Show HN: PyBroker – Algotrading in Python with Machine Learning

📰 Hacker News · pyfreak182

Learn how to use PyBroker, a Python framework for algorithmic trading with machine learning, to develop and fine-tune trading strategies

intermediate Published 9 Mar 2023

Action Steps

Install PyBroker using pip
Import PyBroker and connect to Alpaca or Yahoo Finance for historical data
Create and execute trading rules using PyBroker's backtesting engine
Train and backtest machine learning models using Walkforward Analysis
Evaluate and refine trading strategies based on performance metrics

Who Needs to Know This

Quantitative traders and data scientists can benefit from using PyBroker to build and test algorithmic trading strategies, while developers can contribute to the open-source framework

Key Insight

💡 PyBroker's Walkforward Analysis allows for realistic simulation of trading strategies

Full Article

Hello, I am excited to share PyBroker with you, a free and open-source Python framework that I developed for creating algorithmic trading strategies, including those that utilize machine learning. With PyBroker, you can easily develop and fine-tune trading rules, build powerful ML models, and gain valuable insights into your strategy's performance. Some of the key features of PyBroker include: - A super-fast backtesting engine built using NumPy and accelerated with Numba. - The ability to create and execute trading rules and models across multiple instruments with ease. - Access to historical data from Alpaca and Yahoo Finance. - The option to train and backtest models using Walkforward Analysis, which simulates how the strategy would perform during actual trading. The basic concept behind Walkforward Analysis is that it splits your historical data into multiple time windows and then "walks forward" in time in the same way that the strategy would be executed and retraine

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