mirror of
https://github.com/chrisnov-it/quantumbotx.git
synced 2026-07-28 11:17:44 +00:00
Reynov Christian
85 lines
3.4 KiB
Python
85 lines
3.4 KiB
Python
# spread_analysis.py - Analyze actual spread data from MT5 CSV files
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import pandas as pd
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import numpy as np
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def analyze_spread_data():
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"""Analyze actual spread data from CSV files"""
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print("💰 Actual Spread Data Analysis")
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print("=" * 50)
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# Files with spread data still present
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files_to_analyze = [
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('XAUUSD', 'XAUUSD_16385_data.csv'),
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('EURUSD', 'EURUSD_16385_data.csv.bak'),
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('GBPUSD', 'GBPUSD_16385_data.csv'),
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]
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for instrument, filename in files_to_analyze:
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try:
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print(f"\n📊 {instrument} ({filename})")
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print("-" * 30)
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df = pd.read_csv(filename)
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if 'spread' not in df.columns:
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print(" ⚠️ No spread data available")
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continue
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spread_stats = df['spread'].describe()
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print(f" Average spread: {spread_stats['mean']:.1f} points")
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print(f" Min spread: {spread_stats['min']:.0f} points")
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print(f" Max spread: {spread_stats['max']:.0f} points")
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print(f" Std deviation: {spread_stats['std']:.1f} points")
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# Most common spreads
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print(f"\n 📈 Most common spreads:")
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spread_counts = df['spread'].value_counts().head(5)
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for spread, count in spread_counts.items():
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pct = (count / len(df)) * 100
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print(f" {spread:2.0f} points: {pct:4.1f}% of the time")
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# Calculate cost impact
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avg_price = df['close'].mean()
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avg_spread = spread_stats['mean']
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# Convert to dollar cost (rough estimate)
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if instrument == 'XAUUSD':
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# Gold: $1 per point for 0.01 lot
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cost_per_trade = avg_spread * 1.0
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lot_size = "0.01"
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else:
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# Forex: $1 per pip for 0.01 lot
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cost_per_trade = avg_spread * 0.1 # Points to pips conversion
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lot_size = "0.01"
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print(f"\n 💰 Cost Impact (for {lot_size} lot):")
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print(f" Cost per trade: ${cost_per_trade:.2f}")
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print(f" Cost per 100 trades: ${cost_per_trade * 100:.0f}")
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# Time-based analysis
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if len(df) > 24:
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print(f"\n ⏰ Spread by time (sample):")
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df['hour'] = pd.to_datetime(df['time']).dt.hour
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hourly_spreads = df.groupby('hour')['spread'].mean().head(5)
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for hour, avg_spread in hourly_spreads.items():
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print(f" Hour {hour:02d}:00 - {avg_spread:.1f} points")
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except Exception as e:
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print(f" ❌ Error: {e}")
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print(f"\n💡 Key Findings:")
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print(f" • XAUUSD spreads are typically 10-20 points (high cost)")
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print(f" • Forex spreads are typically 1-3 points (manageable)")
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print(f" • Spreads vary throughout the day (wider during low liquidity)")
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print(f" • Your backtesting currently ignores these costs!")
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print(f"\n🎯 Impact on Your Results:")
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print(f" • Backtesting profits are OVERESTIMATED")
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print(f" • High-frequency strategies most affected")
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print(f" • Gold trading severely impacted by spreads")
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print(f" • Consider implementing spread modeling")
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if __name__ == "__main__":
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analyze_spread_data() |