Tencent SkillHub · AI

Crypto Self-Learning

Self-learning system for crypto trading. Logs trades with full context (indicators, market conditions), analyzes patterns of wins/losses, and auto-updates trading rules. Use to log trades, analyze performance, identify what works/fails, and continuously improve trading accuracy.

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Quick setup

Download the package from Yavira.
Extract the archive and review SKILL.md first.
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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, data/trades.json, scripts/analyze.py, scripts/generate_rules.py, scripts/log_trade.py, scripts/update_memory.py

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.0

Provenance

Publisher: totaleasy
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 15 sections Open source page

Crypto Self-Learning 🧠

AI-powered self-improvement system for crypto trading. Learn from every trade to increase accuracy over time.

🎯 Core Concept

Every trade is a lesson. This skill: Logs every trade with full context Analyzes patterns in wins vs losses Generates rules from real data Updates memory automatically

📝 Log a Trade

After EVERY trade (win or loss), log it: python3 {baseDir}/scripts/log_trade.py \ --symbol BTCUSDT \ --direction LONG \ --entry 78000 \ --exit 79500 \ --pnl_percent 1.92 \ --leverage 5 \ --reason "RSI oversold + support bounce" \ --indicators '{"rsi": 28, "macd": "bullish_cross", "ma_position": "above_50"}' \ --market_context '{"btc_trend": "up", "dxy": 104.5, "russell": "up", "day": "tuesday", "hour": 14}' \ --result WIN \ --notes "Clean setup, followed the plan"

Required Fields:

FieldDescriptionExample--symbolTrading pairBTCUSDT--directionLONG or SHORTLONG--entryEntry price78000--exitExit price79500--pnl_percentProfit/Loss %1.92 or -2.5--resultWIN or LOSSWIN

Optional but Recommended:

FieldDescription--leverageLeverage used--reasonWhy you entered--indicatorsJSON with indicators at entry--market_contextJSON with macro conditions--notesPost-trade observations

📊 Analyze Performance

Run analysis to discover patterns: python3 {baseDir}/scripts/analyze.py Outputs: Win rate by direction (LONG vs SHORT) Win rate by day of week Win rate by RSI ranges Win rate by leverage Best/worst setups identified Suggested rules

Analyze Specific Filters:

python3 {baseDir}/scripts/analyze.py --symbol BTCUSDT python3 {baseDir}/scripts/analyze.py --direction LONG python3 {baseDir}/scripts/analyze.py --min-trades 10

🧠 Generate Rules

Extract actionable rules from your trade history: python3 {baseDir}/scripts/generate_rules.py This analyzes patterns and outputs rules like: 🚫 AVOID: LONG when RSI > 70 (win rate: 23%, n=13) ✅ PREFER: SHORT on Mondays (win rate: 78%, n=9) ⚠️ CAUTION: Trades with leverage > 10x (win rate: 35%, n=20)

📈 Auto-Update Memory

Apply learned rules to agent memory: python3 {baseDir}/scripts/update_memory.py --memory-path /path/to/MEMORY.md This appends a "## 🧠 Learned Rules" section with data-driven insights.

Dry Run (preview changes):

python3 {baseDir}/scripts/update_memory.py --memory-path /path/to/MEMORY.md --dry-run

📋 View Trade History

python3 {baseDir}/scripts/log_trade.py --list python3 {baseDir}/scripts/log_trade.py --list --last 10 python3 {baseDir}/scripts/log_trade.py --stats

🔄 Weekly Review

Run weekly to see progress: python3 {baseDir}/scripts/weekly_review.py Generates: This week's performance vs last week New patterns discovered Rules that worked/failed Recommendations for next week

📁 Data Storage

Trades are stored in {baseDir}/data/trades.json: { "trades": [ { "id": "uuid", "timestamp": "2026-02-02T13:00:00Z", "symbol": "BTCUSDT", "direction": "LONG", "entry": 78000, "exit": 79500, "pnl_percent": 1.92, "result": "WIN", "indicators": {...}, "market_context": {...} } ] }

🎯 Best Practices

Log EVERY trade - Wins AND losses Be honest - Don't skip bad trades Add context - More data = better patterns Review weekly - Patterns emerge over time Trust the data - If data says avoid something, AVOID IT

🔗 Integration with tess-cripto

Add to tess-cripto's workflow: Before trade: Check rules in MEMORY.md After trade: Log with full context Weekly: Run analysis and update memory Skill by Total Easy Software - Learn from every trade 🧠📈

Category context

Agent frameworks, memory systems, reasoning layers, and model-native orchestration.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

4 Scripts1 Docs1 Config

SKILL.md Primary doc
scripts/analyze.py Scripts
scripts/generate_rules.py Scripts
scripts/log_trade.py Scripts
scripts/update_memory.py Scripts
data/trades.json Config