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The NostalgiaForInfinityV7 strategy is a trading strategy implemented as a class in Python. It utilizes various indicators and conditions to generate buy signals for backtesting trading strategies. Here's a breakdown of what the strategy does:
populate_indicators method:
This method takes a DataFrame and metadata as input and returns a DataFrame.
It calculates informative indicators based on the input DataFrame, such as informative_1h.
It merges the informative indicators with the original DataFrame using the merge_informative_pair function.
It calculates additional indicators using the normal_tf_indicators method. The final DataFrame with all indicators is returned. populate_buy_trend method: This method takes a DataFrame and metadata as input and returns a DataFrame. It defines a list of conditions that need to be satisfied for a buy signal. It sets up various buy protections based on user-defined parameters, such as moving averages (ema), close price comparisons, and other technical indicators. It defines buy logic by combining the buy protections and additional conditions, such as RSI (Relative Strength Index), MFI (Money Flow Index), and volume. It assigns the buy logic result to a new column called 'buy_01_trigger' in the DataFrame. If enabled, it adds the 'buy_01_trigger' column to the list of conditions. Similar steps are repeated for 'buy_02_trigger', 'buy_03_trigger', 'buy_04_trigger', and other buy conditions if enabled. The final DataFrame with all buy triggers is returned. Overall, the NostalgiaForInfinityV7 strategy calculates indicators, applies various buy protections and conditions, and generates buy signals based on the defined logic. It allows for customization and testing of multiple buy conditions in a backtesting environment.
It calculates informative indicators based on the input DataFrame, such as informative_1h.
It merges the informative indicators with the original DataFrame using the merge_informative_pair function.
It calculates additional indicators using the normal_tf_indicators method. The final DataFrame with all indicators is returned. populate_buy_trend method: This method takes a DataFrame and metadata as input and returns a DataFrame. It defines a list of conditions that need to be satisfied for a buy signal. It sets up various buy protections based on user-defined parameters, such as moving averages (ema), close price comparisons, and other technical indicators. It defines buy logic by combining the buy protections and additional conditions, such as RSI (Relative Strength Index), MFI (Money Flow Index), and volume. It assigns the buy logic result to a new column called 'buy_01_trigger' in the DataFrame. If enabled, it adds the 'buy_01_trigger' column to the list of conditions. Similar steps are repeated for 'buy_02_trigger', 'buy_03_trigger', 'buy_04_trigger', and other buy conditions if enabled. The final DataFrame with all buy triggers is returned. Overall, the NostalgiaForInfinityV7 strategy calculates indicators, applies various buy protections and conditions, and generates buy signals based on the defined logic. It allows for customization and testing of multiple buy conditions in a backtesting environment.
stoploss: -0.99 timeframe: 5m hash(sha256): 74420c6e2f2190859377d80c877539d6e3368664fd2499252d8203954462bc86 indicators: sell_pump_36_3_1h sma_200_1h upper safe_pump_24_30 close ema_15 sell_pump_36_2 sma_5 sell_pump_24_1_1h buy_22_trigger ema_200_1h ewo buy_17_trigger safe_pump_24_100 volume sma_200_dec_20 buy_09_trigger safe_pump_24_120 safe_dips_70 sell_pump_36_1_1h ema_20 buy_21_trigger safe_pump_48_30 safe_pump_48_100_1h oc_pct_change_48 sell_pump_48_1_1h high tpct_change_2 safe_dips_30 sell_pump_48_3 safe_pump_48_10 safe_dips_80 rsi_1h bb20_2_mid ema_12 tpct_change_12 safe_pump_48_50 sell_pump_48_2 safe_pump_
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last change: 2022-07-06 06:06:51