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The NostalgiaForInfinityNextGen strategy is a trading strategy that involves populating indicators and determining buy trends. Here's a breakdown of what the strategy does:
Populating Indicators: The strategy first populates various indicators based on different timeframes. It fetches BTC (Bitcoin) data and calculates indicators for informative timeframes such as daily, hourly, and the strategy's own timeframe (5 minutes).
It merges these indicators with the main dataframe.
Informative Timeframe: If specified, the strategy calculates indicators for additional informative timeframes such as 1-day and 1-hour.
It merges these informative indicators with the main dataframe. Resampling to Another Timeframe: If specified, the strategy resamples the main dataframe to a different timeframe and calculates indicators for the resampled timeframe. It merges these indicators with the main dataframe. Indicators for the Normal Timeframe: The strategy calculates additional indicators for the normal timeframe (5 minutes) and adds them to the main dataframe. Populating Buy Trend: The strategy determines the buy trend based on a set of conditions. It iterates through the buy protection parameters and checks if the specified conditions are met. These conditions include comparisons between different indicators and thresholds for price changes. If the conditions are met, a buy tag is assigned to the corresponding data point in the dataframe. The strategy essentially fetches and calculates various indicators for different timeframes, merges them with the main dataframe, and determines buy trends based on specified conditions.
It merges these indicators with the main dataframe.
Informative Timeframe: If specified, the strategy calculates indicators for additional informative timeframes such as 1-day and 1-hour.
It merges these informative indicators with the main dataframe. Resampling to Another Timeframe: If specified, the strategy resamples the main dataframe to a different timeframe and calculates indicators for the resampled timeframe. It merges these indicators with the main dataframe. Indicators for the Normal Timeframe: The strategy calculates additional indicators for the normal timeframe (5 minutes) and adds them to the main dataframe. Populating Buy Trend: The strategy determines the buy trend based on a set of conditions. It iterates through the buy protection parameters and checks if the specified conditions are met. These conditions include comparisons between different indicators and thresholds for price changes. If the conditions are met, a buy tag is assigned to the corresponding data point in the dataframe. The strategy essentially fetches and calculates various indicators for different timeframes, merges them with the main dataframe, and determines buy trends based on specified conditions.
stoploss: -0.5 timeframe: 15m hash(sha256): 85ef020c9c29f58746895022e7e5eba1aa042ba57c4ecf73014fcae7a4f9bf51 indicators: sma_200_1h upper open_sha_1d close res1 low_s ema_25 ema_200_1h ewo sma200_rising_val r_480_1h volume sma_200_dec_20 sup_level_1h f"global_buy_protection_paramsclose_under_pivot_type_1d tg_dataframe hl_pct_change_6 ema_20 ewo_ema_1h momdiv_coh hl_pct_change_48_1h rsi_14 high top_grossing_updated close_sha_1d hl_pct_change_36_1h tpct_change_2 close_over_pivot_offset r_480 cci sup_level_1d crossed_below_ema_12_26 vma_20 openclosehighlow bb20_2_mid momdiv_col ema_12 tpct_change_12 btc_not_downtrend
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last change: 2022-09-20 17:22:47