The SimpleBollinger strategy is a trading strategy that uses various technical indicators to generate buy and sell signals based on the price movements of a financial asset. Here is a breakdown of what the strategy does:
Populate Indicators: This method calculates and adds several technical indicators to the given DataFrame. The indicators used in this strategy include ADX (Average Directional Index), MFI (Money Flow Index), MACD (Moving Average Convergence Divergence), Stochastic Fast, RSI (Relative Strength Index), Fisher RSI, Bollinger Bands, EMA (Exponential Moving Average), SAR (Stop and Reverse), and SMA (Simple Moving Average).
Populate Buy Trend: This method populates the buy signal for the DataFrame based on the configured buy conditions.
The conditions include checking the values of the ADX, MFI, MACD, and the price crossing above the upper Bollinger Band.
If all the conditions are met, the 'buy' column of the DataFrame is set to 1. Populate Sell Trend: This method populates the sell signal for the DataFrame based on the configured sell conditions. The conditions include checking the value of MFI and the price crossing below the lower Bollinger Band. If any of the conditions are met, the 'sell' column of the DataFrame is set to 1. Overall, the strategy aims to identify potential buying opportunities when certain indicators suggest a bullish trend and selling opportunities when certain indicators suggest a bearish trend.
Populate Buy Trend: This method populates the buy signal for the DataFrame based on the configured buy conditions.
The conditions include checking the values of the ADX, MFI, MACD, and the price crossing above the upper Bollinger Band.
If all the conditions are met, the 'buy' column of the DataFrame is set to 1. Populate Sell Trend: This method populates the sell signal for the DataFrame based on the configured sell conditions. The conditions include checking the value of MFI and the price crossing below the lower Bollinger Band. If any of the conditions are met, the 'sell' column of the DataFrame is set to 1. Overall, the strategy aims to identify potential buying opportunities when certain indicators suggest a bullish trend and selling opportunities when certain indicators suggest a bearish trend.
stoploss: 0 timeframe: 5m hash(sha256): ef709debbd967044c1ee61241afe6b4fb46b6f7b4af634388c30472f8103d19e indicators: upper close mfi bb_lowerband ema5 fastk sma fastd ema10 sar macdsignal fisher_rsi ema100 macd adx lower rsi bb_upperband ema50
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last change: 2024-04-29 01:33:23