According to AlphaMoat, as of 2026-09, Trading DevBox has 4,489 downloads and 0 stars — ranked #2,150 of 5,078 AI skills overall, and #272 of 577 in Development.
| Platform | Installs | Downloads | Stars | Comments |
|---|---|---|---|---|
| ClawHub ↗ | 149 | 4.4K | - | - |
| SkillHub ↗ | 144 | 4.5K | - | — |
Platforms count differently and update at different times.
The source parses an idea into entry, exit and timeframe parameters, then shows a backtrader strategy example. The example prints a generated status; it does not demonstrate loading market data or producing backtest results. Review code before execution, use an isolated environment and private temporary paths, and do not treat example output as investment advice.
Profile published 2026-10-29
Generated from the skill’s documentation as examples; not results of actual runs.
The counting method changed in 2026-09: skills with the same name are now counted separately by author, and the full catalog is covered. Dashed lines show months before the change; they are not compared with later months, and no growth is computed across the change.
| Month | Downloads | MoM | Stars |
|---|---|---|---|
| 2026-09 | 4.5K | — | - |
| 2026-08 | 4.3K | +4.0% | - |
| 2026-07 | 4.1K | +9.9% | - |
| 2026-06 | 3.8K | +4.6% | - |
| 2026-05 | 3.6K | — | - |
| 2026-03 | 2.3K | — | - |
The author’s original file, shown as plain text without edits or review by AlphaMoat. Links are not clickable; check commands before running them. Fetched 2026-10-23.
--- 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
1. 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)
2. Confirm the parsed parameters with the user before proceeding.
3. Generate a Python backtest strategy using backtrader:
```bash
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
```
4. 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
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Data month: 2026-09 · Downloads, stars and installs are aggregated monthly from public skill registries (ClawHub, SkillHub). See methodology.