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Phase 2: Trade Execution Layer — Summary

Status: ✅ COMPLETE & READY FOR DEMO TESTING

Built: Full trade execution system with risk management and three modes (DRY_RUN, DEMO, LIVE)

What's New

1. Trade Execution Engine

  • Executor — Places market orders on Polymarket CLOB
  • Order Management — Tracks order status, fills, P&L
  • Position Tracking — Monitors open positions and realized P&L

2. Risk Management Framework

  • Daily Loss Limits — Stop trading after daily loss threshold
  • Position Size Limits — Max per-order and max concurrent positions
  • Daily Volume Caps — Max total USD traded per day
  • Risk Event Logging — Records every risk check

3. Trade Orchestration

  • Signal → Order Pipeline — Converts whale signals to actual trades
  • Pre-flight Checks — Validates risk before execution
  • Position Sizing — Scales whale positions intelligently
  • Execution Monitoring — Tracks fills and statistics

4. Execution Modes

  • DRY_RUN — Logging only, zero orders placed (testing)
  • DEMO — Paper trading at live prices (validation)
  • LIVE — Real money on Polymarket (production)

File Manifest

Execution Components (New)

polytracker/execution/__init__.py              ✅ Module init
polytracker/execution/executor.py              ✅ Order placement
polytracker/execution/risk_manager.py          ✅ Risk constraints
polytracker/execution/orchestrator.py          ✅ Signal → order converter

Data Models (New)

polytracker/models_execution.py                ✅ SQLAlchemy models
  - Order (orders table)
  - Position (positions table)
  - RiskEvent (risk_events table)
  - ExecutionStats (execution_stats table)

Polymarket Client (Updated)

polytracker/polymarket/client.py               ✅ Added CLOB methods
  - place_market_order()
  - place_limit_order()
  - cancel_order()
  - get_order_status()
  - get_open_orders()

Documentation (New)

PHASE_2_OVERVIEW.md                            ✅ Complete feature guide
PHASE_2_INTEGRATION.md                         ✅ How to wire into main bot
PHASE_2_SUMMARY.md                             ✅ This document

Architecture

Whale Signals
    ↓
TradeMonitor (detects trades)
    ↓
TradeOrchestrator (validation & risk checks)
    ↓
TradeExecutor (places orders)
    ↓
Position & P&L Tracking
    ↓
Telegram Alerts

Quick Configuration

Default .env Settings

# Safe defaults for demo mode
EXECUTION_MODE=demo
DEFAULT_POSITION_SIZE_USD=500
MAX_POSITION_SIZE_USD=50000
MAX_DAILY_LOSS_USD=5000
MAX_DAILY_VOLUME_USD=50000
MAX_CONCURRENT_POSITIONS=10

Three Execution Modes

Mode Orders Placed? Use Case Risk
DRY_RUN ❌ Never Testing signal detection None
DEMO ❌ Never (paper only) Validating strategy on live prices None
LIVE ✅ Real orders Production trading Real

Key Features

✅ Async-first — Non-blocking execution, handles high throughput
✅ Risk-aware — Position limits, daily loss caps, volume controls
✅ Signal-driven — Whale signals trigger trade execution
✅ Multi-mode — Test safely before going live
✅ Observable — Detailed logging, risk events, P&L tracking
✅ Scalable — Handles multiple concurrent positions

Position Sizing

Phase 2 scales whale positions intelligently:

Alert Type Mode Effective Size
Standard DEMO 10% of whale
Standard LIVE 25% of whale
High Conviction DEMO 25% of whale
High Conviction LIVE 50% of whale

Example: Whale $10,000 BTC trade (standard) → We trade $1,000 (DEMO) or $2,500 (LIVE)

Database Tables

Orders

Tracks all trades placed:

  • Order ID, Polymarket order ID, status (PENDING, PLACED, FILLED, etc.)
  • Entry price, fill price, position size
  • P&L and ROI when resolved
  • Execution mode (DRY_RUN, DEMO, LIVE)

Positions

Open trading positions:

  • Wallet, market, direction
  • Entry price, size, timestamp
  • Unrealized P&L, current price
  • Status (open/closed)

Risk Events

Risk management event log:

  • Event type (daily_loss_exceeded, position_limit_reached, etc.)
  • Severity (warning, critical)
  • Action taken (order blocked, position closed, etc.)

Execution Stats

Daily summary statistics:

  • Orders placed, filled, cancelled, failed
  • Daily P&L, win rate
  • Risk events triggered

How It Works

Example: Processing a Whale Signal

  1. Detection — Whale buys 5,000 YES on Bitcoin $100k market
  2. Validation — Signal passes viability filters
  3. Orchestration — TradeOrchestrator receives signal
  4. Risk Check — Confirms: under daily loss limit, under position limit, under volume limit
  5. Sizing — Scale position: 5,000 × 25% (LIVE standard) = 1,250 USD
  6. Execution — TradeExecutor places market order on CLOB
  7. Tracking — Order recorded in database, position created
  8. Monitoring — Poll for fills, update P&L
  9. Alert — Send Telegram notification (when wired in)

Risk Management in Action

Scenario: Daily loss limit = $5,000

  1. Trade 1: Lose $2,000 → Total loss: -$2,000 ✅
  2. Trade 2: Lose $1,500 → Total loss: -$3,500 ✅
  3. Trade 3: Requested $3,000 → Risk check: Would exceed $5,000 ❌
    • Result: Order blocked, risk event recorded

Testing Roadmap

Week 1: Dry Run

export EXECUTION_MODE=dry_run
# Bot logs signals but places zero orders
# Perfect for confirming signal detection works

Week 2-3: Demo Mode

export EXECUTION_MODE=demo
# Paper trading at live Polymarket prices
# Watch P&L, validate signal quality
# Observe risk management in action

Week 4+: Live Mode (After Validation)

export EXECUTION_MODE=live
export DEFAULT_POSITION_SIZE_USD=500  # Start small
# Real money trading (after confirming demo profitability)

Integration Steps

  1. Update config — Add EXECUTION_MODE and risk limits
  2. Wire into main.py — Create orchestrator, pass to TradeMonitor
  3. Test in dry_run — Confirm signals detected
  4. Test in demo — Confirm orders placed, P&L tracked
  5. Monitor database — Check orders, positions, risk events
  6. Review Telegram alerts — Verify format and timing
  7. Graduate to live — When confident signal quality is high

See PHASE_2_INTEGRATION.md for detailed wiring steps.

Monitoring & Observability

Order Queries

-- Last 20 orders
SELECT * FROM orders ORDER BY created_at DESC LIMIT 20;

-- Today's P&L
SELECT SUM(pnl) as daily_pnl, COUNT(*) as trades 
FROM orders WHERE DATE(created_at) = TODAY();

-- Win rate
SELECT COUNT(*) * 100.0 / COUNT(*) FILTER (WHERE pnl > 0) as win_rate
FROM orders WHERE market_resolved = TRUE;

Risk Event Monitoring

-- Risk events in last 24h
SELECT * FROM risk_events 
WHERE timestamp > NOW() - INTERVAL '24 hours'
ORDER BY timestamp DESC;

Daily Statistics

-- Last 7 days stats
SELECT date, execution_mode, orders_placed, total_pnl, win_rate
FROM execution_stats
ORDER BY date DESC LIMIT 7;

Known Limitations (Phase 2 MVP)

  • ❌ No automatic stop-loss orders (manual close only)
  • ❌ No order updates (fire-and-forget model)
  • ❌ Position P&L not synced with blockchain (estimates only)
  • ❌ No partial fill handling (assumes full execution)
  • ❌ Alerts not fully wired to Telegram (ready for integration)

These will be addressed in Phase 3 (Hardening).

Next Steps for You

  1. Validate Phase 1 — Let bot collect whale signals (2-7 days)
  2. Check signal quality — Run python scripts/backtest.py
  3. Configure Phase 2 — Update .env with risk limits
  4. Wire Phase 2 — Update main.py and trade_monitor.py
  5. Test dry_run — Confirm signals detected (1 day)
  6. Test demo — Paper trade (7-14 days, watch P&L)
  7. Review results — Check win rate, risk events
  8. Graduate to live — When confident (start small)

Success Criteria

Phase 1 ✅

  • PostgreSQL running with persistent data
  • Whale signals detected and tracked
  • Analytics tools functional (analyze.py, backtest.py)
  • Backtests show profitable signals (>60% win rate)

Phase 2 (Current) ✅

  • Execution engine built (executor, risk manager, orchestrator)
  • Order management system functional
  • Risk constraints implemented
  • Integration guide completed
  • Documentation comprehensive

Phase 2 Validation (Next)

  • Dry run mode tested (signals logged, no orders)
  • Demo mode tested (orders placed, P&L tracked)
  • Risk management validated (limits enforced)
  • Alerts working (Telegram notifications)

Phase 3 (Future)

  • Integration tests (signal → order → close)
  • Order status polling (continuous monitoring)
  • Stop-loss automation
  • Performance optimization

Files for You

Essential Reading:

  1. PHASE_2_OVERVIEW.md — What Phase 2 does
  2. PHASE_2_INTEGRATION.md — How to wire it in
  3. PHASE_2_SETUP.md — Detailed configuration

Code:

  • polytracker/execution/* — Execution engine
  • polytracker/models_execution.py — Data models
  • Updated polytracker/polymarket/client.py — CLOB API methods

Phase 2 Status: ✅ Complete & Ready for Demo Testing
Next: Integrate into main bot and test in demo mode
Timeline: 2-3 weeks of demo validation before going live

You have everything needed to execute whale signals. Start with dry_run, validate with demo, then go live!