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The HyperStra_GSN_SMAOnly strategy is designed to backtest trading strategies using simple moving averages (SMA). Here is a breakdown of its key components:
populate_indicators: This function calculates the SMA indicators for various time periods and adds them as columns to the input dataframe. The sma_timeperiods parameter determines the time periods for which the SMA indicators are calculated.
populate_buy_trend: This function identifies buy signals based on specified conditions.
It uses the condition_maker function to evaluate multiple conditions for buying.
If any of the conditions are met, the corresponding rows in the dataframe are marked with a value of 1 in the 'buy' column. populate_sell_trend: This function identifies sell signals based on specified conditions. Similar to populate_buy_trend, it uses the condition_maker function to evaluate multiple conditions for selling. If any of the conditions are met, the corresponding rows in the dataframe are marked with a value of 1 in the 'sell' column. condition_maker: This function evaluates individual conditions for buying or selling. It compares indicators and their values based on different operators (e.g., 'equal', 'above', 'below', 'cross_above', etc.) and returns a boolean result. Normalizer: This function normalizes a given column of data in the dataframe. It subtracts the minimum value, divides by the range (maximum - minimum), and returns the normalized values. Overall, the HyperStra_GSN_SMAOnly strategy calculates SMA indicators, checks conditions for buying and selling, and marks the corresponding rows in the dataframe. It utilizes various operators and normalization techniques to evaluate the indicators and make trading decisions.
populate_buy_trend: This function identifies buy signals based on specified conditions.
It uses the condition_maker function to evaluate multiple conditions for buying.
If any of the conditions are met, the corresponding rows in the dataframe are marked with a value of 1 in the 'buy' column. populate_sell_trend: This function identifies sell signals based on specified conditions. Similar to populate_buy_trend, it uses the condition_maker function to evaluate multiple conditions for selling. If any of the conditions are met, the corresponding rows in the dataframe are marked with a value of 1 in the 'sell' column. condition_maker: This function evaluates individual conditions for buying or selling. It compares indicators and their values based on different operators (e.g., 'equal', 'above', 'below', 'cross_above', etc.) and returns a boolean result. Normalizer: This function normalizes a given column of data in the dataframe. It subtracts the minimum value, divides by the range (maximum - minimum), and returns the normalized values. Overall, the HyperStra_GSN_SMAOnly strategy calculates SMA indicators, checks conditions for buying and selling, and marks the corresponding rows in the dataframe. It utilizes various operators and normalization techniques to evaluate the indicators and make trading decisions.
stoploss: -0.05 timeframe: 5m hash(sha256): d9254aa62eb976592a5d5cc5320b2cc1fadb3d88d52432653de23213c8516b9f
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-02 19:54:08