u/Jabba_au

Have had a few questions around the self learning component. Heres how its built currently

The bot has a simple self-tuning mechanism that adjusts entry filters based on win rate. Here's how it works:

Every 10 completed trades, adapt_thresholds() fires:

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Win rate > 60% → LOOSEN filters (catch more trades)
  - min_price_change: -1% (floor: 5%)
  - volume_spike_multiplier: -0.1x (floor: 1.2x)

Win rate < 35% → TIGHTEN filters (be pickier)
  - min_price_change: +1% (floor: 15%)
  - volume_spike_multiplier: +0.1x (floor: 2.5x)

35-60% → no change

The adjusted values live in learning_state.json and override the config defaults. The scanner (scan_for_entries) reads from the learning state first, falling back to config:

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min_change = learning.get("min_price_change_24h", CFG["min_price_change_24h"])

Current defaults: 8% min price change, 1.5x volume spike. These will shift after the first 10 trades close.

Working on the following different models and compare the best strategy

1. Per-narrative tracking  learn which sectors (AI, L2, privacy) have better hit rates
2. Time-of-day weighting track which scan windows produce winners
3. Dynamic stop/TP  adjust based on volatility (ATR-based) rather than fixed %
4. Decay recent losses heavier weight last 10 trades more than lifetime stats

Now averging a modest $40 per day from $800 investment

I'm a finance broker by trade, and I got tired of manually watching charts. So I built an automated system using AI (OpenClaw + Python) that:

• Scans every USDT pair on Binance with >$5M volume

• Uses 4H klines with RSI(14), 20-period breakout, and volume spike confirmation

• Enters with market orders, 40% position sizing, max 2 concurrent

• Exits with layered take-profits: 20% at +30% (stop to breakeven), 20% at +50% (20% trail), 20% at +100% (10% trail)

• Kill switch at 50% drawdown, daily loss limit 20%

• Adaptive learning: adjusts entry thresholds every 10 trades based on win rate

The interesting part is the adaptive learning if win rate drops below 35%, it tightens entry filters. Above 60%, it loosens them. Simple feedback loop but it keeps the bot aligned with market conditions.

I wrote up the full strategy, code, and deployment process: myclawtrade.com

Happy to answer questions about the approach.

EDIT - Have updated the website to include 2 guides. One for complete for complete beginner and one for advanced user setup

EDIT 2 - have had some people have issues when using coinbase. I have included the coinbase source code that resolves this.