"""Streamlit UI for stock/portfolio analysis.

Run:  streamlit run app.py
"""

from __future__ import annotations

import json
import re
from pathlib import Path

import pandas as pd
import plotly.graph_objects as go
import streamlit as st

import metrics as m
from data import DEFAULT_ROOT, load_bundle, search_symbols
from portfolio import parse_items, parse_weights, portfolio_returns
from portfolios import Portfolio, delete as pf_delete, load_all as pf_load, save as pf_save
from tax import after_tax_portfolio

st.set_page_config(page_title="Stock & Portfolio Analyzer", layout="wide")

# ---------------------------------------------------------------- settings
# user inputs persist across reloads / server restarts / devices
SETTINGS_FILE = Path(__file__).parent / "settings.json"
_settings: dict = {}
try:
    _settings = json.loads(SETTINGS_FILE.read_text())
except Exception:
    pass


def _remember(**kw) -> None:
    """Persist current widget values (write only on change)."""
    global _settings
    changed = False
    for k, v in kw.items():
        if _settings.get(k) != v:
            _settings[k] = v
            changed = True
    if changed:
        try:
            SETTINGS_FILE.write_text(json.dumps(_settings, indent=2))
        except Exception:
            pass

# ---------------------------------------------------------------- sidebar
st.sidebar.title("Stock & Portfolio Analyzer")

root = st.sidebar.text_input("Data root", str(DEFAULT_ROOT))
rebuild = st.sidebar.checkbox("Rebuild parquet cache", value=False)
with st.spinner("Loading data..."):
    bundle = load_bundle(root=Path(root), rebuild=rebuild)

# data symbols are stored lowercase; input is resolved case-insensitively
_LOWER2SYM = {s.lower(): s for s in bundle.adj.columns}


def _resolve_weights(w: dict) -> dict:
    """Map parsed symbols onto the data's symbol case (insensitive match)."""
    return {_LOWER2SYM.get(s.lower(), s): v for s, v in w.items()}


def _replace_token(spec: str, old: str, new: str) -> str:
    """Replace the symbol entry `old` (with or without a ':weight') by `new`.

    Grammar: spaces separate items, commas join elements of one item.
    """
    def fix_item(item: str) -> str:
        out = []
        for p in item.split(","):
            sym = p.rsplit(":", 1)[0]
            if sym.lower() == old.lower():
                p = new if ":" not in p else new + p.rsplit(":", 1)[1]
            out.append(p)
        return ",".join(out)
    return " ".join(fix_item(it) for it in spec.split())


st.sidebar.subheader("Portfolio")
# hand-off from e.g. 'Load saved into field': must be applied before the
# spec widget is (re)instantiated
_handover = st.session_state.pop("spec_handover", None)
if _handover is not None:
    st.session_state["spec"] = _handover
spec = st.sidebar.text_input(
    "Symbol(s) or portfolio(s)", value=_settings.get("spec", ""), key="spec",
    help=("One or more, space-separated — same grammar as the Benchmark "
          "field.\n"
          "MSFT              <- single symbol\n"
          "MSFT:0.6,V:0.4    <- portfolio (commas join its elements, bare "
          "symbols are equal-weight)\n"
          "MSFT V googl:0.5,amzn:0.5   <- three separate entries, each "
          "analyzed on its own\n"
          "The FIRST entry is the 'current' portfolio: it drives the tax "
          "detail tab, the allocation tab and Save."))

saved = pf_load()
if saved:
    st.sidebar.subheader("Saved portfolios")
    lcol1, lcol2 = st.sidebar.columns([3, 1])
    load_name = lcol1.selectbox("Load into field", [p.name for p in saved], key="load_name")
    if lcol2.button("Load"):
        s = next(p.spec for p in saved if p.name == load_name)
        # old specs used ', ' between elements; that would now read as two items
        st.session_state["spec_handover"] = s.replace(", ", ",")
        st.rerun()

_scheme_labels = ["Buy & hold (drift)", "Weekly", "Monthly", "Quarterly", "Yearly"]
scheme = st.sidebar.selectbox(
    "Rebalance scheme", _scheme_labels,
    index=min(int(_settings.get("scheme_index", 3)), len(_scheme_labels) - 1))
freq = {
    "Buy & hold (drift)": None, "Weekly": "1W", "Monthly": "1ME",
    "Quarterly": "QE", "Yearly": "YE",
}[scheme]
cost_bps = st.sidebar.number_input("Trading cost (bps, one-way)", 0.0, 100.0,
                                   float(_settings.get("cost_bps", 5.0)))

st.sidebar.subheader("Tax (after-tax analysis)")
lt_rate = st.sidebar.number_input("Long-term gains %", 0.0, 49.0,
                                  float(_settings.get("lt_rate", 20.0))) / 100
st_rate = st.sidebar.number_input("Short-term gains %", 0.0, 49.0,
                                  float(_settings.get("st_rate", 15.0))) / 100
div_rate = st.sidebar.number_input("Dividends %", 0.0, 49.0,
                                   float(_settings.get("div_rate", 15.0))) / 100

st.sidebar.subheader("Period & benchmark")
_p = int(_settings.get("period", 2010))
period = st.sidebar.selectbox("From year", range(2002, 2026),
                              index=(min(max(_p, 2002), 2025) - 2002))
bench_spec = st.sidebar.text_input(
    "Benchmark (optional)", value=_settings.get("bench_spec", ""), key="bench_spec",
    help=("Same grammar as the symbol field: a single symbol (MSFT) or a "
          "portfolio (MSFT:0.6,V:0.4 — commas join its elements).\n"
          "Spaces separate DISTINCT benchmarks, e.g.\n"
          "MSFT MSFT:0.6,V:0.4\n"
          "Empty = none. Simulated with the same rebalance scheme, cost and "
          "tax rules. Beta/alpha in the stats table refers to the first "
          "benchmark."))

# remember the sidebar inputs now, before any validation st.stop()
_remember(spec=spec, bench_spec=bench_spec, scheme_index=_scheme_labels.index(scheme),
          cost_bps=cost_bps, lt_rate=lt_rate * 100, st_rate=st_rate * 100,
          div_rate=div_rate * 100, period=period)

if not spec.strip():
    st.info("Type symbols or portfolios in the sidebar — e.g. `MSFT` or "
            "`MSFT:0.6,V:0.4`; space-separated entries are each analyzed "
            "separately. The page updates as soon as the input is valid.")
    st.stop()

try:
    items = [_resolve_weights(w) for w in parse_items(spec)]
except ValueError as e:
    st.error(f"Invalid input: {e}")
    st.stop()
weights = items[0]                      # first entry = the 'current' portfolio
all_syms = {s for w in items for s in w}

unknown = [s for s in all_syms if s not in bundle.adj.columns]
if unknown:
    st.error(f"Symbol field — not in the data: {', '.join(unknown)}")
    for u in unknown[:2]:
        cands = [s for s in search_symbols(bundle, u, limit=4) if s not in all_syms]
        if cands:
            st.caption(f"Did you mean {u}:")
            cols = st.sidebar.columns(2)
            for i, s in enumerate(cands):
                if cols[i % 2].button(s, key=f"fix-{u}-{s}", use_container_width=True):
                    st.session_state["spec_handover"] = _replace_token(spec, u, s)
                    st.rerun()
    st.stop()

start = f"{period}-01-01"
use_syms = list(weights)

# ---- save / delete / select saved portfolios -------------------------
st.sidebar.subheader("Saved portfolios")
if len(items) == 1:
    pname = st.sidebar.text_input("Name to save current portfolio as", key="save_name")
    if st.sidebar.button("Save current portfolio"):
        pname = pname.strip()
        if pname:
            # commas without spaces: under the spec grammar that stays ONE portfolio
            spec_str = ",".join(f"{s}:{w:g}" for s, w in weights.items())
            pf_save(Portfolio(pname, spec_str, freq, cost_bps))
            st.sidebar.success(f"Saved '{pname}'.")
            st.rerun()
        else:
            st.sidebar.warning("Give it a name first (field below).")
else:
    st.sidebar.caption("Save: the Symbol field must hold exactly ONE "
                       "portfolio to save (it would save the first entry).")

compare_names: list[str] = []
if saved:
    compare_names = st.sidebar.multiselect(
        "Compare on the chart", [p.name for p in saved],
        default=[p.name for p in saved])
    dcol1, dcol2 = st.sidebar.columns([3, 1])
    del_name = dcol1.text_input("Delete by name", key="del_name")
    if dcol2.button("Delete") and del_name.strip():
        pf_delete(del_name.strip())
        st.rerun()

# ------------------------------------------------------------ compute
# cache the heavy simulations: switching the chart window / curve toggle / tax
# rates only rebuilds the (cheap) HTML — the portfolio+tax runs happen once
@st.cache_data(show_spinner=False)
def _compute_portfolio(w_key: tuple, scheme: str | None, cost_bps: float,
                       start: str, lt: float, st_r: float, div: float,
                       root: str):
    w = dict(w_key)
    r = portfolio_returns(bundle.adj, w, rebalance=scheme, cost_bps=cost_bps, start=start)
    t = after_tax_portfolio(bundle.adj, bundle.div, bundle.capg, w, rebalance=scheme,
                            cost_bps=cost_bps, lt_rate=lt, st_rate=st_r,
                            div_rate=div, start=start)
    return r, t


def build_result(name: str, w: dict, scheme: str | None, cost: float) -> dict:
    r, t = _compute_portfolio(tuple(sorted(w.items())), scheme, cost, start,
                              lt_rate, st_rate, div_rate, str(root))
    return {"name": name, "weights": w, "res": r, "tax": t}

results = []
for k, w in enumerate(items):
    name = "Current" if len(items) == 1 else ", ".join(w)
    results.append(build_result(name, w, freq, cost_bps))
for p in saved:
    if p.name not in compare_names:
        continue
    try:
        w = {s: v for s, v in parse_weights(p.spec).items() if s in bundle.adj.columns}
        if not w:
            st.sidebar.warning(f"'{p.name}': no known symbols, skipped.")
            continue
        results.append(build_result(p.name, w, p.scheme, p.cost_bps))
    except Exception as e:
        st.sidebar.warning(f"'{p.name}': {e}")

res, taxres = results[0]["res"], results[0]["tax"]

# benchmark: same spec format as the main field, simulated with the same
# rebalance scheme and cost; invalid/unknown input just disables it
# benchmarks: ';' separates independent specs; each is simulated exactly
# like the candidate portfolio (same scheme, cost and tax rules)
# each item (line, or space-separated on a line) is one benchmark
benchmarks: list[dict] = []
for i, item in enumerate(bench_spec.split(), 1):
    try:
        w = _resolve_weights(parse_weights(item))
    except ValueError as e:
        st.sidebar.warning(f"Benchmark {i} ignored: {e}")
        continue
    unknown = [s for s in w if s not in bundle.adj.columns]
    if unknown:
        st.sidebar.warning(f"Benchmark {i} ignored: not in the data: "
                           f"{', '.join(unknown)}")
        continue
    try:
        r, t = _compute_portfolio(tuple(sorted(w.items())), freq, cost_bps, start,
                                  lt_rate, st_rate, div_rate, str(root))
    except ValueError as e:
        st.sidebar.warning(f"Benchmark {i} ignored: {e}")
        continue
    benchmarks.append({
        "label": ", ".join(w),
        "price": r.equity.reindex(res.equity.index).ffill(),
        "after": t.equity.reindex(res.equity.index).ffill(),
    })
# first benchmark is the reference for beta/alpha in the stats table
bench_price = benchmarks[0]["price"] if benchmarks else None
bench_after = benchmarks[0]["after"] if benchmarks else None
bench_label = benchmarks[0]["label"] if benchmarks else None

# ------------------------------------------------------------ display
if len(items) == 1:
    if len(use_syms) == 1:
        st.title(f"{use_syms[0]} — single symbol")
    else:
        st.title(f"Portfolio: {', '.join(use_syms)}")
else:
    st.title("Portfolios: " + "  ·  ".join(", ".join(w) for w in items))
if len(results) > 1:
    st.caption(f"Comparing: {', '.join(r['name'] for r in results)}")
st.caption(f"{scheme} · start {start} · cost {cost_bps} bps · "
           f"tax LT/ST/div {lt_rate:.0%}/{st_rate:.0%}/{div_rate:.0%}"
           + (f" · benchmark: {' ; '.join(b['label'] for b in benchmarks)}"
              if benchmarks else ""))

# global curve mode — applies to the stats table AND the chart
_mode_labels = ["Pre-tax", "After-tax", "Pre-tax + after-tax"]
mode = st.radio("Curve", _mode_labels,
                horizontal=True, key="curve_mode",
                index=_mode_labels.index(_settings["curve_mode"])
                if _settings.get("curve_mode") in _mode_labels else 0)
show_pre, show_after, both = mode != "After-tax", mode != "Pre-tax", \
    mode == "Pre-tax + after-tax"

tab_stats, tab_equity, tab_alloc, tab_tax = st.tabs(
    ["Statistics", "Equity curves", "Allocation", "Tax detail"])

with tab_stats:
    # one beta/alpha/bench-return column SET per benchmark
    bench_ms = [(b["label"], b["price"].resample("ME").last()) for b in benchmarks]

    def summarize(price):
        out = m.summary(price, None)
        for blab, bm in bench_ms:
            d = m.summary(price, bm)
            sfx = "" if len(bench_ms) == 1 else f" [{blab}]"
            out[f"beta{sfx}"] = d["beta"]
            out[f"alpha_ann{sfx}"] = d["alpha_ann"]
            out[f"ann_return_bench{sfx}"] = d["ann_return_bench"]
        return out

    # one row per candidate (the whole portfolio, not its components) and
    # per benchmark; the pre/after suffix only appears in 'both' mode
    summaries = {}

    def row(name, pre, after):
        if both:
            summaries[f"{name} (pre-tax)"] = summarize(pre)
            summaries[f"{name} (after-tax)"] = summarize(after)
        elif show_pre:
            summaries[name] = summarize(pre)
        else:
            summaries[name] = summarize(after)

    for r in results:
        row(r["name"], r["res"].equity, r["tax"].equity)
    for b in benchmarks:
        row(f"benchmark: {b['label']}", b["price"], b["after"])
    st.dataframe(m.format_summary_table(summaries), width='stretch')

with tab_equity:
    # Self-contained plotly.js page (see chart_widget.py): full mouse zoom +
    # pan, with every relayout instantly clamping x to the data and re-fitting
    # y to the exact min/max of the visible data. No clipping, no blank space.
    from chart_widget import equity_chart_html

    _win_labels = ["Max", "10Y", "5Y", "3Y", "1Y"]
    win = st.radio("Window", _win_labels,
                   horizontal=True, key="equity_window",
                   index=_win_labels.index(_settings["equity_window"])
                   if _settings.get("equity_window") in _win_labels else 0)
    _remember(curve_mode=mode, equity_window=win)
    idx = res.equity.index
    if win != "Max":
        idx = idx[idx >= idx[-1] - pd.DateOffset(years=int(win[:-1]))]

    # labels carry the pre/after suffix only in 'both' mode
    palette = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728",
               "#9467bd", "#17becf", "#e377c2", "#8c564b"]

    def lab(name, which):
        return f"{name} — {which}" if both else name

    series: dict[str, pd.Series] = {}
    styles: dict[str, dict] = {}
    for k, r in enumerate(results):
        color = palette[k % len(palette)]
        if show_pre:
            series[lab(r["name"], "pre-tax")] = r["res"].equity
            styles[lab(r["name"], "pre-tax")] = {"color": color, "width": 2}
        if show_after:
            s = lab(r["name"], "after-tax")
            series[s] = r["tax"].equity
            # faded only in 'both' mode, to stay distinct from the pre-tax line
            styles[s] = {"color": color, "width": 2,
                         "opacity": 0.55 if both else 1.0}
    for b in benchmarks:
        base = f"benchmark ({b['label']})"
        if show_pre:
            s = lab(base, "pre-tax")
            series[s] = b["price"] / b["price"].iloc[0]
            styles[s] = {"width": 1, "opacity": 0.6}
        if show_after:
            s = lab(base, "after-tax")
            series[s] = b["after"] / b["after"].iloc[0]
            styles[s] = {"width": 1, "opacity": 0.3 if both else 0.6}

    html = equity_chart_html(series, idx, ytitle="growth (1.0 = start)", styles=styles)
    st.iframe(html, height=600)
    def final(name, pre, after):
        if both:
            return f"{name}: pre {pre:.2f}× / after {after:.2f}×"
        return f"{name}: {(pre if show_pre else after):.2f}×"

    finals = " · ".join(final(r["name"], r["res"].equity.iloc[-1],
                              r["tax"].equity.iloc[-1]) for r in results)
    for b in benchmarks:
        finals += " · " + final(f"benchmark ({b['label']})", b["price"].iloc[-1],
                                b["after"].iloc[-1])
    st.caption(f"Final: {finals}.  "
               "Drag to box-zoom, right-drag (or mode-bar hand) to pan, "
               "scroll to zoom, double-click to reset.")

with tab_alloc:
    st.caption(f"Allocation for the **{results[0]['name']}** portfolio "
               "(first entry in the Symbol field).")
    alloc = res.allocation
    st.dataframe(alloc.tail(1).T, width='stretch')
    fig = go.Figure(go.Bar(x=alloc.columns, y=alloc.iloc[-1]))
    fig.update_layout(height=360, title=f"Drifted allocation @ {alloc.index[-1].date()}")
    st.plotly_chart(fig, width='stretch')
    st.caption(f"Final allocation: " +
               ", ".join(f"{k} {v:.1%}" for k, v in alloc.iloc[-1].items()))

with tab_tax:
    st.caption(f"Tax detail for the **{results[0]['name']}** portfolio "
               "(first entry in the Symbol field).")
    # all values below are fractions of the starting account (1.0 = 100%)
    yr_tax = taxres.taxes.resample("YE").sum()
    yr_tax.index = yr_tax.index.year
    yr_tax = yr_tax.map(lambda v: f"{v:.2%}")
    yr_real = taxres.realized.resample("YE").sum()
    yr_real.index = yr_real.index.year
    yr_real = yr_real.map(lambda v: f"{v:+.2%}")
    yr_liq = taxres.liq_tax.resample("YE").last()
    yr_liq.index = yr_liq.index.year
    yr_liq = yr_liq.map(lambda v: f"{v:.2%}")
    st.subheader("Taxes actually paid per year (distributions + rebalance sales)")
    st.dataframe(yr_tax, width='stretch')
    st.subheader("Realized gains/losses per year (at rebalances)")
    st.dataframe(yr_real, width='stretch')
    st.subheader("Unrealized-gain tax you would owe if you sold everything (year-end)")
    st.dataframe(yr_liq, width='stretch')
    st.caption(f"Total taxes paid to date: {taxres.taxes['total'].sum():.2%} of start · "
               f"final after-tax value (sell everything): {taxres.equity.iloc[-1]:.2f}× "
               f"= {taxres.equity.iloc[-1] - 1:+.1%} total return")