Torrent details for "Strimpel J. Python for Algorithmic Trading Cookbook. Recipes...w…" Log in to bookmark
Controls:
×
Report Torrent
Please select a reason for reporting this torrent:
Your report will be reviewed by our moderation team.
×
Report Information
Loading report information...
This torrent has been reported 0 times.
Report Summary:
| User | Reason | Date |
|---|
Failed to load report information.
×
Success
Your report has been submitted successfully.
Checked by:
Category:
Language:
None
Total Size:
14.4 MB
Info Hash:
6BEB4F88E70ED18D397CEFB803090A8B11E796D3
Added By:
Added:
April 22, 2026, 6:53 a.m.
Stats:
|
(Last updated: April 22, 2026, 6:53 a.m.)
| File | Size |
|---|---|
| Code.zip | 4.6 MB |
| Strimpel J. Python for Algorithmic Trading Cookbook. Recipes...with Python 2024.pdf | 9.8 MB |
Name
DL
Uploader
Size
S/L
Added
NOTE
SOURCE: Strimpel J. Python for Algorithmic Trading Cookbook. Recipes...with Python 2024
-----------------------------------------------------------------------------------
COVER
-----------------------------------------------------------------------------------
MEDIAINFO
Textbook in PDF format 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 Manage Orders, Positions, and Portfolios with the IB API
×