The NostalgiaForInfinityNext strategy is a trading strategy implemented as a class that inherits from the IStrategy interface. Here is a brief description of what the strategy does:
populate_indicators method: This method populates the indicators used by the strategy. It takes a DataFrame and metadata as input and returns the modified DataFrame.
It performs the following steps: Retrieves BTC (Bitcoin) data at different timeframes (5 minutes and 1 hour).
Calculates indicators for the BTC data.
Merges the BTC indicators with the original DataFrame. Handles informative timeframes by calculating indicators for the specified timeframe and merging them with the DataFrame. Handles resampling to another timeframe by resampling the DataFrame and calculating indicators for the resampled data. Calculates indicators for the normal (5-minute) timeframe. populate_buy_trend method: This method populates the buy signals for the strategy. It takes a DataFrame and metadata as input and returns the modified DataFrame. It performs the following steps: Defines conditions for generating buy signals based on various parameters. Iterates over buy protection parameters and checks if the buy conditions are enabled. Adds buy protection conditions based on parameters such as exponential moving averages (EMAs), close prices, simple moving averages (SMAs), and others. Defines buy logic based on different index values. Applies buy logic to determine buy signals and adds a 'buy_tag' column to the DataFrame. Overall, the strategy combines multiple indicators and conditions to generate buy signals for trading. It incorporates BTC data, handles informative timeframes and resampling, and uses various technical analysis indicators to make buy decisions.
It performs the following steps: Retrieves BTC (Bitcoin) data at different timeframes (5 minutes and 1 hour).
Calculates indicators for the BTC data.
Merges the BTC indicators with the original DataFrame. Handles informative timeframes by calculating indicators for the specified timeframe and merging them with the DataFrame. Handles resampling to another timeframe by resampling the DataFrame and calculating indicators for the resampled data. Calculates indicators for the normal (5-minute) timeframe. populate_buy_trend method: This method populates the buy signals for the strategy. It takes a DataFrame and metadata as input and returns the modified DataFrame. It performs the following steps: Defines conditions for generating buy signals based on various parameters. Iterates over buy protection parameters and checks if the buy conditions are enabled. Adds buy protection conditions based on parameters such as exponential moving averages (EMAs), close prices, simple moving averages (SMAs), and others. Defines buy logic based on different index values. Applies buy logic to determine buy signals and adds a 'buy_tag' column to the DataFrame. Overall, the strategy combines multiple indicators and conditions to generate buy signals for trading. It incorporates BTC data, handles informative timeframes and resampling, and uses various technical analysis indicators to make buy decisions.
Biased Indicators
chikou_span_1h
stoploss: -0.33 timeframe: 5m hash(sha256): 2d272a0026c5b0cbd92c664200fef350d453f6ceedc6331ebed980992c640591 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