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The "SmoothOperator" strategy is a backtesting strategy that uses various technical indicators to generate buy and sell signals for trading. Here's a short description of what the strategy does:
It starts by populating the dataframe with several indicators such as CCI (Commodity Channel Index), RSI (Relative Strength Index), ADX (Average Directional Movement Index), MFI (Money Flow Index), and their corresponding smoothed versions using exponential moving averages (EMAs). It calculates Bollinger Bands based on the closing price and adds the lower, upper, and middle bands to the dataframe.
It calculates MACD (Moving Average Convergence Divergence) and adds MACD line, MACD signal line, and MACD histogram to the dataframe.
It calculates Bollinger Bands for entry signals using a different standard deviation value and adds the lower, upper, and middle bands to the dataframe.
It calculates a percentage value called "bpercent" that represents the position of the closing price relative to the Bollinger Bands. It calculates a sharpness value called "bsharp" based on the width of the Bollinger Bands. It calculates moving averages of the "bsharp" value at different time periods. It calculates a combined indicator value called "mfi_rsi_cci_smooth" by averaging the smoothed RSI, MFI, and CCI values. It applies a triple exponential moving average (TEMA) to the "mfi_rsi_cci_smooth" value. It calculates the candle size based on the difference between the closing and opening prices. It calculates the average price based on the closing, opening, high, and low prices. It calculates simple moving averages (SMA) at different time periods for the closing price. The "populate_buy_trend" function generates buy signals based on various conditions involving the average price, Bollinger Bands, CCI, RSI, and MFI values. The "populate_sell_trend" function generates sell signals based on conditions involving the "mfi_rsi_cci_smooth" value, a pattern called "eight_green_candles," and high values of CCI and RSI. The strategy also includes a separate class called "StrategyHelper," which may contain additional helper functions or methods not included in the provided code snippet. Overall, the strategy combines multiple indicators and conditions to determine optimal entry and exit points for trading based on the given technical analysis parameters.
It calculates MACD (Moving Average Convergence Divergence) and adds MACD line, MACD signal line, and MACD histogram to the dataframe.
It calculates Bollinger Bands for entry signals using a different standard deviation value and adds the lower, upper, and middle bands to the dataframe.
It calculates a percentage value called "bpercent" that represents the position of the closing price relative to the Bollinger Bands. It calculates a sharpness value called "bsharp" based on the width of the Bollinger Bands. It calculates moving averages of the "bsharp" value at different time periods. It calculates a combined indicator value called "mfi_rsi_cci_smooth" by averaging the smoothed RSI, MFI, and CCI values. It applies a triple exponential moving average (TEMA) to the "mfi_rsi_cci_smooth" value. It calculates the candle size based on the difference between the closing and opening prices. It calculates the average price based on the closing, opening, high, and low prices. It calculates simple moving averages (SMA) at different time periods for the closing price. The "populate_buy_trend" function generates buy signals based on various conditions involving the average price, Bollinger Bands, CCI, RSI, and MFI values. The "populate_sell_trend" function generates sell signals based on conditions involving the "mfi_rsi_cci_smooth" value, a pattern called "eight_green_candles," and high values of CCI and RSI. The strategy also includes a separate class called "StrategyHelper," which may contain additional helper functions or methods not included in the provided code snippet. Overall, the strategy combines multiple indicators and conditions to determine optimal entry and exit points for trading based on the given technical analysis parameters.
stoploss: -0.05 timeframe: 5m hash(sha256): a147ba9acc0df5a004642731aee86b11ed583924360336bdf9fa7708fe36d466
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last change: 2022-07-11 07:19:18