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The NFIX_BB_RPB strategy is a trading strategy that involves backtesting various indicators and conditions to determine buy signals. Here is a breakdown of what the strategy does:
Populating Indicators:
The strategy retrieves BTC price data based on the stake currency specified. If available, it retrieves BTC daily timeframe indicators and merges them with the main dataframe.
If available, it retrieves BTC informative timeframe (1h) indicators and merges them with the main dataframe.
If available, it retrieves BTC base timeframe (5m) indicators and merges them with the main dataframe.
It also merges informative timeframe indicators (1d and 1h) with the main dataframe. If specified, the main dataframe can be resampled to another timeframe and merged with the resampled indicators. Finally, it calculates indicators for the normal timeframe (5m) and returns the updated dataframe. Populating Buy Trend: The strategy sets up a list of conditions for determining buy signals. It iterates through the buy protection parameters defined in the configuration. For each parameter, it checks if the corresponding buy condition is enabled. It then applies various conditions and checks against indicators in the dataframe. These conditions include comparisons with exponential moving averages (EMA), closing price, simple moving averages (SMA), and percentage changes. If safe dip thresholds or safe pump thresholds are defined, they are also considered. Finally, a 'buy_tag' column is added to the dataframe to indicate the buy signal. This strategy utilizes different indicators and conditions at various timeframes to generate buy signals for trading.
If available, it retrieves BTC informative timeframe (1h) indicators and merges them with the main dataframe.
If available, it retrieves BTC base timeframe (5m) indicators and merges them with the main dataframe.
It also merges informative timeframe indicators (1d and 1h) with the main dataframe. If specified, the main dataframe can be resampled to another timeframe and merged with the resampled indicators. Finally, it calculates indicators for the normal timeframe (5m) and returns the updated dataframe. Populating Buy Trend: The strategy sets up a list of conditions for determining buy signals. It iterates through the buy protection parameters defined in the configuration. For each parameter, it checks if the corresponding buy condition is enabled. It then applies various conditions and checks against indicators in the dataframe. These conditions include comparisons with exponential moving averages (EMA), closing price, simple moving averages (SMA), and percentage changes. If safe dip thresholds or safe pump thresholds are defined, they are also considered. Finally, a 'buy_tag' column is added to the dataframe to indicate the buy signal. This strategy utilizes different indicators and conditions at various timeframes to generate buy signals for trading.
stoploss: -0.99 timeframe: 5m hash(sha256): abb86c1fbe80e433cefbaf638f049c77c7027ad58eceaab575554346e4ba6dfe 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 rsi_4 sma_200_dec_20 f"global_buy_protection_paramsclose_under_pivot_type_1d tg_dataframe hl_pct_change_6 ema_20 source hl_pct_change_48_1h bb20_3_low rsi_14 high top_grossing_updated close_sha_1d hl_pct_change_36_1h tpct_change_2 close_over_pivot_offset r_480 ema_8 cci sup_level_1d crossed_below_ema_12_26 final_ub vma_20 openclosehighlow bb20_2_mid atr pm adx ema_12 tpct_change_12 btc_not_downt
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last change: 2024-05-02 19:32:16