---

name: defi-trading-engine description: DeFi Trading Engine - Autonomous DeFi trading bot with self-improving review system for OpenClaw agents. Use when setting up DeFi trading, crypto trading bot, automated trading, Base chain trading, Bankr integration, trading engine, self-improving bot, or trading strategy execution.

DeFi Trading Engine

Autonomous DeFi trading bot with self-improving review system. Scans for opportunities, executes trades, logs performance, and learns from mistakes.

When to Use

Apply this skill when:

• Setting up automated crypto trading on Base or other EVM chains
• Building a self-improving trading system
• Implementing systematic DeFi trading strategies
• Executing DCA, momentum, or mean reversion strategies
• Reviewing and optimizing trading performance
• Managing trading risk (position sizing, drawdown limits)
• Integrating with Bankr CLI or other DEX tools

Architecture

Self-Improvement Loop:

scan → evaluate → execute → log → review → patch params → repeat

Components:

1. Token Scanner (scan-tokens.py) — Finds trading opportunities
2. Risk Manager (risk-manager.py) — Enforces position limits and risk rules
3. Trade Executor (trade-executor.py) — Executes trades via Bankr CLI
4. Daily Review (daily-review.py) — Analyzes performance and suggests improvements
5. Config File (trading-config.json) — Central configuration for all parameters

Quick Start

1. Setup

Create workspace:

mkdir -p ~/trading-bot/{trades,reviews}
cd ~/trading-bot

Copy skill scripts:

cp ~/.openclaw/skills/defi-trading-engine/scripts/* .

2. Configure

Create trading-config.json:

{
  "risk": {
    "max_position_size_usd": 40,
    "take_profit_pct": 4,
    "stop_loss_pct": 8,
    "max_active_positions": 5,
    "max_daily_trades": 8,
    "cooldown_minutes": 30,
    "max_drawdown_pct": 15
  },
  "strategy": {
    "type": "momentum_swing",
    "entry_signal": "volume_spike_and_price_up",
    "exit_signal": "take_profit_or_stop_loss",
    "timeframe": "15min"
  },
  "bankr": {
    "chain": "base",
    "wallet": "trading-wallet",
    "slippage_pct": 1.5
  },
  "data_sources": {
    "use_coingecko_trending": true,
    "use_dexscreener": true,
    "min_liquidity_usd": 50000,
    "min_volume_24h_usd": 100000
  }
}

3. Setup Bankr (if needed)

See references/bankr-setup.md for Bankr CLI setup.

4. Run the Trading Loop

Manual execution:

# 1. Scan for opportunities
python3 scan-tokens.py --output candidates.json

# 2. Review candidates
cat candidates.json

# 3. Execute a trade (after risk check)
python3 trade-executor.py --symbol SOL --action buy --amount 40

# 4. Run daily review
python3 daily-review.py

Automated loop (cron):

# Run scanner every 30 minutes
*/30 * * * * cd ~/trading-bot && python3 scan-tokens.py --output candidates.json

# Run daily review at 23:00
0 23 * * * cd ~/trading-bot && python3 daily-review.py

Core Scripts

scan-tokens.py

Scans for trading opportunities using free APIs.

Data Sources:

• CoinGecko trending coins
• Volume spikes (24h volume vs 7d average)
• Price momentum (1h, 4h, 24h trends)
• Liquidity and market cap filters

Output (candidates.json):

[
  {
    "symbol": "SOL",
    "name": "Solana",
    "price": 145.5,
    "volume_24h": 2800000000,
    "volume_spike_ratio": 1.8,
    "price_change_1h_pct": 2.5,
    "price_change_24h_pct": 5.2,
    "liquidity_usd": 850000000,
    "score": 8.5,
    "signals": ["trending", "volume_spike", "momentum_up"]
  }
]

Usage:

python3 scan-tokens.py --output candidates.json --min-score 7.0

---

risk-manager.py

Enforces risk limits before every trade. Acts as the gatekeeper.

Checks:

• Position size within limit
• Max active positions not exceeded
• Daily trade limit not exceeded
• Cooldown period respected
• Max drawdown not breached

Usage:

python3 risk-manager.py --action check --symbol SOL --amount 40

Exit Codes:

• 0 — Trade approved
• 1 — Trade denied (prints reason)

Example Output:

✅ Risk check passed
  - Position size: $40 (limit: $40)
  - Active positions: 3 (limit: 5)
  - Daily trades: 5 (limit: 8)
  - Cooldown: OK (35 minutes since last trade)
  - Drawdown: 8.5% (limit: 15%)

---

trade-executor.py

Executes trades via Bankr CLI (or generic DEX interface).

Supported Actions:

• buy — Market buy
• sell — Market sell
• limit_buy — Limit order buy
• limit_sell — Limit order sell
• set_stop_loss — Stop-loss order
• set_take_profit — Take-profit order

Usage:

# Market buy
python3 trade-executor.py --symbol SOL --action buy --amount 40

# Sell with stop-loss
python3 trade-executor.py --symbol SOL --action sell --stop-loss-pct 8

Trade Log (trades/YYYY-MM-DD.json):

[
  {
    "timestamp": "2026-03-13T15:45:00Z",
    "symbol": "SOL",
    "action": "buy",
    "amount_usd": 40,
    "price": 145.5,
    "quantity": 0.275,
    "tx_hash": "0xabc123...",
    "status": "success",
    "take_profit_price": 151.32,
    "stop_loss_price": 133.86
  }
]

---

daily-review.py

Analyzes trade history, calculates P&L, identifies weaknesses, and suggests parameter adjustments.

Metrics Calculated:

• Total P&L (realized + unrealized)
• Win rate (% of profitable trades)
• Average win vs average loss
• Sharpe ratio (if enough data)
• Max drawdown
• Best/worst trades

Output (reviews/review-YYYY-MM-DD.md):

# Trading Review — 2026-03-13

## Performance Summary
- **Total P&L:** +$42.50 (+5.3%)
- **Trades:** 8 (6 wins, 2 losses)
- **Win Rate:** 75%
- **Avg Win:** $9.20
- **Avg Loss:** -$5.80
- **Max Drawdown:** 8.5%

## Top Performers
1. SOL: +$18.50 (+12.7%)
2. LINK: +$12.20 (+8.1%)

## Worst Performers
1. UNI: -$8.50 (-5.7%)

## Pattern Analysis
- ✅ Momentum trades (4/5 profitable)
- ⚠️ Low liquidity tokens (1/3 profitable)
- ❌ Entries during high volatility (0/2 profitable)

## Recommended Adjustments
1. Increase `min_liquidity_usd` from $50k to $100k (low liquidity trades underperformed)
2. Add volatility filter (skip trades when VIX > 30)
3. Tighten stop-loss to 6% (avg loss exceeds target)

## Next Actions
- [ ] Update `trading-config.json` with new parameters
- [ ] Backtest on last 30 days with new rules
- [ ] Monitor performance for 1 week before further changes

Usage:

python3 daily-review.py --start-date 2026-03-01 --end-date 2026-03-13

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Configuration Reference

Risk Parameters

Parameter	Default	Purpose
max_position_size_usd	40	Max $ per trade
take_profit_pct	4	Exit when +4% gain
stop_loss_pct	8	Exit when -8% loss
max_active_positions	5	Max concurrent positions
max_daily_trades	8	Max trades per day
cooldown_minutes	30	Wait time between trades
max_drawdown_pct	15	Stop trading if down 15%

Strategy Parameters

Parameter	Options	Purpose
type	momentum_swing, mean_reversion, dca, asymmetric	Strategy type
entry_signal	volume_spike_and_price_up, oversold, breakout	Entry condition
exit_signal	take_profit_or_stop_loss, reversal, time_based	Exit condition
timeframe	5min, 15min, 1h, 4h	Trading timeframe

Bankr Integration

Parameter	Default	Purpose
chain	base	EVM chain (base, ethereum, polygon)
wallet	trading-wallet	Bankr wallet name
slippage_pct	1.5	Max acceptable slippage

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Strategy Templates

See references/strategies.md for detailed strategy implementations:

1. DCA (Dollar-Cost Averaging) — Buy fixed amount on schedule
2. Momentum Swing — Ride short-term momentum with tight stops
3. Mean Reversion — Buy dips, sell rallies
4. Asymmetric Bets — Small positions on high-upside opportunities

---

Risk Management Rules

The risk manager enforces these rules:

Position Sizing

Position size ≤ max_position_size_usd

Active Position Limit

count(open_positions) < max_active_positions

Daily Trade Limit

count(trades_today) < max_daily_trades

Cooldown Period

time_since_last_trade ≥ cooldown_minutes

Max Drawdown Circuit Breaker

if current_drawdown ≥ max_drawdown_pct:
  halt_all_trading()
  send_alert()

When max drawdown is hit, all trading stops until manually reset.

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Self-Improvement Process

The bot learns from performance:

1. Daily Review Run daily-review.py to analyze trades.

2. Pattern Recognition Identify which setups worked:

• Entry conditions with >70% win rate
• Tokens with consistent performance
• Timeframes with best risk/reward

3. Parameter Adjustment Update trading-config.json based on findings:

• Tighten filters if win rate < 60%
• Adjust position size if drawdown too high
• Change timeframe if signals lag

4. Backtest Changes Test new parameters on historical data (manual or automated).

5. Monitor Run new parameters for 7 days, then review again.

Cycle: Weekly reviews → Parameter tweaks → Monitor → Repeat

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Safety Features

✅ DO:

• Start with small position sizes ($40 default)
• Use stop-losses on every trade
• Respect cooldown periods (avoid overtrading)
• Run daily reviews to catch bad patterns early
• Keep max drawdown limit low (15% default)
• Paper trade first (simulate without real funds)

❌ DON'T:

• Disable risk manager checks
• Increase position size without testing
• Remove stop-losses ("this time is different")
• Trade during network congestion (high gas fees)
• Ignore max drawdown signals
• Use leverage (this engine is spot-only by design)

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Monitoring & Alerts

Track bot health:

Check active positions:

jq '.[] | select(.status == "open")' trades/*.json

Check today's P&L:

python3 daily-review.py --start-date $(date +%Y-%m-%d) --end-date $(date +%Y-%m-%d)

Alert on max drawdown:

# Add to cron (every hour)
python3 risk-manager.py --action check_drawdown && echo "Trading halted: max drawdown exceeded"

---

Troubleshooting

Problem: Risk manager denies all trades

Solution: Check trading-config.json limits. May have hit daily trade limit or max drawdown.

---

Problem: Trades execute but P&L is negative

Solution: Run daily-review.py to identify losing patterns. Tighten entry filters or adjust stop-loss.

---

Problem: Bankr CLI errors

Solution: Check wallet balance, network connection, and gas fees. See references/bankr-setup.md.

---

Problem: Scanner returns no candidates

Solution: Lower min_score threshold or relax liquidity filters.

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Advanced Features

Paper Trading Mode

Test strategies without real funds:

{
  "mode": "paper",
  "paper_balance_usd": 1000
}

All trades simulate execution, no real transactions.

Multi-Strategy Support

Run multiple strategies in parallel:

{
  "strategies": [
    {
      "name": "momentum",
      "allocation_pct": 60,
      "config": { ... }
    },
    {
      "name": "mean_reversion",
      "allocation_pct": 40,
      "config": { ... }
    }
  ]
}

Backtesting

Test parameters on historical data (requires historical price data):

python3 backtest.py --start 2026-01-01 --end 2026-03-01 --config trading-config.json

(Backtest script not included — implement based on your data source)

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Resources

• Bankr Setup: references/bankr-setup.md
• Strategy Templates: references/strategies.md
• CoinGecko API: https://www.coingecko.com/en/api/documentation
• Base Chain Docs: https://docs.base.org

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Version: 1.0 Last Updated: 2026-03-13 Security Note: Store API keys and wallet private keys securely. Never commit to Git.