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The ReinforcedQuickie strategy is a trading strategy that uses various technical indicators to generate buy and sell signals for a given financial instrument. Here is a breakdown of what the strategy does:
populate_indicators: This function calculates and populates several technical indicators for the given dataframe. It includes exponential moving averages (EMAs) of different time periods, Bollinger Bands, minimum and maximum values, commodity channel index (CCI), money flow index (MFI), relative strength index (RSI), and average price.
populate_buy_trend: This function populates the buy signal for the dataframe based on the defined conditions.
It checks for two sets of conditions: If the close price is below the short-term EMA, medium-term EMA, minimum value, and the lower Bollinger Band.
If there is a pattern of increasing average prices over the last 5 periods, the previous period's low is below the Bollinger Band's middle line, CCI is below -100, RSI is below 30, and MFI is below 30. Additionally, it checks for volume conditions and the relationship between the moving average and the close price. populate_sell_trend: This function populates the sell signal for the dataframe based on the defined conditions. It checks for two sets of conditions: If the close price is above the short-term EMA, medium-term EMA, maximum value, and the upper Bollinger Band. Additionally, MFI should be above 80. If there is a pattern of increasing open prices over the last 7 periods, and RSI is above 70. The remaining code performs additional data manipulation and resampling. It sets the index of the dataframe to the date, resamples the data based on the provided interval and factor, calculates the simple moving average (SMA) of the resampled data, and merges the resampled data back into the original dataframe. Overall, the strategy aims to generate buy and sell signals based on the calculated technical indicators and specific conditions. It utilizes indicators such as EMAs, Bollinger Bands, CCI, MFI, RSI, and patterns in price and volume to identify potential entry and exit points for trading.
populate_buy_trend: This function populates the buy signal for the dataframe based on the defined conditions.
It checks for two sets of conditions: If the close price is below the short-term EMA, medium-term EMA, minimum value, and the lower Bollinger Band.
If there is a pattern of increasing average prices over the last 5 periods, the previous period's low is below the Bollinger Band's middle line, CCI is below -100, RSI is below 30, and MFI is below 30. Additionally, it checks for volume conditions and the relationship between the moving average and the close price. populate_sell_trend: This function populates the sell signal for the dataframe based on the defined conditions. It checks for two sets of conditions: If the close price is above the short-term EMA, medium-term EMA, maximum value, and the upper Bollinger Band. Additionally, MFI should be above 80. If there is a pattern of increasing open prices over the last 7 periods, and RSI is above 70. The remaining code performs additional data manipulation and resampling. It sets the index of the dataframe to the date, resamples the data based on the provided interval and factor, calculates the simple moving average (SMA) of the resampled data, and merges the resampled data back into the original dataframe. Overall, the strategy aims to generate buy and sell signals based on the calculated technical indicators and specific conditions. It utilizes indicators such as EMAs, Bollinger Bands, CCI, MFI, RSI, and patterns in price and volume to identify potential entry and exit points for trading.
stoploss: -0.05 timeframe: 5m hash(sha256): 4108bcefb832d222032ba577044b623be3ebbc22f32c151d3de2a74159265260
Was not able to fetch indicators from Strategyfile.
last change: 2022-07-02 19:54:08