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The NostalgiaForInfinityNext strategy is a trading strategy that involves backtesting and analyzing various indicators to make buy decisions in the cryptocurrency market. Here is a breakdown of what the strategy does:
Populating Indicators:
Retrieves price and volume data for the chosen cryptocurrency pair. Performs calculations and generates indicators based on different timeframes (5 minutes, 1 hour, daily).
Merges the indicator data with the main dataframe.
Drops unnecessary columns.
Informative Timeframe: If an informative timeframe is specified, additional indicators are calculated for that timeframe. The informative data is merged with the main dataframe. Resampled Timeframe: If a resampled timeframe is specified, the main dataframe is resampled to that timeframe. Indicators specific to the resampled timeframe are calculated. The resampled data is merged with the main dataframe. Normal Timeframe Indicators: Calculates indicators for the normal timeframe (5 minutes). These indicators are added to the main dataframe. Buy Trend: Sets up conditions for determining buy signals. Iterates through different buy protection parameters and builds a list of conditions based on those parameters. Sets buy logic based on specific indices and indicator values. Assigns a buy tag to each row in the dataframe based on the buy conditions. The strategy combines multiple indicators and buy conditions to identify potential buying opportunities in the cryptocurrency market. Please note that the provided code is a part of the strategy implementation and lacks some context.
Merges the indicator data with the main dataframe.
Drops unnecessary columns.
Informative Timeframe: If an informative timeframe is specified, additional indicators are calculated for that timeframe. The informative data is merged with the main dataframe. Resampled Timeframe: If a resampled timeframe is specified, the main dataframe is resampled to that timeframe. Indicators specific to the resampled timeframe are calculated. The resampled data is merged with the main dataframe. Normal Timeframe Indicators: Calculates indicators for the normal timeframe (5 minutes). These indicators are added to the main dataframe. Buy Trend: Sets up conditions for determining buy signals. Iterates through different buy protection parameters and builds a list of conditions based on those parameters. Sets buy logic based on specific indices and indicator values. Assigns a buy tag to each row in the dataframe based on the buy conditions. The strategy combines multiple indicators and buy conditions to identify potential buying opportunities in the cryptocurrency market. Please note that the provided code is a part of the strategy implementation and lacks some context.
Biased Indicators
chikou_span_1h
stoploss: -0.5 timeframe: 5m hash(sha256): 3b695aca287f1dcf5aab221d14dbaf285a3ea8517d2ca96963b21add0fa9d931 indicators: sma_200_1h upper safe_pump_type safe_pump_24_30 leading_senkou_span_b_1h close ema_15 position_adjustment_enable senkou_a sell_pump_36_2 sell_pump_24_1_1h sma_5 res1 safe_pump_period ema_25 ema_200_1h ewo 39 tenkan_sen safe_dump_50 sma200_rising_val r_480_1h safe_pump_24_100 volume rsi_4 sma_200_dec_20 leading_senkou_span_a senkou_a senkou_b safe_pump_24_120 safe_dips_70 sell_pump_36_1_1h ema_20 source safe_pump_48_30 buy_tag oc_pct_change_48 sell_pump_48_1_1h rsi_14 high tpct_change_2 safe_dips
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last change: 2023-09-11 06:36:02