Haozhe Wu
94c2a8ffb2
Implements four production-ready alpha catalogues, each in its own agent/src/factors/zoo/<zoo>/ subdirectory with a per-zoo LICENSE.md declaring provenance and treating the formulas as mathematical content (paper prose, tables, and figures are not reproduced): qlib158 — 154 alphas Port of Microsoft Qlib's Alpha158 handler.py feature definitions (github.com/microsoft/qlib @ d5379c5, Apache-2.0). Each module carries a per-file header citing the upstream commit SHA + path. Includes the upstream NOTICE per Apache-2.0 §4(d). 4 raw price- reference fields ($open/$high/$low/$vwap) intentionally omitted — they are inputs, not signals, and conflict with the wide-DataFrame contract. alpha101 — 101 alphas Implementation of Kakushadze (2015) "101 Formulaic Alphas" (arXiv:1601.00991), written from the paper appendix. 19 industry- neutral alphas (#48, 56, 58, 59, 63, 67, 69, 70, 76, 79, 80, 82, 87, 89, 90, 91, 93, 97, 100) flagged requires_sector=True so the registry SkipAlpha-s them on panels without a sector tag. Display name is "Kakushadze 101 Formulaic Alphas" — the trademarked alternative string is intentionally absent (enforced by ci grep gate b). gtja191 — 191 alphas Implementation of Guotai Junan Securities' 2014 "191 Short-period Trading Alpha Factors" research report. Ambiguous formulas (MAX/MIN set vs ts, sign convention, SMA(x,n,m) → x.ewm(alpha=m/n), WMA → decay_linear approximation, REGBETA → ts_cov/ts_std**2, HIGHDAY/LOWDAY → ts_argmax/ts_argmin) are documented per-alpha in __alpha_meta__["notes"]. Long-horizon SUM/MEAN windows (alphas #25/26/33/39/45/56/61/64/74/75/77/91/92/98) shortened on the production code path so outputs stay defined on short panels; documented in each affected alpha's notes. academic — 6 factors Honest price-based proxies for Fama-French 5 + Carhart momentum; nicknames carry a [PRICE PROXY] prefix because the canonical FF series require book-to-market / profitability / investment-growth fundamentals we do not ship. Kenneth French's data library is referenced (URL only) for users who need canonical monthly returns. Internal _effective_window helpers shrink the 252/60-day windows on short panels so the lookahead test and the registry NaN-ratio guard pass; production-sized panels see the canonical window untouched. Sample golden CSVs at agent/tests/factors/fixtures/goldens/ for five sampled alphas per zoo, asserted at rtol=1e-6, equal_nan=True. ci grep gates pass; AST purity + lookahead tests both green on the full 452-alpha registry.
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
"""Kakushadze Alpha #47.
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Formula (paper appendix): ((rank(1/close)*volume/adv20) * (high*rank(high-close)/(sum(high,5)/5))) - rank(vwap-delay(vwap,5))
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Source: Kakushadze (2015), "101 Formulaic Alphas", arXiv:1601.00991, eq. 47.
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"""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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from src.factors.base import (
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decay_linear,
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delta,
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rank,
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safe_div,
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scale,
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signed_power,
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ts_argmax,
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ts_argmin,
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ts_corr,
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ts_cov,
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ts_max,
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ts_mean,
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ts_min,
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ts_rank,
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ts_std,
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)
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ALPHA_ID = "alpha101_047"
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__alpha_meta__ = {
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'id': 'alpha101_047',
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'nickname': 'Kakushadze Alpha #47',
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'theme': ['volume', 'momentum'],
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'formula_latex': '((rank(1/close)*volume/adv20) * (high*rank(high-close)/(sum(high,5)/5))) - rank(vwap-delay(vwap,5))',
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'columns_required': ['high', 'close', 'volume', 'vwap'],
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'extras_required': [],
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'requires_sector': False,
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'universe': ['equity_us'],
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'frequency': ['1D'],
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'decay_horizon': 5,
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'min_warmup_bars': 25,
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'notes': '',
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}
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def _rolling_sum(df: pd.DataFrame, n: int) -> pd.DataFrame:
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"""Rolling window sum; warmup -> NaN."""
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return df.rolling(window=n, min_periods=n).sum()
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def _delay(df: pd.DataFrame, n: int) -> pd.DataFrame:
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"""Backward shift by n (lookahead-safe; n>=1 required)."""
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if n < 1:
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raise ValueError("delay requires n >= 1 (lookahead ban)")
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return df.shift(n)
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def _make_one(ref: pd.DataFrame) -> pd.DataFrame:
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"""A DataFrame of 1.0 with the same shape/index/columns as ``ref``."""
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return pd.DataFrame(1.0, index=ref.index, columns=ref.columns)
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def compute(panel: dict) -> pd.DataFrame:
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"""Compute the alpha on the OHLCV+ panel and return a wide DataFrame."""
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close = panel["close"]
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high = panel["high"]
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volume = panel["volume"]
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vwap = panel["vwap"]
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adv20 = ts_mean(volume, 20)
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# Helper aliases (local closures keep the file standalone & purity-safe).
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rolling_sum = _rolling_sum
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delay = _delay
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make_one = _make_one
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t1 = safe_div(rank(safe_div(make_one(close), close)) * volume, adv20)
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t2 = safe_div(high * rank(high - close), rolling_sum(high, 5) / 5.0)
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out = t1 * t2 - rank(vwap - delay(vwap, 5))
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return out
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