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The DevilStra strategy is a trading strategy that can be used for backtesting on a trading platform or website. It implements the IStrategy interface. Here's a breakdown of what the strategy does:
populate_indicators: This method is responsible for populating the indicators used in the strategy.
It takes a dataframe and metadata as inputs and returns the updated dataframe.
populate_buy_trend: This method is used to determine the buy signals or conditions for entering a trade.
It retrieves the current whitelist of pairs and the index of the specific pair being evaluated. It then retrieves the buy parameters for that pair from a spell finder function. The buy parameters include indicators, crossed indicators, operators, and real numbers. The method generates conditions based on these parameters using a condition generator function. If there are any conditions, the buy column of the dataframe is set to 1 for the corresponding rows. The updated dataframe is returned. populate_sell_trend: This method is similar to populate_buy_trend but is used to determine the sell signals or conditions for exiting a trade. It follows a similar process as the buy trend, retrieving sell parameters, generating conditions, and updating the sell column of the dataframe. Overall, the strategy evaluates multiple conditions based on the buy and sell parameters specific to each pair being tested. It populates the corresponding columns in the dataframe with 1s to indicate the occurrence of a buy or sell signal. The strategy can be customized by modifying the buy and sell parameters for each pair.
It takes a dataframe and metadata as inputs and returns the updated dataframe.
populate_buy_trend: This method is used to determine the buy signals or conditions for entering a trade.
It retrieves the current whitelist of pairs and the index of the specific pair being evaluated. It then retrieves the buy parameters for that pair from a spell finder function. The buy parameters include indicators, crossed indicators, operators, and real numbers. The method generates conditions based on these parameters using a condition generator function. If there are any conditions, the buy column of the dataframe is set to 1 for the corresponding rows. The updated dataframe is returned. populate_sell_trend: This method is similar to populate_buy_trend but is used to determine the sell signals or conditions for exiting a trade. It follows a similar process as the buy trend, retrieving sell parameters, generating conditions, and updating the sell column of the dataframe. Overall, the strategy evaluates multiple conditions based on the buy and sell parameters specific to each pair being tested. It populates the corresponding columns in the dataframe with 1s to indicate the occurrence of a buy or sell signal. The strategy can be customized by modifying the buy and sell parameters for each pair.
stoploss: -0.97 timeframe: 4h hash(sha256): cdf8d4e0eb3b081d86246ca9fba27c14e2aa184fb343098ae59f9edbe7864fe2 indicators: sell_crossed_indicator2 indicator_trend_sma sell_operator2 crossed_indicator sell_crossed_indicator1 sell_real_num1 indicator buy_indicator1 buy_real_num2 buy_indicator0 sell_operator1 sell_indicator2 volume buy_operator0 sell_indicator1 buy_indicator2 sell_indicator0 sharp_indicator buy_crossed_indicator2 sell_crossed_indicator0 buy_operator2 buy_real_num1 buy_crossed_indicator1 buy_operator1 sell_real_num0 buy_crossed_indicator0 sell_operator0 sell_real_num2 buy_real_num0
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last change: 2022-07-11 14:56:08