The NFI47 strategy is a trading strategy implemented as a class that inherits from the IStrategy class. It consists of several methods that perform different tasks. The populate_indicators method takes a DataFrame and a metadata dictionary as inputs and returns a DataFrame.
It populates the indicators used in the strategy by calling the informative_1h_indicators method and merging the resulting indicators with the original DataFrame using the merge_informative_pair function.
Then, it applies additional indicators using the normal_tf_indicators method and returns the updated DataFrame.
The populate_buy_trend method also takes a DataFrame and a metadata dictionary as inputs and returns a DataFrame. It defines a list of conditions that determine when to initiate a buy order. Each condition is represented as a logical expression involving various indicators and parameters. If a condition evaluates to True, it signifies that the corresponding buy order should be executed. The method appends all the conditions to a list and returns the DataFrame. Overall, the NFI47 strategy involves calculating indicators, merging them with the original DataFrame, and defining conditions for initiating buy orders based on specific criteria.
It populates the indicators used in the strategy by calling the informative_1h_indicators method and merging the resulting indicators with the original DataFrame using the merge_informative_pair function.
Then, it applies additional indicators using the normal_tf_indicators method and returns the updated DataFrame.
The populate_buy_trend method also takes a DataFrame and a metadata dictionary as inputs and returns a DataFrame. It defines a list of conditions that determine when to initiate a buy order. Each condition is represented as a logical expression involving various indicators and parameters. If a condition evaluates to True, it signifies that the corresponding buy order should be executed. The method appends all the conditions to a list and returns the DataFrame. Overall, the NFI47 strategy involves calculating indicators, merging them with the original DataFrame, and defining conditions for initiating buy orders based on specific criteria.
startup_candle_count : 400 ema_100_1h: 0.001% ema_200_1h: 0.078% ewo: 0.086%
stoploss: -0.99 timeframe: 5m hash(sha256): 86d65b71e004927e9be6ba848dd33c1d81fded1adb84d9c3ff22c2c1324af52d 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 ema_200_1h ewo safe_pump_24 volume sma_200_dec_20 sell_pump_36_1_1h ema_20 safe_pump_48_1h safe_pump_24_1h sell_pump_48_1_1h safe_pump_36_1h high safe_pump_24_normal sell_pump_48_3 rsi_1h bb20_2_mid ema_12 sell_pump_48_2 ema_26 safe_pump_24_strict_1h sell_pump_24_1 jaw safe_pump_36 smma_jaw chop safe_pump_36_strict_1h bbdelta bb20_2_upp_1h closedelta safe_dips_loose safe_dips_n
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Strategy: NFI47V2, Similarity Score: 97.62%
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last change: 2024-05-02 16:56:55