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The NostalgiaForInfinityNext strategy is a backtesting strategy that populates indicators and determines buy signals for trading. Here is a breakdown of what the strategy does:
Populating Indicators:
Retrieves BTC information in different timeframes (5 minutes and 1 hour) and merges it with the current dataframe. Drops unnecessary columns from the merged dataframe.
Retrieves informative timeframes (1 day and 1 hour) if specified, calculates indicators for them, and merges them with the current dataframe.
Resamples the dataframe to another specified timeframe if specified, calculates indicators for the resampled data, and merges it back with the current dataframe.
Calculates indicators for the normal timeframe (5 minutes) of the dataframe. Populating Buy Signals: Defines conditions for buy signals based on specified parameters. Checks each condition and appends the result to a list. Updates the 'buy_tag' column of the dataframe based on the combined conditions. The strategy utilizes various indicators such as Exponential Moving Averages (EMA), Simple Moving Average (SMA), percentage change (tpct_change), safe pump, BTC downtrend, pivot points, and more. It also considers the run mode and age filter parameters. Overall, the strategy aims to populate indicators and determine buy signals based on specific conditions for trading.
Retrieves informative timeframes (1 day and 1 hour) if specified, calculates indicators for them, and merges them with the current dataframe.
Resamples the dataframe to another specified timeframe if specified, calculates indicators for the resampled data, and merges it back with the current dataframe.
Calculates indicators for the normal timeframe (5 minutes) of the dataframe. Populating Buy Signals: Defines conditions for buy signals based on specified parameters. Checks each condition and appends the result to a list. Updates the 'buy_tag' column of the dataframe based on the combined conditions. The strategy utilizes various indicators such as Exponential Moving Averages (EMA), Simple Moving Average (SMA), percentage change (tpct_change), safe pump, BTC downtrend, pivot points, and more. It also considers the run mode and age filter parameters. Overall, the strategy aims to populate indicators and determine buy signals based on specific conditions for trading.
Biased Indicators
chikou_span_1h
stoploss: -0.5 timeframe: 5m hash(sha256): b99f37560e4777265a84efba8df7a832a2e4f6229b41da2bd01d8eb99fe262b7 indicators: sma_200_1h upper safe_pump_type safe_pump_24_30 leading_senkou_span_b_1h close ema_15 res1 senkou_a sell_pump_36_2 sell_pump_24_1_1h sma_5 low_s safe_pump_period ema_25 ema_200_1h tenkan_sen 39 sma200_rising_val r_480_1h safe_pump_24_100 volume rsi_4 sma_200_dec_20 leading_senkou_span_a f"global_buy_protection_paramsclose_under_pivot_type_1d senkou_a senkou_b safe_pump_24_120 tg_dataframe sell_pump_36_1_1h ema_20 source safe_pump_48_30 momdiv_coh oc_pct_change_48 rsi_14 sell_pump_48_1_1h high to
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last change: 2023-10-30 12:05:32