IBKR Autonomous Trader

Autonomous paper trading research agent for Interactive Brokers (IBKR). Use this skill whenever the user wants to connect to IBKR, execute trades, manage positions, analyze strategies, run paper trading workflows, backtest signals, or build any automated trading system with TWS or IBKR Gateway. Triggers on: "trade", "IBKR", "Interactive Brokers", "paper trading", "order execution", "position management", "quant strategy", "bracket order", "stop loss", "ib_insync", or any mention of automated trading. Always use this skill for trading-related tasks — even exploratory ones.

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.

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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

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File Map

File	Purpose
scripts/trader.py	Main trading loop — entry point
scripts/strategy_engine.py	Signal generation, indicator logic
scripts/risk_manager.py	Position sizing, kill switch, daily loss limit
scripts/execution_engine.py	Order placement, modification, cancellation
scripts/performance.py	Trade logging, PnL, Sharpe, self-improvement
config/settings.yaml	All tunable parameters
memory/trades.json	Full trade history
memory/strategies.json	Strategy parameter versions
memory/performance.json	Rolling performance metrics

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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

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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

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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

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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

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Adding a New Strategy

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

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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.

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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

Event	Response
risk_event in news	REDUCE_RISK — no new entries, stops tightened
high_impact news	Position size × 0.6, stops × 0.85
News opposes signal AND tech < 0.8	NO_TRADE
Final confidence < 0.45	NO_TRADE
Extreme volatility regime	Volatility score = 0.20

Install

pip install feedparser  # Only new dependency