Write me a simple python script that can back test forex strategy’s using python. I want to use this script to figure out different ways to pass a prop form challenge goal of 8% and a max drawdown of 6%.
Backtesting a Forex trading strategy can be done using historical price data. Below is a simple Python script that illustrates how to backtest a simple Forex strategy with the aim to achieve an 8% account growth and a maximum drawdown of 6%. This script uses NumPy and Pandas libraries for calculations and data handling, so be sure to have them installed via pip:
```bash
pip install pandas numpy matplotlib
```
### Simple Backtesting Script
This is a basic script and is only meant for educational purposes. In a real-world scenario, you would want more sophisticated error checking and perhaps integration with actual trading libraries.
```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Load historical forex data
# For example, use a CSV with 'Date', 'Open', 'High', 'Low', 'Close' columns
data = pd.read_csv('forex_data.csv', parse_dates=['Date'])
data.set_index('Date', inplace=True)
# Parameters
initial_balance = 10000 # starting balance
target_return = 0.08 # 8%
max_drawdown_pct = 0.06 # max drawdown of 6%
lot_size = 0.01 # lot size
# Strategy Parameters (Simple Moving Average Cross Strategy as an example)
short_window = 10
long_window = 30
# Create Moving Averages
data['Short_MA'] = data['Close'].rolling(window=short_window).mean()
data['Long_MA'] = data['Close'].rolling(window=long_window).mean()
# Generate Signals
data['Signal'] = 0
data['Signal'][short_window:] = np.where(data['Short_MA'][short_window:] > data['Long_MA'][short_window:], 1, 0)
data['Position'] = data['Signal'].diff() # 1 = buy, -1 = sell
# Backtesting the Strategy
balance = initial_balance
equity_curve = [balance]
drawdowns = [0]
for i in range(len(data)):
if data['Position'].iloc[i] == 1: # Buy signal
# Calculate profit/loss on buy
balance += lot_size * (data['Close'].iloc[i] - data['Close'].iloc[i - 1])
elif data['Position'].iloc[i] == -1: # Sell signal
# Calculate profit/loss on sell
balance -= lot_size * (data['Close'].iloc[i] - data['Close'].iloc[i - 1])
# Record equity curve
equity_curve.append(balance)
# Calculate drawdown
peak = max(equity_curve)
drawdown = (peak - balance) / peak
drawdowns.append(drawdown)
# Results
final_balance = balance
total_return = (final_balance - initial_balance) / initial_balance
max_drawdown = max(drawdowns)
print(f"Final Balance: ${final_balance:.2f}")
print(f"Total Return: {total_return * 100:.2f}%")
print(f"Max Drawdown: {max_drawdown * 100:.2f}%")
# Check for passing criteria
if total_return >= target_return and max_drawdown <= max_drawdown_pct:
print("Congratulations! The strategy has met the prop challenge criteria.")
else:
print("The strategy did not meet the prop challenge criteria.")
# Plot equity curve
plt.figure(figsize=(12, 6))
plt.plot(equity_curve, label='Equity Curve', color='blue')
plt.title('Equity Curve')
plt.xlabel('Time')
plt.ylabel('Account Balance')
plt.legend()
plt.grid()
plt.show()
```
### Explanation
1. **Data Input**: The script assumes you have historical Forex data in a CSV file with `Date`, `Open`, `High`, `Low`, and `Close`. Adjust the filename in the script as necessary.
2. **Strategy**: A simple Moving Average crossover strategy is implemented, where a buy signal is generated when a short-term moving average crosses above a long-term moving average.
3. **Backtesting**: The script simulates trades based on the generated signals and keeps track of the account balance. It evaluates the total return and maximum drawdown after the entire dataset has been processed.
4. **Results**: The script outputs the final account balance, total return, and max drawdown status. It checks if the strategy meets the specified pass criteria.
### Note:
- This script is very basic. Real-world back-testing strategies take into account factors like spread, slippage, transaction costs, and more.
- Make sure to customize the strategy and its parameters based on your own trading ideas.
- Always conduct extensive testing and analysis using different datasets before applying any strategy in live trading.