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The given code represents a trading strategy implemented in a class called "S03." This strategy is designed for backtesting purposes on a website. The strategy consists of three main functions: populate_indicators, populate_buy_trend, and populate_sell_trend. In the populate_indicators function, the input DataFrame is returned as it is, without any modifications or additional indicators.
The populate_buy_trend function is responsible for generating buy signals based on specified conditions.
It retrieves the current whitelist of trading pairs and determines the index of the pair being processed.
If the length of the whitelist is greater than the number of buy spells defined in the strategy, an error message is displayed, indicating that the strategy needs to be re-optimized. The strategy then fetches the parameters specific to the current pair from a spell_finder function using the buy spells index. It retrieves various indicators, operator types, and threshold values for generating buy conditions. These conditions are then generated using a condition_generator function. The generated conditions are appended to a list. If there are any conditions present, the DataFrame is updated by setting the 'buy' column to 1 where all the conditions evaluate to true. The populate_sell_trend function is similar to the populate_buy_trend function but focuses on generating sell signals. It retrieves the current whitelist, determines the pair index, and fetches the sell parameters specific to the current pair. It then generates sell conditions using a condition_generator function. If there are any conditions, the DataFrame is updated by setting the 'sell' column to 1 where all the conditions evaluate to true. Overall, this strategy generates buy and sell signals based on specified indicators, operators, and threshold values. The signals are added as columns ('buy' and 'sell') in the DataFrame, indicating when to execute buy and sell orders during backtesting.
The populate_buy_trend function is responsible for generating buy signals based on specified conditions.
It retrieves the current whitelist of trading pairs and determines the index of the pair being processed.
If the length of the whitelist is greater than the number of buy spells defined in the strategy, an error message is displayed, indicating that the strategy needs to be re-optimized. The strategy then fetches the parameters specific to the current pair from a spell_finder function using the buy spells index. It retrieves various indicators, operator types, and threshold values for generating buy conditions. These conditions are then generated using a condition_generator function. The generated conditions are appended to a list. If there are any conditions present, the DataFrame is updated by setting the 'buy' column to 1 where all the conditions evaluate to true. The populate_sell_trend function is similar to the populate_buy_trend function but focuses on generating sell signals. It retrieves the current whitelist, determines the pair index, and fetches the sell parameters specific to the current pair. It then generates sell conditions using a condition_generator function. If there are any conditions, the DataFrame is updated by setting the 'sell' column to 1 where all the conditions evaluate to true. Overall, this strategy generates buy and sell signals based on specified indicators, operators, and threshold values. The signals are added as columns ('buy' and 'sell') in the DataFrame, indicating when to execute buy and sell orders during backtesting.
stoploss: -0.28 timeframe: 4h hash(sha256): 02e0d25d2337c191eb8852127e0767d2f077ae2ab2e739c12b1a40c15ea741b4
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-10 21:06:02