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.gitignore vendored
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@ -9,4 +9,3 @@ fundlab/nport_cache/raw/
fundlab/universe_cache/
fundlab/xcheck_run.log
fundlab/streamlit.log
reports/report_data_adhoc.json

253
app.py
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@ -403,17 +403,6 @@ def rng(s):
"""Clip a price/return series (or frame) to the display range."""
return s[(s.index >= lo) & (s.index <= hi)]
# standalone symbols in the Symbol box (not portfolio components) get a
# full per-fund report, built on demand - point the user at it
_standalone = [s for w in items if len(w) == 1 for s in w]
if _standalone:
st.info(
f"Full report(s) generated for: "
f"{', '.join(s.upper() for s in _standalone)} — see the "
f"**Fund Lab** tab, top of the Summary section. (Only symbols "
f"that stand alone in the Symbol box get a report; portfolio "
f"components do not.)")
tab_stats, tab_equity, tab_alloc, tab_tax, tab_corr, tab_fundlab = st.tabs(
["Statistics", "Equity curves", "Allocation", "Tax detail",
"Correlation", "Fund Lab"])
@ -587,110 +576,6 @@ with tab_corr:
# ---------------------------------------------------------------- fund lab
# per-fund report mockup from N-PORT schedules of investments (fundlab)
def render_fund_report(_f: dict) -> None:
"""Render one per-fund report entry (narrative, re-basing equity
chart, period table, drivers, reference mix, tax, cluster peers).
Used by the Summary section for both the pre-built 24 and the
on-demand reports generated for standalone Symbol-box funds."""
_m, _st = _f["meta"], _f.get("stats", {})
_grp = {"candidate": "C", "shortlist": "S"}.get(_m["group"], "A")
# ad-hoc entries carry negative sort orders (they sort first) - show
# the absolute number; and skip the name when it just repeats the
# ticker (ETFs have no fund name on file)
_num = abs(int(_m["order"]))
_nm = _m["name"] if _m["name"].upper() != _m["sym"].upper() else None
_title = (f"{_grp}{_num:02d} · {_m['sym'].upper()}"
+ (f" — {_nm}" if _nm else ""))
if _m.get("verdict"):
_title += f" [{_m['verdict'][:44]}]"
with st.expander(_title):
if _f.get("narrative"):
st.markdown("\n\n".join(_f["narrative"]))
# same re-basing-on-zoom widget as the Equity curves tab: every
# visible window re-bases each line to 1.0 at its left edge, so
# fund/reference/index are comparable no matter where you zoom
_c = _f.get("chart", {})
if _c.get("dates"):
from chart_widget import equity_chart_html
_ds = pd.to_datetime(_c["dates"])
_series = {_m["sym"].upper(): pd.Series(_c["fund"], _ds)}
if _c.get("ref"):
_series["fitted reference"] = pd.Series(
_c["ref"], pd.to_datetime(_c["ref_dates"]))
if _c.get("ivv"):
_series["S&P 500 (IVV)"] = pd.Series(
_c["ivv"], pd.to_datetime(_c["ivv_dates"]))
st.iframe(equity_chart_html(
{k: s / s.iloc[0] for k, s in _series.items()},
_ds, height=460, ytitle="growth (1.0 = start)",
styles={
_m["sym"].upper(): {"width": 2},
"fitted reference": {"width": 1, "dash": "dash",
"opacity": 0.8},
"S&P 500 (IVV)": {"width": 1, "dash": "dot",
"opacity": 0.5},
}), height=490)
if _f.get("perf"):
st.dataframe(pd.DataFrame(
_f["perf"],
columns=["period", "fund", "reference",
"S&P 500", "fund − reference"]),
width="stretch", hide_index=True)
st.caption(
"fund − reference = period alpha/timing (the part of "
"that period the fitted mix does not explain). For "
"cash-anchored funds the reference is the T-bill rate "
"itself.")
if _f.get("drivers"):
st.markdown("\n\n".join(_f["drivers"]))
_sty = _f.get("style")
if _sty and _sty.get("full"):
_f5 = {x["sym"]: x for x in _sty.get("rec5", {}).get("factors", [])}
_srows = []
for x in _sty["full"]["factors"]:
y5 = _f5.get(x["sym"], {})
if abs(x["t"]) < 2 and abs(y5.get("t", 0) or 0) < 2:
continue
_srows.append([
x["name"], f"{x['beta']:+.2f}", f"{x['t']:+.1f}",
(f"{y5['beta']:+.2f}" if y5 else "—"),
(f"{y5['t']:+.1f}" if y5 else "—"),
"✓" if x["sym"] in _sty.get("identified", []) else ""])
st.markdown("**Style tilts** (22 style/asset sleeves regressed "
"on the fund's excess returns; US funds proxy the "
"global factors; 'identified' = survived the BIC "
"forward-selection gate on the full history)")
st.dataframe(pd.DataFrame(
_srows,
columns=["factor", "β full", "t full", "β 5y", "t 5y",
"identified"]),
width="stretch", hide_index=True)
st.markdown("\n\n".join(_sty.get("commentary", [])))
_ref = _f.get("reference")
if _ref and _ref.get("rows"):
st.markdown(f"**Reference mix** ({_ref['netcash']})")
st.dataframe(pd.DataFrame(
_ref["rows"],
columns=["loading", "what it is",
"what it exposes you to"]),
width="stretch", hide_index=True)
st.caption(_ref["text"])
if _f.get("tax"):
st.markdown(f"**Tax** — {_f['tax']}")
_pe = _f.get("peers")
if _pe and _pe.get("rows"):
st.markdown(
f"**Peers — cluster: {_pe['cluster']}** "
f"(n={_pe['n']})")
st.dataframe(pd.DataFrame(
_pe["rows"],
columns=["fund", "5y", "CAGR", "maxDD",
"R² 5y", "alpha 5y", "tax"]),
width="stretch", hide_index=True)
st.caption(_pe["proscons"])
with tab_fundlab:
try:
_FUNDS = json.loads((Path(__file__).parent / "funds.json").read_text())
@ -710,74 +595,25 @@ with tab_fundlab:
_DR = json.loads(_dc.RESULTS.read_text())
except Exception:
_DR = {}
# --- per-fund report: narrative + performance + drivers + peers ----
# static build (fundlab.reportdata) so the page stays fast
_RD = Path(__file__).parent / "reports" / "report_data.json"
if not _RD.exists():
with st.expander("Summary - per-fund reports (not built yet)"):
st.info("Run `.venv/bin/python -m fundlab.reportdata` in the "
"project root, then refresh.")
else:
try:
_rd = json.loads(_RD.read_text())
_rfunds = _rd["funds"]
except Exception as _e: # noqa: BLE001
st.warning(f"report data unreadable: {_e}")
_rfunds = {}
# on-demand reports: a symbol that stands ALONE in the Symbol box
# (not a component of a comma-joined portfolio) gets a full
# report, so any fund can be examined; results are cached on
# disk (reports/report_data_adhoc.json) so re-loading is instant
from fundlab import reportdata as _rdata
_standalone = [s for w in items if len(w) == 1 for s in w]
_new = [s for s in _standalone if s not in _rfunds]
if _new:
with st.spinner(
f"Building report(s) for {', '.join(s.upper() for s in _new)}"
" (first time for a fresh server: up to ~1 min for the "
"driver panel; cached afterwards)"):
_built = _rdata.ensure_adhoc(_new)
_rfunds = {**_built, **_rfunds} # ad-hoc entries first
for _i, (_s, _f) in enumerate(_built.items(), 1):
_f["meta"]["order"] = -_i # ad-hoc block sorts first
_rorder = sorted(_rfunds,
key=lambda s: _rfunds[s]["meta"]["order"])
if _rfunds:
st.subheader(f"Summary - per-fund reports "
f"({len(_rfunds)} funds)")
st.caption(
f"Generated {_rd['generated']}. Each fund: a narrative "
"discussion first (the performance, what drives it, and "
"what we do NOT know), then the equity curve, the period "
"table (fund vs fitted reference vs S&P 500, with the "
"fund-minus-reference gap), the return drivers, the "
"reference mix explained sleeve by sleeve, tax placement, "
"and the best peers from the same return-driver cluster. "
"All alphas are in excess of the 3-mo T-bill rate.")
_ag = []
for _s in _rorder:
_f = _rfunds[_s]
_m, _st = _f["meta"], _f.get("stats", {})
_ag.append({
"fund": (f"{_m['sym'].upper()}"
if _m["name"].upper() == _m["sym"].upper()
else f"{_m['sym'].upper()} — {_m['name'][:44]}"),
"group": _m["group"],
"5y": _st.get("t5y", "—"),
"CAGR": _st.get("cagr", "—"),
"maxDD": _st.get("mdd", "—"),
"R² 5y": _st.get("r2_5y", "—"),
"alpha 5y": _st.get("alpha_5y", "—"),
"cluster": (_f.get("peers") or {}).get("cluster", ""),
})
st.dataframe(pd.DataFrame(_ag), width="stretch",
hide_index=True)
for _s in _rorder:
render_fund_report(_rfunds[_s])
st.caption("← the rest of the Fund Lab continues below: "
"alpha search, return-driver clusters, N-PORT "
"cross-check, drawdown resilience, tax location, "
"and the CEF ranking.")
with st.expander(f"Summary — all {len(_FL_LABELS)} funds"):
_sum_rows = []
for _s in _ORDER:
_d = _DR.get(_s, {})
_rec = _d.get("recent", {})
_comp = ", ".join(f"{c['sym']} {c['beta']:+.2f}"
for c in _rec.get("components", [])[:4]) or \
", ".join(f"{c['sym']} {c['beta']:+.2f}"
for c in _d.get("components", [])[:4])
_sum_rows.append({
"fund": _FL_LABELS.get(_s, _s),
"verdict": _d.get("verdict", "n/a"),
"R² 5y": round(_rec["r2"], 3) if "r2" in _rec else None,
"alpha 5y": (f"{_rec['alpha_ann']*100:+.1f}% "
f"(t={_rec['alpha_t']:+.1f})")
if "alpha_ann" in _rec else None,
"components": _comp or "—",
})
st.dataframe(pd.DataFrame(_sum_rows), width="stretch")
# --- alpha search: all screened funds (shortlist + longlist + harvest)
with st.expander("Alpha search — all screened funds, ranked"):
@ -824,7 +660,6 @@ with tab_fundlab:
else "—"),
"alpha 5y": (f"{_a5*100:+.1f}% (t={_v['alpha_t_5y']:+.1f})"
if isinstance(_a5, (int, float)) else "—"),
"reference (5y)": _v.get("ref_5y") or "—",
"corr port": (f"{_v['corr_portfolio']:.2f}"
if isinstance(_v.get("corr_portfolio"),
(int, float)) else "—"),
@ -835,18 +670,14 @@ with tab_fundlab:
})
st.dataframe(pd.DataFrame(_tbl), width="stretch")
st.caption(
"Screen: daily total returns IN EXCESS OF THE 3-MO T-BILL "
"RATE (BIL) vs 21 broad sleeve axes (same set for every "
"fund); 'alpha 5y' = OLS intercept of the excess returns "
"over the last 5 years (t-stat) — i.e. outperformance vs "
"the fund's OWN fitted sleeve mix (the 'reference' column), "
"not vs one index; a cash position earns exactly the t-bill "
"rate and adds zero alpha. 'corr port' = correlation with "
"your current qspnx/pmaix portfolio; '6m + %' = share of "
"rolling 6-month windows where the fund beat its fitted "
"sleeve mix. CANDIDATE = R²5y < 0.6 (or < 0.85 with strong "
"residual alpha), t5y ≥ 2, t-full ≥ 1.25, ≥ 45% positive "
"windows, portfolio correlation < 0.3.")
"Screen: daily total returns vs 21 broad sleeve axes (same "
"set for every fund); 'alpha 5y' = OLS intercept over the "
"last 5 years (t-stat); 'corr port' = correlation with your "
"current qspnx/pmaix portfolio; '6m + %' = share of rolling "
"6-month windows where the fund beat its fitted sleeve mix. "
"CANDIDATE = R²5y < 0.6 (or < 0.85 with strong residual "
"alpha), t5y ≥ 2, t-full ≥ 1.25, ≥ 45% positive windows, "
"portfolio correlation < 0.3.")
# --- return-driver clusters: k-means on 35-sleeve loading vectors ---
with st.expander(
@ -860,11 +691,7 @@ with tab_fundlab:
_syms, _V = _cl.loading_matrix()
_fr = json.loads((_dc.RESULTS.parent /
"factor_results.json").read_text())
# distance on the cash-emphasized matrix (a pure cash fund's
# level axis is otherwise swallowed by the low-exposure cloud);
# labels below use the raw _V values
_lab = _cl.kmeans(_cl.emphasized(_V, _fac.AXES.index("cash")),
_ck)
_lab = _cl.kmeans(_V, _ck)
_sumrows = []
for c in range(_ck):
idx = [i for i, l in enumerate(_lab) if l == c]
@ -872,7 +699,7 @@ with tab_fundlab:
continue
med = _np.median(_V[idx], 0)
top = _np.argsort(-_np.abs(med))[:4]
prof = {_fac.AXES[t]: float(med[t]) for t in top
prof = {_fac.DRIVERS[t]: float(med[t]) for t in top
if abs(med[t]) >= 0.08}
lab = _cl.label(prof)
t5s = [_fr[_syms[i]].get("alpha_t_5y") for i in idx]
@ -909,7 +736,7 @@ with tab_fundlab:
continue
med = _np.median(_V[idx], 0)
top = _np.argsort(-_np.abs(med))[:4]
prof = {_fac.AXES[t]: float(med[t]) for t in top
prof = {_fac.DRIVERS[t]: float(med[t]) for t in top
if abs(med[t]) >= 0.08}
if _cl.label(prof) == _target:
_members = [(i, idx[0]) for i in idx]
@ -940,14 +767,11 @@ with tab_fundlab:
r.pop("_t")
st.dataframe(pd.DataFrame(_rows), width="stretch")
st.caption(
"Each fund's daily total returns IN EXCESS OF THE 3-MO "
"T-BILL RATE (BIL) are regressed on 34 sleeve axes (equity "
"styles/sizes/intl/EM, the duration ladder, IG/HY/muni/MBS/"
"preferred/EM-debt credit, sectors, gold/oil/commodities, "
"CTA); the loading vector is the fund's 'return-driver "
"signature' — the betas are its NET INVESTED mix — plus a "
"virtual CASH axis = 1 − Σβ (net cash/T-bill position; 1 = "
"fully in cash, < 0 = levered). k-means groups similar "
"Each fund's daily returns are regressed on 35 sleeve axes "
"(equity styles/sizes/intl/EM, the duration ladder, IG/HY/"
"muni/MBS/preferred/EM-debt credit, sectors, gold/oil/"
"commodities, CTA); the 35-d loading vector is the fund's "
"'return-driver signature' and k-means groups similar "
"signatures. 'corr port' is shown for reference only - "
"high-corr funds are REPLACEMENTS for current holdings, "
"not rejections.")
@ -1268,10 +1092,9 @@ with tab_fundlab:
if _dr.get("note"):
st.markdown("**Holdings cross-check:** " + _dr["note"])
st.caption(
"Method: daily total returns IN EXCESS OF THE 3-MO T-BILL RATE "
"(BIL); greedy forward selection on BIC (add only if ΔBIC ≥ 2 and "
"|t| > 2); candidate sleeves curated per fund from the strategy "
"text + N-PORT. β = exposure, not a literal "
"Method: daily total returns; greedy forward selection on BIC "
"(add only if ΔBIC ≥ 2 and |t| > 2); candidate sleeves curated per "
"fund from the strategy text + N-PORT. β = exposure, not a literal "
"holding weight; 'drift' = mean |rolling 1y β − full β| relative to "
"the full β. High-R² + low drift ≈ static mix; low R² with positive "
"alpha ≈ market-neutral/alpha strategy.")

148
data.py
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@ -28,56 +28,6 @@ import pandas as pd
DEFAULT_ROOT = Path("~/prog/fin/stocks").expanduser()
CACHE_DIR = Path(__file__).parent / ".cache"
# Frozen overrides: stale (Yahoo-empty) tickers whose final series is
# snapshotted here by scripts/audit_stale.py. For symbols that have a
# frozen copy, the data-root files are ignored so a future goget
# re-download can never clobber the last known-good series.
FROZEN_DIR = Path(__file__).parent / "overrides" / "frozen"
# Corrections: per-symbol distribution fixes confirmed against official
# sources (see scripts/verify_official.py). Applied on top of whatever the
# data root / frozen dir says, so re-downloads can never clobber them.
# Format, one file per symbol in overrides/corrections/:
# {"dividends": {"remove": [["2008-12-18", 0.292], ...],
# "replace": {"2008-12-18": 0.0},
# "add": [["2007-12-18", 0.292], ...]},
# "capitalGains": {...same ops...},
# "as_of": "2026-08-31", "source": "<filing URL>", "note": "..."}
CORRECTIONS_DIR = Path(__file__).parent / "overrides" / "corrections"
_corr_cache: dict = {}
def _load_corrections(sym: str) -> dict:
f = CORRECTIONS_DIR / f"{sym.upper()}.json"
if f.exists():
try:
if f not in _corr_cache or _corr_cache[f][0] != f.stat().st_mtime_ns:
_corr_cache[f] = (f.stat().st_mtime_ns,
json.loads(f.read_text()))
return _corr_cache[f][1]
except Exception:
return {}
return {}
def _apply_corrections(sym: str, suffix: str, ser: pd.Series) -> pd.Series:
key = {"dividend": "dividends", "capitalGain": "capitalGains"}.get(suffix)
ops = _load_corrections(sym).get(key) if key else None
if not ops:
return ser
d = {ts: float(v) for ts, v in ser.items()}
for r in ops.get("remove", []):
ts = pd.Timestamp(r[0])
amt = r[1] if len(r) > 1 else None
if ts in d and (amt is None or abs(d[ts] - amt) < 1e-9):
del d[ts]
for ts_s, amt in ops.get("replace", {}).items():
d[pd.Timestamp(ts_s)] = float(amt)
for r in ops.get("add", []):
ts = pd.Timestamp(r[0])
d[ts] = d.get(ts, 0.0) + float(r[1])
if not d:
return pd.Series(dtype=float)
return pd.Series(d).sort_index()
_SUFFIXES = ("history", "dividend", "capitalGain")
_PANELS = ("adj", "close", "div", "capg")
@ -100,37 +50,16 @@ def _read_csv_clean(f: Path) -> pd.DataFrame:
return df
def frozen_syms() -> set:
"""Symbols with a frozen history copy in the overrides dir."""
if not FROZEN_DIR.is_dir():
return set()
return {f.stem[: -len("-history")] for f in FROZEN_DIR.glob("*-history.csv")}
def _panel_paths(root: Path, suffix: str) -> list[tuple[str, Path]]:
"""(symbol, path) for every panel file; frozen copies shadow data-root."""
frozen = frozen_syms()
out = []
for f in sorted(root.glob(f"*-{suffix}.csv")):
sym = f.name[: -len(f"-{suffix}.csv")]
if sym in frozen:
continue
out.append((sym, f))
for f in sorted(FROZEN_DIR.glob(f"*-{suffix}.csv")) if FROZEN_DIR.is_dir() else []:
out.append((f.name[: -len(f"-{suffix}.csv")], f))
return out
def _read_panel(root: Path, suffix: str, col: str) -> pd.DataFrame:
cols, syms = [], []
for sym, f in _panel_paths(root, suffix):
for f in sorted(root.glob(f"*-{suffix}.csv")):
try:
df = _read_csv_clean(f)
except Exception:
continue
if col in df.columns:
cols.append(_apply_corrections(sym, suffix, df[col]))
syms.append(sym)
cols.append(df[col])
syms.append(f.name[: -len(f"-{suffix}.csv")])
if not cols:
return pd.DataFrame()
out = pd.concat(cols, axis=1)
@ -167,26 +96,13 @@ REFRESH_CADENCE = 30.0 # min seconds between actual cache rebuilds
_watchers: dict[tuple, threading.Thread] = {}
_gen: dict[tuple, int] = {} # (root, cache) -> bundle generation counter
def _scan_files(root: Path) -> dict:
"""{filename: (mtime_ns, size)} for every csv/json file in the data root
plus the frozen overrides (namespaced under 'frozen/')."""
"""{filename: (mtime_ns, size)} for every csv/json file in the data root."""
out = {}
with os.scandir(root) as it:
for e in it:
if e.is_file() and e.name.endswith((".csv", ".json")):
st = e.stat()
out[e.name] = (st.st_mtime_ns, st.st_size)
if FROZEN_DIR.is_dir():
with os.scandir(FROZEN_DIR) as it:
for e in it:
if e.is_file() and e.name.endswith((".csv", ".json")):
st = e.stat()
out["frozen/" + e.name] = (st.st_mtime_ns, st.st_size)
if CORRECTIONS_DIR.is_dir():
with os.scandir(CORRECTIONS_DIR) as it:
for e in it:
if e.is_file() and e.name.endswith(".json"):
st = e.stat()
out["corrections/" + e.name] = (st.st_mtime_ns, st.st_size)
return out
@ -238,53 +154,14 @@ def _apply_changes(root: Path, cache: Path, old: dict, new: dict) -> None:
return sfx, fname[: -len(f"-{sfx}.csv")]
return None, None
def _path(fname: str) -> Path:
# frozen/ namespaced files live in the overrides dir
return FROZEN_DIR / fname[8:] if fname.startswith("frozen/") else root / fname
updates: dict[str, dict] = {k: {} for k in _PANELS} # panel -> {sym: series}
drops: set[str] = set()
# data-root files of frozen symbols are ignored: their canonical series
# lives in the overrides dir, which is tracked in the same scan
frozen = frozen_syms()
def _recompute_corrected(symfile: str) -> None:
"""corrections/{SYM}.json changed: re-derive the symbol's
dividend/capital-gain series from the base file + corrections."""
for sfx, colname, key in (("dividend", "Dividends", "div"),
("capitalGain", "Capital Gains", "capg")):
sym = None
p = None
for case in (symfile[:-5].lower(), symfile[:-5].upper()):
for base in (FROZEN_DIR, root):
q = base / f"{case}-{sfx}.csv"
if q.exists():
sym, p = case, q
break
if sym:
break
if p is None:
continue
try:
df = _read_csv_clean(p)
except Exception:
continue
if colname not in df.columns:
continue
updates[key][sym] = _apply_corrections(sym, sfx, df[colname]) \
.fillna(0.0)
for fname in changed:
if fname.startswith("corrections/"):
_recompute_corrected(fname[12:])
continue
sfx, sym = _suffix(fname)
if sfx is None:
continue
if not fname.startswith("frozen/") and sym in frozen:
continue
try:
df = _read_csv_clean(_path(fname))
df = _read_csv_clean(root / fname)
except Exception:
continue # e.g. a file mid-download
if sfx == "history":
@ -293,22 +170,11 @@ def _apply_changes(root: Path, cache: Path, old: dict, new: dict) -> None:
if "Close" in df.columns:
updates["close"][sym] = df["Close"]
elif sfx == "dividend" and "Dividends" in df.columns:
updates["div"][sym] = _apply_corrections(sym, sfx, df["Dividends"]) \
.fillna(0.0)
updates["div"][sym] = df["Dividends"].fillna(0.0)
elif sfx == "capitalGain" and "Capital Gains" in df.columns:
updates["capg"][sym] = _apply_corrections(sym, sfx, df["Capital Gains"]) \
.fillna(0.0)
updates["capg"][sym] = df["Capital Gains"].fillna(0.0)
for fname in removed:
sfx, sym = _suffix(fname)
if fname.startswith("corrections/"):
_recompute_corrected(fname[12:])
continue
if fname.startswith("frozen/"):
continue # frozen files are not data-root symbols
if sym in frozen:
continue # data-root file of a frozen symbol vanished; the
# frozen copy remains authoritative
sfx, sym = _suffix(fname)
if sfx is not None:
drops.add(sym)

250
fundlab/RESEARCH.md
View File

@ -933,253 +933,3 @@ the overnight screen and had exited with it.
**App:** Fund Lab tab shows the CEF shortlist table (character
actual-vs-model flag, discount, 5y, vol, scenarios, tenders, score).
### CASH axis: money-market / cash-equivalent funds as a driver
**Problem:** money-market and ultra-short funds fell into the "no
dominant driver" cluster. There WAS a cash sleeve (shv; bil was dropped
from the regression for shv/bil collinearity), but cash funds still
didn't load on it: a fund's yield is a near-CONSTANT in return space,
so OLS puts it in the INTERCEPT (alpha_ann), not in any beta. shv/bil
betas only measure the response to rate CHANGES — tiny for cash-like
funds. Evidence: qcmmrx (Money Market Account) had shv 0.13, bil 0.12,
R² 0.08, but alpha_ann 1.91%/yr = the average cash level over its
history.
**Benchmark:** the canonical cash benchmark is the 13-week T-bill /
0-3m Treasury index (SIXM). Local stand-ins: shv/sgov/bil/gbil
total-return (Adj Close), trailing 1y ≈ 3.7% in the current regime.
**Fix (factors.py + cluster.py + app.py):**
- `factors.cash_yield()`: shv trailing-1y total return (fallback sgov,
bil) — the normalizer.
- `factors.AXES = DRIVERS + ["cash"]`: a VIRTUAL cash axis per fund =
alpha_ann / cash_yield (1.0 = "earns the cash rate"), from the full
window (rec5 fallback when the row falls back to rec5). It is a
clustering/display axis, NOT a regressor (the level is already in
the intercept; regressing on a constant is collinear by design).
- `cluster.loading_matrix()` appends the cash column (winsorized ±4
like the betas). SLEEVE_NAME["cash"] = "cash (yield)".
- `cluster.emphasized(V)`: the cash axis is a LEVEL, not a sensitivity
— in raw Euclidean space a pure cash fund (0,...,0,~0.5) sits inside
the low-exposure balanced/target-date cloud and k-means swallows it.
K-means (app + run_kmeans) runs on the cash-2x-emphasized matrix;
LABELS still use the raw values. The hclust tree stays raw.
- `cluster.label()`: cash-aware — the median cash in a mixed
low-exposure cluster is diluted, so cash counts as the driver at
>=0.25 (vs 0.30 for betas) and must be >=1.5x the runner-up.
**Result (k=30):** the grab-bag 331-fund "no dominant driver" cluster
splits into "cash (yield) +0.64" (n=187: qcmmrx + the ultra-short
government/bond funds), "cash (yield) +1.26" (n=59 pure), and the
genuinely balanced remainder. 637 funds now have CASH as their
dominant axis (>=0.30 and > every beta) — mostly the short-duration
complex, which is correct: their return IS mostly the coupon level.
**Note:** the machine rebooted during this work (12:50) — it silently
kills nohup'd processes (the CEF batch and the first watchdog both
died that way; the batch was resumable so no data was lost).
server_watchdog.sh had to be relaunched manually; consider a
boot-time starter (systemd/cron @reboot) if reboots repeat.
- 2026-08-30 15:09 === stage screen ===
- 2026-08-30 15:09 screen: 2384 funds, 2384 to do
- 2026-08-30 15:10 screen: 100/2384
- 2026-08-30 15:11 screen: 200/2384
- 2026-08-30 15:11 screen: 300/2384
- 2026-08-30 15:12 screen: 400/2384
- 2026-08-30 15:12 screen: 500/2384
- 2026-08-30 15:13 screen: 600/2384
- 2026-08-30 15:13 screen: 700/2384
- 2026-08-30 15:14 screen: 800/2384
- 2026-08-30 15:14 screen: 900/2384
- 2026-08-30 15:15 screen: 1000/2384
- 2026-08-30 15:15 screen: 1100/2384
- 2026-08-30 15:16 screen: 1200/2384
- 2026-08-30 15:17 screen: 1300/2384
- 2026-08-30 15:17 screen: 1400/2384
- 2026-08-30 15:18 screen: 1500/2384
- 2026-08-30 15:18 screen: 1600/2384
- 2026-08-30 15:19 screen: 1700/2384
- 2026-08-30 15:19 screen: 1800/2384
- 2026-08-30 15:20 screen: 1900/2384
- 2026-08-30 15:20 screen: 2000/2384
- 2026-08-30 15:21 screen: 2100/2384
- 2026-08-30 15:21 screen: 2200/2384
- 2026-08-30 15:22 screen: 2300/2384
- 2026-08-30 15:22 screen done: 2384 funds
- 2026-08-30 15:22 === stage finalize ===
- 2026-08-30 15:22 finalize: 2384 funds screened
- 2026-08-30 15:22 1339 sleeve mix
- 2026-08-30 15:22 1001 weak/unstable alpha
- 2026-08-30 15:22 17 no 5y window
- 2026-08-30 15:22 11 CANDIDATE
- 2026-08-30 15:22 10 alpha, but correlated with current portfolio
- 2026-08-30 15:22 6 alpha in 5y window, but not persistent
- 2026-08-30 15:22 candidates v1 name-filter would have missed: ['aguax', 'etsix', 'femdx', 'hicox', 'prfrx', 'puls', 'rctix', 'rpifx', 'scfzx']
- 2026-08-30 15:22 === overnight run finished in 0.2h ===
### Alphas recomputed in EXCESS of the 3-mo T-bill rate (risk-free netting)
**Question that triggered it:** "Are we using cash return as a risk-free
rate in the alpha calculations?" — No. Alpha was the OLS intercept of
RAW daily total returns. For a fund whose net sleeve exposure is not
~100% (under-invested or levered), the raw intercept absorbs the T-bill
yield on the uninvested/levered part, so "alpha" mixed skill with cash
drag. Magnitude (5y, R²>0.5 funds): 582 funds >2%/yr apart, 533 in
1-2%, 345 in 0.5-1%. Sum-of-betas was also polluted by level-matching
(median 1.15; a plain growth fund like bggsx showed 3.7 with a −10%/yr
"alpha" that was pure level-splitting).
**Changes:**
- decompose.py: `rf_series()` = BIL (SPDR 1-3mo T-Bill) daily total
return — the local stand-in for the 3-mo T-bill yield; `excess()`
nets the fund AND the sleeves against it before every regression.
Cash positions now contribute exactly zero (cash IS the rf).
- CASH_SLEEVES {shv, bil} dropped from the regressors: in excess space
they are ~0 columns (ill-conditioning); the cash position is captured
by the residual instead. Same for factors.py (shv added to
DROP_FROM_REGRESSION).
- **FULL_WINDOW moved 1990-01-01 → 2007-06-01** (BIL inception):
mixing raw pre-2007 with excess post-2007 returns breaks the
full-window fit (cosix: R²≈0, zero components selected).
- **CASH axis redefined** from alpha_ann/cash_yield (the old version
was measuring the yield level that no longer lives in the intercept)
to **net cash position = 1 − Σβ**: the betas are the net-invested
mix, the leftover is cash/T-bills. Pure MMF → 1.0; levered → < 0.
Labeled "cash (net posn)". `factors.cash_yield()` removed.
- Alphas in the app are now true excess-return alphas: intercept of
(fund − rf) on (sleeves − rf).
**Verification (probes):** qcmmrx (money market): alpha −0.17%/yr
(t=−1.3, noise), cash 1.00. vsbsx (short-T index): alpha 0.03%, cash
0.77 + short duration. bggsx: Σβ 1.25 (was 3.7), alpha −3.6%
(t=−0.6). IVV: alpha 0.00% (t=+36.7). cosix full-window fixed
(R² 0.60, vweax 0.45/agg 0.15/ief 0.15).
**Consequence — the CANDIDATE list shrank 250 → 11.** Most of the old
250 were under-invested funds (bond funds with Σβ<1) whose "alpha" was
the T-bill yield on their uninvested portion. The 11 that survive are
funds whose excess alpha is real. Verdict mix now: 1339 sleeve mix,
1001 weak/unstable alpha, 11 CANDIDATE, 10 correlated, 6 not
persistent, 17 no 5y window.
**Reference visibility (the "what is alpha computed vs" question):**
alpha is NOT vs one index — it is vs the fund's own fitted sleeve mix
(BIC forward selection). fundlab/refback.py recomputes the forward
selection per fund and stores `ref_5y`/`ref_full` strings in
search_all.json; the app's alpha-search table now has a "reference
(5y)" column.
**Backups of the raw-alpha era (NOT deleted):**
factor_results_rawalpha.json, decompose_results_rawalpha.json,
search_all_rawalpha.json, cluster_kmeans_rawalpha.json.
---
## Per-fund report: HTML + app + narrative engine (2026-08-30)
**Deliverable.** `fundlab/report.py` -> `reports/fund_report.html`
(self-contained, plotly inlined, 20 MB) and `fundlab/reportdata.py` ->
`reports/report_data.json` (3 MB, the app's static build). 24 funds: the
11 excess-return candidates + the 13 unique shortlist funds (pmfkx/lcrix/
egrsx are share classes of pmaix/lcorx/eagmx and are merged).
Per fund: max-history equity curve (fund vs fitted reference vs IVV);
period table (full/5y/1y, the 5 market episodes, 6 calendar years) with
the **fund-minus-reference** column (period alpha/timing); drivers
(reference-model R²/alpha/t + 34-sleeve signature + curated decomposition
verdict + N-PORT cross-check notes); the reference mix explained
sleeve-by-sleeve (SLEEVE_DESC: what each exposure actually is); tax
character + placement; and a peer table of the 4 best-alpha funds in the
same k=30 return-driver cluster with computed adv/dis.
**BUG FOUND AND FIXED — `mix_series()` never applied the betas.** The
"fitted reference" curve summed the RAW sleeve daily returns instead of
β·return. For high-fit funds the reference was garbage (JLPSX "reference"
+407% 5y vs the fund's +123%; CVSIX +465%; PMAIX +1160%). After the fix
the mix tracks the fund (JLPSX ref +127% vs fund +123%). Every reference
curve and table in the HTML report was wrong before this and has been
regenerated. Lesson: a regression on returns does not hand you a price
path — the β weighting is the whole point.
**Weak-fit funds anchor to CASH, not their fitted mix.** For idiosyncratic
funds (R² < 0.5) the BIC forward selection can pick a statistically-thin
OFFSETTING combination (SCFZX: +0.35 VIX futures, −0.23 20+y Treasuries,
+0.10 HY) whose cumulated path is meaningless (a "reference" that loses
79% in 2022 for a fund that lost 2.4%). Below the 0.5 fit gate the
performance table and curve use BIL as the reference; the loadings are
still shown with an explicit "weak fit — read with care" caveat.
**Narrative engine (`fundlab/narrative.py`).** Data-driven English prose,
5 paragraphs per fund: (1) what the fund is (objective, N-PORT buckets,
cluster); (2) the performance in plain terms (CAGR/vol/DD, 5y vs reference,
episode behaviour, calendar-year pattern); (3) what is driving it — tiered
by fit (≥0.7 "mostly the exposures", 0.5–0.7 "mix + residual", <0.5
"idiosyncratic: here is the estimate and its t-stat"); (4) **what we do
NOT know** — stated explicitly: the unexplained share, alpha steadiness
(share of 6m windows trailing), recent-vs-full significance, short sample,
holdings opacity (egrix: 100% in one managed fund, underlying undisclosed);
(5) bottom line (account placement, best cluster peer, portfolio
correlation). Rules learned while reviewing the prose: negative/weak 5y
alpha must not be called "outperformance" (the template assumed positive
alpha and lied about atesx, −1.6% t=−0.5); MIXED tax placement must not be
rendered as "IRA"; a fund with no price history must not be assigned a
cluster (zero loading vector parks everything in the cash cluster);
drift < 0.5 = "roughly stable" (0.44 is not "tactical"); sleeve prose
needs short names (dbb/gsg → "broad commodities") because SLEEVE_DESC
lead-ins reference sibling tickers.
**App integration.** The Fund Lab "Summary" page now renders
`reports/report_data.json`: at-a-glance table (24 funds: 5y, CAGR, maxDD,
R²5y, alpha5y, cluster) + one expander per fund (narrative first, then
chart, period table, drivers, reference mix, tax, peers) + an "expand
all" checkbox. Static build so the page stays fast; regenerate with
`python -m fundlab.reportdata` (~8 s) and `python -m fundlab.report`
(~11 s for the HTML).
**Report charts: re-basing-on-zoom widget (2nd pass).** The first report
build used static st.plotly_chart with pre-rebased series — zooming to a
window left the lines starting at different levels (no comparison
possible). Replaced with the SAME widget as the Equity curves tab
(`chart_widget.equity_chart_html` + st.iframe): on every relayout each
visible line is re-based to 1.0 at its left edge and y fits the visible
data, so fund/reference/index are comparable in any window. report_data
still stores the full-history series; the app divides by each series'
first point before handing it to the widget (per-series 1.0 start, as
the widget expects). Also removed the "expand all" checkbox (24 expanded
sections made the page unusable and hid the sections below) and added a
"rest of the Fund Lab continues below" marker.
**On-demand reports for any Symbol-box fund.** `reportdata.ensure_adhoc(syms)`
builds the same full report entry (narrative, chart, period table, drivers,
reference, tax, peers) for ARBITRARY symbols, cached in memory + on disk
(reports/report_data_adhoc.json, gitignored) so repeat loads are instant and
survive restarts; build time ~0.3-0.9 s per new fund after the one-time
~30-60 s driver-panel warmup. The app triggers it for symbols that stand
ALONE in the Symbol box (a parsed item with a single component) - portfolio
components (comma-joined) are explicitly excluded, per user request. Ad-hoc
entries render at the top of the Fund Lab Summary as group "A" (before the
pre-built 24), the per-fund rendering was extracted into render_fund_report()
shared by both, and a pointer at the top of the page tells the user where
the generated reports are. ETFs with no name on file render as "A01 · TLT"
(no duplicated name).
**Style-tilt battery in the report pipeline (2026-08-30, 3rd pass).**
`fundlab/styletilt.py`: 22 style/asset sleeves (EFA/VWO, growth/value,
IWM/RSP size+equal-weight, USMV/DFLVX low-vol, QUAL, MTUM, HDV/VYM
dividend, TLT, GLD, FXE/FXY/FXU, the five big sectors) regressed on the
fund's excess-of-T-bill returns, full history + 5y. Plain OLS gives every
factor's β + t (exploratory); BIC forward selection gives the IDENTIFIED
tilt stack (strict gate); residual alpha after the identified stack says
"skill left or pure factor exposure". Data-driven commentary: tilt
sentences with strength words (t≥10 strong / ≥5 clear / ≥2 modest),
sign-specific phrasing for the tricky ones (short-euro = USD carry,
anti-quality = flip side of high payout, anti-momentum = contrarian),
5y-only factors called out separately, and an explicit residual-verdict
paragraph. Wired as a "Style tilts" block (factor table + commentary)
into both report builders (app + HTML); ~0.1-0.8 s per fund after the
one-time battery-panel build. All 24 pre-built + 8 ad-hoc reports
rebuilt (23/24 have the block; lcorx has no history). Worked example
(lvhi): identified stack = intl-dev 0.78 + short EUR 0.42 + HDV +0.28
(t+20) + anti-QUAL 0.21 (t-12) + short FXY + short VWO; residual alpha
+2.3%/y t=+1.1 -> "factor exposure, not skill".

45
fundlab/cluster.py
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@ -30,7 +30,7 @@ HERE = Path(__file__).parent
RESULTS = HERE / "factor_results.json"
TREE = HERE / "cluster_tree.json"
KMEANS = HERE / "cluster_kmeans.json"
from fundlab.factors import AXES, DRIVERS # noqa: E402
from fundlab.factors import DRIVERS # noqa: E402
def log(msg: str) -> None:
@ -50,11 +50,6 @@ def loading_matrix() -> tuple[list[str], np.ndarray]:
continue
row = np.array([f["betas"].get(s, 0.0) or 0.0 for s in DRIVERS],
dtype=float)
# virtual CASH axis = net cash position = 1 - sum of the betas.
# Alphas are in excess of the T-bill rate, so the betas are the
# NET INVESTED mix; whatever is left over sits in cash/T-bills.
# Pure money-market fund: all betas ~ 0 -> cash ~ 1.
row = np.append(row, 1.0 - row.sum())
# winsorize at +/-4: everything beyond that is an OLS noise fit
# (R2 ~ 0.01, |t| < 2 - e.g. fyhtx shv +63.8), never a real
# exposure (no open-end fund runs 400% of a sleeve)
@ -125,7 +120,7 @@ def cluster_means(syms: list[str], V: np.ndarray,
idx = [i for i, s in assign.items() if s == c]
med = np.median(V[idx], axis=0)
top = np.argsort(-np.abs(med))[:4]
prof = {AXES[t]: float(med[t]) for t in top
prof = {DRIVERS[t]: float(med[t]) for t in top
if abs(med[t]) >= 0.05}
out.append({"n": len(idx), "top": prof,
"syms": [syms[i] for i in idx]})
@ -145,41 +140,15 @@ SLEEVE_NAME = {
"xlv": "healthcare", "xlp": "staples", "xlu": "utilities",
"xly": "consumer disc", "xlb": "materials", "dbmf": "CTA/futures",
"dbb": "commodities",
"cash": "cash (net posn)",
}
# the CASH axis is a POSITION (net cash, 1.0 = fully in cash), not a
# return SENSITIVITY like the betas. In raw Euclidean space a
# pure cash fund (0,...,0,~0.5) sits inside the cloud of low-exposure
# balanced/target-date funds and k-means swallows it. Emphasize the axis
# for DISTANCE only (labels still use the raw values).
CASH_EMPHASIS = 2.0
def emphasized(V: np.ndarray, ci: int) -> np.ndarray:
W = V.copy()
W[:, ci] *= CASH_EMPHASIS
return W
def label(prof: dict) -> str:
# a real driver shows up as >=0.3 on a median loading; below that the
# cluster is balanced mixes with no single dominant driver. The CASH
# axis is a level, not a beta: in a mixed low-exposure cluster the
# median cash is diluted, so it gets a slightly lower threshold and
# must clearly beat the runner-up to count as THE driver.
if not prof:
# cluster is balanced mixes with no single dominant driver
if not prof or max(abs(v) for v in prof.values()) < 0.30:
return "no dominant driver (balanced/idio)"
names = sorted(prof, key=lambda s: -abs(prof[s]))
top_s, top_v = names[0], prof[names[0]]
runner = abs(prof[names[1]]) if len(names) > 1 else 0.0
if top_s == "cash":
if top_v >= 0.25 and top_v >= 1.5 * runner:
return " + ".join(f"{SLEEVE_NAME.get(s, s)} {prof[s]:+.2f}"
for s in names[:3])
return "no dominant driver (balanced/idio)"
if abs(top_v) < 0.30:
return "no dominant driver (balanced/idio)"
return " + ".join(f"{SLEEVE_NAME.get(s, s)} {prof[s]:+.2f}"
for s in names[:3])
@ -213,7 +182,7 @@ def kmeans(V: np.ndarray, k: int, seed: int = 0,
def run_kmeans(k: int = 30) -> dict:
syms, V = loading_matrix()
lab = kmeans(emphasized(V, AXES.index("cash")), k)
lab = kmeans(V, k)
out: dict[int, dict] = {}
for c in range(k):
idx = [i for i, l in enumerate(lab) if l == c]
@ -221,7 +190,7 @@ def run_kmeans(k: int = 30) -> dict:
continue
med = np.median(V[idx], axis=0)
top = np.argsort(-np.abs(med))[:4]
prof = {AXES[t]: float(med[t]) for t in top
prof = {DRIVERS[t]: float(med[t]) for t in top
if abs(med[t]) >= 0.08}
out[c] = {"n": len(idx), "top": prof, "label": label(prof),
"syms": [syms[i] for i in idx]}
@ -234,7 +203,7 @@ def run() -> None:
log(f"cluster: {len(syms)} funds x {V.shape[1]} drivers")
merges = hclust(V)
TREE.write_text(json.dumps({"syms": syms, "merges": merges,
"n": len(syms), "sleeves": AXES}))
"n": len(syms), "sleeves": DRIVERS}))
log(f"hierarchical tree written in {time.time() - t0:.0f}s -> {TREE.name}")
# NOTE: fixed-k cuts of the tree peel small pure-sleeve clusters off a
# giant blend blob (most funds are multi-sleeve mixes) - the useful

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fundlab/cluster_kmeans_rawalpha.json
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fundlab/decompose.py
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@ -22,7 +22,6 @@ intercept; alphas are annualized (x252); t-stats use the OLS covariance.
from __future__ import annotations
import json
from functools import lru_cache
from pathlib import Path
import numpy as np
@ -50,29 +49,6 @@ def adj_close(sym: str, root: Path = DATA) -> pd.Series | None:
return s
@lru_cache(maxsize=1)
def rf_series() -> pd.Series | None:
"""Daily risk-free rate: BIL (SPDR 1-3 Month T-Bill) total return —
the local stand-in for the 3-month T-bill yield. Alphas are computed
in EXCESS of this rate (both the fund and the sleeves are netted
against it), so the intercept is a true excess-return alpha and a
cash position contributes zero (cash IS the risk-free rate). 0 where
BIL has no history (pre-2007)."""
a = adj_close("bil")
if a is None:
return None
return a.pct_change().fillna(0.0)
def excess(panel: pd.DataFrame) -> pd.DataFrame:
"""Net every column against the daily risk-free rate."""
rf = rf_series()
if rf is None:
return panel
rf = rf.reindex(panel.index).fillna(0.0)
return panel.sub(rf, axis=0)
def returns_panel(symbols: list[str], start: str | None = None) -> pd.DataFrame:
"""Daily simple returns, outer-joined on dates (NaN where a series
has no data). Callers must handle missing values per regressor —
@ -231,17 +207,9 @@ CANDIDATES: dict[str, list[str]] = {
}
# share classes: same underlying fund
ALIAS = {"pmfkx": "pmaix", "lcrix": "lcorx", "egrsx": "eagmx"}
FULL_WINDOW = "2007-06-01" # from BIL inception: alphas are in excess of
# the T-bill rate, which needs the rf series (raw pre-2007 returns
# mixed with excess post-2007 returns breaks the full-window fit)
FULL_WINDOW = "1990-01-01" # effectively all history
RECENT_WINDOW = "2021-01-01" # last ~5 years
# sleeves that ARE the risk-free rate. Alphas are now computed in excess of
# the T-bill rate, so these columns are ~0 in excess space and would make
# the regression ill-conditioned. Cash exposure is captured by the residual
# (1 - sum of betas) instead, so they are dropped from the regressors.
CASH_SLEEVES = {"shv", "bil"}
def _drift(rb: pd.DataFrame, full_beta: np.ndarray) -> dict[str, float]:
"""Mean |rolling beta - full-sample beta| per component, as a fraction
@ -257,14 +225,13 @@ def _drift(rb: pd.DataFrame, full_beta: np.ndarray) -> dict[str, float]:
def decompose(fund: str, start: str = FULL_WINDOW,
candidates: dict[str, list[str]] | None = None) -> dict:
cand_syms = (candidates or CANDIDATES).get(fund, [])
r = excess(returns_panel([fund] + cand_syms, start=start))
r = returns_panel([fund] + cand_syms, start=start)
if fund not in r.columns:
return {"fund": fund, "error": "no return history in the data set",
"verdict": "no return history", "start": start}
y = r[fund].to_numpy()
dates = r.index
cand = {s: r[s].to_numpy() for s in r.columns
if s != fund and s not in CASH_SLEEVES}
cand = {s: r[s].to_numpy() for s in r.columns if s != fund}
y_ok = ~np.isnan(y)
if y_ok.sum() < 252:
return {"fund": fund, "error": "insufficient return history",

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fundlab/factors.py
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@ -69,19 +69,9 @@ SLEEVES_V2 = list(dict.fromkeys(BROAD_SLEEVES + EXTRA_SLEEVES))
# short-rate exposure as huge offsetting shv/bil coefficients (shv +64 /
# bil -54) - shv (1-3m) alone carries the cash/ultra-short axis, shy
# keeps the 1-3y axis.
DROP_FROM_REGRESSION = {"vea", "vug", "finux", "bil", "shv"}
# (bil/shv are the risk-free sleeves - alphas are now computed in
# excess of the T-bill rate, where they are ~0 columns; the cash
# position shows up in the residual / the virtual CASH axis instead)
DROP_FROM_REGRESSION = {"vea", "vug", "finux", "bil"}
DRIVERS = [s for s in SLEEVES_V2 if s not in DROP_FROM_REGRESSION]
# clustering/display basis = the regression sleeves + a virtual CASH axis.
# Alphas are computed in EXCESS of the T-bill rate (decompose.excess), so
# the beta vector is the fund's NET INVESTED mix and the leftover
# (1 - sum of betas) is its cash/T-bill position: a pure money-market
# fund has all betas ~ 0 -> CASH ~ 1, a levered fund -> CASH < 0.
AXES = DRIVERS + ["cash"]
def _resid(y: pd.Series, X: pd.DataFrame) -> pd.Series:
m = y.notna() & X.notna().all(axis=1)
@ -103,7 +93,6 @@ def _panel() -> pd.DataFrame:
log(f"building driver panel ({len(DRIVERS)} axes)")
p = decompose.returns_panel(SLEEVES_V2).sort_index()
p = p[~p.index.duplicated(keep="last")][DRIVERS]
p = decompose.excess(p) # alphas are in excess of the T-bill rate
# pure vol axis: vblix residualized on the core equity/duration axes
core = [c for c in ("ivv", "tlt") if c in p.columns]
if "vblix" in p.columns and core:
@ -153,16 +142,11 @@ def _beta_window(y: pd.Series, X: pd.DataFrame, start: str) -> dict | None:
}
def factor_screen(sym: str, start: str = None) -> dict | None:
if start is None:
start = decompose.FULL_WINDOW
def factor_screen(sym: str, start: str = "1990-01-01") -> dict | None:
fund = decompose.adj_close(sym)
if fund is None or len(fund) < 250:
return None
y = fund.pct_change().dropna()
rf = decompose.rf_series()
if rf is not None:
y = y.sub(rf.reindex(y.index).fillna(0.0))
X = _panel().reindex(y.index)
# sleeves must have history for most of the fund's window
avail = [c for c in X.columns if X[c].notna().mean() > 0.3]

14
fundlab/factors_run.log
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@ -1,14 +0,0 @@
15:03:47 factor screen: 2384 funds, 2384 to do
15:03:47 building driver panel (34 axes)
15:03:55 200/2384
15:04:02 400/2384
15:04:10 600/2384
15:04:18 800/2384
15:04:30 1000/2384
15:04:43 1200/2384
15:04:52 1400/2384
15:04:59 1600/2384
15:05:07 1800/2384
15:05:14 2000/2384
15:05:21 2200/2384
15:05:28 factor screen done: 2384 funds -> factor_results.json

444
fundlab/narrative.py
View File

@ -1,444 +0,0 @@
"""Per-fund narrative: English prose explaining the performance and its
drivers, built from the analysis we have on file.
The prose is data-driven (every number comes from the computed results),
and it is required to be honest about limits: when the return drivers
are unknown or only weakly identified, the text says so explicitly.
Paragraphs:
1. what the fund is (name, stated strategy if on file, what its
N-PORT actually holds, which return-driver cluster it sits in)
2. the performance in plain terms (full history, 5y, recent year,
behaviour in the defined market episodes, calendar-year pattern)
3. what is driving it (fit quality -> whose exposures vs idiosyncratic
alpha; the economic meaning of the top loadings; weight stability)
4. what we do NOT know (unexplained share, alpha steadiness, sample
size, holdings opacity) - stated plainly
5. bottom line (account placement, best cluster peers, role)
Run: .venv/bin/python -m fundlab.narrative (prints all narratives)
"""
from __future__ import annotations
import json
from pathlib import Path
import numpy as np
from fundlab import report as _r
from fundlab.searchlist import BROAD_SLEEVES
from fundlab import decompose as _dc
HERE = Path(__file__).parent
_XC = json.loads((HERE / "xcheck_report.json").read_text()) \
if (HERE / "xcheck_report.json").exists() else {}
def _pct(x, nd=1) -> str:
if x is None or (isinstance(x, float) and np.isnan(x)):
return "no data"
return f"{100 * x:+.{nd}f}%"
def _num(x, nd=2) -> str:
if x is None or (isinstance(x, float) and np.isnan(x)):
return "n/a"
return f"{x:.{nd}f}"
# short prose names for sleeves whose SLEEVE_DESC lead-in references a
# sibling ticker ("same HY exposure as VWEAX via a different fund")
_PROSE_NAME = {
"vea": "international developed markets",
"efa": "international developed markets",
"hyg": "high-yield corporate bonds",
"vweax": "high-yield corporate bonds",
"dbb": "broad commodities",
"gsg": "broad commodities",
"vblix": "crash vol (VIX futures)",
"bil": "cash (T-bills)",
"shv": "cash (T-bills)",
}
def _sleeve_prose(sym: str, beta: float) -> str:
nm, desc = _r.sleeve_desc(sym)
short = _PROSE_NAME.get(sym)
head = short.split(";")[0].strip() if short else \
desc.split(";")[0].strip()
head = head[0].upper() + head[1:] if head else nm
if abs(beta) < 0.15:
return f"a small tilt to {head.lower()}"
if beta > 0:
return f"exposure to {head.lower()}"
return f"short-like exposure to {head.lower()} (it rises when that " \
f"asset falls)"
def _top_sleeves(fr: dict, window: str, n: int = 3) -> list[tuple[str, float]]:
f = fr.get(window) or {}
betas = f.get("betas") or {}
top = sorted(betas.items(), key=lambda kv: -abs(kv[1]))[:n]
return [(s, b) for s, b in top if abs(b) >= 0.08]
def _fit(fr: dict, srow: dict) -> tuple[float | None, float | None,
float | None]:
"""(r2, alpha_ann_5y, t5) preferring the reference-model numbers."""
r2 = srow.get("r2_5y")
a5 = srow.get("alpha_ann_5y")
t5 = srow.get("alpha_t_5y")
if not isinstance(r2, (int, float)):
r2 = (fr.get("rec5") or fr.get("full") or {}).get("r2")
if not isinstance(a5, (int, float)):
a5 = (fr.get("rec5") or fr.get("full") or {}).get("alpha_ann")
if not isinstance(t5, (int, float)):
t5 = (fr.get("rec5") or fr.get("full") or {}).get("alpha_t")
return (r2 if isinstance(r2, (int, float)) else None,
a5 if isinstance(a5, (int, float)) else None,
t5 if isinstance(t5, (int, float)) else None)
def narrate(sym: str) -> list[str]:
sym = sym.lower()
srow = _r.search_row(sym)
fr = _r.factor_row(sym)
dr = _r.decomp_row(sym)
tax = _r.tax_row(sym)
ddr = _r.dd_row(sym)
xcr = _XC.get(sym, {})
p = _r.price(sym)
name = (srow.get("name") or _r.FONDS.get(sym, {}).get("name")
or (fr or {}).get("name") or sym.upper())
out: list[str] = []
# ------------------------------------------------------------ 1. what
who = []
who.append(f"{sym.upper()} is the {name}.")
obj = (_r.FONDS.get(sym) or {}).get("objective")
if obj:
first = obj.split(". ")[0].strip().rstrip(".")
who.append(f"Stated strategy: {first}.")
if xcr and not xcr.get("error"):
b = [x["name"] for x in xcr.get("buckets", [])[:2]]
if b:
who.append(f"Per its latest N-PORT its actual holdings are "
f"mostly {b[0]}"
+ (f" and {b[1]}" if len(b) > 1 else "") + ".")
out.append(" ".join(who))
if p is None:
# no return history: do NOT claim a cluster - the loading vector
# is all zeros and would park the fund in the cash cluster
out.append("There is no local return history for this share "
"class yet (new class), so performance and drivers "
"cannot be measured from returns. The N-PORT shows "
"what it holds: " + ((dr or {}).get("note")
or "see holdings"))
return out
if sym in _r.MEMBER:
_cid, clabel, cn = _r.MEMBER[sym]
who = out[0]
out[0] = (who + f" In the return-driver analysis it sits in the "
f"'{clabel}' cluster with {cn - 1} other funds.")
else:
try:
load = _r.fund_loading_row(fr)
_cid, clabel = _r.cluster_of_row(load)
out[0] = (out[0] + f" Its return-driver signature is closest "
f"to the '{clabel}' cluster.")
except Exception:
pass
st = _r.perf_stats(p)
r2, a5, t5 = _fit(fr, srow)
tf = (fr.get("full") or {}).get("alpha_t")
a_f = (fr.get("full") or {}).get("alpha_ann")
refs = _r.ref_components(sym, fr)
r2f_ = (fr.get("rec5") or fr.get("full") or {}).get("r2")
fit = r2 if r2 is not None else r2f_
r25 = srow.get("r2_5y") if isinstance(srow.get("r2_5y"), (int, float)) \
else r2f_
refs_curve = refs if (fit is not None and fit >= 0.5) else []
mix = _r.mix_series(refs_curve) if refs_curve else _r.price("bil")
ivv = _r.price("ivv")
t5y = _r.window_ret(p, "2021-01-01", st["end"])
t1y = _r.window_ret(p, "2025-09-01", st["end"])
ref5y = _r.window_ret(mix, "2021-01-01", st["end"]) if mix is not None \
else None
iv5y = _r.window_ret(ivv, "2021-01-01", st["end"]) if ivv is not None \
else None
# --------------------------------------------------------- 2. performance
perf = []
tot = p.iloc[-1] / p.iloc[0] - 1
perf.append(
f"Since {st['start'][:4]} it has compounded at "
f"{_pct(st['cagr'])} per year (a {_pct(tot, 0)} total return) "
f"with {_pct(st['vol'], 0)} annualized volatility and a maximum "
f"drawdown of {100 * abs(st['mdd']):.0f}%.")
perf.append(
f"Over the last five years it returned {_pct(t5y)} versus "
f"{_pct(ref5y)} for its reference"
+ (f" and {_pct(iv5y)} for the S&P 500" if iv5y is not None else "")
+ f". The last twelve months have done {_pct(t1y)}.")
yrs = _r.annual_table(p, 7)
pos = sum(1 for _y, v in yrs if v > 0)
perf.append(f"It was positive in {pos} of the last {len(yrs)} "
f"calendar years (including the partial current year).")
# episodes
rets = ddr.get("rets", {})
if rets:
best = max(rets.items(), key=lambda kv: kv[1])
worst = min(rets.items(), key=lambda kv: kv[1])
perf.append(
f"Across the defined market episodes it did best in "
f"'{best[0]}' ({_pct(best[1])}) and worst in '{worst[0]}' "
f"({_pct(worst[1])}) - the peak-to-trough windows when "
f"equities fell hardest.")
_dv = ddr.get("verdict") or ""
if _dv and not _dv.startswith("CANDIDATE"):
perf.append(f"Its drawdown screen verdict: {_dv}.")
out.append(" ".join(perf))
# --------------------------------------------------------- 3. drivers
drv = []
tops = _top_sleeves(fr, "rec5", 3)
if fit is not None and fit >= 0.7:
tops_txt = ", ".join(_sleeve_prose(s, b) for s, b in tops)
drv.append(
f"The fit is strong: {100 * fit:.0f}% of its excess returns "
f"over the last five years are explained by its measured "
f"exposures"
+ (f" - {tops_txt}" if tops else "") + ".")
drv.append(
f"In other words, most of what this fund does is charge you "
f"for those exposures in the form of a fund; what is left - "
f"an alpha of {_pct(a5)} per year (t = {_num(t5, 1)}) - is "
+ ("statistically indistinguishable from zero, i.e. the "
"strategy is not clearly adding anything on top of the "
"mix it owns."
if (t5 or 0) < 1.75 else
"small relative to the beta, but it is what separates "
"this fund from the equivalent index mix.")
)
elif fit is not None and fit >= 0.5:
tops_txt = ", ".join(_sleeve_prose(s, b) for s, b in tops)
drv.append(
f"The measured exposures explain a good share but not all "
f"({100 * fit:.0f}% R²) of its excess returns."
+ (f" The main ones: {tops_txt}." if tops else ""))
drv.append(
f"The residual is {_pct(a5)} per year (t = {_num(t5, 1)}): "
+ ("a real edge on top of the mix, but a material part of "
"the performance IS the mix - judge the exposures and "
"the skill separately."
if (t5 or 0) >= 1.75 else
"not statistically distinct from noise at the five-year "
"horizon, so treat the outperformance as part beta, part "
"luck until it accumulates more history.")
)
else:
tops_txt = ", ".join(_sleeve_prose(s, b) for s, b in tops)
drv.append(
f"The broad sleeve framework explains little of its excess "
f"returns (R² = {_num(fit) if fit is not None else 'n/a'}). "
+ (f"The loadings that do show up - {tops_txt} - are minor "
"tilts, not the story."
if tops else
"No benchmark loading is even large."))
long_ctx = ""
if isinstance(a_f, (int, float)):
long_ctx = (f" Over the full history the same estimate is "
f"{_pct(a_f)} per year (t = {_num(tf, 1)}).")
if (t5 or 0) >= 2:
drv.append(
f"The bulk of the performance is therefore "
f"idiosyncratic: it comes from the fund's own holdings "
f"and decisions, which nothing in our 34-sleeve space "
f"replicates. The estimate of that idiosyncratic return "
f"is {_pct(a5)} per year over the last five years "
f"(t = {_num(t5, 1)}) - statistically significant, so "
f"the outperformance is real; what the returns alone "
f"cannot tell us is its source.{long_ctx}")
elif (a5 or 0) >= 0:
drv.append(
f"The bulk of the performance is therefore "
f"idiosyncratic: it comes from the fund's own holdings "
f"and decisions, which nothing in our 34-sleeve space "
f"replicates. The estimate of that idiosyncratic return "
f"is {_pct(a5)} per year over the last five years "
f"(t = {_num(t5, 1)}) - at that t-stat it is not yet "
f"clearly different from a lucky streak.{long_ctx}")
else:
drv.append(
f"Most of the performance is therefore idiosyncratic "
f"rather than benchmark-like. The honest five-year read "
f"of its excess return is {_pct(a5)} per year "
f"(t = {_num(t5, 1)}): over the recent window this fund "
f"is NOT measurably adding to its fitted reference.{long_ctx}")
if (a5 or 0) < 0 and isinstance(tf, (int, float)) and tf >= 1.25:
drv.append(
f"The longer record is better: over the full history the "
f"alpha is {_pct(a_f)} per year (t = {_num(tf, 1)}), "
f"which IS significant - so the five-year estimate "
f"looks like a flat patch in a longer positive trend, "
f"not evidence the edge has gone.")
if xcr and not xcr.get("error"):
b = [x["name"] for x in xcr.get("buckets", [])[:2]]
if b:
drv.append(
f"The N-PORT is consistent with that: the alpha is "
f"plausibly coming from {b[0]}"
+ (f" and {b[1]}" if len(b) > 1 else "")
+ " - asset classes our sleeve set either does not "
"model directly or models too coarsely. That is an "
"interpretation from the holdings, not something "
"the returns prove.")
if obj and (a5 or 0) > 0:
_o = obj.split(". ")[0].rstrip(".")
if len(_o) > 110:
_o = _o[:110].rsplit(" ", 1)[0] + "..."
drv.append(
f"The stated strategy ('{_o}') is a plausible mechanism "
f"as well, but that is a hypothesis - the returns only "
f"tell us the outperformance exists, not why.")
# stability (curated funds)
roll = (dr or {}).get("rolling") or {}
if "verdict" in (dr or {}) and roll.get("max_drift") is not None:
md = roll["max_drift"]
drv.append(
f"The curated decomposition's read: {dr['verdict']}. "
f"Weight stability: the one-year rolling betas move by at "
f"most {md:.2f} (relative to the full-sample weights) - "
+ ("so the weights are roughly stable over time."
if md < 0.5 else
"so treat this as a tactically active fund, not a "
"static mix."))
out.append(" ".join(drv))
# ------------------------------------------------- 4. what we do NOT know
unk = []
if fit is not None:
if (t5 or 0) >= 2:
unk.append(
f"To be explicit about the limits: "
f"{100 * (1 - fit):.0f}% of this fund's excess return "
f"is NOT attributed to any measured exposure by our "
f"analysis - we know the outperformance is real, we do "
f"not know from returns what produces it; the source "
f"has to be read from the filings before sizing up.")
else:
unk.append(
f"To be explicit about the limits: the excess return is "
f"not measurable against noise at the current sample "
f"size, so there is not even a reliable alpha to "
f"attribute; {100 * (1 - fit):.0f}% of the return is "
f"outside our sleeve model either way.")
pf = srow.get("alpha_pos_frac")
if isinstance(pf, (int, float)) and pf < 0.60:
unk.append(
f"The alpha is also not steady: in {100 * (1 - pf):.0f}% of "
f"rolling six-month windows the fund trailed its reference, "
f"so the five-year number is carrying periods of "
f"underperformance.")
if (t5 or 0) >= 2 and isinstance(tf, (int, float)) and tf < 1.25:
unk.append(
"The significance is recent - over the full history the "
"alpha is not significant, so this is a provisional read on "
"a ~5-year sample.")
if st["years"] < 3:
unk.append(
f"The whole sample is short ({st['years']:.1f} years), so "
f"every number above has a wide error bar.")
if xcr.get("note"):
unk.append(xcr["note"].rstrip(".") + ".")
note = (dr or {}).get("note")
if note and "N-PORT" in note:
# holdings cross-check sentence (shortlist)
for sentence in note.split(". "):
if "N-PORT" in sentence or "holdings" in sentence.lower():
unk.append(sentence.strip().rstrip(".") +
" - the holdings check is the ground truth "
"here, and the return model is only a "
"description of it.")
break
if not unk:
unk.append("No major caveats beyond the usual: the past fit may "
"not persist, and the sleeves are models of the "
"fund, not the fund itself.")
out.append(" ".join(unk))
# --------------------------------------------------------- 5. bottom line
bot = []
loc = tax.get("location")
if loc:
basis = "N-PORT holdings" if tax.get("basis") == "N-PORT" \
else "the sleeve model"
if "MIXED" in loc:
bot.append(
f"Account placement: not settled by the model - the "
f"sleeve model calls it MIXED, so the last 1099-DIV is "
f"the arbiter before choosing taxable vs IRA.")
else:
bot.append(
f"Account placement: "
f"{'taxable' if 'TAXABLE' in loc else 'IRA'} "
f"({basis} says {loc}).")
# peers
if sym in _r.MEMBER:
cid = _r.MEMBER[sym][0]
members = _r.KMEANS["clusters"][cid]["syms"]
peers = []
for s in members:
if s == sym:
continue
v = _r.search_row(s)
if isinstance(v.get("alpha_t_5y"), (int, float)) and \
v["alpha_t_5y"] > 0:
pp = _r.price(s)
if pp is None:
continue
peers.append((v["alpha_t_5y"], v.get("alpha_ann_5y"), s))
peers.sort(reverse=True)
if peers:
b_t, b_a, b_s = peers[0]
bp = _r.price(b_s)
bt5 = _r.window_ret(bp, "2021-01-01",
_r.perf_stats(bp)["end"])
bot.append(
f"Within its cluster the strongest alternative is "
f"{b_s.upper()} (5y {_pct(bt5)}, alpha t = {b_t:+.1f}); "
f"the choice between them should turn on the conviction "
f"in the strategy, the tax fit and the price paid, not "
f"on the statistics - they are the same kind of "
f"position.")
cp = srow.get("corr_portfolio")
if isinstance(cp, (int, float)):
bot.append(
f"Its correlation with your current portfolio is "
f"{cp:+.2f} - "
+ ("a genuine diversifier" if cp < 0.3
else "not very diversified vs what you already own".rstrip("."))
+ ".")
out.append(" ".join(bot))
return out
def main() -> None:
from fundlab.nport import FUND_TOKENS
from fundlab.decompose import ALIAS
cand = [v["sym"] for v in _r.SEARCH.values()
if isinstance(v, dict)
and str(v.get("verdict", "")).startswith("CANDIDATE")]
short = [s for s in FUND_TOKENS if s not in ALIAS]
for s in cand + short:
print(f"=== {s.upper()} ===")
for para in narrate(s):
print(para)
print()
if __name__ == "__main__":
main()

1
fundlab/overnight.pid
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@ -1 +0,0 @@
5847

36
fundlab/overnight_rescreen.log
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@ -1,36 +0,0 @@
15:09:56 === stage screen ===
15:09:56 screen: 2384 funds, 2384 to do
15:10:29 screen: 100/2384
15:11:01 screen: 200/2384
15:11:33 screen: 300/2384
15:12:04 screen: 400/2384
15:12:36 screen: 500/2384
15:13:09 screen: 600/2384
15:13:42 screen: 700/2384
15:14:15 screen: 800/2384
15:14:45 screen: 900/2384
15:15:20 screen: 1000/2384
15:15:56 screen: 1100/2384
15:16:31 screen: 1200/2384
15:17:07 screen: 1300/2384
15:17:38 screen: 1400/2384
15:18:14 screen: 1500/2384
15:18:45 screen: 1600/2384
15:19:16 screen: 1700/2384
15:19:47 screen: 1800/2384
15:20:19 screen: 1900/2384
15:20:51 screen: 2000/2384
15:21:23 screen: 2100/2384
15:21:54 screen: 2200/2384
15:22:26 screen: 2300/2384
15:22:53 screen done: 2384 funds
15:22:53 === stage finalize ===
15:22:53 finalize: 2384 funds screened
15:22:53 1339 sleeve mix
15:22:53 1001 weak/unstable alpha
15:22:53 17 no 5y window
15:22:53 11 CANDIDATE
15:22:53 10 alpha, but correlated with current portfolio
15:22:53 6 alpha in 5y window, but not persistent
15:22:53 candidates v1 name-filter would have missed: ['aguax', 'etsix', 'femdx', 'hicox', 'prfrx', 'puls', 'rctix', 'rpifx', 'scfzx']
15:22:53 === overnight run finished in 0.2h ===

98
fundlab/refback.log
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@ -1,98 +0,0 @@
[2026-08-30 15:40:42] rows=2384 todo=2384
[2026-08-30 15:40:48] 25/2384 done (saved)
[2026-08-30 15:40:53] 50/2384 done (saved)
[2026-08-30 15:40:59] 75/2384 done (saved)
[2026-08-30 15:41:04] 100/2384 done (saved)
[2026-08-30 15:41:10] 125/2384 done (saved)
[2026-08-30 15:41:16] 150/2384 done (saved)
[2026-08-30 15:41:22] 175/2384 done (saved)
[2026-08-30 15:41:28] 200/2384 done (saved)
[2026-08-30 15:41:33] 225/2384 done (saved)
[2026-08-30 15:41:39] 250/2384 done (saved)
[2026-08-30 15:41:45] 275/2384 done (saved)
[2026-08-30 15:41:51] 300/2384 done (saved)
[2026-08-30 15:41:57] 325/2384 done (saved)
[2026-08-30 15:42:03] 350/2384 done (saved)
[2026-08-30 15:42:08] 375/2384 done (saved)
[2026-08-30 15:42:14] 400/2384 done (saved)
[2026-08-30 15:42:20] 425/2384 done (saved)
[2026-08-30 15:42:26] 450/2384 done (saved)
[2026-08-30 15:42:32] 475/2384 done (saved)
[2026-08-30 15:42:37] 500/2384 done (saved)
[2026-08-30 15:42:42] 525/2384 done (saved)
[2026-08-30 15:42:48] 550/2384 done (saved)
[2026-08-30 15:42:54] 575/2384 done (saved)
[2026-08-30 15:43:00] 600/2384 done (saved)
[2026-08-30 15:43:06] 625/2384 done (saved)
[2026-08-30 15:43:11] 650/2384 done (saved)
[2026-08-30 15:43:18] 675/2384 done (saved)
[2026-08-30 15:43:24] 700/2384 done (saved)
[2026-08-30 15:43:29] 725/2384 done (saved)
[2026-08-30 15:43:36] 750/2384 done (saved)
[2026-08-30 15:43:42] 775/2384 done (saved)
[2026-08-30 15:43:48] 800/2384 done (saved)
[2026-08-30 15:43:53] 825/2384 done (saved)
[2026-08-30 15:43:59] 850/2384 done (saved)
[2026-08-30 15:44:05] 875/2384 done (saved)
[2026-08-30 15:44:10] 900/2384 done (saved)
[2026-08-30 15:44:16] 925/2384 done (saved)
[2026-08-30 15:44:22] 950/2384 done (saved)
[2026-08-30 15:44:28] 975/2384 done (saved)
[2026-08-30 15:44:34] 1000/2384 done (saved)
[2026-08-30 15:44:39] 1025/2384 done (saved)
[2026-08-30 15:44:45] 1050/2384 done (saved)
[2026-08-30 15:44:51] 1075/2384 done (saved)
[2026-08-30 15:44:56] 1100/2384 done (saved)
[2026-08-30 15:45:02] 1125/2384 done (saved)
[2026-08-30 15:45:08] 1150/2384 done (saved)
[2026-08-30 15:45:14] 1175/2384 done (saved)
[2026-08-30 15:45:19] 1200/2384 done (saved)
[2026-08-30 15:45:25] 1225/2384 done (saved)
[2026-08-30 15:45:31] 1250/2384 done (saved)
[2026-08-30 15:45:36] 1275/2384 done (saved)
[2026-08-30 15:45:42] 1300/2384 done (saved)
[2026-08-30 15:45:47] 1325/2384 done (saved)
[2026-08-30 15:45:53] 1350/2384 done (saved)
[2026-08-30 15:45:59] 1375/2384 done (saved)
[2026-08-30 15:46:04] 1400/2384 done (saved)
[2026-08-30 15:46:10] 1425/2384 done (saved)
[2026-08-30 15:46:16] 1450/2384 done (saved)
[2026-08-30 15:46:22] 1475/2384 done (saved)
[2026-08-30 15:46:28] 1500/2384 done (saved)
[2026-08-30 15:46:33] 1525/2384 done (saved)
[2026-08-30 15:46:38] 1550/2384 done (saved)
[2026-08-30 15:46:44] 1575/2384 done (saved)
[2026-08-30 15:46:49] 1600/2384 done (saved)
[2026-08-30 15:46:55] 1625/2384 done (saved)
[2026-08-30 15:47:01] 1650/2384 done (saved)
[2026-08-30 15:47:06] 1675/2384 done (saved)
[2026-08-30 15:47:12] 1700/2384 done (saved)
[2026-08-30 15:47:18] 1725/2384 done (saved)
[2026-08-30 15:47:24] 1750/2384 done (saved)
[2026-08-30 15:47:30] 1775/2384 done (saved)
[2026-08-30 15:47:35] 1800/2384 done (saved)
[2026-08-30 15:47:41] 1825/2384 done (saved)
[2026-08-30 15:47:47] 1850/2384 done (saved)
[2026-08-30 15:47:53] 1875/2384 done (saved)
[2026-08-30 15:47:59] 1900/2384 done (saved)
[2026-08-30 15:48:05] 1925/2384 done (saved)
[2026-08-30 15:48:10] 1950/2384 done (saved)
[2026-08-30 15:48:16] 1975/2384 done (saved)
[2026-08-30 15:48:22] 2000/2384 done (saved)
[2026-08-30 15:48:28] 2025/2384 done (saved)
[2026-08-30 15:48:33] 2050/2384 done (saved)
[2026-08-30 15:48:39] 2075/2384 done (saved)
[2026-08-30 15:48:45] 2100/2384 done (saved)
[2026-08-30 15:48:51] 2125/2384 done (saved)
[2026-08-30 15:48:57] 2150/2384 done (saved)
[2026-08-30 15:49:03] 2175/2384 done (saved)
[2026-08-30 15:49:09] 2200/2384 done (saved)
[2026-08-30 15:49:15] 2225/2384 done (saved)
[2026-08-30 15:49:21] 2250/2384 done (saved)
[2026-08-30 15:49:27] 2275/2384 done (saved)
[2026-08-30 15:49:33] 2300/2384 done (saved)
[2026-08-30 15:49:38] 2325/2384 done (saved)
[2026-08-30 15:49:44] 2350/2384 done (saved)
[2026-08-30 15:49:50] 2375/2384 done (saved)
[2026-08-30 15:49:52] 2384/2384 done (saved)
[2026-08-30 15:49:52] done: 2384 rows carry a reference

76
fundlab/refback.py
View File

@ -1,76 +0,0 @@
"""Backfill the per-fund alpha REFERENCE into search_all.json.
The alpha search verdict (fundlab/search.py) computes alpha as the OLS
intercept of the fund's daily total returns against the sleeves BIC
forward-selection picked from the 21 broad sleeve axes (the same set for
every fund). The picked sleeves ARE the fund's reference - a fitted
sleeve mix, not a single index - but the screen never stored them, so
the app couldn't show them.
This script recomputes the forward selection (full + last-5y) for every
screened fund and stores a compact, human-readable reference string on
each row:
ref_5y "ivv +0.62, tlt +0.31" (sleeves picked for the 5y model)
ref_full "ivv +0.38, efa +0.15, ..."
"pure alpha (no sleeve selected)" when the model has no components
Resumable: rows that already carry ref_5y are skipped.
Run: .venv/bin/python -m fundlab.refback (logs to fundlab/refback.log)
"""
from __future__ import annotations
import json
import time
from fundlab import decompose
from fundlab.searchlist import BROAD_SLEEVES
HERE = __import__("pathlib").Path(__file__).parent
SRC = HERE / "search_all.json"
LOG = HERE / "refback.log"
def log(msg: str) -> None:
line = f"[{time.strftime('%Y-%m-%d %H:%M:%S')}] {msg}"
print(line, flush=True)
with LOG.open("a") as f:
f.write(line + "\n")
def _fmt(m: dict) -> str:
comps = m.get("components") or []
if not comps:
return "pure alpha (no sleeve selected)"
return ", ".join(f"{c['sym']} {c['beta']:+.2f}" for c in comps)
def run() -> None:
d = json.loads(SRC.read_text())
todo = [k for k, v in d.items()
if isinstance(v, dict)
and isinstance(v.get("r2_5y"), (int, float))
and "ref_5y" not in v]
log(f"rows={len(d)} todo={len(todo)}")
for i, k in enumerate(todo, 1):
sym = d[k]["sym"]
try:
rec = decompose.decompose(sym, start=decompose.RECENT_WINDOW,
candidates={sym: BROAD_SLEEVES})
full = decompose.decompose(sym, candidates={sym: BROAD_SLEEVES})
d[k]["ref_5y"] = _fmt(rec)
d[k]["ref_full"] = _fmt(full)
except Exception as e: # noqa: BLE001 - batch must not die
d[k]["ref_5y"] = f"(backfill failed: {e})"
if i % 25 == 0 or i == len(todo):
SRC.write_text(json.dumps(d, indent=1))
log(f"{i}/{len(todo)} done (saved)")
SRC.write_text(json.dumps(d, indent=1))
n = sum(1 for v in d.values()
if isinstance(v, dict) and v.get("ref_5y"))
log(f"done: {n} rows carry a reference")
if __name__ == "__main__":
run()

835
fundlab/report.py
View File

@ -1,835 +0,0 @@
"""Fund report: candidates + shortlist, self-contained HTML.
One section per fund:
- equity curve over the maximum available period (fund vs its fitted
reference mix vs IVV),
- performance: full history, calendar years, and the defined market
episodes (2022 bear, 2023 rate shock, 2024 vol spike, 2025 tariff
crash, 2026 Q1) - fund vs reference mix vs IVV, with the fund-vs-
reference gap (period alpha/timing),
- what drove it: the excess-of-T-bill sleeve loadings (full + 5y),
alpha + t, R^2, rolling drift, verdict,
- the reference mix explained: each picked sleeve with what it
actually exposes you to (not just the ticker),
- tax character + taxable/IRA placement,
- cluster peers: the 3-4 best funds of the same k=30 return-driver
cluster, compared, with this fund's advantages/disadvantages.
Run: .venv/bin/python -m fundlab.report -> reports/fund_report.html
"""
from __future__ import annotations
import html
import json
import time
from pathlib import Path
import numpy as np
import pandas as pd
from fundlab import cluster as _cl
from fundlab import decompose
from fundlab import factors
from fundlab.searchlist import BROAD_SLEEVES
HERE = Path(__file__).parent
REPORTS = HERE.parent / "reports"
REPORTS.mkdir(exist_ok=True)
OUT = REPORTS / "fund_report.html"
TAX = json.loads((HERE / "taxplan_results.json").read_text())
DD = json.loads((HERE / "drawdown_results.json").read_text())
FACTOR = json.loads((HERE / "factor_results.json").read_text())
SEARCH = json.loads((HERE / "search_all.json").read_text())
DECOMP = json.loads((HERE / "decompose_results.json").read_text())
FONDS = json.loads((HERE.parent / "funds.json").read_text())
KMEANS = json.loads((HERE / "cluster_kmeans.json").read_text())
# ------------------------------------------------------------------ sleeves
# what each sleeve actually EXPOSES you to (the report must explain the
# exposure, not just name the ticker)
SLEEVE_DESC = {
"qqq": ("US large growth (Nasdaq-100)",
"growth/tech-heavy US equities; high sensitivity to earnings "
"surprises and long-end rates (duration of growth cash flows)"),
"ivv": ("US large blend (S&P 500)",
"core US equity market; the default 'own the economy' exposure"),
"iwm": ("US small cap (Russell 2000)",
"small-cap cycle: domestic credit, margin pressure, IPO window"),
"vea": ("Intl developed ex-US (Vanguard)",
"developed-market equities outside the US (EU, Japan, UK); FX-"
"hedged-off, currency moves matter"),
"efa": ("Intl developed ex-US (MSCI EAFE)",
"developed-market equities outside the US; same exposure as VEA "
"via a different index provider"),
"vwo": ("Emerging-market equity",
"EM corporate profits + EM currency + China/FX flows; "
"high-vol, high-carry, dollar-sensitive"),
"vnq": ("US REITs",
"physical real estate: rents vs rates, leverage in the property "
"sector; equity-like income"),
"bil": ("1-3 month T-bills (cash)",
"the risk-free rate itself; ~zero volatility, pure carry"),
"shv": ("0-3 month T-bills (ultra-short cash)",
"the risk-free rate itself; netted out of the excess-return "
"regression, shown only as part of a fund's cash position"),
"shy": ("1-3 year Treasuries (short duration)",
"short duration: modest rate sensitivity, ~cash-plus-carry"),
"ief": ("7-10 year Treasuries (core duration)",
"the core rate bet: price moves when the Fed path changes"),
"tlt": ("20+ year Treasuries (long duration)",
"levered duration: big moves on rate expectations, steepener/"
"bull-steepener exposure"),
"vblix": ("VIX futures (pure vol axis)",
"crash insurance / short-vol funding; positive loading = "
"long-vol (rises in panic), negative = short-vol carry"),
"agg": ("Aggregate bonds (Treasuries + IG credit)",
"the core bond market: ~60% Treasuries, IG corporates, MBS; "
"moderate duration"),
"vweax": ("High-yield corporate bonds",
"credit spread cycle: HY junk yields, default risk in "
"recessions, strong carry in stable times"),
"hyg": ("High-yield corporate bonds (iShares)",
"same HY credit-spread exposure as VWEAX via a different fund"),
"vmbix": ("Agency RMBS (mortgage-backed)",
"mortgage credit + prepayment/extension risk; the refi cycle"),
"lqd": ("Investment-grade corporate bonds",
"IG credit spreads over Treasuries; milder default risk than HY"),
"finux": ("Intl bonds (Fidelity; TERMINATED 2017)",
"non-US credit/duration; data ends 2017 so recent windows "
"never use it"),
"pff": ("Preferred stocks",
"hybrid: fixed-rate equity-like instruments; rate + credit + "
"equity risk in one"),
"emb": ("Emerging-market debt",
"EM sovereign/corporate carry; EM currency + dollar cycle"),
"tip": ("TIPS (inflation-linked Treasuries)",
"breakeven inflation exposure: rises when inflation "
"expectations rise"),
"vtv": ("US value",
"cheap/asset-rich US names (financials, energy, cyclicals); "
"value-vs-growth style cycle"),
"dbmf": ("CTA / managed futures",
"trend-following across futures; long in crises, earns "
"un-correlated carry otherwise"),
"dbb": ("Broad commodities",
"commodity basket: inflation hedge + global growth proxy"),
"djp": ("Natural gas",
"a single volatile commodity: winter/hedging cycles"),
"gsg": ("Broad commodities (SPDR)",
"same commodity exposure as DBB via a different fund"),
"gld": ("Gold",
"crisis/inflation hedge; real-rate sensitive, no yield"),
"fxe": ("Long euros vs the dollar",
"EUR/USD: carries the euro interest-rate differential"),
"fxy": ("Long yen vs the dollar",
"USD/JPY: carries the Japan rate differential; carry-trade "
"crowding risk"),
"xlk": ("US tech sector", "the tech sector index"),
"xlf": ("US financials sector", "banks/insurance: rate + credit cycle"),
"xle": ("US energy sector", "oil prices + US drilling"),
"xlv": ("US healthcare sector", "defensive pharma/providers"),
"xlp": ("US staples sector", "defensive consumer"),
"xlu": ("US utilities sector", "bond-proxy utilities; rate sensitive"),
"xly": ("US consumer discretionary", "cyclicals: autos, retail, "
"leisure; earnings cycle"),
"xlb": ("US materials sector", "industrial materials: capex cycle"),
}
def sleeve_desc(sym: str) -> tuple[str, str]:
return SLEEVE_DESC.get(sym, (sym.upper(), "no description on file"))
# ------------------------------------------------------------------ data
def price(sym: str) -> pd.Series | None:
f = decompose.DATA / f"{sym.lower()}-history.csv"
if not f.exists():
return None
try:
s = (pd.read_csv(f, parse_dates=["Date"], index_col="Date")
["Adj Close"].dropna())
s = s[~s.index.duplicated(keep="last")].sort_index()
except Exception:
return None
return s if len(s) > 40 else None
EPISODES = [] # (label, start, end) from the drawdown engine
for _e in DD["episodes"]:
EPISODES.append((_e["label"], pd.Timestamp(_e["peak"]),
pd.Timestamp(_e["trough"])))
# k=30 cluster membership (sym -> cluster id/label)
MEMBER = {}
for _c, _v in KMEANS["clusters"].items():
for _s in _v["syms"]:
MEMBER[_s] = (_c, _v["label"], _v["n"])
def loading_matrix_cached():
return _cl.loading_matrix()
CENTROIDS = {} # sym -> (cluster, centroid) for NEW points
def _centroids(syms: list[str], V: np.ndarray) -> dict[int, np.ndarray]:
lab = _cl.kmeans(_cl.emphasized(V, _cl.AXES.index("cash")), 30)
out = {}
for c in range(30):
idx = [i for i, l in enumerate(lab) if l == c]
if idx:
out[c] = np.median(V[idx], 0)
return out
def cluster_of_row(row: np.ndarray) -> tuple[str, str]:
"""Assign a new loading row to the k=30 scheme (distance to cluster
medians in the emphasized space)."""
global CENTROIDS
if not CENTROIDS:
syms, V = loading_matrix_cached()
CENTROIDS = _centroids(syms, V)
r = row.copy()
r[_cl.AXES.index("cash")] *= _cl.CASH_EMPHASIS
best, bd = None, None
for c, cent in CENTROIDS.items():
d = float(np.sum((r - cent) ** 2))
if bd is None or d < bd:
best, bd = c, d
for c, v in KMEANS["clusters"].items():
if int(c) == best:
return (c, v["label"])
return (str(best), "?")
def factor_row(sym: str) -> dict:
"""Full + 5y excess-of-T-bill loadings for ANY fund (shortlist funds
are not in the 2,384 factor file)."""
if sym in FACTOR:
return FACTOR[sym]
r = factors.factor_screen(sym)
return r or {}
_FWD: dict = {}
def ref_components(sym: str, fr: dict, window: str = "rec5") -> list[dict]:
"""The fund's FORWARD-SELECTED reference (the mix its alpha is
measured against): BIC-gated selection from the 21 broad sleeves,
excess-of-T-bill. Recomputed (cached) because the screen stored only
the display strings. Falls back to the 34-sleeve OLS loadings when
the fund is not in the search universe."""
key = (sym, window)
if key in _FWD:
return _FWD[key]
out = None
try:
m = decompose.decompose(sym, start=decompose.RECENT_WINDOW
if window == "rec5" else None,
candidates={sym: BROAD_SLEEVES})
if m.get("components"):
out = [{"sym": c["sym"], "beta": c["beta"]}
for c in m["components"]]
except Exception:
out = None
if out is None:
comps = (fr.get(window) or fr.get("full") or {}).get("betas") or {}
out = [{"sym": s, "beta": b} for s, b in sorted(
comps.items(), key=lambda kv: -abs(kv[1])) if abs(b) >= 0.08]
_FWD[key] = out
return out
def search_row(sym: str) -> dict:
return SEARCH.get(sym, {})
def decomp_row(sym: str) -> dict:
return DECOMP.get(sym, {})
def tax_row(sym: str) -> dict:
up = sym.upper()
if up in TAX["shortlist"]:
return TAX["shortlist"][up]
for k in (sym, up, sym.lower()):
if k in TAX["candidates"]:
return TAX["candidates"][k]
return {}
def dd_row(sym: str) -> dict:
return DD["funds"].get(sym, {})
# ------------------------------------------------------------------ stats
def perf_stats(p: pd.Series, rf_annual: float = 0.037) -> dict:
r = p.pct_change().dropna()
years = (p.index[-1] - p.index[0]).days / 365.25
cagr = (p.iloc[-1] / p.iloc[0]) ** (1 / years) - 1 if years > 0.5 else np.nan
vol = r.std() * np.sqrt(252)
roll_max = p.cummax()
mdd = float(((p / roll_max) - 1).min())
sharpe = (cagr - rf_annual) / vol if vol > 0 else np.nan
return {"start": str(p.index[0].date()), "end": str(p.index[-1].date()),
"years": years, "cagr": cagr, "vol": vol, "mdd": mdd,
"sharpe": sharpe}
def window_ret(p: pd.Series, a, b) -> float | None:
s = p[(p.index >= a) & (p.index <= b)]
if len(s) < 2:
return None
return float(s.iloc[-1] / s.iloc[0] - 1)
def annual_table(p: pd.Series, n: int = 8) -> list[tuple[str, float]]:
yr = p.resample("YE").last().dropna()
out = []
for i in range(len(yr) - 1):
lab = str(yr.index[i].year)
if i == 0:
continue
out.append((lab, float(yr.iloc[i] / yr.iloc[i - 1] - 1)))
# partial current year from last full year-end
out.append(("YTD", float(p.iloc[-1] / yr.iloc[-1] - 1)))
return out[-(n + 1):]
def mix_series(refs: list[dict]) -> pd.Series | None:
"""Fitted reference mix: sum of beta * sleeve daily returns, cumulated
to a price path (rebased 100). Pure beta path - the fund minus this
is the alpha path."""
if not refs:
b = price("bil")
if b is None:
return None
return 100 * b / b.iloc[0]
idx = None
for c in refs:
p = price(c["sym"])
if p is None:
continue
r = c["beta"] * p.pct_change()
idx = r if idx is None else idx.combine(r, lambda x, y: x + y,
fill_value=0.0)
if idx is None:
return None
idx = idx.fillna(0.0)
path = (1 + idx).cumprod()
return 100 * path / path.iloc[0]
# ------------------------------------------------------------------ html
PLOTLY_JS = ""
def load_plotly():
global PLOTLY_JS
try:
import plotly
PLOTLY_JS = plotly.offline.get_plotlyjs()
except Exception:
PLOTLY_JS = None
def fig_html(fig, h: int = 420) -> str:
import plotly.io as pio
s = pio.to_html(fig, full_html=False, include_plotlyjs=False,
config={"displayModeBar": False}, div_id="")
return s.replace("<div>", f'<div style="height:{h}px">')
def equity_figure(sym: str, name: str, refs: list[dict]) -> str:
import plotly.graph_objects as go
p = price(sym)
if p is None:
return "<p>No local price history.</p>"
fig = go.Figure()
fig.add_trace(go.Scatter(x=p.index, y=100 * p / p.iloc[0],
name=sym.upper(), line=dict(width=2)))
m = mix_series(refs)
if m is not None:
fig.add_trace(go.Scatter(x=m.index, y=m, name="fitted reference",
line=dict(width=1.2, dash="dash")))
iv = price("ivv")
if iv is not None:
fig.add_trace(go.Scatter(
x=iv.index, y=100 * iv / iv.iloc[0], name="IVV (S&P 500)",
line=dict(width=1, dash="dot"), opacity=0.7))
fig.update_layout(height=430, margin=dict(l=10, r=10, t=30, b=10),
title=f"{html.escape(name)} - total return since "
f"{p.index[0].year} (rebased 100)",
legend=dict(orientation="h", y=1.08),
hovermode="x unified")
return fig_html(fig)
def fmt_pct(x, nd=1, sign=True):
if x is None or (isinstance(x, float) and np.isnan(x)):
return "—"
s = f"{100 * x:+.{nd}f}%" if sign else f"{100 * x:.{nd}f}%"
return s
def fmt_r2(x):
return "—" if x is None or (isinstance(x, float) and np.isnan(x)) \
else f"{x:.2f}"
def perf_table(p: pd.Series, refs: list[dict]) -> str:
m = mix_series(refs)
iv = price("ivv")
rows = []
def add(label, a, b, ann=False):
fr_ = window_ret(p, a, b)
mr = window_ret(m, a, b) if m is not None else None
ir = window_ret(iv, a, b) if iv is not None else None
gap = (fr_ - mr) if (fr_ is not None and mr is not None) else None
rows.append(f"<tr><td>{label}</td>"
f"<td>{fmt_pct(fr_)}</td><td>{fmt_pct(mr)}</td>"
f"<td>{fmt_pct(ir)}</td><td>{fmt_pct(gap)}</td></tr>")
st = perf_stats(p)
add("Full history", st["start"], st["end"])
add("Last 5y", "2021-01-01", st["end"])
add("Last 1y", "2025-09-01", st["end"])
for lab, a, b in EPISODES:
add(lab, a, b)
# calendar years (last 6)
yr = p.resample("YE").last().dropna()
years = [str(yr.index[i].year) for i in range(1, len(yr))][-6:]
for i, y in enumerate(years):
a = f"{y}-01-01"
b = f"{int(y) + 1}-01-01" if i < len(years) - 1 else st["end"]
add(y, a, b)
return ('<table><tr><th>period</th><th>fund</th><th>reference</th>'
'<th>IVV</th><th>fund − ref</th></tr>' + "".join(rows)
+ '</table><p class="small">fund − reference = period '
'alpha/timing (the part of that period the sleeve mix does '
'not explain). IVV shown for scale - for non-equity funds '
'the IVV column is only context.</p>')
def drivers_section(fr: dict, dr: dict, srow: dict) -> str:
out = []
if srow:
a5 = srow.get("alpha_ann_5y")
t5 = srow.get("alpha_t_5y")
r25 = srow.get("r2_5y")
if isinstance(r25, (int, float)) or isinstance(a5, (int, float)):
out.append(
f"<p><b>Reference model, last 5 years:</b> R² = "
f"{fmt_r2(r25 if isinstance(r25, (int, float)) else None)}, "
f"alpha = {fmt_pct(a5) if isinstance(a5, (int, float)) else '—'}"
f"{' (t = ' + format(t5, '+.1f') + ')' if isinstance(t5, (int, float)) else ''} "
f"vs the fitted reference mix (next section).")
rf = (fr.get("full") or {}).get("alpha_ann")
tf = (fr.get("full") or {}).get("alpha_t")
r2f = srow.get("r2_full")
if isinstance(r2f, (int, float)) or isinstance(rf, (int, float)):
out.append(
f"<p><b>Reference model, full history:</b> R² = "
f"{fmt_r2(r2f if isinstance(r2f, (int, float)) else None)}, "
f"alpha = {fmt_pct(rf) if isinstance(rf, (int, float)) else '—'}"
f"{' (t = ' + format(tf, '+.1f') + ')' if isinstance(tf, (int, float)) else ''}.</p>")
f = fr.get("rec5") or {}
betas = f.get("betas") or {}
top = sorted(betas.items(), key=lambda kv: -abs(kv[1]))[:6]
if top:
out.append(
"<p><b>Return-driver signature (34 sleeves, for clustering "
"context):</b> " + ", ".join(
f"{s} {b:+.2f}" for s, b in top) + " - net cash "
f"{1 - sum(betas.values()):+.2f}.</p>")
# drift + verdict from the curated decomposition, when available
if dr and "verdict" in dr:
out.append(f"<p><b>Decomposition verdict:</b> "
f"{html.escape(dr['verdict'])}</p>")
roll = dr.get("rolling") or {}
if roll.get("max_drift") is not None:
out.append(
f"<p>Weight stability: max 1y β-drift = "
f"{roll['max_drift']:.2f} (relative to full-sample β; "
f"0 = perfectly stable, &gt;1 = the weight is unstable).</p>")
note = dr.get("note")
if note:
out.append(f"<p class='small'>{html.escape(note)}</p>")
if srow:
v = str(srow.get("verdict", ""))
if v:
out.append(f"<p><b>Screen verdict:</b> {html.escape(v)}</p>")
return "".join(out)
def reference_section(refs: list[dict], refs_curve: list[dict],
sym: str) -> str:
weak = bool(refs) and not refs_curve
caveat = ""
if weak:
caveat = ("<p class='small'><b>Weak fit - read with care.</b> "
"These loadings are each individually significant but "
"collectively explain little of the excess return; the "
"economically meaningful picture is a mostly-cash "
"vehicle with idiosyncratic alpha (the performance "
"table anchors to cash, not to this mix).</p>")
if not refs:
srow = search_row(sym)
a = srow.get("alpha_ann_5y")
return caveat + ("<p><b>Reference: cash (the T-bill rate itself)."
"</b> No "
"sleeve passed the forward-selection gates, so the fund's "
"excess returns are not explained by any benchmark mix - "
f"its entire excess performance is idiosyncratic (5y alpha "
f"{fmt_pct(a) if isinstance(a, (int, float)) else '—'}). "
"There is no meaningful 'beta' to this fund; it is a "
"standalone position.</p>")
sb = sum(c["beta"] for c in refs)
cash = 1 - sb
rows = []
for c in refs:
nm, desc = sleeve_desc(c["sym"])
rows.append(f"<tr><td><b>{c['sym'].upper()}</b> {c['beta']:+.2f}"
f"</td><td>{html.escape(nm)}</td>"
f"<td>{html.escape(desc)}</td></tr>")
cash_line = ""
if weak:
cash_line = caveat + cash_line
if cash > 0.05:
cash_line = (f"<p>The loadings sum to {sb:.2f}, i.e. the fund is "
f"~{cash:.0%} NET CASH (earns the T-bill rate; adds "
f"zero excess alpha).</p>")
elif cash < -0.05:
cash_line = (f"The loadings sum to {sb:.2f}, i.e. the fund is ~"
f"{-cash:.0%} NET LEVERED (borrows at ~the T-bill "
f"rate; that financing shows up as negative cash).</p>")
return ('<table><tr><th>loading</th><th>what it is</th>'
'<th>what it exposes you to</th></tr>' + "".join(rows)
+ "</table>" + cash_line
+ '<p class="small">The reference is NOT one index - it is '
'this fitted mix, rebuilt from the fund\'s own returns. '
'"Alpha" everywhere in this report means outperformance vs '
'this mix, in excess of the T-bill rate.</p>')
def tax_section(sym: str) -> str:
t = tax_row(sym)
if not t:
return "<p>No tax classification on file.</p>"
loc = t.get("location", "?")
basis = t.get("basis", "?")
conf = {"N-PORT": "from actual N-PORT holdings (high confidence)",
"sleeves": "from the return-sleeve mix (model, medium "
"confidence)"}.get(basis, basis)
notes = html.escape(t.get("notes") or "")
rec = {"TAXABLE": "Keep in the <b>taxable</b> account.",
"TAXABLE (munis)": "Keep in the <b>taxable</b> account - "
"tax-exempt interest is wasted in an IRA.",
"TAXABLE (defers to LTCG)": "Keep in the <b>taxable</b> "
"account - the income mostly "
"defers to the LTCG/ROC rate."}.get(
loc, f"Recommended account: <b>{html.escape(loc)}</b>.")
return (f"<p>Character score {t.get('score', '?')} "
f"({conf}). <b>Placement: {rec}</b></p>"
+ (f"<p class='small'>{notes}</p>" if notes else ""))
def cluster_section(sym: str, fr: dict, row: np.ndarray | None) -> str:
srow = search_row(sym)
if sym in MEMBER:
cid, clabel, cn = MEMBER[sym]
members = KMEANS["clusters"][cid]["syms"]
else:
if row is None:
return "<p>Not in the cluster scheme (no loading vector).</p>"
cid, clabel = cluster_of_row(row)
cn = KMEANS["clusters"].get(cid, {}).get("n", 0)
members = KMEANS["clusters"].get(cid, {}).get("syms", [])
peers = []
for s in members:
if s == sym:
continue
v = search_row(s)
if not isinstance(v.get("alpha_t_5y"), (int, float)):
continue
peers.append((v["alpha_t_5y"], v.get("r2_5y") or 0, s, v))
# best peers = highest POSITIVE alpha t (a fund with t = -5 is the
# cluster's worst, not a peer worth copying); top-4, positives first
peers.sort(key=lambda p: (p[0] > 0, p[0]), reverse=True)
pos = [p for p in peers if p[0] > 0]
peers = (pos + [p for p in peers if p[0] <= 0])[:4]
peers = [p[2] for p in peers]
if not peers:
return (f"<p>In cluster <b>{html.escape(clabel)}</b> (n={cn}) but "
"no peer with 5y alpha statistics.</p>")
rows = []
def statline(s: str) -> str:
p = price(s)
if p is None:
return ("—", "—", "—", "—", "—", "n/a")
st = perf_stats(p)
v = search_row(s)
t5 = window_ret(p, "2021-01-01", st["end"])
r2 = v.get("r2_5y")
a5 = v.get("alpha_ann_5y")
tt = v.get("alpha_t_5y")
tax = (tax_row(s) or {}).get("location") or "n/a"
return (fmt_pct(t5), fmt_pct(st["cagr"]), fmt_pct(st["mdd"]),
fmt_r2(r2),
(f"{fmt_pct(a5)} (t={tt:+.1f})"
if isinstance(a5, (int, float)) else "—"), tax)
p = price(sym)
st = perf_stats(p) if p is not None else {}
my = statline(sym)
rows.append(f"<tr class='me'><td><b>{sym.upper()} (this fund)</b></td>"
+ "".join(f"<td>{x}</td>" for x in my) + "</tr>")
for s in peers:
rows.append(f"<tr><td>{s.upper()} — "
f"{html.escape((search_row(s) or {}).get('name', '')[:40])}"
f"</td>" + "".join(f"<td>{x}</td>" for x in statline(s))
+ "</tr>")
tbl = ('<table><tr><th>fund</th><th>5y</th><th>CAGR</th>'
'<th>maxDD</th><th>R² 5y</th><th>alpha 5y</th><th>tax</th></tr>'
+ "".join(rows) + "</table>")
# advantages / disadvantages: computed deltas vs the peer set
adv, dis = [], []
if p is not None:
t5 = window_ret(p, "2021-01-01", st["end"])
vals = {}
for s in peers:
pp = price(s)
if pp is None:
continue
ss = perf_stats(pp)
vals[s] = (window_ret(pp, "2021-01-01", ss["end"]), ss["mdd"],
ss["vol"])
if vals:
best_t5 = max(v[0] for v in vals.values() if v[0] is not None)
best_dd = max(v[1] for v in vals.values())
low_vol = min(v[2] for v in vals.values())
if t5 is not None and t5 >= best_t5 - 0.02:
adv.append("5y return at the top of the cluster")
elif t5 is not None and t5 < best_t5 - 0.10:
dis.append(f"5y return trails the best peer by "
f"{100 * (best_t5 - t5):.0f}pp")
if st["mdd"] > best_dd + 0.05:
dis.append(f"deeper drawdown than the calmest peer "
f"({fmt_pct(st['mdd'])} vs {fmt_pct(best_dd)})")
elif st["mdd"] < best_dd - 0.05:
adv.append("sharpest drawdown in the cluster")
if st["vol"] < low_vol - 0.02:
adv.append("lowest volatility in the cluster")
elif st["vol"] > low_vol + 0.05:
dis.append("meaningfully more volatile than the "
"calmest peer")
txt = ""
if adv:
txt += "<p><b>Advantages vs peers:</b> " + "; ".join(adv) + ".</p>"
if dis:
txt += "<p><b>Disadvantages vs peers:</b> " + "; ".join(dis) + ".</p>"
if not txt:
txt = ("<p>The fund sits in the middle of its cluster - no "
"decisive edge on return, drawdown or volatility vs the "
"peers; the choice among them should come down to alpha "
"quality (t-stat), tax fit and the conviction in the "
"strategy.</p>")
return (f"<p>Cluster: <b>{html.escape(clabel)}</b> "
f"(n={cn}, k=30 grouping by return-driver signature).</p>"
+ tbl + txt)
def fund_loading_row(fr: dict) -> np.ndarray:
f = fr.get("full") or fr.get("rec5") or {}
betas = f.get("betas") or {}
row = np.array([betas.get(s, 0.0) or 0.0 for s in factors.DRIVERS],
dtype=float)
return np.append(row, 1.0 - row.sum())
def strategy_block(sym: str) -> str:
f = FONDS.get(sym)
if not f:
return ""
out = ""
if f.get("strategy"):
out += f"<details><summary>Strategy (excerpt from the filing)</summary>"
f"<p class='small'>{html.escape(f['strategy'])}</p></details>"
return out
def style_block(sym: str) -> str:
from fundlab import styletilt
prof = styletilt.style_profile(sym)
if not prof or not prof.get("full"):
return ""
f5 = {x["sym"]: x for x in (prof.get("rec5") or {}).get("factors", [])}
rows = []
for x in prof["full"]["factors"]:
y5 = f5.get(x["sym"], {})
if abs(x["t"]) < 2 and abs(y5.get("t", 0) or 0) < 2:
continue
rows.append(
f"<tr><td>{html.escape(x['name'])}</td>"
f"<td>{x['beta']:+.2f}</td><td>{x['t']:+.1f}</td>"
f"<td>{y5['beta']:+.2f}</td><td>{y5['t']:+.1f}</td>"
f"<td>{'&#10003;' if x['sym'] in prof.get('identified', []) else ''}</td></tr>")
tbl = ('<table><tr><th>factor</th><th>β full</th><th>t full</th>'
'<th>β 5y</th><th>t 5y</th><th>identified</th></tr>'
+ "".join(rows) + "</table>")
para = "".join(f"<p>{html.escape(p)}</p>" for p in prof.get("commentary", []))
return ('<h4>Style tilts</h4>'
'<p class="small">22 style/asset sleeves regressed on the '
'excess returns of the fund (US funds proxy the global '
'factors); "identified" = survived the BIC forward-selection '
'gate on the full history.</p>' + tbl + para)
def narrative_block(sym: str) -> str:
from fundlab.narrative import narrate
return "".join(f"<p>{html.escape(par)}</p>" for par in narrate(sym))
# ------------------------------------------------------------------ build
def fund_section(sym: str, title: str, subtitle: str) -> str:
fr = factor_row(sym)
dr = decomp_row(sym)
srow = search_row(sym)
name = (srow.get("name") or (FONDS.get(sym) or {}).get("name")
or (fr or {}).get("name") or sym.upper())
refs = ref_components(sym, fr)
load = fund_loading_row(fr) if fr else None
# weak-fit (idiosyncratic) funds: the forward-selected mix is a
# statistically-thin spec combination (offsetting VIX/duration legs);
# anchoring the table to it misleads (a "reference" that loses 79% in
# 2022 for a fund that lost 2.4%). Below the fit gate the honest
# reference is CASH - the fund IS ~net-cash + idiosyncratic alpha.
r25 = srow.get("r2_5y") if isinstance(srow.get("r2_5y"),
(int, float)) else None
r2f = (fr.get("rec5") or fr.get("full") or {}).get("r2")
fit = r25 if r25 is not None else r2f
refs_curve = refs if (fit is not None and fit >= 0.5) else []
return f"""
<h3 id="{sym}">{html.escape(title)}
<span class="sub">{html.escape(subtitle)}</span></h3>
<div class="fund">{strategy_block(sym)}
<h4>Discussion</h4>
{narrative_block(sym)}
{equity_figure(sym, name, refs_curve)}
<h4>Performance</h4>
{perf_table(price(sym), refs_curve) if price(sym) is not None else '<p>no price data</p>'}
<h4>What drove the returns</h4>
{drivers_section(fr, dr, srow)}
{style_block(sym)}
<h4>The reference mix - and what it exposes you to</h4>
{reference_section(refs, refs_curve, sym)}
<h4>Tax character &amp; placement</h4>
{tax_section(sym)}
<h4>Peer comparison (same return-driver cluster)</h4>
{cluster_section(sym, fr, load)}
</div>"""
def main() -> None:
t0 = time.time()
load_plotly()
print("building report ...", flush=True)
cand = [v for v in SEARCH.values()
if isinstance(v, dict)
and str(v.get("verdict", "")).startswith("CANDIDATE")]
cand.sort(key=lambda v: -(v.get("alpha_t_5y")
if isinstance(v.get("alpha_t_5y"),
(int, float)) else -9))
# shortlist: the 16 actively-managed funds (3 are share classes of
# the same fund: pmfkx=pmaix, lcrix=lcorx, egrsx=eagmx)
from fundlab.decompose import ALIAS
from fundlab.nport import FUND_TOKENS
short = [s for s in FUND_TOKENS if s not in ALIAS]
toc = []
body = []
for i, v in enumerate(cand, 1):
s = v["sym"]
toc.append(f'<li><a href="#{s}">{s.upper()}</a> — '
f'{html.escape((v.get("name") or "")[:48])}</li>')
body.append(fund_section(
s, f"C{i:02d} · {s.upper()}",
f"{v.get('name', '')} — {str(v.get('verdict', ''))[:60]}"))
for i, s in enumerate(short, 1):
nm = (FONDS.get(s) or {}).get("name", s.upper())
toc.append(f'<li><a href="#{s}">S{i:02d} · {s.upper()}</a> — '
f'{html.escape(nm[:48])}</li>')
body.append(fund_section(s, f"S{i:02d} · {s.upper()}", nm))
doc = f"""<!DOCTYPE html>
<html><head><meta charset="utf-8">
<title>Fund report - candidates &amp; shortlist</title>
<style>
body {{ font-family: -apple-system, 'Segoe UI', Helvetica, Arial, sans-serif;
margin: 0; color: #1a1a2e; background: #fafafa; }}
.wrap {{ display: flex; }}
nav {{ width: 270px; min-width: 270px; position: sticky; top: 0;
height: 100vh; overflow-y: auto; background: #fff;
border-right: 1px solid #ddd; padding: 16px; box-sizing: border-box; }}
main {{ flex: 1; padding: 24px 36px; max-width: 1150px; }}
h1 {{ font-size: 26px; }} h3 {{ border-top: 3px solid #1a1a2e;
padding-top: 18px; margin-top: 40px; }}
h3 .sub {{ font-weight: normal; font-size: 14px; color: #555;
display: block; margin-top: 4px; }}
h4 {{ margin: 18px 0 6px; font-size: 15px; }}
table {{ border-collapse: collapse; margin: 8px 0 14px; font-size: 13px; }}
th, td {{ border: 1px solid #ccc; padding: 4px 9px; text-align: left; }}
th {{ background: #eee; }}
tr.me td {{ background: #fff8e1; }}
.small {{ color: #555; font-size: 12.5px; }}
nav a {{ text-decoration: none; color: #1a1a2e; font-size: 13px;
display: block; padding: 3px 0; }}
nav a:hover {{ color: #b00020; }}
nav h2 {{ font-size: 14px; margin: 14px 0 6px; }}
details {{ margin: 6px 0; }} summary {{ cursor: pointer;
font-size: 13px; color: #333; }}
</style>
{"<script>" + PLOTLY_JS + "</script>" if PLOTLY_JS else
'<script src="https://cdn.plot.ly/plotly-2.35.2.min.js"></script>'}
</head><body><div class="wrap">
<nav>
<b>Return-driver report</b><br><span class="small">generated
{time.strftime('%Y-%m-%d %H:%M')}</span>
<h2>Candidates (alpha screen, excess of T-bill)</h2>
<ol>{''.join(toc[:len(cand)])}</ol>
<h2>Shortlist (your funds)</h2>
<ol start="{len(cand) + 1}">{''.join(toc[len(cand):])}</ol>
</nav>
<main>
<h1>Fund report - {len(cand)} alpha candidates &amp; {len(short)}
shortlist funds</h1>
<p class="small"><b>Method.</b> Every alpha in this report is computed
<b>in excess of the 3-month T-bill rate</b> (BIL total return as the
local risk-free series): the fund's daily total returns are regressed on
sleeve benchmarks that are netted against the same rate, so a cash
position contributes exactly zero. "Reference" is never one index - it
is each fund's <b>own fitted sleeve mix</b> (BIC forward selection on the
broad axes, or the full 34-sleeve OLS for the loadings). R² measures how
much of the excess return the mix explains; alpha is what is left.
"Net cash" = 1 − (sum of loadings). Equity curves are total-return
(Adj Close) over the maximum local history, rebased to 100.</p>
{''.join(body)}
<p class="small" style="margin-top:40px">Generated by fundlab.report -
data as of {time.strftime('%Y-%m-%d')}. Local price histories may lag a
day or two. Cluster = k=30 k-means on the excess-return loading vectors
(34 sleeves + net-cash axis).</p>
</main></div></body></html>"""
OUT.write_text(doc)
print(f"wrote {OUT} in {time.time() - t0:.0f}s "
f"({len(doc) / 1e6:.1f} MB)")
if __name__ == "__main__":
main()

400
fundlab/reportdata.py
View File

@ -1,400 +0,0 @@
"""Build reports/report_data.json: everything the web app (and the HTML
report) need for the per-fund report pages, in one structured file.
One entry per fund (11 alpha candidates + 13 shortlist funds):
meta name, verdict, group, order
narrative list of prose paragraphs (fundlab.narrative)
chart {dates, fund, ref, ivv} (downsampled price paths, rebased)
perf rows [period, fund, reference, ivv, fund-ref] (strings)
drivers markdown lines (reference model, signature, verdicts)
reference {rows [[loading, name, exposure]], weak, netcash, text}
tax plain text
peers {cluster, rows, proscons}
Run: .venv/bin/python -m fundlab.reportdata
"""
from __future__ import annotations
import json
import time
from pathlib import Path
import numpy as np
import pandas as pd
from fundlab import report as _r
from fundlab import decompose
from fundlab import styletilt
from fundlab.narrative import narrate
HERE = Path(__file__).parent
OUT = HERE.parent / "reports" / "report_data.json"
MAX_PTS = 1500
def _down(s: pd.Series) -> tuple[list, list]:
if s is None:
return [], []
if len(s) > MAX_PTS:
s = s.iloc[:: (len(s) // MAX_PTS + 1)]
return [str(d.date()) for d in s.index], [float(v) for v in s]
def _chart(sym: str, refs_curve: list[dict]) -> dict:
p = _r.price(sym)
fd, fv = _down(100 * p / p.iloc[0] if p is not None else None)
if refs_curve:
mix = _r.mix_series(refs_curve)
else:
b = _r.price("bil")
mix = 100 * b / b.iloc[0] if b is not None else None
md, mv = _down(mix)
iv = _r.price("ivv")
id_, ivv = _down(100 * iv / iv.iloc[0] if iv is not None else None)
return {"dates": fd, "fund": fv, "ref_dates": md, "ref": mv,
"ivv_dates": id_, "ivv": ivv}
def _perf_rows(sym: str, refs_curve: list[dict]) -> list[list[str]]:
p = _r.price(sym)
if p is None:
return []
mix = (_r.mix_series(refs_curve) if refs_curve
else _r.price("bil"))
iv = _r.price("ivv")
st = _r.perf_stats(p)
rows = []
def add(label, a, b):
fr_ = _r.window_ret(p, a, b)
mr = _r.window_ret(mix, a, b)
ir = _r.window_ret(iv, a, b)
gap = (fr_ - mr) if (fr_ is not None and mr is not None) else None
rows.append([label, _r.fmt_pct(fr_), _r.fmt_pct(mr),
_r.fmt_pct(ir), _r.fmt_pct(gap)])
add("Full history", st["start"], st["end"])
add("Last 5y", "2021-01-01", st["end"])
add("Last 1y", "2025-09-01", st["end"])
for lab, a, b in _r.EPISODES:
add(lab, a, b)
yr = p.resample("YE").last().dropna()
years = [str(yr.index[i].year) for i in range(1, len(yr))][-6:]
for i, y in enumerate(years):
a = f"{y}-01-01"
b = f"{int(y) + 1}-01-01" if i < len(years) - 1 else st["end"]
add(y, a, b)
return rows
def _drivers(sym: str, fr: dict, srow: dict, dr: dict) -> list[str]:
out = []
a5 = srow.get("alpha_ann_5y")
t5 = srow.get("alpha_t_5y")
r25 = srow.get("r2_5y")
if isinstance(r25, (int, float)) or isinstance(a5, (int, float)):
out.append(
f"**Reference model, last 5 years:** R² = "
f"{_r.fmt_r2(r25 if isinstance(r25, (int, float)) else None)}, "
f"alpha = "
f"{_r.fmt_pct(a5) if isinstance(a5, (int, float)) else '—'}"
f"{' (t = ' + format(t5, '+.1f') + ')' if isinstance(t5, (int, float)) else ''}")
a_f = (fr.get("full") or {}).get("alpha_ann")
t_f = (fr.get("full") or {}).get("alpha_t")
r2f = srow.get("r2_full")
if isinstance(r2f, (int, float)) or isinstance(a_f, (int, float)):
out.append(
f"**Reference model, full history:** R² = "
f"{_r.fmt_r2(r2f if isinstance(r2f, (int, float)) else None)}, "
f"alpha = "
f"{_r.fmt_pct(a_f) if isinstance(a_f, (int, float)) else '—'}"
f"{' (t = ' + format(t_f, '+.1f') + ')' if isinstance(t_f, (int, float)) else ''}.")
f = fr.get("rec5") or {}
betas = f.get("betas") or {}
top = sorted(betas.items(), key=lambda kv: -abs(kv[1]))[:6]
if top:
out.append("**Return-driver signature (34 sleeves):** "
+ ", ".join(f"`{s}` {b:+.2f}" for s, b in top)
+ f" - net cash {1 - sum(betas.values()):+.2f}.")
if dr and "verdict" in dr:
out.append(f"**Decomposition verdict:** {dr['verdict']}")
roll = dr.get("rolling") or {}
if roll.get("max_drift") is not None:
out.append(f"Weight stability: max 1y β-drift = "
f"{roll['max_drift']:.2f} (0 = perfectly stable, "
f">1 = unstable).")
if dr.get("note"):
out.append(f"*{dr['note']}*")
if srow.get("verdict"):
out.append(f"**Screen verdict:** {srow['verdict']}")
return out
def _reference(sym: str, fr: dict, refs: list[dict],
refs_curve: list[dict]) -> dict:
rows = []
for c in refs:
nm, desc = _r.sleeve_desc(c["sym"])
rows.append([f"{c['sym'].upper()} {c['beta']:+.2f}", nm, desc])
sb = sum(c["beta"] for c in refs)
cash = 1 - sb
if cash > 0.05:
net = f"loadings sum to {sb:.2f} → ~{cash:.0%} net cash"
elif cash < -0.05:
net = f"loadings sum to {sb:.2f} → ~{-cash:.0%} net levered"
else:
net = f"loadings sum to {sb:.2f} → fully invested"
weak = bool(refs) and not refs_curve
text = ("**Weak fit - read with care.** These loadings are each "
"individually significant but collectively explain little; "
"the performance table anchors to cash, not this mix."
if weak else
"The reference is the fund's own fitted sleeve mix; "
"'alpha' everywhere means outperformance vs this mix, in "
"excess of the T-bill rate.")
return {"rows": rows, "weak": weak, "netcash": net, "text": text}
def _tax(sym: str) -> str:
t = _r.tax_row(sym)
if not t:
return "No tax classification on file."
loc = t.get("location", "?")
basis = t.get("basis", "?")
conf = "from actual N-PORT holdings (high confidence)" \
if basis == "N-PORT" else "from the return-sleeve mix (model)"
s = (f"Character score {t.get('score', '?')} ({conf}). ")
if "MIXED" in loc:
s += "Model says MIXED - the last 1099-DIV is the arbiter."
elif "TAXABLE" in loc:
if "munis" in loc:
s += ("Keep in the **taxable** account "
"(munis - tax-exempt interest is wasted in an IRA).")
elif "defers" in loc:
s += ("Keep in the **taxable** account "
"(income mostly defers to the LTCG/ROC rate).")
else:
s += "Keep in the **taxable** account."
else:
s += f"Recommended account: **{loc}**."
if t.get("notes"):
s += f" _{t['notes']}_"
return s
def _peers(sym: str, fr: dict) -> dict:
# no return history: a zero loading vector would park the fund in the
# cash cluster - no peer table for a fund with no measured returns
if _r.price(sym) is None:
return {}
if sym in _r.MEMBER:
cid, clabel, cn = _r.MEMBER[sym]
members = _r.KMEANS["clusters"][cid]["syms"]
else:
try:
cid, clabel = _r.cluster_of_row(_r.fund_loading_row(fr))
except Exception:
return {}
cn = _r.KMEANS["clusters"].get(cid, {}).get("n", 0)
members = _r.KMEANS["clusters"].get(cid, {}).get("syms", [])
peers = []
for s in members:
if s == sym:
continue
v = _r.search_row(s)
if isinstance(v.get("alpha_t_5y"), (int, float)):
peers.append((v["alpha_t_5y"], s))
peers.sort(key=lambda x: (x[0] > 0, x[0]), reverse=True)
pos = [p for p in peers if p[0] > 0]
peers = [s for _t, s in (pos + [p for p in peers if p[0] <= 0])[:4]]
def statline(s: str) -> list[str]:
p = _r.price(s)
if p is None:
return ["—", "—", "—", "—", "—", "n/a"]
st = _r.perf_stats(p)
v = _r.search_row(s)
t5 = _r.window_ret(p, "2021-01-01", st["end"])
a5 = v.get("alpha_ann_5y")
tt = v.get("alpha_t_5y")
tax = (_r.tax_row(s) or {}).get("location") or "n/a"
return [_r.fmt_pct(t5), _r.fmt_pct(st["cagr"]),
_r.fmt_pct(st["mdd"]),
_r.fmt_r2(v.get("r2_5y")),
(f"{_r.fmt_pct(a5)} (t={tt:+.1f})"
if isinstance(a5, (int, float)) else "—"), tax]
rows = [[f"{sym.upper()} (this fund)", *statline(sym)]]
for s in peers:
rows.append([s.upper(), *statline(s)])
# pros / cons
adv, dis = [], []
p = _r.price(sym)
if p is not None and peers:
st = _r.perf_stats(p)
t5 = _r.window_ret(p, "2021-01-01", st["end"])
vals = {}
for s in peers:
pp = _r.price(s)
if pp is None:
continue
ss = _r.perf_stats(pp)
vals[s] = (_r.window_ret(pp, "2021-01-01", ss["end"]),
ss["mdd"], ss["vol"])
if vals:
best_t5 = max(v[0] for v in vals.values() if v[0] is not None)
best_dd = max(v[1] for v in vals.values())
low_vol = min(v[2] for v in vals.values())
if t5 is not None and t5 >= best_t5 - 0.02:
adv.append("5y return at the top of the cluster")
elif t5 is not None and t5 < best_t5 - 0.10:
dis.append(f"5y return trails the best peer by "
f"{100 * (best_t5 - t5):.0f}pp")
if st["mdd"] < best_dd - 0.05:
adv.append("sharpest drawdown in the cluster")
elif st["mdd"] > best_dd + 0.05:
dis.append(f"deeper drawdown than the calmest peer "
f"({_r.fmt_pct(st['mdd'])} vs "
f"{_r.fmt_pct(best_dd)})")
if st["vol"] < low_vol - 0.02:
adv.append("lowest volatility in the cluster")
elif st["vol"] > low_vol + 0.05:
dis.append("meaningfully more volatile than the "
"calmest peer")
if adv:
pc = "**Advantages vs peers:** " + "; ".join(adv) + "."
elif dis:
pc = "**Disadvantages vs peers:** " + "; ".join(dis) + "."
else:
pc = ("Middle of the cluster - no decisive edge on return, "
"drawdown or volatility; the choice comes down to alpha "
"quality (t), tax fit and conviction in the strategy.")
if dis and adv:
pc += " **Disadvantages:** " + "; ".join(dis) + "."
return {"cluster": clabel, "n": cn,
"rows": rows, "proscons": pc}
ADHOC_FILE = HERE.parent / "reports" / "report_data_adhoc.json"
_ADHOC_MEM: dict = None # lazy-loaded disk cache
def _adhoc_store() -> dict:
global _ADHOC_MEM
if _ADHOC_MEM is None:
try:
_ADHOC_MEM = json.loads(ADHOC_FILE.read_text())
except Exception:
_ADHOC_MEM = {"funds": {}}
return _ADHOC_MEM
def ensure_adhoc(syms: list[str]) -> dict[str, dict]:
"""Build report entries for ARBITRARY funds (any symbol the user puts
in the portfolio box), cached in memory + on disk so repeat loads are
instant and survive server restarts. The one-time ~30-60 s driver-panel
build happens on the first call in a fresh process."""
store = _adhoc_store()
funds = store["funds"]
missing = [s for s in syms
if s not in funds
and _r.price(s) is not None]
if missing:
for s in missing:
funds[s] = build_fund(s, "adhoc",
order=len(funds) + 1)
ADHOC_FILE.parent.mkdir(exist_ok=True)
ADHOC_FILE.write_text(json.dumps(store))
return {s: funds[s] for s in syms if s in funds}
def build_fund(sym: str, group: str, order: int) -> dict:
t0 = time.time()
fr = _r.factor_row(sym)
dr = _r.decomp_row(sym)
srow = _r.search_row(sym)
name = (srow.get("name") or _r.FONDS.get(sym, {}).get("name")
or (fr or {}).get("name") or sym.upper())
refs = _r.ref_components(sym, fr)
r2f_ = (fr.get("rec5") or fr.get("full") or {}).get("r2")
r25 = srow.get("r2_5y") if isinstance(srow.get("r2_5y"), (int, float)) \
else None
fit = r25 if r25 is not None else r2f_
refs_curve = refs if (fit is not None and fit >= 0.5) else []
p = _r.price(sym)
st_ = _r.perf_stats(p) if p is not None else {}
stats = {"t5y": _r.fmt_pct(_r.window_ret(p, "2021-01-01",
st_["end"])) if p is not None
else "—",
"cagr": _r.fmt_pct(st_.get("cagr")),
"mdd": _r.fmt_pct(st_.get("mdd")),
"r2_5y": _r.fmt_r2(srow.get("r2_5y")),
"alpha_5y": (f"{_r.fmt_pct(srow['alpha_ann_5y'])} "
f"(t={srow['alpha_t_5y']:+.1f})"
if isinstance(srow.get("alpha_ann_5y"),
(int, float)) else "—")}
d = {
"meta": {"sym": sym, "name": name, "group": group, "order": order,
"verdict": srow.get("verdict", ""),
"objective": (_r.FONDS.get(sym) or {}).get("objective",
"")},
"stats": stats,
"narrative": narrate(sym),
"chart": _chart(sym, refs_curve),
"perf": _perf_rows(sym, refs_curve),
"drivers": _drivers(sym, fr, srow, dr),
"reference": _reference(sym, fr, refs, refs_curve),
"tax": _tax(sym),
"peers": _peers(sym, fr),
}
try:
d["style"] = styletilt.style_profile(sym)
except Exception:
d["style"] = None
print(f" {sym:8s} {time.time() - t0:5.1f}s", flush=True)
return d
def main() -> None:
from fundlab.decompose import ALIAS
from fundlab.nport import FUND_TOKENS
t0 = time.time()
cand = [v["sym"] for v in _r.SEARCH.values()
if isinstance(v, dict)
and str(v.get("verdict", "")).startswith("CANDIDATE")]
cand.sort(key=lambda s: -(_r.SEARCH[s]["alpha_t_5y"]
if isinstance(_r.SEARCH[s].get("alpha_t_5y"),
(int, float)) else -9))
short = [s for s in FUND_TOKENS if s not in ALIAS]
data = {"generated": time.strftime("%Y-%m-%d %H:%M"),
"funds": {}}
i = 0
for s in cand:
i += 1
data["funds"][s] = build_fund(s, "candidate", i)
for s in short:
i += 1
data["funds"][s] = build_fund(s, "shortlist", i)
OUT.parent.mkdir(exist_ok=True)
OUT.write_text(json.dumps(data))
# rebuild any on-demand (Symbol-box) reports so they gain the new
# sections too; keep their "adhoc" group
if ADHOC_FILE.exists():
try:
_old = json.loads(ADHOC_FILE.read_text())["funds"]
except Exception:
_old = {}
for j, s in enumerate(_old, 1):
if s in cand or s in short:
continue
_old[s] = build_fund(s, "adhoc", order=-j)
ADHOC_FILE.write_text(json.dumps({"funds": _old}))
print(f"rebuilt {len(_old)} ad-hoc reports")
print(f"wrote {OUT} in {time.time() - t0:.0f}s "
f"({OUT.stat().st_size / 1e6:.1f} MB, {len(data['funds'])} funds)")
if __name__ == "__main__":
main()

47683
fundlab/search_all.json
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fundlab/search_all_rawalpha.json
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fundlab/server_watchdog.log
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@ -1,4 +1,2 @@
2026-08-28 14:52:00
server_watchdog: no answer on :8599 - relaunching
2026-08-30 13:26:18
server_watchdog: no answer on :8599 - relaunching

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fundlab/styletilt.py
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"""Style-tilt battery: mathematical factor identification for one fund.
Regresses the fund's daily excess-of-T-bill returns on a battery of 22
style/asset sleeves (US funds proxying the global style factors; a fund
expresses them in its own universe, so betas are the right sign and
magnitude but approximate), over the full history and the last 5 years:
* plain OLS -> every factor's beta + t-stat (exploratory view)
* BIC forward selection -> the IDENTIFIED tilt stack (the strict gate:
a factor is only in if it measurably improves the model)
* residual alpha after the identified stack -> "is there skill left?"
Plus data-driven English commentary (fundlab.styletilt.commentary).
Caveats stated in the commentary, not hidden: US sleeves proxy global
factors; style sleeves are mutually collinear, so single-OLS t-stats are
indicative and the BIC set is the identification.
"""
from __future__ import annotations
import numpy as np
import pandas as pd
from fundlab import decompose
# factor label -> sleeve ticker (US proxies for the global factors)
BATTERY: dict[str, str] = {
"int-dev (EFA)": "efa", "EM (VWO)": "vwo", "growth (VUG)": "vug",
"value (VTV)": "vtv", "small (IWM)": "iwm", "equalwt (RSP)": "rsp",
"lowvol (USMV)": "usmv", "lowvol (DFLVX)": "dflvx",
"quality (QUAL)": "qual", "momentum (MTUM)": "mtum",
"dividend (HDV)": "hdv", "div-apprec (VYM)": "vym",
"longdur (TLT)": "tlt", "gold (GLD)": "gld",
"EUR (FXE)": "fxe", "JPY (FXY)": "fxy", "AUD (FXU)": "fxu",
"utilities (XLU)": "xlu", "staples (XLP)": "xlp",
"health (XLV)": "xlv", "banks (XLF)": "xlf", "energy (XLE)": "xle",
}
SYMS = list(dict.fromkeys(BATTERY.values()))
INV = {v: k for k, v in BATTERY.items()}
_PANEL: pd.DataFrame | None = None
def _panel() -> pd.DataFrame:
"""Battery sleeves' daily EXCESS-of-T-bill returns, cached per process."""
global _PANEL
if _PANEL is None:
_PANEL = decompose.excess(
decompose.returns_panel(SYMS, start=decompose.FULL_WINDOW))
return _PANEL
def _fund_excess(sym: str) -> pd.Series:
p = decompose.adj_close(sym)
if p is None:
return pd.Series(dtype=float)
y = p.pct_change()
rf = decompose.rf_series()
if rf is not None:
y = y.sub(rf.reindex(y.index).fillna(0.0))
return y
def _fit(y: pd.Series, a: str | None, b: str | None) -> dict:
x = _panel()
idx = x.index
msk = idx >= (a or idx[0])
if b is not None:
msk &= idx <= b
x = x[msk]
y = y.reindex(x.index)
ok = y.notna() & x.notna().all(axis=1)
if ok.sum() < 252:
return {}
X = np.column_stack([np.ones(int(ok.sum()))] +
[x[s][ok].to_numpy() for s in SYMS])
yn = y[ok].to_numpy()
beta, *_ = np.linalg.lstsq(X, yn, rcond=None)
res = yn - X @ beta
dof = max(int(ok.sum()) - len(SYMS) - 1, 1)
sigma2 = float(res @ res) / dof
se = np.sqrt(np.diag(np.linalg.inv(X.T @ X)) * sigma2)
t = np.where(se > 0, beta / se, 0.0)
r2 = 1 - float(res @ res) / float((yn - yn.mean()) @ (yn - yn.mean()))
return {
"n": int(ok.sum()),
"start": str(x.index[ok][0].date()),
"end": str(x.index[ok][-1].date()),
"r2": float(r2),
"alpha_ann": float(beta[0] * 252),
"alpha_t": float(t[0]),
"factors": [
{"name": INV[s], "sym": s, "beta": float(beta[k + 1]),
"t": float(t[k + 1])}
for k, s in enumerate(SYMS)],
}
def _forward(y: pd.Series, a: str | None, b: str | None) -> list[str]:
"""BIC-gated forward selection over the battery -> identified sleeves."""
x = _panel()
idx = x.index
msk = idx >= (a or idx[0])
if b is not None:
msk &= idx <= b
x = x[msk]
y = y.reindex(x.index)
ok = y.notna()
if ok.sum() < 252:
return []
cand = {s: x[s].to_numpy() for s in SYMS}
chosen, _m, _okk = decompose.forward_select(y.to_numpy(), cand, y_ok=ok)
return list(chosen)
# ------------------------------------------------------------- commentary
# human meaning of each sleeve (used for the prose)
MEANING: dict[str, str] = {
"efa": "international developed markets", "vwo": "emerging markets",
"vug": "US growth equities", "vtv": "US value equities",
"iwm": "US small caps", "rsp": "equal-weight (small-tilted) US",
"usmv": "low volatility", "dflvx": "low volatility",
"qual": "quality (high-ROE, low-debt, stable earnings)",
"mtum": "momentum (recent winners)", "hdv": "high dividend",
"vym": "high dividend (appreciation-tilted)",
"tlt": "long-duration Treasuries", "gld": "gold",
"fxe": "the euro", "fxy": "the yen", "fxu": "the Australian dollar",
"xlu": "utilities (defensive bond-proxy)",
"xlp": "consumer staples (defensive)",
"xlv": "healthcare (defensive)", "xlf": "banks/financials",
"xle": "energy",
}
# sign-specific phrasing where the generic 'tilt toward/away from' is wrong
SPECIAL: dict[tuple[str, bool], str] = {
("fxe", False): ("short-euro position that carries the USD/EUR "
"interest-rate differential"),
("fxy", False): ("short-yen position that carries the USD/JPY "
"rate differential"),
("fxu", False): "short-AUD position (USD carry)",
("fxu", True): "long-AUD position (AUD carry)",
("fxe", True): "long-euro position (exposed to EUR moves)",
("fxy", True): "long-yen position (safe-haven/carry)",
("qual", False): ("anti-quality tilt - systematically underweight "
"high-ROE, low-debt names; the mechanical flip side "
"of a high-payout dividend mandate"),
("qual", True): "quality tilt (high-ROE, low-debt, stable earnings)",
("mtum", False): ("anti-momentum character - it does not chase "
"recent winners (low-turnover/contrarian)"),
("mtum", True): "momentum tilt (preference for recent winners)",
("hdv", True): ("high-dividend tilt - a systematic preference for "
"income-paying names"),
("hdv", False): "underweight to high-dividend names",
("vym", True): "dividend-appreciation tilt (growing payers)",
("rsp", False): "cap-weight concentration (tilt away from "
"equal-weight/small)",
("rsp", True): "equal-weight (small-tilted) exposure",
("vwo", False): "developed-only tilt (away from emerging markets)",
("vwo", True): "an emerging-market tilt",
("usmv", True): "low-volatility (defensive beta) tilt",
("dflvx", True): "low-volatility (defensive beta) tilt",
("dflvx", False): "underweight to low-volatility names",
("usmv", False): "underweight to low-volatility names",
}
def _strength(t: float) -> str:
a = abs(t)
return "strong" if a >= 10 else "clear" if a >= 5 else "modest"
def _tilt_sentence(f: dict) -> str:
s = f["sym"]
key = (s, f["beta"] > 0)
if key in SPECIAL:
body = SPECIAL[key]
else:
m = MEANING.get(s, f["name"])
body = (f"tilt toward {m}" if f["beta"] > 0
else f"tilt away from {m}")
return (f"{_strength(f['t'])} {body} "
f"(β {f['beta']:+.2f}, t {f['t']:+.0f})")
def commentary(sym: str, prof: dict) -> list[str]:
"""English prose for the style-tilt block (data-driven)."""
if not prof or not prof.get("full"):
return []
full, rec5 = prof["full"], prof.get("rec5") or {}
sig_full = {f["sym"]: f for f in full["factors"] if abs(f["t"]) >= 2}
sig_5 = {f["sym"]: f for f in rec5.get("factors", [])
if abs(f["t"]) >= 2}
out = []
# paragraph 1: the identified stack
parts = []
core = sig_full.get("efa")
if core:
parts.append(
f"the core exposure is {abs(core['beta']):.0%} of "
f"international developed equities (t {core['t']:+.0f})")
for f in sorted(sig_full.values(), key=lambda x: -abs(x["t"])):
if f["sym"] == "efa":
continue
parts.append(_tilt_sentence(f))
if parts:
out.append(
f"Style-tilt analysis (22 style/asset sleeves regressed on "
f"the fund's excess returns; US funds proxying the global "
f"factors): over {full['start'][:4]}-to-{full['end'][:4]} the "
f"fit explains {full['r2']:.0%} of the excess return (R² "
f"{full['r2']:.2f}). The statistically identified tilt stack: "
+ "; ".join(parts) + ".")
# factors that only appear in the 5y window
recent_only = [s for s in sig_5
if s not in sig_full and s != "efa"]
if recent_only:
out.append(
"Visible only in the recent 5-year window: "
+ "; ".join(_tilt_sentence(sig_5[s]) for s in recent_only)
+ " - newer behavior, or a factor the longer sample dilutes.")
# paragraph 2: the residual - is there skill left?
a, t = full["alpha_ann"], full["alpha_t"]
if abs(t) < 1.75:
out.append(
f"After stripping the identified tilts, the residual excess "
f"return is {a * 100:+.1f}%/year (t {t:+.1f}) - NOT "
f"statistically significant: the fund's outperformance is "
f"factor exposure, not skill alpha.")
else:
out.append(
f"After stripping the identified tilts, a significant "
f"residual of {a * 100:+.1f}%/year (t {t:+.1f}) remains - "
f"genuine alpha on top of the factor stack.")
out.append(
"Caveat: the sleeves are US funds proxying global factors; the "
"fund expresses them in its own holdings, so the betas are the "
"right sign and magnitude but approximate. An index-matched "
"benchmark (the fund's own published index, when one exists) "
"would absorb part of the residual as well.")
return out
def style_profile(sym: str) -> dict | None:
"""Full + 5y battery fits, the BIC-identified stack, and commentary.
Returns None when the fund has no usable local history."""
y = _fund_excess(sym)
if len(y.dropna()) < 252:
return None
full = _fit(y, None, None)
if not full:
return None
rec5 = _fit(y, "2021-01-01", None)
prof = {"full": full,
"rec5": rec5 or {},
"identified": _forward(y, None, None)}
prof["commentary"] = commentary(sym, prof)
return prof

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overrides/corrections/.gitkeep
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overrides/frozen/aaalx-capitalGain.csv
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Date,Capital Gains
2022-12-28,0.007000
1 Date Capital Gains
2 2022-12-28 0.007000

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overrides/frozen/aaalx-dividend.csv
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@ -1,6 +0,0 @@
Date,Dividends
2024-12-20,0.399900
2025-04-08,0.065200
2021-12-28,0.250300
2022-12-28,0.007100
2023-12-21,0.280200
1 Date Dividends
2 2024-12-20 0.399900
3 2025-04-08 0.065200
4 2021-12-28 0.250300
5 2022-12-28 0.007100
6 2023-12-21 0.280200

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overrides/frozen/aaalx-history.csv
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overrides/frozen/aaalx.json
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@ -1,7 +0,0 @@
{
"symbol": "aaalx",
"longName": "American Century Asset Allocation Portfolios Inc. - One Choice Blend 2015\u00a0Portfolio Fund",
"freeze_date": "2025-04-17",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

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overrides/frozen/abqzx-capitalGain.csv
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@ -1,4 +0,0 @@
Date,Capital Gains
2021-12-03,0.018000
2016-12-07,0.040000
2020-12-04,0.145000
1 Date Capital Gains
2 2021-12-03 0.018000
3 2016-12-07 0.040000
4 2020-12-04 0.145000

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overrides/frozen/abqzx-dividend.csv
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Date,Dividends
2019-02-28,0.027378
2019-06-28,0.032440
2020-09-30,0.029451
2021-09-30,0.021411
2023-09-29,0.030585
2024-03-28,0.033582
2024-07-31,0.036753
2019-10-31,0.028129
2021-11-30,0.015153
2019-07-31,0.029595
2019-11-29,0.032274
2024-11-29,0.033543
2020-01-31,0.033029
2020-04-30,0.027369
2022-04-29,0.022921
2024-10-31,0.033149
2019-01-31,0.029606
2021-02-26,0.027489
2021-03-31,0.030588
2021-07-30,0.025064
2023-11-30,0.031725
2021-05-28,0.024247
2021-08-31,0.021447
2021-12-03,0.017700
2024-04-30,0.033871
2020-03-31,0.027427
2021-01-29,0.027906
2022-12-30,0.033246
2023-04-28,0.029077
2024-08-30,0.042260
2020-12-04,0.144700
2022-12-02,0.044800
2023-06-30,0.032841
2024-06-28,0.031146
2023-01-31,0.036682
2019-03-29,0.033541
2019-08-30,0.032235
2020-02-28,0.028150
2020-06-30,0.028395
2020-08-31,0.027902
2021-10-29,0.020966
2022-11-30,0.032311
2024-12-31,0.030620
2022-02-28,0.022998
2022-08-31,0.030780
2020-10-30,0.031190
2020-11-30,0.026206
2020-12-31,0.027202
2022-03-31,0.021258
2022-09-30,0.023069
2023-03-31,0.037445
2023-05-31,0.034867
2024-01-31,0.033743
2019-04-30,0.033428
2020-05-29,0.031073
2021-04-30,0.028135
2022-01-31,0.018929
2023-10-31,0.028526
2024-05-31,0.036527
2019-05-31,0.036744
2019-09-30,0.028360
2019-12-31,0.031747
2022-06-30,0.021610
2022-10-31,0.027292
2023-12-29,0.033158
2024-02-29,0.037804
2021-06-30,0.021983
2021-12-31,0.024863
2022-05-31,0.026023
2022-07-29,0.032376
2023-02-28,0.033407
2024-09-30,0.030860
2020-07-31,0.032462
2023-07-31,0.030966
2023-08-31,0.036161
1 Date Dividends
2 2019-02-28 0.027378
3 2019-06-28 0.032440
4 2020-09-30 0.029451
5 2021-09-30 0.021411
6 2023-09-29 0.030585
7 2024-03-28 0.033582
8 2024-07-31 0.036753
9 2019-10-31 0.028129
10 2021-11-30 0.015153
11 2019-07-31 0.029595
12 2019-11-29 0.032274
13 2024-11-29 0.033543
14 2020-01-31 0.033029
15 2020-04-30 0.027369
16 2022-04-29 0.022921
17 2024-10-31 0.033149
18 2019-01-31 0.029606
19 2021-02-26 0.027489
20 2021-03-31 0.030588
21 2021-07-30 0.025064
22 2023-11-30 0.031725
23 2021-05-28 0.024247
24 2021-08-31 0.021447
25 2021-12-03 0.017700
26 2024-04-30 0.033871
27 2020-03-31 0.027427
28 2021-01-29 0.027906
29 2022-12-30 0.033246
30 2023-04-28 0.029077
31 2024-08-30 0.042260
32 2020-12-04 0.144700
33 2022-12-02 0.044800
34 2023-06-30 0.032841
35 2024-06-28 0.031146
36 2023-01-31 0.036682
37 2019-03-29 0.033541
38 2019-08-30 0.032235
39 2020-02-28 0.028150
40 2020-06-30 0.028395
41 2020-08-31 0.027902
42 2021-10-29 0.020966
43 2022-11-30 0.032311
44 2024-12-31 0.030620
45 2022-02-28 0.022998
46 2022-08-31 0.030780
47 2020-10-30 0.031190
48 2020-11-30 0.026206
49 2020-12-31 0.027202
50 2022-03-31 0.021258
51 2022-09-30 0.023069
52 2023-03-31 0.037445
53 2023-05-31 0.034867
54 2024-01-31 0.033743
55 2019-04-30 0.033428
56 2020-05-29 0.031073
57 2021-04-30 0.028135
58 2022-01-31 0.018929
59 2023-10-31 0.028526
60 2024-05-31 0.036527
61 2019-05-31 0.036744
62 2019-09-30 0.028360
63 2019-12-31 0.031747
64 2022-06-30 0.021610
65 2022-10-31 0.027292
66 2023-12-29 0.033158
67 2024-02-29 0.037804
68 2021-06-30 0.021983
69 2021-12-31 0.024863
70 2022-05-31 0.026023
71 2022-07-29 0.032376
72 2023-02-28 0.033407
73 2024-09-30 0.030860
74 2020-07-31 0.032462
75 2023-07-31 0.030966
76 2023-08-31 0.036161

1516
overrides/frozen/abqzx-history.csv
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7
overrides/frozen/abqzx.json
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@ -1,7 +0,0 @@
{
"symbol": "abqzx",
"longName": "AB Bond Fund, Inc. - AB Total Return Bond Portfolio",
"freeze_date": "2025-01-08",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

1
overrides/frozen/adusx-dividend.csv
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@ -1 +0,0 @@
Date,Dividends,ctime
1 Date Dividends ctime

503
overrides/frozen/adusx-history.csv
View File

@ -1,503 +0,0 @@
Date,Open,High,Low,Close,Adj Close,Volume,ctime
2015-12-21,15.000000,15.000000,15.000000,15.000000,15.000000,0,2017-08-22
2015-12-22,14.960000,14.960000,14.960000,14.960000,14.960000,0,2017-08-22
2015-12-23,14.970000,14.970000,14.970000,14.970000,14.970000,0,2017-08-22
2015-12-24,14.990000,14.990000,14.990000,14.990000,14.990000,0,2017-08-22
2015-12-28,14.990000,14.990000,14.990000,14.990000,14.990000,0,2017-08-22
2015-12-29,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2015-12-30,15.000000,15.000000,15.000000,15.000000,15.000000,0,2017-08-22
2015-12-31,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2016-01-04,14.980000,14.980000,14.980000,14.980000,14.980000,0,2017-08-22
2016-01-05,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2016-01-06,15.050000,15.050000,15.050000,15.050000,15.050000,0,2017-08-22
2016-01-07,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2016-01-08,15.020000,15.020000,15.020000,15.020000,15.020000,0,2017-08-22
2016-01-11,15.090000,15.090000,15.090000,15.090000,15.090000,0,2017-08-22
2016-01-12,15.150000,15.150000,15.150000,15.150000,15.150000,0,2017-08-22
2016-01-13,15.090000,15.090000,15.090000,15.090000,15.090000,0,2017-08-22
2016-01-14,15.080000,15.080000,15.080000,15.080000,15.080000,0,2017-08-22
2016-01-15,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2016-01-19,15.120000,15.120000,15.120000,15.120000,15.120000,0,2017-08-22
2016-01-20,15.090000,15.090000,15.090000,15.090000,15.090000,0,2017-08-22
2016-01-21,15.050000,15.050000,15.050000,15.050000,15.050000,0,2017-08-22
2016-01-22,15.100000,15.100000,15.100000,15.100000,15.100000,0,2017-08-22
2016-01-25,15.090000,15.090000,15.090000,15.090000,15.090000,0,2017-08-22
2016-01-26,15.100000,15.100000,15.100000,15.100000,15.100000,0,2017-08-22
2016-01-27,15.090000,15.090000,15.090000,15.090000,15.090000,0,2017-08-22
2016-01-28,15.140000,15.140000,15.140000,15.140000,15.140000,0,2017-08-22
2016-01-29,15.170000,15.170000,15.170000,15.170000,15.170000,0,2017-08-22
2016-02-01,15.190000,15.190000,15.190000,15.190000,15.190000,0,2017-08-22
2016-02-02,15.220000,15.220000,15.220000,15.220000,15.220000,0,2017-08-22
2016-02-03,15.140000,15.140000,15.140000,15.140000,15.140000,0,2017-08-22
2016-02-04,15.020000,15.020000,15.020000,15.020000,15.020000,0,2017-08-22
2016-02-05,15.000000,15.000000,15.000000,15.000000,15.000000,0,2017-08-22
2016-02-08,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2016-02-09,15.050000,15.050000,15.050000,15.050000,15.050000,0,2017-08-22
2016-02-10,15.070000,15.070000,15.070000,15.070000,15.070000,0,2017-08-22
2016-02-11,15.090000,15.090000,15.090000,15.090000,15.090000,0,2017-08-22
2016-02-12,15.060000,15.060000,15.060000,15.060000,15.060000,0,2017-08-22
2016-02-16,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2016-02-17,14.960000,14.960000,14.960000,14.960000,14.960000,0,2017-08-22
2016-02-18,14.920000,14.920000,14.920000,14.920000,14.920000,0,2017-08-22
2016-02-19,14.940000,14.940000,14.940000,14.940000,14.940000,0,2017-08-22
2016-02-22,14.860000,14.860000,14.860000,14.860000,14.860000,0,2017-08-22
2016-02-23,14.890000,14.890000,14.890000,14.890000,14.890000,0,2017-08-22
2016-02-24,14.890000,14.890000,14.890000,14.890000,14.890000,0,2017-08-22
2016-02-25,14.910000,14.910000,14.910000,14.910000,14.910000,0,2017-08-22
2016-02-26,14.840000,14.840000,14.840000,14.840000,14.840000,0,2017-08-22
2016-02-29,14.790000,14.790000,14.790000,14.790000,14.790000,0,2017-08-22
2016-03-01,14.710000,14.710000,14.710000,14.710000,14.710000,0,2017-08-22
2016-03-02,14.710000,14.710000,14.710000,14.710000,14.710000,0,2017-08-22
2016-03-03,14.640000,14.640000,14.640000,14.640000,14.640000,0,2017-08-22
2016-03-04,14.600000,14.600000,14.600000,14.600000,14.600000,0,2017-08-22
2016-03-07,14.520000,14.520000,14.520000,14.520000,14.520000,0,2017-08-22
2016-03-08,14.610000,14.610000,14.610000,14.610000,14.610000,0,2017-08-22
2016-03-09,14.640000,14.640000,14.640000,14.640000,14.640000,0,2017-08-22
2016-03-10,14.700000,14.700000,14.700000,14.700000,14.700000,0,2017-08-22
2016-03-11,14.710000,14.710000,14.710000,14.710000,14.710000,0,2017-08-22
2016-03-14,14.740000,14.740000,14.740000,14.740000,14.740000,0,2017-08-22
2016-03-15,14.790000,14.790000,14.790000,14.790000,14.790000,0,2017-08-22
2016-03-16,14.780000,14.780000,14.780000,14.780000,14.780000,0,2017-08-22
2016-03-17,14.720000,14.720000,14.720000,14.720000,14.720000,0,2017-08-22
2016-03-18,14.710000,14.710000,14.710000,14.710000,14.710000,0,2017-08-22
2016-03-21,14.740000,14.740000,14.740000,14.740000,14.740000,0,2017-08-22
2016-03-22,14.720000,14.720000,14.720000,14.720000,14.720000,0,2017-08-22
2016-03-23,14.800000,14.800000,14.800000,14.800000,14.800000,0,2017-08-22
2016-03-24,14.790000,14.790000,14.790000,14.790000,14.790000,0,2017-08-22
2016-03-28,14.790000,14.790000,14.790000,14.790000,14.790000,0,2017-08-22
2016-03-29,14.800000,14.800000,14.800000,14.800000,14.800000,0,2017-08-22
2016-03-30,14.810000,14.810000,14.810000,14.810000,14.810000,0,2017-08-22
2016-03-31,14.790000,14.790000,14.790000,14.790000,14.790000,0,2017-08-22
2016-04-01,14.820000,14.820000,14.820000,14.820000,14.820000,0,2017-08-22
2016-04-04,14.880000,14.880000,14.880000,14.880000,14.880000,0,2017-08-22
2016-04-05,14.950000,14.950000,14.950000,14.950000,14.950000,0,2017-08-22
2016-04-06,14.980000,14.980000,14.980000,14.980000,14.980000,0,2017-08-22
2016-04-07,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2016-04-08,15.020000,15.020000,15.020000,15.020000,15.020000,0,2017-08-22
2016-04-11,14.990000,14.990000,14.990000,14.990000,14.990000,0,2017-08-22
2016-04-12,15.000000,15.000000,15.000000,15.000000,15.000000,0,2017-08-22
2016-04-13,14.880000,14.880000,14.880000,14.880000,14.880000,0,2017-08-22
2016-04-14,14.880000,14.880000,14.880000,14.880000,14.880000,0,2017-08-22
2016-04-15,14.870000,14.870000,14.870000,14.870000,14.870000,0,2017-08-22
2016-04-18,14.870000,14.870000,14.870000,14.870000,14.870000,0,2017-08-22
2016-04-19,14.890000,14.890000,14.890000,14.890000,14.890000,0,2017-08-22
2016-04-20,14.870000,14.870000,14.870000,14.870000,14.870000,0,2017-08-22
2016-04-21,14.820000,14.820000,14.820000,14.820000,14.820000,0,2017-08-22
2016-04-22,14.720000,14.720000,14.720000,14.720000,14.720000,0,2017-08-22
2016-04-25,14.800000,14.800000,14.800000,14.800000,14.800000,0,2017-08-22
2016-04-26,14.780000,14.780000,14.780000,14.780000,14.780000,0,2017-08-22
2016-04-27,14.760000,14.760000,14.760000,14.760000,14.760000,0,2017-08-22
2016-04-28,14.800000,14.800000,14.800000,14.800000,14.800000,0,2017-08-22
2016-04-29,14.790000,14.790000,14.790000,14.790000,14.790000,0,2017-08-22
2016-05-02,14.850000,14.850000,14.850000,14.850000,14.850000,0,2017-08-22
2016-05-03,14.870000,14.870000,14.870000,14.870000,14.870000,0,2017-08-22
2016-05-04,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2016-05-05,15.050000,15.050000,15.050000,15.050000,15.050000,0,2017-08-22
2016-05-06,15.060000,15.060000,15.060000,15.060000,15.060000,0,2017-08-22
2016-05-09,15.150000,15.150000,15.150000,15.150000,15.150000,0,2017-08-22
2016-05-10,15.170000,15.170000,15.170000,15.170000,15.170000,0,2017-08-22
2016-05-11,15.160000,15.160000,15.160000,15.160000,15.160000,0,2017-08-22
2016-05-12,15.180000,15.180000,15.180000,15.180000,15.180000,0,2017-08-22
2016-05-13,15.210000,15.210000,15.210000,15.210000,15.210000,0,2017-08-22
2016-05-16,15.190000,15.190000,15.190000,15.190000,15.190000,0,2017-08-22
2016-05-17,15.100000,15.100000,15.100000,15.100000,15.100000,0,2017-08-22
2016-05-18,15.170000,15.170000,15.170000,15.170000,15.170000,0,2017-08-22
2016-05-20,15.140000,15.140000,15.140000,15.140000,15.140000,0,2017-08-22
2016-05-23,15.080000,15.080000,15.080000,15.080000,15.080000,0,2017-08-22
2016-05-24,15.100000,15.100000,15.100000,15.100000,15.100000,0,2017-08-22
2016-05-25,15.040000,15.040000,15.040000,15.040000,15.040000,0,2017-08-22
2016-05-26,15.080000,15.080000,15.080000,15.080000,15.080000,0,2017-08-22
2016-05-27,15.070000,15.070000,15.070000,15.070000,15.070000,0,2017-08-22
2016-05-31,15.040000,15.040000,15.040000,15.040000,15.040000,0,2017-08-22
2016-06-01,15.060000,15.060000,15.060000,15.060000,15.060000,0,2017-08-22
2016-06-02,15.030000,15.030000,15.030000,15.030000,15.030000,0,2017-08-22
2016-06-03,15.030000,15.030000,15.030000,15.030000,15.030000,0,2017-08-22
2016-06-06,14.930000,14.930000,14.930000,14.930000,14.930000,0,2017-08-22
2016-06-07,14.910000,14.910000,14.910000,14.910000,14.910000,0,2017-08-22
2016-06-08,14.950000,14.950000,14.950000,14.950000,14.950000,0,2017-08-22
2016-06-09,14.990000,14.990000,14.990000,14.990000,14.990000,0,2017-08-22
2016-06-10,15.040000,15.040000,15.040000,15.040000,15.040000,0,2017-08-22
2016-06-13,15.070000,15.070000,15.070000,15.070000,15.070000,0,2017-08-22
2016-06-14,15.090000,15.090000,15.090000,15.090000,15.090000,0,2017-08-22
2016-06-15,15.050000,15.050000,15.050000,15.050000,15.050000,0,2017-08-22
2016-06-16,15.060000,15.060000,15.060000,15.060000,15.060000,0,2017-08-22
2016-06-17,15.000000,15.000000,15.000000,15.000000,15.000000,0,2017-08-22
2016-06-20,14.920000,14.920000,14.920000,14.920000,14.920000,0,2017-08-22
2016-06-21,14.990000,14.990000,14.990000,14.990000,14.990000,0,2017-08-22
2016-06-22,14.980000,14.980000,14.980000,14.980000,14.980000,0,2017-08-22
2016-06-23,14.910000,14.910000,14.910000,14.910000,14.910000,0,2017-08-22
2016-06-24,15.090000,15.090000,15.090000,15.090000,15.090000,0,2017-08-22
2016-06-27,15.250000,15.250000,15.250000,15.250000,15.250000,0,2017-08-22
2016-06-28,15.190000,15.190000,15.190000,15.190000,15.190000,0,2017-08-22
2016-06-29,15.140000,15.140000,15.140000,15.140000,15.140000,0,2017-08-22
2016-06-30,15.150000,15.150000,15.150000,15.150000,15.150000,0,2017-08-22
2016-07-01,15.070000,15.070000,15.070000,15.070000,15.070000,0,2017-08-22
2016-07-05,15.210000,15.210000,15.210000,15.210000,15.210000,0,2017-08-22
2016-07-06,15.180000,15.180000,15.180000,15.180000,15.180000,0,2017-08-22
2016-07-07,15.210000,15.210000,15.210000,15.210000,15.210000,0,2017-08-22
2016-07-08,15.170000,15.170000,15.170000,15.170000,15.170000,0,2017-08-22
2016-07-11,15.120000,15.120000,15.120000,15.120000,15.120000,0,2017-08-22
2016-07-12,14.970000,14.970000,14.970000,14.970000,14.970000,0,2017-08-22
2016-07-13,14.970000,14.970000,14.970000,14.970000,14.970000,0,2017-08-22
2016-07-14,14.990000,14.990000,14.990000,14.990000,14.990000,0,2017-08-22
2016-07-15,15.000000,15.000000,15.000000,15.000000,15.000000,0,2017-08-22
2016-07-18,14.990000,14.990000,14.990000,14.990000,14.990000,0,2017-08-22
2016-07-19,15.110000,15.110000,15.110000,15.110000,15.110000,0,2017-08-22
2016-07-20,15.070000,15.070000,15.070000,15.070000,15.070000,0,2017-08-22
2016-07-21,14.980000,14.980000,14.980000,14.980000,14.980000,0,2017-08-22
2016-07-22,15.020000,15.020000,15.020000,15.020000,15.020000,0,2017-08-22
2016-07-25,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2016-07-26,14.870000,14.870000,14.870000,14.870000,14.870000,0,2017-08-22
2016-07-27,14.890000,14.890000,14.890000,14.890000,14.890000,0,2017-08-22
2016-07-28,14.960000,14.960000,14.960000,14.960000,14.960000,0,2017-08-22
2016-07-29,14.960000,14.960000,14.960000,14.960000,14.960000,0,2017-08-22
2016-08-01,14.990000,14.990000,14.990000,14.990000,14.990000,0,2017-08-22
2016-08-02,15.070000,15.070000,15.070000,15.070000,15.070000,0,2017-08-22
2016-08-03,15.060000,15.060000,15.060000,15.060000,15.060000,0,2017-08-22
2016-08-04,15.010000,15.010000,15.010000,15.010000,15.010000,0,2017-08-22
2016-08-05,14.950000,14.950000,14.950000,14.950000,14.950000,0,2017-08-22
2016-08-08,14.920000,14.920000,14.920000,14.920000,14.920000,0,2017-08-22
2016-08-09,14.940000,14.940000,14.940000,14.940000,14.940000,0,2017-08-22
2016-08-10,14.930000,14.930000,14.930000,14.930000,14.930000,0,2017-08-22
2016-08-11,14.900000,14.900000,14.900000,14.900000,14.900000,0,2017-08-22
2016-08-12,14.910000,14.910000,14.910000,14.910000,14.910000,0,2017-08-22
2016-08-15,14.880000,14.880000,14.880000,14.880000,14.880000,0,2017-08-22
2016-08-16,14.780000,14.780000,14.780000,14.780000,14.780000,0,2017-08-22
2016-08-17,14.850000,14.850000,14.850000,14.850000,14.850000,0,2017-08-22
2016-08-18,14.840000,14.840000,14.840000,14.840000,14.840000,0,2017-08-22
2016-08-19,14.840000,14.840000,14.840000,14.840000,14.840000,0,2017-08-22
2016-08-22,14.870000,14.870000,14.870000,14.870000,14.870000,0,2017-08-22
2016-08-23,14.870000,14.870000,14.870000,14.870000,14.870000,0,2017-08-22
2016-08-24,14.850000,14.850000,14.850000,14.850000,14.850000,0,2017-08-22
2016-08-25,14.780000,14.780000,14.780000,14.780000,14.780000,0,2017-08-22
2016-08-26,14.820000,14.820000,14.820000,14.820000,14.820000,0,2017-08-22
2016-08-29,14.810000,14.810000,14.810000,14.810000,14.810000,0,2017-08-22
2016-08-30,14.830000,14.830000,14.830000,14.830000,14.830000,0,2017-08-22
2016-08-31,14.840000,14.840000,14.840000,14.840000,14.840000,0,2017-08-22
2016-09-01,14.850000,14.850000,14.850000,14.850000,14.850000,0,2017-08-22
2016-09-02,14.830000,14.830000,14.830000,14.830000,14.830000,0,2017-08-22
2016-09-06,14.790000,14.790000,14.790000,14.790000,14.790000,0,2017-08-22
2016-09-07,14.770000,14.770000,14.770000,14.770000,14.770000,0,2017-08-22
2016-09-08,14.790000,14.790000,14.790000,14.790000,14.790000,0,2017-08-22
2016-09-09,14.910000,14.910000,14.910000,14.910000,14.910000,0,2017-08-22
2016-09-12,14.860000,14.860000,14.860000,14.860000,14.860000,0,2017-08-22
2016-09-13,14.910000,14.910000,14.910000,14.910000,14.910000,0,2017-08-22
2016-09-14,14.960000,14.960000,14.960000,14.960000,14.960000,0,2017-08-22
2016-09-15,14.930000,14.930000,14.930000,14.930000,14.930000,0,2017-08-22
2016-09-16,14.960000,14.960000,14.960000,14.960000,14.960000,0,2017-08-22
2016-09-19,14.950000,14.950000,14.950000,14.950000,14.950000,0,2017-08-22
2016-09-20,14.980000,14.980000,14.980000,14.980000,14.980000,0,2017-08-22
2016-09-21,14.950000,14.950000,14.950000,14.950000,14.950000,0,2017-08-22
2016-09-22,14.930000,14.930000,14.930000,14.930000,14.930000,0,2017-08-22
2016-09-23,14.910000,14.910000,14.910000,14.910000,14.910000,0,2017-08-22
2016-09-26,14.900000,14.900000,14.900000,14.900000,14.900000,0,2017-08-22
2016-09-27,14.880000,14.880000,14.880000,14.880000,14.880000,0,2017-08-22
2016-09-28,14.840000,14.840000,14.840000,14.840000,14.840000,0,2017-08-22
2016-09-29,14.790000,14.790000,14.790000,14.790000,14.790000,0,2017-08-22
2016-09-30,14.770000,14.770000,14.770000,14.770000,14.770000,0,2017-08-22
2016-10-03,14.770000,14.770000,14.770000,14.770000,14.770000,0,2017-08-22
2016-10-04,14.810000,14.810000,14.810000,14.810000,14.810000,0,2017-08-22
2016-10-05,14.810000,14.810000,14.810000,14.810000,14.810000,0,2017-08-22
2016-10-06,14.810000,14.810000,14.810000,14.810000,14.810000,0,2017-08-22
2016-10-07,14.850000,14.850000,14.850000,14.850000,14.850000,0,2017-08-22
2016-10-10,14.910000,14.910000,14.910000,14.910000,14.910000,0,2017-08-22
2016-10-11,14.890000,14.890000,14.890000,14.890000,14.890000,0,2017-08-22
2016-10-12,14.870000,14.870000,14.870000,14.870000,14.870000,0,2017-08-22
2016-10-13,14.860000,14.860000,14.860000,14.860000,14.860000,0,2017-08-22
2016-10-14,14.880000,14.880000,14.880000,14.880000,14.880000,0,2017-08-22
2016-10-17,14.860000,14.860000,14.860000,14.860000,14.860000,0,2017-08-22
2016-10-18,14.810000,14.810000,14.810000,14.810000,14.810000,0,2017-08-22
2016-10-19,14.730000,14.730000,14.730000,14.730000,14.730000,0,2017-08-22
2016-10-20,14.750000,14.750000,14.750000,14.750000,14.750000,0,2017-08-22
2016-10-21,14.780000,14.780000,14.780000,14.780000,14.780000,0,2017-08-22
2016-10-24,14.780000,14.780000,14.780000,14.780000,14.780000,0,2017-08-22
2016-10-25,14.800000,14.800000,14.800000,14.800000,14.800000,0,2017-08-22
2016-10-26,14.800000,14.800000,14.800000,14.800000,14.800000,0,2017-08-22
2016-10-27,14.750000,14.750000,14.750000,14.750000,14.750000,0,2017-08-22
2016-10-28,14.770000,14.770000,14.770000,14.770000,14.770000,0,2017-08-22
2016-10-31,14.710000,14.710000,14.710000,14.710000,14.710000,0,2017-08-22
2016-11-01,14.730000,14.730000,14.730000,14.730000,14.730000,0,2017-08-22
2016-11-02,14.620000,14.620000,14.620000,14.620000,14.620000,0,2017-08-22
2016-11-03,14.620000,14.620000,14.620000,14.620000,14.620000,0,2017-08-22
2016-11-04,14.600000,14.600000,14.600000,14.600000,14.600000,0,2017-08-22
2016-11-07,14.590000,14.590000,14.590000,14.590000,14.590000,0,2017-08-22
2016-11-08,14.600000,14.600000,14.600000,14.600000,14.600000,0,2017-08-22
2016-11-09,14.600000,14.600000,14.600000,14.600000,14.600000,0,2017-08-22
2016-11-10,14.560000,14.560000,14.560000,14.560000,14.560000,0,2017-08-22
2016-11-11,14.500000,14.500000,14.500000,14.500000,14.500000,0,2017-08-22
2016-11-14,14.510000,14.510000,14.510000,14.510000,14.510000,0,2017-08-22
2016-11-15,14.550000,14.550000,14.550000,14.550000,14.550000,0,2017-08-22
2016-11-16,14.510000,14.510000,14.510000,14.510000,14.510000,0,2017-08-22
2016-11-17,14.580000,14.580000,14.580000,14.580000,14.580000,0,2017-08-22
2016-11-18,14.560000,14.560000,14.560000,14.560000,14.560000,0,2017-08-22
2016-11-21,14.550000,14.550000,14.550000,14.550000,14.550000,0,2017-08-22
2016-11-22,14.450000,14.450000,14.450000,14.450000,14.450000,0,2017-08-22
2016-11-23,14.460000,14.460000,14.460000,14.460000,14.460000,0,2017-08-22
2016-11-25,14.440000,14.440000,14.440000,14.440000,14.440000,0,2017-08-22
2016-11-28,14.380000,14.380000,14.380000,14.380000,14.380000,0,2017-08-22
2016-11-29,14.420000,14.420000,14.420000,14.420000,14.420000,0,2017-08-22
2016-11-30,14.510000,14.510000,14.510000,14.510000,14.510000,0,2017-08-22
2016-12-01,14.510000,14.510000,14.510000,14.510000,14.510000,0,2017-08-22
2016-12-02,14.500000,14.500000,14.500000,14.500000,14.500000,0,2017-08-22
2016-12-05,14.490000,14.490000,14.490000,14.490000,14.490000,0,2017-08-22
2016-12-06,14.480000,14.480000,14.480000,14.480000,14.480000,0,2017-08-22
2016-12-07,14.420000,14.420000,14.420000,14.420000,14.420000,0,2017-08-22
2016-12-08,14.440000,14.440000,14.440000,14.440000,14.440000,0,2017-08-22
2016-12-09,14.420000,14.420000,14.420000,14.420000,14.420000,0,2017-08-22
2016-12-12,14.350000,14.350000,14.350000,14.350000,14.350000,0,2017-08-22
2016-12-13,14.390000,14.390000,14.390000,14.390000,14.390000,0,2017-08-22
2016-12-14,14.440000,14.440000,14.440000,14.440000,14.440000,0,2017-08-22
2016-12-15,14.440000,14.440000,14.440000,14.440000,14.440000,0,2017-08-22
2016-12-16,14.410000,14.410000,14.410000,14.410000,14.410000,0,2017-08-22
2016-12-19,14.390000,14.390000,14.390000,14.390000,14.390000,0,2017-08-22
2016-12-20,14.380000,14.380000,14.380000,14.380000,14.380000,0,2017-08-22
2016-12-21,14.390000,14.390000,14.390000,14.390000,14.390000,0,2017-08-22
2016-12-22,14.400000,14.400000,14.400000,14.400000,14.400000,0,2017-08-22
2016-12-23,14.400000,14.400000,14.400000,14.400000,14.400000,0,2017-08-22
2016-12-27,14.390000,14.390000,14.390000,14.390000,14.390000,0,2017-08-22
2016-12-28,14.390000,14.390000,14.390000,14.390000,14.390000,0,2017-08-22
2016-12-29,14.400000,14.400000,14.400000,14.400000,14.400000,0,2017-08-22
2016-12-30,14.380000,14.380000,14.380000,14.380000,14.380000,0,2017-08-22
2017-01-03,14.360000,14.360000,14.360000,14.360000,14.360000,0,2017-08-22
2017-01-04,14.330000,14.330000,14.330000,14.330000,14.330000,0,2017-08-22
2017-01-05,14.370000,14.370000,14.370000,14.370000,14.370000,0,2017-08-22
2017-01-06,14.330000,14.330000,14.330000,14.330000,14.330000,0,2017-08-22
2017-01-09,14.360000,14.360000,14.360000,14.360000,14.360000,0,2017-08-22
2017-01-10,14.350000,14.350000,14.350000,14.350000,14.350000,0,2017-08-22
2017-01-11,14.420000,14.420000,14.420000,14.420000,14.420000,0,2017-08-22
2017-01-12,14.370000,14.370000,14.370000,14.370000,14.370000,0,2017-08-22
2017-01-13,14.430000,14.430000,14.430000,14.430000,14.430000,0,2017-08-22
2017-01-17,14.390000,14.390000,14.390000,14.390000,14.390000,0,2017-08-22
2017-01-18,14.420000,14.420000,14.420000,14.420000,14.420000,0,2017-08-22
2017-01-19,14.430000,14.430000,14.430000,14.430000,14.430000,0,2017-08-22
2017-01-20,14.470000,14.470000,14.470000,14.470000,14.470000,0,2017-08-22
2017-01-23,14.470000,14.470000,14.470000,14.470000,14.470000,0,2017-08-22
2017-01-24,14.430000,14.430000,14.430000,14.430000,14.430000,0,2017-08-22
2017-01-25,14.450000,14.450000,14.450000,14.450000,14.450000,0,2017-08-22
2017-01-26,14.490000,14.490000,14.490000,14.490000,14.490000,0,2017-08-22
2017-01-27,14.480000,14.480000,14.480000,14.480000,14.480000,0,2017-08-22
2017-01-30,14.440000,14.440000,14.440000,14.440000,14.440000,0,2017-08-22
2017-01-31,14.420000,14.420000,14.420000,14.420000,14.420000,0,2017-08-22
2017-02-01,14.440000,14.440000,14.440000,14.440000,14.440000,0,2017-08-22
2017-02-02,14.450000,14.450000,14.450000,14.450000,14.450000,0,2017-08-22
2017-02-03,14.430000,14.430000,14.430000,14.430000,14.430000,0,2017-08-22
2017-02-06,14.390000,14.390000,14.390000,14.390000,14.390000,0,2017-08-22
2017-02-07,14.390000,14.390000,14.390000,14.390000,14.390000,0,2017-08-22
2017-02-08,14.330000,14.330000,14.330000,14.330000,14.330000,0,2017-08-22
2017-02-09,14.340000,14.340000,14.340000,14.340000,14.340000,0,2017-08-22
2017-02-10,14.300000,14.300000,14.300000,14.300000,14.300000,0,2017-08-22
2017-02-13,14.310000,14.310000,14.310000,14.310000,14.310000,0,2017-08-22
2017-02-14,14.300000,14.300000,14.300000,14.300000,14.300000,0,2017-08-22
2017-02-15,14.280000,14.280000,14.280000,14.280000,14.280000,0,2017-08-22
2017-02-16,14.200000,14.200000,14.200000,14.200000,14.200000,0,2017-08-22
2017-02-17,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-02-21,14.170000,14.170000,14.170000,14.170000,14.170000,0,2017-08-22
2017-02-22,14.160000,14.160000,14.160000,14.160000,14.160000,0,2017-08-22
2017-02-23,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-02-24,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-02-27,14.110000,14.110000,14.110000,14.110000,14.110000,0,2017-08-22
2017-02-28,14.140000,14.140000,14.140000,14.140000,14.140000,0,2017-08-22
2017-03-01,14.110000,14.110000,14.110000,14.110000,14.110000,0,2017-08-22
2017-03-02,14.080000,14.080000,14.080000,14.080000,14.080000,0,2017-08-22
2017-03-03,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-03-06,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-03-07,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-03-08,14.060000,14.060000,14.060000,14.060000,14.060000,0,2017-08-22
2017-03-09,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-03-10,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-03-13,14.180000,14.180000,14.180000,14.180000,14.180000,0,2017-08-22
2017-03-14,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-03-15,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-03-16,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-03-17,14.130000,14.130000,14.130000,14.130000,14.130000,0,2017-08-22
2017-03-20,14.100000,14.100000,14.100000,14.100000,14.100000,0,2017-08-22
2017-03-21,14.020000,14.020000,14.020000,14.020000,14.020000,0,2017-08-22
2017-03-22,14.020000,14.020000,14.020000,14.020000,14.020000,0,2017-08-22
2017-03-23,14.030000,14.030000,14.030000,14.030000,14.030000,0,2017-08-22
2017-03-24,14.030000,14.030000,14.030000,14.030000,14.030000,0,2017-08-22
2017-03-27,14.060000,14.060000,14.060000,14.060000,14.060000,0,2017-08-22
2017-03-28,14.100000,14.100000,14.100000,14.100000,14.100000,0,2017-08-22
2017-03-29,14.110000,14.110000,14.110000,14.110000,14.110000,0,2017-08-22
2017-03-30,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-03-31,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-04-03,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-04-04,14.200000,14.200000,14.200000,14.200000,14.200000,0,2017-08-22
2017-04-05,14.180000,14.180000,14.180000,14.180000,14.180000,0,2017-08-22
2017-04-06,14.210000,14.210000,14.210000,14.210000,14.210000,0,2017-08-22
2017-04-07,14.220000,14.220000,14.220000,14.220000,14.220000,0,2017-08-22
2017-04-10,14.170000,14.170000,14.170000,14.170000,14.170000,0,2017-08-22
2017-04-11,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-04-12,14.130000,14.130000,14.130000,14.130000,14.130000,0,2017-08-22
2017-04-13,14.100000,14.100000,14.100000,14.100000,14.100000,0,2017-08-22
2017-04-17,14.080000,14.080000,14.080000,14.080000,14.080000,0,2017-08-22
2017-04-18,14.060000,14.060000,14.060000,14.060000,14.060000,0,2017-08-22
2017-04-19,14.010000,14.010000,14.010000,14.010000,14.010000,0,2017-08-22
2017-04-20,14.030000,14.030000,14.030000,14.030000,14.030000,0,2017-08-22
2017-04-21,14.000000,14.000000,14.000000,14.000000,14.000000,0,2017-08-22
2017-04-24,14.050000,14.050000,14.050000,14.050000,14.050000,0,2017-08-22
2017-04-25,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-04-26,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-04-27,14.070000,14.070000,14.070000,14.070000,14.070000,0,2017-08-22
2017-04-28,14.040000,14.040000,14.040000,14.040000,14.040000,0,2017-08-22
2017-05-01,14.080000,14.080000,14.080000,14.080000,14.080000,0,2017-08-22
2017-05-02,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-05-03,14.110000,14.110000,14.110000,14.110000,14.110000,0,2017-08-22
2017-05-04,14.080000,14.080000,14.080000,14.080000,14.080000,0,2017-08-22
2017-05-05,14.060000,14.060000,14.060000,14.060000,14.060000,0,2017-08-22
2017-05-08,14.040000,14.040000,14.040000,14.040000,14.040000,0,2017-08-22
2017-05-09,14.000000,14.000000,14.000000,14.000000,14.000000,0,2017-08-22
2017-05-10,14.000000,14.000000,14.000000,14.000000,14.000000,0,2017-08-22
2017-05-11,14.020000,14.020000,14.020000,14.020000,14.020000,0,2017-08-22
2017-05-12,14.040000,14.040000,14.040000,14.040000,14.040000,0,2017-08-22
2017-05-15,14.060000,14.060000,14.060000,14.060000,14.060000,0,2017-08-22
2017-05-16,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-05-17,13.970000,13.970000,13.970000,13.970000,13.970000,0,2017-08-22
2017-05-18,14.030000,14.030000,14.030000,14.030000,14.030000,0,2017-08-22
2017-05-19,14.050000,14.050000,14.050000,14.050000,14.050000,0,2017-08-22
2017-05-22,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-05-23,14.140000,14.140000,14.140000,14.140000,14.140000,0,2017-08-22
2017-05-24,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-05-25,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-05-26,14.190000,14.190000,14.190000,14.190000,14.190000,0,2017-08-22
2017-05-30,14.130000,14.130000,14.130000,14.130000,14.130000,0,2017-08-22
2017-05-31,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-06-01,14.060000,14.060000,14.060000,14.060000,14.060000,0,2017-08-22
2017-06-02,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-06-05,14.100000,14.100000,14.100000,14.100000,14.100000,0,2017-08-22
2017-06-06,14.130000,14.130000,14.130000,14.130000,14.130000,0,2017-08-22
2017-06-07,14.190000,14.190000,14.190000,14.190000,14.190000,0,2017-08-22
2017-06-08,14.230000,14.230000,14.230000,14.230000,14.230000,0,2017-08-22
2017-06-09,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-06-12,14.010000,14.010000,14.010000,14.010000,14.010000,0,2017-08-22
2017-06-13,14.060000,14.060000,14.060000,14.060000,14.060000,0,2017-08-22
2017-06-14,14.050000,14.050000,14.050000,14.050000,14.050000,0,2017-08-22
2017-06-15,14.040000,14.040000,14.040000,14.040000,14.040000,0,2017-08-22
2017-06-16,14.030000,14.030000,14.030000,14.030000,14.030000,0,2017-08-22
2017-06-19,14.100000,14.100000,14.100000,14.100000,14.100000,0,2017-08-22
2017-06-20,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-06-21,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-06-22,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-06-23,14.100000,14.100000,14.100000,14.100000,14.100000,0,2017-08-22
2017-06-26,14.040000,14.040000,14.040000,14.040000,14.040000,0,2017-08-22
2017-06-27,14.000000,14.000000,14.000000,14.000000,14.000000,0,2017-08-22
2017-06-28,14.050000,14.050000,14.050000,14.050000,14.050000,0,2017-08-22
2017-06-29,13.980000,13.980000,13.980000,13.980000,13.980000,0,2017-08-22
2017-06-30,13.960000,13.960000,13.960000,13.960000,13.960000,0,2017-08-22
2017-07-03,13.910000,13.910000,13.910000,13.910000,13.910000,0,2017-08-22
2017-07-05,14.040000,14.040000,14.040000,14.040000,14.040000,0,2017-08-22
2017-07-06,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-07-07,14.110000,14.110000,14.110000,14.110000,14.110000,0,2017-08-22
2017-07-10,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-07-11,14.130000,14.130000,14.130000,14.130000,14.130000,0,2017-08-22
2017-07-12,14.170000,14.170000,14.170000,14.170000,14.170000,0,2017-08-22
2017-07-13,14.140000,14.140000,14.140000,14.140000,14.140000,0,2017-08-22
2017-07-14,14.130000,14.130000,14.130000,14.130000,14.130000,0,2017-08-22
2017-07-17,14.090000,14.090000,14.090000,14.090000,14.090000,0,2017-08-22
2017-07-18,14.120000,14.120000,14.120000,14.120000,14.120000,0,2017-08-22
2017-07-19,14.170000,14.170000,14.170000,14.170000,14.170000,0,2017-08-22
2017-07-20,14.170000,14.170000,14.170000,14.170000,14.170000,0,2017-08-22
2017-07-21,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-07-24,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-07-25,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-07-26,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-07-27,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-07-28,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-07-31,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-01,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-02,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-03,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-04,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-07,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-08,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-09,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-10,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-11,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-14,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-15,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-16,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-17,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-18,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-21,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-22
2017-08-22,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-24 08:26:38
2017-08-23,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-24 08:26:38
2017-08-24,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-25 07:50:50
2017-08-25,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-26 07:47:11
2017-08-28,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-29 07:18:44
2017-08-29,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-29 22:27:18
2017-08-30,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-08-30 21:26:47
2017-08-31,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-01 08:18:04
2017-09-01,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-05 07:48:27
2017-09-05,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-06 07:03:16
2017-09-06,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-07 07:38:19
2017-09-07,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-08 07:09:10
2017-09-08,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-09 08:43:50
2017-09-11,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-13 06:44:33
2017-09-12,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-13 14:29:06
2017-09-13,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-15 07:54:07
2017-09-14,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-15 07:54:07
2017-09-15,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-18 07:57:00
2017-09-18,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-19 07:04:50
2017-09-19,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-20 07:02:59
2017-09-20,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-21 06:52:01
2017-09-21,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-22 07:46:25
2017-09-22,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-23 08:05:02
2017-09-25,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-26 07:33:50
2017-09-26,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-27 07:38:44
2017-09-27,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-28 06:48:39
2017-09-28,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-09-29 08:00:20
2017-09-29,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-04 07:54:54
2017-10-02,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-04 07:54:54
2017-10-03,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-04 07:54:54
2017-10-04,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-05 07:13:33
2017-10-05,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-06 06:58:57
2017-10-06,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-09 07:35:40
2017-10-09,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-10 07:30:35
2017-10-10,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-11 07:10:33
2017-10-11,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-12 07:02:18
2017-10-12,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-13 07:44:41
2017-10-13,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-15 21:31:35
2017-10-16,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-17 07:24:53
2017-10-17,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-18 06:38:28
2017-10-18,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-19 07:41:01
2017-10-19,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-20 07:48:13
2017-10-20,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-23 07:36:15
2017-10-23,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-24 07:44:32
2017-10-24,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-26 06:30:50
2017-10-25,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-26 06:30:50
2017-10-26,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-31 08:26:49
2017-10-27,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-31 08:26:49
2017-10-30,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-10-31 08:26:49
2017-10-31,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-01 07:49:35
2017-11-01,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-07 10:58:03
2017-11-02,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-07 10:58:03
2017-11-03,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-07 10:58:03
2017-11-06,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-07 10:58:03
2017-11-07,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-08 07:52:27
2017-11-08,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-15 07:55:39
2017-11-09,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-15 07:55:39
2017-11-10,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-15 07:55:39
2017-11-13,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-15 07:55:39
2017-11-14,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-15 07:55:39
2017-11-15,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-20 07:43:46
2017-11-16,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-20 07:43:46
2017-11-17,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-20 07:43:46
2017-11-20,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-28 13:58:38
2017-11-21,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-28 13:58:38
2017-11-22,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-28 13:58:38
2017-11-24,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-28 13:58:38
2017-11-27,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-28 13:58:38
2017-11-28,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-30 07:17:01
2017-11-29,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-11-30 07:17:01
2017-11-30,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-02 19:04:46
2017-12-01,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-02 19:04:46
2017-12-04,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-06 14:41:27
2017-12-05,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-06 14:41:27
2017-12-06,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-11 20:07:47
2017-12-07,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-11 20:07:47
2017-12-08,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-11 20:07:47
2017-12-11,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-12 07:18:16
2017-12-12,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-13 10:46:22
2017-12-13,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-14 07:15:00
2017-12-14,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-15 07:46:46
2017-12-15,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-19 07:11:54
2017-12-18,14.150000,14.150000,14.150000,14.150000,14.150000,0,2017-12-19 07:11:54
1 Date Open High Low Close Adj Close Volume ctime
2 2015-12-21 15.000000 15.000000 15.000000 15.000000 15.000000 0 2017-08-22
3 2015-12-22 14.960000 14.960000 14.960000 14.960000 14.960000 0 2017-08-22
4 2015-12-23 14.970000 14.970000 14.970000 14.970000 14.970000 0 2017-08-22
5 2015-12-24 14.990000 14.990000 14.990000 14.990000 14.990000 0 2017-08-22
6 2015-12-28 14.990000 14.990000 14.990000 14.990000 14.990000 0 2017-08-22
7 2015-12-29 15.010000 15.010000 15.010000 15.010000 15.010000 0 2017-08-22
8 2015-12-30 15.000000 15.000000 15.000000 15.000000 15.000000 0 2017-08-22
9 2015-12-31 15.010000 15.010000 15.010000 15.010000 15.010000 0 2017-08-22
10 2016-01-04 14.980000 14.980000 14.980000 14.980000 14.980000 0 2017-08-22
11 2016-01-05 15.010000 15.010000 15.010000 15.010000 15.010000 0 2017-08-22
12 2016-01-06 15.050000 15.050000 15.050000 15.050000 15.050000 0 2017-08-22
13 2016-01-07 15.010000 15.010000 15.010000 15.010000 15.010000 0 2017-08-22
14 2016-01-08 15.020000 15.020000 15.020000 15.020000 15.020000 0 2017-08-22
15 2016-01-11 15.090000 15.090000 15.090000 15.090000 15.090000 0 2017-08-22
16 2016-01-12 15.150000 15.150000 15.150000 15.150000 15.150000 0 2017-08-22
17 2016-01-13 15.090000 15.090000 15.090000 15.090000 15.090000 0 2017-08-22
18 2016-01-14 15.080000 15.080000 15.080000 15.080000 15.080000 0 2017-08-22
19 2016-01-15 15.010000 15.010000 15.010000 15.010000 15.010000 0 2017-08-22
20 2016-01-19 15.120000 15.120000 15.120000 15.120000 15.120000 0 2017-08-22
21 2016-01-20 15.090000 15.090000 15.090000 15.090000 15.090000 0 2017-08-22
22 2016-01-21 15.050000 15.050000 15.050000 15.050000 15.050000 0 2017-08-22
23 2016-01-22 15.100000 15.100000 15.100000 15.100000 15.100000 0 2017-08-22
24 2016-01-25 15.090000 15.090000 15.090000 15.090000 15.090000 0 2017-08-22
25 2016-01-26 15.100000 15.100000 15.100000 15.100000 15.100000 0 2017-08-22
26 2016-01-27 15.090000 15.090000 15.090000 15.090000 15.090000 0 2017-08-22
27 2016-01-28 15.140000 15.140000 15.140000 15.140000 15.140000 0 2017-08-22
28 2016-01-29 15.170000 15.170000 15.170000 15.170000 15.170000 0 2017-08-22
29 2016-02-01 15.190000 15.190000 15.190000 15.190000 15.190000 0 2017-08-22
30 2016-02-02 15.220000 15.220000 15.220000 15.220000 15.220000 0 2017-08-22
31 2016-02-03 15.140000 15.140000 15.140000 15.140000 15.140000 0 2017-08-22
32 2016-02-04 15.020000 15.020000 15.020000 15.020000 15.020000 0 2017-08-22
33 2016-02-05 15.000000 15.000000 15.000000 15.000000 15.000000 0 2017-08-22
34 2016-02-08 15.010000 15.010000 15.010000 15.010000 15.010000 0 2017-08-22
35 2016-02-09 15.050000 15.050000 15.050000 15.050000 15.050000 0 2017-08-22
36 2016-02-10 15.070000 15.070000 15.070000 15.070000 15.070000 0 2017-08-22
37 2016-02-11 15.090000 15.090000 15.090000 15.090000 15.090000 0 2017-08-22
38 2016-02-12 15.060000 15.060000 15.060000 15.060000 15.060000 0 2017-08-22
39 2016-02-16 15.010000 15.010000 15.010000 15.010000 15.010000 0 2017-08-22
40 2016-02-17 14.960000 14.960000 14.960000 14.960000 14.960000 0 2017-08-22
41 2016-02-18 14.920000 14.920000 14.920000 14.920000 14.920000 0 2017-08-22
42 2016-02-19 14.940000 14.940000 14.940000 14.940000 14.940000 0 2017-08-22
43 2016-02-22 14.860000 14.860000 14.860000 14.860000 14.860000 0 2017-08-22
44 2016-02-23 14.890000 14.890000 14.890000 14.890000 14.890000 0 2017-08-22
45 2016-02-24 14.890000 14.890000 14.890000 14.890000 14.890000 0 2017-08-22
46 2016-02-25 14.910000 14.910000 14.910000 14.910000 14.910000 0 2017-08-22
47 2016-02-26 14.840000 14.840000 14.840000 14.840000 14.840000 0 2017-08-22
48 2016-02-29 14.790000 14.790000 14.790000 14.790000 14.790000 0 2017-08-22
49 2016-03-01 14.710000 14.710000 14.710000 14.710000 14.710000 0 2017-08-22
50 2016-03-02 14.710000 14.710000 14.710000 14.710000 14.710000 0 2017-08-22
51 2016-03-03 14.640000 14.640000 14.640000 14.640000 14.640000 0 2017-08-22
52 2016-03-04 14.600000 14.600000 14.600000 14.600000 14.600000 0 2017-08-22
53 2016-03-07 14.520000 14.520000 14.520000 14.520000 14.520000 0 2017-08-22
54 2016-03-08 14.610000 14.610000 14.610000 14.610000 14.610000 0 2017-08-22
55 2016-03-09 14.640000 14.640000 14.640000 14.640000 14.640000 0 2017-08-22
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295 2017-02-22 14.160000 14.160000 14.160000 14.160000 14.160000 0 2017-08-22
296 2017-02-23 14.120000 14.120000 14.120000 14.120000 14.120000 0 2017-08-22
297 2017-02-24 14.090000 14.090000 14.090000 14.090000 14.090000 0 2017-08-22
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299 2017-02-28 14.140000 14.140000 14.140000 14.140000 14.140000 0 2017-08-22
300 2017-03-01 14.110000 14.110000 14.110000 14.110000 14.110000 0 2017-08-22
301 2017-03-02 14.080000 14.080000 14.080000 14.080000 14.080000 0 2017-08-22
302 2017-03-03 14.120000 14.120000 14.120000 14.120000 14.120000 0 2017-08-22
303 2017-03-06 14.120000 14.120000 14.120000 14.120000 14.120000 0 2017-08-22
304 2017-03-07 14.120000 14.120000 14.120000 14.120000 14.120000 0 2017-08-22
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309 2017-03-14 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-08-22
310 2017-03-15 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-08-22
311 2017-03-16 14.120000 14.120000 14.120000 14.120000 14.120000 0 2017-08-22
312 2017-03-17 14.130000 14.130000 14.130000 14.130000 14.130000 0 2017-08-22
313 2017-03-20 14.100000 14.100000 14.100000 14.100000 14.100000 0 2017-08-22
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366 2017-06-05 14.100000 14.100000 14.100000 14.100000 14.100000 0 2017-08-22
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428 2017-08-31 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-09-01 08:18:04
429 2017-09-01 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-09-05 07:48:27
430 2017-09-05 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-09-06 07:03:16
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434 2017-09-11 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-09-13 06:44:33
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445 2017-09-26 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-09-27 07:38:44
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460 2017-10-17 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-10-18 06:38:28
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486 2017-11-22 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-11-28 13:58:38
487 2017-11-24 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-11-28 13:58:38
488 2017-11-27 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-11-28 13:58:38
489 2017-11-28 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-11-30 07:17:01
490 2017-11-29 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-11-30 07:17:01
491 2017-11-30 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-12-02 19:04:46
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493 2017-12-04 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-12-06 14:41:27
494 2017-12-05 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-12-06 14:41:27
495 2017-12-06 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-12-11 20:07:47
496 2017-12-07 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-12-11 20:07:47
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499 2017-12-12 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-12-13 10:46:22
500 2017-12-13 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-12-14 07:15:00
501 2017-12-14 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-12-15 07:46:46
502 2017-12-15 14.150000 14.150000 14.150000 14.150000 14.150000 0 2017-12-19 07:11:54
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7
overrides/frozen/adusx.json
View File

@ -1,7 +0,0 @@
{
"symbol": "adusx",
"longName": "?",
"freeze_date": "2017-12-18",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

5
overrides/frozen/aectx-capitalGain.csv
View File

@ -1,5 +0,0 @@
Date,Capital Gains
2021-12-21,1.204000
2017-12-19,0.016000
2018-12-18,0.414000
2020-12-22,0.608000
1 Date Capital Gains
2 2021-12-21 1.204000
3 2017-12-19 0.016000
4 2018-12-18 0.414000
5 2020-12-22 0.608000

10
overrides/frozen/aectx-dividend.csv
View File

@ -1,10 +0,0 @@
Date,Dividends
2019-12-17,0.092000
2020-12-22,0.608000
2022-12-21,0.291000
2024-12-17,0.052000
2016-12-20,0.124000
2017-12-19,0.206000
2018-12-18,0.414000
2021-12-21,1.276000
2024-03-19,0.164000
1 Date Dividends
2 2019-12-17 0.092000
3 2020-12-22 0.608000
4 2022-12-21 0.291000
5 2024-12-17 0.052000
6 2016-12-20 0.124000
7 2017-12-19 0.206000
8 2018-12-18 0.414000
9 2021-12-21 1.276000
10 2024-03-19 0.164000

2
overrides/frozen/aectx-history.csv
View File

@ -1,2 +0,0 @@
Date,Open,High,Low,Close,Adj Close,Volume
2025-09-23,14.420000,14.420000,14.420000,14.420000,14.420000,0
1 Date Open High Low Close Adj Close Volume
2 2025-09-23 14.420000 14.420000 14.420000 14.420000 14.420000 0

7
overrides/frozen/aectx.json
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@ -1,7 +0,0 @@
{
"symbol": "aectx",
"longName": "American Century World Mutual Funds, Inc. - Emerging Markets Small Cap Fund",
"freeze_date": "2025-09-23",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

478
overrides/frozen/afxpx-dividend.csv
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@ -1,478 +0,0 @@
Date,Dividends,ctime
2013-04-01,0.007,2017-08-22
2017-07-03,0.014,2017-08-22
2014-08-01,0.012,2017-08-22
2017-01-03,0.014,2017-08-22
2014-02-03,0.007,2017-08-22
2013-07-01,0.031,2017-08-22
2013-01-02,0.007,2017-08-22
2012-06-01,0.013,2017-08-22
2015-12-22,0.22,2017-08-22
2013-10-01,0.007,2017-08-22
2014-12-01,0.009,2017-08-22
2011-12-30,0.004,2017-08-22
2016-06-01,0.016,2017-08-22
2015-05-01,0.005,2017-08-22
2015-03-02,0.004,2017-08-22
2012-09-04,0.036,2017-08-22
2012-04-02,0.016,2017-08-22
2013-08-01,0.025,2017-08-22
2016-04-01,0.014,2017-08-22
2012-03-01,0.057,2017-08-22
2015-02-02,0.006,2017-08-22
2016-12-01,0.014,2017-08-22
2012-07-02,0.038,2017-08-22
2016-10-03,0.013,2017-08-22
2013-11-01,0.009,2017-08-22
2014-01-02,0.007,2017-08-22
2016-07-01,0.028,2017-08-22
2012-10-01,0.046,2017-08-22
2015-06-01,0.01,2017-08-22
2015-12-01,0.013,2017-08-22
2014-05-01,0.01,2017-08-22
2017-04-03,0.018,2017-08-22
2012-12-20,0.137,2017-08-22
2015-09-01,0.005,2017-08-22
2016-03-01,0.013,2017-08-22
2011-11-30,0.003,2017-08-22
2014-11-03,0.011,2017-08-22
2016-11-01,0.013,2017-08-22
2012-08-01,0.023,2017-08-22
2015-04-01,0.004,2017-08-22
2012-05-01,0.006,2017-08-22
2016-02-01,0.013,2017-08-22
2016-08-01,0.016,2017-08-22
2015-07-01,0.024,2017-08-22
2015-01-02,0.02,2017-08-22
2012-01-31,0.004,2017-08-22
2012-12-03,0.018,2017-08-22
2014-06-02,0.013,2017-08-22
2015-10-01,0.014,2017-08-22
2013-03-01,0.004,2017-08-22
2017-08-01,0.025,2017-08-22
2013-05-01,0.009,2017-08-22
2013-12-20,0.069,2017-08-22
2014-09-02,0.015,2017-08-22
2017-03-01,0.013,2017-08-22
2015-11-02,0.011,2017-08-22
2017-05-01,0.01,2017-08-22
2014-04-01,0.009,2017-08-22
2016-01-04,0.025,2017-08-22
2013-02-01,0.006,2017-08-22
2015-08-03,0.009,2017-08-22
2017-06-01,0.021,2017-08-22
2014-07-01,0.028,2017-08-22
2014-12-22,0.105,2017-08-22
2016-05-03,0.015,2017-08-22
2013-06-03,0.02,2017-08-22
2013-12-02,0.008,2017-08-22
2014-10-01,0.013,2017-08-22
2011-10-31,0.003,2017-08-22
2017-02-01,0.025,2017-08-22
2014-03-03,0.012,2017-08-22
2013-09-03,0.026,2017-08-22
2017-10-02,0.015,2017-10-10 07:30:35
2013-01-02,0.007,2017-10-20 09:54:16
2012-06-01,0.013,2017-10-20 09:54:16
2015-12-22,0.22,2017-10-20 09:54:16
2013-10-01,0.007,2017-10-20 09:54:16
2014-12-01,0.009,2017-10-20 09:54:16
2011-12-30,0.004,2017-10-20 09:54:16
2016-06-01,0.016,2017-10-20 09:54:16
2015-05-01,0.005,2017-10-20 09:54:16
2015-03-02,0.004,2017-10-20 09:54:16
2012-09-04,0.036,2017-10-20 09:54:16
2012-04-02,0.016,2017-10-20 09:54:16
2013-08-01,0.025,2017-10-20 09:54:16
2016-04-01,0.014,2017-10-20 09:54:16
2012-03-01,0.057,2017-10-20 09:54:16
2015-02-02,0.006,2017-10-20 09:54:16
2016-12-01,0.014,2017-10-20 09:54:16
2012-07-02,0.038,2017-10-20 09:54:16
2016-10-03,0.013,2017-10-20 09:54:16
2013-11-01,0.009,2017-10-20 09:54:16
2014-01-02,0.007,2017-10-20 09:54:16
2016-07-01,0.028,2017-10-20 09:54:16
2012-10-01,0.046,2017-10-20 09:54:16
2015-06-01,0.01,2017-10-20 09:54:16
2015-12-01,0.013,2017-10-20 09:54:16
2014-05-01,0.01,2017-10-20 09:54:16
2017-04-03,0.018,2017-10-20 09:54:16
2012-12-20,0.137,2017-10-20 09:54:16
2015-09-01,0.005,2017-10-20 09:54:16
2016-03-01,0.013,2017-10-20 09:54:16
2011-11-30,0.003,2017-10-20 09:54:16
2014-11-03,0.011,2017-10-20 09:54:16
2016-11-01,0.013,2017-10-20 09:54:16
2012-08-01,0.023,2017-10-20 09:54:16
2015-04-01,0.004,2017-10-20 09:54:16
2012-05-01,0.006,2017-10-20 09:54:16
2016-02-01,0.013,2017-10-20 09:54:16
2016-08-01,0.016,2017-10-20 09:54:16
2015-07-01,0.024,2017-10-20 09:54:16
2015-01-02,0.02,2017-10-20 09:54:16
2012-01-31,0.004,2017-10-20 09:54:16
2012-12-03,0.018,2017-10-20 09:54:16
2014-06-02,0.013,2017-10-20 09:54:16
2015-10-01,0.014,2017-10-20 09:54:16
2013-03-01,0.004,2017-10-20 09:54:16
2017-08-01,0.025,2017-10-20 09:54:16
2013-05-01,0.009,2017-10-20 09:54:16
2013-12-20,0.069,2017-10-20 09:54:16
2014-09-02,0.015,2017-10-20 09:54:16
2017-03-01,0.013,2017-10-20 09:54:16
2015-11-02,0.011,2017-10-20 09:54:16
2017-05-01,0.01,2017-10-20 09:54:16
2014-04-01,0.009,2017-10-20 09:54:16
2016-01-04,0.025,2017-10-20 09:54:16
2013-02-01,0.006,2017-10-20 09:54:16
2015-08-03,0.009,2017-10-20 09:54:16
2017-06-01,0.021,2017-10-20 09:54:16
2014-07-01,0.028,2017-10-20 09:54:16
2014-12-22,0.105,2017-10-20 09:54:16
2016-05-03,0.015,2017-10-20 09:54:16
2017-09-01,0.015,2017-10-20 09:54:16
2013-06-03,0.02,2017-10-20 09:54:16
2013-12-02,0.008,2017-10-20 09:54:16
2014-10-01,0.013,2017-10-20 09:54:16
2011-10-31,0.003,2017-10-20 09:54:16
2017-02-01,0.025,2017-10-20 09:54:16
2014-03-03,0.012,2017-10-20 09:54:16
2013-09-03,0.026,2017-10-20 09:54:16
2017-11-01,0.016,2017-11-15 07:55:39
2013-08-01,0.025,2017-11-15 07:55:39
2016-04-01,0.014,2017-11-15 07:55:39
2012-03-01,0.057,2017-11-15 07:55:39
2015-02-02,0.006,2017-11-15 07:55:39
2016-12-01,0.014,2017-11-15 07:55:39
2012-07-02,0.038,2017-11-15 07:55:39
2016-10-03,0.013,2017-11-15 07:55:39
2013-11-01,0.009,2017-11-15 07:55:39
2014-01-02,0.007,2017-11-15 07:55:39
2016-07-01,0.028,2017-11-15 07:55:39
2012-10-01,0.046,2017-11-15 07:55:39
2015-06-01,0.01,2017-11-15 07:55:39
2015-12-01,0.013,2017-11-15 07:55:39
2014-05-01,0.01,2017-11-15 07:55:39
2017-04-03,0.018,2017-11-15 07:55:39
2012-12-20,0.137,2017-11-15 07:55:39
2015-09-01,0.005,2017-11-15 07:55:39
2016-03-01,0.013,2017-11-15 07:55:39
2011-11-30,0.003,2017-11-15 07:55:39
2014-11-03,0.011,2017-11-15 07:55:39
2016-11-01,0.013,2017-11-15 07:55:39
2012-08-01,0.023,2017-11-15 07:55:39
2015-04-01,0.004,2017-11-15 07:55:39
2012-05-01,0.006,2017-11-15 07:55:39
2016-02-01,0.013,2017-11-15 07:55:39
2016-08-01,0.016,2017-11-15 07:55:39
2015-07-01,0.024,2017-11-15 07:55:39
2015-01-02,0.02,2017-11-15 07:55:39
2012-01-31,0.004,2017-11-15 07:55:39
2012-12-03,0.018,2017-11-15 07:55:39
2014-06-02,0.013,2017-11-15 07:55:39
2015-10-01,0.014,2017-11-15 07:55:39
2013-03-01,0.004,2017-11-15 07:55:39
2017-08-01,0.025,2017-11-15 07:55:39
2013-05-01,0.009,2017-11-15 07:55:39
2013-12-20,0.069,2017-11-15 07:55:39
2014-09-02,0.015,2017-11-15 07:55:39
2017-03-01,0.013,2017-11-15 07:55:39
2015-11-02,0.011,2017-11-15 07:55:39
2017-05-01,0.01,2017-11-15 07:55:39
2014-04-01,0.009,2017-11-15 07:55:39
2016-01-04,0.025,2017-11-15 07:55:39
2013-02-01,0.006,2017-11-15 07:55:39
2015-08-03,0.009,2017-11-15 07:55:39
2017-06-01,0.021,2017-11-15 07:55:39
2014-07-01,0.028,2017-11-15 07:55:39
2014-12-22,0.105,2017-11-15 07:55:39
2016-05-03,0.015,2017-11-15 07:55:39
2017-09-01,0.015,2017-11-15 07:55:39
2013-06-03,0.02,2017-11-15 07:55:39
2013-12-02,0.008,2017-11-15 07:55:39
2014-10-01,0.013,2017-11-15 07:55:39
2011-10-31,0.003,2017-11-15 07:55:39
2017-02-01,0.025,2017-11-15 07:55:39
2014-03-03,0.012,2017-11-15 07:55:39
2013-09-03,0.026,2017-11-15 07:55:39
2017-12-01,0.017,2017-12-11 20:07:47
2015-07-01,0.024,2017-12-11 20:07:47
2015-01-02,0.02,2017-12-11 20:07:47
2012-01-31,0.004,2017-12-11 20:07:47
2012-12-03,0.018,2017-12-11 20:07:47
2014-06-02,0.013,2017-12-11 20:07:47
2015-10-01,0.014,2017-12-11 20:07:47
2013-03-01,0.004,2017-12-11 20:07:47
2017-08-01,0.025,2017-12-11 20:07:47
2013-05-01,0.009,2017-12-11 20:07:47
2013-12-20,0.069,2017-12-11 20:07:47
2014-09-02,0.015,2017-12-11 20:07:47
2017-03-01,0.013,2017-12-11 20:07:47
2015-11-02,0.011,2017-12-11 20:07:47
2017-05-01,0.01,2017-12-11 20:07:47
2014-04-01,0.009,2017-12-11 20:07:47
2016-01-04,0.025,2017-12-11 20:07:47
2013-02-01,0.006,2017-12-11 20:07:47
2015-08-03,0.009,2017-12-11 20:07:47
2017-06-01,0.021,2017-12-11 20:07:47
2014-07-01,0.028,2017-12-11 20:07:47
2014-12-22,0.105,2017-12-11 20:07:47
2016-05-03,0.015,2017-12-11 20:07:47
2017-09-01,0.015,2017-12-11 20:07:47
2013-06-03,0.02,2017-12-11 20:07:47
2013-12-02,0.008,2017-12-11 20:07:47
2014-10-01,0.013,2017-12-11 20:07:47
2011-10-31,0.003,2017-12-11 20:07:47
2017-02-01,0.025,2017-12-11 20:07:47
2014-03-03,0.012,2017-12-11 20:07:47
2013-09-03,0.026,2017-12-11 20:07:47
2018-01-02,0.023,2018-01-16 07:54:11
2016-04-01,0.014,2018-01-16 07:54:11
2012-03-01,0.057,2018-01-16 07:54:11
2015-02-02,0.006,2018-01-16 07:54:11
2016-12-01,0.014,2018-01-16 07:54:11
2012-07-02,0.038,2018-01-16 07:54:11
2016-10-03,0.013,2018-01-16 07:54:11
2013-11-01,0.009,2018-01-16 07:54:11
2014-01-02,0.007,2018-01-16 07:54:11
2016-07-01,0.028,2018-01-16 07:54:11
2012-10-01,0.046,2018-01-16 07:54:11
2015-06-01,0.01,2018-01-16 07:54:11
2015-12-01,0.013,2018-01-16 07:54:11
2014-05-01,0.01,2018-01-16 07:54:11
2017-04-03,0.018,2018-01-16 07:54:11
2012-12-20,0.137,2018-01-16 07:54:11
2015-09-01,0.005,2018-01-16 07:54:11
2016-03-01,0.013,2018-01-16 07:54:11
2011-11-30,0.003,2018-01-16 07:54:11
2014-11-03,0.011,2018-01-16 07:54:11
2016-11-01,0.013,2018-01-16 07:54:11
2012-08-01,0.023,2018-01-16 07:54:11
2015-04-01,0.004,2018-01-16 07:54:11
2012-05-01,0.006,2018-01-16 07:54:11
2016-02-01,0.013,2018-01-16 07:54:11
2016-08-01,0.016,2018-01-16 07:54:11
2017-12-01,0.017,2018-01-16 07:54:11
2015-07-01,0.024,2018-01-16 07:54:11
2015-01-02,0.02,2018-01-16 07:54:11
2012-01-31,0.004,2018-01-16 07:54:11
2012-12-03,0.018,2018-01-16 07:54:11
2014-06-02,0.013,2018-01-16 07:54:11
2015-10-01,0.014,2018-01-16 07:54:11
2013-03-01,0.004,2018-01-16 07:54:11
2017-08-01,0.025,2018-01-16 07:54:11
2013-05-01,0.009,2018-01-16 07:54:11
2013-12-20,0.069,2018-01-16 07:54:11
2014-09-02,0.015,2018-01-16 07:54:11
2017-03-01,0.013,2018-01-16 07:54:11
2015-11-02,0.011,2018-01-16 07:54:11
2017-05-01,0.01,2018-01-16 07:54:11
2014-04-01,0.009,2018-01-16 07:54:11
2016-01-04,0.025,2018-01-16 07:54:11
2013-02-01,0.006,2018-01-16 07:54:11
2015-08-03,0.009,2018-01-16 07:54:11
2017-06-01,0.021,2018-01-16 07:54:11
2014-07-01,0.028,2018-01-16 07:54:11
2014-12-22,0.105,2018-01-16 07:54:11
2016-05-03,0.015,2018-01-16 07:54:11
2017-09-01,0.015,2018-01-16 07:54:11
2013-06-03,0.02,2018-01-16 07:54:11
2013-12-02,0.008,2018-01-16 07:54:11
2014-10-01,0.013,2018-01-16 07:54:11
2011-10-31,0.003,2018-01-16 07:54:11
2017-02-01,0.025,2018-01-16 07:54:11
2014-03-03,0.012,2018-01-16 07:54:11
2013-09-03,0.026,2018-01-16 07:54:11
2018-02-01,0.017,2018-02-26 21:44:31
2015-03-02,0.004,2018-02-26 21:44:31
2012-09-04,0.036,2018-02-26 21:44:31
2012-04-02,0.016,2018-02-26 21:44:31
2017-11-01,0.016,2018-02-26 21:44:31
2013-08-01,0.025,2018-02-26 21:44:31
2018-01-02,0.023,2018-02-26 21:44:31
2016-04-01,0.014,2018-02-26 21:44:31
2012-03-01,0.057,2018-02-26 21:44:31
2015-02-02,0.006,2018-02-26 21:44:31
2016-12-01,0.014,2018-02-26 21:44:31
2012-07-02,0.038,2018-02-26 21:44:31
2016-10-03,0.013,2018-02-26 21:44:31
2013-11-01,0.009,2018-02-26 21:44:31
2014-01-02,0.007,2018-02-26 21:44:31
2016-07-01,0.028,2018-02-26 21:44:31
2012-10-01,0.046,2018-02-26 21:44:31
2015-06-01,0.01,2018-02-26 21:44:31
2015-12-01,0.013,2018-02-26 21:44:31
2014-05-01,0.01,2018-02-26 21:44:31
2017-04-03,0.018,2018-02-26 21:44:31
2012-12-20,0.137,2018-02-26 21:44:31
2015-09-01,0.005,2018-02-26 21:44:31
2016-03-01,0.013,2018-02-26 21:44:31
2011-11-30,0.003,2018-02-26 21:44:31
2014-11-03,0.011,2018-02-26 21:44:31
2016-11-01,0.013,2018-02-26 21:44:31
2012-08-01,0.023,2018-02-26 21:44:31
2015-04-01,0.004,2018-02-26 21:44:31
2012-05-01,0.006,2018-02-26 21:44:31
2016-02-01,0.013,2018-02-26 21:44:31
2016-08-01,0.016,2018-02-26 21:44:31
2017-12-01,0.017,2018-02-26 21:44:31
2015-07-01,0.024,2018-02-26 21:44:31
2015-01-02,0.02,2018-02-26 21:44:31
2012-01-31,0.004,2018-02-26 21:44:31
2012-12-03,0.018,2018-02-26 21:44:31
2014-06-02,0.013,2018-02-26 21:44:31
2015-10-01,0.014,2018-02-26 21:44:31
2013-03-01,0.004,2018-02-26 21:44:31
2017-08-01,0.025,2018-02-26 21:44:31
2013-05-01,0.009,2018-02-26 21:44:31
2013-12-20,0.069,2018-02-26 21:44:31
2014-09-02,0.015,2018-02-26 21:44:31
2017-03-01,0.013,2018-02-26 21:44:31
2015-11-02,0.011,2018-02-26 21:44:31
2017-05-01,0.01,2018-02-26 21:44:31
2014-04-01,0.009,2018-02-26 21:44:31
2016-01-04,0.025,2018-02-26 21:44:31
2013-02-01,0.006,2018-02-26 21:44:31
2015-08-03,0.009,2018-02-26 21:44:31
2017-06-01,0.021,2018-02-26 21:44:31
2014-07-01,0.028,2018-02-26 21:44:31
2014-12-22,0.105,2018-02-26 21:44:31
2016-05-03,0.015,2018-02-26 21:44:31
2017-09-01,0.015,2018-02-26 21:44:31
2013-06-03,0.02,2018-02-26 21:44:31
2013-12-02,0.008,2018-02-26 21:44:31
2014-10-01,0.013,2018-02-26 21:44:31
2011-10-31,0.003,2018-02-26 21:44:31
2017-02-01,0.025,2018-02-26 21:44:31
2014-03-03,0.012,2018-02-26 21:44:31
2013-09-03,0.026,2018-02-26 21:44:31
2018-03-01,0.019,2018-03-09 21:59:16
2017-09-01,0.015,2018-03-09 21:59:16
2013-06-03,0.02,2018-03-09 21:59:16
2013-12-02,0.008,2018-03-09 21:59:16
2014-10-01,0.013,2018-03-09 21:59:16
2011-10-31,0.003,2018-03-09 21:59:16
2017-02-01,0.025,2018-03-09 21:59:16
2014-03-03,0.012,2018-03-09 21:59:16
2013-09-03,0.026,2018-03-09 21:59:16
2018-04-02,0.021,2018-04-13 15:05:34
2015-05-01,0.005,2018-04-13 15:05:34
2018-02-01,0.017,2018-04-13 15:05:34
2015-03-02,0.004,2018-04-13 15:05:34
2012-09-04,0.036,2018-04-13 15:05:34
2012-04-02,0.016,2018-04-13 15:05:34
2017-11-01,0.016,2018-04-13 15:05:34
2013-08-01,0.025,2018-04-13 15:05:34
2018-01-02,0.023,2018-04-13 15:05:34
2016-04-01,0.014,2018-04-13 15:05:34
2012-03-01,0.057,2018-04-13 15:05:34
2015-02-02,0.006,2018-04-13 15:05:34
2016-12-01,0.014,2018-04-13 15:05:34
2012-07-02,0.038,2018-04-13 15:05:34
2016-10-03,0.013,2018-04-13 15:05:34
2013-11-01,0.009,2018-04-13 15:05:34
2014-01-02,0.007,2018-04-13 15:05:34
2016-07-01,0.028,2018-04-13 15:05:34
2012-10-01,0.046,2018-04-13 15:05:34
2015-06-01,0.01,2018-04-13 15:05:34
2015-12-01,0.013,2018-04-13 15:05:34
2014-05-01,0.01,2018-04-13 15:05:34
2017-04-03,0.018,2018-04-13 15:05:34
2012-12-20,0.137,2018-04-13 15:05:34
2015-09-01,0.005,2018-04-13 15:05:34
2016-03-01,0.013,2018-04-13 15:05:34
2011-11-30,0.003,2018-04-13 15:05:34
2014-11-03,0.011,2018-04-13 15:05:34
2016-11-01,0.013,2018-04-13 15:05:34
2012-08-01,0.023,2018-04-13 15:05:34
2015-04-01,0.004,2018-04-13 15:05:34
2012-05-01,0.006,2018-04-13 15:05:34
2016-02-01,0.013,2018-04-13 15:05:34
2016-08-01,0.016,2018-04-13 15:05:34
2017-12-01,0.017,2018-04-13 15:05:34
2015-07-01,0.024,2018-04-13 15:05:34
2015-01-02,0.02,2018-04-13 15:05:34
2012-01-31,0.004,2018-04-13 15:05:34
2012-12-03,0.018,2018-04-13 15:05:34
2014-06-02,0.013,2018-04-13 15:05:34
2015-10-01,0.014,2018-04-13 15:05:34
2013-03-01,0.004,2018-04-13 15:05:34
2017-08-01,0.025,2018-04-13 15:05:34
2013-05-01,0.009,2018-04-13 15:05:34
2013-12-20,0.069,2018-04-13 15:05:34
2014-09-02,0.015,2018-04-13 15:05:34
2017-03-01,0.013,2018-04-13 15:05:34
2015-11-02,0.011,2018-04-13 15:05:34
2017-05-01,0.01,2018-04-13 15:05:34
2014-04-01,0.009,2018-04-13 15:05:34
2016-01-04,0.025,2018-04-13 15:05:34
2013-02-01,0.006,2018-04-13 15:05:34
2015-08-03,0.009,2018-04-13 15:05:34
2017-06-01,0.021,2018-04-13 15:05:34
2014-07-01,0.028,2018-04-13 15:05:34
2014-12-22,0.105,2018-04-13 15:05:34
2016-05-03,0.015,2018-04-13 15:05:34
2018-03-01,0.019,2018-04-13 15:05:34
2017-09-01,0.015,2018-04-13 15:05:34
2013-06-03,0.02,2018-04-13 15:05:34
2013-12-02,0.008,2018-04-13 15:05:34
2014-10-01,0.013,2018-04-13 15:05:34
2011-10-31,0.003,2018-04-13 15:05:34
2017-02-01,0.025,2018-04-13 15:05:34
2014-03-03,0.012,2018-04-13 15:05:34
2013-09-03,0.026,2018-04-13 15:05:34
2018-05-01,0.023,2018-12-20 11:23:51
2012-10-01,0.046,2018-12-20 11:23:51
2015-06-01,0.01,2018-12-20 11:23:51
2015-12-01,0.013,2018-12-20 11:23:51
2018-08-01,0.033,2018-12-20 11:23:51
2014-05-01,0.01,2018-12-20 11:23:51
2017-04-03,0.018,2018-12-20 11:23:51
2012-12-20,0.137,2018-12-20 11:23:51
2015-09-01,0.005,2018-12-20 11:23:51
2016-03-01,0.013,2018-12-20 11:23:51
2011-11-30,0.003,2018-12-20 11:23:51
2014-11-03,0.011,2018-12-20 11:23:51
2016-11-01,0.013,2018-12-20 11:23:51
2012-08-01,0.023,2018-12-20 11:23:51
2015-04-01,0.004,2018-12-20 11:23:51
2012-05-01,0.006,2018-12-20 11:23:51
2016-02-01,0.013,2018-12-20 11:23:51
2016-08-01,0.016,2018-12-20 11:23:51
2017-12-01,0.017,2018-12-20 11:23:51
2018-06-01,0.021,2018-12-20 11:23:51
2015-07-01,0.024,2018-12-20 11:23:51
2015-01-02,0.02,2018-12-20 11:23:51
2012-01-31,0.004,2018-12-20 11:23:51
2012-12-03,0.018,2018-12-20 11:23:51
2014-06-02,0.013,2018-12-20 11:23:51
2015-10-01,0.014,2018-12-20 11:23:51
2018-09-04,0.023,2018-12-20 11:23:51
2013-03-01,0.004,2018-12-20 11:23:51
2017-08-01,0.025,2018-12-20 11:23:51
2013-05-01,0.009,2018-12-20 11:23:51
2013-12-20,0.069,2018-12-20 11:23:51
2014-09-02,0.015,2018-12-20 11:23:51
2017-03-01,0.013,2018-12-20 11:23:51
2015-11-02,0.011,2018-12-20 11:23:51
2017-05-01,0.01,2018-12-20 11:23:51
2014-04-01,0.009,2018-12-20 11:23:51
2016-01-04,0.025,2018-12-20 11:23:51
2018-07-02,0.022,2018-12-20 11:23:51
2013-02-01,0.006,2018-12-20 11:23:51
2015-08-03,0.009,2018-12-20 11:23:51
2017-06-01,0.021,2018-12-20 11:23:51
2014-07-01,0.028,2018-12-20 11:23:51
2018-10-01,0.008,2018-12-20 11:23:51
2014-12-22,0.105,2018-12-20 11:23:51
2016-05-03,0.015,2018-12-20 11:23:51
2018-03-01,0.019,2018-12-20 11:23:51
2017-09-01,0.015,2018-12-20 11:23:51
2013-06-03,0.02,2018-12-20 11:23:51
2013-12-02,0.008,2018-12-20 11:23:51
2014-10-01,0.013,2018-12-20 11:23:51
2011-10-31,0.003,2018-12-20 11:23:51
2017-02-01,0.025,2018-12-20 11:23:51
2014-03-03,0.012,2018-12-20 11:23:51
2013-09-03,0.026,2018-12-20 11:23:51
1 Date Dividends ctime
2 2013-04-01 0.007 2017-08-22
3 2017-07-03 0.014 2017-08-22
4 2014-08-01 0.012 2017-08-22
5 2017-01-03 0.014 2017-08-22
6 2014-02-03 0.007 2017-08-22
7 2013-07-01 0.031 2017-08-22
8 2013-01-02 0.007 2017-08-22
9 2012-06-01 0.013 2017-08-22
10 2015-12-22 0.22 2017-08-22
11 2013-10-01 0.007 2017-08-22
12 2014-12-01 0.009 2017-08-22
13 2011-12-30 0.004 2017-08-22
14 2016-06-01 0.016 2017-08-22
15 2015-05-01 0.005 2017-08-22
16 2015-03-02 0.004 2017-08-22
17 2012-09-04 0.036 2017-08-22
18 2012-04-02 0.016 2017-08-22
19 2013-08-01 0.025 2017-08-22
20 2016-04-01 0.014 2017-08-22
21 2012-03-01 0.057 2017-08-22
22 2015-02-02 0.006 2017-08-22
23 2016-12-01 0.014 2017-08-22
24 2012-07-02 0.038 2017-08-22
25 2016-10-03 0.013 2017-08-22
26 2013-11-01 0.009 2017-08-22
27 2014-01-02 0.007 2017-08-22
28 2016-07-01 0.028 2017-08-22
29 2012-10-01 0.046 2017-08-22
30 2015-06-01 0.01 2017-08-22
31 2015-12-01 0.013 2017-08-22
32 2014-05-01 0.01 2017-08-22
33 2017-04-03 0.018 2017-08-22
34 2012-12-20 0.137 2017-08-22
35 2015-09-01 0.005 2017-08-22
36 2016-03-01 0.013 2017-08-22
37 2011-11-30 0.003 2017-08-22
38 2014-11-03 0.011 2017-08-22
39 2016-11-01 0.013 2017-08-22
40 2012-08-01 0.023 2017-08-22
41 2015-04-01 0.004 2017-08-22
42 2012-05-01 0.006 2017-08-22
43 2016-02-01 0.013 2017-08-22
44 2016-08-01 0.016 2017-08-22
45 2015-07-01 0.024 2017-08-22
46 2015-01-02 0.02 2017-08-22
47 2012-01-31 0.004 2017-08-22
48 2012-12-03 0.018 2017-08-22
49 2014-06-02 0.013 2017-08-22
50 2015-10-01 0.014 2017-08-22
51 2013-03-01 0.004 2017-08-22
52 2017-08-01 0.025 2017-08-22
53 2013-05-01 0.009 2017-08-22
54 2013-12-20 0.069 2017-08-22
55 2014-09-02 0.015 2017-08-22
56 2017-03-01 0.013 2017-08-22
57 2015-11-02 0.011 2017-08-22
58 2017-05-01 0.01 2017-08-22
59 2014-04-01 0.009 2017-08-22
60 2016-01-04 0.025 2017-08-22
61 2013-02-01 0.006 2017-08-22
62 2015-08-03 0.009 2017-08-22
63 2017-06-01 0.021 2017-08-22
64 2014-07-01 0.028 2017-08-22
65 2014-12-22 0.105 2017-08-22
66 2016-05-03 0.015 2017-08-22
67 2013-06-03 0.02 2017-08-22
68 2013-12-02 0.008 2017-08-22
69 2014-10-01 0.013 2017-08-22
70 2011-10-31 0.003 2017-08-22
71 2017-02-01 0.025 2017-08-22
72 2014-03-03 0.012 2017-08-22
73 2013-09-03 0.026 2017-08-22
74 2017-10-02 0.015 2017-10-10 07:30:35
75 2013-01-02 0.007 2017-10-20 09:54:16
76 2012-06-01 0.013 2017-10-20 09:54:16
77 2015-12-22 0.22 2017-10-20 09:54:16
78 2013-10-01 0.007 2017-10-20 09:54:16
79 2014-12-01 0.009 2017-10-20 09:54:16
80 2011-12-30 0.004 2017-10-20 09:54:16
81 2016-06-01 0.016 2017-10-20 09:54:16
82 2015-05-01 0.005 2017-10-20 09:54:16
83 2015-03-02 0.004 2017-10-20 09:54:16
84 2012-09-04 0.036 2017-10-20 09:54:16
85 2012-04-02 0.016 2017-10-20 09:54:16
86 2013-08-01 0.025 2017-10-20 09:54:16
87 2016-04-01 0.014 2017-10-20 09:54:16
88 2012-03-01 0.057 2017-10-20 09:54:16
89 2015-02-02 0.006 2017-10-20 09:54:16
90 2016-12-01 0.014 2017-10-20 09:54:16
91 2012-07-02 0.038 2017-10-20 09:54:16
92 2016-10-03 0.013 2017-10-20 09:54:16
93 2013-11-01 0.009 2017-10-20 09:54:16
94 2014-01-02 0.007 2017-10-20 09:54:16
95 2016-07-01 0.028 2017-10-20 09:54:16
96 2012-10-01 0.046 2017-10-20 09:54:16
97 2015-06-01 0.01 2017-10-20 09:54:16
98 2015-12-01 0.013 2017-10-20 09:54:16
99 2014-05-01 0.01 2017-10-20 09:54:16
100 2017-04-03 0.018 2017-10-20 09:54:16
101 2012-12-20 0.137 2017-10-20 09:54:16
102 2015-09-01 0.005 2017-10-20 09:54:16
103 2016-03-01 0.013 2017-10-20 09:54:16
104 2011-11-30 0.003 2017-10-20 09:54:16
105 2014-11-03 0.011 2017-10-20 09:54:16
106 2016-11-01 0.013 2017-10-20 09:54:16
107 2012-08-01 0.023 2017-10-20 09:54:16
108 2015-04-01 0.004 2017-10-20 09:54:16
109 2012-05-01 0.006 2017-10-20 09:54:16
110 2016-02-01 0.013 2017-10-20 09:54:16
111 2016-08-01 0.016 2017-10-20 09:54:16
112 2015-07-01 0.024 2017-10-20 09:54:16
113 2015-01-02 0.02 2017-10-20 09:54:16
114 2012-01-31 0.004 2017-10-20 09:54:16
115 2012-12-03 0.018 2017-10-20 09:54:16
116 2014-06-02 0.013 2017-10-20 09:54:16
117 2015-10-01 0.014 2017-10-20 09:54:16
118 2013-03-01 0.004 2017-10-20 09:54:16
119 2017-08-01 0.025 2017-10-20 09:54:16
120 2013-05-01 0.009 2017-10-20 09:54:16
121 2013-12-20 0.069 2017-10-20 09:54:16
122 2014-09-02 0.015 2017-10-20 09:54:16
123 2017-03-01 0.013 2017-10-20 09:54:16
124 2015-11-02 0.011 2017-10-20 09:54:16
125 2017-05-01 0.01 2017-10-20 09:54:16
126 2014-04-01 0.009 2017-10-20 09:54:16
127 2016-01-04 0.025 2017-10-20 09:54:16
128 2013-02-01 0.006 2017-10-20 09:54:16
129 2015-08-03 0.009 2017-10-20 09:54:16
130 2017-06-01 0.021 2017-10-20 09:54:16
131 2014-07-01 0.028 2017-10-20 09:54:16
132 2014-12-22 0.105 2017-10-20 09:54:16
133 2016-05-03 0.015 2017-10-20 09:54:16
134 2017-09-01 0.015 2017-10-20 09:54:16
135 2013-06-03 0.02 2017-10-20 09:54:16
136 2013-12-02 0.008 2017-10-20 09:54:16
137 2014-10-01 0.013 2017-10-20 09:54:16
138 2011-10-31 0.003 2017-10-20 09:54:16
139 2017-02-01 0.025 2017-10-20 09:54:16
140 2014-03-03 0.012 2017-10-20 09:54:16
141 2013-09-03 0.026 2017-10-20 09:54:16
142 2017-11-01 0.016 2017-11-15 07:55:39
143 2013-08-01 0.025 2017-11-15 07:55:39
144 2016-04-01 0.014 2017-11-15 07:55:39
145 2012-03-01 0.057 2017-11-15 07:55:39
146 2015-02-02 0.006 2017-11-15 07:55:39
147 2016-12-01 0.014 2017-11-15 07:55:39
148 2012-07-02 0.038 2017-11-15 07:55:39
149 2016-10-03 0.013 2017-11-15 07:55:39
150 2013-11-01 0.009 2017-11-15 07:55:39
151 2014-01-02 0.007 2017-11-15 07:55:39
152 2016-07-01 0.028 2017-11-15 07:55:39
153 2012-10-01 0.046 2017-11-15 07:55:39
154 2015-06-01 0.01 2017-11-15 07:55:39
155 2015-12-01 0.013 2017-11-15 07:55:39
156 2014-05-01 0.01 2017-11-15 07:55:39
157 2017-04-03 0.018 2017-11-15 07:55:39
158 2012-12-20 0.137 2017-11-15 07:55:39
159 2015-09-01 0.005 2017-11-15 07:55:39
160 2016-03-01 0.013 2017-11-15 07:55:39
161 2011-11-30 0.003 2017-11-15 07:55:39
162 2014-11-03 0.011 2017-11-15 07:55:39
163 2016-11-01 0.013 2017-11-15 07:55:39
164 2012-08-01 0.023 2017-11-15 07:55:39
165 2015-04-01 0.004 2017-11-15 07:55:39
166 2012-05-01 0.006 2017-11-15 07:55:39
167 2016-02-01 0.013 2017-11-15 07:55:39
168 2016-08-01 0.016 2017-11-15 07:55:39
169 2015-07-01 0.024 2017-11-15 07:55:39
170 2015-01-02 0.02 2017-11-15 07:55:39
171 2012-01-31 0.004 2017-11-15 07:55:39
172 2012-12-03 0.018 2017-11-15 07:55:39
173 2014-06-02 0.013 2017-11-15 07:55:39
174 2015-10-01 0.014 2017-11-15 07:55:39
175 2013-03-01 0.004 2017-11-15 07:55:39
176 2017-08-01 0.025 2017-11-15 07:55:39
177 2013-05-01 0.009 2017-11-15 07:55:39
178 2013-12-20 0.069 2017-11-15 07:55:39
179 2014-09-02 0.015 2017-11-15 07:55:39
180 2017-03-01 0.013 2017-11-15 07:55:39
181 2015-11-02 0.011 2017-11-15 07:55:39
182 2017-05-01 0.01 2017-11-15 07:55:39
183 2014-04-01 0.009 2017-11-15 07:55:39
184 2016-01-04 0.025 2017-11-15 07:55:39
185 2013-02-01 0.006 2017-11-15 07:55:39
186 2015-08-03 0.009 2017-11-15 07:55:39
187 2017-06-01 0.021 2017-11-15 07:55:39
188 2014-07-01 0.028 2017-11-15 07:55:39
189 2014-12-22 0.105 2017-11-15 07:55:39
190 2016-05-03 0.015 2017-11-15 07:55:39
191 2017-09-01 0.015 2017-11-15 07:55:39
192 2013-06-03 0.02 2017-11-15 07:55:39
193 2013-12-02 0.008 2017-11-15 07:55:39
194 2014-10-01 0.013 2017-11-15 07:55:39
195 2011-10-31 0.003 2017-11-15 07:55:39
196 2017-02-01 0.025 2017-11-15 07:55:39
197 2014-03-03 0.012 2017-11-15 07:55:39
198 2013-09-03 0.026 2017-11-15 07:55:39
199 2017-12-01 0.017 2017-12-11 20:07:47
200 2015-07-01 0.024 2017-12-11 20:07:47
201 2015-01-02 0.02 2017-12-11 20:07:47
202 2012-01-31 0.004 2017-12-11 20:07:47
203 2012-12-03 0.018 2017-12-11 20:07:47
204 2014-06-02 0.013 2017-12-11 20:07:47
205 2015-10-01 0.014 2017-12-11 20:07:47
206 2013-03-01 0.004 2017-12-11 20:07:47
207 2017-08-01 0.025 2017-12-11 20:07:47
208 2013-05-01 0.009 2017-12-11 20:07:47
209 2013-12-20 0.069 2017-12-11 20:07:47
210 2014-09-02 0.015 2017-12-11 20:07:47
211 2017-03-01 0.013 2017-12-11 20:07:47
212 2015-11-02 0.011 2017-12-11 20:07:47
213 2017-05-01 0.01 2017-12-11 20:07:47
214 2014-04-01 0.009 2017-12-11 20:07:47
215 2016-01-04 0.025 2017-12-11 20:07:47
216 2013-02-01 0.006 2017-12-11 20:07:47
217 2015-08-03 0.009 2017-12-11 20:07:47
218 2017-06-01 0.021 2017-12-11 20:07:47
219 2014-07-01 0.028 2017-12-11 20:07:47
220 2014-12-22 0.105 2017-12-11 20:07:47
221 2016-05-03 0.015 2017-12-11 20:07:47
222 2017-09-01 0.015 2017-12-11 20:07:47
223 2013-06-03 0.02 2017-12-11 20:07:47
224 2013-12-02 0.008 2017-12-11 20:07:47
225 2014-10-01 0.013 2017-12-11 20:07:47
226 2011-10-31 0.003 2017-12-11 20:07:47
227 2017-02-01 0.025 2017-12-11 20:07:47
228 2014-03-03 0.012 2017-12-11 20:07:47
229 2013-09-03 0.026 2017-12-11 20:07:47
230 2018-01-02 0.023 2018-01-16 07:54:11
231 2016-04-01 0.014 2018-01-16 07:54:11
232 2012-03-01 0.057 2018-01-16 07:54:11
233 2015-02-02 0.006 2018-01-16 07:54:11
234 2016-12-01 0.014 2018-01-16 07:54:11
235 2012-07-02 0.038 2018-01-16 07:54:11
236 2016-10-03 0.013 2018-01-16 07:54:11
237 2013-11-01 0.009 2018-01-16 07:54:11
238 2014-01-02 0.007 2018-01-16 07:54:11
239 2016-07-01 0.028 2018-01-16 07:54:11
240 2012-10-01 0.046 2018-01-16 07:54:11
241 2015-06-01 0.01 2018-01-16 07:54:11
242 2015-12-01 0.013 2018-01-16 07:54:11
243 2014-05-01 0.01 2018-01-16 07:54:11
244 2017-04-03 0.018 2018-01-16 07:54:11
245 2012-12-20 0.137 2018-01-16 07:54:11
246 2015-09-01 0.005 2018-01-16 07:54:11
247 2016-03-01 0.013 2018-01-16 07:54:11
248 2011-11-30 0.003 2018-01-16 07:54:11
249 2014-11-03 0.011 2018-01-16 07:54:11
250 2016-11-01 0.013 2018-01-16 07:54:11
251 2012-08-01 0.023 2018-01-16 07:54:11
252 2015-04-01 0.004 2018-01-16 07:54:11
253 2012-05-01 0.006 2018-01-16 07:54:11
254 2016-02-01 0.013 2018-01-16 07:54:11
255 2016-08-01 0.016 2018-01-16 07:54:11
256 2017-12-01 0.017 2018-01-16 07:54:11
257 2015-07-01 0.024 2018-01-16 07:54:11
258 2015-01-02 0.02 2018-01-16 07:54:11
259 2012-01-31 0.004 2018-01-16 07:54:11
260 2012-12-03 0.018 2018-01-16 07:54:11
261 2014-06-02 0.013 2018-01-16 07:54:11
262 2015-10-01 0.014 2018-01-16 07:54:11
263 2013-03-01 0.004 2018-01-16 07:54:11
264 2017-08-01 0.025 2018-01-16 07:54:11
265 2013-05-01 0.009 2018-01-16 07:54:11
266 2013-12-20 0.069 2018-01-16 07:54:11
267 2014-09-02 0.015 2018-01-16 07:54:11
268 2017-03-01 0.013 2018-01-16 07:54:11
269 2015-11-02 0.011 2018-01-16 07:54:11
270 2017-05-01 0.01 2018-01-16 07:54:11
271 2014-04-01 0.009 2018-01-16 07:54:11
272 2016-01-04 0.025 2018-01-16 07:54:11
273 2013-02-01 0.006 2018-01-16 07:54:11
274 2015-08-03 0.009 2018-01-16 07:54:11
275 2017-06-01 0.021 2018-01-16 07:54:11
276 2014-07-01 0.028 2018-01-16 07:54:11
277 2014-12-22 0.105 2018-01-16 07:54:11
278 2016-05-03 0.015 2018-01-16 07:54:11
279 2017-09-01 0.015 2018-01-16 07:54:11
280 2013-06-03 0.02 2018-01-16 07:54:11
281 2013-12-02 0.008 2018-01-16 07:54:11
282 2014-10-01 0.013 2018-01-16 07:54:11
283 2011-10-31 0.003 2018-01-16 07:54:11
284 2017-02-01 0.025 2018-01-16 07:54:11
285 2014-03-03 0.012 2018-01-16 07:54:11
286 2013-09-03 0.026 2018-01-16 07:54:11
287 2018-02-01 0.017 2018-02-26 21:44:31
288 2015-03-02 0.004 2018-02-26 21:44:31
289 2012-09-04 0.036 2018-02-26 21:44:31
290 2012-04-02 0.016 2018-02-26 21:44:31
291 2017-11-01 0.016 2018-02-26 21:44:31
292 2013-08-01 0.025 2018-02-26 21:44:31
293 2018-01-02 0.023 2018-02-26 21:44:31
294 2016-04-01 0.014 2018-02-26 21:44:31
295 2012-03-01 0.057 2018-02-26 21:44:31
296 2015-02-02 0.006 2018-02-26 21:44:31
297 2016-12-01 0.014 2018-02-26 21:44:31
298 2012-07-02 0.038 2018-02-26 21:44:31
299 2016-10-03 0.013 2018-02-26 21:44:31
300 2013-11-01 0.009 2018-02-26 21:44:31
301 2014-01-02 0.007 2018-02-26 21:44:31
302 2016-07-01 0.028 2018-02-26 21:44:31
303 2012-10-01 0.046 2018-02-26 21:44:31
304 2015-06-01 0.01 2018-02-26 21:44:31
305 2015-12-01 0.013 2018-02-26 21:44:31
306 2014-05-01 0.01 2018-02-26 21:44:31
307 2017-04-03 0.018 2018-02-26 21:44:31
308 2012-12-20 0.137 2018-02-26 21:44:31
309 2015-09-01 0.005 2018-02-26 21:44:31
310 2016-03-01 0.013 2018-02-26 21:44:31
311 2011-11-30 0.003 2018-02-26 21:44:31
312 2014-11-03 0.011 2018-02-26 21:44:31
313 2016-11-01 0.013 2018-02-26 21:44:31
314 2012-08-01 0.023 2018-02-26 21:44:31
315 2015-04-01 0.004 2018-02-26 21:44:31
316 2012-05-01 0.006 2018-02-26 21:44:31
317 2016-02-01 0.013 2018-02-26 21:44:31
318 2016-08-01 0.016 2018-02-26 21:44:31
319 2017-12-01 0.017 2018-02-26 21:44:31
320 2015-07-01 0.024 2018-02-26 21:44:31
321 2015-01-02 0.02 2018-02-26 21:44:31
322 2012-01-31 0.004 2018-02-26 21:44:31
323 2012-12-03 0.018 2018-02-26 21:44:31
324 2014-06-02 0.013 2018-02-26 21:44:31
325 2015-10-01 0.014 2018-02-26 21:44:31
326 2013-03-01 0.004 2018-02-26 21:44:31
327 2017-08-01 0.025 2018-02-26 21:44:31
328 2013-05-01 0.009 2018-02-26 21:44:31
329 2013-12-20 0.069 2018-02-26 21:44:31
330 2014-09-02 0.015 2018-02-26 21:44:31
331 2017-03-01 0.013 2018-02-26 21:44:31
332 2015-11-02 0.011 2018-02-26 21:44:31
333 2017-05-01 0.01 2018-02-26 21:44:31
334 2014-04-01 0.009 2018-02-26 21:44:31
335 2016-01-04 0.025 2018-02-26 21:44:31
336 2013-02-01 0.006 2018-02-26 21:44:31
337 2015-08-03 0.009 2018-02-26 21:44:31
338 2017-06-01 0.021 2018-02-26 21:44:31
339 2014-07-01 0.028 2018-02-26 21:44:31
340 2014-12-22 0.105 2018-02-26 21:44:31
341 2016-05-03 0.015 2018-02-26 21:44:31
342 2017-09-01 0.015 2018-02-26 21:44:31
343 2013-06-03 0.02 2018-02-26 21:44:31
344 2013-12-02 0.008 2018-02-26 21:44:31
345 2014-10-01 0.013 2018-02-26 21:44:31
346 2011-10-31 0.003 2018-02-26 21:44:31
347 2017-02-01 0.025 2018-02-26 21:44:31
348 2014-03-03 0.012 2018-02-26 21:44:31
349 2013-09-03 0.026 2018-02-26 21:44:31
350 2018-03-01 0.019 2018-03-09 21:59:16
351 2017-09-01 0.015 2018-03-09 21:59:16
352 2013-06-03 0.02 2018-03-09 21:59:16
353 2013-12-02 0.008 2018-03-09 21:59:16
354 2014-10-01 0.013 2018-03-09 21:59:16
355 2011-10-31 0.003 2018-03-09 21:59:16
356 2017-02-01 0.025 2018-03-09 21:59:16
357 2014-03-03 0.012 2018-03-09 21:59:16
358 2013-09-03 0.026 2018-03-09 21:59:16
359 2018-04-02 0.021 2018-04-13 15:05:34
360 2015-05-01 0.005 2018-04-13 15:05:34
361 2018-02-01 0.017 2018-04-13 15:05:34
362 2015-03-02 0.004 2018-04-13 15:05:34
363 2012-09-04 0.036 2018-04-13 15:05:34
364 2012-04-02 0.016 2018-04-13 15:05:34
365 2017-11-01 0.016 2018-04-13 15:05:34
366 2013-08-01 0.025 2018-04-13 15:05:34
367 2018-01-02 0.023 2018-04-13 15:05:34
368 2016-04-01 0.014 2018-04-13 15:05:34
369 2012-03-01 0.057 2018-04-13 15:05:34
370 2015-02-02 0.006 2018-04-13 15:05:34
371 2016-12-01 0.014 2018-04-13 15:05:34
372 2012-07-02 0.038 2018-04-13 15:05:34
373 2016-10-03 0.013 2018-04-13 15:05:34
374 2013-11-01 0.009 2018-04-13 15:05:34
375 2014-01-02 0.007 2018-04-13 15:05:34
376 2016-07-01 0.028 2018-04-13 15:05:34
377 2012-10-01 0.046 2018-04-13 15:05:34
378 2015-06-01 0.01 2018-04-13 15:05:34
379 2015-12-01 0.013 2018-04-13 15:05:34
380 2014-05-01 0.01 2018-04-13 15:05:34
381 2017-04-03 0.018 2018-04-13 15:05:34
382 2012-12-20 0.137 2018-04-13 15:05:34
383 2015-09-01 0.005 2018-04-13 15:05:34
384 2016-03-01 0.013 2018-04-13 15:05:34
385 2011-11-30 0.003 2018-04-13 15:05:34
386 2014-11-03 0.011 2018-04-13 15:05:34
387 2016-11-01 0.013 2018-04-13 15:05:34
388 2012-08-01 0.023 2018-04-13 15:05:34
389 2015-04-01 0.004 2018-04-13 15:05:34
390 2012-05-01 0.006 2018-04-13 15:05:34
391 2016-02-01 0.013 2018-04-13 15:05:34
392 2016-08-01 0.016 2018-04-13 15:05:34
393 2017-12-01 0.017 2018-04-13 15:05:34
394 2015-07-01 0.024 2018-04-13 15:05:34
395 2015-01-02 0.02 2018-04-13 15:05:34
396 2012-01-31 0.004 2018-04-13 15:05:34
397 2012-12-03 0.018 2018-04-13 15:05:34
398 2014-06-02 0.013 2018-04-13 15:05:34
399 2015-10-01 0.014 2018-04-13 15:05:34
400 2013-03-01 0.004 2018-04-13 15:05:34
401 2017-08-01 0.025 2018-04-13 15:05:34
402 2013-05-01 0.009 2018-04-13 15:05:34
403 2013-12-20 0.069 2018-04-13 15:05:34
404 2014-09-02 0.015 2018-04-13 15:05:34
405 2017-03-01 0.013 2018-04-13 15:05:34
406 2015-11-02 0.011 2018-04-13 15:05:34
407 2017-05-01 0.01 2018-04-13 15:05:34
408 2014-04-01 0.009 2018-04-13 15:05:34
409 2016-01-04 0.025 2018-04-13 15:05:34
410 2013-02-01 0.006 2018-04-13 15:05:34
411 2015-08-03 0.009 2018-04-13 15:05:34
412 2017-06-01 0.021 2018-04-13 15:05:34
413 2014-07-01 0.028 2018-04-13 15:05:34
414 2014-12-22 0.105 2018-04-13 15:05:34
415 2016-05-03 0.015 2018-04-13 15:05:34
416 2018-03-01 0.019 2018-04-13 15:05:34
417 2017-09-01 0.015 2018-04-13 15:05:34
418 2013-06-03 0.02 2018-04-13 15:05:34
419 2013-12-02 0.008 2018-04-13 15:05:34
420 2014-10-01 0.013 2018-04-13 15:05:34
421 2011-10-31 0.003 2018-04-13 15:05:34
422 2017-02-01 0.025 2018-04-13 15:05:34
423 2014-03-03 0.012 2018-04-13 15:05:34
424 2013-09-03 0.026 2018-04-13 15:05:34
425 2018-05-01 0.023 2018-12-20 11:23:51
426 2012-10-01 0.046 2018-12-20 11:23:51
427 2015-06-01 0.01 2018-12-20 11:23:51
428 2015-12-01 0.013 2018-12-20 11:23:51
429 2018-08-01 0.033 2018-12-20 11:23:51
430 2014-05-01 0.01 2018-12-20 11:23:51
431 2017-04-03 0.018 2018-12-20 11:23:51
432 2012-12-20 0.137 2018-12-20 11:23:51
433 2015-09-01 0.005 2018-12-20 11:23:51
434 2016-03-01 0.013 2018-12-20 11:23:51
435 2011-11-30 0.003 2018-12-20 11:23:51
436 2014-11-03 0.011 2018-12-20 11:23:51
437 2016-11-01 0.013 2018-12-20 11:23:51
438 2012-08-01 0.023 2018-12-20 11:23:51
439 2015-04-01 0.004 2018-12-20 11:23:51
440 2012-05-01 0.006 2018-12-20 11:23:51
441 2016-02-01 0.013 2018-12-20 11:23:51
442 2016-08-01 0.016 2018-12-20 11:23:51
443 2017-12-01 0.017 2018-12-20 11:23:51
444 2018-06-01 0.021 2018-12-20 11:23:51
445 2015-07-01 0.024 2018-12-20 11:23:51
446 2015-01-02 0.02 2018-12-20 11:23:51
447 2012-01-31 0.004 2018-12-20 11:23:51
448 2012-12-03 0.018 2018-12-20 11:23:51
449 2014-06-02 0.013 2018-12-20 11:23:51
450 2015-10-01 0.014 2018-12-20 11:23:51
451 2018-09-04 0.023 2018-12-20 11:23:51
452 2013-03-01 0.004 2018-12-20 11:23:51
453 2017-08-01 0.025 2018-12-20 11:23:51
454 2013-05-01 0.009 2018-12-20 11:23:51
455 2013-12-20 0.069 2018-12-20 11:23:51
456 2014-09-02 0.015 2018-12-20 11:23:51
457 2017-03-01 0.013 2018-12-20 11:23:51
458 2015-11-02 0.011 2018-12-20 11:23:51
459 2017-05-01 0.01 2018-12-20 11:23:51
460 2014-04-01 0.009 2018-12-20 11:23:51
461 2016-01-04 0.025 2018-12-20 11:23:51
462 2018-07-02 0.022 2018-12-20 11:23:51
463 2013-02-01 0.006 2018-12-20 11:23:51
464 2015-08-03 0.009 2018-12-20 11:23:51
465 2017-06-01 0.021 2018-12-20 11:23:51
466 2014-07-01 0.028 2018-12-20 11:23:51
467 2018-10-01 0.008 2018-12-20 11:23:51
468 2014-12-22 0.105 2018-12-20 11:23:51
469 2016-05-03 0.015 2018-12-20 11:23:51
470 2018-03-01 0.019 2018-12-20 11:23:51
471 2017-09-01 0.015 2018-12-20 11:23:51
472 2013-06-03 0.02 2018-12-20 11:23:51
473 2013-12-02 0.008 2018-12-20 11:23:51
474 2014-10-01 0.013 2018-12-20 11:23:51
475 2011-10-31 0.003 2018-12-20 11:23:51
476 2017-02-01 0.025 2018-12-20 11:23:51
477 2014-03-03 0.012 2018-12-20 11:23:51
478 2013-09-03 0.026 2018-12-20 11:23:51

16800
overrides/frozen/afxpx-history.csv
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7
overrides/frozen/afxpx.json
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@ -1,7 +0,0 @@
{
"symbol": "afxpx",
"longName": "?",
"freeze_date": "2018-12-11",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

7
overrides/frozen/agazx-capitalGain.csv
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@ -1,7 +0,0 @@
Date,Capital Gains
2015-12-03,0.093000
2017-12-05,0.759000
2018-12-07,0.334000
2021-12-09,0.367000
2024-12-12,0.260000
2025-02-21,1.364000
1 Date Capital Gains
2 2015-12-03 0.093000
3 2017-12-05 0.759000
4 2018-12-07 0.334000
5 2021-12-09 0.367000
6 2024-12-12 0.260000
7 2025-02-21 1.364000

9
overrides/frozen/agazx-dividend.csv
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@ -1,9 +0,0 @@
Date,Dividends
2023-12-27,0.342230
2024-12-12,0.260230
2024-12-27,0.210040
2025-02-21,1.363530
2025-03-25,0.134190
2019-12-09,0.032500
2021-12-09,0.367200
2022-12-28,0.098270
1 Date Dividends
2 2023-12-27 0.342230
3 2024-12-12 0.260230
4 2024-12-27 0.210040
5 2025-02-21 1.363530
6 2025-03-25 0.134190
7 2019-12-09 0.032500
8 2021-12-09 0.367200
9 2022-12-28 0.098270

1572
overrides/frozen/agazx-history.csv
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7
overrides/frozen/agazx.json
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@ -1,7 +0,0 @@
{
"symbol": "agazx",
"longName": "Allspring Funds Trust - Allspring Global Long/Short Equity Fund",
"freeze_date": "2025-04-07",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

13
overrides/frozen/akrex-capitalGain.csv
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@ -1,13 +0,0 @@
Date,Capital Gains
2021-12-10,2.340000
2023-12-08,2.044000
2024-12-06,3.273000
2012-12-14,0.252000
2014-12-12,0.467000
2016-12-16,0.046000
2017-12-15,0.200000
2019-12-13,1.331000
2025-08-25,3.020000
2013-12-13,0.404000
2018-12-14,0.188000
2022-12-09,3.120000
1 Date Capital Gains
2 2021-12-10 2.340000
3 2023-12-08 2.044000
4 2024-12-06 3.273000
5 2012-12-14 0.252000
6 2014-12-12 0.467000
7 2016-12-16 0.046000
8 2017-12-15 0.200000
9 2019-12-13 1.331000
10 2025-08-25 3.020000
11 2013-12-13 0.404000
12 2018-12-14 0.188000
13 2022-12-09 3.120000

7
overrides/frozen/akrex-dividend.csv
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@ -1,7 +0,0 @@
Date,Dividends
2021-12-10,2.340140
2022-12-09,3.120430
2023-12-08,2.044210
2024-12-06,3.272590
2025-08-25,3.020290
2019-12-13,1.330690
1 Date Dividends
2 2021-12-10 2.340140
3 2022-12-09 3.120430
4 2023-12-08 2.044210
5 2024-12-06 3.272590
6 2025-08-25 3.020290
7 2019-12-13 1.330690

1702
overrides/frozen/akrex-history.csv
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7
overrides/frozen/akrex.json
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{
"symbol": "akrex",
"longName": "Akre Focus Retail",
"freeze_date": "2025-10-09",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

9
overrides/frozen/alasx-dividend.csv
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@ -1,9 +0,0 @@
Date,Dividends,ctime
2014-12-18,0.078,2017-08-22
2015-12-17,0.042,2017-08-22
2016-12-15,0.117,2017-08-22
2015-03-10,0.007,2017-08-22
2014-09-09,0.042,2017-08-22
2017-12-14,0.08,2017-12-27 07:42:40
2015-03-10,0.007,2017-12-27 07:42:40
2014-09-09,0.042,2017-12-27 07:42:40
1 Date Dividends ctime
2 2014-12-18 0.078 2017-08-22
3 2015-12-17 0.042 2017-08-22
4 2016-12-15 0.117 2017-08-22
5 2015-03-10 0.007 2017-08-22
6 2014-09-09 0.042 2017-08-22
7 2017-12-14 0.08 2017-12-27 07:42:40
8 2015-03-10 0.007 2017-12-27 07:42:40
9 2014-09-09 0.042 2017-12-27 07:42:40

8399
overrides/frozen/alasx-history.csv
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7
overrides/frozen/alasx.json
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{
"symbol": "alasx",
"longName": "?",
"freeze_date": "2018-04-27",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

16
overrides/frozen/amfzx-dividend.csv
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@ -1,16 +0,0 @@
Date,Dividends,ctime
2012-12-18,0.011,2017-08-22
2013-12-17,0.403,2017-08-22
2014-12-17,0.292,2017-08-22
2019-02-07,4.44,2019-12-19 07:25:20
2019-03-07,4.43,2019-04-16 10:50:03
2019-02-07,4.44,2019-04-16 10:50:03
2019-04-09,4.45,2019-04-16 10:50:03
2019-12-09,0.158,2019-12-19 07:25:20
2012-12-18,0.0,2021-03-01 22:16:32
2012-12-18,0.011,2019-12-19 07:25:20
2013-12-17,0.0,2021-03-01 22:16:32
2013-12-17,0.403,2019-12-19 07:25:20
2014-12-17,0.0,2021-03-01 22:16:32
2014-12-17,0.292,2019-12-19 07:25:20
2019-03-07,4.43,2019-12-19 07:25:20
1 Date Dividends ctime
2 2012-12-18 0.011 2017-08-22
3 2013-12-17 0.403 2017-08-22
4 2014-12-17 0.292 2017-08-22
5 2019-02-07 4.44 2019-12-19 07:25:20
6 2019-03-07 4.43 2019-04-16 10:50:03
7 2019-02-07 4.44 2019-04-16 10:50:03
8 2019-04-09 4.45 2019-04-16 10:50:03
9 2019-12-09 0.158 2019-12-19 07:25:20
10 2012-12-18 0.0 2021-03-01 22:16:32
11 2012-12-18 0.011 2019-12-19 07:25:20
12 2013-12-17 0.0 2021-03-01 22:16:32
13 2013-12-17 0.403 2019-12-19 07:25:20
14 2014-12-17 0.0 2021-03-01 22:16:32
15 2014-12-17 0.292 2019-12-19 07:25:20
16 2019-03-07 4.43 2019-12-19 07:25:20

573
overrides/frozen/amfzx-history.csv
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@ -1,573 +0,0 @@
Date,Open,High,Low,Close,Adj Close,Volume
2019-01-02,11.430000,11.430000,11.430000,11.430000,11.430000,0
2019-01-03,11.430000,11.430000,11.430000,11.430000,11.430000,0
2019-01-04,11.530000,11.530000,11.530000,11.530000,11.530000,0
2019-01-07,11.470000,11.470000,11.470000,11.470000,11.470000,0
2019-01-08,11.360000,11.360000,11.360000,11.360000,11.360000,0
2019-01-09,11.300000,11.300000,11.300000,11.300000,11.300000,0
2019-01-10,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-01-11,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-01-14,11.300000,11.300000,11.300000,11.300000,11.300000,0
2019-01-15,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-01-16,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-01-17,11.170000,11.170000,11.170000,11.170000,11.170000,0
2019-01-18,11.040000,11.040000,11.040000,11.040000,11.040000,0
2019-01-22,11.220000,11.220000,11.220000,11.220000,11.220000,0
2019-01-23,11.220000,11.220000,11.220000,11.220000,11.220000,0
2019-01-24,11.230000,11.230000,11.230000,11.230000,11.230000,0
2019-01-25,11.210000,11.210000,11.210000,11.210000,11.210000,0
2019-01-28,11.290000,11.290000,11.290000,11.290000,11.290000,0
2019-01-29,11.290000,11.290000,11.290000,11.290000,11.290000,0
2019-01-30,11.260000,11.260000,11.260000,11.260000,11.260000,0
2019-01-31,11.230000,11.230000,11.230000,11.230000,11.230000,0
2019-02-01,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-02-04,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-02-05,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-02-06,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-02-07,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-02-08,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-02-11,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-02-12,11.260000,11.260000,11.260000,11.260000,11.260000,0
2019-02-13,11.260000,11.260000,11.260000,11.260000,11.260000,0
2019-02-14,11.260000,11.260000,11.260000,11.260000,11.260000,0
2019-02-15,11.260000,11.260000,11.260000,11.260000,11.260000,0
2019-02-19,11.260000,11.260000,11.260000,11.260000,11.260000,0
2019-02-20,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-02-21,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-02-22,11.300000,11.300000,11.300000,11.300000,11.300000,0
2019-02-25,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-02-26,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-02-27,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-02-28,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-03-01,11.300000,11.300000,11.300000,11.300000,11.300000,0
2019-03-04,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-03-05,11.270000,11.270000,11.270000,11.270000,11.270000,0
2019-03-06,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-03-07,11.200000,11.200000,11.200000,11.200000,11.200000,0
2019-03-08,11.200000,11.200000,11.200000,11.200000,11.200000,0
2019-03-11,11.400000,11.400000,11.400000,11.400000,11.400000,0
2019-03-12,11.420000,11.420000,11.420000,11.420000,11.420000,0
2019-03-13,11.430000,11.430000,11.430000,11.430000,11.430000,0
2019-03-14,11.430000,11.430000,11.430000,11.430000,11.430000,0
2019-03-15,11.450000,11.450000,11.450000,11.450000,11.450000,0
2019-03-18,11.440000,11.440000,11.440000,11.440000,11.440000,0
2019-03-19,11.440000,11.440000,11.440000,11.440000,11.440000,0
2019-03-20,11.430000,11.430000,11.430000,11.430000,11.430000,0
2019-03-21,11.440000,11.440000,11.440000,11.440000,11.440000,0
2019-03-22,11.390000,11.390000,11.390000,11.390000,11.390000,0
2019-03-25,11.410000,11.410000,11.410000,11.410000,11.410000,0
2019-03-26,11.450000,11.450000,11.450000,11.450000,11.450000,0
2019-03-27,11.430000,11.430000,11.430000,11.430000,11.430000,0
2019-03-28,11.470000,11.470000,11.470000,11.470000,11.470000,0
2019-03-29,11.490000,11.490000,11.490000,11.490000,11.490000,0
2019-04-01,11.500000,11.500000,11.500000,11.500000,11.500000,0
2019-04-02,11.500000,11.500000,11.500000,11.500000,11.500000,0
2019-04-03,11.500000,11.500000,11.500000,11.500000,11.500000,0
2019-04-04,11.510000,11.510000,11.510000,11.510000,11.510000,0
2019-04-05,11.520000,11.520000,11.520000,11.520000,11.520000,0
2019-04-08,11.520000,11.520000,11.520000,11.520000,11.520000,0
2019-04-09,11.500000,11.500000,11.500000,11.500000,11.500000,0
2019-04-10,11.530000,11.530000,11.530000,11.530000,11.530000,0
2019-04-11,11.540000,11.540000,11.540000,11.540000,11.540000,0
2019-04-12,11.580000,11.580000,11.580000,11.580000,11.580000,0
2019-04-15,11.580000,11.580000,11.580000,11.580000,11.580000,0
2019-04-16,11.580000,11.580000,11.580000,11.580000,11.580000,0
2019-04-17,11.580000,11.580000,11.580000,11.580000,11.580000,0
2019-04-18,11.580000,11.580000,11.580000,11.580000,11.580000,0
2019-04-22,11.590000,11.590000,11.590000,11.590000,11.590000,0
2019-04-23,11.640000,11.640000,11.640000,11.640000,11.640000,0
2019-04-24,11.630000,11.630000,11.630000,11.630000,11.630000,0
2019-04-25,11.620000,11.620000,11.620000,11.620000,11.620000,0
2019-04-26,11.640000,11.640000,11.640000,11.640000,11.640000,0
2019-04-29,11.640000,11.640000,11.640000,11.640000,11.640000,0
2019-04-30,11.640000,11.640000,11.640000,11.640000,11.640000,0
2019-05-01,11.610000,11.610000,11.610000,11.610000,11.610000,0
2019-05-02,11.600000,11.600000,11.600000,11.600000,11.600000,0
2019-05-03,11.620000,11.620000,11.620000,11.620000,11.620000,0
2019-05-06,11.570000,11.570000,11.570000,11.570000,11.570000,0
2019-05-07,11.570000,11.570000,11.570000,11.570000,11.570000,0
2019-05-08,11.560000,11.560000,11.560000,11.560000,11.560000,0
2019-05-09,11.510000,11.510000,11.510000,11.510000,11.510000,0
2019-05-10,11.540000,11.540000,11.540000,11.540000,11.540000,0
2019-05-13,11.180000,11.180000,11.180000,11.180000,11.180000,0
2019-05-14,11.330000,11.330000,11.330000,11.330000,11.330000,0
2019-05-15,11.330000,11.330000,11.330000,11.330000,11.330000,0
2019-05-16,11.330000,11.330000,11.330000,11.330000,11.330000,0
2019-05-17,11.330000,11.330000,11.330000,11.330000,11.330000,0
2019-05-20,11.320000,11.320000,11.320000,11.320000,11.320000,0
2019-05-21,11.380000,11.380000,11.380000,11.380000,11.380000,0
2019-05-22,11.360000,11.360000,11.360000,11.360000,11.360000,0
2019-05-23,11.290000,11.290000,11.290000,11.290000,11.290000,0
2019-05-24,11.350000,11.350000,11.350000,11.350000,11.350000,0
2019-05-28,11.270000,11.270000,11.270000,11.270000,11.270000,0
2019-05-29,11.160000,11.160000,11.160000,11.160000,11.160000,0
2019-05-30,11.190000,11.190000,11.190000,11.190000,11.190000,0
2019-05-31,11.040000,11.040000,11.040000,11.040000,11.040000,0
2019-06-03,10.990000,10.990000,10.990000,10.990000,10.990000,0
2019-06-04,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-06-05,11.230000,11.230000,11.230000,11.230000,11.230000,0
2019-06-06,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-06-07,11.230000,11.230000,11.230000,11.230000,11.230000,0
2019-06-10,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-06-11,11.230000,11.230000,11.230000,11.230000,11.230000,0
2019-06-12,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-06-13,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-06-14,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-06-17,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-06-18,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-06-19,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-06-20,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-06-21,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-06-24,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-06-25,11.220000,11.220000,11.220000,11.220000,11.220000,0
2019-06-26,11.220000,11.220000,11.220000,11.220000,11.220000,0
2019-06-27,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-06-28,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-07-01,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-07-02,11.270000,11.270000,11.270000,11.270000,11.270000,0
2019-07-03,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-07-05,11.270000,11.270000,11.270000,11.270000,11.270000,0
2019-07-08,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-07-09,11.250000,11.250000,11.250000,11.250000,11.250000,0
2019-07-10,11.290000,11.290000,11.290000,11.290000,11.290000,0
2019-07-11,11.290000,11.290000,11.290000,11.290000,11.290000,0
2019-07-12,11.300000,11.300000,11.300000,11.300000,11.300000,0
2019-07-15,11.300000,11.300000,11.300000,11.300000,11.300000,0
2019-07-16,11.300000,11.300000,11.300000,11.300000,11.300000,0
2019-07-17,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-07-18,11.310000,11.310000,11.310000,11.310000,11.310000,0
2019-07-19,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-07-22,11.290000,11.290000,11.290000,11.290000,11.290000,0
2019-07-23,11.350000,11.350000,11.350000,11.350000,11.350000,0
2019-07-24,11.360000,11.360000,11.360000,11.360000,11.360000,0
2019-07-25,11.360000,11.360000,11.360000,11.360000,11.360000,0
2019-07-26,11.420000,11.420000,11.420000,11.420000,11.420000,0
2019-07-29,11.430000,11.430000,11.430000,11.430000,11.430000,0
2019-07-30,11.390000,11.390000,11.390000,11.390000,11.390000,0
2019-07-31,11.350000,11.350000,11.350000,11.350000,11.350000,0
2019-08-01,11.280000,11.280000,11.280000,11.280000,11.280000,0
2019-08-02,11.210000,11.210000,11.210000,11.210000,11.210000,0
2019-08-05,10.820000,10.820000,10.820000,10.820000,10.820000,0
2019-08-06,10.980000,10.980000,10.980000,10.980000,10.980000,0
2019-08-07,11.010000,11.010000,11.010000,11.010000,11.010000,0
2019-08-08,11.170000,11.170000,11.170000,11.170000,11.170000,0
2019-08-09,11.180000,11.180000,11.180000,11.180000,11.180000,0
2019-08-12,11.150000,11.150000,11.150000,11.150000,11.150000,0
2019-08-13,11.300000,11.300000,11.300000,11.300000,11.300000,0
2019-08-14,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-08-15,11.240000,11.240000,11.240000,11.240000,11.240000,0
2019-08-16,11.420000,11.420000,11.420000,11.420000,11.420000,0
2019-08-19,11.410000,11.410000,11.410000,11.410000,11.410000,0
2019-08-20,11.440000,11.440000,11.440000,11.440000,11.440000,0
2019-08-21,11.400000,11.400000,11.400000,11.400000,11.400000,0
2019-08-22,11.390000,11.390000,11.390000,11.390000,11.390000,0
2019-08-23,11.370000,11.370000,11.370000,11.370000,11.370000,0
2019-08-26,11.500000,11.500000,11.500000,11.500000,11.500000,0
2019-08-27,11.400000,11.400000,11.400000,11.400000,11.400000,0
2019-08-28,11.500000,11.500000,11.500000,11.500000,11.500000,0
2019-08-29,11.660000,11.660000,11.660000,11.660000,11.660000,0
2019-08-30,11.660000,11.660000,11.660000,11.660000,11.660000,0
2019-09-03,11.660000,11.660000,11.660000,11.660000,11.660000,0
2019-09-04,11.670000,11.670000,11.670000,11.670000,11.670000,0
2019-09-05,11.690000,11.690000,11.690000,11.690000,11.690000,0
2019-09-06,11.730000,11.730000,11.730000,11.730000,11.730000,0
2019-09-09,11.730000,11.730000,11.730000,11.730000,11.730000,0
2019-09-10,11.730000,11.730000,11.730000,11.730000,11.730000,0
2019-09-11,11.670000,11.670000,11.670000,11.670000,11.670000,0
2019-09-12,11.670000,11.670000,11.670000,11.670000,11.670000,0
2019-09-13,11.680000,11.680000,11.680000,11.680000,11.680000,0
2019-09-16,11.650000,11.650000,11.650000,11.650000,11.650000,0
2019-09-17,11.670000,11.670000,11.670000,11.670000,11.670000,0
2019-09-18,11.700000,11.700000,11.700000,11.700000,11.700000,0
2019-09-19,11.690000,11.690000,11.690000,11.690000,11.690000,0
2019-09-20,11.640000,11.640000,11.640000,11.640000,11.640000,0
2019-09-23,11.670000,11.670000,11.670000,11.670000,11.670000,0
2019-09-24,11.550000,11.550000,11.550000,11.550000,11.550000,0
2019-09-25,11.640000,11.640000,11.640000,11.640000,11.640000,0
2019-09-26,11.630000,11.630000,11.630000,11.630000,11.630000,0
2019-09-27,11.610000,11.610000,11.610000,11.610000,11.610000,0
2019-09-30,11.680000,11.680000,11.680000,11.680000,11.680000,0
2019-10-01,11.660000,11.660000,11.660000,11.660000,11.660000,0
2019-10-02,11.660000,11.660000,11.660000,11.660000,11.660000,0
2019-10-03,11.750000,11.750000,11.750000,11.750000,11.750000,0
2019-10-04,11.790000,11.790000,11.790000,11.790000,11.790000,0
2019-10-07,11.790000,11.790000,11.790000,11.790000,11.790000,0
2019-10-08,11.790000,11.790000,11.790000,11.790000,11.790000,0
2019-10-09,11.870000,11.870000,11.870000,11.870000,11.870000,0
2019-10-10,11.890000,11.890000,11.890000,11.890000,11.890000,0
2019-10-11,11.880000,11.880000,11.880000,11.880000,11.880000,0
2019-10-14,11.900000,11.900000,11.900000,11.900000,11.900000,0
2019-10-15,11.910000,11.910000,11.910000,11.910000,11.910000,0
2019-10-16,11.920000,11.920000,11.920000,11.920000,11.920000,0
2019-10-17,11.920000,11.920000,11.920000,11.920000,11.920000,0
2019-10-18,11.920000,11.920000,11.920000,11.920000,11.920000,0
2019-10-21,11.970000,11.970000,11.970000,11.970000,11.970000,0
2019-10-22,11.950000,11.950000,11.950000,11.950000,11.950000,0
2019-10-23,11.970000,11.970000,11.970000,11.970000,11.970000,0
2019-10-24,11.980000,11.980000,11.980000,11.980000,11.980000,0
2019-10-25,12.000000,12.000000,12.000000,12.000000,12.000000,0
2019-10-28,11.990000,11.990000,11.990000,11.990000,11.990000,0
2019-10-29,12.000000,12.000000,12.000000,12.000000,12.000000,0
2019-10-30,12.020000,12.020000,12.020000,12.020000,12.020000,0
2019-10-31,12.030000,12.030000,12.030000,12.030000,12.030000,0
2019-11-01,12.130000,12.130000,12.130000,12.130000,12.130000,0
2019-11-04,12.130000,12.130000,12.130000,12.130000,12.130000,0
2019-11-05,12.120000,12.120000,12.120000,12.120000,12.120000,0
2019-11-06,12.130000,12.130000,12.130000,12.130000,12.130000,0
2019-11-07,12.130000,12.130000,12.130000,12.130000,12.130000,0
2019-11-08,12.160000,12.160000,12.160000,12.160000,12.160000,0
2019-11-11,12.160000,12.160000,12.160000,12.160000,12.160000,0
2019-11-12,12.170000,12.170000,12.170000,12.170000,12.170000,0
2019-11-13,12.160000,12.160000,12.160000,12.160000,12.160000,0
2019-11-14,12.170000,12.170000,12.170000,12.170000,12.170000,0
2019-11-15,12.200000,12.200000,12.200000,12.200000,12.200000,0
2019-11-18,12.200000,12.200000,12.200000,12.200000,12.200000,0
2019-11-19,12.200000,12.200000,12.200000,12.200000,12.200000,0
2019-11-20,12.190000,12.190000,12.190000,12.190000,12.190000,0
2019-11-21,12.160000,12.160000,12.160000,12.160000,12.160000,0
2019-11-22,12.190000,12.190000,12.190000,12.190000,12.190000,0
2019-11-25,12.220000,12.220000,12.220000,12.220000,12.220000,0
2019-11-26,12.220000,12.220000,12.220000,12.220000,12.220000,0
2019-11-27,12.220000,12.220000,12.220000,12.220000,12.220000,0
2019-11-29,12.220000,12.220000,12.220000,12.220000,12.220000,0
2019-12-02,12.170000,12.170000,12.170000,12.170000,12.170000,0
2019-12-03,12.090000,12.090000,12.090000,12.090000,12.090000,0
2019-12-04,12.150000,12.150000,12.150000,12.150000,12.150000,0
2019-12-05,12.150000,12.150000,12.150000,12.150000,12.150000,0
2019-12-06,12.160000,12.160000,12.160000,12.160000,12.160000,0
2019-12-09,11.950000,11.950000,11.950000,11.950000,11.950000,0
2019-12-10,11.950000,11.950000,11.950000,11.950000,11.950000,0
2019-12-11,11.950000,11.950000,11.950000,11.950000,11.950000,0
2019-12-12,11.960000,11.960000,11.960000,11.960000,11.960000,0
2019-12-13,11.990000,11.990000,11.990000,11.990000,11.990000,0
2019-12-16,12.000000,12.000000,12.000000,12.000000,12.000000,0
2019-12-17,12.000000,12.000000,12.000000,12.000000,12.000000,0
2019-12-18,12.000000,12.000000,12.000000,12.000000,12.000000,0
2019-12-19,12.010000,12.010000,12.010000,12.010000,12.010000,0
2019-12-20,12.000000,12.000000,12.000000,12.000000,12.000000,0
2019-12-23,12.000000,12.000000,12.000000,12.000000,12.000000,0
2019-12-24,12.010000,12.010000,12.010000,12.010000,12.010000,0
2019-12-26,12.010000,12.010000,12.010000,12.010000,12.010000,0
2019-12-27,11.990000,11.990000,11.990000,11.990000,11.990000,0
2019-12-30,11.970000,11.970000,11.970000,11.970000,11.970000,0
2019-12-31,12.000000,12.000000,12.000000,12.000000,12.000000,0
2020-01-02,12.010000,12.010000,12.010000,12.010000,12.010000,0
2020-01-03,11.970000,11.970000,11.970000,11.970000,11.970000,0
2020-01-06,11.970000,11.970000,11.970000,11.970000,11.970000,0
2020-01-07,11.980000,11.980000,11.980000,11.980000,11.980000,0
2020-01-08,12.000000,12.000000,12.000000,12.000000,12.000000,0
2020-01-09,12.010000,12.010000,12.010000,12.010000,12.010000,0
2020-01-10,12.010000,12.010000,12.010000,12.010000,12.010000,0
2020-01-13,12.050000,12.050000,12.050000,12.050000,12.050000,0
2020-01-14,12.060000,12.060000,12.060000,12.060000,12.060000,0
2020-01-15,12.060000,12.060000,12.060000,12.060000,12.060000,0
2020-01-16,12.070000,12.070000,12.070000,12.070000,12.070000,0
2020-01-17,12.070000,12.070000,12.070000,12.070000,12.070000,0
2020-01-21,12.070000,12.070000,12.070000,12.070000,12.070000,0
2020-01-22,12.070000,12.070000,12.070000,12.070000,12.070000,0
2020-01-23,12.070000,12.070000,12.070000,12.070000,12.070000,0
2020-01-24,11.990000,11.990000,11.990000,11.990000,11.990000,0
2020-01-27,11.890000,11.890000,11.890000,11.890000,11.890000,0
2020-01-28,12.020000,12.020000,12.020000,12.020000,12.020000,0
2020-01-29,12.000000,12.000000,12.000000,12.000000,12.000000,0
2020-01-30,12.000000,12.000000,12.000000,12.000000,12.000000,0
2020-01-31,11.900000,11.900000,11.900000,11.900000,11.900000,0
2020-02-03,12.010000,12.010000,12.010000,12.010000,12.010000,0
2020-02-04,12.020000,12.020000,12.020000,12.020000,12.020000,0
2020-02-05,12.030000,12.030000,12.030000,12.030000,12.030000,0
2020-02-06,12.030000,12.030000,12.030000,12.030000,12.030000,0
2020-02-07,12.020000,12.020000,12.020000,12.020000,12.020000,0
2020-02-10,12.030000,12.030000,12.030000,12.030000,12.030000,0
2020-02-11,12.030000,12.030000,12.030000,12.030000,12.030000,0
2020-02-12,12.060000,12.060000,12.060000,12.060000,12.060000,0
2020-02-13,12.060000,12.060000,12.060000,12.060000,12.060000,0
2020-02-14,12.060000,12.060000,12.060000,12.060000,12.060000,0
2020-02-18,12.050000,12.050000,12.050000,12.050000,12.050000,0
2020-02-19,12.050000,12.050000,12.050000,12.050000,12.050000,0
2020-02-20,12.040000,12.040000,12.040000,12.040000,12.040000,0
2020-02-21,12.030000,12.030000,12.030000,12.030000,12.030000,0
2020-02-24,11.750000,11.750000,11.750000,11.750000,11.750000,0
2020-02-25,11.420000,11.420000,11.420000,11.420000,11.420000,0
2020-02-26,11.340000,11.340000,11.340000,11.340000,11.340000,0
2020-02-27,10.820000,10.820000,10.820000,10.820000,10.820000,0
2020-02-28,10.800000,10.800000,10.800000,10.800000,10.800000,0
2020-03-02,11.140000,11.140000,11.140000,11.140000,11.140000,0
2020-03-03,11.020000,11.020000,11.020000,11.020000,11.020000,0
2020-03-04,11.390000,11.390000,11.390000,11.390000,11.390000,0
2020-03-05,11.420000,11.420000,11.420000,11.420000,11.420000,0
2020-03-06,11.340000,11.340000,11.340000,11.340000,11.340000,0
2020-03-09,10.580000,10.580000,10.580000,10.580000,10.580000,0
2020-03-10,10.930000,10.930000,10.930000,10.930000,10.930000,0
2020-03-11,10.680000,10.680000,10.680000,10.680000,10.680000,0
2020-03-12,9.870000,9.870000,9.870000,9.870000,9.870000,0
2020-03-13,10.630000,10.630000,10.630000,10.630000,10.630000,0
2020-03-16,9.410000,9.410000,9.410000,9.410000,9.410000,0
2020-03-17,9.830000,9.830000,9.830000,9.830000,9.830000,0
2020-03-18,9.410000,9.410000,9.410000,9.410000,9.410000,0
2020-03-19,9.510000,9.510000,9.510000,9.510000,9.510000,0
2020-03-20,9.240000,9.240000,9.240000,9.240000,9.240000,0
2020-03-23,9.040000,9.040000,9.040000,9.040000,9.040000,0
2020-03-24,9.840000,9.840000,9.840000,9.840000,9.840000,0
2020-03-25,9.870000,9.870000,9.870000,9.870000,9.870000,0
2020-03-26,10.280000,10.280000,10.280000,10.280000,10.280000,0
2020-03-27,10.210000,10.210000,10.210000,10.210000,10.210000,0
2020-03-30,10.240000,10.240000,10.240000,10.240000,10.240000,0
2020-03-31,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-04-01,9.770000,9.770000,9.770000,9.770000,9.770000,0
2020-04-02,9.980000,9.980000,9.980000,9.980000,9.980000,0
2020-04-03,9.960000,9.960000,9.960000,9.960000,9.960000,0
2020-04-06,10.170000,10.170000,10.170000,10.170000,10.170000,0
2020-04-07,10.180000,10.180000,10.180000,10.180000,10.180000,0
2020-04-08,9.940000,9.940000,9.940000,9.940000,9.940000,0
2020-04-09,9.760000,9.760000,9.760000,9.760000,9.760000,0
2020-04-13,9.890000,9.890000,9.890000,9.890000,9.890000,0
2020-04-14,9.590000,9.590000,9.590000,9.590000,9.590000,0
2020-04-15,9.820000,9.820000,9.820000,9.820000,9.820000,0
2020-04-16,9.820000,9.820000,9.820000,9.820000,9.820000,0
2020-04-17,9.820000,9.820000,9.820000,9.820000,9.820000,0
2020-04-20,9.860000,9.860000,9.860000,9.860000,9.860000,0
2020-04-21,9.900000,9.900000,9.900000,9.900000,9.900000,0
2020-04-22,10.060000,10.060000,10.060000,10.060000,10.060000,0
2020-04-23,10.060000,10.060000,10.060000,10.060000,10.060000,0
2020-04-24,10.010000,10.010000,10.010000,10.010000,10.010000,0
2020-04-27,9.820000,9.820000,9.820000,9.820000,9.820000,0
2020-04-28,9.770000,9.770000,9.770000,9.770000,9.770000,0
2020-04-29,9.550000,9.550000,9.550000,9.550000,9.550000,0
2020-04-30,9.700000,9.700000,9.700000,9.700000,9.700000,0
2020-05-01,9.820000,9.820000,9.820000,9.820000,9.820000,0
2020-05-04,9.860000,9.860000,9.860000,9.860000,9.860000,0
2020-05-05,9.910000,9.910000,9.910000,9.910000,9.910000,0
2020-05-06,9.920000,9.920000,9.920000,9.920000,9.920000,0
2020-05-07,9.910000,9.910000,9.910000,9.910000,9.910000,0
2020-05-08,9.870000,9.870000,9.870000,9.870000,9.870000,0
2020-05-11,9.890000,9.890000,9.890000,9.890000,9.890000,0
2020-05-12,10.140000,10.140000,10.140000,10.140000,10.140000,0
2020-05-13,9.920000,9.920000,9.920000,9.920000,9.920000,0
2020-05-14,10.010000,10.010000,10.010000,10.010000,10.010000,0
2020-05-15,10.040000,10.040000,10.040000,10.040000,10.040000,0
2020-05-18,9.930000,9.930000,9.930000,9.930000,9.930000,0
2020-05-19,10.030000,10.030000,10.030000,10.030000,10.030000,0
2020-05-20,9.890000,9.890000,9.890000,9.890000,9.890000,0
2020-05-21,9.920000,9.920000,9.920000,9.920000,9.920000,0
2020-05-22,9.890000,9.890000,9.890000,9.890000,9.890000,0
2020-05-26,9.800000,9.800000,9.800000,9.800000,9.800000,0
2020-05-27,9.660000,9.660000,9.660000,9.660000,9.660000,0
2020-05-28,9.720000,9.720000,9.720000,9.720000,9.720000,0
2020-05-29,9.750000,9.750000,9.750000,9.750000,9.750000,0
2020-06-01,9.740000,9.740000,9.740000,9.740000,9.740000,0
2020-06-02,9.690000,9.690000,9.690000,9.690000,9.690000,0
2020-06-03,9.570000,9.570000,9.570000,9.570000,9.570000,0
2020-06-04,9.570000,9.570000,9.570000,9.570000,9.570000,0
2020-06-05,9.380000,9.380000,9.380000,9.380000,9.380000,0
2020-06-08,9.260000,9.260000,9.260000,9.260000,9.260000,0
2020-06-09,9.350000,9.350000,9.350000,9.350000,9.350000,0
2020-06-10,9.420000,9.420000,9.420000,9.420000,9.420000,0
2020-06-11,9.370000,9.370000,9.370000,9.370000,9.370000,0
2020-06-12,9.430000,9.430000,9.430000,9.430000,9.430000,0
2020-06-15,9.490000,9.490000,9.490000,9.490000,9.490000,0
2020-06-16,9.560000,9.560000,9.560000,9.560000,9.560000,0
2020-06-17,9.610000,9.610000,9.610000,9.610000,9.610000,0
2020-06-18,9.630000,9.630000,9.630000,9.630000,9.630000,0
2020-06-19,9.610000,9.610000,9.610000,9.610000,9.610000,0
2020-06-22,9.610000,9.610000,9.610000,9.610000,9.610000,0
2020-06-23,9.610000,9.610000,9.610000,9.610000,9.610000,0
2020-06-24,9.760000,9.760000,9.760000,9.760000,9.760000,0
2020-06-25,9.880000,9.880000,9.880000,9.880000,9.880000,0
2020-06-26,9.810000,9.810000,9.810000,9.810000,9.810000,0
2020-06-29,9.970000,9.970000,9.970000,9.970000,9.970000,0
2020-06-30,10.090000,10.090000,10.090000,10.090000,10.090000,0
2020-07-01,10.100000,10.100000,10.100000,10.100000,10.100000,0
2020-07-02,10.050000,10.050000,10.050000,10.050000,10.050000,0
2020-07-06,9.970000,9.970000,9.970000,9.970000,9.970000,0
2020-07-07,10.090000,10.090000,10.090000,10.090000,10.090000,0
2020-07-08,10.080000,10.080000,10.080000,10.080000,10.080000,0
2020-07-09,10.080000,10.080000,10.080000,10.080000,10.080000,0
2020-07-10,10.080000,10.080000,10.080000,10.080000,10.080000,0
2020-07-13,10.080000,10.080000,10.080000,10.080000,10.080000,0
2020-07-14,10.060000,10.060000,10.060000,10.060000,10.060000,0
2020-07-15,10.050000,10.050000,10.050000,10.050000,10.050000,0
2020-07-16,10.090000,10.090000,10.090000,10.090000,10.090000,0
2020-07-17,10.070000,10.070000,10.070000,10.070000,10.070000,0
2020-07-20,10.160000,10.160000,10.160000,10.160000,10.160000,0
2020-07-21,10.120000,10.120000,10.120000,10.120000,10.120000,0
2020-07-22,10.110000,10.110000,10.110000,10.110000,10.110000,0
2020-07-23,10.110000,10.110000,10.110000,10.110000,10.110000,0
2020-07-24,10.060000,10.060000,10.060000,10.060000,10.060000,0
2020-07-27,10.150000,10.150000,10.150000,10.150000,10.150000,0
2020-07-28,10.140000,10.140000,10.140000,10.140000,10.140000,0
2020-07-29,10.140000,10.140000,10.140000,10.140000,10.140000,0
2020-07-30,10.140000,10.140000,10.140000,10.140000,10.140000,0
2020-07-31,10.150000,10.150000,10.150000,10.150000,10.150000,0
2020-08-03,10.200000,10.200000,10.200000,10.200000,10.200000,0
2020-08-04,10.200000,10.200000,10.200000,10.200000,10.200000,0
2020-08-05,10.200000,10.200000,10.200000,10.200000,10.200000,0
2020-08-06,10.200000,10.200000,10.200000,10.200000,10.200000,0
2020-08-07,10.200000,10.200000,10.200000,10.200000,10.200000,0
2020-08-10,10.180000,10.180000,10.180000,10.180000,10.180000,0
2020-08-11,10.130000,10.130000,10.130000,10.130000,10.130000,0
2020-08-12,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-13,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-14,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-17,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-18,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-19,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-20,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-21,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-24,10.240000,10.240000,10.240000,10.240000,10.240000,0
2020-08-25,10.240000,10.240000,10.240000,10.240000,10.240000,0
2020-08-26,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-27,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-28,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-08-31,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-09-01,10.240000,10.240000,10.240000,10.240000,10.240000,0
2020-09-02,10.240000,10.240000,10.240000,10.240000,10.240000,0
2020-09-03,10.240000,10.240000,10.240000,10.240000,10.240000,0
2020-09-04,10.140000,10.140000,10.140000,10.140000,10.140000,0
2020-09-08,10.160000,10.160000,10.160000,10.160000,10.160000,0
2020-09-09,10.160000,10.160000,10.160000,10.160000,10.160000,0
2020-09-10,10.160000,10.160000,10.160000,10.160000,10.160000,0
2020-09-11,10.130000,10.130000,10.130000,10.130000,10.130000,0
2020-09-14,10.290000,10.290000,10.290000,10.290000,10.290000,0
2020-09-15,10.300000,10.300000,10.300000,10.300000,10.300000,0
2020-09-16,10.300000,10.300000,10.300000,10.300000,10.300000,0
2020-09-17,10.300000,10.300000,10.300000,10.300000,10.300000,0
2020-09-18,10.230000,10.230000,10.230000,10.230000,10.230000,0
2020-09-21,10.210000,10.210000,10.210000,10.210000,10.210000,0
2020-09-22,10.200000,10.200000,10.200000,10.200000,10.200000,0
2020-09-23,10.130000,10.130000,10.130000,10.130000,10.130000,0
2020-09-24,10.140000,10.140000,10.140000,10.140000,10.140000,0
2020-09-25,10.220000,10.220000,10.220000,10.220000,10.220000,0
2020-09-28,10.300000,10.300000,10.300000,10.300000,10.300000,0
2020-09-29,10.300000,10.300000,10.300000,10.300000,10.300000,0
2020-09-30,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-10-01,10.350000,10.350000,10.350000,10.350000,10.350000,0
2020-10-02,10.350000,10.350000,10.350000,10.350000,10.350000,0
2020-10-05,10.370000,10.370000,10.370000,10.370000,10.370000,0
2020-10-06,10.370000,10.370000,10.370000,10.370000,10.370000,0
2020-10-07,10.420000,10.420000,10.420000,10.420000,10.420000,0
2020-10-08,10.420000,10.420000,10.420000,10.420000,10.420000,0
2020-10-09,10.420000,10.420000,10.420000,10.420000,10.420000,0
2020-10-12,10.420000,10.420000,10.420000,10.420000,10.420000,0
2020-10-13,10.420000,10.420000,10.420000,10.420000,10.420000,0
2020-10-14,10.420000,10.420000,10.420000,10.420000,10.420000,0
2020-10-15,10.420000,10.420000,10.420000,10.420000,10.420000,0
2020-10-16,10.400000,10.400000,10.400000,10.400000,10.400000,0
2020-10-19,10.370000,10.370000,10.370000,10.370000,10.370000,0
2020-10-20,10.380000,10.380000,10.380000,10.380000,10.380000,0
2020-10-21,10.380000,10.380000,10.380000,10.380000,10.380000,0
2020-10-22,10.390000,10.390000,10.390000,10.390000,10.390000,0
2020-10-23,10.390000,10.390000,10.390000,10.390000,10.390000,0
2020-10-26,10.390000,10.390000,10.390000,10.390000,10.390000,0
2020-10-27,10.390000,10.390000,10.390000,10.390000,10.390000,0
2020-10-28,10.270000,10.270000,10.270000,10.270000,10.270000,0
2020-10-29,10.270000,10.270000,10.270000,10.270000,10.270000,0
2020-10-30,10.270000,10.270000,10.270000,10.270000,10.270000,0
2020-11-02,10.270000,10.270000,10.270000,10.270000,10.270000,0
2020-11-03,10.270000,10.270000,10.270000,10.270000,10.270000,0
2020-11-04,10.260000,10.260000,10.260000,10.260000,10.260000,0
2020-11-05,10.260000,10.260000,10.260000,10.260000,10.260000,0
2020-11-06,10.260000,10.260000,10.260000,10.260000,10.260000,0
2020-11-09,10.260000,10.260000,10.260000,10.260000,10.260000,0
2020-11-10,10.260000,10.260000,10.260000,10.260000,10.260000,0
2020-11-11,10.260000,10.260000,10.260000,10.260000,10.260000,0
2020-11-12,10.260000,10.260000,10.260000,10.260000,10.260000,0
2020-11-13,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-11-16,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-11-17,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-11-18,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-11-19,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-11-20,10.300000,10.300000,10.300000,10.300000,10.300000,0
2020-11-23,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-11-24,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-11-25,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-11-27,10.310000,10.310000,10.310000,10.310000,10.310000,0
2020-11-30,10.310000,10.310000,10.310000,10.310000,10.310000,0
2020-12-01,10.330000,10.330000,10.330000,10.330000,10.330000,0
2020-12-02,10.330000,10.330000,10.330000,10.330000,10.330000,0
2020-12-03,10.330000,10.330000,10.330000,10.330000,10.330000,0
2020-12-04,10.330000,10.330000,10.330000,10.330000,10.330000,0
2020-12-07,10.330000,10.330000,10.330000,10.330000,10.330000,0
2020-12-08,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-12-09,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-12-10,10.330000,10.330000,10.330000,10.330000,10.330000,0
2020-12-11,10.320000,10.320000,10.320000,10.320000,10.320000,0
2020-12-14,10.310000,10.310000,10.310000,10.310000,10.310000,0
2020-12-15,10.340000,10.340000,10.340000,10.340000,10.340000,0
2020-12-16,10.340000,10.340000,10.340000,10.340000,10.340000,0
2020-12-17,10.340000,10.340000,10.340000,10.340000,10.340000,0
2020-12-18,10.340000,10.340000,10.340000,10.340000,10.340000,0
2020-12-21,10.340000,10.340000,10.340000,10.340000,10.340000,0
2020-12-22,10.330000,10.330000,10.330000,10.330000,10.330000,0
2020-12-23,10.340000,10.340000,10.340000,10.340000,10.340000,0
2020-12-24,10.340000,10.340000,10.340000,10.340000,10.340000,0
2020-12-28,10.330000,10.330000,10.330000,10.330000,10.330000,0
2020-12-29,10.330000,10.330000,10.330000,10.330000,10.330000,0
2020-12-30,10.350000,10.350000,10.350000,10.350000,10.350000,0
2020-12-31,10.350000,10.350000,10.350000,10.350000,10.350000,0
2021-01-04,10.350000,10.350000,10.350000,10.350000,10.350000,0
2021-01-05,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-06,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-07,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-08,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-11,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-12,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-13,10.360000,10.360000,10.360000,10.360000,10.360000,0
2021-01-14,10.360000,10.360000,10.360000,10.360000,10.360000,0
2021-01-15,10.360000,10.360000,10.360000,10.360000,10.360000,0
2021-01-19,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-20,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-21,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-22,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-25,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-26,10.370000,10.370000,10.370000,10.370000,10.370000,0
2021-01-27,10.360000,10.360000,10.360000,10.360000,10.360000,0
2021-01-28,10.380000,10.380000,10.380000,10.380000,10.380000,0
2021-01-29,10.380000,10.380000,10.380000,10.380000,10.380000,0
2021-02-01,10.480000,10.480000,10.480000,10.480000,10.480000,0
2021-02-02,10.510000,10.510000,10.510000,10.510000,10.510000,0
2021-02-03,10.510000,10.510000,10.510000,10.510000,10.510000,0
2021-02-04,10.510000,10.510000,10.510000,10.510000,10.510000,0
2021-02-05,10.510000,10.510000,10.510000,10.510000,10.510000,0
2021-02-08,10.500000,10.500000,10.500000,10.500000,10.500000,0
2021-02-09,10.500000,10.500000,10.500000,10.500000,10.500000,0
2021-02-10,10.500000,10.500000,10.500000,10.500000,10.500000,0
2021-02-11,10.500000,10.500000,10.500000,10.500000,10.500000,0
2021-02-12,10.500000,10.500000,10.500000,10.500000,10.500000,0
2021-02-16,10.500000,10.500000,10.500000,10.500000,10.500000,0
2021-02-17,10.500000,10.500000,10.500000,10.500000,10.500000,0
2021-02-18,10.470000,10.470000,10.470000,10.470000,10.470000,0
2021-02-19,10.470000,10.470000,10.470000,10.470000,10.470000,0
2021-02-22,10.470000,10.470000,10.470000,10.470000,10.470000,0
2021-02-23,10.460000,10.460000,10.460000,10.460000,10.460000,0
2021-02-24,10.470000,10.470000,10.470000,10.470000,10.470000,0
2021-02-25,10.420000,10.420000,10.420000,10.420000,10.420000,0
2021-02-26,10.420000,10.420000,10.420000,10.420000,10.420000,0
2021-03-01,10.500000,10.500000,10.500000,10.500000,10.500000,0
2021-03-02,10.490000,10.490000,10.490000,10.490000,10.490000,0
2021-03-03,10.490000,10.490000,10.490000,10.490000,10.490000,0
2021-03-04,10.470000,10.470000,10.470000,10.470000,10.470000,0
2021-03-05,10.560000,10.560000,10.560000,10.560000,10.560000,0
2021-03-08,10.560000,10.560000,10.560000,10.560000,10.560000,0
2021-03-09,10.620000,10.620000,10.620000,10.620000,10.620000,0
2021-03-10,10.620000,10.620000,10.620000,10.620000,10.620000,0
2021-03-11,10.620000,10.620000,10.620000,10.620000,10.620000,0
2021-03-12,10.620000,10.620000,10.620000,10.620000,10.620000,0
2021-03-15,10.610000,10.610000,10.610000,10.610000,10.610000,0
2021-03-16,10.610000,10.610000,10.610000,10.610000,10.610000,0
2021-03-17,10.620000,10.620000,10.620000,10.620000,10.620000,0
2021-03-18,10.620000,10.620000,10.620000,10.620000,10.620000,0
2021-03-19,10.640000,10.640000,10.640000,10.640000,10.640000,0
2021-03-22,10.630000,10.630000,10.630000,10.630000,10.630000,0
2021-03-23,10.580000,10.580000,10.580000,10.580000,10.580000,0
2021-03-24,10.540000,10.540000,10.540000,10.540000,10.540000,0
2021-03-25,10.580000,10.580000,10.580000,10.580000,10.580000,0
2021-03-26,10.630000,10.630000,10.630000,10.630000,10.630000,0
2021-03-29,10.590000,10.590000,10.590000,10.590000,10.590000,0
2021-03-30,10.600000,10.600000,10.600000,10.600000,10.600000,0
2021-03-31,10.640000,10.640000,10.640000,10.640000,10.640000,0
2021-04-01,10.640000,10.640000,10.640000,10.640000,10.640000,0
2021-04-05,10.640000,10.640000,10.640000,10.640000,10.640000,0
2021-04-06,10.640000,10.640000,10.640000,10.640000,10.640000,0
2021-04-07,10.640000,10.640000,10.640000,10.640000,10.640000,0
2021-04-08,10.640000,10.640000,10.640000,10.640000,10.640000,0
2021-04-09,10.640000,10.640000,10.640000,10.640000,10.640000,0
1 Date Open High Low Close Adj Close Volume
2 2019-01-02 11.430000 11.430000 11.430000 11.430000 11.430000 0
3 2019-01-03 11.430000 11.430000 11.430000 11.430000 11.430000 0
4 2019-01-04 11.530000 11.530000 11.530000 11.530000 11.530000 0
5 2019-01-07 11.470000 11.470000 11.470000 11.470000 11.470000 0
6 2019-01-08 11.360000 11.360000 11.360000 11.360000 11.360000 0
7 2019-01-09 11.300000 11.300000 11.300000 11.300000 11.300000 0
8 2019-01-10 11.250000 11.250000 11.250000 11.250000 11.250000 0
9 2019-01-11 11.250000 11.250000 11.250000 11.250000 11.250000 0
10 2019-01-14 11.300000 11.300000 11.300000 11.300000 11.300000 0
11 2019-01-15 11.280000 11.280000 11.280000 11.280000 11.280000 0
12 2019-01-16 11.250000 11.250000 11.250000 11.250000 11.250000 0
13 2019-01-17 11.170000 11.170000 11.170000 11.170000 11.170000 0
14 2019-01-18 11.040000 11.040000 11.040000 11.040000 11.040000 0
15 2019-01-22 11.220000 11.220000 11.220000 11.220000 11.220000 0
16 2019-01-23 11.220000 11.220000 11.220000 11.220000 11.220000 0
17 2019-01-24 11.230000 11.230000 11.230000 11.230000 11.230000 0
18 2019-01-25 11.210000 11.210000 11.210000 11.210000 11.210000 0
19 2019-01-28 11.290000 11.290000 11.290000 11.290000 11.290000 0
20 2019-01-29 11.290000 11.290000 11.290000 11.290000 11.290000 0
21 2019-01-30 11.260000 11.260000 11.260000 11.260000 11.260000 0
22 2019-01-31 11.230000 11.230000 11.230000 11.230000 11.230000 0
23 2019-02-01 11.250000 11.250000 11.250000 11.250000 11.250000 0
24 2019-02-04 11.240000 11.240000 11.240000 11.240000 11.240000 0
25 2019-02-05 11.240000 11.240000 11.240000 11.240000 11.240000 0
26 2019-02-06 11.250000 11.250000 11.250000 11.250000 11.250000 0
27 2019-02-07 11.240000 11.240000 11.240000 11.240000 11.240000 0
28 2019-02-08 11.240000 11.240000 11.240000 11.240000 11.240000 0
29 2019-02-11 11.240000 11.240000 11.240000 11.240000 11.240000 0
30 2019-02-12 11.260000 11.260000 11.260000 11.260000 11.260000 0
31 2019-02-13 11.260000 11.260000 11.260000 11.260000 11.260000 0
32 2019-02-14 11.260000 11.260000 11.260000 11.260000 11.260000 0
33 2019-02-15 11.260000 11.260000 11.260000 11.260000 11.260000 0
34 2019-02-19 11.260000 11.260000 11.260000 11.260000 11.260000 0
35 2019-02-20 11.280000 11.280000 11.280000 11.280000 11.280000 0
36 2019-02-21 11.280000 11.280000 11.280000 11.280000 11.280000 0
37 2019-02-22 11.300000 11.300000 11.300000 11.300000 11.300000 0
38 2019-02-25 11.280000 11.280000 11.280000 11.280000 11.280000 0
39 2019-02-26 11.280000 11.280000 11.280000 11.280000 11.280000 0
40 2019-02-27 11.280000 11.280000 11.280000 11.280000 11.280000 0
41 2019-02-28 11.280000 11.280000 11.280000 11.280000 11.280000 0
42 2019-03-01 11.300000 11.300000 11.300000 11.300000 11.300000 0
43 2019-03-04 11.280000 11.280000 11.280000 11.280000 11.280000 0
44 2019-03-05 11.270000 11.270000 11.270000 11.270000 11.270000 0
45 2019-03-06 11.240000 11.240000 11.240000 11.240000 11.240000 0
46 2019-03-07 11.200000 11.200000 11.200000 11.200000 11.200000 0
47 2019-03-08 11.200000 11.200000 11.200000 11.200000 11.200000 0
48 2019-03-11 11.400000 11.400000 11.400000 11.400000 11.400000 0
49 2019-03-12 11.420000 11.420000 11.420000 11.420000 11.420000 0
50 2019-03-13 11.430000 11.430000 11.430000 11.430000 11.430000 0
51 2019-03-14 11.430000 11.430000 11.430000 11.430000 11.430000 0
52 2019-03-15 11.450000 11.450000 11.450000 11.450000 11.450000 0
53 2019-03-18 11.440000 11.440000 11.440000 11.440000 11.440000 0
54 2019-03-19 11.440000 11.440000 11.440000 11.440000 11.440000 0
55 2019-03-20 11.430000 11.430000 11.430000 11.430000 11.430000 0
56 2019-03-21 11.440000 11.440000 11.440000 11.440000 11.440000 0
57 2019-03-22 11.390000 11.390000 11.390000 11.390000 11.390000 0
58 2019-03-25 11.410000 11.410000 11.410000 11.410000 11.410000 0
59 2019-03-26 11.450000 11.450000 11.450000 11.450000 11.450000 0
60 2019-03-27 11.430000 11.430000 11.430000 11.430000 11.430000 0
61 2019-03-28 11.470000 11.470000 11.470000 11.470000 11.470000 0
62 2019-03-29 11.490000 11.490000 11.490000 11.490000 11.490000 0
63 2019-04-01 11.500000 11.500000 11.500000 11.500000 11.500000 0
64 2019-04-02 11.500000 11.500000 11.500000 11.500000 11.500000 0
65 2019-04-03 11.500000 11.500000 11.500000 11.500000 11.500000 0
66 2019-04-04 11.510000 11.510000 11.510000 11.510000 11.510000 0
67 2019-04-05 11.520000 11.520000 11.520000 11.520000 11.520000 0
68 2019-04-08 11.520000 11.520000 11.520000 11.520000 11.520000 0
69 2019-04-09 11.500000 11.500000 11.500000 11.500000 11.500000 0
70 2019-04-10 11.530000 11.530000 11.530000 11.530000 11.530000 0
71 2019-04-11 11.540000 11.540000 11.540000 11.540000 11.540000 0
72 2019-04-12 11.580000 11.580000 11.580000 11.580000 11.580000 0
73 2019-04-15 11.580000 11.580000 11.580000 11.580000 11.580000 0
74 2019-04-16 11.580000 11.580000 11.580000 11.580000 11.580000 0
75 2019-04-17 11.580000 11.580000 11.580000 11.580000 11.580000 0
76 2019-04-18 11.580000 11.580000 11.580000 11.580000 11.580000 0
77 2019-04-22 11.590000 11.590000 11.590000 11.590000 11.590000 0
78 2019-04-23 11.640000 11.640000 11.640000 11.640000 11.640000 0
79 2019-04-24 11.630000 11.630000 11.630000 11.630000 11.630000 0
80 2019-04-25 11.620000 11.620000 11.620000 11.620000 11.620000 0
81 2019-04-26 11.640000 11.640000 11.640000 11.640000 11.640000 0
82 2019-04-29 11.640000 11.640000 11.640000 11.640000 11.640000 0
83 2019-04-30 11.640000 11.640000 11.640000 11.640000 11.640000 0
84 2019-05-01 11.610000 11.610000 11.610000 11.610000 11.610000 0
85 2019-05-02 11.600000 11.600000 11.600000 11.600000 11.600000 0
86 2019-05-03 11.620000 11.620000 11.620000 11.620000 11.620000 0
87 2019-05-06 11.570000 11.570000 11.570000 11.570000 11.570000 0
88 2019-05-07 11.570000 11.570000 11.570000 11.570000 11.570000 0
89 2019-05-08 11.560000 11.560000 11.560000 11.560000 11.560000 0
90 2019-05-09 11.510000 11.510000 11.510000 11.510000 11.510000 0
91 2019-05-10 11.540000 11.540000 11.540000 11.540000 11.540000 0
92 2019-05-13 11.180000 11.180000 11.180000 11.180000 11.180000 0
93 2019-05-14 11.330000 11.330000 11.330000 11.330000 11.330000 0
94 2019-05-15 11.330000 11.330000 11.330000 11.330000 11.330000 0
95 2019-05-16 11.330000 11.330000 11.330000 11.330000 11.330000 0
96 2019-05-17 11.330000 11.330000 11.330000 11.330000 11.330000 0
97 2019-05-20 11.320000 11.320000 11.320000 11.320000 11.320000 0
98 2019-05-21 11.380000 11.380000 11.380000 11.380000 11.380000 0
99 2019-05-22 11.360000 11.360000 11.360000 11.360000 11.360000 0
100 2019-05-23 11.290000 11.290000 11.290000 11.290000 11.290000 0
101 2019-05-24 11.350000 11.350000 11.350000 11.350000 11.350000 0
102 2019-05-28 11.270000 11.270000 11.270000 11.270000 11.270000 0
103 2019-05-29 11.160000 11.160000 11.160000 11.160000 11.160000 0
104 2019-05-30 11.190000 11.190000 11.190000 11.190000 11.190000 0
105 2019-05-31 11.040000 11.040000 11.040000 11.040000 11.040000 0
106 2019-06-03 10.990000 10.990000 10.990000 10.990000 10.990000 0
107 2019-06-04 11.240000 11.240000 11.240000 11.240000 11.240000 0
108 2019-06-05 11.230000 11.230000 11.230000 11.230000 11.230000 0
109 2019-06-06 11.240000 11.240000 11.240000 11.240000 11.240000 0
110 2019-06-07 11.230000 11.230000 11.230000 11.230000 11.230000 0
111 2019-06-10 11.240000 11.240000 11.240000 11.240000 11.240000 0
112 2019-06-11 11.230000 11.230000 11.230000 11.230000 11.230000 0
113 2019-06-12 11.240000 11.240000 11.240000 11.240000 11.240000 0
114 2019-06-13 11.240000 11.240000 11.240000 11.240000 11.240000 0
115 2019-06-14 11.240000 11.240000 11.240000 11.240000 11.240000 0
116 2019-06-17 11.240000 11.240000 11.240000 11.240000 11.240000 0
117 2019-06-18 11.240000 11.240000 11.240000 11.240000 11.240000 0
118 2019-06-19 11.250000 11.250000 11.250000 11.250000 11.250000 0
119 2019-06-20 11.250000 11.250000 11.250000 11.250000 11.250000 0
120 2019-06-21 11.240000 11.240000 11.240000 11.240000 11.240000 0
121 2019-06-24 11.240000 11.240000 11.240000 11.240000 11.240000 0
122 2019-06-25 11.220000 11.220000 11.220000 11.220000 11.220000 0
123 2019-06-26 11.220000 11.220000 11.220000 11.220000 11.220000 0
124 2019-06-27 11.250000 11.250000 11.250000 11.250000 11.250000 0
125 2019-06-28 11.250000 11.250000 11.250000 11.250000 11.250000 0
126 2019-07-01 11.250000 11.250000 11.250000 11.250000 11.250000 0
127 2019-07-02 11.270000 11.270000 11.270000 11.270000 11.270000 0
128 2019-07-03 11.280000 11.280000 11.280000 11.280000 11.280000 0
129 2019-07-05 11.270000 11.270000 11.270000 11.270000 11.270000 0
130 2019-07-08 11.250000 11.250000 11.250000 11.250000 11.250000 0
131 2019-07-09 11.250000 11.250000 11.250000 11.250000 11.250000 0
132 2019-07-10 11.290000 11.290000 11.290000 11.290000 11.290000 0
133 2019-07-11 11.290000 11.290000 11.290000 11.290000 11.290000 0
134 2019-07-12 11.300000 11.300000 11.300000 11.300000 11.300000 0
135 2019-07-15 11.300000 11.300000 11.300000 11.300000 11.300000 0
136 2019-07-16 11.300000 11.300000 11.300000 11.300000 11.300000 0
137 2019-07-17 11.280000 11.280000 11.280000 11.280000 11.280000 0
138 2019-07-18 11.310000 11.310000 11.310000 11.310000 11.310000 0
139 2019-07-19 11.240000 11.240000 11.240000 11.240000 11.240000 0
140 2019-07-22 11.290000 11.290000 11.290000 11.290000 11.290000 0
141 2019-07-23 11.350000 11.350000 11.350000 11.350000 11.350000 0
142 2019-07-24 11.360000 11.360000 11.360000 11.360000 11.360000 0
143 2019-07-25 11.360000 11.360000 11.360000 11.360000 11.360000 0
144 2019-07-26 11.420000 11.420000 11.420000 11.420000 11.420000 0
145 2019-07-29 11.430000 11.430000 11.430000 11.430000 11.430000 0
146 2019-07-30 11.390000 11.390000 11.390000 11.390000 11.390000 0
147 2019-07-31 11.350000 11.350000 11.350000 11.350000 11.350000 0
148 2019-08-01 11.280000 11.280000 11.280000 11.280000 11.280000 0
149 2019-08-02 11.210000 11.210000 11.210000 11.210000 11.210000 0
150 2019-08-05 10.820000 10.820000 10.820000 10.820000 10.820000 0
151 2019-08-06 10.980000 10.980000 10.980000 10.980000 10.980000 0
152 2019-08-07 11.010000 11.010000 11.010000 11.010000 11.010000 0
153 2019-08-08 11.170000 11.170000 11.170000 11.170000 11.170000 0
154 2019-08-09 11.180000 11.180000 11.180000 11.180000 11.180000 0
155 2019-08-12 11.150000 11.150000 11.150000 11.150000 11.150000 0
156 2019-08-13 11.300000 11.300000 11.300000 11.300000 11.300000 0
157 2019-08-14 11.240000 11.240000 11.240000 11.240000 11.240000 0
158 2019-08-15 11.240000 11.240000 11.240000 11.240000 11.240000 0
159 2019-08-16 11.420000 11.420000 11.420000 11.420000 11.420000 0
160 2019-08-19 11.410000 11.410000 11.410000 11.410000 11.410000 0
161 2019-08-20 11.440000 11.440000 11.440000 11.440000 11.440000 0
162 2019-08-21 11.400000 11.400000 11.400000 11.400000 11.400000 0
163 2019-08-22 11.390000 11.390000 11.390000 11.390000 11.390000 0
164 2019-08-23 11.370000 11.370000 11.370000 11.370000 11.370000 0
165 2019-08-26 11.500000 11.500000 11.500000 11.500000 11.500000 0
166 2019-08-27 11.400000 11.400000 11.400000 11.400000 11.400000 0
167 2019-08-28 11.500000 11.500000 11.500000 11.500000 11.500000 0
168 2019-08-29 11.660000 11.660000 11.660000 11.660000 11.660000 0
169 2019-08-30 11.660000 11.660000 11.660000 11.660000 11.660000 0
170 2019-09-03 11.660000 11.660000 11.660000 11.660000 11.660000 0
171 2019-09-04 11.670000 11.670000 11.670000 11.670000 11.670000 0
172 2019-09-05 11.690000 11.690000 11.690000 11.690000 11.690000 0
173 2019-09-06 11.730000 11.730000 11.730000 11.730000 11.730000 0
174 2019-09-09 11.730000 11.730000 11.730000 11.730000 11.730000 0
175 2019-09-10 11.730000 11.730000 11.730000 11.730000 11.730000 0
176 2019-09-11 11.670000 11.670000 11.670000 11.670000 11.670000 0
177 2019-09-12 11.670000 11.670000 11.670000 11.670000 11.670000 0
178 2019-09-13 11.680000 11.680000 11.680000 11.680000 11.680000 0
179 2019-09-16 11.650000 11.650000 11.650000 11.650000 11.650000 0
180 2019-09-17 11.670000 11.670000 11.670000 11.670000 11.670000 0
181 2019-09-18 11.700000 11.700000 11.700000 11.700000 11.700000 0
182 2019-09-19 11.690000 11.690000 11.690000 11.690000 11.690000 0
183 2019-09-20 11.640000 11.640000 11.640000 11.640000 11.640000 0
184 2019-09-23 11.670000 11.670000 11.670000 11.670000 11.670000 0
185 2019-09-24 11.550000 11.550000 11.550000 11.550000 11.550000 0
186 2019-09-25 11.640000 11.640000 11.640000 11.640000 11.640000 0
187 2019-09-26 11.630000 11.630000 11.630000 11.630000 11.630000 0
188 2019-09-27 11.610000 11.610000 11.610000 11.610000 11.610000 0
189 2019-09-30 11.680000 11.680000 11.680000 11.680000 11.680000 0
190 2019-10-01 11.660000 11.660000 11.660000 11.660000 11.660000 0
191 2019-10-02 11.660000 11.660000 11.660000 11.660000 11.660000 0
192 2019-10-03 11.750000 11.750000 11.750000 11.750000 11.750000 0
193 2019-10-04 11.790000 11.790000 11.790000 11.790000 11.790000 0
194 2019-10-07 11.790000 11.790000 11.790000 11.790000 11.790000 0
195 2019-10-08 11.790000 11.790000 11.790000 11.790000 11.790000 0
196 2019-10-09 11.870000 11.870000 11.870000 11.870000 11.870000 0
197 2019-10-10 11.890000 11.890000 11.890000 11.890000 11.890000 0
198 2019-10-11 11.880000 11.880000 11.880000 11.880000 11.880000 0
199 2019-10-14 11.900000 11.900000 11.900000 11.900000 11.900000 0
200 2019-10-15 11.910000 11.910000 11.910000 11.910000 11.910000 0
201 2019-10-16 11.920000 11.920000 11.920000 11.920000 11.920000 0
202 2019-10-17 11.920000 11.920000 11.920000 11.920000 11.920000 0
203 2019-10-18 11.920000 11.920000 11.920000 11.920000 11.920000 0
204 2019-10-21 11.970000 11.970000 11.970000 11.970000 11.970000 0
205 2019-10-22 11.950000 11.950000 11.950000 11.950000 11.950000 0
206 2019-10-23 11.970000 11.970000 11.970000 11.970000 11.970000 0
207 2019-10-24 11.980000 11.980000 11.980000 11.980000 11.980000 0
208 2019-10-25 12.000000 12.000000 12.000000 12.000000 12.000000 0
209 2019-10-28 11.990000 11.990000 11.990000 11.990000 11.990000 0
210 2019-10-29 12.000000 12.000000 12.000000 12.000000 12.000000 0
211 2019-10-30 12.020000 12.020000 12.020000 12.020000 12.020000 0
212 2019-10-31 12.030000 12.030000 12.030000 12.030000 12.030000 0
213 2019-11-01 12.130000 12.130000 12.130000 12.130000 12.130000 0
214 2019-11-04 12.130000 12.130000 12.130000 12.130000 12.130000 0
215 2019-11-05 12.120000 12.120000 12.120000 12.120000 12.120000 0
216 2019-11-06 12.130000 12.130000 12.130000 12.130000 12.130000 0
217 2019-11-07 12.130000 12.130000 12.130000 12.130000 12.130000 0
218 2019-11-08 12.160000 12.160000 12.160000 12.160000 12.160000 0
219 2019-11-11 12.160000 12.160000 12.160000 12.160000 12.160000 0
220 2019-11-12 12.170000 12.170000 12.170000 12.170000 12.170000 0
221 2019-11-13 12.160000 12.160000 12.160000 12.160000 12.160000 0
222 2019-11-14 12.170000 12.170000 12.170000 12.170000 12.170000 0
223 2019-11-15 12.200000 12.200000 12.200000 12.200000 12.200000 0
224 2019-11-18 12.200000 12.200000 12.200000 12.200000 12.200000 0
225 2019-11-19 12.200000 12.200000 12.200000 12.200000 12.200000 0
226 2019-11-20 12.190000 12.190000 12.190000 12.190000 12.190000 0
227 2019-11-21 12.160000 12.160000 12.160000 12.160000 12.160000 0
228 2019-11-22 12.190000 12.190000 12.190000 12.190000 12.190000 0
229 2019-11-25 12.220000 12.220000 12.220000 12.220000 12.220000 0
230 2019-11-26 12.220000 12.220000 12.220000 12.220000 12.220000 0
231 2019-11-27 12.220000 12.220000 12.220000 12.220000 12.220000 0
232 2019-11-29 12.220000 12.220000 12.220000 12.220000 12.220000 0
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515 2021-01-14 10.360000 10.360000 10.360000 10.360000 10.360000 0
516 2021-01-15 10.360000 10.360000 10.360000 10.360000 10.360000 0
517 2021-01-19 10.370000 10.370000 10.370000 10.370000 10.370000 0
518 2021-01-20 10.370000 10.370000 10.370000 10.370000 10.370000 0
519 2021-01-21 10.370000 10.370000 10.370000 10.370000 10.370000 0
520 2021-01-22 10.370000 10.370000 10.370000 10.370000 10.370000 0
521 2021-01-25 10.370000 10.370000 10.370000 10.370000 10.370000 0
522 2021-01-26 10.370000 10.370000 10.370000 10.370000 10.370000 0
523 2021-01-27 10.360000 10.360000 10.360000 10.360000 10.360000 0
524 2021-01-28 10.380000 10.380000 10.380000 10.380000 10.380000 0
525 2021-01-29 10.380000 10.380000 10.380000 10.380000 10.380000 0
526 2021-02-01 10.480000 10.480000 10.480000 10.480000 10.480000 0
527 2021-02-02 10.510000 10.510000 10.510000 10.510000 10.510000 0
528 2021-02-03 10.510000 10.510000 10.510000 10.510000 10.510000 0
529 2021-02-04 10.510000 10.510000 10.510000 10.510000 10.510000 0
530 2021-02-05 10.510000 10.510000 10.510000 10.510000 10.510000 0
531 2021-02-08 10.500000 10.500000 10.500000 10.500000 10.500000 0
532 2021-02-09 10.500000 10.500000 10.500000 10.500000 10.500000 0
533 2021-02-10 10.500000 10.500000 10.500000 10.500000 10.500000 0
534 2021-02-11 10.500000 10.500000 10.500000 10.500000 10.500000 0
535 2021-02-12 10.500000 10.500000 10.500000 10.500000 10.500000 0
536 2021-02-16 10.500000 10.500000 10.500000 10.500000 10.500000 0
537 2021-02-17 10.500000 10.500000 10.500000 10.500000 10.500000 0
538 2021-02-18 10.470000 10.470000 10.470000 10.470000 10.470000 0
539 2021-02-19 10.470000 10.470000 10.470000 10.470000 10.470000 0
540 2021-02-22 10.470000 10.470000 10.470000 10.470000 10.470000 0
541 2021-02-23 10.460000 10.460000 10.460000 10.460000 10.460000 0
542 2021-02-24 10.470000 10.470000 10.470000 10.470000 10.470000 0
543 2021-02-25 10.420000 10.420000 10.420000 10.420000 10.420000 0
544 2021-02-26 10.420000 10.420000 10.420000 10.420000 10.420000 0
545 2021-03-01 10.500000 10.500000 10.500000 10.500000 10.500000 0
546 2021-03-02 10.490000 10.490000 10.490000 10.490000 10.490000 0
547 2021-03-03 10.490000 10.490000 10.490000 10.490000 10.490000 0
548 2021-03-04 10.470000 10.470000 10.470000 10.470000 10.470000 0
549 2021-03-05 10.560000 10.560000 10.560000 10.560000 10.560000 0
550 2021-03-08 10.560000 10.560000 10.560000 10.560000 10.560000 0
551 2021-03-09 10.620000 10.620000 10.620000 10.620000 10.620000 0
552 2021-03-10 10.620000 10.620000 10.620000 10.620000 10.620000 0
553 2021-03-11 10.620000 10.620000 10.620000 10.620000 10.620000 0
554 2021-03-12 10.620000 10.620000 10.620000 10.620000 10.620000 0
555 2021-03-15 10.610000 10.610000 10.610000 10.610000 10.610000 0
556 2021-03-16 10.610000 10.610000 10.610000 10.610000 10.610000 0
557 2021-03-17 10.620000 10.620000 10.620000 10.620000 10.620000 0
558 2021-03-18 10.620000 10.620000 10.620000 10.620000 10.620000 0
559 2021-03-19 10.640000 10.640000 10.640000 10.640000 10.640000 0
560 2021-03-22 10.630000 10.630000 10.630000 10.630000 10.630000 0
561 2021-03-23 10.580000 10.580000 10.580000 10.580000 10.580000 0
562 2021-03-24 10.540000 10.540000 10.540000 10.540000 10.540000 0
563 2021-03-25 10.580000 10.580000 10.580000 10.580000 10.580000 0
564 2021-03-26 10.630000 10.630000 10.630000 10.630000 10.630000 0
565 2021-03-29 10.590000 10.590000 10.590000 10.590000 10.590000 0
566 2021-03-30 10.600000 10.600000 10.600000 10.600000 10.600000 0
567 2021-03-31 10.640000 10.640000 10.640000 10.640000 10.640000 0
568 2021-04-01 10.640000 10.640000 10.640000 10.640000 10.640000 0
569 2021-04-05 10.640000 10.640000 10.640000 10.640000 10.640000 0
570 2021-04-06 10.640000 10.640000 10.640000 10.640000 10.640000 0
571 2021-04-07 10.640000 10.640000 10.640000 10.640000 10.640000 0
572 2021-04-08 10.640000 10.640000 10.640000 10.640000 10.640000 0
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7
overrides/frozen/amfzx.json
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@ -1,7 +0,0 @@
{
"symbol": "amfzx",
"longName": "Investment Managers Series Trust - 361 Managed Futures Strategy Fund",
"freeze_date": "2021-04-09",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

3
overrides/frozen/andrx-capitalGain.csv
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@ -1,3 +0,0 @@
Date,Capital Gains
2014-12-19,0.282000
2017-12-20,0.015000
1 Date Capital Gains
2 2014-12-19 0.282000
3 2017-12-20 0.015000

9
overrides/frozen/andrx-dividend.csv
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@ -1,9 +0,0 @@
Date,Dividends
2025-12-17,0.784130
2026-04-28,6.557670
2019-12-23,0.354020
2020-12-22,0.262650
2021-12-23,0.400300
2022-12-19,0.267040
2023-12-18,0.433520
2024-12-17,0.336460
1 Date Dividends
2 2025-12-17 0.784130
3 2026-04-28 6.557670
4 2019-12-23 0.354020
5 2020-12-22 0.262650
6 2021-12-23 0.400300
7 2022-12-19 0.267040
8 2023-12-18 0.433520
9 2024-12-17 0.336460

1843
overrides/frozen/andrx-history.csv
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File diff suppressed because it is too large Load Diff

7
overrides/frozen/andrx.json
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@ -1,7 +0,0 @@
{
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"created": "2026-08-31",
"reason": "yahoo-empty"
}

14
overrides/frozen/arwdx-capitalGain.csv
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@ -1,14 +0,0 @@
Date,Capital Gains
2019-12-30,0.866000
2021-12-28,0.665000
2024-12-20,0.317000
2025-06-10,0.977000
2016-12-29,0.128000
2017-10-19,0.113000
2017-12-28,0.010000
2020-12-29,0.770000
2022-12-28,0.254000
2023-12-21,0.247000
2014-12-30,0.048000
2015-12-30,0.155000
2018-12-27,0.556000
1 Date Capital Gains
2 2019-12-30 0.866000
3 2021-12-28 0.665000
4 2024-12-20 0.317000
5 2025-06-10 0.977000
6 2016-12-29 0.128000
7 2017-10-19 0.113000
8 2017-12-28 0.010000
9 2020-12-29 0.770000
10 2022-12-28 0.254000
11 2023-12-21 0.247000
12 2014-12-30 0.048000
13 2015-12-30 0.155000
14 2018-12-27 0.556000

16
overrides/frozen/arwdx-dividend.csv
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@ -1,16 +0,0 @@
Date,Dividends
2014-12-30,0.284000
2017-10-19,0.218000
2021-12-28,1.273000
2023-12-21,0.593000
2025-06-10,1.092000
2017-12-28,0.179000
2018-12-31,0.421000
2020-12-29,0.991000
2015-12-30,0.343000
2016-12-29,0.277000
2024-12-20,0.743000
2013-12-30,0.147000
2018-12-27,0.977000
2019-12-30,1.142000
2022-12-28,0.622000
1 Date Dividends
2 2014-12-30 0.284000
3 2017-10-19 0.218000
4 2021-12-28 1.273000
5 2023-12-21 0.593000
6 2025-06-10 1.092000
7 2017-12-28 0.179000
8 2018-12-31 0.421000
9 2020-12-29 0.991000
10 2015-12-30 0.343000
11 2016-12-29 0.277000
12 2024-12-20 0.743000
13 2013-12-30 0.147000
14 2018-12-27 0.977000
15 2019-12-30 1.142000
16 2022-12-28 0.622000

2
overrides/frozen/arwdx-history.csv
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@ -1,2 +0,0 @@
Date,Open,High,Low,Close,Adj Close,Volume
2025-09-23,8.650000,8.650000,8.650000,8.650000,8.650000,0
1 Date Open High Low Close Adj Close Volume
2 2025-09-23 8.650000 8.650000 8.650000 8.650000 8.650000 0

7
overrides/frozen/arwdx.json
View File

@ -1,7 +0,0 @@
{
"symbol": "arwdx",
"longName": "American Century Asset Allocation Portfolios, Inc. - One Choice 2025 Portfolio",
"freeze_date": "2025-09-23",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

5
overrides/frozen/atezx-capitalGain.csv
View File

@ -1,5 +0,0 @@
Date,Capital Gains
2021-12-07,17.691000
2022-12-06,2.947000
2024-12-10,11.705000
2025-12-09,18.145000
1 Date Capital Gains
2 2021-12-07 17.691000
3 2022-12-06 2.947000
4 2024-12-10 11.705000
5 2025-12-09 18.145000

6
overrides/frozen/atezx-dividend.csv
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@ -1,6 +0,0 @@
Date,Dividends
2022-12-06,3.798000
2023-12-05,0.469000
2024-12-10,11.705000
2025-12-09,18.145000
2021-12-07,17.691000
1 Date Dividends
2 2022-12-06 3.798000
3 2023-12-05 0.469000
4 2024-12-10 11.705000
5 2025-12-09 18.145000
6 2021-12-07 17.691000

2
overrides/frozen/atezx-history.csv
View File

@ -1,2 +0,0 @@
Date,Open,High,Low,Close,Adj Close,Volume
2026-07-17,137.520004,137.520004,137.520004,137.520004,137.520004,0
1 Date Open High Low Close Adj Close Volume
2 2026-07-17 137.520004 137.520004 137.520004 137.520004 137.520004 0

7
overrides/frozen/atezx.json
View File

@ -1,7 +0,0 @@
{
"symbol": "atezx",
"longName": "AB Sustainable Global Thematic Z",
"freeze_date": "2026-07-17",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

491
overrides/frozen/atmdx-dividend.csv
View File

@ -1,491 +0,0 @@
Date,Dividends,ctime
2014-07-31,0.016,2017-08-22
2011-02-28,0.006,2017-08-22
2011-08-31,0.008,2017-08-22
2017-03-31,0.02,2017-08-22
2016-12-30,0.022,2017-08-22
2017-06-30,0.024,2017-08-22
2009-06-30,0.009,2017-08-22
2011-12-28,0.023,2017-08-22
2011-12-30,0.007,2017-08-22
2013-06-28,0.003,2017-08-22
2012-08-31,0.006,2017-08-22
2015-04-30,0.014,2017-08-22
2016-08-31,0.018,2017-08-22
2012-02-29,0.012,2017-08-22
2013-04-30,0.011,2017-08-22
2013-10-31,0.004,2017-08-22
2013-12-31,0.008,2017-08-22
2012-03-30,0.014,2017-08-22
2011-01-31,0.008,2017-08-22
2015-11-30,0.016,2017-08-22
2012-06-29,0.013,2017-08-22
2015-08-31,0.015,2017-08-22
2016-02-29,0.019,2017-08-22
2012-09-28,0.003,2017-08-22
2011-11-30,0.008,2017-08-22
2015-03-31,0.007,2017-08-22
2012-04-30,0.015,2017-08-22
2009-05-29,0.001,2017-08-22
2012-10-31,0.003,2017-08-22
2015-06-30,0.015,2017-08-22
2011-03-31,0.007,2017-08-22
2016-10-31,0.019,2017-08-22
2012-11-30,0.002,2017-08-22
2014-05-30,0.012,2017-08-22
2009-12-31,0.016,2017-08-22
2011-06-30,0.008,2017-08-22
2013-12-27,0.014,2017-08-22
2016-11-30,0.02,2017-08-22
2014-08-29,0.015,2017-08-22
2015-12-29,0.263,2017-08-22
2015-12-31,0.019,2017-08-22
2010-02-26,0.012,2017-08-22
2013-05-31,0.007,2017-08-22
2013-11-29,0.006,2017-08-22
2014-09-30,0.016,2017-08-22
2011-10-31,0.009,2017-08-22
2017-05-31,0.021,2017-08-22
2009-12-09,0.043,2017-08-22
2013-08-30,0.001,2017-08-22
2014-02-28,0.007,2017-08-22
2010-09-30,0.008,2017-08-22
2014-01-31,0.008,2017-08-22
2010-01-29,0.018,2017-08-22
2010-07-30,0.008,2017-08-22
2014-10-31,0.017,2017-08-22
2013-03-28,0.004,2017-08-22
2014-11-28,0.014,2017-08-22
2013-09-30,0.003,2017-08-22
2010-10-29,0.008,2017-08-22
2016-05-31,0.018,2017-08-22
2010-12-08,0.007,2017-08-22
2015-02-27,0.006,2017-08-22
2009-09-30,0.014,2017-08-22
2010-11-30,0.011,2017-08-22
2012-05-31,0.015,2017-08-22
2013-07-31,0.001,2017-08-22
2010-08-31,0.007,2017-08-22
2016-03-31,0.02,2017-08-22
2010-05-28,0.013,2017-08-22
2017-07-31,0.02,2017-08-22
2009-07-31,0.023,2017-08-22
2016-06-30,0.018,2017-08-22
2015-05-29,0.016,2017-08-22
2009-10-30,0.027,2017-08-22
2012-12-27,0.068,2017-08-22
2014-04-30,0.007,2017-08-22
2011-05-31,0.012,2017-08-22
2009-08-31,0.015,2017-08-22
2016-01-29,0.017,2017-08-22
2016-07-29,0.017,2017-08-22
2013-02-28,0.006,2017-08-22
2014-12-29,0.111,2017-08-22
2014-12-31,0.01,2017-08-22
2012-01-31,0.005,2017-08-22
2012-07-31,0.007,2017-08-22
2015-09-30,0.017,2017-08-22
2017-04-28,0.019,2017-08-22
2011-09-30,0.009,2017-08-22
2017-02-28,0.017,2017-08-22
2014-03-31,0.005,2017-08-22
2011-04-29,0.009,2017-08-22
2015-07-31,0.015,2017-08-22
2014-06-30,0.016,2017-08-22
2011-07-29,0.006,2017-08-22
2010-03-31,0.011,2017-08-22
2015-10-30,0.017,2017-08-22
2010-06-30,0.008,2017-08-22
2010-12-31,0.01,2017-08-22
2017-01-31,0.017,2017-08-22
2009-11-30,0.014,2017-08-22
2016-04-29,0.018,2017-08-22
2017-09-29,0.023,2017-10-20 09:54:16
2012-08-31,0.006,2017-10-20 09:54:16
2015-04-30,0.014,2017-10-20 09:54:16
2016-08-31,0.018,2017-10-20 09:54:16
2012-02-29,0.012,2017-10-20 09:54:16
2013-04-30,0.011,2017-10-20 09:54:16
2013-10-31,0.004,2017-10-20 09:54:16
2013-12-31,0.008,2017-10-20 09:54:16
2012-03-30,0.014,2017-10-20 09:54:16
2011-01-31,0.008,2017-10-20 09:54:16
2015-11-30,0.016,2017-10-20 09:54:16
2012-06-29,0.013,2017-10-20 09:54:16
2015-08-31,0.015,2017-10-20 09:54:16
2016-02-29,0.019,2017-10-20 09:54:16
2012-09-28,0.003,2017-10-20 09:54:16
2011-11-30,0.008,2017-10-20 09:54:16
2015-03-31,0.007,2017-10-20 09:54:16
2012-04-30,0.015,2017-10-20 09:54:16
2009-05-29,0.001,2017-10-20 09:54:16
2012-10-31,0.003,2017-10-20 09:54:16
2015-06-30,0.015,2017-10-20 09:54:16
2011-03-31,0.007,2017-10-20 09:54:16
2016-10-31,0.019,2017-10-20 09:54:16
2012-11-30,0.002,2017-10-20 09:54:16
2014-05-30,0.012,2017-10-20 09:54:16
2009-12-31,0.016,2017-10-20 09:54:16
2011-06-30,0.008,2017-10-20 09:54:16
2013-12-27,0.014,2017-10-20 09:54:16
2016-11-30,0.02,2017-10-20 09:54:16
2014-08-29,0.015,2017-10-20 09:54:16
2015-12-29,0.263,2017-10-20 09:54:16
2015-12-31,0.019,2017-10-20 09:54:16
2010-02-26,0.012,2017-10-20 09:54:16
2013-05-31,0.007,2017-10-20 09:54:16
2013-11-29,0.006,2017-10-20 09:54:16
2014-09-30,0.016,2017-10-20 09:54:16
2011-10-31,0.009,2017-10-20 09:54:16
2017-05-31,0.021,2017-10-20 09:54:16
2009-12-09,0.043,2017-10-20 09:54:16
2013-08-30,0.001,2017-10-20 09:54:16
2014-02-28,0.007,2017-10-20 09:54:16
2010-09-30,0.008,2017-10-20 09:54:16
2014-01-31,0.008,2017-10-20 09:54:16
2010-01-29,0.018,2017-10-20 09:54:16
2010-07-30,0.008,2017-10-20 09:54:16
2014-10-31,0.017,2017-10-20 09:54:16
2013-03-28,0.004,2017-10-20 09:54:16
2014-11-28,0.014,2017-10-20 09:54:16
2013-09-30,0.003,2017-10-20 09:54:16
2010-10-29,0.008,2017-10-20 09:54:16
2016-05-31,0.018,2017-10-20 09:54:16
2010-12-08,0.007,2017-10-20 09:54:16
2015-02-27,0.006,2017-10-20 09:54:16
2009-09-30,0.014,2017-10-20 09:54:16
2010-11-30,0.011,2017-10-20 09:54:16
2012-05-31,0.015,2017-10-20 09:54:16
2013-07-31,0.001,2017-10-20 09:54:16
2010-08-31,0.007,2017-10-20 09:54:16
2016-03-31,0.02,2017-10-20 09:54:16
2010-05-28,0.013,2017-10-20 09:54:16
2017-07-31,0.02,2017-10-20 09:54:16
2009-07-31,0.023,2017-10-20 09:54:16
2016-06-30,0.018,2017-10-20 09:54:16
2015-05-29,0.016,2017-10-20 09:54:16
2009-10-30,0.027,2017-10-20 09:54:16
2012-12-27,0.068,2017-10-20 09:54:16
2014-04-30,0.007,2017-10-20 09:54:16
2011-05-31,0.012,2017-10-20 09:54:16
2009-08-31,0.015,2017-10-20 09:54:16
2016-01-29,0.017,2017-10-20 09:54:16
2016-07-29,0.017,2017-10-20 09:54:16
2013-02-28,0.006,2017-10-20 09:54:16
2014-12-29,0.111,2017-10-20 09:54:16
2014-12-31,0.01,2017-10-20 09:54:16
2012-01-31,0.005,2017-10-20 09:54:16
2012-07-31,0.007,2017-10-20 09:54:16
2015-09-30,0.017,2017-10-20 09:54:16
2017-04-28,0.019,2017-10-20 09:54:16
2011-09-30,0.009,2017-10-20 09:54:16
2017-02-28,0.017,2017-10-20 09:54:16
2014-03-31,0.005,2017-10-20 09:54:16
2011-04-29,0.009,2017-10-20 09:54:16
2015-07-31,0.015,2017-10-20 09:54:16
2014-06-30,0.016,2017-10-20 09:54:16
2011-07-29,0.006,2017-10-20 09:54:16
2010-03-31,0.011,2017-10-20 09:54:16
2015-10-30,0.017,2017-10-20 09:54:16
2017-08-31,0.021,2017-10-20 09:54:16
2010-06-30,0.008,2017-10-20 09:54:16
2010-12-31,0.01,2017-10-20 09:54:16
2017-01-31,0.017,2017-10-20 09:54:16
2009-11-30,0.014,2017-10-20 09:54:16
2016-04-29,0.018,2017-10-20 09:54:16
2017-10-31,0.022,2017-11-15 07:55:39
2012-02-29,0.012,2017-11-15 07:55:39
2013-04-30,0.011,2017-11-15 07:55:39
2013-10-31,0.004,2017-11-15 07:55:39
2013-12-31,0.008,2017-11-15 07:55:39
2012-03-30,0.014,2017-11-15 07:55:39
2011-01-31,0.008,2017-11-15 07:55:39
2015-11-30,0.016,2017-11-15 07:55:39
2012-06-29,0.013,2017-11-15 07:55:39
2015-08-31,0.015,2017-11-15 07:55:39
2016-02-29,0.019,2017-11-15 07:55:39
2012-09-28,0.003,2017-11-15 07:55:39
2011-11-30,0.008,2017-11-15 07:55:39
2015-03-31,0.007,2017-11-15 07:55:39
2012-04-30,0.015,2017-11-15 07:55:39
2009-05-29,0.001,2017-11-15 07:55:39
2012-10-31,0.003,2017-11-15 07:55:39
2015-06-30,0.015,2017-11-15 07:55:39
2011-03-31,0.007,2017-11-15 07:55:39
2016-10-31,0.019,2017-11-15 07:55:39
2012-11-30,0.002,2017-11-15 07:55:39
2014-05-30,0.012,2017-11-15 07:55:39
2009-12-31,0.016,2017-11-15 07:55:39
2011-06-30,0.008,2017-11-15 07:55:39
2013-12-27,0.014,2017-11-15 07:55:39
2016-11-30,0.02,2017-11-15 07:55:39
2014-08-29,0.015,2017-11-15 07:55:39
2015-12-29,0.263,2017-11-15 07:55:39
2015-12-31,0.019,2017-11-15 07:55:39
2010-02-26,0.012,2017-11-15 07:55:39
2013-05-31,0.007,2017-11-15 07:55:39
2013-11-29,0.006,2017-11-15 07:55:39
2014-09-30,0.016,2017-11-15 07:55:39
2011-10-31,0.009,2017-11-15 07:55:39
2017-05-31,0.021,2017-11-15 07:55:39
2009-12-09,0.043,2017-11-15 07:55:39
2013-08-30,0.001,2017-11-15 07:55:39
2014-02-28,0.007,2017-11-15 07:55:39
2010-09-30,0.008,2017-11-15 07:55:39
2014-01-31,0.008,2017-11-15 07:55:39
2010-01-29,0.018,2017-11-15 07:55:39
2010-07-30,0.008,2017-11-15 07:55:39
2014-10-31,0.017,2017-11-15 07:55:39
2013-03-28,0.004,2017-11-15 07:55:39
2014-11-28,0.014,2017-11-15 07:55:39
2013-09-30,0.003,2017-11-15 07:55:39
2010-10-29,0.008,2017-11-15 07:55:39
2016-05-31,0.018,2017-11-15 07:55:39
2010-12-08,0.007,2017-11-15 07:55:39
2015-02-27,0.006,2017-11-15 07:55:39
2009-09-30,0.014,2017-11-15 07:55:39
2010-11-30,0.011,2017-11-15 07:55:39
2012-05-31,0.015,2017-11-15 07:55:39
2013-07-31,0.001,2017-11-15 07:55:39
2010-08-31,0.007,2017-11-15 07:55:39
2016-03-31,0.02,2017-11-15 07:55:39
2010-05-28,0.013,2017-11-15 07:55:39
2017-07-31,0.02,2017-11-15 07:55:39
2009-07-31,0.023,2017-11-15 07:55:39
2016-06-30,0.018,2017-11-15 07:55:39
2015-05-29,0.016,2017-11-15 07:55:39
2009-10-30,0.027,2017-11-15 07:55:39
2012-12-27,0.068,2017-11-15 07:55:39
2014-04-30,0.007,2017-11-15 07:55:39
2011-05-31,0.012,2017-11-15 07:55:39
2009-08-31,0.015,2017-11-15 07:55:39
2016-01-29,0.017,2017-11-15 07:55:39
2016-07-29,0.017,2017-11-15 07:55:39
2013-02-28,0.006,2017-11-15 07:55:39
2014-12-29,0.111,2017-11-15 07:55:39
2014-12-31,0.01,2017-11-15 07:55:39
2012-01-31,0.005,2017-11-15 07:55:39
2012-07-31,0.007,2017-11-15 07:55:39
2015-09-30,0.017,2017-11-15 07:55:39
2017-04-28,0.019,2017-11-15 07:55:39
2011-09-30,0.009,2017-11-15 07:55:39
2017-02-28,0.017,2017-11-15 07:55:39
2014-03-31,0.005,2017-11-15 07:55:39
2011-04-29,0.009,2017-11-15 07:55:39
2015-07-31,0.015,2017-11-15 07:55:39
2014-06-30,0.016,2017-11-15 07:55:39
2011-07-29,0.006,2017-11-15 07:55:39
2010-03-31,0.011,2017-11-15 07:55:39
2015-10-30,0.017,2017-11-15 07:55:39
2017-08-31,0.021,2017-11-15 07:55:39
2010-06-30,0.008,2017-11-15 07:55:39
2010-12-31,0.01,2017-11-15 07:55:39
2017-01-31,0.017,2017-11-15 07:55:39
2009-11-30,0.014,2017-11-15 07:55:39
2016-04-29,0.018,2017-11-15 07:55:39
2017-11-30,0.023,2017-12-11 20:07:47
2014-12-29,0.111,2017-12-11 20:07:47
2014-12-31,0.01,2017-12-11 20:07:47
2012-01-31,0.005,2017-12-11 20:07:47
2012-07-31,0.007,2017-12-11 20:07:47
2015-09-30,0.017,2017-12-11 20:07:47
2017-04-28,0.019,2017-12-11 20:07:47
2011-09-30,0.009,2017-12-11 20:07:47
2017-02-28,0.017,2017-12-11 20:07:47
2014-03-31,0.005,2017-12-11 20:07:47
2011-04-29,0.009,2017-12-11 20:07:47
2015-07-31,0.015,2017-12-11 20:07:47
2014-06-30,0.016,2017-12-11 20:07:47
2011-07-29,0.006,2017-12-11 20:07:47
2010-03-31,0.011,2017-12-11 20:07:47
2015-10-30,0.017,2017-12-11 20:07:47
2017-08-31,0.021,2017-12-11 20:07:47
2010-06-30,0.008,2017-12-11 20:07:47
2010-12-31,0.01,2017-12-11 20:07:47
2017-01-31,0.017,2017-12-11 20:07:47
2009-11-30,0.014,2017-12-11 20:07:47
2016-04-29,0.018,2017-12-11 20:07:47
2017-12-27,0.192,2018-01-16 07:54:11
2012-03-30,0.014,2018-01-16 07:54:11
2011-01-31,0.008,2018-01-16 07:54:11
2017-12-29,0.026,2018-01-16 07:54:11
2015-11-30,0.016,2018-01-16 07:54:11
2012-06-29,0.013,2018-01-16 07:54:11
2015-08-31,0.015,2018-01-16 07:54:11
2016-02-29,0.019,2018-01-16 07:54:11
2012-09-28,0.003,2018-01-16 07:54:11
2011-11-30,0.008,2018-01-16 07:54:11
2015-03-31,0.007,2018-01-16 07:54:11
2012-04-30,0.015,2018-01-16 07:54:11
2009-05-29,0.001,2018-01-16 07:54:11
2012-10-31,0.003,2018-01-16 07:54:11
2015-06-30,0.015,2018-01-16 07:54:11
2011-03-31,0.007,2018-01-16 07:54:11
2016-10-31,0.019,2018-01-16 07:54:11
2012-11-30,0.002,2018-01-16 07:54:11
2014-05-30,0.012,2018-01-16 07:54:11
2009-12-31,0.016,2018-01-16 07:54:11
2011-06-30,0.008,2018-01-16 07:54:11
2013-12-27,0.014,2018-01-16 07:54:11
2016-11-30,0.02,2018-01-16 07:54:11
2014-08-29,0.015,2018-01-16 07:54:11
2015-12-29,0.263,2018-01-16 07:54:11
2015-12-31,0.019,2018-01-16 07:54:11
2010-02-26,0.012,2018-01-16 07:54:11
2013-05-31,0.007,2018-01-16 07:54:11
2013-11-29,0.006,2018-01-16 07:54:11
2014-09-30,0.016,2018-01-16 07:54:11
2011-10-31,0.009,2018-01-16 07:54:11
2017-05-31,0.021,2018-01-16 07:54:11
2009-12-09,0.043,2018-01-16 07:54:11
2013-08-30,0.001,2018-01-16 07:54:11
2014-02-28,0.007,2018-01-16 07:54:11
2010-09-30,0.008,2018-01-16 07:54:11
2014-01-31,0.008,2018-01-16 07:54:11
2010-01-29,0.018,2018-01-16 07:54:11
2010-07-30,0.008,2018-01-16 07:54:11
2014-10-31,0.017,2018-01-16 07:54:11
2013-03-28,0.004,2018-01-16 07:54:11
2014-11-28,0.014,2018-01-16 07:54:11
2013-09-30,0.003,2018-01-16 07:54:11
2010-10-29,0.008,2018-01-16 07:54:11
2016-05-31,0.018,2018-01-16 07:54:11
2010-12-08,0.007,2018-01-16 07:54:11
2015-02-27,0.006,2018-01-16 07:54:11
2009-09-30,0.014,2018-01-16 07:54:11
2010-11-30,0.011,2018-01-16 07:54:11
2012-05-31,0.015,2018-01-16 07:54:11
2013-07-31,0.001,2018-01-16 07:54:11
2010-08-31,0.007,2018-01-16 07:54:11
2016-03-31,0.02,2018-01-16 07:54:11
2010-05-28,0.013,2018-01-16 07:54:11
2017-07-31,0.02,2018-01-16 07:54:11
2009-07-31,0.023,2018-01-16 07:54:11
2016-06-30,0.018,2018-01-16 07:54:11
2015-05-29,0.016,2018-01-16 07:54:11
2009-10-30,0.027,2018-01-16 07:54:11
2012-12-27,0.068,2018-01-16 07:54:11
2014-04-30,0.007,2018-01-16 07:54:11
2011-05-31,0.012,2018-01-16 07:54:11
2009-08-31,0.015,2018-01-16 07:54:11
2016-01-29,0.017,2018-01-16 07:54:11
2016-07-29,0.017,2018-01-16 07:54:11
2013-02-28,0.006,2018-01-16 07:54:11
2017-11-30,0.023,2018-01-16 07:54:11
2014-12-29,0.111,2018-01-16 07:54:11
2014-12-31,0.01,2018-01-16 07:54:11
2012-01-31,0.005,2018-01-16 07:54:11
2012-07-31,0.007,2018-01-16 07:54:11
2015-09-30,0.017,2018-01-16 07:54:11
2017-04-28,0.019,2018-01-16 07:54:11
2011-09-30,0.009,2018-01-16 07:54:11
2017-02-28,0.017,2018-01-16 07:54:11
2014-03-31,0.005,2018-01-16 07:54:11
2011-04-29,0.009,2018-01-16 07:54:11
2015-07-31,0.015,2018-01-16 07:54:11
2014-06-30,0.016,2018-01-16 07:54:11
2011-07-29,0.006,2018-01-16 07:54:11
2010-03-31,0.011,2018-01-16 07:54:11
2015-10-30,0.017,2018-01-16 07:54:11
2017-08-31,0.021,2018-01-16 07:54:11
2010-06-30,0.008,2018-01-16 07:54:11
2010-12-31,0.01,2018-01-16 07:54:11
2017-01-31,0.017,2018-01-16 07:54:11
2009-11-30,0.014,2018-01-16 07:54:11
2016-04-29,0.018,2018-01-16 07:54:11
2018-01-31,0.027,2018-02-26 21:44:31
2009-09-30,0.014,2018-02-26 21:44:31
2010-11-30,0.011,2018-02-26 21:44:31
2012-05-31,0.015,2018-02-26 21:44:31
2013-07-31,0.001,2018-02-26 21:44:31
2010-08-31,0.007,2018-02-26 21:44:31
2016-03-31,0.02,2018-02-26 21:44:31
2010-05-28,0.013,2018-02-26 21:44:31
2017-07-31,0.02,2018-02-26 21:44:31
2009-07-31,0.023,2018-02-26 21:44:31
2016-06-30,0.018,2018-02-26 21:44:31
2015-05-29,0.016,2018-02-26 21:44:31
2009-10-30,0.027,2018-02-26 21:44:31
2012-12-27,0.068,2018-02-26 21:44:31
2014-04-30,0.007,2018-02-26 21:44:31
2011-05-31,0.012,2018-02-26 21:44:31
2009-08-31,0.015,2018-02-26 21:44:31
2016-01-29,0.017,2018-02-26 21:44:31
2016-07-29,0.017,2018-02-26 21:44:31
2013-02-28,0.006,2018-02-26 21:44:31
2017-11-30,0.023,2018-02-26 21:44:31
2014-12-29,0.111,2018-02-26 21:44:31
2014-12-31,0.01,2018-02-26 21:44:31
2012-01-31,0.005,2018-02-26 21:44:31
2012-07-31,0.007,2018-02-26 21:44:31
2015-09-30,0.017,2018-02-26 21:44:31
2017-04-28,0.019,2018-02-26 21:44:31
2011-09-30,0.009,2018-02-26 21:44:31
2017-02-28,0.017,2018-02-26 21:44:31
2014-03-31,0.005,2018-02-26 21:44:31
2011-04-29,0.009,2018-02-26 21:44:31
2015-07-31,0.015,2018-02-26 21:44:31
2014-06-30,0.016,2018-02-26 21:44:31
2011-07-29,0.006,2018-02-26 21:44:31
2010-03-31,0.011,2018-02-26 21:44:31
2015-10-30,0.017,2018-02-26 21:44:31
2017-08-31,0.021,2018-02-26 21:44:31
2010-06-30,0.008,2018-02-26 21:44:31
2010-12-31,0.01,2018-02-26 21:44:31
2017-01-31,0.017,2018-02-26 21:44:31
2009-11-30,0.014,2018-02-26 21:44:31
2016-04-29,0.018,2018-02-26 21:44:31
2018-02-28,0.025,2018-03-09 21:59:16
2014-01-31,0.008,2018-03-09 21:59:16
2010-01-29,0.018,2018-03-09 21:59:16
2010-07-30,0.008,2018-03-09 21:59:16
2014-10-31,0.017,2018-03-09 21:59:16
2013-03-28,0.004,2018-03-09 21:59:16
2014-11-28,0.014,2018-03-09 21:59:16
2013-09-30,0.003,2018-03-09 21:59:16
2010-10-29,0.008,2018-03-09 21:59:16
2016-05-31,0.018,2018-03-09 21:59:16
2010-12-08,0.007,2018-03-09 21:59:16
2015-02-27,0.006,2018-03-09 21:59:16
2018-01-31,0.027,2018-03-09 21:59:16
2009-09-30,0.014,2018-03-09 21:59:16
2010-11-30,0.011,2018-03-09 21:59:16
2012-05-31,0.015,2018-03-09 21:59:16
2013-07-31,0.001,2018-03-09 21:59:16
2010-08-31,0.007,2018-03-09 21:59:16
2016-03-31,0.02,2018-03-09 21:59:16
2010-05-28,0.013,2018-03-09 21:59:16
2017-07-31,0.02,2018-03-09 21:59:16
2009-07-31,0.023,2018-03-09 21:59:16
2016-06-30,0.018,2018-03-09 21:59:16
2015-05-29,0.016,2018-03-09 21:59:16
2009-10-30,0.027,2018-03-09 21:59:16
2012-12-27,0.068,2018-03-09 21:59:16
2014-04-30,0.007,2018-03-09 21:59:16
2011-05-31,0.012,2018-03-09 21:59:16
2009-08-31,0.015,2018-03-09 21:59:16
2016-01-29,0.017,2018-03-09 21:59:16
2016-07-29,0.017,2018-03-09 21:59:16
2013-02-28,0.006,2018-03-09 21:59:16
2017-11-30,0.023,2018-03-09 21:59:16
2014-12-29,0.111,2018-03-09 21:59:16
2014-12-31,0.01,2018-03-09 21:59:16
2012-01-31,0.005,2018-03-09 21:59:16
2012-07-31,0.007,2018-03-09 21:59:16
2015-09-30,0.017,2018-03-09 21:59:16
2017-04-28,0.019,2018-03-09 21:59:16
2011-09-30,0.009,2018-03-09 21:59:16
2017-02-28,0.017,2018-03-09 21:59:16
2014-03-31,0.005,2018-03-09 21:59:16
2011-04-29,0.009,2018-03-09 21:59:16
2015-07-31,0.015,2018-03-09 21:59:16
2014-06-30,0.016,2018-03-09 21:59:16
2011-07-29,0.006,2018-03-09 21:59:16
2010-03-31,0.011,2018-03-09 21:59:16
2015-10-30,0.017,2018-03-09 21:59:16
2017-08-31,0.021,2018-03-09 21:59:16
2010-06-30,0.008,2018-03-09 21:59:16
2010-12-31,0.01,2018-03-09 21:59:16
2017-01-31,0.017,2018-03-09 21:59:16
2009-11-30,0.014,2018-03-09 21:59:16
2016-04-29,0.018,2018-03-09 21:59:16
1 Date Dividends ctime
2 2014-07-31 0.016 2017-08-22
3 2011-02-28 0.006 2017-08-22
4 2011-08-31 0.008 2017-08-22
5 2017-03-31 0.02 2017-08-22
6 2016-12-30 0.022 2017-08-22
7 2017-06-30 0.024 2017-08-22
8 2009-06-30 0.009 2017-08-22
9 2011-12-28 0.023 2017-08-22
10 2011-12-30 0.007 2017-08-22
11 2013-06-28 0.003 2017-08-22
12 2012-08-31 0.006 2017-08-22
13 2015-04-30 0.014 2017-08-22
14 2016-08-31 0.018 2017-08-22
15 2012-02-29 0.012 2017-08-22
16 2013-04-30 0.011 2017-08-22
17 2013-10-31 0.004 2017-08-22
18 2013-12-31 0.008 2017-08-22
19 2012-03-30 0.014 2017-08-22
20 2011-01-31 0.008 2017-08-22
21 2015-11-30 0.016 2017-08-22
22 2012-06-29 0.013 2017-08-22
23 2015-08-31 0.015 2017-08-22
24 2016-02-29 0.019 2017-08-22
25 2012-09-28 0.003 2017-08-22
26 2011-11-30 0.008 2017-08-22
27 2015-03-31 0.007 2017-08-22
28 2012-04-30 0.015 2017-08-22
29 2009-05-29 0.001 2017-08-22
30 2012-10-31 0.003 2017-08-22
31 2015-06-30 0.015 2017-08-22
32 2011-03-31 0.007 2017-08-22
33 2016-10-31 0.019 2017-08-22
34 2012-11-30 0.002 2017-08-22
35 2014-05-30 0.012 2017-08-22
36 2009-12-31 0.016 2017-08-22
37 2011-06-30 0.008 2017-08-22
38 2013-12-27 0.014 2017-08-22
39 2016-11-30 0.02 2017-08-22
40 2014-08-29 0.015 2017-08-22
41 2015-12-29 0.263 2017-08-22
42 2015-12-31 0.019 2017-08-22
43 2010-02-26 0.012 2017-08-22
44 2013-05-31 0.007 2017-08-22
45 2013-11-29 0.006 2017-08-22
46 2014-09-30 0.016 2017-08-22
47 2011-10-31 0.009 2017-08-22
48 2017-05-31 0.021 2017-08-22
49 2009-12-09 0.043 2017-08-22
50 2013-08-30 0.001 2017-08-22
51 2014-02-28 0.007 2017-08-22
52 2010-09-30 0.008 2017-08-22
53 2014-01-31 0.008 2017-08-22
54 2010-01-29 0.018 2017-08-22
55 2010-07-30 0.008 2017-08-22
56 2014-10-31 0.017 2017-08-22
57 2013-03-28 0.004 2017-08-22
58 2014-11-28 0.014 2017-08-22
59 2013-09-30 0.003 2017-08-22
60 2010-10-29 0.008 2017-08-22
61 2016-05-31 0.018 2017-08-22
62 2010-12-08 0.007 2017-08-22
63 2015-02-27 0.006 2017-08-22
64 2009-09-30 0.014 2017-08-22
65 2010-11-30 0.011 2017-08-22
66 2012-05-31 0.015 2017-08-22
67 2013-07-31 0.001 2017-08-22
68 2010-08-31 0.007 2017-08-22
69 2016-03-31 0.02 2017-08-22
70 2010-05-28 0.013 2017-08-22
71 2017-07-31 0.02 2017-08-22
72 2009-07-31 0.023 2017-08-22
73 2016-06-30 0.018 2017-08-22
74 2015-05-29 0.016 2017-08-22
75 2009-10-30 0.027 2017-08-22
76 2012-12-27 0.068 2017-08-22
77 2014-04-30 0.007 2017-08-22
78 2011-05-31 0.012 2017-08-22
79 2009-08-31 0.015 2017-08-22
80 2016-01-29 0.017 2017-08-22
81 2016-07-29 0.017 2017-08-22
82 2013-02-28 0.006 2017-08-22
83 2014-12-29 0.111 2017-08-22
84 2014-12-31 0.01 2017-08-22
85 2012-01-31 0.005 2017-08-22
86 2012-07-31 0.007 2017-08-22
87 2015-09-30 0.017 2017-08-22
88 2017-04-28 0.019 2017-08-22
89 2011-09-30 0.009 2017-08-22
90 2017-02-28 0.017 2017-08-22
91 2014-03-31 0.005 2017-08-22
92 2011-04-29 0.009 2017-08-22
93 2015-07-31 0.015 2017-08-22
94 2014-06-30 0.016 2017-08-22
95 2011-07-29 0.006 2017-08-22
96 2010-03-31 0.011 2017-08-22
97 2015-10-30 0.017 2017-08-22
98 2010-06-30 0.008 2017-08-22
99 2010-12-31 0.01 2017-08-22
100 2017-01-31 0.017 2017-08-22
101 2009-11-30 0.014 2017-08-22
102 2016-04-29 0.018 2017-08-22
103 2017-09-29 0.023 2017-10-20 09:54:16
104 2012-08-31 0.006 2017-10-20 09:54:16
105 2015-04-30 0.014 2017-10-20 09:54:16
106 2016-08-31 0.018 2017-10-20 09:54:16
107 2012-02-29 0.012 2017-10-20 09:54:16
108 2013-04-30 0.011 2017-10-20 09:54:16
109 2013-10-31 0.004 2017-10-20 09:54:16
110 2013-12-31 0.008 2017-10-20 09:54:16
111 2012-03-30 0.014 2017-10-20 09:54:16
112 2011-01-31 0.008 2017-10-20 09:54:16
113 2015-11-30 0.016 2017-10-20 09:54:16
114 2012-06-29 0.013 2017-10-20 09:54:16
115 2015-08-31 0.015 2017-10-20 09:54:16
116 2016-02-29 0.019 2017-10-20 09:54:16
117 2012-09-28 0.003 2017-10-20 09:54:16
118 2011-11-30 0.008 2017-10-20 09:54:16
119 2015-03-31 0.007 2017-10-20 09:54:16
120 2012-04-30 0.015 2017-10-20 09:54:16
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396 2018-01-31 0.027 2018-02-26 21:44:31
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398 2010-11-30 0.011 2018-02-26 21:44:31
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406 2016-06-30 0.018 2018-02-26 21:44:31
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448 2010-12-08 0.007 2018-03-09 21:59:16
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467 2016-01-29 0.017 2018-03-09 21:59:16
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481 2015-07-31 0.015 2018-03-09 21:59:16
482 2014-06-30 0.016 2018-03-09 21:59:16
483 2011-07-29 0.006 2018-03-09 21:59:16
484 2010-03-31 0.011 2018-03-09 21:59:16
485 2015-10-30 0.017 2018-03-09 21:59:16
486 2017-08-31 0.021 2018-03-09 21:59:16
487 2010-06-30 0.008 2018-03-09 21:59:16
488 2010-12-31 0.01 2018-03-09 21:59:16
489 2017-01-31 0.017 2018-03-09 21:59:16
490 2009-11-30 0.014 2018-03-09 21:59:16
491 2016-04-29 0.018 2018-03-09 21:59:16

33583
overrides/frozen/atmdx-history.csv
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7
overrides/frozen/atmdx.json
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@ -1,7 +0,0 @@
{
"symbol": "atmdx",
"longName": "?",
"freeze_date": "2018-03-23",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

5
overrides/frozen/avgyx-capitalGain.csv
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@ -1,5 +0,0 @@
Date,Capital Gains
2021-12-20,2.743000
2016-12-23,0.029000
2017-12-22,0.817000
2019-12-23,0.331000
1 Date Capital Gains
2 2021-12-20 2.743000
3 2016-12-23 0.029000
4 2017-12-22 0.817000
5 2019-12-23 0.331000

6
overrides/frozen/avgyx-dividend.csv
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@ -1,6 +0,0 @@
Date,Dividends
2019-12-23,0.558100
2021-12-20,2.859500
2022-12-20,0.222000
2023-12-19,0.702600
2024-12-18,1.305000
1 Date Dividends
2 2019-12-23 0.558100
3 2021-12-20 2.859500
4 2022-12-20 0.222000
5 2023-12-19 0.702600
6 2024-12-18 1.305000

1715
overrides/frozen/avgyx-history.csv
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7
overrides/frozen/avgyx.json
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@ -1,7 +0,0 @@
{
"symbol": "avgyx",
"longName": "BNY Mellon Advantage Funds, Inc. - BNY Mellon Dynamic Total Return Fund",
"freeze_date": "2025-10-24",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

5
overrides/frozen/bafmx-capitalGain.csv
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@ -1,5 +0,0 @@
Date,Capital Gains
2018-12-17,0.114000
2019-12-16,0.036000
2021-12-14,1.749000
2022-12-13,0.814000
1 Date Capital Gains
2 2018-12-17 0.114000
3 2019-12-16 0.036000
4 2021-12-14 1.749000
5 2022-12-13 0.814000

5
overrides/frozen/bafmx-dividend.csv
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@ -1,5 +0,0 @@
Date,Dividends
2019-12-16,0.035660
2021-12-14,1.749320
2022-12-13,0.813930
2025-12-15,7.440010
1 Date Dividends
2 2019-12-16 0.035660
3 2021-12-14 1.749320
4 2022-12-13 0.813930
5 2025-12-15 7.440010

2
overrides/frozen/bafmx-history.csv
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@ -1,2 +0,0 @@
Date,Open,High,Low,Close,Adj Close,Volume
2026-04-24,9.920400,9.920400,9.920400,9.920400,9.920400,0
1 Date Open High Low Close Adj Close Volume
2 2026-04-24 9.920400 9.920400 9.920400 9.920400 9.920400 0

7
overrides/frozen/bafmx.json
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@ -1,7 +0,0 @@
{
"symbol": "bafmx",
"longName": "Brown Advisory Mid-Cap Growth Instl",
"freeze_date": "2026-04-24",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

9
overrides/frozen/bbgpx-capitalGain.csv
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@ -1,9 +0,0 @@
Date,Capital Gains
2018-12-21,0.017000
2019-12-23,0.004000
2022-12-19,0.002000
2009-12-22,0.192000
2010-12-22,0.365000
2011-12-21,0.125000
2012-12-21,0.201000
2017-12-22,0.004000
1 Date Capital Gains
2 2018-12-21 0.017000
3 2019-12-23 0.004000
4 2022-12-19 0.002000
5 2009-12-22 0.192000
6 2010-12-22 0.365000
7 2011-12-21 0.125000
8 2012-12-21 0.201000
9 2017-12-22 0.004000

284
overrides/frozen/bbgpx-dividend.csv
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@ -1,284 +0,0 @@
Date,Dividends
2003-10-31,0.052000
2014-02-28,0.019000
2017-11-30,0.026000
2008-12-31,0.035000
2021-02-26,0.017000
2021-07-30,0.013000
2024-06-28,0.026000
2002-12-31,0.015000
2012-07-31,0.033000
2014-07-31,0.019000
2014-08-29,0.021000
2014-10-31,0.021000
2016-06-30,0.022000
2005-05-31,0.035000
2006-11-30,0.042000
2015-06-30,0.017000
2018-08-31,0.030000
2021-08-31,0.011000
2024-03-28,0.023000
2006-03-31,0.037000
2011-08-31,0.031000
2011-10-31,0.034000
2016-04-29,0.024000
2019-04-30,0.030000
2020-07-31,0.028000
2023-01-31,0.019000
2013-05-31,0.034000
2013-07-31,0.033000
2015-01-30,0.020000
2017-02-28,0.024000
2019-10-31,0.025000
2025-09-30,0.029000
2025-10-31,0.029000
2006-01-31,0.041000
2015-07-31,0.021000
2016-02-29,0.024000
2004-10-29,0.022000
2007-12-31,0.042000
2009-12-31,0.027000
2011-12-21,0.125000
2013-03-28,0.032000
2023-07-31,0.023000
2013-04-30,0.034000
2015-08-31,0.023000
2020-10-30,0.021000
2021-03-31,0.016000
2008-11-28,0.034000
2017-10-31,0.028000
2021-12-31,0.013000
2025-03-31,0.028000
2005-09-30,0.035000
2007-10-31,0.044000
2009-06-30,0.040000
2010-05-28,0.031000
2018-12-31,0.029000
2019-12-23,0.004000
2020-06-30,0.029000
2024-09-30,0.026000
2009-12-22,0.192000
2012-03-30,0.037000
2021-05-28,0.014000
2004-03-31,0.049000
2008-06-30,0.039000
2009-09-30,0.026000
2010-08-31,0.024000
2011-11-30,0.035000
2013-01-31,0.029000
2019-03-29,0.029000
2003-04-30,0.052000
2005-11-30,0.035000
2013-11-29,0.027000
2022-07-29,0.020000
2006-06-30,0.037000
2008-07-31,0.039000
2018-12-21,0.017000
2022-12-30,0.019000
2024-11-29,0.026000
2003-05-30,0.052000
2005-02-28,0.035000
2007-03-30,0.042000
2012-05-31,0.034000
2023-10-31,0.026000
2003-11-28,0.052000
2004-12-31,0.042000
2008-08-29,0.036000
2011-05-31,0.030000
2021-04-30,0.017000
2024-05-31,0.028000
2004-05-28,0.049000
2005-07-29,0.035000
2007-04-30,0.043000
2011-07-29,0.032000
2018-01-31,0.026000
2018-03-29,0.026000
2021-06-30,0.015000
2017-06-30,0.026000
2008-01-31,0.042000
2008-10-31,0.031000
2010-11-30,0.026000
2012-04-30,0.035000
2012-12-31,0.028000
2015-10-30,0.025000
2016-01-29,0.024000
2019-09-30,0.025000
2024-07-31,0.024000
2003-06-30,0.052000
2005-01-31,0.035000
2011-03-31,0.024000
2013-12-31,0.021000
2020-05-29,0.028000
2008-02-29,0.042000
2020-01-31,0.026000
2020-12-31,0.019000
2006-08-31,0.042000
2007-11-30,0.044000
2010-02-26,0.027000
2011-04-29,0.027000
2011-06-30,0.031000
2020-08-31,0.022000
2022-09-30,0.020000
2024-01-31,0.023000
2006-04-28,0.037000
2007-06-29,0.043000
2010-10-29,0.025000
2013-02-28,0.033000
2013-10-31,0.028000
2020-02-28,0.028000
2023-05-31,0.021000
2004-07-30,0.039000
2010-12-22,0.365000
2003-07-31,0.052000
2009-02-27,0.032000
2012-09-28,0.030000
2013-06-28,0.036000
2018-11-30,0.028000
2010-03-31,0.031000
2010-04-30,0.040000
2017-09-29,0.031000
2022-11-30,0.019000
2011-02-28,0.023000
2013-09-30,0.030000
2023-09-29,0.024000
2009-04-30,0.037000
2018-09-28,0.030000
2022-05-31,0.018000
2006-05-31,0.037000
2007-09-28,0.043000
2023-12-29,0.023000
2024-12-31,0.026000
2004-06-30,0.049000
2004-11-30,0.042000
2007-07-31,0.046000
2017-04-28,0.023000
2018-05-31,0.028000
2019-08-30,0.026000
2022-08-31,0.020000
2008-03-31,0.041000
2011-01-31,0.026000
2017-08-31,0.030000
2019-01-31,0.027000
2023-04-28,0.020000
2006-07-31,0.042000
2006-12-29,0.042000
2024-08-30,0.026000
2025-05-30,0.028000
2012-02-29,0.034000
2022-01-31,0.011000
2008-05-30,0.040000
2017-07-31,0.028000
2019-02-28,0.028000
2015-05-29,0.017000
2024-10-31,0.026000
2010-07-30,0.025000
2011-12-30,0.037000
2012-08-31,0.031000
2012-12-21,0.201000
2003-09-30,0.052000
2007-08-31,0.043000
2016-10-31,0.023000
2007-02-28,0.042000
2009-10-30,0.031000
2022-03-31,0.014000
2023-02-28,0.022000
2005-12-30,0.108000
2012-06-29,0.034000
2014-12-31,0.020000
2018-02-28,0.028000
2019-12-31,0.026000
2017-01-31,0.024000
2021-01-29,0.021000
2011-09-30,0.033000
2016-09-30,0.023000
2005-10-31,0.035000
2020-03-31,0.028000
2022-10-31,0.020000
2022-12-19,0.002000
2024-04-30,0.027000
2025-06-30,0.028000
2020-09-30,0.024000
2021-11-30,0.012000
2025-11-28,0.029000
2003-08-29,0.052000
2007-01-31,0.042000
2015-02-27,0.020000
2015-04-30,0.016000
2017-03-31,0.022000
2003-03-31,0.052000
2004-08-31,0.022000
2007-05-31,0.043000
2008-09-30,0.037000
2009-08-31,0.036000
2010-12-31,0.029000
2013-08-30,0.030000
2017-05-31,0.025000
2006-02-28,0.038000
2014-01-31,0.021000
2019-05-31,0.029000
2020-04-30,0.030000
2003-12-31,0.052000
2014-03-31,0.018000
2023-11-30,0.023000
2009-01-30,0.031000
2016-07-29,0.024000
2004-09-30,0.022000
2006-09-29,0.042000
2009-05-29,0.040000
2010-01-29,0.027000
2010-06-30,0.026000
2010-09-30,0.025000
2015-09-30,0.027000
2014-04-30,0.019000
2014-11-28,0.021000
2016-03-31,0.024000
2016-11-30,0.024000
2020-11-30,0.022000
2025-08-29,0.028000
2022-02-28,0.012000
2023-08-31,0.025000
2025-01-31,0.027000
2014-09-30,0.023000
2015-03-31,0.018000
2016-12-30,0.024000
2025-04-30,0.027000
2003-02-28,0.052000
2016-05-31,0.023000
2019-11-29,0.026000
2025-07-31,0.028000
2005-04-29,0.035000
2009-03-31,0.035000
2018-04-30,0.027000
2022-06-30,0.021000
2024-02-29,0.023000
2005-06-30,0.035000
2008-04-30,0.041000
2012-01-31,0.037000
2015-11-30,0.025000
2018-07-31,0.028000
2021-09-30,0.015000
2023-03-31,0.021000
2009-07-31,0.039000
2004-01-30,0.052000
2016-08-31,0.023000
2019-07-31,0.027000
2025-02-28,0.027000
2023-06-30,0.021000
2004-02-27,0.052000
2004-04-30,0.049000
2012-11-30,0.029000
2014-05-30,0.019000
2018-06-29,0.028000
2018-10-31,0.029000
2022-04-29,0.016000
2012-10-31,0.030000
2014-06-30,0.019000
2015-12-31,0.025000
2005-08-31,0.035000
2009-11-30,0.026000
2019-06-28,0.027000
2005-03-31,0.035000
2017-12-22,0.004000
2021-10-29,0.014000
2006-10-31,0.042000
2017-12-29,0.027000
1 Date Dividends
2 2003-10-31 0.052000
3 2014-02-28 0.019000
4 2017-11-30 0.026000
5 2008-12-31 0.035000
6 2021-02-26 0.017000
7 2021-07-30 0.013000
8 2024-06-28 0.026000
9 2002-12-31 0.015000
10 2012-07-31 0.033000
11 2014-07-31 0.019000
12 2014-08-29 0.021000
13 2014-10-31 0.021000
14 2016-06-30 0.022000
15 2005-05-31 0.035000
16 2006-11-30 0.042000
17 2015-06-30 0.017000
18 2018-08-31 0.030000
19 2021-08-31 0.011000
20 2024-03-28 0.023000
21 2006-03-31 0.037000
22 2011-08-31 0.031000
23 2011-10-31 0.034000
24 2016-04-29 0.024000
25 2019-04-30 0.030000
26 2020-07-31 0.028000
27 2023-01-31 0.019000
28 2013-05-31 0.034000
29 2013-07-31 0.033000
30 2015-01-30 0.020000
31 2017-02-28 0.024000
32 2019-10-31 0.025000
33 2025-09-30 0.029000
34 2025-10-31 0.029000
35 2006-01-31 0.041000
36 2015-07-31 0.021000
37 2016-02-29 0.024000
38 2004-10-29 0.022000
39 2007-12-31 0.042000
40 2009-12-31 0.027000
41 2011-12-21 0.125000
42 2013-03-28 0.032000
43 2023-07-31 0.023000
44 2013-04-30 0.034000
45 2015-08-31 0.023000
46 2020-10-30 0.021000
47 2021-03-31 0.016000
48 2008-11-28 0.034000
49 2017-10-31 0.028000
50 2021-12-31 0.013000
51 2025-03-31 0.028000
52 2005-09-30 0.035000
53 2007-10-31 0.044000
54 2009-06-30 0.040000
55 2010-05-28 0.031000
56 2018-12-31 0.029000
57 2019-12-23 0.004000
58 2020-06-30 0.029000
59 2024-09-30 0.026000
60 2009-12-22 0.192000
61 2012-03-30 0.037000
62 2021-05-28 0.014000
63 2004-03-31 0.049000
64 2008-06-30 0.039000
65 2009-09-30 0.026000
66 2010-08-31 0.024000
67 2011-11-30 0.035000
68 2013-01-31 0.029000
69 2019-03-29 0.029000
70 2003-04-30 0.052000
71 2005-11-30 0.035000
72 2013-11-29 0.027000
73 2022-07-29 0.020000
74 2006-06-30 0.037000
75 2008-07-31 0.039000
76 2018-12-21 0.017000
77 2022-12-30 0.019000
78 2024-11-29 0.026000
79 2003-05-30 0.052000
80 2005-02-28 0.035000
81 2007-03-30 0.042000
82 2012-05-31 0.034000
83 2023-10-31 0.026000
84 2003-11-28 0.052000
85 2004-12-31 0.042000
86 2008-08-29 0.036000
87 2011-05-31 0.030000
88 2021-04-30 0.017000
89 2024-05-31 0.028000
90 2004-05-28 0.049000
91 2005-07-29 0.035000
92 2007-04-30 0.043000
93 2011-07-29 0.032000
94 2018-01-31 0.026000
95 2018-03-29 0.026000
96 2021-06-30 0.015000
97 2017-06-30 0.026000
98 2008-01-31 0.042000
99 2008-10-31 0.031000
100 2010-11-30 0.026000
101 2012-04-30 0.035000
102 2012-12-31 0.028000
103 2015-10-30 0.025000
104 2016-01-29 0.024000
105 2019-09-30 0.025000
106 2024-07-31 0.024000
107 2003-06-30 0.052000
108 2005-01-31 0.035000
109 2011-03-31 0.024000
110 2013-12-31 0.021000
111 2020-05-29 0.028000
112 2008-02-29 0.042000
113 2020-01-31 0.026000
114 2020-12-31 0.019000
115 2006-08-31 0.042000
116 2007-11-30 0.044000
117 2010-02-26 0.027000
118 2011-04-29 0.027000
119 2011-06-30 0.031000
120 2020-08-31 0.022000
121 2022-09-30 0.020000
122 2024-01-31 0.023000
123 2006-04-28 0.037000
124 2007-06-29 0.043000
125 2010-10-29 0.025000
126 2013-02-28 0.033000
127 2013-10-31 0.028000
128 2020-02-28 0.028000
129 2023-05-31 0.021000
130 2004-07-30 0.039000
131 2010-12-22 0.365000
132 2003-07-31 0.052000
133 2009-02-27 0.032000
134 2012-09-28 0.030000
135 2013-06-28 0.036000
136 2018-11-30 0.028000
137 2010-03-31 0.031000
138 2010-04-30 0.040000
139 2017-09-29 0.031000
140 2022-11-30 0.019000
141 2011-02-28 0.023000
142 2013-09-30 0.030000
143 2023-09-29 0.024000
144 2009-04-30 0.037000
145 2018-09-28 0.030000
146 2022-05-31 0.018000
147 2006-05-31 0.037000
148 2007-09-28 0.043000
149 2023-12-29 0.023000
150 2024-12-31 0.026000
151 2004-06-30 0.049000
152 2004-11-30 0.042000
153 2007-07-31 0.046000
154 2017-04-28 0.023000
155 2018-05-31 0.028000
156 2019-08-30 0.026000
157 2022-08-31 0.020000
158 2008-03-31 0.041000
159 2011-01-31 0.026000
160 2017-08-31 0.030000
161 2019-01-31 0.027000
162 2023-04-28 0.020000
163 2006-07-31 0.042000
164 2006-12-29 0.042000
165 2024-08-30 0.026000
166 2025-05-30 0.028000
167 2012-02-29 0.034000
168 2022-01-31 0.011000
169 2008-05-30 0.040000
170 2017-07-31 0.028000
171 2019-02-28 0.028000
172 2015-05-29 0.017000
173 2024-10-31 0.026000
174 2010-07-30 0.025000
175 2011-12-30 0.037000
176 2012-08-31 0.031000
177 2012-12-21 0.201000
178 2003-09-30 0.052000
179 2007-08-31 0.043000
180 2016-10-31 0.023000
181 2007-02-28 0.042000
182 2009-10-30 0.031000
183 2022-03-31 0.014000
184 2023-02-28 0.022000
185 2005-12-30 0.108000
186 2012-06-29 0.034000
187 2014-12-31 0.020000
188 2018-02-28 0.028000
189 2019-12-31 0.026000
190 2017-01-31 0.024000
191 2021-01-29 0.021000
192 2011-09-30 0.033000
193 2016-09-30 0.023000
194 2005-10-31 0.035000
195 2020-03-31 0.028000
196 2022-10-31 0.020000
197 2022-12-19 0.002000
198 2024-04-30 0.027000
199 2025-06-30 0.028000
200 2020-09-30 0.024000
201 2021-11-30 0.012000
202 2025-11-28 0.029000
203 2003-08-29 0.052000
204 2007-01-31 0.042000
205 2015-02-27 0.020000
206 2015-04-30 0.016000
207 2017-03-31 0.022000
208 2003-03-31 0.052000
209 2004-08-31 0.022000
210 2007-05-31 0.043000
211 2008-09-30 0.037000
212 2009-08-31 0.036000
213 2010-12-31 0.029000
214 2013-08-30 0.030000
215 2017-05-31 0.025000
216 2006-02-28 0.038000
217 2014-01-31 0.021000
218 2019-05-31 0.029000
219 2020-04-30 0.030000
220 2003-12-31 0.052000
221 2014-03-31 0.018000
222 2023-11-30 0.023000
223 2009-01-30 0.031000
224 2016-07-29 0.024000
225 2004-09-30 0.022000
226 2006-09-29 0.042000
227 2009-05-29 0.040000
228 2010-01-29 0.027000
229 2010-06-30 0.026000
230 2010-09-30 0.025000
231 2015-09-30 0.027000
232 2014-04-30 0.019000
233 2014-11-28 0.021000
234 2016-03-31 0.024000
235 2016-11-30 0.024000
236 2020-11-30 0.022000
237 2025-08-29 0.028000
238 2022-02-28 0.012000
239 2023-08-31 0.025000
240 2025-01-31 0.027000
241 2014-09-30 0.023000
242 2015-03-31 0.018000
243 2016-12-30 0.024000
244 2025-04-30 0.027000
245 2003-02-28 0.052000
246 2016-05-31 0.023000
247 2019-11-29 0.026000
248 2025-07-31 0.028000
249 2005-04-29 0.035000
250 2009-03-31 0.035000
251 2018-04-30 0.027000
252 2022-06-30 0.021000
253 2024-02-29 0.023000
254 2005-06-30 0.035000
255 2008-04-30 0.041000
256 2012-01-31 0.037000
257 2015-11-30 0.025000
258 2018-07-31 0.028000
259 2021-09-30 0.015000
260 2023-03-31 0.021000
261 2009-07-31 0.039000
262 2004-01-30 0.052000
263 2016-08-31 0.023000
264 2019-07-31 0.027000
265 2025-02-28 0.027000
266 2023-06-30 0.021000
267 2004-02-27 0.052000
268 2004-04-30 0.049000
269 2012-11-30 0.029000
270 2014-05-30 0.019000
271 2018-06-29 0.028000
272 2018-10-31 0.029000
273 2022-04-29 0.016000
274 2012-10-31 0.030000
275 2014-06-30 0.019000
276 2015-12-31 0.025000
277 2005-08-31 0.035000
278 2009-11-30 0.026000
279 2019-06-28 0.027000
280 2005-03-31 0.035000
281 2017-12-22 0.004000
282 2021-10-29 0.014000
283 2006-10-31 0.042000
284 2017-12-29 0.027000

5792
overrides/frozen/bbgpx-history.csv
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7
overrides/frozen/bbgpx.json
View File

@ -1,7 +0,0 @@
{
"symbol": "bbgpx",
"longName": "BlackRock ETF Trust II - iShares Mortgage-Backed Securities Active ETF",
"freeze_date": "2025-12-26",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

11
overrides/frozen/bcmix-capitalGain.csv
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@ -1,11 +0,0 @@
Date,Capital Gains
2012-12-14,0.354000
2016-12-13,0.611000
2019-12-12,0.613000
2020-12-11,0.453000
2022-12-12,1.859000
2014-12-12,1.809000
2015-12-11,11.223000
2017-12-12,2.419000
2018-12-12,1.137000
2021-12-10,0.500000
1 Date Capital Gains
2 2012-12-14 0.354000
3 2016-12-13 0.611000
4 2019-12-12 0.613000
5 2020-12-11 0.453000
6 2022-12-12 1.859000
7 2014-12-12 1.809000
8 2015-12-11 11.223000
9 2017-12-12 2.419000
10 2018-12-12 1.137000
11 2021-12-10 0.500000

11
overrides/frozen/bcmix-dividend.csv
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@ -1,11 +0,0 @@
Date,Dividends
2019-12-12,0.613000
2021-12-10,0.500000
2022-12-12,1.859000
2012-12-14,0.354000
2014-12-12,1.809000
2015-12-11,11.223000
2016-12-13,0.611000
2017-12-12,2.419000
2018-12-12,1.137000
2020-12-11,0.453000
1 Date Dividends
2 2019-12-12 0.613000
3 2021-12-10 0.500000
4 2022-12-12 1.859000
5 2012-12-14 0.354000
6 2014-12-12 1.809000
7 2015-12-11 11.223000
8 2016-12-13 0.611000
9 2017-12-12 2.419000
10 2018-12-12 1.137000
11 2020-12-11 0.453000

3269
overrides/frozen/bcmix-history.csv
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7
overrides/frozen/bcmix.json
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@ -1,7 +0,0 @@
{
"symbol": "bcmix",
"longName": "Brown Capital Management Mutual Funds - The Brown Capital Management Mid Company Fund",
"freeze_date": "2024-12-11",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

2
overrides/frozen/bfslx-dividend.csv
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@ -1,2 +0,0 @@
Date,Dividends,ctime
2014-12-02,0.191,2017-08-22
1 Date Dividends ctime
2 2014-12-02 0.191 2017-08-22

2712
overrides/frozen/bfslx-history.csv
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7
overrides/frozen/bfslx.json
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@ -1,7 +0,0 @@
{
"symbol": "bfslx",
"longName": "?",
"freeze_date": "2018-03-16",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

4
overrides/frozen/bgaqx-dividend.csv
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@ -1,4 +0,0 @@
Date,Dividends
2017-12-06,0.007000
2018-12-06,0.114000
2019-12-04,0.724000
1 Date Dividends
2 2017-12-06 0.007000
3 2018-12-06 0.114000
4 2019-12-04 0.724000

19
overrides/frozen/bgaqx-history.csv
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@ -1,19 +0,0 @@
Date,Open,High,Low,Close,Adj Close,Volume
2020-03-05,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-06,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-09,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-10,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-11,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-12,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-13,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-16,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-17,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-18,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-19,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-20,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-23,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-24,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-25,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-26,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-27,0.100000,0.100000,0.100000,0.100000,0.100000,0
2020-03-30,0.100000,0.100000,0.100000,0.100000,0.100000,0
1 Date Open High Low Close Adj Close Volume
2 2020-03-05 0.100000 0.100000 0.100000 0.100000 0.100000 0
3 2020-03-06 0.100000 0.100000 0.100000 0.100000 0.100000 0
4 2020-03-09 0.100000 0.100000 0.100000 0.100000 0.100000 0
5 2020-03-10 0.100000 0.100000 0.100000 0.100000 0.100000 0
6 2020-03-11 0.100000 0.100000 0.100000 0.100000 0.100000 0
7 2020-03-12 0.100000 0.100000 0.100000 0.100000 0.100000 0
8 2020-03-13 0.100000 0.100000 0.100000 0.100000 0.100000 0
9 2020-03-16 0.100000 0.100000 0.100000 0.100000 0.100000 0
10 2020-03-17 0.100000 0.100000 0.100000 0.100000 0.100000 0
11 2020-03-18 0.100000 0.100000 0.100000 0.100000 0.100000 0
12 2020-03-19 0.100000 0.100000 0.100000 0.100000 0.100000 0
13 2020-03-20 0.100000 0.100000 0.100000 0.100000 0.100000 0
14 2020-03-23 0.100000 0.100000 0.100000 0.100000 0.100000 0
15 2020-03-24 0.100000 0.100000 0.100000 0.100000 0.100000 0
16 2020-03-25 0.100000 0.100000 0.100000 0.100000 0.100000 0
17 2020-03-26 0.100000 0.100000 0.100000 0.100000 0.100000 0
18 2020-03-27 0.100000 0.100000 0.100000 0.100000 0.100000 0
19 2020-03-30 0.100000 0.100000 0.100000 0.100000 0.100000 0

7
overrides/frozen/bgaqx.json
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@ -1,7 +0,0 @@
{
"symbol": "bgaqx",
"longName": "BMO Global Long/Short Equity Fund Class A",
"freeze_date": "2020-03-30",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

4
overrides/frozen/bgrsx-capitalGain.csv
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@ -1,4 +0,0 @@
Date,Capital Gains
2018-12-13,0.203000
2024-09-19,1.467000
2015-12-17,0.044000
1 Date Capital Gains
2 2018-12-13 0.203000
3 2024-09-19 1.467000
4 2015-12-17 0.044000

8
overrides/frozen/bgrsx-dividend.csv
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@ -1,8 +0,0 @@
Date,Dividends
2016-12-15,0.015000
2018-12-13,0.203000
2019-12-12,0.104000
2022-12-08,0.304000
2023-12-14,0.201000
2024-09-19,1.592000
2015-12-17,0.044000
1 Date Dividends
2 2016-12-15 0.015000
3 2018-12-13 0.203000
4 2019-12-12 0.104000
5 2022-12-08 0.304000
6 2023-12-14 0.201000
7 2024-09-19 1.592000
8 2015-12-17 0.044000

2698
overrides/frozen/bgrsx-history.csv
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7
overrides/frozen/bgrsx.json
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@ -1,7 +0,0 @@
{
"symbol": "bgrsx",
"longName": "The RBB Fund, Inc. - Boston Partners Global Long/Short Fund",
"freeze_date": "2024-12-30",
"created": "2026-08-31",
"reason": "yahoo-empty"
}

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