backtester

Files (2)

• SKILL.md
• backtest_runner.py · scripts/

SKILL.md (first lines)

# Backtester

Test trading strategies against historical data before risking real money.

## Overview

- **Historical Data** - Load OHLCV from exchanges
- **Strategy Testing** - Simulate trades with rules
- **Performance Metrics** - Win rate, Sharpe, drawdown
- **Report Generation** - Detailed analysis

## Commands

### Load Historical Data

```bash
python3 -c "
import ccxt
import pandas as pd
from datetime import datetime, timedelta

symbol = 'BTC/USDT'
timeframe = '1d'
exchange = ccxt.binance()

# Fetch 1 year of data
since = exchange.parse8601((datetime.now() - timedelta(days=365)).isoformat())
ohlcv = exchange.fetch_ohlcv(symbol, timeframe, since=since, limit=365)

df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
df['date'] = pd.to_datetime(df['timestamp'], unit='ms')

print(f'📊 HISTORICAL DATA: {symbol}')
print('=' * 50)
print(f'Timeframe: {timeframe}')
print(f'Period: {df[\"date\"].iloc[0].date()} to {df[\"date\"].iloc[-1].date()}')
print(f'Candles: {len(df)}')
print(f'Price Range: \${df[\"low\"].min():,.2f} - \${df[\"high\"].max():,.2f}')

# Save for backtesting
# df.to_csv(f'{symbol.replace(\"/\", \"_\")}_{timeframe}.csv', index=False)
"
```

### Simple RSI Backtest

```bash
python3 -c "
import ccxt
import ta
import pandas as pd
import numpy as np

# Load data
symbol = 'BTC/USDT'
exchange = ccxt.binance()
ohlcv = exchange.fetch_ohlcv(symbol, '1d', limit=365)
df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])

# Calculate RSI
df['rsi'] = ta.momentum.RSIIndicator(df['close'], 14).rsi()

# Strategy: Buy RSI < 30, Sell RSI > 70
initial_capital = 10000
capital = initial_capital
position = 0
trades = []

for i in range(1, len(df)):
    rsi = df['rsi'].iloc[i]
    price = df['close'].iloc[i]
    
    if rsi < 30 and position == 0:  # Buy signal
        position = capital / price
        capital = 0
        trades.append({'type': 'buy', 'price': price, 'rsi': rsi})
    
    elif rsi > 70 and position > 0:  # Sell signal
        capital = position * price
        position = 0
        trades.append({'type': 'sell', 'price': price, 'rsi': rsi})

# Close final position
if position > 0:
    capital = position * df['close'].iloc[-1]

final_value = capital
total_return = ((final_value - initial_capital) / initial_capital) * 100
buy_hold_return = ((df['close'].iloc[-1] - df['close'].iloc[0]) / df['close'].iloc[0]) * 100

print(f'📊 RSI STRATEGY BACKTEST: {symbol}')
print('=' * 50)
print(f'Period: {len(df)} days')
print(f'Initial Capital: \${initial_capital:,.2f}')
print(f'Final Value: \${final_value:,.2f}')
print()
print(f'Strategy Return: {total_return:+.2f}%')
print(f'Buy & Hold Return: {buy_hold_return:+.2f}%')
print(f'Outperformance: {total_return - buy_hold_return:+.2f}%')
print()
print(f'Total Trades: {len(trades)}')
"
```

### Moving Average Crossover Backtest

```bash
python3 -c "
import ccxt
import ta
import pandas as pd
import numpy as np

symbol = 'BTC/USDT'
exchange = ccxt.binance()
ohlcv = exchange.fetch_ohlcv(symbol, '4h', limit=500)
df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])

# Calculate EMAs

From Signal-Execution-Labs/forex-trading-ai-agent, MIT.

More from Signal-Execution-Labs/forex-trading-ai-agent