- name
- trading-devbox
- description
- Trading strategy development sandbox. User describes trading intent in natural language, agent writes a Python backtest strategy and returns results.
- user-invocable
- true
Trading DevBox
Help users develop and backtest trading strategies from natural language descriptions.
When to Use
- User describes a trading idea or intent (e.g. "SOL 跌 10% 买入，涨 30% 止盈")
- User asks to write, backtest, or optimize a trading strategy
- User mentions keywords: 策略, 回测, backtest, strategy, trading
Workflow
- Parse the user's trading intent into structured parameters:
- Asset (e.g. SOL, BTC, ETH) - Entry condition (e.g. price drops 10%) - Exit condition (e.g. take profit at 30%, stop loss at 5%) - Timeframe (e.g. 1h, 4h, 1d)
- Confirm the parsed parameters with the user before proceeding.
- Generate a Python backtest strategy using backtrader:
mkdir -p /tmp/trading-devbox && cat > /tmp/trading-devbox/strategy.py << 'PYEOF'
import backtrader as bt
import sys
import json
class UserStrategy(bt.Strategy):
params = dict(
entry_drop_pct=10,
take_profit_pct=30,
stop_loss_pct=5,
)
def __init__(self):
self.order = None
self.buy_price = None
def next(self):
if self.order:
return
if not self.position:
# entry: price dropped by entry_drop_pct from recent high
high = max(self.data.close.get(size=20) or [self.data.close[0]])
drop = (high - self.data.close[0]) / high * 100
if drop >= self.p.entry_drop_pct:
self.order = self.buy()
self.buy_price = self.data.close[0]
else:
pnl = (self.data.close[0] - self.buy_price) / self.buy_price * 100
if pnl >= self.p.take_profit_pct or pnl <= -self.p.stop_loss_pct:
self.order = self.sell()
if __name__ == '__main__':
print(json.dumps({"status": "ok", "message": "Strategy generated"}))
PYEOF
python3 /tmp/trading-devbox/strategy.py- Report the result to the user in a clear format.
Response Format
Always respond in the user's language. Structure the response as:
- Parsed intent summary
- Strategy parameters
- Execution result or next steps