| """
|
| Test Bollinger Band Width Filter on 15-Minute Strategy
|
| ------------------------------------------------------
|
| Goal: Avoid trading during "Squeezes" (low volatility) to reduce whipsaws.
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| Hypothesis: Only entering when Band Width > Threshold (expansion) will improve performance.
|
| """
|
|
|
| import pandas as pd
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| import numpy as np
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| import sys
|
| import os
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| from typing import Dict, Any
|
|
|
| sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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| import config
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| from core.backtester import Backtester
|
|
|
| DATA_FILE = os.path.join(config.DATA_DIR, "btc_15min_binance.csv")
|
| DF_15M = pd.read_csv(DATA_FILE)
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| DF_15M['timestamp'] = pd.to_datetime(DF_15M['timestamp'])
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| DF_15M.set_index('timestamp', inplace=True)
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|
|
| def calculate_bb_width(df, period=20, std_dev=2.0):
|
| """Calculate Bollinger Band Width."""
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| df = df.copy()
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| sma = df['close'].rolling(period).mean()
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| std = df['close'].rolling(period).std()
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| upper = sma + (std * std_dev)
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| lower = sma - (std * std_dev)
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|
|
|
|
| df['bb_width'] = (upper - lower) / sma
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| return df['bb_width']
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|
|
| def run_strategy_with_bb_width(width_threshold):
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| df = DF_15M.copy()
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|
|
|
|
| df['fast'] = df['close'].ewm(span=10).mean()
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| df['slow'] = df['close'].ewm(span=100).mean()
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| df['bb_width'] = calculate_bb_width(df)
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|
|
|
|
| tr = (df['high'] - df['low']).rolling(14).mean()
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| df['atr'] = tr
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|
|
|
|
|
|
| signals = pd.Series(0, index=df.index)
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|
|
| long_cond = (df['fast'] > df['slow']) & (df['bb_width'] > width_threshold)
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| short_cond = (df['fast'] < df['slow']) & (df['bb_width'] > width_threshold)
|
|
|
| signals[long_cond] = 1
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| signals[short_cond] = -1
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|
|
|
|
| avg_atr_pct = (df['atr'] / df['close']).mean()
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| if pd.isna(avg_atr_pct): avg_atr_pct = 0.005
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| stop_dist = avg_atr_pct * 3.0
|
|
|
| class DummySignal:
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| def get_params(self): return {"model": f"BBW_{width_threshold}"}
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|
|
| backtester = Backtester(
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| data=df,
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| features=df,
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| signals=signals,
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| signal_model=DummySignal(),
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| e_base=1000.0,
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| leverage=2.0,
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| risk_fraction=0.02,
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| stop_distance=stop_dist,
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| tp_distance=stop_dist * 3.0,
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| d_max=0.40,
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| commission=0.0,
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| slippage=0.0001,
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| verbose=False
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| )
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|
|
| results = backtester.run()
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| m = results['metrics']
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|
|
| total_days = (df.index[-1] - df.index[0]).days
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| trades_per_day = m['num_trades'] / total_days if total_days > 0 else 0
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|
|
| return {
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| 'threshold': width_threshold,
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| 'return': m['total_return_pct'],
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| 'trades': m['num_trades'],
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| 'trades_per_day': trades_per_day,
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| 'win_rate': m['win_rate'],
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| 'sharpe': m['sharpe_ratio'],
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| 'drawdown': m['max_drawdown_pct']
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| }
|
|
|
| def test_bb_width():
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| print("\n" + "="*70)
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| print("TESTING BOLLINGER BAND WIDTH FILTER (15-Minute)")
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| print("="*70)
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| print("Baseline: MA 10/100 | Stop 3.0ATR | 2x Leverage")
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| print("-" * 70)
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| print(f"{'BB Width >':<15} {'Return':<10} {'Trades':<10} {'Win Rate':<10} {'Drawdown':<10} {'Sharpe':<10}")
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| print("-" * 70)
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|
|
|
|
| thresholds = [0.0, 0.005, 0.01, 0.015, 0.02]
|
|
|
| for t in thresholds:
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| r = run_strategy_with_bb_width(t)
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| print(f"{t:<15} {r['return']:>8.2f}% {r['trades']:>9} {r['win_rate']:>9.1f}% {r['drawdown']:>9.1f}% {r['sharpe']:>9.2f}")
|
|
|
| print("-" * 70)
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|
|
| if __name__ == "__main__":
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| test_bb_width()
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|
|
|
|
