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The ReinforcedQuickie strategy is a trading strategy that uses various technical analysis indicators to generate buy and sell signals. Here's a breakdown of what the strategy does:
Populate Indicators: This method calculates and adds several technical indicators to the input dataframe. The indicators include Exponential Moving Averages (EMA) of different time periods, Bollinger Bands, Minimum and Maximum values, Commodity Channel Index (CCI), Money Flow Index (MFI), Relative Strength Index (RSI), and others.
Populate Buy Trend: This method populates the "buy" column in the dataframe based on specific conditions.
The conditions include checks such as the closing price being below certain EMAs, being at the minimum value, being below the lower Bollinger Band, and a series of conditions involving moving averages, CCI, RSI, and MFI.
Populate Sell Trend: This method populates the "sell" column in the dataframe based on certain conditions. The conditions include checks such as the closing price being above certain EMAs, being at the maximum value, being above the upper Bollinger Band, and a series of conditions involving opening and closing prices, as well as RSI. The remaining code performs additional data manipulation and resampling operations on the dataframe, including setting the index, resampling the data at a specified interval, calculating a simple moving average (SMA), interpolation, merging with the original dataframe, and returning the updated dataframe. Overall, the ReinforcedQuickie strategy utilizes a combination of technical indicators to identify potential buy and sell signals in the market data.
Populate Buy Trend: This method populates the "buy" column in the dataframe based on specific conditions.
The conditions include checks such as the closing price being below certain EMAs, being at the minimum value, being below the lower Bollinger Band, and a series of conditions involving moving averages, CCI, RSI, and MFI.
Populate Sell Trend: This method populates the "sell" column in the dataframe based on certain conditions. The conditions include checks such as the closing price being above certain EMAs, being at the maximum value, being above the upper Bollinger Band, and a series of conditions involving opening and closing prices, as well as RSI. The remaining code performs additional data manipulation and resampling operations on the dataframe, including setting the index, resampling the data at a specified interval, calculating a simple moving average (SMA), interpolation, merging with the original dataframe, and returning the updated dataframe. Overall, the ReinforcedQuickie strategy utilizes a combination of technical indicators to identify potential buy and sell signals in the market data.
startup_candle_count : 1000 Recursive Analysis found no issues while using 1000 startup_candle_count
Biased Indicators
resample_sma
stoploss: -0.05 timeframe: 5m hash(sha256): d825b7a77f73ee68538b1de7e6d8cbd4c3732f5700bca37d003ca10ef7f12184 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
Similar Strategies: (based on used indicators)
Strategy: DuperFivish, Similarity Score: 96.3%
Strategy: Leveraged, Similarity Score: 96.3%
Strategy: ReinforcedQuickieHyperStrategy, Similarity Score: 96.3%
Strategy: ReinforcedQuickie_2, Similarity Score: 96.3%
Strategy: ReinforcedQuickie_253, Similarity Score: 96.3%
Strategy: ReinforcedQuickie_3, Similarity Score: 96.3%
Strategy: ReinforcedQuickie_4, Similarity Score: 96.3%
Strategy: ReinforcedQuickie_86, Similarity Score: 96.3%
Strategy: YouPig, Similarity Score: 96.3%
Strategy: SmoothOperator_4, Similarity Score: 88.89%
last change: 2023-07-04 04:19:40