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The NostalgiaForInfinityNext strategy is a trading strategy implemented as a class that inherits from the IStrategy class. Here's a short description of what the strategy does:
The strategy first populates indicators using the populate_indicators function. It retrieves informative 1-hour indicators and merges them with the main timeframe indicators.
The function returns the updated dataframe with indicators.
Next, the strategy determines the buying conditions using the populate_buy_trend function.
It checks various conditions for buying and defines multiple sets of protections and logic for each condition. The protections include checks based on exponential moving averages (EMA), close price relative to EMAs, simple moving averages (SMA), safe dips, and safe pumps. The logic involves combining the protections using logical operators like AND (&). The resulting logical conditions are stored in different trigger columns in the dataframe based on the condition number (e.g., buy_01_trigger, buy_02_trigger, etc.). The strategy allows enabling or disabling different conditions through configuration parameters. The conditions are stored in a list, and if enabled, the respective trigger column is added to the list. Overall, the strategy aims to identify buying opportunities based on various technical indicators such as EMAs, SMAs, RSI, MFI, Bollinger Bands, and volume. By combining different conditions and protections, the strategy tries to identify favorable entry points for trading. Please note that the provided code snippet may be incomplete or require additional context for a complete understanding of the strategy.
The function returns the updated dataframe with indicators.
Next, the strategy determines the buying conditions using the populate_buy_trend function.
It checks various conditions for buying and defines multiple sets of protections and logic for each condition. The protections include checks based on exponential moving averages (EMA), close price relative to EMAs, simple moving averages (SMA), safe dips, and safe pumps. The logic involves combining the protections using logical operators like AND (&). The resulting logical conditions are stored in different trigger columns in the dataframe based on the condition number (e.g., buy_01_trigger, buy_02_trigger, etc.). The strategy allows enabling or disabling different conditions through configuration parameters. The conditions are stored in a list, and if enabled, the respective trigger column is added to the list. Overall, the strategy aims to identify buying opportunities based on various technical indicators such as EMAs, SMAs, RSI, MFI, Bollinger Bands, and volume. By combining different conditions and protections, the strategy tries to identify favorable entry points for trading. Please note that the provided code snippet may be incomplete or require additional context for a complete understanding of the strategy.
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.99 timeframe: 5m hash(sha256): 2575f8b2c29ce415c80a3f7cca839693816610324af63997198d25725bd07694 indicators: sell_pump_36_3_1h sma_200_1h upper close ema_15 sell_pump_36_2 sma_5 safe_pump_36_strict sell_pump_24_1_1h bb_lowerband buy_22_trigger ema_200_1h ewo buy_17_trigger volume buy_09_trigger sell_pump_36_1_1h ema_20 buy_21_trigger sell_pump_48_1_1h bb_upperband_1h high safe_pump_24_normal sell_pump_48_3 rsi_1h ema_12 sell_pump_48_2 ema_26 buy_04_trigger sell_pump_24_1 chop bbdelta closedelta safe_dips_loose safe_dips_normal buy_03_trigger safe_pump_48_loose volume_mean_4 buy_19_trigger buy_08_trigge
No similar strategies found. (based on used indicators)
last change: 2024-08-02 07:32:08