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This strategy, referred to as S04, is an implementation of the IStrategy interface for backtesting trading strategies. Let's break down its components:
The populate_indicators method takes a DataFrame and metadata as input and returns the same DataFrame. In this strategy, it doesn't modify the indicators and simply returns the original DataFrame.
The populate_buy_trend method is responsible for determining the conditions for buying.
It creates a list called conditions and appends a single condition to it.
This condition uses the qtpylib.crossed_above function to check if a specific value has crossed above another value multiplied by a vertical push factor. The values used for this calculation are obtained from the DataFrame and specified by the buy_fast_key, buy_slow_key, buy_horizontal_push, and buy_vertical_push attributes. If the conditions list is not empty (indicating that the buying condition is met), the method sets the 'buy' column of the DataFrame to 1 for the corresponding rows where the conditions are satisfied. Finally, it returns the modified DataFrame. The populate_sell_trend method is similar to populate_buy_trend but handles the conditions for selling. It creates a list called conditions and appends a condition using the qtpylib.crossed_below function to check if a specific value has crossed below another value multiplied by a vertical push factor. The values used for this calculation are obtained from the DataFrame and specified by the sell_fast_key, sell_slow_key, sell_horizontal_push, and sell_vertical_push attributes. If the conditions list is not empty (indicating that the selling condition is met), the method sets the 'sell' column of the DataFrame to 1 for the corresponding rows where the conditions are satisfied. Finally, it returns the modified DataFrame. Overall, this strategy uses specific indicators and their crossing points to generate buy and sell signals based on configurable parameters.
The populate_buy_trend method is responsible for determining the conditions for buying.
It creates a list called conditions and appends a single condition to it.
This condition uses the qtpylib.crossed_above function to check if a specific value has crossed above another value multiplied by a vertical push factor. The values used for this calculation are obtained from the DataFrame and specified by the buy_fast_key, buy_slow_key, buy_horizontal_push, and buy_vertical_push attributes. If the conditions list is not empty (indicating that the buying condition is met), the method sets the 'buy' column of the DataFrame to 1 for the corresponding rows where the conditions are satisfied. Finally, it returns the modified DataFrame. The populate_sell_trend method is similar to populate_buy_trend but handles the conditions for selling. It creates a list called conditions and appends a condition using the qtpylib.crossed_below function to check if a specific value has crossed below another value multiplied by a vertical push factor. The values used for this calculation are obtained from the DataFrame and specified by the sell_fast_key, sell_slow_key, sell_horizontal_push, and sell_vertical_push attributes. If the conditions list is not empty (indicating that the selling condition is met), the method sets the 'sell' column of the DataFrame to 1 for the corresponding rows where the conditions are satisfied. Finally, it returns the modified DataFrame. Overall, this strategy uses specific indicators and their crossing points to generate buy and sell signals based on configurable parameters.
stoploss: -0.271 timeframe: 5m hash(sha256): a8aec0e57b63c598ecd3e82ce0b911b00c701793c83226d4548c15a61280a1af
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-02 19:54:08