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The Leveraged strategy is a backtesting strategy that uses various technical indicators to generate buy and sell signals for trading. Here is a brief explanation of what the strategy does:
populate_indicators method:
Resamples the input dataframe based on the specified timeframe and resample factor. Calculates Exponential Moving Averages (EMAs) of short-term, medium-term, and long-term periods.
Calculates Bollinger Bands using the typical price and a window of 20 periods with 2 standard deviations.
Calculates the minimum and maximum values over the medium-term period.
Calculates the Commodity Channel Index (CCI), Money Flow Index (MFI), and Relative Strength Index (RSI). Calculates the average price based on the close, open, high, and low prices. Calculates Bollinger Bands for the close price with the same parameters as before. Calculates Moving Average Convergence Divergence (MACD) and its components. populate_buy_trend method: Determines buy signals based on specific conditions: The close price is below the short-term EMA, medium-term EMA, minimum value, and lower Bollinger Band. A pattern of decreasing averages and other indicators signaling oversold conditions. The close price is below the lower Bollinger Band, and other indicators signaling oversold conditions. Additional conditions related to volume and simple moving average. populate_sell_trend method: Determines sell signals based on specific conditions: The close price is above the short-term EMA, medium-term EMA, maximum value, upper Bollinger Band, and high MFI. A pattern of increasing open and close prices and high RSI. The CCI and RSI indicators are above certain thresholds. Additional processing steps: Copies the input dataframe and sets the index as a DateTimeIndex. Resamples the dataframe based on a specified interval, using open, high, low, and close prices to generate OHLC data. Calculates a simple moving average (SMA) with a period of 25 on the resampled dataframe. Interpolates missing values in the resampled dataframe using the time method. Merges the processed dataframe back with the original dataframe based on the date column. The strategy aims to identify potential buying and selling opportunities based on the calculated indicators and patterns in the market data.
Calculates Bollinger Bands using the typical price and a window of 20 periods with 2 standard deviations.
Calculates the minimum and maximum values over the medium-term period.
Calculates the Commodity Channel Index (CCI), Money Flow Index (MFI), and Relative Strength Index (RSI). Calculates the average price based on the close, open, high, and low prices. Calculates Bollinger Bands for the close price with the same parameters as before. Calculates Moving Average Convergence Divergence (MACD) and its components. populate_buy_trend method: Determines buy signals based on specific conditions: The close price is below the short-term EMA, medium-term EMA, minimum value, and lower Bollinger Band. A pattern of decreasing averages and other indicators signaling oversold conditions. The close price is below the lower Bollinger Band, and other indicators signaling oversold conditions. Additional conditions related to volume and simple moving average. populate_sell_trend method: Determines sell signals based on specific conditions: The close price is above the short-term EMA, medium-term EMA, maximum value, upper Bollinger Band, and high MFI. A pattern of increasing open and close prices and high RSI. The CCI and RSI indicators are above certain thresholds. Additional processing steps: Copies the input dataframe and sets the index as a DateTimeIndex. Resamples the dataframe based on a specified interval, using open, high, low, and close prices to generate OHLC data. Calculates a simple moving average (SMA) with a period of 25 on the resampled dataframe. Interpolates missing values in the resampled dataframe using the time method. Merges the processed dataframe back with the original dataframe based on the date column. The strategy aims to identify potential buying and selling opportunities based on the calculated indicators and patterns in the market data.
startup_candle_count : 50 ema_21: -0.011% rsi: 0.269%
Biased Indicators
resample_sma
stoploss: -0.05 timeframe: 5m hash(sha256): 3f9091652d0110ebb4617625b75e35582cb3392b8bce304511b61499a90de05b indicators: upper close bb_lowerband mfi macdhist volume average date max open high macdsignal mid cci macd open high low close min lower bb_middleband rsi bb_upperband low resample_sma
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last change: 2023-07-05 05:05:08