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The NostalgiaForInfinityNext strategy is designed for backtesting trading strategies. It performs several tasks to prepare the data for analysis. Here is a breakdown of what the strategy does:
Populating Indicators:
Retrieves BTC (Bitcoin) data on different timeframes (5 minutes and 1 hour) and applies specific indicators to the data.
Merges the BTC data with the original dataset, filling missing values.
Removes unnecessary columns related to the BTC data.
Informative Timeframe: If an informative timeframe is specified, applies additional indicators to the data at that timeframe. Merges the informative timeframe data with the original dataset, filling missing values. Removes unnecessary columns related to the informative timeframe data. Resampled Timeframe: If a resampled timeframe is specified, resamples the data to the specified timeframe. Applies indicators specific to the resampled timeframe. Merges the resampled data with the original dataset, filling missing values. Renames columns related to the resampled timeframe. Removes unnecessary columns related to the resampled timeframe data. Indicators for the Normal Timeframe: Applies indicators specific to the normal timeframe (5 minutes). Modifies the dataset by adding calculated indicators. Populating Buy Trend: Defines conditions for buying based on various parameters. Applies logic to determine when to execute a buy order. Adds a "buy_tag" column to the dataset, indicating the buy signal. Overall, the NostalgiaForInfinityNext strategy prepares the data by populating indicators at different timeframes, merging data, and applying specific conditions to determine buy signals.
Merges the BTC data with the original dataset, filling missing values.
Removes unnecessary columns related to the BTC data.
Informative Timeframe: If an informative timeframe is specified, applies additional indicators to the data at that timeframe. Merges the informative timeframe data with the original dataset, filling missing values. Removes unnecessary columns related to the informative timeframe data. Resampled Timeframe: If a resampled timeframe is specified, resamples the data to the specified timeframe. Applies indicators specific to the resampled timeframe. Merges the resampled data with the original dataset, filling missing values. Renames columns related to the resampled timeframe. Removes unnecessary columns related to the resampled timeframe data. Indicators for the Normal Timeframe: Applies indicators specific to the normal timeframe (5 minutes). Modifies the dataset by adding calculated indicators. Populating Buy Trend: Defines conditions for buying based on various parameters. Applies logic to determine when to execute a buy order. Adds a "buy_tag" column to the dataset, indicating the buy signal. Overall, the NostalgiaForInfinityNext strategy prepares the data by populating indicators at different timeframes, merging data, and applying specific conditions to determine buy signals.
stoploss: -0.99 timeframe: 5m hash(sha256): ea1c7a712fbfd1a41d31cd8fa52dcc92dce75bb484e7f10ba489262a96ce2910 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 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 efi tpct_change_2 safe_dips_30 r_480 sell_pump_48_3 chikou_span ssl_down_
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last change: 2022-07-02 19:54:08