How to Create a Python Trading Library

Introduction

Creating a Python trading library allows traders and developers to automate strategies, analyze market data, and execute trades efficiently. In this article, we will go through the essential steps to build a robust trading library in Python.

1. Setting Up the Project

Before writing any code, structure your project properly:

trading_library/
|-- src/
|   |-- __init__.py
|   |-- data_fetcher.py
|   |-- strategy.py
|   |-- backtester.py
|   |-- broker.py
|   |-- utils.py
|-- tests/
|   |-- test_data_fetcher.py
|   |-- test_strategy.py
|   |-- test_backtester.py
|-- requirements.txt
|-- setup.py
|-- README.md

Dependencies and Installation

To ensure smooth development, create a requirements.txt file:

yfinance
alpaca-trade-api
pandas
numpy
matplotlib
pytest

Then, install dependencies:

pip install -r requirements.txt

2. Fetching Market Data

A trading library needs reliable market data. Use APIs like Alpha Vantage, Yahoo Finance, or Binance API.

Example using yfinance:

import yfinance as yf