The BBMod1 strategy is a trading strategy that uses various indicators and conditions to determine buying opportunities. Here is a summary of what the strategy does:
Populate Indicators: This method calculates and adds additional indicators to the given dataframe, using both the current timeframe and a 1-hour informative timeframe. Populate Buy Trend: This method identifies buy signals based on several conditions.
Here are some of the important conditions used:
is_dip: Checks for a potential dip in the market based on RMI (Relative Momentum Index), CCI (Commodity Channel Index), and SRSI (Slow Stochastic).
is_break: Looks for a breakout opportunity based on Bollinger Bands, delta, width, and price movement.
is_local_uptrend: Identifies a local uptrend by comparing exponential moving averages (EMA) and Bollinger Bands. is_local_uptrend2: Similar to is_local_uptrend but with different parameter values. is_local_dip: Searches for a local dip in the market using EMA, RSI (Relative Strength Index), CRSI (Commodity Relative Strength Index), and price movement. is_ewo: Examines EMA, RSI, and EWO (Elliott Wave Oscillator) for a potential buying opportunity. is_clucha: Considers various factors such as rate of change, Bollinger Bands, and candlestick patterns (HA: Heikin-Ashi) to find a favorable entry point. is_clucha2: Similar to is_clucha but with different parameter values. is_cofi: Uses EMA, fastk/fastd crossover, ADX (Average Directional Index), EWO, CTI (Candlestick Trend Index), and R_14 (Simple Moving Average) to identify a buying opportunity. is_nfi_32, is_nfi_33, is_nfi_38: These conditions represent different variations of NFI (Next Gen Financials Indicator) strategy, considering factors like RSI, EWO, CTI, R_14, and price levels. is_nfix_5, is_nfix_39, is_nfix_49: These conditions represent different variations of NFIX (Next Fix Indicator) strategy, incorporating EMA, BB (Bollinger Bands), delta, closedelta, tail, and EMA comparison. The strategy combines these conditions to determine when to generate buy signals. It assigns a "buy_tag" value to the dataframe to indicate potential buying opportunities.