The provided code represents two classes, IMTest and IMTestOpt, that implement trading strategies for backtesting. Here is a short description of what each strategy does:
IMTest Strategy:
The populate_indicators method takes a dataframe and metadata as input. It iterates over a collection of indicators and populates the dataframe with their values.
Informative indicators are skipped.
The populate_buy_trend method populates the 'buy' column of the dataframe based on specified conditions.
It iterates over a collection of buy comparisons and evaluates each condition using the compare method. If any of the conditions are met, the corresponding row in the 'buy' column is set to 1. The populate_sell_trend method populates the 'sell' column of the dataframe based on specified conditions. It works similarly to populate_buy_trend but uses sell comparisons instead. IMTestOpt Strategy:
The populate_indicators method is the same as in IMTest, populating the dataframe with indicator values. The populate_buy_trend method generates buy conditions using the get_conditions function from ct. It evaluates the conditions and sets the 'buy' column to 1 for rows where all conditions are met. The populate_sell_trend method generates sell conditions using the create_conditions function from iopt. It evaluates the conditions and sets the 'sell' column to 1 for rows where any condition is met. Both strategies follow a similar structure, where indicators are populated, and then buy and sell conditions are evaluated to determine the corresponding signals in the 'buy' and 'sell' columns of the dataframe.