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The NostalgiaForInfinityNext_v772 strategy is a trading strategy implemented in Python. It is designed to be used for backtesting on a trading platform or website. Here is a brief description of what the strategy does:
populate_indicators function:
It receives a DataFrame and metadata as input.
The function fetches BTC (Bitcoin) data for informative timeframes (5 minutes and 1 hour).
It calculates various indicators for the BTC data and merges them with the input DataFrame.
Unnecessary columns are dropped from the DataFrame. It also handles informative timeframes and resampling to another timeframe if specified. populate_buy_trend function: It receives a DataFrame and metadata as input. The function defines a set of conditions for generating buy signals. It iterates over buy protection parameters and applies corresponding conditions based on the configuration. It calculates various indicators and compares them with predefined thresholds to generate buy signals. The buy signals are marked in the DataFrame using a 'buy_tag' column. Overall, the strategy involves fetching and merging BTC data, calculating indicators, and applying specific conditions to generate buy signals. This description provides a high-level overview of the strategy's functionality, but the specific details of each indicator and condition are not mentioned.
The function fetches BTC (Bitcoin) data for informative timeframes (5 minutes and 1 hour).
It calculates various indicators for the BTC data and merges them with the input DataFrame.
Unnecessary columns are dropped from the DataFrame. It also handles informative timeframes and resampling to another timeframe if specified. populate_buy_trend function: It receives a DataFrame and metadata as input. The function defines a set of conditions for generating buy signals. It iterates over buy protection parameters and applies corresponding conditions based on the configuration. It calculates various indicators and compares them with predefined thresholds to generate buy signals. The buy signals are marked in the DataFrame using a 'buy_tag' column. Overall, the strategy involves fetching and merging BTC data, calculating indicators, and applying specific conditions to generate buy signals. This description provides a high-level overview of the strategy's functionality, but the specific details of each indicator and condition are not mentioned.
stoploss: -0.99 timeframe: 5m hash(sha256): 2ee239b034e9b43fc4a5bef06b8ce64e0249cdc7e516bae58b8fd2d9bdeb0cff indicators: sma_200_1h upper safe_pump_type safe_pump_24_30 leading_senkou_span_b_1h close ema_15 senkou_a sell_pump_36_2 sma_5 sell_pump_24_1_1h safe_pump_period ema_25 ema_200_1h tenkan_sen 39 safe_dump_50 ewo sma200_rising_val r_480_1h safe_pump_24_100 volume rsi_4 sma_200_dec_20 leading_senkou_span_a senkou_a senkou_b safe_pump_24_120 safe_dips_70 sell_pump_36_1_1h ema_20 source safe_pump_48_30 hrsi buy_tag oc_pct_change_48 sell_pump_48_1_1h rsi_14 high efi tpct_change_2 safe_dips_30 r_480 sell_pump_48_
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last change: 2022-07-02 19:54:08