Frequently Asked Questions

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There are a few challenges to keep in mind when building infrastructure to run strategies yourself:

  1. Data infrastructure and quality control
    Access to high-quality data is not straightforward. Even with strong vendors, you still need to continuously ingest, normalize, and curate data, while handling issues like corporate actions, ticker changes, market halts, and missing prints. Options data alone can easily exceed 60GB per day, and strategies often require the full depth of the feed at all times, not just snapshots.
  2. Continuous operations and reliability
    Strategies don’t run “when convenient”, they must operate throughout the entire trading day with strict timing constraints. This requires production-grade infrastructure with redundancy, failover systems, monitoring, and the ability to recover instantly from outages or data gaps without corrupting positions.
  3. Derivatives modeling and calibration complexity
    Real-world derivatives modeling goes far beyond textbook Black-Scholes. Even simple replication or delta-hedging requires continuously calibrated inputs such as interest rate curves, dividend forecasts, and full volatility surfaces. These inputs are themselves noisy, high-dimensional, and market-dependent, and small errors can compound quickly into large P&L deviations.
  4. Research, backtesting, and simulation integrity (often underestimated)
    Developing strategies is not just about coding a signal, it requires a robust research pipeline where backtests are realistic, bias-free, and reproducible. This includes handling look-ahead bias, survivorship bias, execution assumptions, and realistic transaction costs (including slippage and impact). Many strategies that look strong in research fail once implemented because the simulation environment was not sufficiently faithful to live trading conditions.