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Könyv Quantitative Trading Strategies with Python Hayden Van Der Post

Quantitative Trading Strategies with Python

Implementing Econometrics, Machine Learning, and Algorithmic Execution

Szerző: Hayden Van Der Post, Vincent Bisette, Alice Schwartz
Nyelv: Angol
Kötés: Puha kötésű
Kiadó: Independently published
Elérhetőség: Várható készletfeltöltés
Küldés 30. 06. 2026
15 076 Ft
Reactive PublishingDiscover the power of data-driven trading with Quantitative Trading Strategies wi...

Információk a könyvről

Szerző
Hayden Van Der Post, Vincent Bisette, Alice Schwartz
Nyelv
Angol
Kötés
Könyv - Puha kötésű
Kiadva
2026
oldal
450
EAN
9798184353500
Enbook ID
53026090
Kiadó
Independently published
Súly
541
Méretek
152 x 229 x 28

Teljes leírás

Reactive Publishing

Discover the power of data-driven trading with Quantitative Trading Strategies with Python. Whether you're a seasoned quant, aspiring algorithmic trader, or Python developer looking to break into finance, this practical guide delivers battle-tested techniques to design, backtest, and deploy profitable trading systems in today's competitive markets.

Master the full quantitative trading pipeline:

  • Econometrics Foundations: Build robust statistical models using OLS, ARIMA, GARCH, cointegration, and regime-switching frameworks to identify market inefficiencies and forecast volatility.
  • Machine Learning for Alpha Generation: Implement supervised and unsupervised algorithms, including random forests, gradient boosting, LSTM networks, and reinforcement learning, to discover hidden patterns and adaptive trading signals.
  • Algorithmic Execution: Engineer high-performance execution engines with vectorized backtesting (pandas & NumPy), event-driven simulations (Backtrader & Zipline), risk management overlays, and low-latency order routing strategies.
  • End-to-End System Design: Combine econometrics, ML, and execution into production-ready strategies with walk-forward optimization, Monte Carlo simulations, transaction cost modeling, and portfolio optimization.

Packed with complete, ready-to-run Python code (GitHub repository included), real-world market data examples, and clear explanations, this book bridges theory and practice. Learn how to avoid common pitfalls like overfitting, survivorship bias, and curve-fitting while implementing institutional-grade risk controls and performance attribution.

Perfect for:

  • Quantitative analysts and traders seeking to systematize their edge
  • Python developers entering fintech and hedge funds
  • Students and researchers in financial engineering
  • Anyone ready to move beyond discretionary trading into automated, data-driven profits

Vincent Bisette draws on years of hands-on experience in quantitative research and algorithmic trading to provide clear, actionable insights that translate directly into trading performance.

Take your trading to the next level, turn data into consistent alpha with Python. Start building sophisticated quantitative strategies today.