The "alma" strategy is a backtesting strategy implemented in Python for trading. It uses the ALMA (Arnaud Legoux Moving Average) indicator from the "talipp" library to make buy and sell decisions based on the price data. Here's a breakdown of what the strategy does:
It sets the parameters for the strategy, such as stop loss, timeframe, and minimal return on investment (ROI).
It defines the indicators that will be used for the strategy.
In this case, it calculates the ALMA indicator for different parameter values and adds them as columns to the input dataframe.
In the "populate_buy_trend" function, it defines the conditions for a buy signal. It checks if the closing price is greater than the ALMA indicator calculated with the current parameter values. In the "populate_sell_trend" function, it defines the conditions for a sell signal. It checks if the closing price is less than the ALMA indicator calculated with the current parameter values. If the conditions for a buy or sell signal are met, it sets the corresponding values in the "buy" or "sell" columns of the dataframe. Overall, the strategy uses the ALMA indicator with different parameter values to generate buy and sell signals based on the relationship between the closing price and the indicator.
It defines the indicators that will be used for the strategy.
In this case, it calculates the ALMA indicator for different parameter values and adds them as columns to the input dataframe.
In the "populate_buy_trend" function, it defines the conditions for a buy signal. It checks if the closing price is greater than the ALMA indicator calculated with the current parameter values. In the "populate_sell_trend" function, it defines the conditions for a sell signal. It checks if the closing price is less than the ALMA indicator calculated with the current parameter values. If the conditions for a buy or sell signal are met, it sets the corresponding values in the "buy" or "sell" columns of the dataframe. Overall, the strategy uses the ALMA indicator with different parameter values to generate buy and sell signals based on the relationship between the closing price and the indicator.
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stoploss: -0.015 timeframe: 5m hash(sha256): 77f0dc37f9eda7da17c40d326c17c6071cb99dd3b4a47b676e9e6e680fed882a indicators: close
No similar strategies found. (based on used indicators)
last change: 2024-07-27 15:04:23