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The strategy, named "CombinedBinHClucAndMADV9," is a trading strategy implemented as a class that inherits from the base class "IStrategy." It consists of two main functions: "populate_indicators" and "populate_entry_trend," as well as a third function, "populate_exit_trend." Here's a breakdown of what each function does:
populate_indicators:
This function takes a DataFrame and a metadata dictionary as input. It applies informative indicators on the 1-hour timeframe and merges them with the main dataframe. It applies additional normal timeframe indicators on the main dataframe.
Finally, it returns the updated dataframe.
populate_entry_trend: This function takes a DataFrame and a metadata dictionary as input.
It populates the "buy" column of the dataframe based on various conditions. The conditions are defined using logical operators and refer to different indicators and their values. Each condition represents a potential buy signal, and if any of the conditions are met, the "buy" column is set to 1. The conditions consider factors such as close price, exponential moving averages (ema), Bollinger Bands (bb), volume, and relative strength index (rsi). The function returns the updated dataframe. populate_exit_trend: This function takes a DataFrame and a metadata dictionary as input. It populates the "sell" column of the dataframe based on a condition. The condition checks if the close price is above the middle Bollinger Band by a certain threshold and if the volume is greater than 0. If the condition is met, the "sell" column is set to 1. The function returns the updated dataframe. In summary, the "CombinedBinHClucAndMADV9" strategy applies informative and normal timeframe indicators to a given dataset, identifies potential entry points based on a set of conditions involving price, indicators, and volume, and determines exit points based on a condition involving the price and volume.
Finally, it returns the updated dataframe.
populate_entry_trend: This function takes a DataFrame and a metadata dictionary as input.
It populates the "buy" column of the dataframe based on various conditions. The conditions are defined using logical operators and refer to different indicators and their values. Each condition represents a potential buy signal, and if any of the conditions are met, the "buy" column is set to 1. The conditions consider factors such as close price, exponential moving averages (ema), Bollinger Bands (bb), volume, and relative strength index (rsi). The function returns the updated dataframe. populate_exit_trend: This function takes a DataFrame and a metadata dictionary as input. It populates the "sell" column of the dataframe based on a condition. The condition checks if the close price is above the middle Bollinger Band by a certain threshold and if the volume is greater than 0. If the condition is met, the "sell" column is set to 1. The function returns the updated dataframe. In summary, the "CombinedBinHClucAndMADV9" strategy applies informative and normal timeframe indicators to a given dataset, identifies potential entry points based on a set of conditions involving price, indicators, and volume, and determines exit points based on a condition involving the price and volume.
stoploss: -0.99 timeframe: 5m hash(sha256): 7766659371874a5b8701c53edda4c730eeabe816f5ea7886f022e3ddb5c753df
Was not able to fetch indicators from Strategyfile.
last change: 2023-04-20 17:49:42