The strategy, called CombinedBinHClucAndMADV5, is implemented as a class that inherits from the IStrategy class. It consists of two main functions: populate_indicators and populate_buy_trend. In the populate_indicators function, various indicators are calculated and populated in the input dataframe.

These indicators are derived from different timeframes, including an informative 1-hour timeframe.

The informative 1-hour indicators are merged with the original dataframe using the merge_informative_pair function.

The populate_buy_trend function is responsible for determining the buy signals based on specific conditions. The strategy involves multiple sub-strategies combined together using logical operators (| for OR conditions). Here are the important parts of the strategy: Strategy ClucMay72018: The close price should be above the 200-day exponential moving average (ema_200). The close price should be above the 1-hour exponential moving average (ema_200_1h). The close price should be below the slow exponential moving average (ema_slow). The close price should be below 0.99 times the lower Bollinger Band (bb_lowerband). The volume should be less than 21 times the previous slow volume mean. The volume should be greater than zero. Strategy MACD Low buy: The close price should be above the 200-day exponential moving average (ema_200). The close price should be above the 1-hour exponential moving average (ema_200_1h). The MACD line (ema_26 - ema_12) should be positive. The difference between the MACD line and the signal line (ema_26 - ema_12) should be greater than 2% of the open price. The difference between the previous MACD line and the previous signal line should be greater than 1% of the open price. The volume should be less than four times the previous volume. The close price should be below the lower Bollinger Band (bb_lowerband). The volume should be greater than zero. Other conditions: The close price should be below the 5-day simple moving average (sma_5). The SSL up indicator (ssl_up_1h) should be greater than the SSL down indicator (ssl_down_1h). The 50-day exponential moving average (ema_50) should be above the 200-day exponential moving average (ema_200). The 50-hour exponential moving average (ema_50_1h) should be above the 200-hour exponential moving average (ema_200_1h). The RSI (Relative Strength Index) should be below the RSI of the previous hour minus 43.276. The volume should be greater than zero. If any of these conditions are met, the corresponding row in the dataframe is marked as a buy signal by assigning the value of 1. Additionally, there is a populate_sell_trend function that determines the sell signals based on a condition: The close price should be above 1.01 times the middle Bollinger Band (bb_middleband). The volume should be greater than zero. If this condition is met, the corresponding row in the dataframe is marked as a sell signal by assigning the value of 1. The functions modify the dataframe and return it as the output.

These indicators are derived from different timeframes, including an informative 1-hour timeframe.

The informative 1-hour indicators are merged with the original dataframe using the merge_informative_pair function.

The populate_buy_trend function is responsible for determining the buy signals based on specific conditions. The strategy involves multiple sub-strategies combined together using logical operators (| for OR conditions). Here are the important parts of the strategy: Strategy ClucMay72018: The close price should be above the 200-day exponential moving average (ema_200). The close price should be above the 1-hour exponential moving average (ema_200_1h). The close price should be below the slow exponential moving average (ema_slow). The close price should be below 0.99 times the lower Bollinger Band (bb_lowerband). The volume should be less than 21 times the previous slow volume mean. The volume should be greater than zero. Strategy MACD Low buy: The close price should be above the 200-day exponential moving average (ema_200). The close price should be above the 1-hour exponential moving average (ema_200_1h). The MACD line (ema_26 - ema_12) should be positive. The difference between the MACD line and the signal line (ema_26 - ema_12) should be greater than 2% of the open price. The difference between the previous MACD line and the previous signal line should be greater than 1% of the open price. The volume should be less than four times the previous volume. The close price should be below the lower Bollinger Band (bb_lowerband). The volume should be greater than zero. Other conditions: The close price should be below the 5-day simple moving average (sma_5). The SSL up indicator (ssl_up_1h) should be greater than the SSL down indicator (ssl_down_1h). The 50-day exponential moving average (ema_50) should be above the 200-day exponential moving average (ema_200). The 50-hour exponential moving average (ema_50_1h) should be above the 200-hour exponential moving average (ema_200_1h). The RSI (Relative Strength Index) should be below the RSI of the previous hour minus 43.276. The volume should be greater than zero. If any of these conditions are met, the corresponding row in the dataframe is marked as a buy signal by assigning the value of 1. Additionally, there is a populate_sell_trend function that determines the sell signals based on a condition: The close price should be above 1.01 times the middle Bollinger Band (bb_middleband). The volume should be greater than zero. If this condition is met, the corresponding row in the dataframe is marked as a sell signal by assigning the value of 1. The functions modify the dataframe and return it as the output.

startup_candle_count :200ema_50_1h:0.001%

stoploss:-0.99timeframe:5mhash(sha256):ec954f77cea1c12ec400a8678fdab881fd3f5ff05722f879f96ec61f25cd4414indicators:upper ema_200 ema_50 close sma_5 tail bb_lowerband ema_200_1h bbdelta volume smaHigh ATR ssl_up closedelta 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_1hSimilar Strategies:(based on used indicators)

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Strategy: MadV9HO, Similarity Score: 86.11%last change: 2023-11-02 23:37:37