The NostalgiaForInfinityV7 strategy is a trading strategy implemented as a class in Python. It inherits from the IStrategy class. Here is a brief description of what the strategy does:
The populate_indicators method takes a DataFrame and metadata as input and populates indicators for the strategy.
It merges informative 1-hour indicators with the current timeframe indicators and returns the updated DataFrame.
The populate_buy_trend method populates buy conditions for the strategy based on various protection and logic criteria.
It appends buy conditions to a list called conditions. a. For buy condition 1, it checks for multiple protections such as exponential moving averages (EMA) being above the 200-day EMA, close price being above certain EMAs, rising moving averages, safe dips, and safe pumps. It also includes logic checks based on close price, relative strength index (RSI), and money flow index (MFI). b. For buy condition 2, it checks for similar protections as in buy condition 1, but with additional logic checks based on RSI difference, MFI, close price relative to Bollinger Bands, and volume. c. For buy condition 3, it again checks for protections and includes logic checks based on Bollinger Bands, close price, and tail. The resulting buy triggers are stored in the DataFrame under the columns 'buy_01_trigger', 'buy_02_trigger', 'buy_03_trigger', etc., depending on the number of buy conditions enabled. Note: The provided code snippet is incomplete and lacks important details about the strategy's sell conditions and execution logic.
It merges informative 1-hour indicators with the current timeframe indicators and returns the updated DataFrame.
The populate_buy_trend method populates buy conditions for the strategy based on various protection and logic criteria.
It appends buy conditions to a list called conditions. a. For buy condition 1, it checks for multiple protections such as exponential moving averages (EMA) being above the 200-day EMA, close price being above certain EMAs, rising moving averages, safe dips, and safe pumps. It also includes logic checks based on close price, relative strength index (RSI), and money flow index (MFI). b. For buy condition 2, it checks for similar protections as in buy condition 1, but with additional logic checks based on RSI difference, MFI, close price relative to Bollinger Bands, and volume. c. For buy condition 3, it again checks for protections and includes logic checks based on Bollinger Bands, close price, and tail. The resulting buy triggers are stored in the DataFrame under the columns 'buy_01_trigger', 'buy_02_trigger', 'buy_03_trigger', etc., depending on the number of buy conditions enabled. Note: The provided code snippet is incomplete and lacks important details about the strategy's sell conditions and execution logic.
stoploss: -0.118 timeframe: 5m hash(sha256): 36a7f5c42be46d840aaf65737ee0608261c1af6af2dc3227f1130a616fcccbe7 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: 2024-05-02 11:15:44