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Airflow for Algorithmic Trading Automating Market Data, Backtests, Models, and Live Execution A Comprehensive Guide [#1015573]
Airflow for Algorithmic Trading: Automating Market Data, Backtests, Models, and Live Execution: A Comprehensive Guide by Hayden Van Der Post, Vincent Bisette, Alice Schwartz
English | December 7, 2025 | ISBN: N/A | ASIN: B0G5JPNBLW | 718 pages | EPUB | 0.74 Mb
Reactive Publishing
Algorithmic trading systems fail for one reason more than any other:
their pipelines are brittle.
Airflow for Algorithmic Trading is the first complete guide to designing, orchestrating, and automating robust trading infrastructure using Apache Airflow. This book shows you how to build the backbone of a modern quant operation - one where data ingestion, feature engineering, backtests, risk controls, and live execution are coordinated with precision.
Across hedge funds, prop firms, and fintech platforms, Airflow has quietly become the orchestration engine that keeps trading models running reliably. In this book, you will learn how to apply Airflow to the unique demands of financial markets: real-time ingestion, event-driven triggers, multi-source pipelines, ML-based signal generation, and resilient execution pathways.
You will not just learn Airflow as a tool. You will learn it as an engineering discipline central to quant trading success.What You Will Build Inside This BookAutomated market data ingestion pipelines for equities, futures, FX, and optionsETL/ELT systems for transforming raw tick, OHLC, and alternative datasetsFeature engineering and factor model pipelines that recompute cleanly and reliablyAutomated backtesting workflows that evaluate strategies on scheduleML-driven signal-generation pipelines with training, validation, and model refresh cyclesEvent-driven trading workflows triggered by economic releases, volatility shifts, or portfolio signalsLive trading orchestration with failover logic, error notifications, and task-level risk controlsComprehensive monitoring dashboards, SLA enforcement, and trade-level audit trailsYou Will Learn How ToDesign DAGs for financial workflows that must run with absolute reliabilityStructure robust dependencies, retries, conditional logic, and idempotent transformationsIntegrate Airflow with Interactive Brokers, Polygon, Tradier, Tiingo, Binance, and custom APIsDeploy Airflow using Docker, Kubernetes, CeleryExecutor, or managed cloud servicesBuild guardrails using sensors, hooks, triggers, and event propagationScale your pipelines as your capital, complexity, and compute demands growWho This Book Is ForAlgorithmic tradersQuantitative analystsData engineers in financeML practitioners building trading pipelinesPortfolio managers who need reliable automationAnyone moving from "scripts" to real production trading systemsYou do not need prior Airflow experience, only a foundation in Python and a desire to build resilient, automated trading infrastructure.Why This Book Matters
Algorithmic trading is not just models, it is pipelines.
Alpha dies without reliability, speed, and automation.
Airflow for Algorithmic Trading gives you the architecture, the patterns, and the implementation detail required to operate at a professional level.
Build the systems behind the strategies.
The edge begins with the pipeline.
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