The general idea behind backtesting is to evaluate the performance of a trading strategy—built using some heuristics or technical indicators—by applying it to historical data.

In this recipe, we introduce one of the available frameworks for backtesting in Python: backtrader. Key features of this framework include:

• A vast amount of available technical indicators (backtrader also provides a wrapper around the popular TA-Lib library) and performance measures
• Ease of building and applying new indicators
• Multiple data sources available (including Yahoo Finance, Quandl)
• Simulating many aspects of real brokers, such as different types of orders (market, limit, stop), slippage (the difference between the intended and actual execution prices of an order), commission, going long/short, and so on
• A one-line call for a plot, with all results

For this recipe, we consider a basic strategy based on the SMA. The key points of the strategy are as follows:

• When the close price becomes higher than the 20-day SMA, buy one share.
• When the close price becomes lower than the 20-day SMA and we have a share, sell it.
• We can only have a maximum of one share at any given time.
• No short selling is allowed.

We run the backtesting of this strategy, using Apple's stock prices from the year 2018.