feat: finalize portfolio system and quantitative validation- Finalized MA_Crossover(30,100) and TrendFiltered_MA(30,100,ADX=15)
- 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
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scripts/research/wf_optimize_strategies.py
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257
scripts/research/wf_optimize_strategies.py
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# scripts/research/wf_optimize_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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import seaborn as sns
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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.optimization.opt_moving_average import MACrossoverOptimization
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from src.strategies.optimization.opt_trend_filtered import TrendFilteredMAOptimization
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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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COMMISSION = 0.001
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SLIPPAGE = 0.0005
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OUTPUT_DIR = Path(__file__).parent / "output/wf_optimize"
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OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
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TOP_N_PRINT = 15
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OPTIMIZERS = [
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MACrossoverOptimization,
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TrendFilteredMAOptimization,
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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(
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symbol=SYMBOL,
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timeframe=TIMEFRAME,
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use_cache=True,
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)
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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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def run_wf(data: pd.DataFrame, strategy_factory) -> dict:
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capital = INITIAL_CAPITAL
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window_returns = []
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worst_dd = 0.0
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windows = 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=strategy_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=COMMISSION,
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slippage=SLIPPAGE,
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)
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res = engine.run(test_data)
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windows += 1
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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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capital *= (1 + res["total_return_pct"] / 100)
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start += timedelta(days=STEP_DAYS)
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wr = np.array(window_returns, dtype=float)
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return {
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"windows": windows,
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"final_capital": round(capital, 2),
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"return_mean": round(wr.mean(), 4),
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"return_median": round(np.median(wr), 4),
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"max_dd_worst": round(worst_dd, 4),
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"win_rate": round((wr > 0).mean(), 4),
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}
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def score_row(row):
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dd_penalty = abs(row["max_dd_worst"]) / 100.0
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return (
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(row["final_capital"] / INITIAL_CAPITAL)
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+ row["win_rate"] * 0.5
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- dd_penalty * 1.5
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)
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# --------------------------------------------------
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def run():
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data = load_data()
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rows = []
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for optimizer in OPTIMIZERS:
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log.info(f"🧪 Optimizing {optimizer.name}")
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for params in optimizer.parameter_grid():
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def factory(p=params):
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return optimizer.build_strategy(p)
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metrics = run_wf(data, factory)
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row = {
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"strategy": optimizer.name,
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**params,
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**metrics,
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}
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row["score"] = score_row(row)
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rows.append(row)
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df = pd.DataFrame(rows)
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df = df.sort_values("score", ascending=False)
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# --------------------------------------------------
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# SAVE CSV
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# --------------------------------------------------
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csv_path = OUTPUT_DIR / "wf_optimization_results.csv"
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df.to_csv(csv_path, index=False)
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log.info(f"✅ Saved CSV: {csv_path}")
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# --------------------------------------------------
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# PRINT TOP CONFIGS
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# --------------------------------------------------
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print("\n" + "=" * 100)
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print("🏆 TOP CONFIGS (by score)")
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print("=" * 100)
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for strat in df["strategy"].unique():
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top = df[df["strategy"] == strat].head(TOP_N_PRINT)
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print(f"\n--- {strat} ---")
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print(
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top[
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[
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"fast_period",
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"slow_period",
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"adx_threshold",
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"final_capital",
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"return_mean",
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"win_rate",
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"max_dd_worst",
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"score",
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]
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].to_string(index=False)
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)
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# --------------------------------------------------
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# 📊 VISUALIZATIONS
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# --------------------------------------------------
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sns.set(style="whitegrid")
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# A) Scatter: Score vs DD
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plt.figure(figsize=(10, 6))
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sns.scatterplot(
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data=df,
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x="max_dd_worst",
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y="score",
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hue="strategy",
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size="final_capital",
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sizes=(40, 200),
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alpha=0.7,
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)
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plt.title("Score vs Max Drawdown")
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plt.tight_layout()
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plt.savefig(OUTPUT_DIR / "scatter_score_vs_dd.png")
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plt.close()
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# B) Boxplot: Score by Strategy
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plt.figure(figsize=(8, 5))
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sns.boxplot(data=df, x="strategy", y="score")
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plt.title("Score Distribution by Strategy")
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plt.tight_layout()
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plt.savefig(OUTPUT_DIR / "boxplot_score_by_strategy.png")
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plt.close()
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# C) Heatmaps
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for strat in df["strategy"].unique():
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sub = df[df["strategy"] == strat]
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if "adx_threshold" in sub.columns and sub["adx_threshold"].notna().any():
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for adx in sorted(sub["adx_threshold"].dropna().unique()):
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pivot = sub[sub["adx_threshold"] == adx].pivot(
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index="fast_period",
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columns="slow_period",
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values="final_capital",
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)
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plt.figure(figsize=(10, 6))
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sns.heatmap(pivot, annot=False, cmap="viridis")
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plt.title(f"{strat} – Final Capital (ADX={adx})")
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plt.tight_layout()
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plt.savefig(
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OUTPUT_DIR / f"heatmap_{strat}_adx_{int(adx)}.png"
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)
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plt.close()
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else:
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pivot = sub.pivot(
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index="fast_period",
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columns="slow_period",
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values="final_capital",
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)
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plt.figure(figsize=(10, 6))
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sns.heatmap(pivot, annot=False, cmap="viridis")
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plt.title(f"{strat} – Final Capital")
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plt.tight_layout()
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plt.savefig(OUTPUT_DIR / f"heatmap_{strat}.png")
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plt.close()
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if __name__ == "__main__":
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run()
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