Algorithmic Trader

Role Overview Seeking an experienced Algorithmic Trading Engineer with deep expertise in futures trading and algorithmic trading systems to operate, maintain, and enhance a proprietary Renkobased trading platform for US index futures (ES, NQ, MES, MNQ). This role combines hands-on coding with real-market execution responsibility.Location: Remote (India) Experience: 3–7 yearsType: Contract About the Trading System The Trading System is a production-grade algorithmic trading platform that: Trades ES, NQ, MES, MNQ futures via automated strategies Uses Renko chart-based logic with dynamic brick sizing Integrates with Interactive Brokers (IB) for market data and execution Manages trades via a state machine–driven engine (multiple position states) Incorporates volatility analysis using VIX/VXN Includes a full back testing framework against historical tick data Uses a queue-based execution layer for robust order handlingKey Responsibilities 1. Live System Execution & Operations Deploy, configure, and run the trading system in paper and live environments Set up and maintain MySQL schemas, connections, and performance Configure and monitor IB TWS/Gateway sessions Oversee live strategy execution during US market hours (9:30 AM–4:00 PM ET) Monitor system health and ensure near-continuous uptime during trading Diagnose and resolve issues with connectivity, data feeds, and order execution 2. Strategy Maintenance & Enhancements Understand and maintain the V221GLi strategy and related versions (V3, V4, V221, etc.) Adjust parameters based on backtests and changing market conditions Implement new strategy versions within the existing modular architecture Debug and fix issues in entry/exit logic, state transitions, and trade management Optimize Renko brick logic, ATR sizing, and indicator computations 3. Backtesting & Performance Analysis Run comprehensive backtests using historical data (e.g., historical_ticks) Analyze results, generate P&L and performance reports, and validate changes Compare strategy variants and recommend improvements based on data Produce day-by-day and trade-by-trade analytics and summaries 4. Data Management & Quality Import and manage historical market data (CSV, Excel, database imports) Ensure data integrity, handle missing/dirty data, and implement recovery workflows Maintain real-time data feeds from IB and monitor tick_temp / index_temp tables Address data synchronization and latency issues 5. Risk Management & Monitoring Implement and monitor stop-loss, profit target, and position sizing rules Track unrealized P&L, drawdowns, and risk metrics in real time Configure alerts (e.g., Slack) for critical events and risk breaches Enforce risk limits, including after-hours and Friday-close logic 6. Codebase Maintenance & New Features Refactor and optimize Python code for robustness and performance Fix bugs and edge cases in data handling and trading logic Add new indicators, strategy modules, or asset coverage as needed Maintain documentation and version control hygiene Write unit tests for core trading and risk functionsRequired Technical Skills Core Technologies Python 3.9+ (expert) OOP and modular design State machine implementations Async/concurrent programming for real-time systems SQL/MySQL (advanced) Schema design and optimization Complex queries/joins, indexing, and performance tuning Transaction and connection managementTrading & Analytics Stack Interactive Brokers API (ibapi) – hands-on experience required Market data subscriptions Order routing and execution Contract and account management TA-Lib and technical indicators ATR, EMA, and custom indicators Pandas / NumPy Time-series manipulation and analysis Edicient numeric computationsSystem & Integration Skills MySQL setup, tuning, and maintenance Working with REST APIs and WebSockets Data formats: CSV, Excel (e.g., xlwings), JSON Logging, error handling, and runtime monitoringRequired Domain Knowledge Futures Trading (Must Have) Strong knowledge of ES, NQ, MES, MNQ futures Contract specs, tick sizes, multipliers, rollover mechanics Practical experience (paper or live) with futures execution US session structure: regular hours, pre-/post-market nuances Algorithmic Trading Concepts Renko charts and brick-based strategies State machine–driven trade lifecycle management Technical indicators: EMA, ATR, MCO, etc. Volatility analysis with VIX/VXN Volume/flow-based conditions and filters Risk management: stops, targets, position sizing Backtesting and walk-forward evaluation

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