Strategy: DevilStra2
Downloaded: 20220116
Stoploss: -1
Downloaded: 20220116
Stoploss: -1
The DevilStra2 strategy is a trading strategy implemented as a class in Python. It is used for backtesting on a trading platform or website. Here is a breakdown of what the strategy does:
populate_indicators function:
This function takes a dataframe and metadata as input and returns the same dataframe.
It is responsible for populating indicators in the dataframe.
However, in the given code, this function doesn't modify the dataframe or add any indicators.
populate_buy_trend function: This function takes a dataframe and metadata as input and returns the modified dataframe. It implements the buy logic of the strategy. It retrieves the list of trading pairs and their corresponding index. It checks the length of the buy_spell value (a parameter used for buying) and compares it with the length of the trading pairs. If the lengths don't match, it prints an error message and exits. It retrieves the buy parameters based on the pair index and constructs conditions based on these parameters. It generates conditions using the condition_generator function for up to three sets of buy indicators, buy crossed indicators, buy operators, and buy real numbers. If conditions are generated, it updates the 'buy' column of the dataframe to indicate buying signals. Finally, it returns the modified dataframe. populate_sell_trend function: This function takes a dataframe and metadata as input and returns the modified dataframe. It implements the sell logic of the strategy. It follows a similar structure as the populate_buy_trend function but for sell signals. It retrieves the sell parameters based on the pair index and constructs conditions based on these parameters. It generates conditions using the condition_generator function for up to three sets of sell indicators, sell crossed indicators, sell operators, and sell real numbers. If conditions are generated, it updates the 'sell' column of the dataframe to indicate selling signals. Finally, it returns the modified dataframe. Overall, the strategy defines conditions based on various indicators and their thresholds for buying and selling signals. It populates the 'buy' and 'sell' columns of the dataframe accordingly to indicate the occurrence of these signals.
It is responsible for populating indicators in the dataframe.
However, in the given code, this function doesn't modify the dataframe or add any indicators.
populate_buy_trend function: This function takes a dataframe and metadata as input and returns the modified dataframe. It implements the buy logic of the strategy. It retrieves the list of trading pairs and their corresponding index. It checks the length of the buy_spell value (a parameter used for buying) and compares it with the length of the trading pairs. If the lengths don't match, it prints an error message and exits. It retrieves the buy parameters based on the pair index and constructs conditions based on these parameters. It generates conditions using the condition_generator function for up to three sets of buy indicators, buy crossed indicators, buy operators, and buy real numbers. If conditions are generated, it updates the 'buy' column of the dataframe to indicate buying signals. Finally, it returns the modified dataframe. populate_sell_trend function: This function takes a dataframe and metadata as input and returns the modified dataframe. It implements the sell logic of the strategy. It follows a similar structure as the populate_buy_trend function but for sell signals. It retrieves the sell parameters based on the pair index and constructs conditions based on these parameters. It generates conditions using the condition_generator function for up to three sets of sell indicators, sell crossed indicators, sell operators, and sell real numbers. If conditions are generated, it updates the 'sell' column of the dataframe to indicate selling signals. Finally, it returns the modified dataframe. Overall, the strategy defines conditions based on various indicators and their thresholds for buying and selling signals. It populates the 'buy' and 'sell' columns of the dataframe accordingly to indicate the occurrence of these signals.
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -1 timeframe: 5m hash(sha256): 464de4abb66e751407901baf43a4965886bf1f644647804bbb7ca65bfe8ecec7 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
Similar Strategies: (based on used indicators)
Strategy: DevilStra, Similarity Score: 96.67%
Strategy: DevilStra_2, Similarity Score: 96.67%
Strategy: DevilStra_679, Similarity Score: 96.67%
Strategy: DevilStra_866, Similarity Score: 96.67%
last change: 2024-07-28 02:27:31
Freqtrade Strategy Ninja
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