research: add strategy evaluation and exploration scripts
Add 28 research scripts covering DCA simulation, momentum evaluation, Sharpe optimization, trend rider analysis, and US fundamentals exploration.
This commit is contained in:
291
research/strategy_sharpe_boost.py
Normal file
291
research/strategy_sharpe_boost.py
Normal file
@@ -0,0 +1,291 @@
|
||||
"""
|
||||
Sharpe boost research: blend pure momentum into the Ensemble signal.
|
||||
|
||||
Root cause of Sharpe=1.32 (not 1.5+):
|
||||
- 2021: recovery signals returned +3% vs SPY +30.5%
|
||||
- In low-vol steady uptrends, "bouncing from bottom" stocks don't exist
|
||||
- Pure 12-1 momentum captures "steady grinders" that do well in these regimes
|
||||
|
||||
Approach: Add a 3rd signal (pure momentum rank) to the ensemble with weight α,
|
||||
reducing existing signals to (1-α)/2 each.
|
||||
Test α ∈ {0.20, 0.25, 0.30, 0.35, 0.40} and pick the one that maximizes Sharpe
|
||||
without materially hurting CAGR.
|
||||
|
||||
Also test: market-DD dampener ON TOP of the blended signal (risk-managed version).
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
||||
|
||||
from strategies.base import Strategy
|
||||
|
||||
|
||||
def _rank(df):
|
||||
return df.rank(axis=1, pct=True, na_option="keep")
|
||||
|
||||
|
||||
class MomentumBlendEnsembleStrategy(Strategy):
|
||||
"""
|
||||
Ensemble of 3 signals: rec_mfilt+deep_upvol, recovery63+mom, pure momentum.
|
||||
The pure momentum signal provides diversification in low-vol steady trends.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
rebal_freq: int = 21,
|
||||
top_n: int = 10,
|
||||
mom_blend: float = 0.30, # weight on pure momentum signal
|
||||
dd_floor: float = 0.40,
|
||||
dd_denom: float = 0.20,
|
||||
risk_managed: bool = True,
|
||||
):
|
||||
self.rebal_freq = rebal_freq
|
||||
self.top_n = top_n
|
||||
self.mom_blend = mom_blend
|
||||
self.dd_floor = dd_floor
|
||||
self.dd_denom = dd_denom
|
||||
self.risk_managed = risk_managed
|
||||
|
||||
def generate_signals(self, data: pd.DataFrame) -> pd.DataFrame:
|
||||
p = data
|
||||
|
||||
# === Signal A: rec_mfilt + deep_upvol ===
|
||||
rec_126 = p / p.rolling(126, min_periods=126).min() - 1
|
||||
mom_filter = p.shift(21).pct_change(105)
|
||||
rec_mfilt = rec_126.where(mom_filter > 0, np.nan)
|
||||
rec_mfilt_r = _rank(rec_mfilt)
|
||||
|
||||
ret = p.pct_change()
|
||||
up_vol = ret.where(ret > 0, 0).rolling(20, min_periods=15).sum()
|
||||
deep_upvol = _rank(rec_126) * _rank(up_vol)
|
||||
deep_upvol_r = _rank(deep_upvol)
|
||||
|
||||
signal_a = 0.5 * rec_mfilt_r + 0.5 * deep_upvol_r
|
||||
|
||||
# === Signal B: Recovery 63d + 12-1 momentum ===
|
||||
rec_63 = p / p.rolling(63, min_periods=63).min() - 1
|
||||
mom_12_1 = p.shift(21).pct_change(231)
|
||||
|
||||
rec_63_r = _rank(rec_63)
|
||||
mom_r = _rank(mom_12_1)
|
||||
|
||||
signal_b = 0.5 * rec_63_r + 0.5 * mom_r
|
||||
|
||||
# === Signal C: Pure 12-1 momentum (diversification in melt-ups) ===
|
||||
signal_c = mom_r # already computed above
|
||||
|
||||
# === Ensemble: weighted average ===
|
||||
α = self.mom_blend
|
||||
ensemble = (1 - α) / 2.0 * signal_a + (1 - α) / 2.0 * signal_b + α * signal_c
|
||||
|
||||
# === Select top_n ===
|
||||
rank = ensemble.rank(axis=1, ascending=False, na_option="bottom")
|
||||
n_valid = ensemble.notna().sum(axis=1)
|
||||
enough = n_valid >= self.top_n
|
||||
top_mask = (rank <= self.top_n) & enough.values.reshape(-1, 1)
|
||||
|
||||
# Equal weight
|
||||
raw = top_mask.astype(float)
|
||||
row_sums = raw.sum(axis=1).replace(0, np.nan)
|
||||
signals = raw.div(row_sums, axis=0).fillna(0.0)
|
||||
|
||||
# === Monthly rebalance ===
|
||||
warmup = 252
|
||||
rebal_mask = pd.Series(False, index=data.index)
|
||||
rebal_indices = list(range(warmup, len(data), self.rebal_freq))
|
||||
rebal_mask.iloc[rebal_indices] = True
|
||||
|
||||
signals[~rebal_mask] = np.nan
|
||||
signals = signals.ffill().fillna(0.0)
|
||||
signals.iloc[:warmup] = 0.0
|
||||
|
||||
signals = signals.shift(1).fillna(0.0) # PIT
|
||||
|
||||
# === Risk management: market-DD dampener ===
|
||||
if self.risk_managed:
|
||||
daily_rets = data.pct_change().fillna(0.0)
|
||||
mkt_rets = daily_rets.mean(axis=1)
|
||||
mkt_eq = (1 + mkt_rets).cumprod()
|
||||
mkt_dd = mkt_eq / mkt_eq.cummax() - 1
|
||||
dd_scale = (1.0 + mkt_dd / self.dd_denom).clip(
|
||||
lower=self.dd_floor, upper=1.0
|
||||
)
|
||||
dd_scale_lagged = dd_scale.shift(1).fillna(1.0)
|
||||
signals = signals.mul(dd_scale_lagged, axis=0)
|
||||
|
||||
return signals
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Evaluation
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
|
||||
def compute_metrics(daily_rets: pd.Series) -> dict:
|
||||
"""Compute standard performance metrics from daily returns."""
|
||||
eq = (1 + daily_rets).cumprod()
|
||||
n_years = len(daily_rets) / 252.0
|
||||
cagr = eq.iloc[-1] ** (1.0 / n_years) - 1.0
|
||||
vol = daily_rets.std() * np.sqrt(252)
|
||||
sharpe = daily_rets.mean() / daily_rets.std() * np.sqrt(252) if daily_rets.std() > 0 else 0
|
||||
running_max = eq.cummax()
|
||||
dd = eq / running_max - 1
|
||||
max_dd = dd.min()
|
||||
calmar = cagr / abs(max_dd) if max_dd != 0 else 0
|
||||
return {
|
||||
"cagr": cagr,
|
||||
"vol": vol,
|
||||
"sharpe": sharpe,
|
||||
"max_dd": max_dd,
|
||||
"calmar": calmar,
|
||||
}
|
||||
|
||||
|
||||
def yearly_returns(daily_rets: pd.Series) -> pd.Series:
|
||||
"""Compute annual returns."""
|
||||
eq = (1 + daily_rets).cumprod()
|
||||
yearly = eq.resample("YE").last().pct_change()
|
||||
yearly.iloc[0] = eq.resample("YE").last().iloc[0] - 1
|
||||
yearly.index = yearly.index.year
|
||||
return yearly
|
||||
|
||||
|
||||
_DATA_CACHE = {}
|
||||
|
||||
|
||||
def backtest_strategy(strategy, start="2016-04-01", end="2026-05-13"):
|
||||
"""Run backtest and return daily portfolio returns."""
|
||||
import data_manager
|
||||
|
||||
if "data" not in _DATA_CACHE:
|
||||
from universe import get_sp500
|
||||
tickers = get_sp500()
|
||||
data_manager.update("us", tickers)
|
||||
_DATA_CACHE["data"] = data_manager.load("us")
|
||||
|
||||
data = _DATA_CACHE["data"]
|
||||
if data is None:
|
||||
raise RuntimeError("No data loaded")
|
||||
|
||||
weights = strategy.generate_signals(data)
|
||||
daily_rets = (weights * data.pct_change().fillna(0.0)).sum(axis=1)
|
||||
|
||||
# Trim to evaluation period
|
||||
daily_rets = daily_rets.loc[start:end]
|
||||
return daily_rets
|
||||
|
||||
|
||||
def main():
|
||||
print("=" * 80)
|
||||
print("SHARPE BOOST: Momentum blend into Ensemble signal")
|
||||
print("=" * 80)
|
||||
|
||||
# --- Parameter sweep: mom_blend ---
|
||||
blends = [0.0, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40]
|
||||
|
||||
print("\n--- Sweep: mom_blend (risk_managed=False) ---")
|
||||
print(f"{'blend':>6s} {'CAGR':>7s} {'Vol':>7s} {'Sharpe':>7s} {'MaxDD':>7s} {'Calmar':>7s}")
|
||||
print("-" * 50)
|
||||
|
||||
results_no_rm = {}
|
||||
for α in blends:
|
||||
strat = MomentumBlendEnsembleStrategy(
|
||||
top_n=10, mom_blend=α, risk_managed=False
|
||||
)
|
||||
rets = backtest_strategy(strat)
|
||||
m = compute_metrics(rets)
|
||||
results_no_rm[α] = {"rets": rets, "metrics": m}
|
||||
print(
|
||||
f"{α:>6.2f} {m['cagr']*100:>6.1f}% {m['vol']*100:>6.1f}% "
|
||||
f"{m['sharpe']:>7.2f} {m['max_dd']*100:>6.1f}% {m['calmar']:>7.2f}"
|
||||
)
|
||||
|
||||
print("\n--- Sweep: mom_blend (risk_managed=True, dd_floor=0.40, dd_denom=0.20) ---")
|
||||
print(f"{'blend':>6s} {'CAGR':>7s} {'Vol':>7s} {'Sharpe':>7s} {'MaxDD':>7s} {'Calmar':>7s}")
|
||||
print("-" * 50)
|
||||
|
||||
results_rm = {}
|
||||
for α in blends:
|
||||
strat = MomentumBlendEnsembleStrategy(
|
||||
top_n=10, mom_blend=α, risk_managed=True
|
||||
)
|
||||
rets = backtest_strategy(strat)
|
||||
m = compute_metrics(rets)
|
||||
results_rm[α] = {"rets": rets, "metrics": m}
|
||||
print(
|
||||
f"{α:>6.2f} {m['cagr']*100:>6.1f}% {m['vol']*100:>6.1f}% "
|
||||
f"{m['sharpe']:>7.2f} {m['max_dd']*100:>6.1f}% {m['calmar']:>7.2f}"
|
||||
)
|
||||
|
||||
# --- Best config: yearly breakdown ---
|
||||
best_α = max(results_rm, key=lambda k: results_rm[k]["metrics"]["sharpe"])
|
||||
print(f"\n{'=' * 80}")
|
||||
print(f"BEST CONFIG: mom_blend={best_α:.2f} + risk_managed=True")
|
||||
print(f"{'=' * 80}")
|
||||
|
||||
best_rets = results_rm[best_α]["rets"]
|
||||
best_m = results_rm[best_α]["metrics"]
|
||||
print(f"CAGR: {best_m['cagr']*100:.1f}% Vol: {best_m['vol']*100:.1f}% "
|
||||
f"Sharpe: {best_m['sharpe']:.2f} MaxDD: {best_m['max_dd']*100:.1f}% "
|
||||
f"Calmar: {best_m['calmar']:.2f}")
|
||||
|
||||
print("\n--- Yearly returns ---")
|
||||
yr = yearly_returns(best_rets)
|
||||
for year, ret in yr.items():
|
||||
print(f" {year}: {ret*100:>+7.1f}%")
|
||||
|
||||
# --- IS/OOS validation ---
|
||||
print(f"\n{'=' * 80}")
|
||||
print("IS/OOS VALIDATION")
|
||||
print(f"{'=' * 80}")
|
||||
|
||||
strat_best = MomentumBlendEnsembleStrategy(
|
||||
top_n=10, mom_blend=best_α, risk_managed=True
|
||||
)
|
||||
|
||||
is_rets = backtest_strategy(strat_best, start="2016-04-01", end="2022-12-31")
|
||||
oos_rets = backtest_strategy(strat_best, start="2023-01-01", end="2026-05-13")
|
||||
|
||||
is_m = compute_metrics(is_rets)
|
||||
oos_m = compute_metrics(oos_rets)
|
||||
|
||||
print(f" IS (2016-2022): CAGR {is_m['cagr']*100:.1f}% Sharpe {is_m['sharpe']:.2f} MaxDD {is_m['max_dd']*100:.1f}%")
|
||||
print(f" OOS (2023-2026): CAGR {oos_m['cagr']*100:.1f}% Sharpe {oos_m['sharpe']:.2f} MaxDD {oos_m['max_dd']*100:.1f}%")
|
||||
print(f" OOS/IS CAGR ratio: {oos_m['cagr']/is_m['cagr']:.2f}")
|
||||
print(f" OOS/IS Sharpe ratio: {oos_m['sharpe']/is_m['sharpe']:.2f}")
|
||||
|
||||
# --- Bootstrap confidence intervals ---
|
||||
print(f"\n{'=' * 80}")
|
||||
print("BLOCK BOOTSTRAP (5000 resamples, block=21 days)")
|
||||
print(f"{'=' * 80}")
|
||||
|
||||
from research.trend_rider_p0 import block_bootstrap, bootstrap_summary
|
||||
|
||||
boot = block_bootstrap(best_rets, n_boot=5000, block_len=21)
|
||||
summary = bootstrap_summary(boot)
|
||||
print(summary[["p0250", "p0500", "mean", "p0500", "p0750", "p0950"]].to_string())
|
||||
print(f"\n P(Sharpe < 1.0): {(boot['sharpe'] < 1.0).mean()*100:.1f}%")
|
||||
print(f" P(Sharpe < 1.5): {(boot['sharpe'] < 1.5).mean()*100:.1f}%")
|
||||
print(f" P(MaxDD > 30%): {(boot['max_drawdown'].abs() > 0.30).mean()*100:.1f}%")
|
||||
print(f" P(MaxDD > 25%): {(boot['max_drawdown'].abs() > 0.25).mean()*100:.1f}%")
|
||||
|
||||
# --- Compare with baseline (no momentum blend) ---
|
||||
print(f"\n{'=' * 80}")
|
||||
print("COMPARISON: Baseline (α=0) vs Best (α={best_α:.2f})")
|
||||
print(f"{'=' * 80}")
|
||||
|
||||
base_m = results_rm[0.0]["metrics"]
|
||||
print(f" Baseline: CAGR {base_m['cagr']*100:.1f}% Sharpe {base_m['sharpe']:.2f} MaxDD {base_m['max_dd']*100:.1f}%")
|
||||
print(f" Best: CAGR {best_m['cagr']*100:.1f}% Sharpe {best_m['sharpe']:.2f} MaxDD {best_m['max_dd']*100:.1f}%")
|
||||
print(f" Δ Sharpe: {best_m['sharpe'] - base_m['sharpe']:+.2f}")
|
||||
print(f" Δ CAGR: {(best_m['cagr'] - base_m['cagr'])*100:+.1f}pp")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user