The TuplaBollinger strategy is a trading strategy that uses Bollinger Bands and an Exponential Moving Average (EMA) to identify potential buy and sell signals. Here's a breakdown of what the strategy does:
It sets the long-term EMA period to 200. It defines a minimal return on investment (ROI) for the strategy, specifying different levels of ROI for different holding periods.
It sets the stoploss value to -0.25, indicating the maximum acceptable loss before exiting a trade.
It selects a timeframe of 5 hours for analyzing the price data.
The populate_indicators function calculates the Bollinger Bands and EMA indicators for the given dataframe. It calculates the inner Bollinger Bands using a window of 20 and standard deviations of 1. It adds columns for the lower band, middle band (bb_middleband), and upper band of the inner Bollinger Bands. It calculates the outer Bollinger Bands using a window of 20 and standard deviations of 2. It adds columns for the lower band and upper band of the outer Bollinger Bands. It calculates the EMA with a period of 200 and adds a corresponding column to the dataframe. The populate_buy_trend function determines the buy signals based on the defined conditions: If the close price is below the inner lower band and the previous close price is lower than the current close price, a buy signal is generated. It sets the 'buy' column to 1 for the corresponding rows. The populate_sell_trend function determines the sell signals based on the defined conditions: If the close price is above the inner upper band and the previous close price is higher than the current close price, a sell signal is generated. It sets the 'sell' column to 1 for the corresponding rows. Overall, the strategy aims to identify buying opportunities when the price is below the inner Bollinger Band and selling opportunities when the price is above the inner Bollinger Band. The EMA indicator is used as a trend filter to provide additional confirmation.
It sets the stoploss value to -0.25, indicating the maximum acceptable loss before exiting a trade.
It selects a timeframe of 5 hours for analyzing the price data.
The populate_indicators function calculates the Bollinger Bands and EMA indicators for the given dataframe. It calculates the inner Bollinger Bands using a window of 20 and standard deviations of 1. It adds columns for the lower band, middle band (bb_middleband), and upper band of the inner Bollinger Bands. It calculates the outer Bollinger Bands using a window of 20 and standard deviations of 2. It adds columns for the lower band and upper band of the outer Bollinger Bands. It calculates the EMA with a period of 200 and adds a corresponding column to the dataframe. The populate_buy_trend function determines the buy signals based on the defined conditions: If the close price is below the inner lower band and the previous close price is lower than the current close price, a buy signal is generated. It sets the 'buy' column to 1 for the corresponding rows. The populate_sell_trend function determines the sell signals based on the defined conditions: If the close price is above the inner upper band and the previous close price is higher than the current close price, a sell signal is generated. It sets the 'sell' column to 1 for the corresponding rows. Overall, the strategy aims to identify buying opportunities when the price is below the inner Bollinger Band and selling opportunities when the price is above the inner Bollinger Band. The EMA indicator is used as a trend filter to provide additional confirmation.
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stoploss: -0.25 timeframe: 5h hash(sha256): 550027bae6533959eba8dddca6cf881e76d422dcbcb065ed83fd130ab4221ea8 indicators: outer_lowerband upper mid lower outer_upperband bb_middleband inner_lowerband close inner_upperband
No similar strategies found. (based on used indicators)
last change: 2024-07-27 23:19:27