Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python - Image 1

Price loading...

Packt Publishing Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python

Name: Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python
Brand: Packt Publishing

Professional Finance

Price History & Forecast

No Price Data Available

Price history will appear here once data is collected from Amazon.

Price Distribution

No price data available for histogram

Description

Harness the power of Python libraries to transform freely available financial market data into algorithmic trading strategies and deploy them into a live trading environment Get With Your Book: PDF Copy, AI Assistant, and Next-Gen Reader Free Key Features Follow practical Python recipes to acquire, visualize, and store market data for market research Design, backtest, and evaluate the performance of trading strategies using professional techniques Deploy trading strategies built in Python to a live trading environment with API connectivity Book Description Discover how Python has made algorithmic trading accessible to non-professionals with unparalleled expertise and practical insights from Jason Strimpel, founder of PyQuant News and a seasoned professional with global experience in trading and risk management. This book guides you through from the basics of quantitative finance and data acquisition to advanced stages of backtesting and live trading. Detailed recipes will help you leverage the cutting-edge OpenBB SDK to gather freely available data for stocks, options, and futures, and build your own research environment using lightning-fast storage techniques like SQLite, HDF5, and ArcticDB. This book shows you how to use SciPy and statsmodels to identify alpha factors and hedge risk, and construct momentum and mean-reversion factors. You’ll optimize strategy parameters with walk-forward optimization using VectorBT and construct a production-ready backtest using Zipline Reloaded. Implementing all that you’ve learned, you’ll set up and deploy your algorithmic trading strategies in a live trading environment using the Interactive Brokers API, allowing you to stream tick-level data, submit orders, and retrieve portfolio details. By the end of this algorithmic trading book, you'll not only have grasped the essential concepts but also the practical skills needed to implement and execute sophisticated trading strategies using Python. What you will learn Acquire and process freely available market data with the OpenBB Platform Build a research environment and populate it with financial market data Use machine learning to identify alpha factors and engineer them into signals Use VectorBT to find strategy parameters using walk-forward optimization Build production-ready backtests with Zipline Reloaded and evaluate factor performance Set up the code framework to connect and send an order to Interactive Brokers Who this book is for Python for Algorithmic Trading Cookbook equips traders, investors, and Python developers with code to design, backtest, and deploy algorithmic trading strategies. You should have experience investing in the stock market, knowledge of Python data structures, and a basic understanding of using Python libraries like pandas. This book is also ideal for individuals with Python experience who are already active in the market or are aspiring to be. Table of Contents Acquire Free Financial Market Data with Cutting-edge Python Libraries Analyze and Transform Financial Market Data with pandas Visualize Financial Market Data with Matplotlib, Seaborn, and Plotly Dash Store Financial Market Data on Your Computer Build Alpha Factors for Stock Portfolios Vector-Based Backtesting with VectorBT Event-Based Backtesting Factor Portfolios with Zipline Reloaded Evaluate Factor Risk and Performance with Alphalens Reloaded Assess Backtest Risk and Performance Metrics with Pyfolio Set Up the Interactive Brokers Python API (N.B. Please use the Read Sample option to see further chapters)

Product Specifications

Brand: Packt Publishing
Format: Paperback
ASIN: 1835084702
Category: Books > Subjects > Business, Finance & Law > Professional Finance
Domain: Amazon UK
Release Date: 16 August 2024
Listed Since: 09 July 2024

Barcode

No barcode data available

Packt Publishing Python for Algorithmic Trading Cookbook: Recipes for designing, building, and deploying algorithmic trading strategies with Python

Price History & Forecast

No Price Data Available

Price Distribution

Description

Product Specifications

Barcode

Similar Products You Might Like

Machine Learning for Algorithmic Trading: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Machine Learning for Algorithmic Trading - Second Edition: Predictive models to extract signals from market and alternative data for systematic trading strategies with Python

Algorithmic Trading Hands-On Approach Using Python

Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Market Making Algorithms: 33 Comprehensive Powerful Algorithms With Full Python Code

Python for Algorithmic Trading: From Idea to Cloud Deployment

Algorithmic Pattern Recognition in Day Trading (The Artificial Edge: Quantitative Trading Strategies with Python)

Quantitative Trading Strategies Using Python: Technical Analysis, Statistical Testing, and Machine Learning

Quantitative Algorithm Design for FX Markets: Building High-Frequency Trading Systems With Python (The Artificial Edge: Quantitative Trading Strategies with Python)

Day Trade With AI: Theoretical foundations for discretionary, algorithmic, and diversified trading with AI

Quant_Py: Python for Quantitative Finance

Options Pricing with AI (The Artificial Edge: Quantitative Trading Strategies with Python)

Forward–Backward SDEs in Algorithmic Trading: BSDEs, Deep BSDE Solvers, and Risk-Sensitive Control With Python (Computational Mathematics Library)

The Backtesting Masterclass: Build, Validate, and Optimize with Python: Ideas are cheap. Verified performance is priceless.

Practical Quantitative Finance with Python: A Comprehensive Guide to Quantitative Analysis and Trading Strategy Development

Statistical Arbitrage in Algorithmic Trading: Stationarity, Cointegration, and Mean-Reverting Stochastic Processes With Python (Computational Mathematics Library)

Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection

High-Frequency Trading and Order Book Dynamics (The Artificial Edge: Quantitative Trading Strategies with Python)

Machine Learning for Factor Investing: Python Version (Chapman and Hall/CRC Financial Mathematics Series)

Quantitative Trading Systems: How to Build Automated Options and Volatility Strategies in Python: A Comprehensive Guide

Stochastic Finance with Python: Design Financial Models from Probabilistic Perspective

Full stack Expert Advisor Programming For Meta trader 5: Learn How to Develop the Perfect Trading Algorithm for Gold /Forex Market

Ultimate Quant Job Interview Questions Workbook: Brief Crash Courses and Real Interview Questions taking you from Beginner to Wall Street Offers (The ... Quantitative Trading Strategies with Python)