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The NostalgiaForInfinityNext strategy is a trading strategy that involves populating indicators and defining conditions for buying trends. Here is a breakdown of what the strategy does:
Populating Indicators: The strategy populates various indicators based on different timeframes and pairs. It retrieves information on Bitcoin (BTC) and merges it with the current dataframe.
Indicators include daily, hourly, and base timeframe indicators.
Informative Timeframe: The strategy includes informative timeframes for additional indicators.
It calculates indicators for the 1-day and 1-hour informative timeframes and merges them with the current dataframe. Resampled Timeframe: The strategy resamples the dataframe to a specified timeframe and calculates indicators for the resampled data. The resampled dataframe is merged with the original dataframe, and the columns are renamed accordingly. Normal Timeframe Indicators: The strategy calculates indicators for the normal 5-minute timeframe. Buy Trend Conditions: The strategy defines conditions for determining buy trends. It iterates through a list of buy protection parameters and checks if specific conditions are met. These conditions include exponential moving average (EMA) crossovers, close price above EMAs, rising moving averages, safe dips, safe pumps, BTC not in a downtrend, close price above/below pivot types, and additional filters depending on the run mode. Buy Trend Logic: The strategy applies logical operations to determine the buy logic. It checks if the conditions defined in the buy protection parameters are met. Additionally, for index 1, it includes specific conditions such as price increase, RSI (Relative Strength Index), R (rate of change), and MFI (Money Flow Index) thresholds. This strategy aims to use various indicators and conditions to identify potential buy trends in trading.
Indicators include daily, hourly, and base timeframe indicators.
Informative Timeframe: The strategy includes informative timeframes for additional indicators.
It calculates indicators for the 1-day and 1-hour informative timeframes and merges them with the current dataframe. Resampled Timeframe: The strategy resamples the dataframe to a specified timeframe and calculates indicators for the resampled data. The resampled dataframe is merged with the original dataframe, and the columns are renamed accordingly. Normal Timeframe Indicators: The strategy calculates indicators for the normal 5-minute timeframe. Buy Trend Conditions: The strategy defines conditions for determining buy trends. It iterates through a list of buy protection parameters and checks if specific conditions are met. These conditions include exponential moving average (EMA) crossovers, close price above EMAs, rising moving averages, safe dips, safe pumps, BTC not in a downtrend, close price above/below pivot types, and additional filters depending on the run mode. Buy Trend Logic: The strategy applies logical operations to determine the buy logic. It checks if the conditions defined in the buy protection parameters are met. Additionally, for index 1, it includes specific conditions such as price increase, RSI (Relative Strength Index), R (rate of change), and MFI (Money Flow Index) thresholds. This strategy aims to use various indicators and conditions to identify potential buy trends in trading.
Biased Indicators
chikou_span_1h
stoploss: -0.5 timeframe: 5m hash(sha256): db8a036a5ed47f50a766e7da679e2bd74999405613fe97a33ada805ee3b4af28 indicators: sma_200_1h upper safe_pump_type safe_pump_24_30 leading_senkou_span_b_1h close empty 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 32 33 34 35 36 37 38 3940 43 ema_15 position_adjustment_enable res1 senkou_a sell_pump_24_1_1h sell_pump_36_2 sma_5 safe_pump_period list_length 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 f"global_buy_protection_paramsclose_under_pivot_type_1d senkou_a senkou_b safe_
Similar Strategies: (based on used indicators)
Strategy: NostalgiaForInfinityNext1_327, Similarity Score: 98.39%
Strategy: NostalgiaForInfinityNext_7_13_0, Similarity Score: 96.77%
Strategy: NostalgiaForInfinity_7_13_0, Similarity Score: 96.77%
last change: 2023-08-16 11:45:47