272 lines
10 KiB
Python
272 lines
10 KiB
Python
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#!/usr/bin/env python3
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"""
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Plot original strategy results from trades CSV file.
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Shows buy/sell signals and portfolio value over time.
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"""
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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 matplotlib.dates as mdates
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from datetime import datetime
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import os
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import sys
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# Add project root to path
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sys.path.insert(0, os.path.abspath('..'))
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def load_and_process_trades(trades_file, initial_usd=10000):
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"""Load trades and calculate portfolio value over time."""
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# Load trades data
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trades_df = pd.read_csv(trades_file)
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# Convert timestamps
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trades_df['entry_time'] = pd.to_datetime(trades_df['entry_time'])
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trades_df['exit_time'] = pd.to_datetime(trades_df['exit_time'], errors='coerce')
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# Separate buy and sell signals
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buy_signals = trades_df[trades_df['type'] == 'BUY'].copy()
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sell_signals = trades_df[trades_df['type'] != 'BUY'].copy()
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print(f"Loaded {len(buy_signals)} buy signals and {len(sell_signals)} sell signals")
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# Calculate portfolio value using compounding
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portfolio_value = initial_usd
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portfolio_history = []
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# Create timeline from all trade times
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all_times = []
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all_times.extend(buy_signals['entry_time'].tolist())
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all_times.extend(sell_signals['exit_time'].dropna().tolist())
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all_times = sorted(set(all_times))
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print(f"Processing {len(all_times)} trade events...")
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# Track portfolio value at each trade
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current_value = initial_usd
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for sell_trade in sell_signals.itertuples():
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# Apply the profit/loss from this trade
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profit_pct = sell_trade.profit_pct
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current_value *= (1 + profit_pct)
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portfolio_history.append({
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'timestamp': sell_trade.exit_time,
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'portfolio_value': current_value,
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'trade_type': 'SELL',
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'price': sell_trade.exit_price,
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'profit_pct': profit_pct * 100
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})
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# Convert to DataFrame
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portfolio_df = pd.DataFrame(portfolio_history)
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portfolio_df = portfolio_df.sort_values('timestamp').reset_index(drop=True)
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# Calculate performance metrics
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final_value = current_value
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total_return = (final_value - initial_usd) / initial_usd * 100
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num_trades = len(sell_signals)
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winning_trades = len(sell_signals[sell_signals['profit_pct'] > 0])
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win_rate = winning_trades / num_trades * 100 if num_trades > 0 else 0
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avg_trade = sell_signals['profit_pct'].mean() * 100 if num_trades > 0 else 0
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best_trade = sell_signals['profit_pct'].max() * 100 if num_trades > 0 else 0
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worst_trade = sell_signals['profit_pct'].min() * 100 if num_trades > 0 else 0
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performance = {
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'initial_value': initial_usd,
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'final_value': final_value,
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'total_return': total_return,
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'num_trades': num_trades,
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'win_rate': win_rate,
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'avg_trade': avg_trade,
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'best_trade': best_trade,
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'worst_trade': worst_trade
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}
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return buy_signals, sell_signals, portfolio_df, performance
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def create_comprehensive_plot(buy_signals, sell_signals, portfolio_df, performance, save_path="original_strategy_analysis.png"):
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"""Create comprehensive plot with signals and portfolio value."""
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# Create figure with subplots
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fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 12),
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gridspec_kw={'height_ratios': [2, 1]})
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# Plot 1: Price chart with buy/sell signals
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# Get price range for the chart
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all_prices = []
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all_prices.extend(buy_signals['entry_price'].tolist())
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all_prices.extend(sell_signals['exit_price'].tolist())
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price_min = min(all_prices)
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price_max = max(all_prices)
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# Create a price line by connecting buy and sell points
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price_timeline = []
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value_timeline = []
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# Combine and sort all signals by time
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all_signals = []
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for _, buy in buy_signals.iterrows():
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all_signals.append({
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'time': buy['entry_time'],
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'price': buy['entry_price'],
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'type': 'BUY'
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})
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for _, sell in sell_signals.iterrows():
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all_signals.append({
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'time': sell['exit_time'],
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'price': sell['exit_price'],
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'type': 'SELL'
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})
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all_signals = sorted(all_signals, key=lambda x: x['time'])
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# Create price line
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for signal in all_signals:
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price_timeline.append(signal['time'])
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value_timeline.append(signal['price'])
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# Plot price line
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if price_timeline:
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ax1.plot(price_timeline, value_timeline, color='black', linewidth=1.5, alpha=0.7, label='Price Action')
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# Plot buy signals
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ax1.scatter(buy_signals['entry_time'], buy_signals['entry_price'],
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color='green', marker='^', s=80, label=f"Buy Signals ({len(buy_signals)})",
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zorder=5, alpha=0.9, edgecolors='white', linewidth=1)
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# Plot sell signals with different colors based on profit/loss
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profitable_sells = sell_signals[sell_signals['profit_pct'] > 0]
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losing_sells = sell_signals[sell_signals['profit_pct'] <= 0]
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if len(profitable_sells) > 0:
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ax1.scatter(profitable_sells['exit_time'], profitable_sells['exit_price'],
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color='blue', marker='v', s=80, label=f"Profitable Sells ({len(profitable_sells)})",
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zorder=5, alpha=0.9, edgecolors='white', linewidth=1)
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if len(losing_sells) > 0:
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ax1.scatter(losing_sells['exit_time'], losing_sells['exit_price'],
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color='red', marker='v', s=80, label=f"Losing Sells ({len(losing_sells)})",
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zorder=5, alpha=0.9, edgecolors='white', linewidth=1)
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ax1.set_title('Original Strategy - Trading Signals', fontsize=16, fontweight='bold')
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ax1.set_ylabel('Price (USD)', fontsize=12)
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ax1.legend(loc='upper left', fontsize=10)
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ax1.grid(True, alpha=0.3)
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# Format y-axis for price
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ax1.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'${x:,.0f}'))
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# Plot 2: Portfolio Value Over Time
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if len(portfolio_df) > 0:
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ax2.plot(portfolio_df['timestamp'], portfolio_df['portfolio_value'],
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color='purple', linewidth=2, label='Portfolio Value')
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# Add horizontal line for initial value
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ax2.axhline(y=performance['initial_value'], color='gray',
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linestyle='--', alpha=0.7, label='Initial Value ($10,000)')
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# Add profit/loss shading
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initial_value = performance['initial_value']
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profit_mask = portfolio_df['portfolio_value'] > initial_value
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loss_mask = portfolio_df['portfolio_value'] < initial_value
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if profit_mask.any():
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ax2.fill_between(portfolio_df['timestamp'], portfolio_df['portfolio_value'], initial_value,
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where=profit_mask, color='green', alpha=0.2, label='Profit Zone')
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if loss_mask.any():
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ax2.fill_between(portfolio_df['timestamp'], portfolio_df['portfolio_value'], initial_value,
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where=loss_mask, color='red', alpha=0.2, label='Loss Zone')
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ax2.set_title('Portfolio Value Over Time', fontsize=14, fontweight='bold')
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ax2.set_ylabel('Portfolio Value (USD)', fontsize=12)
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ax2.set_xlabel('Date', fontsize=12)
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ax2.legend(loc='upper left', fontsize=10)
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ax2.grid(True, alpha=0.3)
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# Format y-axis for portfolio value
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ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f'${x:,.0f}'))
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# Format x-axis for both plots
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for ax in [ax1, ax2]:
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ax.xaxis.set_major_locator(mdates.MonthLocator())
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ax.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
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plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)
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# Add performance text box
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perf_text = f"""
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PERFORMANCE SUMMARY
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{'='*30}
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Initial Value: ${performance['initial_value']:,.0f}
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Final Value: ${performance['final_value']:,.0f}
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Total Return: {performance['total_return']:+.1f}%
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Trading Statistics:
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• Number of Trades: {performance['num_trades']}
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• Win Rate: {performance['win_rate']:.1f}%
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• Average Trade: {performance['avg_trade']:+.2f}%
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• Best Trade: {performance['best_trade']:+.1f}%
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• Worst Trade: {performance['worst_trade']:+.1f}%
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Period: {buy_signals['entry_time'].min().strftime('%Y-%m-%d')} to {sell_signals['exit_time'].max().strftime('%Y-%m-%d')}
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"""
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# Add text box to the plot
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ax2.text(1.02, 0.98, perf_text, transform=ax2.transAxes, fontsize=10,
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verticalalignment='top', fontfamily='monospace',
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bbox=dict(boxstyle="round,pad=0.5", facecolor="lightgray", alpha=0.9))
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# Adjust layout and save
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plt.tight_layout()
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plt.subplots_adjust(right=0.75) # Make room for text box
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.show()
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print(f"Plot saved to: {save_path}")
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def main():
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"""Main function to run the analysis."""
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print("🚀 Starting Original Strategy Analysis")
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print("=" * 50)
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# File paths
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trades_file = "../results/trades_15min(15min)_ST3pct.csv"
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output_file = "../results/original_strategy_analysis.png"
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if not os.path.exists(trades_file):
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print(f"❌ Error: Trades file not found: {trades_file}")
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return
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try:
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# Load and process trades
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buy_signals, sell_signals, portfolio_df, performance = load_and_process_trades(trades_file)
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# Print performance summary
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print(f"\n📊 PERFORMANCE SUMMARY:")
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print(f"Initial Value: ${performance['initial_value']:,.0f}")
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print(f"Final Value: ${performance['final_value']:,.0f}")
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print(f"Total Return: {performance['total_return']:+.1f}%")
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print(f"Number of Trades: {performance['num_trades']}")
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print(f"Win Rate: {performance['win_rate']:.1f}%")
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print(f"Average Trade: {performance['avg_trade']:+.2f}%")
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# Create plot
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create_comprehensive_plot(buy_signals, sell_signals, portfolio_df, performance, output_file)
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print(f"\n✅ Analysis completed successfully!")
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except Exception as e:
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print(f"❌ Error during analysis: {e}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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main()
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