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The BcmbigzV1 strategy is a trading strategy implemented as a class that inherits from IStrategy. Here is a short description of what the strategy does:
The populate_indicators function is responsible for calculating the indicators used in the strategy. It takes a DataFrame of market data and a metadata dictionary as input and returns the updated DataFrame with additional indicators.
The populate_buy_trend function is where the buy conditions of the strategy are defined.
It takes the DataFrame of market data and metadata dictionary as input and returns the updated DataFrame with buy signals based on the defined conditions.
The strategy uses various conditions to determine when to buy. Here are some of the important conditions used in the strategy: Condition 13: The close price is greater than the 1-hour exponential moving average (ema_200_1h), the Chaikin Money Flow (cmf) is less than -0.435, the Relative Strength Index (rsi) is less than 22, the slow volume mean is higher than 48-period ago, and the current volume is greater than 0. Condition 12: Similar to Condition 13 but with additional conditions like the close price being less than the lower Bollinger Band (bb_lowerband) multiplied by 0.993, the low price being less than the lower Bollinger Band multiplied by 0.985, and more. Condition 11: The close price is greater than the exponential moving average (ema_200), the histogram (hist) is positive, the Bollinger Band middle band (bb_middleband) has a positive slope, and more. Condition 0: The close price is greater than the exponential moving average (ema_200), the rsi is less than 30, the close price multiplied by 1.024 is less than the open price 3 periods ago, and more. Condition 1: Similar to Condition 0 but with additional conditions like the close price being less than the lower Bollinger Band multiplied by a certain value and the rsi_1h being less than 69. Condition 2: The close price is greater than the exponential moving average (ema_200), the close price is less than the lower Bollinger Band multiplied by a certain value, and more. Condition 3: The close price is greater than the 1-hour exponential moving average (ema_200_1h), the close price is less than the lower Bollinger Band, the rsi is less than a certain value, and more. Condition 4: The 1-hour rsi (rsi_1h) is less than a certain value, the close price is less than the lower Bollinger Band, and more. Condition 5: The close price is greater than the exponential moving average (ema_200), the close price is greater than the 1-hour exponential moving average (ema_200_1h), the MACD indicator has a positive slope, and more. These conditions represent different scenarios in which the strategy would generate a buy signal. The strategy likely incorporates additional logic and rules for selling and managing positions, but those details are not provided in the code snippet.
The populate_buy_trend function is where the buy conditions of the strategy are defined.
It takes the DataFrame of market data and metadata dictionary as input and returns the updated DataFrame with buy signals based on the defined conditions.
The strategy uses various conditions to determine when to buy. Here are some of the important conditions used in the strategy: Condition 13: The close price is greater than the 1-hour exponential moving average (ema_200_1h), the Chaikin Money Flow (cmf) is less than -0.435, the Relative Strength Index (rsi) is less than 22, the slow volume mean is higher than 48-period ago, and the current volume is greater than 0. Condition 12: Similar to Condition 13 but with additional conditions like the close price being less than the lower Bollinger Band (bb_lowerband) multiplied by 0.993, the low price being less than the lower Bollinger Band multiplied by 0.985, and more. Condition 11: The close price is greater than the exponential moving average (ema_200), the histogram (hist) is positive, the Bollinger Band middle band (bb_middleband) has a positive slope, and more. Condition 0: The close price is greater than the exponential moving average (ema_200), the rsi is less than 30, the close price multiplied by 1.024 is less than the open price 3 periods ago, and more. Condition 1: Similar to Condition 0 but with additional conditions like the close price being less than the lower Bollinger Band multiplied by a certain value and the rsi_1h being less than 69. Condition 2: The close price is greater than the exponential moving average (ema_200), the close price is less than the lower Bollinger Band multiplied by a certain value, and more. Condition 3: The close price is greater than the 1-hour exponential moving average (ema_200_1h), the close price is less than the lower Bollinger Band, the rsi is less than a certain value, and more. Condition 4: The 1-hour rsi (rsi_1h) is less than a certain value, the close price is less than the lower Bollinger Band, and more. Condition 5: The close price is greater than the exponential moving average (ema_200), the close price is greater than the 1-hour exponential moving average (ema_200_1h), the MACD indicator has a positive slope, and more. These conditions represent different scenarios in which the strategy would generate a buy signal. The strategy likely incorporates additional logic and rules for selling and managing positions, but those details are not provided in the code snippet.
startup_candle_count : 200 ema_50_1h: -0.001% macd: -0.001% signal: -0.002% hist: 0.002%
stoploss: -0.99 timeframe: 5m hash(sha256): bdf457464be5b1b864c0964a89c3ea78ed6b008a1b5c8b15d21aa339cfa43c04 indicators: upper ema_200 ema_50 close bb_lowerband ema_200_1h volume open volume_mean_slow open_1h high mid mfv cmf macd rsi_1h bb_lowerband_1h hist lower cm ema_12 signal bb_middleband rsi bb_upperband ema_26 low close_1h
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last change: 2023-07-03 19:37:35