The CCIStrategy is a trading strategy implemented in Python for backtesting purposes. Here is a short description of what the strategy does:
The strategy uses various technical indicators such as CCI (Commodity Channel Index), RSI (Relative Strength Index), MFI (Money Flow Index), and Chaikin Money Flow (CMF) to make buy and sell decisions. The populate_indicators method calculates the indicators for the given price data, including CCI, RSI, MFI, and CMF.
It also calculates Bollinger Bands for graphing purposes.
The populate_buy_trend method generates buy signals based on specific conditions.
It checks if the CCI values are below -100, the CMF is below -0.1, the MFI is below 25, and there is a bullish crossover between moving averages. The populate_sell_trend method generates sell signals based on specific conditions. It checks if the CCI values are above 100, the CMF is above 0.3, and there is a bearish crossover between moving averages. The chaikin_mf function calculates the Chaikin Money Flow indicator using the high, low, close prices, and volume. The resample function resamples the dataframe to a different time interval and calculates additional moving averages. It interpolates missing values and merges the resampled data with the original dataframe. Overall, the CCIStrategy combines multiple technical indicators to identify potential buy and sell signals based on the defined conditions.
It also calculates Bollinger Bands for graphing purposes.
The populate_buy_trend method generates buy signals based on specific conditions.
It checks if the CCI values are below -100, the CMF is below -0.1, the MFI is below 25, and there is a bullish crossover between moving averages. The populate_sell_trend method generates sell signals based on specific conditions. It checks if the CCI values are above 100, the CMF is above 0.3, and there is a bearish crossover between moving averages. The chaikin_mf function calculates the Chaikin Money Flow indicator using the high, low, close prices, and volume. The resample function resamples the dataframe to a different time interval and calculates additional moving averages. It interpolates missing values and merges the resampled data with the original dataframe. Overall, the CCIStrategy combines multiple technical indicators to identify potential buy and sell signals based on the defined conditions.
stoploss: -0.02 timeframe: 1m hash(sha256): e668403ca5fccaf1ac45133cc859793b99d52d3c5516a385c2ac849feddc6554 indicators: resample_medium upper resample_long cci_one close mfi bb_lowerband volume resample_short date high mid cmf open high low close lower cm bb_middleband rsi bb_upperband resample_sma low cci_two
Similar Strategies: (based on used indicators)
Strategy: CCIStrategy_124, Similarity Score: 96.15%
Strategy: CCIStrategy_2, Similarity Score: 96.15%
Strategy: CCIStrategy_263, Similarity Score: 96.15%
Strategy: CCIStrategy_4, Similarity Score: 96.15%
Strategy: CCIStrategy_5, Similarity Score: 96.15%
Strategy: tSmoothScalp20230110, Similarity Score: 96.15%
Strategy: ZBCDstra, Similarity Score: 88.46%
last change: 2024-04-29 00:15:41