TradeClaw went last week. Here are the real numbers.
The Numbers
| Metric | Value |
|---|---|
| Total Signals | 354 |
| Resolved | 103 |
| Win Rate | 38.8% |
| Avg P&L per Signal | +0.21% |
| Total P&L | +21.83% |
| Starting Balance | $10,000 |
| Current Balance | $12,183 |
| Max Drawdown | -10.66% |
| Sharpe Ratio | 2.5 |
38.8% win rate. Looks bad on paper.
+21.83% total P&L in 48 hours. Looks less bad.
The Point
Most people obsess over win rate. They want to be right. They want 70%, 80%, 90% accuracy.
TradeClaw doesn't try to be right. It tries to be asymmetric.
The system cuts losers fast and lets winners run. When it's wrong, it loses small. When it's right, it captures the full move. That math works even when you're wrong more than half the time.
This is not a new idea. It's how every serious edge works. But most AI trading tools still optimize for accuracy instead of risk-adjusted returns. They chase win rate because it looks good in a demo. It doesn't survive real markets.
What TradeClaw Actually Does
It's an open-source AI trading signal engine. Python. Free APIs. Free compute. No paid infrastructure.
It scans crypto futures pairs on Binance (15m timeframe), detects three setup types:
- Momentum Breakout — volume spike + key level break
- Mean Reversion — liquidity grab / wick rejection at support/resistance
- Trend Continuation — pullback to EMA21 in a trending market
Every setup gets graded A+ through C. Only A+ and A setups get auto-executed on testnet. Every signal is tracked. Every outcome is recorded. The track record page at tradeclaw.win shows all of it — no cherry-picking.
Built With Zero Budget
No paid APIs. No cloud servers. No subscription services.
The entire stack runs on free resources. If you have Python and an internet connection, you can run it.
This matters because most trading tools charge $50-200/month for signal access. TradeClaw gives you the signals, the logic, and the source code. You can verify every decision the system makes.
What I Need From You
This is an open-source project and I'm building it in public. I need:
Bug reports. The system is 2 days old. Things will break. If you find something, open an issue.
Ideas. New setup detection patterns, better risk management logic, additional pairs, different timeframes — if you have a thesis, I want to hear it.
Code. PRs are welcome. The codebase is Python, straightforward, and documented enough to jump into.
Stars. If the approach makes sense to you, star the repo. It helps with visibility and tells me the direction is worth pushing further.
Try It
Live track record: tradeclaw.win
The win rate will probably stay under 50%. The P&L won't care.
Cut the losers. Let the winners run. That's the whole system.
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