Building a Profitable Trading Strategy with Backtrader: A Crossover Strategy Tutorial
Introduction
The crossover strategy is a popular trading strategy used by many traders. It involves plotting two moving averages – one with a shorter time frame (i.e., the "fast" MA) and one with a longer time frame (i.e., the "slow" MA). When the fast MA crosses over the slow MA, it signals a buy, and when the slow MA crosses over the fast MA, it signals a sell. In this article, I will discuss how to code the crossover strategy using the backtrader python library.
How Does the Crossover Strategy Work?
The crossover strategy works by plotting two moving averages – one with a shorter time frame (i.e., the "fast" MA) and one with a longer time frame (i.e., the "slow" MA). This strategy is based on the idea that short-term price movements are more volatile than long-term price movements. By taking advantage of this volatility, traders can make profitable trades. Some of the benefits of the crossover strategy include the following:
It is easy to understand and implement
Reliable since short-term price movements tend to be more volatile than long-term.
Flexible since traders can adjust the time frames of the moving averages to match their trading style.
Now that we understand the crossover strategy, it's time to code! I will use the backtrader python library to build a simple moving average crossover strategy.
Intro to Backtrader
Backtrader is an open-source Python library for building automated trading strategies. It is used to develop, backtest, and optimize trading strategies using historical data, making it easy to build our simple moving average crossover strategy. The first step is to set up the backtrader environment. To do this, I will need to import the math and backtrader library and create a Strategy object. Next, I will set up the two moving averages – the fast and slow-moving averages.
The next step is to code the buy and sell conditions. First, I'll define the buy condition as when the fast-moving average crosses over the slow-moving average, and I am not already in a trade. I will only use 50% of my account cash balance when entering a trade. The sell condition will be defined as when the slow-moving average crosses over the fast-moving average, and I am in a position. I will exit the entire position when the sell condition is met.
Finally, I will run the strategy to see how it performs. To do this, I will need to create a Cerebro object and add our strategy. Then, I will need to add a data feed and run the strategy. I will use the yfinance Python library to download historical data from Yahoo Finance. I will use the SPY ticker and download daily data between 2022-01-01 and 2022-12-31 to determine how the strategy performs. Finally, I will set the initial account balance to 100000 and run a buy-and-hold strategy that invests during the same period to compare how our strategy would perform relative to being invested in the market throughout the duration.
Backtesting results show that the moving average crossover strategy entered into six buy and six sell entries, starting with a portfolio value of $100,000 on 2022-01-01. The final portfolio value on 2022-12-31 was $100,978.24, resulting in a profit of $978.24. During the same period, if a buy-and-hold strategy had been used instead, the account value on 2022-12-31 would have been $80,054.84 - a loss of $19,945.15. This shows that while the crossover strategy made a small profit, it avoided an almost 20% loss.
In conclusion, the crossover strategy is a popular trading strategy used by many traders. It is based on the idea that short-term price movements are more volatile than long-term price movements. In this tutorial, I discussed how to code the crossover strategy using the backtrader python library. We saw that the strategy was able to make a small profit and avoid an almost 20% loss.
If you are interested in learning more about trading strategies or would like me to code and deploy a strategy for you using real money, please don't hesitate to reach out.
