{"id":5228,"date":"2024-03-20T15:21:50","date_gmt":"2024-03-20T15:21:50","guid":{"rendered":"https:\/\/eodhd.com\/financial-academy\/?p=5228"},"modified":"2025-02-05T12:50:56","modified_gmt":"2025-02-05T12:50:56","slug":"using-delisted-stocks-for-backtesting-a-3-ema-strategy-with-python","status":"publish","type":"post","link":"https:\/\/eodhd.com\/financial-academy\/backtesting-strategies-examples\/using-delisted-stocks-for-backtesting-a-3-ema-strategy-with-python","title":{"rendered":"Using Delisted Stocks for Backtesting a 3-EMA Strategy with Python"},"content":{"rendered":"
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The Exponential Moving Average (EMA) is one of the most widely used technical indicators in the trading world, yet in most cases, it is not used to its full potential. People are still using generic strategies like the EMA 50 & 200 crossover which is not a bad thing but since it’s overused by a lot of traders, its commonality has deteriorated the strategy’s efficiency. Today, we are going to explore a new EMA crossover strategy and check if it beats the market by outperforming the results of the buy\/hold strategy. <\/p>\n\n\n\n

But there’s a catch. We’re going to backtest the strategy on delisted stocks. Why? Because the price movements of delisted stocks are more volatile than of listed stocks, giving us an opportunity to test our strategy in all types of market scenarios.<\/p>\n\n\n\n

In this article, we will first extract the historical data of delisted stocks using EODHD’s end-of-day data API endpoint<\/a>, then we’ll extract the EMA data using EODHD’s technical indicator API<\/a>, and followed by that, we’ll construct the trading strategy and backtest it on a delisted stock. We will also compare the results of our EMA crossover strategy with that of the buy\/hold strategy for validation.<\/p>\n\n\n\n

With that said, 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 Trading Strategy <\/h2>\n\n\n\n

Before actually moving on to coding the trading strategy, it’s very essential to gain some background on our EMA crossover trading strategy. Our strategy consists of three EMAs with different lookback periods, and it generates signals based on certain crossover criteria.<\/p>\n\n\n\n

The following are the mechanics of our strategy:<\/p>\n\n\n\n

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  1. We enter the market if: <\/strong>EMA 10 crosses above EMA 30 and EMA 10 is greater than EMA 50<\/li>\n\n\n\n
  2. We exit the market if: <\/strong>EMA 10 crosses below EMA 30 and EMA 9 is lower than EMA 50<\/li>\n<\/ol>\n\n\n\n

    This is a simple yet effective crossover strategy that helps in capturing the direction of the price movements and provides trading signals accordingly. <\/p>\n\n\n\n

    Now that we have a good background of the trading strategy that we\u2019re going to implement, let\u2019s move on to the coding part to transform this idea into an actual useful strategy.<\/p>\n\n\n\n

    Importing Packages<\/h2>\n\n\n\n

    The first and foremost step is to import all the required packages into our Python environment. In this article, we\u2019ll be using four packages which are:<\/p>\n\n\n\n