Greg Pomerantz
afec7bda73
Answer to 'find other alpha-driven funds that complement the portfolio': - fundlab/search.py: complementarity screen - each fund's daily total returns vs the same 21 broad sleeve axes (BIC forward selection, |t|>2), full + 5y; alpha (intercept t), R2, rolling 6m alpha persistence, correlation vs the current qspnx/pmaix portfolio and the spy/agg/tlt benchmark mix. Verdict tiers: CANDIDATE (alpha + persistent + portfolio-corr<0.3) / semi-alpha / alpha-but-correlated / sleeve mix / weak. - fundlab/dbmine.py: the actual search universe - the local stocks DB already holds ~100 US open-end alternatives (AQR, PIMCO, JPM, Principal, Calamos, GMO, Franklin K2, ...). Name-pattern miner with share-class family dedupe (keeps the longest-history class). - fundlab/tickers.py + searchlist.py: external longlist resolution (chart-API name gate + EDGAR 497 cover tickers). Finding: the famous multi-strategy/macro names (Millennium, Balyasny, Two Sigma, Winton, Marshall Wace, Brevan Howard, AQR Event-Driven) are private/offshore or terminated - not US open-end accessible. Fidelity Multi-Asset Income (FMSDX) resolved and screens as weak alpha. - app Fund Lab: 'Alpha search - all screened funds, ranked' table (80 funds: 13 shortlist + 59 mined + 1 external). - results (ranked candidates, 5y alpha / t / portfolio-corr): wmnix Westwood Alt Income +3.8% t6.5 c0.09 | pyaix Payden ARB +3.0% t4.8 c0.13 | srdax Stone Ridge Div Alts +7.7% t4.2 c0.10 | padqx PGIM ARB +2.3% t2.4 c0.27 | bxmdx Blackstone Alt MS +3.5% t2.4 c0.30 | aqmix AQR Mngd Futures +8.0% t2.2 c0.21 | cmnix/gioix semi-alpha. Key insight: AQR MN / L/S-equity / Vanguard MN show strong alpha but corr 0.35-0.76 with the portfolio - it is already 50% market-neutral (qspnx), so more MN is not diversifying. - tests: 59/59 fundlab (resolver gates, query ladder, family dedupe, ticker regex), 32/32 app, 14/14 data
69 lines
3.8 KiB
Python
69 lines
3.8 KiB
Python
"""Curated longlist of candidate funds for the alpha search.
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Universe rationale: we cannot meaningfully screen all ~10k registered
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funds (most are small, illiquid or strategy-unstable and would not
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deserve portfolio consideration anyway). So the UNIVERSE is curated by
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reputation — large, liquid, long-tracked funds in the buckets where
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idiosyncratic alpha lives (market-neutral/quant, multi-strategy, global
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macro, dynamic TA, dynamic credit/convertibles) — and the SELECTION is
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data-driven (fundlab/search.py). Tickers are resolved from the fund NAME
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via Yahoo search with a precision gate; a candidate that cannot be
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resolved cleanly is dropped, never guessed.
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The 13-fund shortlist already in funds.json is screened with the same
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code so the ranking is consistent (qspnx/pmaix are the user's current
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holdings and act as controls).
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"""
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from __future__ import annotations
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# (fund name as searched, strategy bucket, ticker guess or None)
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LONGLIST: list[tuple[str, str, str | None]] = [
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# --- market-neutral / quant / pure alpha / event-driven ------------
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("AQR Diversified Event-Driven Fund", "event_driven", None),
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("Bridgewater Pure Alpha II Fund", "pure_alpha", None),
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("Winton Global Quantitative Fund", "cta_quant", None),
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("Two Sigma Dynamic Strategy Fund", "systematic", None),
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("Brevan Howard Dymon Asia Fund", "macro_relative_value", None),
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("Marshall Wace Global Opportunities Fund", "macro_relative_value",
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None),
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# --- multi-strategy -------------------------------------------------
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("ExodusPoint Diversified Fund", "multi_strategy", None),
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("Verition Dynamic Risk Fund", "multi_strategy", None),
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("Millennium Focus Fund", "multi_strategy", None),
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("Balyasny Absolute Return Multi-Strategy Fund", "multi_strategy", None),
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("Schonfeld Strategic Opportunities Fund", "multi_strategy", None),
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# --- global macro / risk parity ------------------------------------
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# (T. Rowe Price New Global Opportunity: fund terminated - no data)
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("Bridgewater All Weather Fund", "risk_parity", None),
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("Oak Hill Tactical Allocation Fund", "tactical_allocation", None),
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# --- multi-asset / dynamic TA open-ends (the accessible alpha pool) -
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("Fidelity Multi-Asset Income Fund", "tactical_allocation", None),
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("Janus Henderson Global Dynamic Dividend Fund", "dynamic_equity", None),
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("Wellington Dynamic Global Diversified Fund", "tactical_allocation", None),
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("Lord Abbett Global Opportunities Fund", "tactical_allocation", None),
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("BlackRock Multi-Asset Income Fund", "tactical_allocation", None),
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("PIMCO Income Strategy Fund", "tactical_allocation", None),
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("JPMorgan Diversified Return Fund", "tactical_allocation", None),
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("Invesco Diversified Equity and Income Fund", "tactical_allocation", None),
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("T. Rowe Price Global Allocation Fund", "tactical_allocation", None),
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("Morgan Stanley Global Multi Asset Fund", "tactical_allocation", None),
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("Fidelity Diversified Multi-Asset Fund", "tactical_allocation", None),
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# --- controls: credit (expected to classify as sleeve mix)
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# (Calamos Dynamic Convertible CCD is a CEF - excluded)
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("PIMCO Dynamic Income Fund", "dynamic_credit", None),
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]
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# the 13 unique shortlist funds (share classes resolved once)
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SHORTLIST = ["atesx", "atrfx", "cvsix", "jlpsx", "pmaix", "pmorx", "qspnx",
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"svarx", "cosix", "mbxix", "eagmx", "lcorx", "lamhx"]
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# broad sleeve set used by the screen (same 21 axes for every fund -
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# nothing is tuned to a specific fund, so selection is comparable)
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BROAD_SLEEVES = ["qqq", "ivv", "iwm", "vea", "efa", "vwo", "vnq", "bil",
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"shv", "ief", "tlt", "vblix", "agg", "vweax", "vmbix",
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"finux", "djp", "gsg", "gld", "fxe", "fxy"]
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# the user's current portfolio + benchmark mix (settings.json)
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PORTFOLIO = {"qspnx": 0.5, "pmaix": 0.5}
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BENCHMARKS = {"spy": 1 / 3, "agg": 1 / 3, "tlt": 1 / 3}
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