IBKR Autonomous Trader Skill

A modular, safe, self-improving paper trading agent for Interactive Brokers.

⚠️ Safety First: ENABLE_LIVE_TRADING defaults to False. The agent operates in paper trading mode unless explicitly toggled and confirmed. Never flip this flag without user confirmation.

Quick Start

pip install ib_insync pandas numpy pyyaml loguru
# Ensure TWS or IB Gateway is running with API enabled on port 7497 (paper)
python skills/ibkr-autonomous-trader/scripts/trader.py

File Map

Agent Workflow (One Loop Iteration)

1. Connect to IBKR
2. Load config + memory
3. Fetch market data (bars + live quotes)
4. Scan watchlist assets
5. Run strategy_engine → generate signals
6. Run risk_manager → validate each signal
7. Size position
8. Execute order via execution_engine (bracket with stop loss)
9. Monitor open positions
10. Log trade details to memory/trades.json
11. Periodically: run performance.py → evaluate + tune parameters
12. Sleep → repeat

Safety Controls

• ENABLE_LIVE_TRADING = False in config/settings.yaml — paper only by default
• Kill switch triggers if daily drawdown exceeds MAX_DAILY_LOSS_PCT
• Max concurrent positions enforced via MAX_POSITIONS
• Every order validated by risk_manager.py before submission
• Duplicate order detection prevents double-fills
• All actions logged via loguru

Key Functions (see individual scripts for full signatures)

execution_engine.py

• execute_trade(signal) — Places bracket order with auto stop loss
• close_trade(position) — Market-closes an open position
• adjust_stop(position, new_stop) — Modifies trailing stop
• cancel_trade(order_id) — Cancels a pending order
• get_positions() — Returns all open positions
• get_open_orders() — Returns all pending orders

risk_manager.py

• validate_signal(signal, portfolio_state) — Returns (approved: bool, reason: str)
• size_position(signal, account_value) — Returns share quantity
• check_kill_switch(daily_pnl, account_value) — Halts trading if threshold breached

strategy_engine.py

• generate_signals(bars_dict) — Returns list of Signal objects
• compute_indicators(bars) — RSI, MACD, ATR, EMA
• score_signal(signal) — Confidence 0–1

performance.py

• log_trade(trade) — Appends to trades.json
• evaluate_performance() — Returns stats dict (win rate, Sharpe, drawdown, profit factor)
• suggest_improvements(stats) — Returns parameter adjustment recommendations
• apply_improvements(recommendations) — Updates strategies.json

Reading the Reference Files

When implementing or modifying any module, read the corresponding reference:

• Connection + auth patterns → references/ibkr_connection.md
• Order types and bracket logic → references/order_types.md
• Indicator formulas → references/indicators.md
• Self-improvement algorithm → references/self_improvement.md

Adding a New Strategy

1. Add a function strategy_\x3Cname>(bars) -> list[Signal] in strategy_engine.py
2. Register it in config/settings.yaml under strategies.enabled
3. Add parameter defaults under strategies.params.\x3Cname>
4. The self-improvement loop will automatically tune its parameters over time

Extending Order Types

To add OCO (One-Cancels-Other) or trailing stops, see references/order_types.md. execution_engine.py is structured to accept any ib_insync Order object.

News Intelligence & Decision System (v2)

Two new modules extend the base skill with multi-factor decision making.

news_engine.py

• Fetches Google News RSS for 8 market topics + per-symbol feeds (no API key needed)
• Scores each item: sentiment (bullish/bearish/neutral/risk_event), event type, impact level, time sensitivity
• fetch_all() → list of NewsItem (cached for 5 min)
• get_market_sentiment(items) → aggregate score dict used by decision engine
• get_symbol_sentiment(symbol, items) → float score for a specific ticker
• evaluate_news_utility() → learns which news types predict trade outcomes
• All items logged to memory/news_log.json

decision_engine.py

• Never lets a trade fire from news alone — technical signal always required
• Combines 4 scores with configurable weights:
  • technical (default 50%) — strategy_engine confidence
  • news (default 25%) — directional alignment with sentiment
  • risk (default 15%) — inverse portfolio exposure
  • volatility (default 10%) — ATR/price regime penalty
• Returns a Decision with: action, confidence, position_size_modifier, stop_modifier
• Risk event override: any risk_event news → REDUCE_RISK, no new entries
• Conflict rule: strong opposing news + weak tech signal → NO_TRADE
• Weights auto-tune after min_trades_for_weight_update closed trades

Updated Trading Loop (trader.py v2)

1. Fetch market data
2. Fetch + analyze news → market_sentiment
3. Generate technical signals
4. For each signal:
   a. risk_manager.validate_signal()
   b. news_engine.get_symbol_sentiment()
   c. decision_engine.decide() → Decision
   d. Apply position_size_modifier + stop_modifier
   e. execution_engine.execute_trade()
5. Monitor positions
6. Periodic eval → parameter tuning + news utility learning

Defensive Mode Triggers

Install

pip install feedparser  # Only new dependency