Predictive models to extract signals from market and alternative data for systematic trading strategies with Python, 2nd Edition
by Stefan Jansen
Recommended by Kirk Borne
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Recommended by 1 source.
Leverage machine learning to design and backtest automated trading strategies for realworld markets using pandas, TALib, scikitlearn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio. Key Features Design, train, and evaluate machine learning algorithms that underpin automated trading strategies Create a researc...
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Why recommended
Recommended by 1 source.
People and public figures who have recommended this book.
Recommendation proof is sourced from public posts, interviews, reading lists, and cited references.
Kirk Borne
“A pathway to learning #Python for #AlgorithmicTrading: ————— #BigData #DataScience #AI #MachineLearning #Coding #DataScientists #IoT #IoTPL #TimeSeries #PredictiveAnalytics #Statistics ———— + See this brilliant book: by @ml4trading”
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Consider Life 3.0 by Max Tegmark. Recommended by 18 sources.
“Life 3.0 reads like a long, wide-ranging conversation with a physicist who loves big if-then thought experiments. The useful part is its panoramic sweep across possible AI futures—from job automation to cosmic colonization—forcing you to consider timelines you might otherwise avoid. The limitation is that the speculative breadth often outruns the depth; chapters can feel meandering, and some readers will find the cosmic-scale scenarios too detached from practical concerns, making it hard to ground in real urgency.”
Each recommendation is collected from a public source — interviews, articles, or curated lists — and linked to its original URL. Books with many verifiable recommendations from respected people rank higher.
