Backtesting

Backtesting allows you to evaluate the VRVP Strategy against historical data before risking real capital.

Running a Backtest

Basic Command


python main.py backtest -i EUR_USD -s 2023-01-01 -e 2024-01-01 -b 10000

Command Line Options

Option	Short	Required	Description
`--instrument`	`-i`	Yes	Trading pair (e.g., EUR_USD)
`--start`	`-s`	Yes	Start date (YYYY-MM-DD)
`--end`	`-e`	Yes	End date (YYYY-MM-DD)
`--balance`	`-b`	Yes	Initial account balance
`--output`	`-o`	No	Output file for results

Example with All Options


python main.py backtest \
  -i EUR_USD \
  -s 2023-01-01 \
  -e 2024-01-01 \
  -b 10000 \
  -o results/eur_usd_2023.json

Understanding Backtest Results

Performance Metrics

After running a backtest, you’ll see output like:


========================================
BACKTEST RESULTS - EUR_USD
========================================
Period: 2023-01-01 to 2024-01-01
Initial Balance: $10,000.00
Final Balance: $12,450.00
----------------------------------------
Net Profit: $2,450.00 (24.5%)
Total Trades: 48
Winning Trades: 29 (60.4%)
Losing Trades: 19 (39.6%)
----------------------------------------
Profit Factor: 1.85
Sharpe Ratio: 1.42
Max Drawdown: 8.3%
Average Win: $145.00
Average Loss: $78.00
Risk/Reward: 1:1.86
========================================

Key Metrics Explained

Metric	Description	Good Value
Win Rate	Percentage of profitable trades	> 50%
Profit Factor	Gross profit / Gross loss	> 1.5
Sharpe Ratio	Risk-adjusted return	> 1.0
Max Drawdown	Largest peak-to-trough decline	< 15%
Risk/Reward	Average win / Average loss	> 1.5:1

Multi-Instrument Backtesting

Test multiple instruments sequentially:


# Using a loop
for pair in EUR_USD GBP_USD USD_JPY; do
  python main.py backtest -i $pair -s 2023-01-01 -e 2024-01-01 -b 10000
done

Analyzing Results

Trade Log

The backtester generates a detailed trade log:


[
  {
    "id": 1,
    "instrument": "EUR_USD",
    "direction": "LONG",
    "entry_time": "2023-01-15 14:00:00",
    "entry_price": 1.0850,
    "exit_time": "2023-01-16 09:00:00",
    "exit_price": 1.0920,
    "pnl": 145.00,
    "pnl_percent": 1.45,
    "exit_reason": "TAKE_PROFIT"
  }
]

Exit Reasons

Reason	Description
`TAKE_PROFIT`	Target price reached
`STOP_LOSS`	Stop loss triggered
`TRAILING_STOP`	Trailing stop triggered
`SIGNAL_EXIT`	Strategy signal reversed
`END_OF_DATA`	Backtest period ended

Optimization Tips

Parameter Optimization

Test different parameter combinations:


# Example: Test different Supertrend multipliers
multipliers = [2.0, 2.5, 3.0, 3.5, 4.0]
for mult in multipliers:
    # Update config and run backtest
    config.supertrend.multiplier = mult
    results = run_backtest(config)
    print(f"Multiplier {mult}: PF={results.profit_factor}")

Walk-Forward Analysis

Prevent overfitting with walk-forward testing:

In-Sample Period: Optimize parameters (e.g., Jan-Jun 2023)
Out-of-Sample Period: Validate on unseen data (e.g., Jul-Dec 2023)
Repeat: Roll forward and repeat

Common Pitfalls

Avoid These Mistakes:

Overfitting: Don’t optimize too many parameters
Survivorship Bias: Use complete historical data
Look-Ahead Bias: Strategy uses shifted HTF data to prevent this
Ignoring Slippage: Real execution differs from backtest

Data Sources

Historical Data

The backtester uses data from:

Capital.com API: Real historical OHLCV data
CSV Files: Local data files in data/historical/

Loading CSV Data


from data.historical import load_csv_data
 
# Load from CSV
df = load_csv_data(
    path="data/historical/EUR_USD_1H.csv",
    start_date="2023-01-01",
    end_date="2024-01-01"
)

Performance Visualization

While the CLI provides text output, you can export results for visualization:


# Export to JSON
python main.py backtest -i EUR_USD -s 2023-01-01 -e 2024-01-01 -b 10000 -o results.json
 
# Then use Python for visualization


import json
import matplotlib.pyplot as plt
 
with open("results.json") as f:
    results = json.load(f)
 
# Plot equity curve
plt.figure(figsize=(12, 6))
plt.plot(results["equity_curve"])
plt.title("Equity Curve - EUR/USD 2023")
plt.xlabel("Trade #")
plt.ylabel("Account Balance ($)")
plt.grid(True)
plt.savefig("equity_curve.png")

Next Steps

Deployment Guide - Move to production
Configuration - Fine-tune parameters
API Reference - Use the REST API