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The NostalgiaForInfinityV6HO strategy is a trading strategy implemented as a class. It utilizes various indicators and conditions to generate buy signals for backtesting purposes. Here is a brief description of what the strategy does:
populate_indicators method: This function takes a DataFrame and additional metadata as input and populates the indicators needed for the strategy.
It merges informative 1-hour indicators with the current timeframe indicators and returns the updated DataFrame.
populate_buy_trend method: This function generates buy signals based on a set of conditions.
The conditions are defined using different protection measures and logic. Here are the key steps involved: It sets up empty lists for conditions and protections. For each protection, it checks if the corresponding indicator values meet certain criteria and appends the result to the protections list. It sets up empty lists for logic related to buy conditions (buy_01_logic, buy_02_logic, etc.). For each buy condition, it appends the logic expressions based on indicator values, thresholds, and comparisons. It computes a trigger column for each buy condition by reducing the logic expressions using the bitwise AND operator. If a buy condition is enabled, it appends the trigger column to the conditions list. Finally, it returns the DataFrame with the added trigger columns. Overall, this strategy combines multiple indicators, protections, and logic expressions to generate buy signals based on specific conditions. The exact details of each protection and logic expression can be found in the code provided.
It merges informative 1-hour indicators with the current timeframe indicators and returns the updated DataFrame.
populate_buy_trend method: This function generates buy signals based on a set of conditions.
The conditions are defined using different protection measures and logic. Here are the key steps involved: It sets up empty lists for conditions and protections. For each protection, it checks if the corresponding indicator values meet certain criteria and appends the result to the protections list. It sets up empty lists for logic related to buy conditions (buy_01_logic, buy_02_logic, etc.). For each buy condition, it appends the logic expressions based on indicator values, thresholds, and comparisons. It computes a trigger column for each buy condition by reducing the logic expressions using the bitwise AND operator. If a buy condition is enabled, it appends the trigger column to the conditions list. Finally, it returns the DataFrame with the added trigger columns. Overall, this strategy combines multiple indicators, protections, and logic expressions to generate buy signals based on specific conditions. The exact details of each protection and logic expression can be found in the code provided.
stoploss: -0.99 timeframe: 5m hash(sha256): 3d156f518f82e17b508f9fe2489fbdcc6ff3a8801e4272070084ba318362842e indicators: sell_pump_36_2_1h sell_pump_36_3_1h sma_200_1h upper safe_pump_36_normal_1h ema_35_1h cmf_1h length ema_200 ema_50 sell_pump_36_1 sell_pump_24_1 close ema_15 sell_pump_36_2 tail safe_pump_36_strict sell_pump_24_1_1h bb_lowerband safe_pump_24_strict sma_5 safe_pump_48_strict mfi ema_200_1h bbdelta ewo safe_pump_48_normal volume sell_pump_36_3 ema_100_1h closedelta sell_pump_48_1 sell_pump_36_1_1h ema_20 open safe_pump_36_loose safe_dips_loose safe_dips_normal safe_dips_strict sell_pump_48_1_1h vo
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last change: 2023-06-28 07:26:38