The BB_RPB_TSL_v103 strategy is a trading strategy implemented as a class in Python. Here is a brief description of what the strategy does:
populate_indicators: This function takes a DataFrame and metadata as input and populates the indicators for the strategy. It merges informative data from a 1-hour timeframe and applies normal time frame indicators.
populate_buy_trend: This function determines the conditions for buying based on various indicators.
It initializes an empty list of conditions and assigns an empty string to the 'buy_tag' column in the DataFrame.
The strategy considers the following conditions for buying:
is_dip: Determines if the RMI, CCI, and SRSI indicators meet specific criteria. is_break: Checks if the BB delta, BB width, and closedelta meet specific values. is_local_uptrend: Considers the EMA indicators, open values, and BB lowerband to identify a local uptrend. is_local_dip: Checks for a local dip based on EMA indicators, open values, RSI, CRSI, and closedelta. is_ewo: Considers RSI, EMA, and EWO indicators for buying. is_ewo_2: Considers a combination of indicators including EMA, RSI, and EWO for buying. is_nfi_13: Checks specific conditions involving EMA, SMA, CTI, and EWO for buying. is_nfi_32: Checks specific conditions involving RSI, SMA, CTI, and close values for buying. is_nfi_33: Checks specific conditions involving EMA, EWO, RSI, volume, and r_14 for buying. is_nfi_38: Considers various indicators including PM, SMA, EWO, CTI, r_14, and CRSI_1h for buying. The strategy appends the respective buy tags to the 'buy_tag' column based on the fulfilled conditions. If any conditions are met, an additional check is performed using the roc_1h and bb_width_1h indicators. Overall, the strategy combines multiple indicators to determine potential buying opportunities based on specific criteria.