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The ClucHAnix_BB_RPB_MOD strategy is designed to backtest trading strategies using various indicators and conditions. Here is a short description of what the strategy does:
It starts by populating indicators based on the input dataframe, including Heikin Ashi candles, exponential moving averages (EMAs), simple moving averages (SMAs), relative strength index (RSI), Commodity Channel Index (CTI), stochastic fast indicators, average directional index (ADX), Bollinger Bands, closedelta, ha_closedelta, tail, ema_fast, ema_slow, rate of change ratio (ROCR), and Elliott Wave Oscillator (EWO). It merges informative data on a higher timeframe (1 hour) to the input dataframe and adds additional features such as ha_close, rocr, btc_1m, btc_1d, zema_30, zema_200, and pump_strength.
The populate_buy_trend function populates buy conditions based on the populated indicators and user-defined parameters.
The strategy evaluates multiple conditions for potential buying opportunities, including BTC safety, pump safety, lambo1, lambo2, local uptrend, NFI_32, EWO_1, EWO_low, and COFI.
These conditions are based on comparisons between various indicators, moving averages, thresholds, and ratios. The strategy assigns a buy_tag to each potential buying opportunity based on the fulfilled conditions. The strategy returns the modified dataframe with the populated buy_tag and other relevant information. Note: The provided code snippet represents a part of the strategy implementation and may require additional code for complete functionality.
The populate_buy_trend function populates buy conditions based on the populated indicators and user-defined parameters.
The strategy evaluates multiple conditions for potential buying opportunities, including BTC safety, pump safety, lambo1, lambo2, local uptrend, NFI_32, EWO_1, EWO_low, and COFI.
These conditions are based on comparisons between various indicators, moving averages, thresholds, and ratios. The strategy assigns a buy_tag to each potential buying opportunity based on the fulfilled conditions. The strategy returns the modified dataframe with the populated buy_tag and other relevant information. Note: The provided code snippet represents a part of the strategy implementation and may require additional code for complete functionality.
stoploss: -0.99 timeframe: 1m hash(sha256): 4d71e1469b844764c40aec90bc3be1a45b153429ce43ad498157f72b827b1876 indicators: upper local_trend_bb_factor lambo1_ema_14_factor close cofi_ema bb_lowerband lambo1_rsi_4_limit lambo2_rsi_4_limit volume rsi_4 date close ha_closedelta clucha_bbdelta_close rsi_14 bb_upperband2 high ewo_low_offset ema_8 ewo_low_enabled antipump_threshold pump_strength ha_high cofi_enabled adx ema_slow ema_26 ewo_candles_buy local_trend_enabled ewo_high ewo_low rsi_20 bbdelta bb_middleband2 cofi_fastk cofi_ewo_high nfi32_cti_limit closedelta zema_30 ema_fast ha_open sma_15 nfi32_sma_factor cofi_
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last change: 2022-07-02 19:54:08