The CBPete9 strategy is a trading strategy implemented as a class in Python. It utilizes various indicators and conditions to determine buy and sell signals for trading. In the populate_indicators method, the strategy populates indicators based on the provided dataframe and metadata.
The populate_buy_trend method implements the buy conditions of the strategy.
It checks multiple conditions for each candle in the dataframe to determine if a buy signal should be generated.
Some of the important conditions include: Checking if the close price is above the 200-day exponential moving average (ema_200) and the 1-hour exponential moving average (ema_200_1h). Checking if the close price is below the lower Bollinger Band (bb_lowerband) multiplied by a specific value (buy_bb20_close_bblowerband_safe_1). Checking volume-related conditions such as comparing the current volume with the volume 30 periods ago (shifted) and applying certain thresholds (buy_volume_pump_1, buy_volume_drop_1). Checking the difference between the open and close prices in relation to the Bollinger Bands (bb_upperband, bb_lowerband) shifted by 2 periods. Checking the relative strength index (rsi) and applying a threshold (buy_rsi_3). Similar checks are performed for other buy conditions (buy_condition_2_enable, buy_condition_3_enable, and so on) using different indicators and parameters. In the populate_sell_trend method, the strategy determines the sell conditions. If the close price is above the middle Bollinger Band (bb_middleband) multiplied by 1.01 and the volume is greater than zero, a sell signal is generated. The strategy returns the modified dataframe with 'buy' and 'sell' signals marked accordingly. Overall, the CBPete9 strategy combines multiple technical indicators, moving averages, Bollinger Bands, and volume analysis to generate buy and sell signals for trading.
The populate_buy_trend method implements the buy conditions of the strategy.
It checks multiple conditions for each candle in the dataframe to determine if a buy signal should be generated.
Some of the important conditions include: Checking if the close price is above the 200-day exponential moving average (ema_200) and the 1-hour exponential moving average (ema_200_1h). Checking if the close price is below the lower Bollinger Band (bb_lowerband) multiplied by a specific value (buy_bb20_close_bblowerband_safe_1). Checking volume-related conditions such as comparing the current volume with the volume 30 periods ago (shifted) and applying certain thresholds (buy_volume_pump_1, buy_volume_drop_1). Checking the difference between the open and close prices in relation to the Bollinger Bands (bb_upperband, bb_lowerband) shifted by 2 periods. Checking the relative strength index (rsi) and applying a threshold (buy_rsi_3). Similar checks are performed for other buy conditions (buy_condition_2_enable, buy_condition_3_enable, and so on) using different indicators and parameters. In the populate_sell_trend method, the strategy determines the sell conditions. If the close price is above the middle Bollinger Band (bb_middleband) multiplied by 1.01 and the volume is greater than zero, a sell signal is generated. The strategy returns the modified dataframe with 'buy' and 'sell' signals marked accordingly. Overall, the CBPete9 strategy combines multiple technical indicators, moving averages, Bollinger Bands, and volume analysis to generate buy and sell signals for trading.
startup_candle_count : 200 ema_50_1h: 0.001%
stoploss: -0.99 timeframe: 5m hash(sha256): 590f9240d3b9b4f5103da30525640a254ebf870477b684ea51430ffb4d372371 indicators: upper ema_200 ema_50 close sma_5 bb_lowerband ema_200_1h volume smaHigh ATR ssl_up sslDown open hlv volume_mean_slow ema_50_1h smaLow high sslUp mid ssl_down_1h ssl_down rsi_1h lower ema_12 bb_middleband rsi bb_upperband ema_26 low ssl_up_1h
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last change: 2024-05-02 13:32:16