{"id":4545,"date":"2024-01-15T09:30:49","date_gmt":"2024-01-15T09:30:49","guid":{"rendered":"https:\/\/eodhd.com\/financial-academy\/?p=4545"},"modified":"2025-02-05T12:52:40","modified_gmt":"2025-02-05T12:52:40","slug":"backtesting-a-killer-mean-reversion-trading-strategy-with-python","status":"publish","type":"post","link":"https:\/\/eodhd.com\/financial-academy\/backtesting-strategies-examples\/backtesting-a-killer-mean-reversion-trading-strategy-with-python","title":{"rendered":"Backtesting a Killer Mean Reversion Trading Strategy with Python"},"content":{"rendered":"\n

There is an ocean of strategies existing in the algo trading space, but what’s tough is filtering and choosing the most reliable and profitable ones out of them. In this article, we are going to build one such strategy (a mean-reversion strategy to be specific) that ticks all the boxes of a “good strategy” and backtest it on Python to analyze its results and profitability. We’ll be using EODHD’s stock tick data API<\/a> accessed via the EODHD python package for effective backtesting. So without further ado, let’s dive into the article!<\/p>\n\n\n\n

Register & Get Data<\/span><\/a><\/p>\n\n\n\n\n\n\n

The Backtesting a Mean-Reversion Strategy<\/strong> Description<\/strong><\/h2>\n\n\n\n

Before moving on to the coding part, it’s essential to have a good background about the strategy we are going to build today. The strategy is a mean-reversion trading strategy which means it’s a strategy that relies on the idea that the stock price will return to its average or normal price after a certain point (usually an extreme price movement). <\/p>\n\n\n\n

One of the best indicators used for analyzing mean reversions is the Relative Strength Index (RSI) indicator. We’ll be incorporating this indicator along with SMA to create our own custom mean-reversion trading strategy.<\/p>\n\n\n\n

We enter the market if: the RSI 10 is lower than 30 and the market is greater than the SMA 200<\/p>\n\n\n\n

We exit the market if: the RSI 10 is greater than 70 and the market is below the SMA 200<\/p>\n\n\n\n

Though our strategy is barely complex and lies more on the simplistic side, it’s still a highly effective trading strategy. Now, let’s move on to the coding part to see if our strategy proves this point.<\/p>\n\n\n\n

Step 1\/6:<\/strong> Importing Packages<\/strong><\/h3>\n\n\n\n

The first and foremost step in this process of creating a mean-reversion trading strategy is importing the required packages into the Python environment is a non-skippable step. The primary packages are going to be eodhd for extracting historical stock data, Pandas for data formatting and manipulations, and Pandas TA-Lib for calculating technical indicators. The secondary packages are going to be Math for mathematical functions, Datetime for working with dates, and Termcolor for font customization (optional).<\/p>\n\n\n\n

\n
\n \n
# IMPORTING PACKAGES\n\nimport pandas as pd\nfrom eodhd import APIClient\nimport pandas_ta as ta\nfrom datetime import datetime\nfrom termcolor import colored as cl\nimport math <\/code><\/pre>\n\n                <\/div>\n                
\n