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The NostalgiaForInfinityNextGen strategy is a trading strategy that involves populating indicators and determining buy trends based on various conditions. Here is a breakdown of what the strategy does:
Populating Indicators: The strategy starts by populating indicators using different timeframes and informative pairs. It retrieves BTC (Bitcoin) information in different timeframes (5 minutes, 1 hour, and 1 day) and merges it with the current dataframe.
The strategy also resamples the data to a specified timeframe if required.
It calculates various indicators such as moving averages (EMA, SMA), volume, and price changes.
Informative Timeframe: If specified, the strategy populates indicators for informative timeframes such as 1 day and 1 hour. These indicators provide additional context for decision-making. Resampled Timeframe: If specified, the strategy resamples the data to a different timeframe. It calculates indicators specific to the resampled timeframe and merges them with the existing dataframe. Indicators for Normal Timeframe: The strategy calculates additional indicators for the normal timeframe (5 minutes). These indicators provide insights into market conditions and trends. Populating Buy Trends: After populating indicators, the strategy proceeds to determine buy trends. It applies various conditions based on the configured parameters. These conditions include checks on moving averages (EMA, SMA), closing price compared to moving averages, rising trends of moving averages, safe dips thresholds, and safe pump thresholds. Buy Tags: For each buy trend, the strategy assigns a buy tag to the corresponding data point in the dataframe. The strategy aims to backtest and evaluate different trading strategies by simulating trades based on the calculated indicators and buy trends.
The strategy also resamples the data to a specified timeframe if required.
It calculates various indicators such as moving averages (EMA, SMA), volume, and price changes.
Informative Timeframe: If specified, the strategy populates indicators for informative timeframes such as 1 day and 1 hour. These indicators provide additional context for decision-making. Resampled Timeframe: If specified, the strategy resamples the data to a different timeframe. It calculates indicators specific to the resampled timeframe and merges them with the existing dataframe. Indicators for Normal Timeframe: The strategy calculates additional indicators for the normal timeframe (5 minutes). These indicators provide insights into market conditions and trends. Populating Buy Trends: After populating indicators, the strategy proceeds to determine buy trends. It applies various conditions based on the configured parameters. These conditions include checks on moving averages (EMA, SMA), closing price compared to moving averages, rising trends of moving averages, safe dips thresholds, and safe pump thresholds. Buy Tags: For each buy trend, the strategy assigns a buy tag to the corresponding data point in the dataframe. The strategy aims to backtest and evaluate different trading strategies by simulating trades based on the calculated indicators and buy trends.
stoploss: -0.5 timeframe: 15m hash(sha256): aa99e918f86aa31785673a091baba32de59efbb3c4e426ab748f3b2cd1b5fb07 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: 2023-08-08 22:35:36