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The NostalgiaForInfinityNext strategy is designed to backtest trading strategies using various indicators and timeframes. Here's a breakdown of what it does:
Populating Indicators:
The strategy fetches BTC price data and calculates indicators for different timeframes (5 minutes, 1 hour, and daily). It merges the indicator data with the main dataframe, removing unnecessary columns.
Informative Timeframe: If specified, the strategy calculates additional indicators for informative timeframes (1 day and 1 hour).
The indicators are merged with the main dataframe, and unnecessary columns are dropped.
Resampled Timeframe: If specified, the strategy resamples the data to another timeframe and calculates corresponding indicators. The resampled data is merged with the main dataframe, and column names are adjusted. Unnecessary columns are dropped. Normal Timeframe Indicators: The strategy calculates additional indicators specifically for the normal 5-minute timeframe. Populating Buy Trend: The strategy defines conditions for triggering buy signals based on various parameters. It checks each condition and sets a buy tag in the dataframe for qualifying signals. This strategy aims to provide a comprehensive framework for backtesting trading strategies by incorporating different indicators, timeframes, and buy signal conditions.
Informative Timeframe: If specified, the strategy calculates additional indicators for informative timeframes (1 day and 1 hour).
The indicators are merged with the main dataframe, and unnecessary columns are dropped.
Resampled Timeframe: If specified, the strategy resamples the data to another timeframe and calculates corresponding indicators. The resampled data is merged with the main dataframe, and column names are adjusted. Unnecessary columns are dropped. Normal Timeframe Indicators: The strategy calculates additional indicators specifically for the normal 5-minute timeframe. Populating Buy Trend: The strategy defines conditions for triggering buy signals based on various parameters. It checks each condition and sets a buy tag in the dataframe for qualifying signals. This strategy aims to provide a comprehensive framework for backtesting trading strategies by incorporating different indicators, timeframes, and buy signal conditions.
Biased Indicators
chikou_span_1h
stoploss: -0.5 timeframe: 5m hash(sha256): 763c3d1d0f7d4ec164a04decbf3fd245d1c3bf0f29cd2f6f449520f887c58a2f 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 position_adjustment_enable res1 senkou_a sell_pump_24_1_1h sell_pump_36_2 sma_5 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_
Similar Strategies: (based on used indicators)
Strategy: NostalgiaForInfinityNext1, Similarity Score: 98.39%
Strategy: NostalgiaForInfinityNext_7_13_0, Similarity Score: 96.77%
Strategy: NostalgiaForInfinity_7_13_0, Similarity Score: 96.77%
last change: 2024-02-02 20:50:50