Python for Finance, 3rd Edition

Chapter 25. Automated Deployment of Trading Strategies

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Once a strategy and engine design are in place, the remaining challenge is to run them automatically and observably over time. This chapter turns the market and broker engine into an automated deployment platform that reuses the baseline machine-learning strategy from Vectorized and Event-Based Backtesting, first through historical replay and then through simulated real-time streaming with logging, monitoring, and operational safeguards.

This chapter connects the earlier event-based backtest to the engine package from Chapter 24. The same baseline logistic-regression strategy is wrapped in a deployable interface, fed with resampled bars derived from incoming ticks, and run first on historical end-of-day replay and then on irregular simulated tick streams. Along the way, this chapter adds the operational layer that a deployment needs: model refits, stateful order generation, structured logs, and monitoring summaries. The examples assume ...