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The NostalgiaForInfinityNext strategy is designed to backtest trading strategies using various indicators and timeframes. Here is a brief description of what the strategy does:
Populating Indicators:
Retrieves BTC (Bitcoin) data on informative timeframes (5 minutes and 1 hour). Computes indicators for the BTC data and merges them with the strategy's main dataframe.
Removes unnecessary columns from the main dataframe.
Informative Timeframe: If an informative timeframe is specified, computes informative indicators on the main timeframe and merges them with the main dataframe.
Removes unnecessary columns from the main dataframe. Resampled Timeframe: If a resampled timeframe is specified, resamples the main dataframe to the specified timeframe. Computes indicators for the resampled data and merges them with the main dataframe. Renames columns related to resampling. Removes unnecessary columns from the main dataframe. Indicators for Normal Timeframe: Computes indicators for the normal timeframe (5 minutes) and updates the main dataframe. Populating Buy Trend: Sets up conditions for triggering a buy signal. Iterates through the specified buy protection parameters and checks if the conditions are met. Constructs logical conditions based on various indicators and parameters. Assigns a buy tag to the main dataframe based on the conditions. The strategy focuses on populating indicators, handling different timeframes, and determining buy signals based on specified conditions.
Removes unnecessary columns from the main dataframe.
Informative Timeframe: If an informative timeframe is specified, computes informative indicators on the main timeframe and merges them with the main dataframe.
Removes unnecessary columns from the main dataframe. Resampled Timeframe: If a resampled timeframe is specified, resamples the main dataframe to the specified timeframe. Computes indicators for the resampled data and merges them with the main dataframe. Renames columns related to resampling. Removes unnecessary columns from the main dataframe. Indicators for Normal Timeframe: Computes indicators for the normal timeframe (5 minutes) and updates the main dataframe. Populating Buy Trend: Sets up conditions for triggering a buy signal. Iterates through the specified buy protection parameters and checks if the conditions are met. Constructs logical conditions based on various indicators and parameters. Assigns a buy tag to the main dataframe based on the conditions. The strategy focuses on populating indicators, handling different timeframes, and determining buy signals based on specified conditions.
Biased Indicators
chikou_span_1h
stoploss: -0.99 timeframe: 5m hash(sha256): f1f8bea63d8b538c59c8e8a758fd62baf2e2b6471d39cd4e1d4dcc77680715c7 indicators: sma_200_1h upper safe_pump_type safe_pump_24_30 leading_senkou_span_b_1h close ema_15 senkou_a sell_pump_36_2 sma_5 sell_pump_24_1_1h safe_pump_period ema_25 ema_200_1h tenkan_sen 39 safe_dump_50 ewo 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 hrsi buy_tag oc_pct_change_48 sell_pump_48_1_1h rsi_14 high efi tpct_change_2 safe_dips_30 r_480 sell_pump_48_
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last change: 2023-07-05 10:19:15