My master's degree completely failed to teach me how to test trading strategies.

So I spent 40 hours looking for Python backtesting libraries.

Then I started using the best ones.

But unlike my quant finance degree, these won't cost you $90,000.

Here they are for free.

AutoTrader

AutoTrader is Python-based platform intended to help in the development, optimisation and deployment of automated trading

Zipline

From Quantopian (acquired by Robinhood) the first to democratize quant trading, comes Zipline. It's a robust, fully-featured backtesting library which features slippage models, robust data handing and rich

backtrader

backtrader features live data and trading, filters and multiple data feeds at

backtesting .py

backtesting .py features a simple well-documented API, fast execution and a library of built-in base

9 trading strategies everyone should know (with Python code):

Bollinger Bands Pattern Recognition

The mid band is the moving average on the price series.

The upper and lower bands are two moving standard deviations away from the mid band.

MACD oscillator

MACD refers to Moving Average Convergence/Divergence.

MACD is a momentum trading strategy.

It assumes momentum has more impact on short-term moving average than long-term moving average.

Pairs Trading

Pairs trading is a basic form of statistical arbitrage.

It assumes that two cointegrated stocks do not drift too far away from each other.

When they do, it generates the trading signal.

Heikin-Ashi Candlestick

Heikin-Ashi refers to 'Average Bar' in Japanese.

It's an alternative style of candlestick chart.

The rules of Heiki-Ashi are designed to filter out the noise for momentum trading.

8 trading and backtesting libraries in Python that will help you find your edge:

QuantSoftware Toolkit

Python-based open source software framework designed to support portfolio construction and

quantitative

Quantitative finance, and backtesting

analyzer

Python framework for real-time financial and backtesting trading

pybacktest

Vectorized backtesting framework in Python / pandas, designed to make your backtesting

I've been using pandas since 2011.

It's the most important tool for analysts working in Python today.

If you're using Python, you need pandas.

Here's the 7 tutorials I wish I had when I started:

10 minutes to pandas

This is a short introduction to pandas, geared mainly for new

pandas tutorial on W3 Schools

A 14 section getting started guide for pandas. From an introduction to the library to more advanced features like correlation and

A Complete Introduction for Beginners

Learn some of the most important pandas features for exploring, cleaning, transforming, visualizing, and learning from

Hands-On Pandas

Solve short hands-on challenges to perfect your data manipulation

Myth:

You need a computer science degree to get started with Python.

Reality:

You need YouTube.

Get started with these 7 channels:

Anaconda Inc.

Anaconda is the world’s most popular Python data science platform. Videos directly from the folks that built

CodingEntreprenuers

Coding for Entrepreneurs is a programming series for non-technical

Enthought

Enthought has built AI solutions with science and engineering at the core. In addition to the company-developed content, it has tutorials specifically focused on the advancement of scientific

Michael Kennedy (Talk Python)

Videos, demos, and lectures about Python and web