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The HyperStra_SMAOnly strategy is a trading strategy that uses simple moving averages (SMA) to generate buy and sell signals. Here's a breakdown of what the strategy does:
populate_indicators: This method calculates the moving averages (MA) for different time periods and adds them to the input dataframe. populate_buy_trend: This method populates the buy signal based on certain conditions.
It checks multiple conditions using the condition_maker function, which compares indicators and values using different operators (e.g., equal, above, below, cross above, etc.).
If all the conditions are met, the 'buy' column in the dataframe is set to 1.
populate_sell_trend: Similar to the populate_buy_trend method, this method populates the sell signal based on specified conditions. If all the conditions are met, the 'sell' column in the dataframe is set to 1. condition_maker: This function compares indicators based on the given operator and returns a boolean result. It supports various operators such as equal, above, below, cross above, divide greater, divide smaller, normalized equal, normalized smaller, normalized bigger, normalized divided equal, normalized divided smaller, and normalized divided bigger. Normalizer: This function normalizes a given indicator by subtracting the minimum value and dividing by the range (max - min) to obtain values between 0 and 1. Overall, the strategy calculates moving averages, checks conditions for generating buy and sell signals, and performs normalization on indicators.
It checks multiple conditions using the condition_maker function, which compares indicators and values using different operators (e.g., equal, above, below, cross above, etc.).
If all the conditions are met, the 'buy' column in the dataframe is set to 1.
populate_sell_trend: Similar to the populate_buy_trend method, this method populates the sell signal based on specified conditions. If all the conditions are met, the 'sell' column in the dataframe is set to 1. condition_maker: This function compares indicators based on the given operator and returns a boolean result. It supports various operators such as equal, above, below, cross above, divide greater, divide smaller, normalized equal, normalized smaller, normalized bigger, normalized divided equal, normalized divided smaller, and normalized divided bigger. Normalizer: This function normalizes a given indicator by subtracting the minimum value and dividing by the range (max - min) to obtain values between 0 and 1. Overall, the strategy calculates moving averages, checks conditions for generating buy and sell signals, and performs normalization on indicators.
startup_candle_count : 440 Recursive Analysis found no issues while using 440 startup_candle_count
Biased Entry Signals:
20Biased Exit Signals:
19
stoploss: -0.05 timeframe: 5m hash(sha256): 441c29d71c2f21235e9933239b1b106def00109fefb8be15834f0e70e5cd62e9 indicators: buy_1_indicator_sec buy_1_indicator buy_3_real_number sell_3_indicator sell_1_real_number sell_1_indicator_sec buy_2_indicator_sec sell_2_operator sell_2_real_number volume sell_2_indicator_sec sell_2_indicator buy_2_indicator sell_3_operator buy_2_operator sell_3_indicator_sec buy_3_indicator_sec buy_2_real_number buy_3_operator ma_m_timeperiod sell_1_indicator sell_3_real_number indicator_1 buy_1_operator buy_3_indicator sell_1_operator buy_1_real_number
Similar Strategies: (based on used indicators)
Strategy: HGSMA, Similarity Score: 96.43%
Strategy: HSMA, Similarity Score: 96.43%
Strategy: HyperSMASwing, Similarity Score: 96.43%
Strategy: HyperStra_GSN_SMAOnly_2, Similarity Score: 96.43%
last change: 2023-07-05 16:54:49