DaM
f85c522f22
- Implemented portfolio engine with risk-based allocation (50/50) - Added equity-based metrics for system-level evaluation - Validated portfolio against standalone strategies - Reduced max drawdown and volatility at system level - Quantitative decision closed before paper trading phase
276 lines
8.0 KiB
Python
276 lines
8.0 KiB
Python
# scripts/research/wf_compare_strategies.py
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import os
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import sys
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from pathlib import Path
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from dotenv import load_dotenv
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from datetime import timedelta
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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from src.core.engine import Engine
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from src.data.storage import StorageManager
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from src.risk.sizing.percent_risk import PercentRiskSizer
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from src.risk.stops.trailing_stop import TrailingStop
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from src.strategies.moving_average import MovingAverageCrossover
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from src.strategies.trend_filtered import TrendFilteredMACrossover
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from src.strategies.breakout import DonchianBreakout
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from src.strategies.mean_reversion import RSIMeanReversion
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from src.utils.logger import log
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# --------------------------------------------------
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# CONFIG
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# --------------------------------------------------
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SYMBOL = "BTC/USDT"
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TIMEFRAME = "1h"
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TRAIN_DAYS = 120
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TEST_DAYS = 30
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STEP_DAYS = 30
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INITIAL_CAPITAL = 10_000
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RISK = PercentRiskSizer(0.01)
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STOP = TrailingStop(0.02)
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OUTPUT_DIR = Path(__file__).parent / "output/wf_strategies"
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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# --------------------------------------------------
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# STRATEGIES
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# --------------------------------------------------
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STRATEGIES = {
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"MA_Crossover": lambda: MovingAverageCrossover(20, 50, "ema", False),
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"TrendFiltered_MA": lambda: TrendFilteredMACrossover(20, 50, "ema", 20),
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"Donchian": lambda: DonchianBreakout(20),
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"RSI_Reversion": lambda: RSIMeanReversion(14, 30, 70),
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}
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# --------------------------------------------------
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def setup_env():
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env_path = Path(__file__).parent.parent.parent / "config" / "secrets.env"
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load_dotenv(env_path)
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def load_data():
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setup_env()
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storage = StorageManager(
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db_host=os.getenv("DB_HOST"),
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db_port=int(os.getenv("DB_PORT", 5432)),
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db_name=os.getenv("DB_NAME"),
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db_user=os.getenv("DB_USER"),
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db_password=os.getenv("DB_PASSWORD"),
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)
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data = storage.load_ohlcv(SYMBOL, TIMEFRAME, use_cache=True)
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storage.close()
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if data.empty:
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raise RuntimeError("No data loaded")
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return data
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# --------------------------------------------------
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def compute_percentiles(curves, n_points=100):
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x_common = np.linspace(0, 1, n_points)
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interpolated = []
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for curve in curves:
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x = np.linspace(0, 1, len(curve))
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interpolated.append(np.interp(x_common, x, curve.values))
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arr = np.vstack(interpolated)
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return (
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x_common,
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np.percentile(arr, 10, axis=0),
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np.percentile(arr, 50, axis=0),
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np.percentile(arr, 90, axis=0),
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)
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# --------------------------------------------------
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def run():
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log.info("🧪 WF MULTI-STRATEGY TEST (FULL VISUAL + STATS)")
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data = load_data()
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results_summary = []
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all_accumulated = {}
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all_dispersion_percentiles = {}
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for name, strat_factory in STRATEGIES.items():
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log.info(f"▶ Strategy: {name}")
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capital = INITIAL_CAPITAL
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equity_accumulated = []
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dispersion_curves = []
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window_returns = []
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worst_dd = 0.0
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start = data.index[0]
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while True:
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train_end = start + timedelta(days=TRAIN_DAYS)
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test_end = train_end + timedelta(days=TEST_DAYS)
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if test_end > data.index[-1]:
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break
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test_data = data.loc[train_end:test_end]
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engine = Engine(
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strategy=strat_factory(),
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initial_capital=capital,
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position_sizer=RISK,
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stop_loss=STOP,
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commission=0.001,
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slippage=0.0005,
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)
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res = engine.run(test_data)
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# ---- accumulated
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capital *= (1 + res["total_return_pct"] / 100)
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equity_accumulated.append((res["timestamps"][-1], capital))
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# ---- dispersion (normalized reset)
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eq = pd.Series(res["equity_curve"], index=res["timestamps"])
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dispersion_curves.append(eq / eq.iloc[0])
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window_returns.append(res["total_return_pct"])
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worst_dd = min(worst_dd, res["max_drawdown_pct"])
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start += timedelta(days=STEP_DAYS)
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# ===============================
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# STORE ACCUMULATED
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# ===============================
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acc_df = pd.DataFrame(equity_accumulated, columns=["time", "capital"]).set_index("time")
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all_accumulated[name] = acc_df
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# ===============================
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# 1️⃣ DISPERSION – RAW (OLD)
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# ===============================
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plt.figure(figsize=(12, 5))
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for eq in dispersion_curves:
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plt.plot(eq.index, eq.values, alpha=0.3)
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plt.title(f"WF Equity Dispersion (Raw) – {name}")
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plt.ylabel("Normalized Equity")
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plt.grid(alpha=0.3)
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path = OUTPUT_DIR / f"wf_equity_dispersion_raw_{name}.png"
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plt.tight_layout()
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plt.savefig(path)
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plt.close()
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# ===============================
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# 2️⃣ DISPERSION – PERCENTILES (NEW)
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# ===============================
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x, p10, p50, p90 = compute_percentiles(dispersion_curves)
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all_dispersion_percentiles[name] = (x, p10, p50, p90)
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plt.figure(figsize=(12, 5))
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plt.plot(x, p50, label="P50", linewidth=2)
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plt.fill_between(x, p10, p90, alpha=0.3, label="P10–P90")
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plt.title(f"WF Equity Dispersion (Percentiles) – {name}")
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plt.ylabel("Normalized Equity")
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plt.xlabel("Window Progress")
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plt.legend()
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plt.grid(alpha=0.3)
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path = OUTPUT_DIR / f"wf_equity_dispersion_percentiles_{name}.png"
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plt.tight_layout()
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plt.savefig(path)
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plt.close()
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# ===============================
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# 3️⃣ RETURN DISTRIBUTION (OLD)
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# ===============================
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plt.figure(figsize=(10, 5))
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plt.hist(window_returns, bins=20, density=True, alpha=0.7)
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plt.title(f"WF Return Distribution – {name}")
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plt.xlabel("Return per window (%)")
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plt.ylabel("Density")
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plt.grid(alpha=0.3)
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path = OUTPUT_DIR / f"wf_return_distribution_{name}.png"
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plt.tight_layout()
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plt.savefig(path)
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plt.close()
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# ===============================
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# SUMMARY
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# ===============================
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results_summary.append({
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"strategy": name,
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"windows": len(window_returns),
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"return_mean": round(np.mean(window_returns), 2),
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"return_median": round(np.median(window_returns), 2),
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"max_dd_worst": round(worst_dd, 2),
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"final_capital": round(capital, 2),
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})
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# ===============================
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# 4️⃣ ACCUMULATED EQUITY – ALL
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# ===============================
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plt.figure(figsize=(13, 6))
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for name, acc_df in all_accumulated.items():
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plt.plot(acc_df.index, acc_df["capital"], linewidth=2, label=name)
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plt.title("WF Accumulated Equity – Strategy Comparison")
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plt.ylabel("Capital")
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plt.legend()
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plt.grid(alpha=0.3)
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path = OUTPUT_DIR / "wf_equity_accumulated_ALL.png"
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plt.tight_layout()
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plt.savefig(path)
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plt.close()
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# ===============================
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# 5️⃣ DISPERSION COMPARISON – P50
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# ===============================
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plt.figure(figsize=(12, 6))
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for name, (x, _, p50, _) in all_dispersion_percentiles.items():
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plt.plot(x, p50, linewidth=2, label=name)
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plt.title("WF Dispersion Comparison (Median – P50)")
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plt.ylabel("Normalized Equity")
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plt.xlabel("Window Progress")
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plt.legend()
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plt.grid(alpha=0.3)
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path = OUTPUT_DIR / "wf_dispersion_comparison_P50.png"
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plt.tight_layout()
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plt.savefig(path)
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plt.close()
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# ===============================
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# FINAL TABLE
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# ===============================
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summary_df = pd.DataFrame(results_summary).set_index("strategy")
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print("\n" + "=" * 80)
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print("📊 WF SUMMARY (ACCUMULATED)")
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print("=" * 80)
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print(summary_df)
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print("=" * 80)
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if __name__ == "__main__":
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run()
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