209 lines
8.5 KiB
Python
209 lines
8.5 KiB
Python
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#!/usr/bin/env python3
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"""
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Compare Trade Timing Between Strategies
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=======================================
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This script analyzes the timing differences between the original and incremental
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strategies to understand why there's still a performance difference despite
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having similar exit conditions.
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"""
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import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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from datetime import datetime, timedelta
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def load_and_compare_trades():
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"""Load and compare trade timing between strategies."""
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print("🔍 COMPARING TRADE TIMING BETWEEN STRATEGIES")
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print("=" * 80)
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# Load original strategy trades
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original_file = "../results/trades_15min(15min)_ST3pct.csv"
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incremental_file = "../results/trades_incremental_15min(15min)_ST3pct.csv"
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print(f"📊 Loading original trades from: {original_file}")
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original_df = pd.read_csv(original_file)
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original_df['entry_time'] = pd.to_datetime(original_df['entry_time'])
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original_df['exit_time'] = pd.to_datetime(original_df['exit_time'])
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print(f"📊 Loading incremental trades from: {incremental_file}")
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incremental_df = pd.read_csv(incremental_file)
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incremental_df['entry_time'] = pd.to_datetime(incremental_df['entry_time'])
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incremental_df['exit_time'] = pd.to_datetime(incremental_df['exit_time'])
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# Filter to only buy signals for entry timing comparison
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original_buys = original_df[original_df['type'] == 'BUY'].copy()
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incremental_buys = incremental_df[incremental_df['type'] == 'BUY'].copy()
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print(f"\n📈 TRADE COUNT COMPARISON:")
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print(f"Original strategy: {len(original_buys)} buy signals")
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print(f"Incremental strategy: {len(incremental_buys)} buy signals")
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print(f"Difference: {len(incremental_buys) - len(original_buys)} more in incremental")
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# Compare first 10 trades
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print(f"\n🕐 FIRST 10 TRADE TIMINGS:")
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print("-" * 60)
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print("Original Strategy:")
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for i, row in original_buys.head(10).iterrows():
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print(f" {i//2 + 1:2d}. {row['entry_time']} - ${row['entry_price']:.0f}")
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print("\nIncremental Strategy:")
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for i, row in incremental_buys.head(10).iterrows():
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print(f" {i//2 + 1:2d}. {row['entry_time']} - ${row['entry_price']:.0f}")
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# Analyze timing differences
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analyze_timing_differences(original_buys, incremental_buys)
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# Analyze price differences
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analyze_price_differences(original_buys, incremental_buys)
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return original_buys, incremental_buys
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def analyze_timing_differences(original_buys, incremental_buys):
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"""Analyze the timing differences between strategies."""
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print(f"\n🕐 TIMING ANALYSIS:")
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print("-" * 60)
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# Find the earliest and latest trades
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orig_start = original_buys['entry_time'].min()
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orig_end = original_buys['entry_time'].max()
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inc_start = incremental_buys['entry_time'].min()
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inc_end = incremental_buys['entry_time'].max()
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print(f"Original strategy:")
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print(f" First trade: {orig_start}")
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print(f" Last trade: {orig_end}")
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print(f" Duration: {orig_end - orig_start}")
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print(f"\nIncremental strategy:")
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print(f" First trade: {inc_start}")
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print(f" Last trade: {inc_end}")
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print(f" Duration: {inc_end - inc_start}")
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# Check if incremental strategy misses early trades
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time_diff = inc_start - orig_start
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print(f"\n⏰ TIME DIFFERENCE:")
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print(f"Incremental starts {time_diff} after original")
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if time_diff > timedelta(hours=1):
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print("⚠️ SIGNIFICANT DELAY DETECTED!")
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print("The incremental strategy is missing early profitable trades!")
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# Count how many original trades happened before incremental started
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early_trades = original_buys[original_buys['entry_time'] < inc_start]
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print(f"📊 Original trades before incremental started: {len(early_trades)}")
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if len(early_trades) > 0:
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early_profits = []
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for i in range(0, len(early_trades) * 2, 2):
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if i + 1 < len(original_buys.index):
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profit_pct = original_buys.iloc[i + 1]['profit_pct']
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early_profits.append(profit_pct)
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if early_profits:
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avg_early_profit = np.mean(early_profits) * 100
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total_early_profit = np.sum(early_profits) * 100
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print(f"📈 Average profit of early trades: {avg_early_profit:.2f}%")
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print(f"📈 Total profit from early trades: {total_early_profit:.2f}%")
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def analyze_price_differences(original_buys, incremental_buys):
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"""Analyze price differences at similar times."""
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print(f"\n💰 PRICE ANALYSIS:")
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print("-" * 60)
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# Find trades that happen on the same day
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original_buys['date'] = original_buys['entry_time'].dt.date
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incremental_buys['date'] = incremental_buys['entry_time'].dt.date
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common_dates = set(original_buys['date']) & set(incremental_buys['date'])
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print(f"📅 Common trading dates: {len(common_dates)}")
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# Compare prices on common dates
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price_differences = []
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for date in sorted(list(common_dates))[:10]: # First 10 common dates
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orig_trades = original_buys[original_buys['date'] == date]
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inc_trades = incremental_buys[incremental_buys['date'] == date]
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if len(orig_trades) > 0 and len(inc_trades) > 0:
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orig_price = orig_trades.iloc[0]['entry_price']
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inc_price = inc_trades.iloc[0]['entry_price']
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price_diff = ((inc_price - orig_price) / orig_price) * 100
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price_differences.append(price_diff)
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print(f" {date}: Original ${orig_price:.0f}, Incremental ${inc_price:.0f} ({price_diff:+.2f}%)")
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if price_differences:
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avg_price_diff = np.mean(price_differences)
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print(f"\n📊 Average price difference: {avg_price_diff:+.2f}%")
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if avg_price_diff > 1:
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print("⚠️ Incremental strategy consistently buys at higher prices!")
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elif avg_price_diff < -1:
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print("✅ Incremental strategy consistently buys at lower prices!")
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def create_timing_visualization(original_buys, incremental_buys):
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"""Create a visualization of trade timing differences."""
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print(f"\n📊 CREATING TIMING VISUALIZATION...")
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(15, 10))
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# Plot 1: Trade timing over time
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ax1.scatter(original_buys['entry_time'], original_buys['entry_price'],
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alpha=0.6, label='Original Strategy', color='blue', s=30)
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ax1.scatter(incremental_buys['entry_time'], incremental_buys['entry_price'],
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alpha=0.6, label='Incremental Strategy', color='red', s=30)
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ax1.set_title('Trade Entry Timing Comparison')
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ax1.set_xlabel('Date')
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ax1.set_ylabel('Entry Price ($)')
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ax1.legend()
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ax1.grid(True, alpha=0.3)
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# Plot 2: Cumulative trade count
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original_buys_sorted = original_buys.sort_values('entry_time')
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incremental_buys_sorted = incremental_buys.sort_values('entry_time')
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ax2.plot(original_buys_sorted['entry_time'], range(1, len(original_buys_sorted) + 1),
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label='Original Strategy', color='blue', linewidth=2)
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ax2.plot(incremental_buys_sorted['entry_time'], range(1, len(incremental_buys_sorted) + 1),
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label='Incremental Strategy', color='red', linewidth=2)
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ax2.set_title('Cumulative Trade Count Over Time')
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ax2.set_xlabel('Date')
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ax2.set_ylabel('Cumulative Trades')
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ax2.legend()
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ax2.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig('../results/trade_timing_comparison.png', dpi=300, bbox_inches='tight')
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print("📊 Timing visualization saved to: ../results/trade_timing_comparison.png")
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def main():
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"""Main analysis function."""
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try:
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original_buys, incremental_buys = load_and_compare_trades()
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create_timing_visualization(original_buys, incremental_buys)
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print(f"\n🎯 SUMMARY:")
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print("=" * 80)
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print("Key findings from trade timing analysis:")
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print("1. Check if incremental strategy starts trading later")
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print("2. Compare entry prices on same dates")
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print("3. Identify any systematic timing delays")
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print("4. Quantify impact of timing differences on performance")
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return True
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except Exception as e:
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print(f"\n❌ Error during analysis: {e}")
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import traceback
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traceback.print_exc()
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return False
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if __name__ == "__main__":
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success = main()
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exit(0 if success else 1)
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