Python Algorithmic Trading for Stocks: Build, Backtest, and Automate Winning Strategies

Monika Gupta‏

تیر ۲۰۲۶ · BookCollection

درباره این ای-کتاب

Turn market data into repeatable, testable results with Python Algorithmic Trading for Stocks by Monika Gupta. This practical, step-by-step guide shows you how to design, backtest, and automate stock trading strategies using modern Python tools and rigorous research methods. You’ll learn how to structure an algorithmic workflow—from sourcing and cleaning price/volume data to building trading signals, defining risk controls, and evaluating performance with metrics that matter. Inside, you’ll develop strategies using proven quantitative ideas such as moving-average and momentum systems, volatility-aware position sizing, and rule-based entry/exit logic. You’ll also learn to validate ideas responsibly through walk-forward testing and out-of-sample evaluation, helping reduce overfitting and improve confidence before going live. Whether you’re a beginner ready to move beyond basic scripts or an experienced coder seeking a structured approach to trading development, this book emphasizes clear implementation and actionable experimentation. You’ll finish with a toolkit for converting research into automation—so strategies can run consistently, monitor live conditions, and manage orders with disciplined execution. With Python-centric examples and a focus on measurable outcomes, Python Algorithmic Trading for Stocks is your roadmap to building strategies that are not only profitable on paper, but engineered for real-world decision making.

درباره نویسنده

Monika Gupta is a quantitative developer and educator focused on practical algorithmic trading systems. She bridges the gap between trading theory and software implementation, helping readers build strategies they can measure, test, and automate. Her approach emphasizes disciplined backtesting, robust evaluation, and risk-aware design using Python. Monika writes to make complex quantitative concepts accessible, while still grounded in real-world constraints and dependable engineering practices. Through her work, she empowers readers to turn ideas into automated workflows with repeatable results.