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The MultiMA_TSL strategy is a trading strategy that uses multiple moving averages (MAs), along with other indicators, to determine buy and sell signals. Here is a breakdown of what the strategy does:
In the populate_indicators method:
Calculates the exponential moving average (EMA) of a specified number of candles as a filter offset. Calculates the Elder's Force Index (EWO) using user-defined fast and slow EWO values.
Calculates the relative strength index (RSI) with a time period of 14.
Calculates a faster RSI with a time period of 4.
Returns the updated dataframe with these indicators. In the populate_buy_trend method: Defines empty conditions list. Calculates the offset values for different types of moving averages (SMA, EMA, TRIMA) multiplied by respective low offsets. Initializes a 'buy_tag' column in the dataframe. Defines buy conditions for each type of moving average: For SMA: If the close price is below the SMA offset and certain EWO and RSI conditions are met. For EMA: If the close price is below the EMA offset and certain EWO and RSI conditions are met. For TRIMA: If the close price is below the TRIMA offset and certain EWO and RSI conditions are met. Appends the buy conditions to the conditions list. Adds additional check conditions related to RSI and close price for selling. If there are any buy conditions, sets the 'buy' column in the dataframe based on the conditions. Returns the updated dataframe. In the populate_sell_trend method: Checks if the strategy is running in hyperopt mode. If in hyperopt mode: Calculates the EMA offset for selling using the specified number of candles and high offset. Calculates the EMA of a specified number of candles as a filter offset. Defines an empty conditions list. Adds a condition for selling if the close price is above the EMA offset and the volume is greater than 0. If there are any sell conditions, sets the 'sell' column in the dataframe based on the conditions. Returns the updated dataframe. The last part of the code (outside the class) calculates the difference between two EMAs (sma1 and sma2) divided by the close price and multiplied by 100. This calculates the percentage difference between the two EMAs and returns the result. Overall, the strategy uses various moving averages, EWO, RSI, and volume conditions to generate buy and sell signals. The specific indicators and conditions used can be adjusted through the configuration parameters of the strategy.
Calculates the relative strength index (RSI) with a time period of 14.
Calculates a faster RSI with a time period of 4.
Returns the updated dataframe with these indicators. In the populate_buy_trend method: Defines empty conditions list. Calculates the offset values for different types of moving averages (SMA, EMA, TRIMA) multiplied by respective low offsets. Initializes a 'buy_tag' column in the dataframe. Defines buy conditions for each type of moving average: For SMA: If the close price is below the SMA offset and certain EWO and RSI conditions are met. For EMA: If the close price is below the EMA offset and certain EWO and RSI conditions are met. For TRIMA: If the close price is below the TRIMA offset and certain EWO and RSI conditions are met. Appends the buy conditions to the conditions list. Adds additional check conditions related to RSI and close price for selling. If there are any buy conditions, sets the 'buy' column in the dataframe based on the conditions. Returns the updated dataframe. In the populate_sell_trend method: Checks if the strategy is running in hyperopt mode. If in hyperopt mode: Calculates the EMA offset for selling using the specified number of candles and high offset. Calculates the EMA of a specified number of candles as a filter offset. Defines an empty conditions list. Adds a condition for selling if the close price is above the EMA offset and the volume is greater than 0. If there are any sell conditions, sets the 'sell' column in the dataframe based on the conditions. Returns the updated dataframe. The last part of the code (outside the class) calculates the difference between two EMAs (sma1 and sma2) divided by the close price and multiplied by 100. This calculates the percentage difference between the two EMAs and returns the result. Overall, the strategy uses various moving averages, EWO, RSI, and volume conditions to generate buy and sell signals. The specific indicators and conditions used can be adjusted through the configuration parameters of the strategy.
stoploss: -0.15 timeframe: 5m hash(sha256): b2aeee76356da0bad90b398e23a077b934df8e02ee795fe5b9b5879f030bf23c
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-29 02:22:37