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The NostalgiaForInfinityNext strategy is a trading strategy that involves populating indicators and determining buy signals for trading. Here is a breakdown of what the strategy does:
Populating Indicators:
The strategy first checks the stake currency and determines the trading pair for Bitcoin (BTC). It retrieves informative data for BTC on different timeframes (5 minutes, 1 hour, and daily) and merges it with the input dataframe.
Various indicators are calculated for the BTC data on each timeframe, such as moving averages (EMA and SMA).
The strategy also supports resampling the data to a different timeframe if specified.
Indicators for the normal timeframe (5 minutes) are calculated. Populating Buy Signals: The strategy defines conditions for determining buy signals based on user-defined parameters. It iterates through the buy protection parameters and evaluates the conditions for each parameter. Conditions include checks for moving averages, close price above moving averages, rising SMA values, safe dips thresholds, safe pumps, BTC not in a downtrend, and close price relative to pivot points. The conditions are added to a list, and if all conditions are met, a buy tag is assigned to the corresponding data point in the dataframe. The strategy aims to provide a backtesting framework for evaluating trading strategies by populating indicators and generating buy signals based on specified conditions.
Various indicators are calculated for the BTC data on each timeframe, such as moving averages (EMA and SMA).
The strategy also supports resampling the data to a different timeframe if specified.
Indicators for the normal timeframe (5 minutes) are calculated. Populating Buy Signals: The strategy defines conditions for determining buy signals based on user-defined parameters. It iterates through the buy protection parameters and evaluates the conditions for each parameter. Conditions include checks for moving averages, close price above moving averages, rising SMA values, safe dips thresholds, safe pumps, BTC not in a downtrend, and close price relative to pivot points. The conditions are added to a list, and if all conditions are met, a buy tag is assigned to the corresponding data point in the dataframe. The strategy aims to provide a backtesting framework for evaluating trading strategies by populating indicators and generating buy signals based on specified conditions.
Biased Indicators
chikou_span_1h
stoploss: -0.5 timeframe: 5m hash(sha256): 2576444bdfee47d0d33003e69365a0c5af899dccc04616af1078e8e85c13e358 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: 2023-07-04 00:38:16