The CombinedBinHClucAndMADV6 strategy is a trading strategy implemented as a class in Python. It is used for backtesting trading strategies on a website. Here is a description of what the strategy does in simplified terms:
The strategy consists of two main parts: indicator population and trend population.
In the indicator population part (populate_indicators function), the strategy calculates various indicators based on the provided dataframe and metadata.
It merges informative indicators from a 1-hour timeframe (informative_1h) with the main dataframe.
It also calculates additional indicators for the normal timeframe. In the trend population part, the strategy determines the conditions for entering a buy trade (populate_buy_trend function) and selling the trade (populate_sell_trend function). The buy conditions include: Price crossing above the 200-day exponential moving average (ema_200) and the 1-hour exponential moving average (ema_200_1h). Price below the slow exponential moving average (ema_slow) and 0.99 times the lower Bollinger Band (bb_lowerband). Volume mean slow is greater than the volume mean slow shifted by 30 periods multiplied by 0.4. Volume is greater than 0. The strategy also has additional buy conditions based on the MACD indicator and other technical factors. The sell condition is when the price crosses above 1.01 times the middle Bollinger Band (bb_middleband) and the volume is greater than 0. Overall, the strategy combines multiple conditions based on various indicators to determine the entry and exit points for trading. Please note that this description is a simplified interpretation of the code provided, and there may be additional details or nuances not captured in this summary.
In the indicator population part (populate_indicators function), the strategy calculates various indicators based on the provided dataframe and metadata.
It merges informative indicators from a 1-hour timeframe (informative_1h) with the main dataframe.
It also calculates additional indicators for the normal timeframe. In the trend population part, the strategy determines the conditions for entering a buy trade (populate_buy_trend function) and selling the trade (populate_sell_trend function). The buy conditions include: Price crossing above the 200-day exponential moving average (ema_200) and the 1-hour exponential moving average (ema_200_1h). Price below the slow exponential moving average (ema_slow) and 0.99 times the lower Bollinger Band (bb_lowerband). Volume mean slow is greater than the volume mean slow shifted by 30 periods multiplied by 0.4. Volume is greater than 0. The strategy also has additional buy conditions based on the MACD indicator and other technical factors. The sell condition is when the price crosses above 1.01 times the middle Bollinger Band (bb_middleband) and the volume is greater than 0. Overall, the strategy combines multiple conditions based on various indicators to determine the entry and exit points for trading. Please note that this description is a simplified interpretation of the code provided, and there may be additional details or nuances not captured in this summary.
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.99 timeframe: 5m hash(sha256): bf15eb667775d3b10792244e498e0b437e7ea96b476a341a93b5a50510c6a1b4 indicators: upper ema_200 ema_50 close sma_5 bb_lowerband ema_200_1h volume smaHigh ATR ssl_up sslDown open hlv volume_mean_slow ema_50_1h smaLow high sslUp mid ssl_down_1h ssl_down rsi_1h lower ema_12 bb_middleband rsi bb_upperband ema_slow ema_26 low ssl_up_1h
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last change: 2024-07-26 22:47:50