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The strategy, named "NostalgiaForInfinityNext_7_13_0," is designed for backtesting trading strategies. It performs various tasks to populate indicators and generate buy signals. Here is a breakdown of its key components:
Populating Indicators: This part involves fetching and merging different dataframes to populate indicators.
It includes obtaining informative data for Bitcoin (BTC) at different timeframes (5 minutes, 1 hour, and daily) and merging it with the main dataframe.
The unnecessary columns are dropped to optimize the data.
Informative Timeframe: The strategy supports additional informative timeframes, such as 1-day and 1-hour. Indicators specific to these timeframes are calculated and merged with the main dataframe. Resampling to Another Timeframe: The strategy allows resampling the data to a different timeframe if specified. The dataframe is resampled, additional indicators are calculated for the resampled data, and the resampled dataframe is merged with the main dataframe. Indicators for Normal Timeframe (5 minutes): This step calculates indicators specific to the normal timeframe (5 minutes) and adds them to the main dataframe. Populating Buy Trend: This part involves generating buy signals based on specified conditions. The strategy evaluates various conditions and creates a buy tag for data points that meet the conditions. Overall, the strategy fetches and merges different dataframes, calculates various indicators, and generates buy signals based on predefined conditions. It provides a framework for backtesting trading strategies using historical data.
It includes obtaining informative data for Bitcoin (BTC) at different timeframes (5 minutes, 1 hour, and daily) and merging it with the main dataframe.
The unnecessary columns are dropped to optimize the data.
Informative Timeframe: The strategy supports additional informative timeframes, such as 1-day and 1-hour. Indicators specific to these timeframes are calculated and merged with the main dataframe. Resampling to Another Timeframe: The strategy allows resampling the data to a different timeframe if specified. The dataframe is resampled, additional indicators are calculated for the resampled data, and the resampled dataframe is merged with the main dataframe. Indicators for Normal Timeframe (5 minutes): This step calculates indicators specific to the normal timeframe (5 minutes) and adds them to the main dataframe. Populating Buy Trend: This part involves generating buy signals based on specified conditions. The strategy evaluates various conditions and creates a buy tag for data points that meet the conditions. Overall, the strategy fetches and merges different dataframes, calculates various indicators, and generates buy signals based on predefined conditions. It provides a framework for backtesting trading strategies using historical data.
stoploss: -0.5 timeframe: 5m hash(sha256): 37171b4938777f46c5ec4db9e3e0dcd89c5fbae6a7d9b564e679514e391be79c indicators: sma_200_1h upper safe_pump_type safe_pump_24_30 leading_senkou_span_b_1h close empty 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 32 33 34 35 36 37 38 3940 43 ema_15 res1 senkou_a sell_pump_36_2 sell_pump_24_1_1h sma_5 low_s safe_pump_period list_length ema_25 ema_200_1h ewo 39 tenkan_sen safe_dump_50 sma200_rising_val r_480_1h safe_pump_24_100 volume rsi_4 sma_200_dec_20 leading_senkou_span_a f"global_buy_protection_paramsclose_under_pivot_type_1d senkou_a senkou_b safe_pump_24_120 safe_dips
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Strategy: NostalgiaForInfinity_7_13_0, Similarity Score: 98.41%
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last change: 2022-10-05 09:08:27