In this post, we will explore a simple moving average (SMA) cross-over strategy for trading financial assets. The strategy is implemented using the backtesting library, which allows us to test our strategy on historical data and evaluate its performance. This is the code. We will go through it step by step. The SMA cross-over strategy is based on the idea that when a short-term SMA crosses above a long-term SMA, it is a signal to buy the asset, and when the short-term SMA crosses below the long-term SMA, it is a signal to sell the asset. This strategy can be implemented in a few lines of code using the backtesting library. First, we import the Backtest and Strategy classes from the backtesting library, as well as the crossover function, which we will use to check for SMA cross-overs: Next, we define a SMA function that calculates the simple moving average of a given series of values. This function takes two arguments: values, which is a list or array of values, and n, which is the number of previous values to take into account when calculating the SMA: Now, we can define our SmaCross strategy class, which subclasses the Strategy class from the backtesting library. The class defines two class variables, n1 and n2, which represent the two moving average lags that are used in the strategy. The init method of the class precomputes the two SMAs using the SMA function, which we defined earlier: The next method of the class is called at each step of the backtesting process and implements the logic of the strategy. If the first SMA crosses above the second SMA, any existing short trades are closed and the asset is bought; if the second SMA crosses above the first SMA, any existing long trades are closed and the asset is sold: Now that we have implemented our SmaCross strategy class, we can use it to test our strategy on some historical data. We will use the Backtest class from the backtesting library to run the backtesting process. First, we need to import the historical data that we will use for the backtesting. In this example, we will use the GOOG data, which is included in the backtesting library and represents the daily closing prices of Google's stock. Next, we create an instance of the Backtest class, passing it the GOOG data, the SmaCross strategy class that we defined earlier, and some additional parameters such as the initial cash balance and the commission rate: Finally, we call the run method on the Backtest instance to run the backtesting process and store the resulting stats in a stats variable: Now that we have run the backtesting process, we can use the stats variable to evaluate the performance of our strategy. The stats variable is a dictionary that contains a variety of metrics and plots that can help us understand how the strategy performed. We can extract the equity curve from it and store it in a equity variable. We can then use the create_tearsheet method of the TimeseriesTools class to create a tearsheet, which is a plot that displays the equity curve and some additional metrics such as the Sharpe ratio and the maximum drawdown. This plot can help us visualize the performance of the strategy and understand how it evolved over time. Our tearsheet will create these graphs! You’ll find the tearsheet here:  Also check out some other posts!