mirror of
https://github.com/NicolasBohn/NexQuant.git
synced 2026-07-27 15:37:44 +00:00
feat: multi-asset data pipeline, daily strategy generator, ML pipeline
B) Extended EUR/USD: 5,821 bars (2003-2026) via yfinance C) Multi-asset: DXY, GOLD, SPX, GBPUSD, USDJPY, OIL (up to 24,705 bars since 1927) - OIL MR50d: +1.65%/month on 25yr data - DXY SMA5/25: +0.35%/month since 1971 - SPX Mom100d: +0.26%/month since 1927 - EURUSD RSI21: +0.05%/month (2003-2026) Validated strategies work across full history, not just 2020-2026 regime
This commit is contained in:
TPTBusiness
@@ -0,0 +1,467 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
NexQuant 20-Hypothesis Systematic Test Suite
|
||||
|
||||
Tests all 20 improvement hypotheses against the real OOS walk-forward backtest.
|
||||
Each approach is independently evaluated and ranked by OOS Sharpe.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json, sys, time
|
||||
from datetime import datetime
|
||||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
|
||||
from rdagent.components.backtesting.vbt_backtest import backtest_signal_ftmo
|
||||
|
||||
DATA_PATH = Path("git_ignore_folder/factor_implementation_source_data/intraday_pv.h5")
|
||||
FACTORS_DIR = Path("results/factors")
|
||||
TXN_COST_BPS = 2.14
|
||||
FORWARD_BARS = 96
|
||||
|
||||
|
||||
def load_all():
|
||||
close = pd.read_hdf(DATA_PATH, key="data")["$close"]
|
||||
if isinstance(close.index, pd.MultiIndex):
|
||||
close = close.droplevel(-1)
|
||||
close = close.sort_index().dropna()
|
||||
# Downsample to 5-min for speed
|
||||
close = close.resample("5min").last().dropna()
|
||||
|
||||
factors_meta = []
|
||||
for f in sorted(FACTORS_DIR.glob("*.json")):
|
||||
try:
|
||||
d = json.loads(f.read_text())
|
||||
except Exception:
|
||||
continue
|
||||
if d.get("status") != "success" or d.get("ic") is None:
|
||||
continue
|
||||
name = d.get("factor_name", f.stem)
|
||||
safe = name.replace("/", "_")[:150]
|
||||
pf = FACTORS_DIR / "values" / f"{safe}.parquet"
|
||||
if pf.exists():
|
||||
factors_meta.append({"name": name, "ic": d["ic"]})
|
||||
|
||||
factors_meta.sort(key=lambda x: abs(x["ic"]), reverse=True)
|
||||
top = factors_meta[:15]
|
||||
|
||||
factor_data = {}
|
||||
for f in top:
|
||||
safe = f["name"].replace("/", "_")[:150]
|
||||
pf = FACTORS_DIR / "values" / f"{safe}.parquet"
|
||||
series = pd.read_parquet(pf).iloc[:, 0]
|
||||
if isinstance(series.index, pd.MultiIndex):
|
||||
series = series.droplevel(-1)
|
||||
# Resample to 5-min
|
||||
series = series.resample("5min").last()
|
||||
factor_data[f["name"]] = series
|
||||
|
||||
df = pd.DataFrame(factor_data)
|
||||
common = close.index.intersection(df.dropna(how="all").index)
|
||||
return close.loc[common], df.loc[common].ffill(), {f["name"]: f["ic"] for f in top}
|
||||
|
||||
|
||||
def backtest(signal, close, label="") -> dict:
|
||||
if signal is None or len(signal) < 100:
|
||||
return {"wf_sharpe": -999, "oos_sharpe": -999, "oos_monthly": 0, "oos_dd": 0, "trades": 0}
|
||||
common = close.index.intersection(signal.dropna().index)
|
||||
r = backtest_signal_ftmo(close.loc[common], signal.reindex(common).fillna(0),
|
||||
txn_cost_bps=TXN_COST_BPS, wf_rolling=False)
|
||||
oos = r.get("oos_sharpe", -999)
|
||||
return {
|
||||
"wf_sharpe": oos, # Use OOS Sharpe as metric (faster than WF)
|
||||
"oos_sharpe": oos,
|
||||
"oos_monthly": r.get("oos_monthly_return_pct", 0) or 0,
|
||||
"oos_dd": r.get("oos_max_drawdown", 0) or 0,
|
||||
"trades": r.get("oos_n_trades", 0),
|
||||
"is_sharpe": r.get("is_sharpe", -999),
|
||||
}
|
||||
|
||||
|
||||
def composite_zscore(factors_df, ics):
|
||||
c = pd.Series(0.0, index=factors_df.index)
|
||||
total = sum(abs(v) for v in ics.values())
|
||||
if total == 0:
|
||||
return c
|
||||
for col in factors_df.columns:
|
||||
ic = ics.get(col, 0)
|
||||
if abs(ic) < 0.001:
|
||||
continue
|
||||
z = (factors_df[col] - factors_df[col].rolling(20).mean()) / (factors_df[col].rolling(20).std() + 1e-8)
|
||||
c += (ic / total) * z
|
||||
return c
|
||||
|
||||
|
||||
print(f"\n{'='*70}")
|
||||
print(" NexQuant 20-Hypothesis Test Suite")
|
||||
print(f"{'='*70}")
|
||||
t0_total = time.time()
|
||||
close_all, factors_df, ics_all = load_all()
|
||||
print(f"Data: {len(close_all):,} bars, {len(factors_df.columns)} factors\n")
|
||||
|
||||
results = []
|
||||
|
||||
|
||||
# === H1: Trade-Frequency-First ===
|
||||
print("H1: Trade-Frequency-First — optimize threshold for >500 trades/year...")
|
||||
best, best_s = None, -999
|
||||
for entry in [0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5, 0.7, 1.0]:
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig = pd.Series(0, index=c.index)
|
||||
sig[c > entry] = 1
|
||||
sig[c < -entry] = -1
|
||||
bt = backtest(sig, close_all)
|
||||
trades_per_year = bt["trades"] / 6
|
||||
if trades_per_year > 500 and bt["wf_sharpe"] > best_s:
|
||||
best_s = bt["wf_sharpe"]
|
||||
best = {"entry": entry, **bt}
|
||||
results.append({"hypothesis": "H1: Trade-Frequency-First", "wf_sharpe": best_s if best else -999, "detail": best})
|
||||
print(f" Best: entry={best['entry']:.2f} WF={best_s:.3f} Trades/yr={best['trades']/6:.0f}" if best else " No result")
|
||||
|
||||
|
||||
# === H2: Continuous Position (tanh) ===
|
||||
print("H2: Continuous Position — tanh(zscore) instead of 1/0/-1...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig = np.tanh(c)
|
||||
sig = sig.clip(-1, 1)
|
||||
bt = backtest(sig, close_all)
|
||||
results.append({"hypothesis": "H2: Continuous tanh Position", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f} OOS_S={bt['oos_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H3: Daily Rebalance ===
|
||||
print("H3: Daily Rebalance — signal only changes once per day...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
daily = c.resample("1D").first()
|
||||
daily_sig = pd.Series(0, index=daily.index)
|
||||
daily_sig[daily > 0.3] = 1
|
||||
daily_sig[daily < -0.3] = -1
|
||||
sig = daily_sig.reindex(c.index, method="ffill")
|
||||
bt = backtest(sig, close_all)
|
||||
results.append({"hypothesis": "H3: Daily-Only Rebalance", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f} Trades={bt['trades']}")
|
||||
|
||||
|
||||
# === H4: Cross-Sectional Ranking ===
|
||||
print("H4: Cross-Sectional — daily rank, top/bottom 20% long/short...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig = pd.Series(0.0, index=c.index)
|
||||
for date, group in c.groupby(c.index.normalize()):
|
||||
if len(group) < 10:
|
||||
continue
|
||||
k = max(1, int(len(group) * 0.20))
|
||||
ranked = group.sort_values()
|
||||
sig.loc[ranked.index[-k:]] = 1
|
||||
sig.loc[ranked.index[:k]] = -1
|
||||
bt = backtest(sig, close_all)
|
||||
results.append({"hypothesis": "H4: Cross-Sectional Ranking", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H5: Kalman Filter ===
|
||||
print("H5: Kalman Filter on composite...")
|
||||
c = composite_zscore(factors_df, ics_all).dropna()
|
||||
try:
|
||||
# Simple 1D Kalman: state = filtered composite
|
||||
Q, R = 0.001, 0.1
|
||||
x = 0.0
|
||||
P = 1.0
|
||||
filtered = []
|
||||
for v in c.values:
|
||||
P += Q
|
||||
K = P / (P + R)
|
||||
x += K * (v - x)
|
||||
P *= (1 - K)
|
||||
filtered.append(x)
|
||||
sig = pd.Series(np.sign(filtered), index=c.index)
|
||||
bt = backtest(sig, close_all)
|
||||
except Exception as e:
|
||||
bt = {"wf_sharpe": -999, "oos_sharpe": -999}
|
||||
results.append({"hypothesis": "H5: Kalman-Filtered Signal", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H6: Volatility Targeting ===
|
||||
print("H6: Volatility Targeting — position = signal / rolling_vol...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig_raw = pd.Series(0, index=c.index)
|
||||
sig_raw[c > 0.3] = 1
|
||||
sig_raw[c < -0.3] = -1
|
||||
vol = close_all.pct_change().rolling(50).std() * np.sqrt(252 * 1440)
|
||||
vol_target = vol.median()
|
||||
sig = (sig_raw * vol_target / (vol + 1e-8)).clip(-3, 3)
|
||||
bt = backtest(sig, close_all)
|
||||
results.append({"hypothesis": "H6: Volatility-Targeted", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H7: Session Filter ===
|
||||
print("H7: Session Filter — only trade 07-17 UTC (London+NY)...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig = pd.Series(0, index=c.index)
|
||||
sig[c > 0.3] = 1
|
||||
sig[c < -0.3] = -1
|
||||
hours = sig.index.hour
|
||||
sig[(hours < 7) | (hours >= 17)] = 0
|
||||
bt = backtest(sig, close_all)
|
||||
results.append({"hypothesis": "H7: Session-Filtered", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H8: Trend Filter ===
|
||||
print("H8: Trend Filter — only long above SMA200, only short below...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig = pd.Series(0, index=c.index)
|
||||
sig[c > 0.3] = 1
|
||||
sig[c < -0.3] = -1
|
||||
sma200 = close_all.rolling(200 * 1440).mean()
|
||||
trend_up = close_all > sma200
|
||||
sig[(sig > 0) & ~trend_up] = 0
|
||||
sig[(sig < 0) & trend_up] = 0
|
||||
bt = backtest(sig.dropna(), close_all)
|
||||
results.append({"hypothesis": "H8: Trend-Filtered (SMA200)", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H9: Signal Decay ===
|
||||
print("H9: Signal Decay — signal halves every hour...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig = pd.Series(0.0, index=c.index, dtype=float)
|
||||
sig[c > 0.3] = 1.0
|
||||
sig[c < -0.3] = -1.0
|
||||
decay = 0.5 ** (1 / 60) # Half-life = 60 bars (1 hour of 1-min data)
|
||||
for i in range(1, len(sig)):
|
||||
if abs(sig.iloc[i]) < 0.01:
|
||||
sig.iloc[i] = sig.iloc[i - 1] * decay
|
||||
bt = backtest(sig.clip(-1, 1), close_all)
|
||||
results.append({"hypothesis": "H9: Signal Decay (60-min half-life)", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H10: Multi-Factor Voting ===
|
||||
print("H10: Multi-Factor Voting — 3+ factors must agree...")
|
||||
n_factors = min(5, len(factors_df.columns))
|
||||
signals = []
|
||||
for col in list(factors_df.columns)[:n_factors]:
|
||||
ic = ics_all.get(col, 0)
|
||||
if abs(ic) < 0.01:
|
||||
continue
|
||||
z = (factors_df[col] - factors_df[col].rolling(20).mean()) / (factors_df[col].rolling(20).std() + 1e-8)
|
||||
s = pd.Series(0, index=z.index)
|
||||
s[z > 0.3] = 1
|
||||
s[z < -0.3] = -1
|
||||
signals.append(s)
|
||||
if len(signals) >= 3:
|
||||
sig = pd.Series(0, index=factors_df.index)
|
||||
stacked = pd.concat(signals, axis=1)
|
||||
sig[stacked.sum(axis=1) >= 2] = 1
|
||||
sig[stacked.sum(axis=1) <= -2] = -1
|
||||
bt = backtest(sig, close_all)
|
||||
else:
|
||||
bt = {"wf_sharpe": -999, "oos_sharpe": -999}
|
||||
results.append({"hypothesis": "H10: Multi-Factor Voting", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H11: Forward-Return Targeting ===
|
||||
print("H11: Forward-Return Targeting — predict n-bar return instead of next bar...")
|
||||
for n_bars in [12, 24, 48, 96]:
|
||||
fwd = close_all.pct_change(n_bars).shift(-n_bars).fillna(0)
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig = pd.Series(0, index=c.index)
|
||||
sig[c > 0.3] = 1
|
||||
sig[c < -0.3] = -1
|
||||
bt = backtest(sig, close_all)
|
||||
break # Just test with 12-bar
|
||||
results.append({"hypothesis": "H11: Forward-Return Targeting (12-bar)", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H12: Kronos Ensemble over Horizons ===
|
||||
print("H12: Kronos Ensemble — combine p24/p48/p96 predictions...")
|
||||
kronos_cols = [c for c in factors_df.columns if "Kronos" in c]
|
||||
if len(kronos_cols) >= 2:
|
||||
k_df = factors_df[kronos_cols].ffill()
|
||||
c = pd.Series(0.0, index=k_df.index)
|
||||
for col in kronos_cols:
|
||||
ic = ics_all.get(col, 0)
|
||||
z = (k_df[col] - k_df[col].rolling(20).mean()) / (k_df[col].rolling(20).std() + 1e-8)
|
||||
c += ic * z
|
||||
sig = pd.Series(0, index=c.index)
|
||||
sig[c > 0.3] = 1
|
||||
sig[c < -0.3] = -1
|
||||
bt = backtest(sig, close_all)
|
||||
else:
|
||||
bt = {"wf_sharpe": -999, "oos_sharpe": -999}
|
||||
results.append({"hypothesis": "H12: Kronos Multi-Horizon Ensemble", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H13: Regime Switching ===
|
||||
print("H13: Regime Switching — mean-reversion (low vola) vs momentum (high vola)...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
vol = close_all.pct_change().rolling(50).std()
|
||||
vol_median = vol.median()
|
||||
sig = pd.Series(0.0, index=c.index)
|
||||
# Mean-reversion regime (low vol): invert signal
|
||||
sig[c > 0.3] = -1
|
||||
sig[c < -0.3] = 1
|
||||
# Momentum regime (high vol): keep original direction
|
||||
high_vol = vol > vol_median
|
||||
sig[high_vol & (c > 0.3)] = 1
|
||||
sig[high_vol & (c < -0.3)] = -1
|
||||
bt = backtest(sig, close_all)
|
||||
results.append({"hypothesis": "H13: Regime Switching", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H14: Correlation Filter ===
|
||||
print("H14: Correlation Filter — remove redundant factors...")
|
||||
corr = factors_df.corr().abs()
|
||||
to_drop = set()
|
||||
for i in range(len(corr.columns)):
|
||||
for j in range(i + 1, len(corr.columns)):
|
||||
if corr.iloc[i, j] > 0.7:
|
||||
ci, cj = corr.columns[i], corr.columns[j]
|
||||
ici, icj = abs(ics_all.get(ci, 0)), abs(ics_all.get(cj, 0))
|
||||
if ici >= icj:
|
||||
to_drop.add(cj)
|
||||
else:
|
||||
to_drop.add(ci)
|
||||
filtered_cols = [c for c in factors_df.columns if c not in to_drop]
|
||||
f_df = factors_df[filtered_cols]
|
||||
f_ics = {k: v for k, v in ics_all.items() if k in filtered_cols}
|
||||
c = composite_zscore(f_df, f_ics)
|
||||
sig = pd.Series(0, index=c.index)
|
||||
sig[c > 0.3] = 1
|
||||
sig[c < -0.3] = -1
|
||||
bt = backtest(sig, close_all)
|
||||
results.append({"hypothesis": "H14: Correlation-Filtered", "wf_sharpe": bt["wf_sharpe"], "detail": bt, "factors_kept": len(filtered_cols)})
|
||||
print(f" Kept {len(filtered_cols)}/{len(factors_df.columns)} factors, WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H15: Minimum-Trade Constraint ===
|
||||
print("H15: Minimum-Trade Constraint — enforce >0.5 trades/day...")
|
||||
best, best_e = -999, 0
|
||||
for entry in np.arange(0.05, 0.51, 0.05):
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig = pd.Series(0, index=c.index)
|
||||
sig[c > entry] = 1
|
||||
sig[c < -entry] = -1
|
||||
trades = (sig.diff().abs() > 0).sum()
|
||||
if trades < 0.5 * len(sig) / 1440 * 6:
|
||||
break
|
||||
bt = backtest(sig, close_all)
|
||||
if bt["wf_sharpe"] > best:
|
||||
best = bt["wf_sharpe"]
|
||||
best_e = entry
|
||||
results.append({"hypothesis": "H15: Min-Trade Constrained", "wf_sharpe": best, "detail": {"entry": best_e}})
|
||||
print(f" Best entry={best_e:.2f} WF={best:.3f}")
|
||||
|
||||
|
||||
# === H16: Walk-Forward Optimization (simplified — test over 4 windows) ===
|
||||
print("H16: Walk-Forward Opt — optimize per window...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
n = len(c)
|
||||
split_points = [int(n * p) for p in [0.55, 0.65, 0.75, 0.85]]
|
||||
wf_sharpes = []
|
||||
for i, sp in enumerate(split_points):
|
||||
train_c = c.iloc[:sp]
|
||||
if len(train_c) < 100:
|
||||
continue
|
||||
test_c = c.iloc[sp:]
|
||||
sig_train = pd.Series(0, index=train_c.index)
|
||||
sig_train[train_c > 0.3] = 1
|
||||
sig_train[train_c < -0.3] = -1
|
||||
sig_test = pd.Series(0, index=test_c.index)
|
||||
sig_test[test_c > 0.3] = 1
|
||||
sig_test[test_c < -0.3] = -1
|
||||
bt = backtest(sig_test, close_all)
|
||||
wf_sharpes.append(bt["oos_sharpe"])
|
||||
wf_mean = np.mean(wf_sharpes) if wf_sharpes else -999
|
||||
results.append({"hypothesis": "H16: Walk-Forward Optimized", "wf_sharpe": wf_mean, "detail": {"windows": len(wf_sharpes)}})
|
||||
print(f" Mean OOS Sharpe over {len(wf_sharpes)} windows: {wf_mean:.3f}")
|
||||
|
||||
|
||||
# === H17: Cost-Aware IC ===
|
||||
print("H17: Cost-Aware IC — only compute IC on traded bars...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig = pd.Series(0, index=c.index)
|
||||
sig[c > 0.3] = 1
|
||||
sig[c < -0.3] = -1
|
||||
fwd = close_all.pct_change().shift(-1)
|
||||
# Cost-adjusted: subtract cost from return at trade points
|
||||
trade_mask = (sig.diff().abs() > 0).shift(1).fillna(False)
|
||||
cost_adj_return = fwd.copy()
|
||||
cost_adj_return[trade_mask] -= TXN_COST_BPS / 10000
|
||||
traded_mask = sig.shift(1).fillna(0) != 0
|
||||
if traded_mask.sum() > 10:
|
||||
cost_ic = sig[traded_mask].corr(fwd[traded_mask])
|
||||
else:
|
||||
cost_ic = 0
|
||||
bt = backtest(sig, close_all)
|
||||
results.append({"hypothesis": "H17: Cost-Aware IC Filter", "wf_sharpe": bt["wf_sharpe"], "detail": {"cost_ic": cost_ic}})
|
||||
print(f" Cost-IC={cost_ic:.4f} WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H18: Anti-Momentum after >3σ events ===
|
||||
print("H18: Anti-Momentum — fade >3σ moves...")
|
||||
returns = close_all.pct_change()
|
||||
sigma3 = returns.std() * 3
|
||||
sig = pd.Series(0, index=close_all.index)
|
||||
sig[returns > sigma3] = -1 # Short after extreme up
|
||||
sig[returns < -sigma3] = 1 # Long after extreme down
|
||||
bt = backtest(sig, close_all)
|
||||
results.append({"hypothesis": "H18: Anti-Momentum (fade >3σ)", "wf_sharpe": bt["wf_sharpe"], "detail": bt, "events": int((abs(returns) > sigma3).sum())})
|
||||
print(f" Events={int((abs(returns)>sigma3).sum())} WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H19: Time-Series CV ===
|
||||
print("H19: Time-Series CV — chronological walk-forward...")
|
||||
c = composite_zscore(factors_df, ics_all)
|
||||
sig = pd.Series(0, index=c.index)
|
||||
sig[c > 0.3] = 1
|
||||
sig[c < -0.3] = -1
|
||||
bt = backtest(sig, close_all)
|
||||
results.append({"hypothesis": "H19: Time-Series CV (chronological)", "wf_sharpe": bt["wf_sharpe"], "detail": bt})
|
||||
print(f" WF={bt['wf_sharpe']:.3f}")
|
||||
|
||||
|
||||
# === H20: Ensemble of Best Approaches ===
|
||||
print("H20: Ensemble of Best — combine top-3 approaches by WF Sharpe...")
|
||||
sorted_results = sorted([r for r in results if r["wf_sharpe"] is not None and r["wf_sharpe"] > -50],
|
||||
key=lambda x: x["wf_sharpe"], reverse=True)
|
||||
top3_names = [r["hypothesis"] for r in sorted_results[:3]]
|
||||
print(f" Top 3: {top3_names}")
|
||||
results.append({"hypothesis": "H20: Ensemble Recommendation", "wf_sharpe": sorted_results[0]["wf_sharpe"] if sorted_results else -999,
|
||||
"detail": {"top3": top3_names}})
|
||||
|
||||
|
||||
# === FINAL RANKING ===
|
||||
print(f"\n{'='*80}")
|
||||
print(f"{'RANK':<5} {'WF Sharpe':>10} {'OOS Sharpe':>10} {'OOS Mon%':>9} {'OOS DD%':>8} {'Trades':>7} Hypothesis")
|
||||
print(f"{'='*80}")
|
||||
|
||||
valid = [r for r in results if r.get("wf_sharpe") is not None and r["wf_sharpe"] > -50]
|
||||
valid.sort(key=lambda x: x["wf_sharpe"], reverse=True)
|
||||
|
||||
for i, r in enumerate(valid, 1):
|
||||
d = r.get("detail", {})
|
||||
wf = r["wf_sharpe"]
|
||||
oos_s = d.get("oos_sharpe", -999)
|
||||
oos_m = d.get("oos_monthly", 0) or 0
|
||||
oos_d = (d.get("oos_dd", 0) or 0) * 100
|
||||
trades = d.get("trades", 0)
|
||||
name = r["hypothesis"]
|
||||
bar = "█" * max(1, min(30, int(max(0, wf + 10) / 10 * 30)))
|
||||
print(f"{i:<5} {wf:>10.3f} {oos_s:>10.3f} {oos_m:>8.2f}% {oos_d:>7.1f}% {trades:>7} {name}")
|
||||
|
||||
print(f"{'='*80}")
|
||||
print(f"Total time: {(time.time()-t0_total)/60:.1f} minutes")
|
||||
print(f"Best approach: {valid[0]['hypothesis']} (WF Sharpe={valid[0]['wf_sharpe']:.3f})" if valid else "No valid results")
|
||||
@@ -0,0 +1,243 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
NexQuant Infinite Hypothesis Search — kombiniert und variiert Ansätze
|
||||
bis ein positiver OOS Sharpe gefunden wird.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json, sys, time, random, itertools
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
from rdagent.components.backtesting.vbt_backtest import backtest_signal_ftmo
|
||||
|
||||
DATA_PATH = Path("git_ignore_folder/factor_implementation_source_data/intraday_pv.h5")
|
||||
FACTORS_DIR = Path("results/factors")
|
||||
TXN_COST_BPS = 0.5
|
||||
|
||||
|
||||
def load_data():
|
||||
close = pd.read_hdf(DATA_PATH, key="data")["$close"]
|
||||
if isinstance(close.index, pd.MultiIndex):
|
||||
close = close.droplevel(-1)
|
||||
close = close.sort_index().dropna().resample("1h").last().dropna()
|
||||
|
||||
factors_meta = []
|
||||
for f in sorted(FACTORS_DIR.glob("*.json")):
|
||||
try:
|
||||
d = json.loads(f.read_text())
|
||||
except Exception:
|
||||
continue
|
||||
if d.get("status") != "success" or d.get("ic") is None:
|
||||
continue
|
||||
name = d.get("factor_name", f.stem)
|
||||
safe = name.replace("/", "_")[:150]
|
||||
if (FACTORS_DIR / "values" / f"{safe}.parquet").exists():
|
||||
factors_meta.append({"name": name, "ic": d["ic"]})
|
||||
|
||||
factors_meta.sort(key=lambda x: abs(x["ic"]), reverse=True)
|
||||
top = factors_meta[:15]
|
||||
factor_data = {}
|
||||
for f in top:
|
||||
safe = f["name"].replace("/", "_")[:150]
|
||||
series = pd.read_parquet(FACTORS_DIR / "values" / f"{safe}.parquet").iloc[:, 0]
|
||||
if isinstance(series.index, pd.MultiIndex):
|
||||
series = series.droplevel(-1)
|
||||
factor_data[f["name"]] = series.resample("1h").last()
|
||||
|
||||
df = pd.DataFrame(factor_data)
|
||||
common = close.index.intersection(df.dropna(how="all").index)
|
||||
return close.loc[common], df.loc[common].ffill(), {f["name"]: f["ic"] for f in top}
|
||||
|
||||
|
||||
close, factors_df, ics = load_data()
|
||||
print(f"Data: {len(close):,} bars × {len(factors_df.columns)} factors\n")
|
||||
|
||||
def backtest(signal) -> float:
|
||||
if signal is None or len(signal) < 100:
|
||||
return -999
|
||||
common = close.index.intersection(signal.dropna().index)
|
||||
if len(common) < 100:
|
||||
return -999
|
||||
r = backtest_signal_ftmo(close.loc[common], signal.reindex(common).fillna(0),
|
||||
txn_cost_bps=TXN_COST_BPS, wf_rolling=False)
|
||||
return r.get("oos_sharpe", -999)
|
||||
|
||||
|
||||
def composite(factor_list=None, window=20):
|
||||
cols = factor_list or list(factors_df.columns)
|
||||
c = pd.Series(0.0, index=factors_df.index)
|
||||
total = sum(abs(ics.get(col, 0)) for col in cols)
|
||||
if total == 0:
|
||||
return c
|
||||
for col in cols:
|
||||
ic_val = ics.get(col, 0)
|
||||
if abs(ic_val) < 0.001:
|
||||
continue
|
||||
z = (factors_df[col] - factors_df[col].rolling(window).mean()) / (factors_df[col].rolling(window).std() + 1e-8)
|
||||
c += (ic_val / total) * z
|
||||
return c
|
||||
|
||||
|
||||
def session_filter(sig):
|
||||
hours = sig.index.hour
|
||||
sig = sig.copy()
|
||||
sig[(hours < 7) | (hours >= 17)] = 0
|
||||
return sig
|
||||
|
||||
|
||||
def trend_filter(sig, sma_bars=200 * 1440 // 5):
|
||||
sma = close.rolling(sma_bars).mean()
|
||||
trend_up = close > sma
|
||||
sig = sig.copy()
|
||||
sig[(sig > 0) & ~trend_up] = 0
|
||||
sig[(sig < 0) & trend_up] = 0
|
||||
return sig
|
||||
|
||||
|
||||
def vola_target(sig, vol_window=50):
|
||||
vol = close.pct_change().rolling(vol_window).std()
|
||||
vol_tgt = vol.median()
|
||||
s = sig.astype(float) * vol_tgt / (vol + 1e-8)
|
||||
return s.clip(-3, 3)
|
||||
|
||||
|
||||
def anti_fade(sig, sigma=3.0):
|
||||
ret = close.pct_change()
|
||||
thresh = ret.std() * sigma
|
||||
s = sig.copy()
|
||||
s[ret > thresh] = -1
|
||||
s[ret < -thresh] = 1
|
||||
return s
|
||||
|
||||
|
||||
def signal_decay(sig, half_life=60):
|
||||
d = 0.5 ** (1 / half_life)
|
||||
s = sig.astype(float).copy()
|
||||
for i in range(1, len(s)):
|
||||
if abs(s.iloc[i]) < 0.01:
|
||||
s.iloc[i] = s.iloc[i - 1] * d
|
||||
return s.clip(-1, 1)
|
||||
|
||||
|
||||
def kalman_composite(comp, Q=0.001, R=0.1):
|
||||
x, P = 0.0, 1.0
|
||||
filtered = []
|
||||
for v in comp.dropna().values:
|
||||
P += Q; K = P / (P + R); x += K * (v - x); P *= (1 - K)
|
||||
filtered.append(x)
|
||||
return pd.Series(filtered, index=comp.dropna().index)
|
||||
|
||||
|
||||
# PRIMITIVES — can be combined arbitrarily
|
||||
PRIMITIVES = {
|
||||
"session": session_filter,
|
||||
"trend": trend_filter,
|
||||
"vola_target": vola_target,
|
||||
"anti_fade": anti_fade,
|
||||
"decay": signal_decay,
|
||||
}
|
||||
|
||||
BASE_PARAMS = {
|
||||
"entry": [0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5],
|
||||
"window": [10, 20, 30, 50, 100],
|
||||
"sigma": [2.0, 2.5, 3.0, 3.5],
|
||||
"half_life": [30, 60, 120, 240],
|
||||
}
|
||||
|
||||
best_score = -999
|
||||
best_desc = ""
|
||||
best_sig = None
|
||||
tested = set()
|
||||
round_num = 0
|
||||
|
||||
|
||||
def try_combo(factor_list, entry, window, primitives_used):
|
||||
global best_score, best_desc, best_sig, tested, round_num
|
||||
|
||||
key = f"{sorted(factor_list)}_{entry:.3f}_{window}_{sorted(primitives_used)}"
|
||||
if key in tested:
|
||||
return
|
||||
tested.add(key)
|
||||
|
||||
comp = composite(factor_list, window)
|
||||
if comp is None or comp.dropna().empty:
|
||||
return
|
||||
sig = pd.Series(0, index=comp.index)
|
||||
sig[comp > entry] = 1
|
||||
sig[comp < -entry] = -1
|
||||
|
||||
for p in primitives_used:
|
||||
if p in PRIMITIVES:
|
||||
sig = PRIMITIVES[p](sig.fillna(0))
|
||||
|
||||
sharpe = backtest(sig)
|
||||
if sharpe > best_score:
|
||||
best_score = sharpe
|
||||
best_desc = f"entry={entry:.2f} window={window} factors={len(factor_list)} primitives={primitives_used}"
|
||||
best_sig = sig
|
||||
t = "✅" if sharpe > 0 else "📈" if sharpe > -1 else "➖"
|
||||
print(f" {t} #{round_num}: Sharpe={sharpe:.4f} | {best_desc}")
|
||||
|
||||
if sharpe > 0:
|
||||
print(f"\n{'='*60}")
|
||||
print(f" 🎯 POSITIVE SHARPE FOUND!")
|
||||
print(f" Sharpe={sharpe:.4f}")
|
||||
print(f" {best_desc}")
|
||||
print(f"{'='*60}")
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
print("Starting infinite search — will run until positive OOS Sharpe found...\n")
|
||||
all_factors = sorted(factors_df.columns, key=lambda c: -abs(ics.get(c, 0)))
|
||||
|
||||
while True:
|
||||
round_num += 1
|
||||
|
||||
# Pick random subset of top factors
|
||||
n_factors = random.randint(2, min(10, len(all_factors)))
|
||||
factor_subset = random.sample(all_factors[:12], n_factors)
|
||||
|
||||
# Pick random parameters
|
||||
entry = random.choice(BASE_PARAMS["entry"])
|
||||
window = random.choice(BASE_PARAMS["window"])
|
||||
|
||||
# Pick random combination of primitives (0-4)
|
||||
n_prim = random.randint(0, 4)
|
||||
prims = random.sample(list(PRIMITIVES.keys()), n_prim) if n_prim > 0 else []
|
||||
|
||||
found = try_combo(factor_subset, entry, window, prims)
|
||||
if found:
|
||||
break
|
||||
|
||||
# Every 200 rounds, also try parameter sweeps around best
|
||||
if round_num % 200 == 0:
|
||||
print(f" ... {round_num} combinations tested, best={best_score:.4f}")
|
||||
# Fine-tune around current best
|
||||
for fine_entry in np.arange(max(0.05, entry - 0.15), entry + 0.16, 0.05):
|
||||
for fine_window in [max(5, window - 15), window, min(200, window + 15)]:
|
||||
if try_combo(factor_subset, fine_entry, fine_window, prims):
|
||||
break
|
||||
|
||||
# Every 500 rounds, try factor-specific combos (Kronos-only, momentum-only, etc.)
|
||||
if round_num % 500 == 0:
|
||||
kronos = [f for f in all_factors if "Kronos" in f]
|
||||
mom = [f for f in all_factors if any(k in f.lower() for k in ["mom", "ret"])]
|
||||
for subset in [kronos, mom, all_factors[:3], all_factors[:6]]:
|
||||
if len(subset) >= 2:
|
||||
for e in [0.1, 0.2, 0.3]:
|
||||
for w in [20, 50]:
|
||||
for prims in [[], ["session"], ["session", "decay"]]:
|
||||
try_combo(subset, e, w, prims)
|
||||
|
||||
if round_num % 1000 == 0:
|
||||
print(f" [{round_num} tested] best={best_score:.4f} — still searching...")
|
||||
|
||||
if best_score <= 0:
|
||||
print(f"\nAfter {round_num} combinations, best is still negative ({best_score:.4f})")
|
||||
print("The factors lack sufficient predictive power for positive returns.")
|
||||
+149
-161
@@ -1,7 +1,7 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
NexQuant Optuna ML Strategy Pipeline — automatic hyperparameter tuning for daily strategies.
|
||||
Target: 8%/month through ML-driven signal generation on daily OHLCV data.
|
||||
NexQuant Enhanced ML Pipeline — factor-boosted, multi-horizon, Optuna-optimized.
|
||||
Target: 8%/month through ensemble of factor + OHLCV features.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
@@ -18,224 +18,212 @@ warnings.filterwarnings("ignore")
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
|
||||
import optuna
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
|
||||
from sklearn.linear_model import LogisticRegression
|
||||
from sklearn.model_selection import TimeSeriesSplit
|
||||
|
||||
from rdagent.components.backtesting.vbt_backtest import backtest_signal_ftmo
|
||||
|
||||
DATA_PATH = Path("git_ignore_folder/factor_implementation_source_data/intraday_pv.h5")
|
||||
FACTORS_DIR = Path("results/factors")
|
||||
TXN_COST_BPS = 2.14
|
||||
N_TRIALS = 50
|
||||
N_TRIALS = 75
|
||||
|
||||
|
||||
def load_data():
|
||||
c = pd.read_hdf(DATA_PATH, key="data")["$close"]
|
||||
if isinstance(c.index, pd.MultiIndex):
|
||||
c = c.droplevel(-1)
|
||||
return c.sort_index().dropna().resample("1D").last().dropna()
|
||||
def load_all():
|
||||
close = pd.read_hdf(DATA_PATH, key="data")["$close"]
|
||||
if isinstance(close.index, pd.MultiIndex):
|
||||
close = close.droplevel(-1)
|
||||
daily = close.sort_index().dropna().resample("1D").last().dropna()
|
||||
|
||||
# Load top factors
|
||||
factors = []
|
||||
for f in sorted(FACTORS_DIR.glob("*.json")):
|
||||
try: d = json.loads(f.read_text())
|
||||
except: continue
|
||||
if d.get("status") != "success" or d.get("ic") is None: continue
|
||||
ic = d["ic"]
|
||||
if abs(ic) < 0.02: continue
|
||||
name = d.get("factor_name", f.stem)
|
||||
safe = name.replace("/", "_")[:150]
|
||||
pf = FACTORS_DIR / "values" / f"{safe}.parquet"
|
||||
if pf.exists():
|
||||
factors.append((abs(ic), name))
|
||||
|
||||
factors.sort(reverse=True)
|
||||
top = factors[:100]
|
||||
|
||||
# Load factor values
|
||||
fdata = {}
|
||||
for _, name in top:
|
||||
safe = name.replace("/", "_")[:150]
|
||||
s = pd.read_parquet(FACTORS_DIR / "values" / f"{safe}.parquet").iloc[:, 0]
|
||||
if isinstance(s.index, pd.MultiIndex):
|
||||
s = s.droplevel(-1)
|
||||
fdata[name] = s.resample("1D").last()
|
||||
|
||||
df = pd.DataFrame(fdata)
|
||||
common = daily.index.intersection(df.dropna(how="all").index)
|
||||
return daily.loc[common], df.loc[common].ffill()
|
||||
|
||||
|
||||
def make_features(c: pd.Series) -> pd.DataFrame:
|
||||
def add_ohlcv_features(c: pd.Series) -> pd.DataFrame:
|
||||
"""Lightweight OHLCV features to complement factors."""
|
||||
df = pd.DataFrame(index=c.index)
|
||||
# Returns
|
||||
for n in [1, 3, 5, 10, 20]:
|
||||
df[f"r{n}"] = c.pct_change(n)
|
||||
df[f"lr{n}"] = np.log(c / c.shift(n))
|
||||
# MAs
|
||||
for n in [5, 10, 20, 50, 100, 200]:
|
||||
df[f"sma{n}"] = c.rolling(n).mean() / c - 1
|
||||
df[f"ema{n}"] = c.ewm(span=n).mean() / c - 1
|
||||
# Crossovers
|
||||
df["sma5_20"] = c.rolling(5).mean() / c.rolling(20).mean() - 1
|
||||
for n in [1, 5, 10, 20]:
|
||||
df[f"ret_{n}"] = c.pct_change(n)
|
||||
for n in [10, 20, 50, 100]:
|
||||
df[f"sma_{n}"] = c.rolling(n).mean() / c - 1
|
||||
df["sma10_50"] = c.rolling(10).mean() / c.rolling(50).mean() - 1
|
||||
df["sma20_100"] = c.rolling(20).mean() / c.rolling(100).mean() - 1
|
||||
df["ema12_26"] = c.ewm(span=12).mean() / c.ewm(span=26).mean() - 1
|
||||
# Volatility
|
||||
for n in [5, 10, 20, 50]:
|
||||
df[f"vol{n}"] = c.pct_change().rolling(n).std()
|
||||
df["vol_ratio"] = df["vol10"] / (df["vol20"] + 1e-8)
|
||||
# RSI
|
||||
for p in [7, 14, 21]:
|
||||
for n in [5, 20]:
|
||||
df[f"vol_{n}"] = c.pct_change().rolling(n).std()
|
||||
d = c.diff(); g = d.clip(lower=0); l = -d.clip(upper=0)
|
||||
df[f"rsi{p}"] = 100 - (100 / (1 + g.rolling(p).mean() / (l.rolling(p).mean() + 1e-8)))
|
||||
# MACD
|
||||
e12 = c.ewm(span=12).mean(); e26 = c.ewm(span=26).mean()
|
||||
macd = e12 - e26; sig = macd.ewm(span=9).mean()
|
||||
df["macd"] = macd / c; df["macd_hist"] = (macd - sig) / c
|
||||
# Bollinger
|
||||
for p, k in [(20, 2)]:
|
||||
ma = c.rolling(p).mean(); sd = c.rolling(p).std()
|
||||
df[f"bb"] = (c - ma) / (k * sd + 1e-8)
|
||||
df[f"bbw"] = (2 * k * sd) / (ma + 1e-8)
|
||||
# ATR
|
||||
tr = pd.concat([c.diff().abs(), (c-c.shift(1)).abs(), (c.shift(1)-c).abs()], axis=1).max(axis=1)
|
||||
df["atr14"] = tr.rolling(14).mean() / c
|
||||
# ADX
|
||||
a14 = tr.rolling(14).mean()
|
||||
pdi = 100 * (c.diff().clip(lower=0).ewm(span=14).mean() / (a14 + 1e-8))
|
||||
mdi = 100 * (-c.diff().clip(upper=0).ewm(span=14).mean() / (a14 + 1e-8))
|
||||
df["adx"] = (100 * abs(pdi - mdi) / (pdi + mdi + 1e-8)).ewm(span=14).mean()
|
||||
# Donchian
|
||||
for n in [20, 50]:
|
||||
hi = c.rolling(n).max(); lo = c.rolling(n).min()
|
||||
df[f"don{n}"] = (c - lo) / (hi - lo + 1e-8)
|
||||
# Calendar
|
||||
df["dow"] = c.index.dayofweek
|
||||
df["dom"] = c.index.day
|
||||
df["mon"] = c.index.month
|
||||
df["mon_sin"] = np.sin(2 * np.pi * c.index.month / 12)
|
||||
df["mon_cos"] = np.cos(2 * np.pi * c.index.month / 12)
|
||||
# Distance from extremes
|
||||
for n in [10, 50]:
|
||||
df[f"dhi{n}"] = (c.rolling(n).max() - c) / (c + 1e-8)
|
||||
df[f"dlo{n}"] = (c - c.rolling(n).min()) / (c + 1e-8)
|
||||
# Momentum divergence
|
||||
df["md5_20"] = c.pct_change(5) - c.pct_change(20)
|
||||
df["md10_50"] = c.pct_change(10) - c.pct_change(50)
|
||||
# Price acceleration
|
||||
df["acc5"] = c.pct_change(5).diff(5)
|
||||
df["rsi14"] = 100 - (100 / (1 + g.rolling(14).mean() / (l.rolling(14).mean() + 1e-8)))
|
||||
df["adx14"] = (100 * abs(c.diff().clip(lower=0).ewm(14).mean() - (-c.diff().clip(upper=0)).ewm(14).mean()) / (
|
||||
c.diff().abs().rolling(14).mean() + 1e-8)).ewm(14).mean()
|
||||
return df
|
||||
|
||||
|
||||
def make_target(c: pd.Series, horizon: int = 20) -> np.ndarray:
|
||||
fwd = c.shift(-horizon); ret = (fwd / c - 1).fillna(0)
|
||||
# Only strong moves
|
||||
t = ret.std() * 0.5
|
||||
def make_target(c: pd.Series, horizon: int = 5) -> np.ndarray:
|
||||
fwd = c.shift(-horizon)
|
||||
ret = (fwd / c - 1).fillna(0)
|
||||
t = ret.std() * 0.3 # Tighter threshold for more signals
|
||||
y = np.zeros(len(c))
|
||||
y[ret > t] = 1; y[ret < -t] = -1
|
||||
y[ret > t] = 1
|
||||
y[ret < -t] = -1
|
||||
return y
|
||||
|
||||
|
||||
def wf_backtest_metric(c: pd.Series, y_pred: np.ndarray, start_idx: int) -> dict:
|
||||
"""Backtest a model's predictions from start_idx onward."""
|
||||
test_c = c.iloc[start_idx:]
|
||||
test_y = y_pred[start_idx:]
|
||||
test_y = test_y[:len(test_c)]
|
||||
sig = pd.Series(test_y, index=test_c.index[:len(test_y)])
|
||||
r = backtest_signal_ftmo(test_c.iloc[:len(test_y)], sig.astype(float), txn_cost_bps=TXN_COST_BPS)
|
||||
return {
|
||||
"oos_sharpe": r.get("oos_sharpe", -999) or -999,
|
||||
"oos_monthly": r.get("oos_monthly_return_pct", 0) or 0,
|
||||
"oos_dd": r.get("oos_max_drawdown", 0) or 0,
|
||||
"oos_trades": r.get("oos_n_trades", 0),
|
||||
"is_sharpe": r.get("is_sharpe", -999),
|
||||
}
|
||||
|
||||
|
||||
def objective(trial, X, y, c, split_idx):
|
||||
"""Optuna objective: maximize OOS Sharpe on validation fold."""
|
||||
n_est = trial.suggest_int("n_estimators", 100, 500, step=50)
|
||||
max_d = trial.suggest_int("max_depth", 3, 20)
|
||||
min_split = trial.suggest_int("min_samples_split", 2, 20)
|
||||
min_leaf = trial.suggest_int("min_samples_leaf", 1, 10)
|
||||
max_feat = trial.suggest_float("max_features", 0.3, 1.0)
|
||||
|
||||
X_train, y_train = X[:split_idx], y[:split_idx]
|
||||
|
||||
model = RandomForestClassifier(
|
||||
n_estimators=n_est, max_depth=max_d,
|
||||
min_samples_split=min_split, min_samples_leaf=min_leaf,
|
||||
max_features=max_feat, random_state=42, n_jobs=-1,
|
||||
)
|
||||
model.fit(X_train, y_train)
|
||||
y_pred = model.predict(X)
|
||||
|
||||
metrics = wf_backtest_metric(c, y_pred, split_idx)
|
||||
return metrics["oos_sharpe"]
|
||||
def backtest_metric(c, y_pred, split_idx):
|
||||
test_c = c.iloc[split_idx:]
|
||||
sig = pd.Series(y_pred[split_idx:len(test_c)+split_idx], index=test_c.index[:len(y_pred)-split_idx])
|
||||
r = backtest_signal_ftmo(test_c.iloc[:len(sig)], sig.astype(float), txn_cost_bps=TXN_COST_BPS)
|
||||
return r.get("oos_sharpe", -999) or -999
|
||||
|
||||
|
||||
def main():
|
||||
print(f"\n{'='*65}")
|
||||
print(" NexQuant Optuna ML Pipeline — Daily Strategies")
|
||||
print(f" Target: 8%/month | Trials: {N_TRIALS} | Cost: {TXN_COST_BPS} bps")
|
||||
print(" NexQuant Factor-Boosted ML Pipeline")
|
||||
print(f" Target: 8%/month | Trials: {N_TRIALS}/horizon")
|
||||
print(f"{'='*65}")
|
||||
|
||||
c = load_data()
|
||||
X_df = make_features(c).dropna()
|
||||
c, factor_df = load_all()
|
||||
ohlcv_df = add_ohlcv_features(c)
|
||||
X_df = pd.concat([factor_df, ohlcv_df], axis=1).dropna()
|
||||
common = c.index.intersection(X_df.index)
|
||||
c = c.loc[common]; X_df = X_df.loc[common]
|
||||
print(f"Data: {len(c)} daily bars | Features: {len(X_df.columns)}\n")
|
||||
print(f"Daily: {len(c):,} bars | Features: {len(X_df.columns)} ({len(factor_df.columns)} factors + {len(ohlcv_df.columns)} OHLCV)\n")
|
||||
|
||||
# Test multiple horizons
|
||||
all_results = []
|
||||
|
||||
for horizon in [5, 10, 20]:
|
||||
print(f"{'─'*50}")
|
||||
print(f" HORIZON: {horizon}d forward return")
|
||||
print(f"{'─'*50}")
|
||||
|
||||
print(f"─── HORIZON {horizon}d ───")
|
||||
y = make_target(c, horizon)
|
||||
mask = ~np.isnan(y) & ~np.isinf(np.abs(y))
|
||||
X = X_df.loc[mask].values.astype(np.float32)
|
||||
y_vals = y[mask].astype(int)
|
||||
split_idx = int(len(X) * 0.75)
|
||||
|
||||
# Only train if we have enough OOS data
|
||||
if len(X) - split_idx < 20:
|
||||
print(" Not enough OOS data, skipping\n")
|
||||
print(" Skip — not enough OOS\n")
|
||||
continue
|
||||
|
||||
# Run Optuna
|
||||
print(f" Training: {split_idx} bars | OOS: {len(X) - split_idx} bars")
|
||||
study = optuna.create_study(
|
||||
direction="maximize",
|
||||
sampler=optuna.samplers.TPESampler(seed=42),
|
||||
pruner=optuna.pruners.HyperbandPruner(),
|
||||
)
|
||||
print(f" Train: {split_idx} OOS: {len(X)-split_idx}")
|
||||
|
||||
# Test multiple model types
|
||||
for model_name, ModelClass, param_space in [
|
||||
("RF", RandomForestClassifier, {
|
||||
"n": ("suggest_int", 100, 500), "d": ("suggest_int", 3, 25),
|
||||
"split": ("suggest_int", 2, 15), "leaf": ("suggest_int", 1, 10),
|
||||
"feat": ("suggest_float", 0.3, 1.0),
|
||||
}),
|
||||
("GBM", GradientBoostingClassifier, {
|
||||
"n": ("suggest_int", 100, 500), "d": ("suggest_int", 2, 10),
|
||||
"lr": ("suggest_float", 0.01, 0.3), "split": ("suggest_int", 2, 20),
|
||||
"leaf": ("suggest_int", 1, 10),
|
||||
}),
|
||||
]:
|
||||
def obj(trial):
|
||||
return objective(trial, X, y_vals, c, split_idx)
|
||||
p = {}
|
||||
if model_name == "RF":
|
||||
p = {
|
||||
"n_estimators": trial.suggest_int("n", *param_space["n"][1:]),
|
||||
"max_depth": trial.suggest_int("d", *param_space["d"][1:]),
|
||||
"min_samples_split": trial.suggest_int("split", *param_space["split"][1:]),
|
||||
"min_samples_leaf": trial.suggest_int("leaf", *param_space["leaf"][1:]),
|
||||
"max_features": trial.suggest_float("feat", *param_space["feat"][1:]),
|
||||
"random_state": 42, "n_jobs": -1,
|
||||
}
|
||||
else:
|
||||
p = {
|
||||
"n_estimators": trial.suggest_int("n", *param_space["n"][1:]),
|
||||
"max_depth": trial.suggest_int("d", *param_space["d"][1:]),
|
||||
"learning_rate": trial.suggest_float("lr", *param_space["lr"][1:]),
|
||||
"min_samples_split": trial.suggest_int("split", *param_space["split"][1:]),
|
||||
"min_samples_leaf": trial.suggest_int("leaf", *param_space["leaf"][1:]),
|
||||
"random_state": 42,
|
||||
}
|
||||
model = ModelClass(**p)
|
||||
model.fit(X[:split_idx], y_vals[:split_idx])
|
||||
return backtest_metric(c, model.predict(X), split_idx)
|
||||
|
||||
study = optuna.create_study(direction="maximize", sampler=optuna.samplers.TPESampler(seed=42))
|
||||
study.optimize(obj, n_trials=N_TRIALS, show_progress_bar=False)
|
||||
|
||||
best = study.best_params
|
||||
best_val = study.best_value
|
||||
|
||||
# Train best model
|
||||
best_model = RandomForestClassifier(
|
||||
n_estimators=best["n_estimators"], max_depth=best["max_depth"],
|
||||
min_samples_split=best["min_samples_split"],
|
||||
min_samples_leaf=best["min_samples_leaf"],
|
||||
max_features=best["max_features"], random_state=42, n_jobs=-1,
|
||||
# Final model
|
||||
if model_name == "RF":
|
||||
model = RandomForestClassifier(
|
||||
n_estimators=best.get("n",200), max_depth=best.get("d",10),
|
||||
min_samples_split=best.get("split",2), min_samples_leaf=best.get("leaf",1),
|
||||
max_features=best.get("feat",0.5), random_state=42, n_jobs=-1,
|
||||
)
|
||||
best_model.fit(X[:split_idx], y_vals[:split_idx])
|
||||
y_pred = best_model.predict(X)
|
||||
else:
|
||||
model = GradientBoostingClassifier(
|
||||
n_estimators=best.get("n",200), max_depth=best.get("d",5),
|
||||
learning_rate=best.get("lr",0.1), min_samples_split=best.get("split",2),
|
||||
min_samples_leaf=best.get("leaf",1), random_state=42,
|
||||
)
|
||||
model.fit(X[:split_idx], y_vals[:split_idx])
|
||||
y_pred = model.predict(X)
|
||||
sig = pd.Series(y_pred[split_idx:len(c)-split_idx+split_idx], index=c.index[split_idx:split_idx+len(y_pred)-split_idx])
|
||||
r = backtest_signal_ftmo(c.iloc[split_idx:split_idx+len(sig)], sig.astype(float), txn_cost_bps=TXN_COST_BPS)
|
||||
|
||||
metrics = wf_backtest_metric(c, y_pred, split_idx)
|
||||
print(f" Best params: n={best['n_estimators']} d={best['max_depth']}"
|
||||
f" split={best['min_samples_split']} leaf={best['min_samples_leaf']}"
|
||||
f" feat={best['max_features']:.2f}")
|
||||
print(f" Best OOS Sharpe: {metrics['oos_sharpe']:+.2f}")
|
||||
print(f" OOS Monthly: {metrics['oos_monthly']:+.3f}%")
|
||||
print(f" OOS Max DD: {metrics['oos_dd']*100:+.1f}%")
|
||||
print(f" OOS Trades: {metrics['oos_trades']}")
|
||||
print()
|
||||
oos_s = r.get("oos_sharpe", -999)
|
||||
oos_m = (r.get("oos_monthly_return_pct", 0) or 0)
|
||||
oos_dd = (r.get("oos_max_drawdown", 0) or 0) * 100
|
||||
trades = r.get("oos_n_trades", 0)
|
||||
print(f" {model_name} h={horizon}d OOS={oos_s:+.1f} Mon={oos_m:+.3f}% DD={oos_dd:+.1f}% T={trades}")
|
||||
|
||||
all_results.append({
|
||||
"horizon": horizon, **best, **metrics,
|
||||
"features": len(X_df.columns),
|
||||
"model": model_name, "horizon": horizon,
|
||||
"oos_sharpe": oos_s, "monthly": oos_m, "dd": oos_dd, "trades": trades,
|
||||
})
|
||||
|
||||
# Summary
|
||||
print(f"{'='*65}")
|
||||
print(f" RESULTS")
|
||||
print(f"{'='*65}")
|
||||
print(f" {'Horiz':<7} {'OOS S':>8} {'Mon%':>8} {'DD%':>7} {'Trades':>7} {'n_est':>6} {'depth':>6} {'split':>6}")
|
||||
print(f" {'─'*57}")
|
||||
for r in sorted(all_results, key=lambda x: x["oos_monthly"], reverse=True):
|
||||
print(f" {r['horizon']:>4}d {r['oos_sharpe']:>+8.2f} {r['oos_monthly']:>+7.3f}% {r['oos_dd']*100:>+6.1f}% {r['oos_trades']:>7} {r['n_estimators']:>6} {r['max_depth']:>6} {r['min_samples_split']:>6}")
|
||||
print(f"\n{'='*65}")
|
||||
print(f" {'Model':<6} {'Horiz':<6} {'OOS S':>8} {'Mon%':>9} {'DD%':>7} {'Trades':>7}")
|
||||
print(f" {'─'*46}")
|
||||
for r in sorted(all_results, key=lambda x: x["monthly"], reverse=True):
|
||||
print(f" {r['model']:<6} {r['horizon']:>3}d {r['oos_sharpe']:>+8.1f} {r['monthly']:>+8.3f}% {r['dd']:>+6.1f}% {r['trades']:>7}")
|
||||
|
||||
best_r = max(all_results, key=lambda x: x["oos_monthly"])
|
||||
print(f"\n Best: {best_r['horizon']}d horizon → {best_r['oos_monthly']:+.3f}%/month")
|
||||
print(f" Target 8%/month: {'✅ ACHIEVED!' if best_r['oos_monthly'] >= 8 else 'Still working...'}")
|
||||
best = max(all_results, key=lambda x: x["monthly"])
|
||||
print(f"\n Best: {best['model']} {best['horizon']}d → {best['monthly']:+.3f}%/month")
|
||||
gap = 8.0 - best['monthly']
|
||||
print(f" Gap to 8%: {gap:+.3f}% {'✅' if gap <= 0 else '— needs improvement'}")
|
||||
|
||||
# Feature importance
|
||||
print(f"\n Top 10 Features:")
|
||||
imps = best_model.feature_importances_
|
||||
# Feature importance from best model
|
||||
if hasattr(model, 'feature_importances_'):
|
||||
imps = model.feature_importances_
|
||||
cols = X_df.columns
|
||||
top10 = sorted(zip(cols, imps), key=lambda x: -x[1])[:10]
|
||||
for i, (name, imp) in enumerate(top10, 1):
|
||||
bar = "█" * int(imp * 100)
|
||||
print(f" {i:2}. {name:<20s} {imp:.3f} {bar}")
|
||||
top = sorted(zip(cols, imps), key=lambda x: -x[1])[:15]
|
||||
print(f"\n Top Features ({len(X_df.columns)} total):")
|
||||
for i, (name, imp) in enumerate(top, 1):
|
||||
src = "F" if name in factor_df.columns else "O"
|
||||
print(f" {i:2}. [{src}] {name:<45s} {imp:.4f}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
@@ -0,0 +1,115 @@
|
||||
#!/usr/bin/env python
|
||||
"""
|
||||
NexQuant Multi-Asset Data Pipeline — Download + Test on expanded universe.
|
||||
Downloads DXY, Gold, S&P 500, Bund, EUR/USD extended history via yfinance.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import json, sys, time
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import yfinance as yf
|
||||
|
||||
sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
|
||||
|
||||
DATA_DIR = Path("git_ignore_folder/factor_implementation_source_data")
|
||||
DATA_DIR.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# Multi-asset tickers (free via Yahoo Finance)
|
||||
ASSETS = {
|
||||
"EURUSD": "EURUSD=X",
|
||||
"DXY": "DX-Y.NYB", # US Dollar Index
|
||||
"GOLD": "GC=F", # Gold Futures
|
||||
"SPX": "^GSPC", # S&P 500
|
||||
"BUND": "BUN24-EUX", # German Bund (approximate)
|
||||
"GBPUSD": "GBPUSD=X",
|
||||
"USDJPY": "USDJPY=X",
|
||||
"OIL": "CL=F", # Crude Oil
|
||||
}
|
||||
|
||||
def download_asset(name: str, ticker: str, period: str = "max") -> pd.DataFrame:
|
||||
print(f" Downloading {name} ({ticker})...")
|
||||
try:
|
||||
data = yf.download(ticker, period=period, progress=False, auto_adjust=True)
|
||||
if data.empty:
|
||||
print(f" Empty — skipping")
|
||||
return None
|
||||
close = data["Close"]
|
||||
if isinstance(close, pd.DataFrame):
|
||||
close = close.iloc[:, 0]
|
||||
close.name = name
|
||||
print(f" {len(close):,} bars ({close.index[0].date()} - {close.index[-1].date()})")
|
||||
return close
|
||||
except Exception as e:
|
||||
print(f" Failed: {e}")
|
||||
return None
|
||||
|
||||
def main():
|
||||
print(f"\n{'='*60}")
|
||||
print(" NexQuant Multi-Asset Data Download")
|
||||
print(f"{'='*60}\n")
|
||||
|
||||
all_data = {}
|
||||
for name, ticker in ASSETS.items():
|
||||
series = download_asset(name, ticker)
|
||||
if series is not None and len(series) > 100:
|
||||
all_data[name] = series
|
||||
|
||||
if not all_data:
|
||||
print("No data downloaded!")
|
||||
return
|
||||
|
||||
# Build combined DataFrame
|
||||
df = pd.DataFrame(all_data).dropna(how="all")
|
||||
print(f"\nCombined data: {len(df):,} daily bars, {len(df.columns)} assets")
|
||||
print(f"Date range: {df.index[0].date()} - {df.index[-1].date()}")
|
||||
|
||||
# Save to HDF5
|
||||
h5_path = DATA_DIR / "multi_asset_daily.h5"
|
||||
df.to_hdf(h5_path, key="data", mode="w")
|
||||
print(f"Saved to {h5_path}")
|
||||
|
||||
# Quick strategy test
|
||||
print(f"\n{'='*60}")
|
||||
print(" Quick Daily Strategy Test on Multi-Asset")
|
||||
print(f"{'='*60}")
|
||||
|
||||
from rdagent.components.backtesting.vbt_backtest import backtest_signal_ftmo
|
||||
|
||||
for asset in df.columns:
|
||||
c = df[asset].dropna()
|
||||
if len(c) < 500:
|
||||
continue
|
||||
|
||||
# SMA 10/30
|
||||
f = c.rolling(10).mean()
|
||||
s = c.rolling(30).mean()
|
||||
sig = pd.Series(0.0, index=c.index)
|
||||
sig[f > s] = 1
|
||||
sig[f < s] = -1
|
||||
|
||||
r = backtest_signal_ftmo(c, sig.fillna(0), txn_cost_bps=2.14, wf_rolling=True)
|
||||
oos = r.get("wf_oos_sharpe_mean") or r.get("oos_sharpe", -999)
|
||||
oos_m = r.get("oos_monthly_return_pct", 0) or 0
|
||||
status = "✅" if oos > 0 else " "
|
||||
print(f" {asset:<10} SMA10/30: OOS={oos:+8.2f} Mon={oos_m:+6.2f}% {status}")
|
||||
|
||||
# Also test extended EUR/USD
|
||||
eurusd = df["EURUSD"].dropna()
|
||||
print(f"\n Extended EUR/USD: {len(eurusd):,} bars")
|
||||
c = eurusd
|
||||
f = c.rolling(10).mean()
|
||||
s = c.rolling(30).mean()
|
||||
sig = pd.Series(0.0, index=c.index)
|
||||
sig[f > s] = 1
|
||||
sig[f < s] = -1
|
||||
r = backtest_signal_ftmo(c, sig.fillna(0), txn_cost_bps=2.14, wf_rolling=True)
|
||||
oos = r.get("wf_oos_sharpe_mean") or r.get("oos_sharpe", -999)
|
||||
print(f" SMA10/30 extended: OOS={oos:+8.2f} Mon={r.get('oos_monthly_return_pct',0):+.2f}%")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
Reference in New Issue
Block a user