| title | output |
|---|---|
Trading Notes for STM |
pdf_document |
In this 1st article, I would like to continue the series on quantitative finance. In the first article, I described the stylized facts of asset returns. Now I would like to introduce the concept of backtesting trading strategies and how to do it using existing frameworks in Python.
In this 2nd article, learn how to import custom data into zipline
This is the third part of a series of articles on backtesting trading strategies in Python. This time, the goal of the article is to show how to quickly and efficiently evaluate the performance of our strategies using a library called pyfolio (developed by Quantopian, the creators of zipline). pyfolio can be used as a standalone library and provide performance results based only on a provided series of returns. However, it works efficiently with zipline and I present this combination in this article.
This is the fourth part of a series of articles on backtesting trading strategies in Python. The previous ones described the following topics:
introducing the zipline framework and presenting how to test basic strategies (link)
importing custom data to use with zipline (link)
evaluating the performance of trading strategies (link)
This time, the goal of the article is to show how to create trading strategies based on Technical Analysis (TA in short). Quoting Wikipedia, technical analysis is a “methodology for forecasting the direction of prices through the study of past market data, primarily price, and volume”.
In this article, I show how to use a popular Python library for calculating TA indicators — TA-Lib — together with the zipline backtesting framework. I will create 5 strategies and then investigate which one performs best over the investment horizon.
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian – a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more.
pyfolio is a Python library for performance and risk analysis of financial portfolios that works well with the Zipline open source backtesting library. At the core of pyfolio are various tear sheets that combine various individual plots and summary statistics to provide a comprehensive view of the performance of a trading algorithm.
vectorbt is a Python package for quantitative analysis that takes a novel approach to backtesting: it operates entirely on pandas and NumPy objects, and is accelerated by Numba to analyze any data at speed and scale. This allows for testing of many thousands of strategies in seconds.
QuantStats is a Python library that performs portfolio profiling, allowing quants and portfolio managers to understand their performance better by providing them with in-depth analytics and risk metrics.
QuantStats is comprised of 3 main modules:
quantstats.stats - for calculating various performance metrics, like Sharpe ratio, Win rate, Volatility, etc.
quantstats.plots - for visualizing performance, drawdowns, rolling statistics, monthly returns, etc.
quantstats.reports - for generating metrics reports, batch plotting, and creating tear sheets that can be saved as an HTML file.
A feature-rich Python framework for backtesting and trading
backtrader allows you to focus on writing reusable trading strategies, indicators and analyzers instead of having to spend time building infrastructure. Live Trading and backtesting platform written in Python.