The Strategy003 class is a trading strategy that uses various technical analysis (TA) indicators to generate buy and sell signals for a given financial dataset. The strategy consists of three main functions: populate_indicators, populate_buy_trend, and populate_sell_trend. populate_indicators: This function calculates and adds several TA indicators to the DataFrame.
The indicators used in this strategy are: MFI (Money Flow Index) Stochastic Fast (fastk and fastd) RSI (Relative Strength Index) Fisher RSI Bollinger Bands (lower band) EMA (Exponential Moving Average) with different time periods (5, 10, 50, and 100) SAR (Stop and Reverse) SMA (Simple Moving Average) The indicators are calculated using the input DataFrame, and the calculated values are stored in additional columns of the DataFrame.
populate_buy_trend: This function generates the buy signals based on the TA indicators.
It checks various conditions to determine when to buy. The conditions used in this strategy are: If the RSI is below a specified threshold (self.buy_rsi.value) and above 0. If the close price is below the SMA (Simple Moving Average). If the Fisher RSI is below a specified threshold (self.buy_fisher.value). If the MFI (Money Flow Index) is less than or equal to a specified threshold (self.buy_mfi.value). If either the EMA50 (Exponential Moving Average with a time period of 50) is greater than the EMA100 or if the EMA5 (Exponential Moving Average with a time period of 5) has crossed above the EMA10 (Exponential Moving Average with a time period of 10). If the Stochastic Fast (fastd) is greater than the Stochastic Fast (fastk) and greater than 0. If any of these conditions are met, the corresponding rows in the 'buy' column of the DataFrame are set to 1. populate_sell_trend: This function generates the sell signals based on the TA indicators. It checks various conditions to determine when to sell. The conditions used in this strategy are: If the 'sell_hold_enabled' flag is enabled, no sell signals are generated. Instead, all rows in the 'sell' column of the DataFrame are set to 0. If the SAR (Stop and Reverse) is greater than the close price. If the Fisher RSI is above a specified threshold (self.sell_fisher.value). If any of these conditions are met, the corresponding rows in the 'sell' column of the DataFrame are set to 1. Overall, this strategy aims to generate buy and sell signals based on the values of various TA indicators, allowing for backtesting and evaluation of the strategy's performance.
The indicators used in this strategy are: MFI (Money Flow Index) Stochastic Fast (fastk and fastd) RSI (Relative Strength Index) Fisher RSI Bollinger Bands (lower band) EMA (Exponential Moving Average) with different time periods (5, 10, 50, and 100) SAR (Stop and Reverse) SMA (Simple Moving Average) The indicators are calculated using the input DataFrame, and the calculated values are stored in additional columns of the DataFrame.
populate_buy_trend: This function generates the buy signals based on the TA indicators.
It checks various conditions to determine when to buy. The conditions used in this strategy are: If the RSI is below a specified threshold (self.buy_rsi.value) and above 0. If the close price is below the SMA (Simple Moving Average). If the Fisher RSI is below a specified threshold (self.buy_fisher.value). If the MFI (Money Flow Index) is less than or equal to a specified threshold (self.buy_mfi.value). If either the EMA50 (Exponential Moving Average with a time period of 50) is greater than the EMA100 or if the EMA5 (Exponential Moving Average with a time period of 5) has crossed above the EMA10 (Exponential Moving Average with a time period of 10). If the Stochastic Fast (fastd) is greater than the Stochastic Fast (fastk) and greater than 0. If any of these conditions are met, the corresponding rows in the 'buy' column of the DataFrame are set to 1. populate_sell_trend: This function generates the sell signals based on the TA indicators. It checks various conditions to determine when to sell. The conditions used in this strategy are: If the 'sell_hold_enabled' flag is enabled, no sell signals are generated. Instead, all rows in the 'sell' column of the DataFrame are set to 0. If the SAR (Stop and Reverse) is greater than the close price. If the Fisher RSI is above a specified threshold (self.sell_fisher.value). If any of these conditions are met, the corresponding rows in the 'sell' column of the DataFrame are set to 1. Overall, this strategy aims to generate buy and sell signals based on the values of various TA indicators, allowing for backtesting and evaluation of the strategy's performance.
stoploss: 0 timeframe: 5m hash(sha256): 24c942f28fdc305b04275a8cbec88ec0100a0de5c5f0909700eb3baffffa8bfa indicators: lower fisher_rsi ema100 close rsi mfi bb_lowerband ema5 ema50 fastd ema10 fastk sar sma
Similar Strategies: (based on used indicators)
Strategy: BTCBigDrop, Similarity Score: 93.33%
Strategy: BTCBigDrop_2, Similarity Score: 93.33%
Strategy: BTCJump, Similarity Score: 93.33%
Strategy: BTCNDrop, Similarity Score: 93.33%
Strategy: BTCNDrop_2, Similarity Score: 93.33%
Strategy: BTCNSeq, Similarity Score: 93.33%
Strategy: BTCNSeq_2, Similarity Score: 93.33%
Strategy: BigDrop, Similarity Score: 93.33%
Strategy: BigDrop_2, Similarity Score: 93.33%
Strategy: BollingerBounce, Similarity Score: 93.33%
Strategy: BollingerBounce_2, Similarity Score: 93.33%
Strategy: BuyDips, Similarity Score: 93.33%
Strategy: BuyDips_2, Similarity Score: 93.33%
Strategy: ComboHold, Similarity Score: 93.33%
Strategy: DCBBBounce, Similarity Score: 93.33%
Strategy: Discord_strat3, Similarity Score: 93.33%
Strategy: DonchianBounce, Similarity Score: 93.33%
Strategy: DonchianBounce_2, Similarity Score: 93.33%
Strategy: DonchianChannel, Similarity Score: 93.33%
Strategy: DonchianChannel_2, Similarity Score: 93.33%
Strategy: KeltnerBounce, Similarity Score: 93.33%
Strategy: KeltnerBounce_2, Similarity Score: 93.33%
Strategy: KeltnerChannels, Similarity Score: 93.33%
Strategy: KeltnerChannels_2, Similarity Score: 93.33%
Strategy: MACD003_2, Similarity Score: 93.33%
Strategy: MFI2, Similarity Score: 93.33%
Strategy: MFI2_2, Similarity Score: 93.33%
Strategy: NDrop, Similarity Score: 93.33%
Strategy: NDrop_2, Similarity Score: 93.33%
Strategy: NSeq, Similarity Score: 93.33%
Strategy: NSeq_2, Similarity Score: 93.33%
Strategy: Patterns, Similarity Score: 93.33%
Strategy: STRATEGY_003_RSI_MFI_FISHERRSI, Similarity Score: 93.33%
Strategy: SimpleBollinger, Similarity Score: 93.33%
Strategy: SimpleBollinger_2, Similarity Score: 93.33%
Strategy: Strategy003, Similarity Score: 93.33%
Strategy: Strategy003_13, Similarity Score: 93.33%
Strategy: Strategy003_7, Similarity Score: 93.33%
Strategy: Strategy003_74, Similarity Score: 93.33%
Strategy: Strategy003_8, Similarity Score: 93.33%
Strategy: Strategy003_869, Similarity Score: 93.33%
Strategy: strategy003_3, Similarity Score: 93.33%
Strategy: strategy003_4, Similarity Score: 93.33%
last change: 2024-04-29 00:19:56