Not Enough Data to display!
Average Overall
| Not Enough Data! / Avg statistics not populated yet. |
|---|
The NostalgiaForInfinityNextGen strategy is a trading strategy that involves backtesting various indicators and conditions to determine buy signals for trading. Here is a breakdown of what the strategy does:
populate_indicators: This function populates the indicators used in the strategy. It fetches and merges different informative timeframes, such as daily, hourly, and base timeframe (5 minutes).
It also resamples the data to another timeframe if specified.
The indicators are calculated for each timeframe and merged with the main dataframe.
populate_buy_trend: This function determines the buy signals based on specific conditions. It iterates over the defined buy protection parameters and checks various conditions such as exponential moving averages (EMA), simple moving averages (SMA), close prices above EMAs, rising SMAs, safe dips thresholds, and safe pump thresholds. If the conditions are met, a buy tag is assigned to the corresponding data point. The strategy utilizes a combination of indicators and conditions to identify potential buying opportunities in the market. By backtesting this strategy on historical data, you can evaluate its effectiveness and make informed decisions for actual trading.
It also resamples the data to another timeframe if specified.
The indicators are calculated for each timeframe and merged with the main dataframe.
populate_buy_trend: This function determines the buy signals based on specific conditions. It iterates over the defined buy protection parameters and checks various conditions such as exponential moving averages (EMA), simple moving averages (SMA), close prices above EMAs, rising SMAs, safe dips thresholds, and safe pump thresholds. If the conditions are met, a buy tag is assigned to the corresponding data point. The strategy utilizes a combination of indicators and conditions to identify potential buying opportunities in the market. By backtesting this strategy on historical data, you can evaluate its effectiveness and make informed decisions for actual trading.
stoploss: -0.5 timeframe: 15m hash(sha256): 9340ace9bbcecaa5ec84a23f69d0d08b86e8b50781883222fc23d7c7f19310cf 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
Similar Strategies: (based on used indicators)
Strategy: NFINextGen, Similarity Score: 97.5%
Strategy: NostalgiaForInfinityNextGen_141, Similarity Score: 97.5%
Strategy: NostalgiaForInfinityNextGen_142, Similarity Score: 97.5%
Strategy: NostalgiaForInfinityNextGen_2, Similarity Score: 97.5%
Strategy: NostalgiaForInfinityNextGen_295, Similarity Score: 97.5%
Strategy: NostalgiaForInfinityNextGen_3, Similarity Score: 97.5%
Strategy: NostalgiaForInfinityNextGen_637, Similarity Score: 97.5%
Strategy: NostalgiaForInfinityNextGen_742, Similarity Score: 97.5%
Strategy: NFIX_BB_RPB, Similarity Score: 85%
Strategy: NFIX_BB_RPB_v103, Similarity Score: 85%
last change: 2023-06-27 12:39:47