Defi Trading Engine

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:

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:

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:

Usage:

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

Exit Codes:

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:

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:

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

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 |

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.

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:

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

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

Safety Features

✅ DO:

❌ DON'T:

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.

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)*

Resources

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

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

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.