The MACDTurn strategy is a trading strategy that utilizes various technical indicators to generate buy and sell signals. Here is a breakdown of what the strategy does:
populate_indicators: This function adds several technical indicators to the given DataFrame, including:
ADX (Average Directional Index)
PLUS_DM and MINUS_DM (Plus Directional Movement and Minus Directional Movement)
PLUS_DI and MINUS_DI (Plus Directional Indicator and Minus Directional Indicator)
RSI (Relative Strength Index)
Fisher RSI
Stochastic Fast %D and %K
MACD (Moving Average Convergence Divergence)
MFI (Money Flow Index)
Bollinger Bands
EMA (Exponential Moving Average)
SAR (Stop and Reverse)
TEMA (Triple Exponential Moving Average)
The function returns the DataFrame with all the added indicators. populate_buy_trend: This function populates the buy signal for the DataFrame based on the indicator values.
It applies several conditions to determine when to buy, including: ADX value is greater than or equal to a specified threshold (buy_adx) Difference between PLUS_DM and MINUS_DM is greater than 0 MFI (Money Flow Index) value is greater than a specified threshold (buy_mfi) Close price is below the SAR (Stop and Reverse) value Fisher RSI value is below a specified threshold (buy_fisher) MACD value is negative Bollinger Bands gain is greater than or equal to a specified threshold (buy_bb_gain) MACD histogram value is negative MACD histogram slope is not null and crosses above 0 If all the conditions are met, the 'buy' column of the DataFrame is set to 1.
populate_sell_trend: This function populates the sell signal for the DataFrame based on the indicator values.
It applies conditions to determine when to sell, including: If the 'sell_hold' parameter is set to true, the 'sell' column is set to 0 for all rows. If the MACD value is greater than 0, the 'sell' column is set to 1. If any of the conditions are met, the 'sell' column of the DataFrame is set to 1. The strategy uses these indicators and conditions to generate buy and sell signals, which can be used for backtesting different trading strategies on the website.
It applies several conditions to determine when to buy, including: ADX value is greater than or equal to a specified threshold (buy_adx) Difference between PLUS_DM and MINUS_DM is greater than 0 MFI (Money Flow Index) value is greater than a specified threshold (buy_mfi) Close price is below the SAR (Stop and Reverse) value Fisher RSI value is below a specified threshold (buy_fisher) MACD value is negative Bollinger Bands gain is greater than or equal to a specified threshold (buy_bb_gain) MACD histogram value is negative MACD histogram slope is not null and crosses above 0 If all the conditions are met, the 'buy' column of the DataFrame is set to 1.
populate_sell_trend: This function populates the sell signal for the DataFrame based on the indicator values.
It applies conditions to determine when to sell, including: If the 'sell_hold' parameter is set to true, the 'sell' column is set to 0 for all rows. If the MACD value is greater than 0, the 'sell' column is set to 1. If any of the conditions are met, the 'sell' column of the DataFrame is set to 1. The strategy uses these indicators and conditions to generate buy and sell signals, which can be used for backtesting different trading strategies on the website.
stoploss: 0 timeframe: 5m hash(sha256): f48866b69a0c3c708c38efc67f302aa1323f3305e5df63be2244eca22b1bd528 indicators: upper tema close dm_delta bb_gain mfi bb_lowerband bb_percent macdhist_ave fastk macdhist fastd dm_plus sar macdsignal mid fisher_rsi bb_width di_plus fisher_rsi_norma macd di_delta ema7 ema25 adx lower bb_middleband rsi di_minus bb_upperband macdhist_slope dm_minus
Similar Strategies: (based on used indicators)
Strategy: MACDTurn_2, Similarity Score: 96.97%
Strategy: BTCMACDCross, Similarity Score: 90.91%
Strategy: BTCMACDCross_2, Similarity Score: 90.91%
Strategy: MACDCross_2, Similarity Score: 90.91%
Strategy: SARCross, Similarity Score: 90.91%
Strategy: SARCross_2, Similarity Score: 90.91%
last change: 2024-04-28 09:01:56