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The akivaHOTSL strategy is a trading strategy that uses various indicators to determine buy and sell signals. Here's a breakdown of what the strategy does:
Populate Indicators: This function calculates and adds several indicators to the input dataframe. These indicators include:
Exponential Moving Averages (EMA) for different time periods.
Hull Moving Average (HMA) with a window of 50.
EMA with a time period of 100.
Simple Moving Average (SMA) with a time period of 9. Elder's Force Index (EWO). Relative Strength Index (RSI) with different time periods. Populate Buy Trend: This function identifies buy signals based on specific conditions. If the conditions are met, it marks the corresponding rows in the dataframe with a buy signal and a buy tag. The conditions include: RSI fast is less than 35. The closing price is below the buy EMA multiplied by a low offset. EWO is greater than a specified high value. RSI is less than a specified buy value. Volume is greater than 0. The closing price is below the sell EMA multiplied by a high offset. Populate Sell Trend: This function determines sell signals based on specific conditions. If the conditions are met, it marks the corresponding rows in the dataframe with a sell signal. The conditions include: The closing price is above the simple moving average (SMA) with a time period of 9. The closing price is above the sell EMA multiplied by a high offset. RSI is greater than 50. Volume is greater than 0. RSI fast is greater than RSI slow. Alternatively, if the closing price is below the Hull Moving Average (HMA) with a window of 50 and above the sell EMA multiplied by a high offset, and RSI fast is greater than RSI slow. Calculate smadif: This function calculates the difference between two exponential moving averages (EMA) and normalizes it by dividing by the closing price and multiplying by 100. It returns the resulting smadif value. Overall, the akivaHOTSL strategy uses indicators such as EMAs, HMAs, RSIs, and volume to generate buy and sell signals based on specified conditions. The strategy aims to capture potential trading opportunities in the market.
Hull Moving Average (HMA) with a window of 50.
EMA with a time period of 100.
Simple Moving Average (SMA) with a time period of 9. Elder's Force Index (EWO). Relative Strength Index (RSI) with different time periods. Populate Buy Trend: This function identifies buy signals based on specific conditions. If the conditions are met, it marks the corresponding rows in the dataframe with a buy signal and a buy tag. The conditions include: RSI fast is less than 35. The closing price is below the buy EMA multiplied by a low offset. EWO is greater than a specified high value. RSI is less than a specified buy value. Volume is greater than 0. The closing price is below the sell EMA multiplied by a high offset. Populate Sell Trend: This function determines sell signals based on specific conditions. If the conditions are met, it marks the corresponding rows in the dataframe with a sell signal. The conditions include: The closing price is above the simple moving average (SMA) with a time period of 9. The closing price is above the sell EMA multiplied by a high offset. RSI is greater than 50. Volume is greater than 0. RSI fast is greater than RSI slow. Alternatively, if the closing price is below the Hull Moving Average (HMA) with a window of 50 and above the sell EMA multiplied by a high offset, and RSI fast is greater than RSI slow. Calculate smadif: This function calculates the difference between two exponential moving averages (EMA) and normalizes it by dividing by the closing price and multiplying by 100. It returns the resulting smadif value. Overall, the akivaHOTSL strategy uses indicators such as EMAs, HMAs, RSIs, and volume to generate buy and sell signals based on specified conditions. The strategy aims to capture potential trading opportunities in the market.
stoploss: -0.04 timeframe: 5m hash(sha256): 2adaec6010c374a7bb1c8232a2e1f533abd85a0c75930d5501dcd207fb57a800
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-11 14:07:22