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The BB_RPB_TSL_RNG_2 strategy is a backtesting strategy that utilizes multiple indicators and conditions to determine buy signals in trading. Here's a breakdown of its main components:
Indicator Calculation:
Bollinger Bands with a window of 20 and standard deviation of 2 are calculated and stored in bb_lowerband2, bb_middleband2, and bb_upperband2. Bollinger Bands with a window of 20 and standard deviation of 3 are calculated and stored in bb_lowerband3, bb_middleband3, and bb_upperband3.
Other indicators such as CCI (Commodity Channel Index), RMI (Relative Momentum Index), STOCHRSI (Stochastic RSI), SMA (Simple Moving Average), EMA (Exponential Moving Average), EWO (Elliott Wave Oscillator), ADX (Average Directional Index), and Williams %R are calculated and stored in various dataframe columns.
Informative Data: Data from the 'BTC/USDT' trading pair with a timeframe of 5 minutes is retrieved and stored in the informative variable.
Past data from the informative dataframe is shifted and used to calculate different thresholds and deltas. Buy Trend Conditions: Various conditions are checked to determine buy signals: Dip condition: RMI, CCI, and Stochastic RSI values are checked against specific thresholds. Break condition: Bollinger Bands delta and width, along with the percentage change in closing price, are compared against predefined values. Local uptrend condition: Moving averages and price differentials are evaluated. EWO condition: RSI, moving averages, and EWO values are compared against thresholds. COFI condition: Opening price, fastk/fastd crossover, ADX, and EWO values are checked against specific values. Buy Signal Assignment: If any of the buy conditions are met, a 'buy_tag' is assigned to the corresponding row in the dataframe. The strategy combines these indicators and conditions to identify potential buying opportunities when certain criteria are satisfied.
Other indicators such as CCI (Commodity Channel Index), RMI (Relative Momentum Index), STOCHRSI (Stochastic RSI), SMA (Simple Moving Average), EMA (Exponential Moving Average), EWO (Elliott Wave Oscillator), ADX (Average Directional Index), and Williams %R are calculated and stored in various dataframe columns.
Informative Data: Data from the 'BTC/USDT' trading pair with a timeframe of 5 minutes is retrieved and stored in the informative variable.
Past data from the informative dataframe is shifted and used to calculate different thresholds and deltas. Buy Trend Conditions: Various conditions are checked to determine buy signals: Dip condition: RMI, CCI, and Stochastic RSI values are checked against specific thresholds. Break condition: Bollinger Bands delta and width, along with the percentage change in closing price, are compared against predefined values. Local uptrend condition: Moving averages and price differentials are evaluated. EWO condition: RSI, moving averages, and EWO values are compared against thresholds. COFI condition: Opening price, fastk/fastd crossover, ADX, and EWO values are checked against specific values. Buy Signal Assignment: If any of the buy conditions are met, a 'buy_tag' is assigned to the corresponding row in the dataframe. The strategy combines these indicators and conditions to identify potential buying opportunities when certain criteria are satisfied.
stoploss: -0.99 timeframe: 5m hash(sha256): 5214f68f052c6e2c954e1470a498d28aaf5888662745aed85334b67140ebb99f indicators: upper bb_middleband3 bb_upperband3 btc_1d EWO btc_5m ema_13 close high_offset_2 cci_length_val fastk rsi_fast ma_sell_val srsi_fk r_14 bb_middleband2 volume srsi_fd cci_long closedelta bb_bottom_cross high_offset open bb_delta btc_diff fastd btc_threshold sma_15 bb_upperband2 high volume_mean_4 mid bb_width ema_8 ema_16 cci rmi_length_val base_nb_candles_sell hma_50 bb_lowerband3 cti sma_9 ema_100 adx lower ema_12 sma_30 rsi_slow rsi ema_26 low btc_dif bb_lowerband2
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last change: 2023-12-02 23:17:03