Add 32 factor-combo strategies with configurable rebalancing frequency
New FactorComboStrategy class (strategies/factor_combo.py) implements
8 champion factor signals (4 US, 4 CN) discovered through iterative
factor research, each at 4 rebalancing frequencies (daily/weekly/
biweekly/monthly). Registered in trader.py as fc_{signal}_{freq}.
Existing strategies and state files are untouched — safe to git pull
and restart monitor on server.
Also includes factor research scripts (factor_loop.py, factor_research.py,
etc.) used to discover and validate these factors.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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factor_yearly_fresh.py
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factor_yearly_fresh.py
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"""
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Rebalancing frequency comparison: daily (1d) vs weekly (5d) vs biweekly (10d) vs monthly (21d).
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Shows yearly returns and max drawdown for each frequency, for all champion strategies.
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"""
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from __future__ import annotations
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import warnings
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import numpy as np
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import pandas as pd
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import data_manager
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from factor_loop import (
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strat, bt, stats, combo,
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f_rec_mom, f_rec_126, f_rec_63,
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f_mom_12_1, f_mom_6_1, f_mom_intermediate,
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f_above_ma200, f_golden_cross,
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f_up_volume_proxy, f_gap_up_freq,
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f_rec_mom_filtered, f_down_resilience,
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f_up_capture, f_52w_high, f_str_10d,
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f_earnings_drift, f_reversal_vol,
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)
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warnings.filterwarnings("ignore")
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INITIAL = 10_000
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REBAL_CONFIGS = [
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("daily", 1),
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("weekly", 5),
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("biweekly", 10),
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("monthly", 21),
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]
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def f_quality_mom(p):
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mom = f_mom_12_1(p)
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consist = (p.pct_change() > 0).astype(float).rolling(252, min_periods=126).mean()
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mom_r = mom.rank(axis=1, pct=True, na_option="keep")
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con_r = consist.rank(axis=1, pct=True, na_option="keep")
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up_r = f_up_volume_proxy(p).rank(axis=1, pct=True, na_option="keep")
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return 0.4 * mom_r + 0.3 * con_r + 0.3 * up_r
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def f_mom_x_gap(p):
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return (f_mom_12_1(p).rank(axis=1, pct=True, na_option="keep") *
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f_gap_up_freq(p).rank(axis=1, pct=True, na_option="keep"))
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def run_equity(func, prices, rebal=21, cost=0.001):
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w = strat(prices, func, top_n=10, rebal=rebal)
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eq = bt(w, prices, cost=cost)
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return eq / eq.iloc[0] * INITIAL
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def year_returns(eq: pd.Series) -> dict[int, float]:
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dr = eq.pct_change().fillna(0)
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return {y: float((1 + dr[dr.index.year == y]).prod() - 1)
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for y in sorted(dr.index.year.unique())}
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def max_drawdown(eq: pd.Series) -> float:
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rm = eq.cummax()
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dd = (eq - rm) / rm
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return float(dd.min())
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def max_drawdown_yearly(eq: pd.Series) -> dict[int, float]:
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result = {}
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for y in sorted(eq.index.year.unique()):
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chunk = eq[eq.index.year == y]
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if len(chunk) < 5:
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continue
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rm = chunk.cummax()
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dd = (chunk - rm) / rm
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result[y] = float(dd.min())
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return result
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def cagr(eq: pd.Series) -> float:
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dr = eq.pct_change().dropna()
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if len(dr) < 100:
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return np.nan
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ny = len(dr) / 252
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tot = eq.iloc[-1] / eq.iloc[0] - 1
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return (1 + tot) ** (1 / ny) - 1
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def sharpe(eq: pd.Series) -> float:
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dr = eq.pct_change().dropna()
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if len(dr) < 100 or dr.std() == 0:
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return np.nan
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return float(dr.mean() / dr.std() * np.sqrt(252))
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def turnover_annual(func, prices, rebal):
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"""Estimate annualised turnover (one-way)."""
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w = strat(prices, func, top_n=10, rebal=rebal)
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daily_turn = w.diff().abs().sum(axis=1).mean()
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return daily_turn * 252
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def print_by_year(strat_defs, prices, bench_eq, bench_label, market_label, years):
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"""For each year, print a table: strategies as rows, rebal frequencies as columns."""
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freq_labels = [r for r, _ in REBAL_CONFIGS]
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# Pre-compute all equities and returns
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all_eqs = {} # {(sname, freq): equity}
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for sname, func in strat_defs.items():
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for rlabel, rdays in REBAL_CONFIGS:
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all_eqs[(sname, rlabel)] = run_equity(func, prices, rebal=rdays)
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all_rets = {} # {(sname, freq): {year: ret}}
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for key, eq in all_eqs.items():
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all_rets[key] = year_returns(eq)
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bench_rets = year_returns(bench_eq)
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snames = list(strat_defs.keys())
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name_w = max(len(s) for s in snames) + 1
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for year in years:
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line_w = name_w + 4 + 20 * (len(freq_labels) + 1)
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print(f"\n{'=' * line_w}")
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print(f" {market_label} — {year} (fresh $10,000)")
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print(f"{'=' * line_w}")
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# Header
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print(f" {'Strategy':<{name_w}}", end="")
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for f in freq_labels:
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print(f" {f:>18}", end="")
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print(f" {bench_label:>18}")
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print(f" {'-'*name_w}", end="")
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for _ in range(len(freq_labels) + 1):
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print(f" {'-'*18}", end="")
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print()
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for sname in snames:
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print(f" {sname:<{name_w}}", end="")
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# Find best freq for this strategy this year
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freq_vals = {}
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for f in freq_labels:
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r = all_rets[(sname, f)].get(year)
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if r is not None and abs(r) > 0.0005:
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freq_vals[f] = r
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best_f = max(freq_vals, key=freq_vals.get) if freq_vals else None
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for f in freq_labels:
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r = all_rets[(sname, f)].get(year)
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if r is not None and abs(r) > 0.0005:
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v = INITIAL * (1 + r)
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marker = " ★" if f == best_f else " "
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print(f" ${v:>9,.0f} {r:>+5.0%}{marker}", end="")
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else:
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print(f" {'—':>18}", end="")
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# Benchmark (same for all strategies)
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br = bench_rets.get(year)
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if br is not None and abs(br) > 0.0005:
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print(f" ${INITIAL*(1+br):>9,.0f} {br:>+5.0%} ", end="")
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else:
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print(f" {'—':>18}", end="")
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print()
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# Best strategy per freq
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print(f" {'-'*name_w}", end="")
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for _ in range(len(freq_labels) + 1):
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print(f" {'-'*18}", end="")
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print()
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print(f" {'BEST':<{name_w}}", end="")
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for f in freq_labels:
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best_r = -999
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best_s = ""
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for sname in snames:
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r = all_rets[(sname, f)].get(year)
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if r is not None and abs(r) > 0.0005 and r > best_r:
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best_r = r
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best_s = sname
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if best_r > -999:
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print(f" ${INITIAL*(1+best_r):>9,.0f} {best_r:>+5.0%} ", end="")
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else:
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print(f" {'—':>18}", end="")
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# bench
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br = bench_rets.get(year)
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if br is not None and abs(br) > 0.0005:
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print(f" ${INITIAL*(1+br):>9,.0f} {br:>+5.0%} ", end="")
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else:
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print(f" {'—':>18}", end="")
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print()
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def main():
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years = list(range(2015, 2027))
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# ===== US =====
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print(f"\n{'#'*130}")
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print(f"{'#'*50} US MARKET {'#'*50}")
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print(f"{'#'*130}")
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prices_us = data_manager.load("us")
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bench_us = prices_us["SPY"].dropna()
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stocks_us = prices_us.drop(columns=["SPY"], errors="ignore")
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eq_spy = bench_us / bench_us.iloc[0] * INITIAL
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us_strats = {
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"rec_mfilt+deep×upvol": combo([
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(f_rec_mom_filtered, 0.5),
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(combo([(f_rec_126, 0.5), (f_up_volume_proxy, 0.5)]), 0.5),
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]),
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"ma200+mom7m+rec126": combo([
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(f_above_ma200, 0.33), (f_mom_intermediate, 0.33), (f_rec_126, 0.34)
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]),
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"rec_mfilt+ma200": combo([
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(f_rec_mom_filtered, 0.5), (f_above_ma200, 0.5)
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]),
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"mom7m+rec126": combo([
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(f_mom_intermediate, 0.5), (f_rec_126, 0.5)
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]),
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"BASELINE:rec+mom": f_rec_mom,
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}
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print_by_year(us_strats, stocks_us, eq_spy, "SPY", "US", years)
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# ===== CN =====
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print(f"\n\n{'#'*130}")
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print(f"{'#'*50} CN MARKET {'#'*50}")
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print(f"{'#'*130}")
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prices_cn = data_manager.load("cn")
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bench_cn = prices_cn["000300.SS"].dropna() if "000300.SS" in prices_cn.columns else None
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stocks_cn = prices_cn.drop(columns=["000300.SS"], errors="ignore")
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cn_strats = {
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"up_cap+quality_mom": combo([
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(f_up_capture, 0.5), (f_quality_mom, 0.5)
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]),
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"down_resil+qual_mom": combo([
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(f_down_resilience, 0.5), (f_quality_mom, 0.5)
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]),
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"rec63+mom×gap": combo([
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(f_rec_63, 0.5), (f_mom_x_gap, 0.5)
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]),
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"up_cap+mom×gap": combo([
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(f_up_capture, 0.5), (f_mom_x_gap, 0.5)
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]),
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"BASELINE:rec+mom": f_rec_mom,
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}
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if bench_cn is not None:
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eq_csi = bench_cn / bench_cn.iloc[0] * INITIAL
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else:
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eq_csi = pd.Series(dtype=float)
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print_by_year(cn_strats, stocks_cn, eq_csi, "CSI300", "CN", years)
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if __name__ == "__main__":
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main()
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