auto-trader

Files (2)

• SKILL.md
• position_calculator.py · scripts/

SKILL.md (first lines)

# Auto Trader

Automated trading execution with risk management.

## Overview

- **Strategy Execution** - Run predefined trading strategies
- **Risk Management** - Position sizing, max drawdown limits
- **Order Management** - Stop-loss, take-profit, trailing stops
- **Trade Logging** - Complete audit trail

⚠️ **WARNING**: Automated trading involves significant risk. Always test with small amounts first!

## Configuration

Trading config in `~/.kit/auto-trader.json`:

```json
{
  "exchange": "binance",
  "sandbox": true,
  "risk": {
    "max_position_pct": 5,
    "max_daily_loss_pct": 3,
    "default_stop_loss_pct": 2,
    "default_take_profit_pct": 4
  },
  "strategies": ["rsi_reversal", "ma_crossover"],
  "symbols": ["BTC/USDT", "ETH/USDT"]
}
```

## Commands

### Position Sizing Calculator

```bash
python3 -c "
account_balance = 10000  # USD
risk_per_trade_pct = 2   # Risk 2% per trade
entry_price = 45000      # BTC entry
stop_loss_price = 44000  # Stop loss

risk_amount = account_balance * (risk_per_trade_pct / 100)
price_risk = entry_price - stop_loss_price
position_size = risk_amount / price_risk

print('📊 POSITION SIZE CALCULATOR')
print('=' * 50)
print(f'Account Balance: \${account_balance:,.2f}')
print(f'Risk per Trade: {risk_per_trade_pct}% (\${risk_amount:,.2f})')
print(f'Entry Price: \${entry_price:,.2f}')
print(f'Stop Loss: \${stop_loss_price:,.2f}')
print(f'Price Risk: \${price_risk:,.2f} per unit')
print()
print(f'✅ Position Size: {position_size:.6f} BTC')
print(f'✅ Position Value: \${position_size * entry_price:,.2f}')
"
```

### Simple RSI Strategy

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

# Strategy: Buy when RSI < 30, Sell when RSI > 70
symbol = 'BTC/USDT'
exchange = ccxt.binance()

ohlcv = exchange.fetch_ohlcv(symbol, '1h', limit=100)
df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
df['rsi'] = ta.momentum.RSIIndicator(df['close'], 14).rsi()

current_rsi = df['rsi'].iloc[-1]
current_price = df['close'].iloc[-1]

print(f'📊 RSI STRATEGY: {symbol}')
print('=' * 50)
print(f'Price: \${current_price:,.2f}')
print(f'RSI(14): {current_rsi:.1f}')
print()

if current_rsi < 30:
    print('🟢 SIGNAL: BUY (RSI oversold)')
    print(f'   Entry: \${current_price:,.2f}')
    print(f'   Stop Loss: \${current_price * 0.98:,.2f} (-2%)')
    print(f'   Take Profit: \${current_price * 1.04:,.2f} (+4%)')
elif current_rsi > 70:
    print('🔴 SIGNAL: SELL (RSI overbought)')
else:
    print('⚪ NO SIGNAL: RSI in neutral zone (30-70)')
"
```

### Moving Average Crossover Strategy

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

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

ohlcv = exchange.fetch_ohlcv(symbol, '4h', limit=100)
df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])

df['ema_12'] = ta.trend.ema_indicator(df['close'], 12)
df['ema_26'] = ta.trend.ema_indicator(df['close'], 26)

current = df.iloc[-1]
previous = df.iloc[-2]
price = current['close']

print(f'📊 MA CROSSOVER STRATEGY: {symbol}')

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

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