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The MultiMA_TSL strategy is a trading strategy that uses multiple moving averages (MAs) and various indicators to generate buy and sell signals. Here's a breakdown of what the strategy does:
In the populate_indicators function:
Calculates the EWO (Elliott Wave Oscillator) based on the specified fast and slow periods. Calculates the RSI (Relative Strength Index) with a time period of 14.
Calculates a rolling 48-period percent change of the closing price and finds the maximum value within a 12-period rolling window.
The result is multiplied by 100 and stored in the "roc_max" column of the dataframe.
In the populate_buy_trend function: Calculates various buy offsets based on different moving averages (EMA, ZEMA, TRIMA) and their corresponding offset values. Sets up conditions for triggering a buy signal based on the offset values, EWO values, and RSI values. Updates the "buy_tag" column with the relevant offset indicators. Updates the "buy_copy" and "buy" columns with 1 for the rows that meet the buy conditions. In the populate_sell_trend function: Calculates sell offsets based on moving averages (EMA, TRIMA) and their corresponding offset values. Sets up conditions for triggering a sell signal based on the offset values and volume. Updates the "sell_copy" and "sell" columns with 1 for the rows that meet the sell conditions. The populate_sell_trend function also includes additional logic for scenarios when the strategy is not running in backtest or hyperopt modes. In such cases, it sets the "sell" column to 0 for all rows. The final part of the code calculates the difference between two exponential moving averages (EMA) with specified lengths and divides it by the closing price to get the percentage difference. This is returned as the "smadif" value. Overall, the strategy uses indicators such as EWO, RSI, and moving averages (EMA, ZEMA, TRIMA) to determine buy and sell signals based on specific conditions and offsets. The aim is to capture potential market trends and generate profitable trading opportunities.
Calculates a rolling 48-period percent change of the closing price and finds the maximum value within a 12-period rolling window.
The result is multiplied by 100 and stored in the "roc_max" column of the dataframe.
In the populate_buy_trend function: Calculates various buy offsets based on different moving averages (EMA, ZEMA, TRIMA) and their corresponding offset values. Sets up conditions for triggering a buy signal based on the offset values, EWO values, and RSI values. Updates the "buy_tag" column with the relevant offset indicators. Updates the "buy_copy" and "buy" columns with 1 for the rows that meet the buy conditions. In the populate_sell_trend function: Calculates sell offsets based on moving averages (EMA, TRIMA) and their corresponding offset values. Sets up conditions for triggering a sell signal based on the offset values and volume. Updates the "sell_copy" and "sell" columns with 1 for the rows that meet the sell conditions. The populate_sell_trend function also includes additional logic for scenarios when the strategy is not running in backtest or hyperopt modes. In such cases, it sets the "sell" column to 0 for all rows. The final part of the code calculates the difference between two exponential moving averages (EMA) with specified lengths and divides it by the closing price to get the percentage difference. This is returned as the "smadif" value. Overall, the strategy uses indicators such as EWO, RSI, and moving averages (EMA, ZEMA, TRIMA) to determine buy and sell signals based on specific conditions and offsets. The aim is to capture potential market trends and generate profitable trading opportunities.
stoploss: -0.15 timeframe: 5m hash(sha256): 416cad96198852bf2ca9a6bb88564b7869377508f53488332d9b21f73b736fbe
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-02 19:54:08