125d4f7d52
- Introduced BacktestCharts class in charts.py to plot profit ratio vs stop loss and average trade vs stop loss for different timeframes. - Updated main.py to integrate new charting functionality and streamline data processing without monthly splits. - Enhanced backtesting logic in TrendDetectorSimple to include transaction costs and improved stop loss handling using 1-minute data for accuracy. - Added functionality to write results to individual CSV files for better organization and analysis.
87 lines
3.3 KiB
Python
87 lines
3.3 KiB
Python
import os
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import matplotlib.pyplot as plt
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class BacktestCharts:
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def __init__(self, charts_dir="charts"):
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self.charts_dir = charts_dir
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os.makedirs(self.charts_dir, exist_ok=True)
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def plot_profit_ratio_vs_stop_loss(self, results, filename="profit_ratio_vs_stop_loss.png"):
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"""
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Plots profit ratio vs stop loss percentage for each timeframe.
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Parameters:
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- results: list of dicts, each with keys: 'timeframe', 'stop_loss_pct', 'profit_ratio'
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- filename: output filename (will be saved in charts_dir)
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"""
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# Organize data by timeframe
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from collections import defaultdict
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data = defaultdict(lambda: {"stop_loss_pct": [], "profit_ratio": []})
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for row in results:
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tf = row["timeframe"]
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data[tf]["stop_loss_pct"].append(row["stop_loss_pct"])
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data[tf]["profit_ratio"].append(row["profit_ratio"])
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plt.figure(figsize=(10, 6))
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for tf, vals in data.items():
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# Sort by stop_loss_pct for smooth lines
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sorted_pairs = sorted(zip(vals["stop_loss_pct"], vals["profit_ratio"]))
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stop_loss, profit_ratio = zip(*sorted_pairs)
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plt.plot(
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[s * 100 for s in stop_loss], # Convert to percent
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profit_ratio,
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marker="o",
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label=tf
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)
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plt.xlabel("Stop Loss (%)")
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plt.ylabel("Profit Ratio")
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plt.title("Profit Ratio vs Stop Loss (%) per Timeframe")
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plt.legend(title="Timeframe")
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plt.grid(True, linestyle="--", alpha=0.5)
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plt.tight_layout()
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output_path = os.path.join(self.charts_dir, filename)
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plt.savefig(output_path)
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plt.close()
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def plot_average_trade_vs_stop_loss(self, results, filename="average_trade_vs_stop_loss.png"):
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"""
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Plots average trade vs stop loss percentage for each timeframe.
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Parameters:
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- results: list of dicts, each with keys: 'timeframe', 'stop_loss_pct', 'average_trade'
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- filename: output filename (will be saved in charts_dir)
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"""
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from collections import defaultdict
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data = defaultdict(lambda: {"stop_loss_pct": [], "average_trade": []})
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for row in results:
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tf = row["timeframe"]
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if "average_trade" not in row:
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continue # Skip rows without average_trade
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data[tf]["stop_loss_pct"].append(row["stop_loss_pct"])
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data[tf]["average_trade"].append(row["average_trade"])
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plt.figure(figsize=(10, 6))
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for tf, vals in data.items():
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# Sort by stop_loss_pct for smooth lines
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sorted_pairs = sorted(zip(vals["stop_loss_pct"], vals["average_trade"]))
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stop_loss, average_trade = zip(*sorted_pairs)
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plt.plot(
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[s * 100 for s in stop_loss], # Convert to percent
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average_trade,
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marker="o",
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label=tf
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)
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plt.xlabel("Stop Loss (%)")
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plt.ylabel("Average Trade")
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plt.title("Average Trade vs Stop Loss (%) per Timeframe")
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plt.legend(title="Timeframe")
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plt.grid(True, linestyle="--", alpha=0.5)
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plt.tight_layout()
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output_path = os.path.join(self.charts_dir, filename)
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plt.savefig(output_path)
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plt.close()
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