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The NostalgiaForInfinityX2 strategy is a trading strategy that involves backtesting and analyzing various indicators to make buy and sell decisions. Here's a breakdown of what the strategy does:
populate_indicators method:
Retrieves informative indicators related to Bitcoin (BTC) and merges them with the main DataFrame. Drops unnecessary columns from the DataFrame.
Adds indicators on different timeframes to the DataFrame.
Calculates indicators for the base timeframe (5 minutes).
populate_buy_trend method: Defines conditions for determining buy signals. Checks the conditions against the DataFrame and assigns a buy tag to qualifying rows. Combines the conditions using logical AND and assigns the result to the 'buy' column in the DataFrame. populate_sell_trend method: Initializes the 'sell' column in the DataFrame to 0 (no sell signals). time_in_force method: Retrieves the analyzed DataFrame for the specified pair and timeframe. Checks if the length of the DataFrame is less than 1 (no data available). Compares the current rate with the previous closing rate to calculate slippage. Returns true if the slippage is below 0.038, indicating a valid buy signal. Logs a warning and returns false if the slippage is above the threshold. Additional helper functions: reduce function: Performs logical AND or OR operations on a list of conditions. sma1 and sma2 calculations: Calculate the Simple Moving Average (SMA) for the given lengths. Overall, the strategy combines multiple indicators and conditions to identify potential buying opportunities in the market.
Adds indicators on different timeframes to the DataFrame.
Calculates indicators for the base timeframe (5 minutes).
populate_buy_trend method: Defines conditions for determining buy signals. Checks the conditions against the DataFrame and assigns a buy tag to qualifying rows. Combines the conditions using logical AND and assigns the result to the 'buy' column in the DataFrame. populate_sell_trend method: Initializes the 'sell' column in the DataFrame to 0 (no sell signals). time_in_force method: Retrieves the analyzed DataFrame for the specified pair and timeframe. Checks if the length of the DataFrame is less than 1 (no data available). Compares the current rate with the previous closing rate to calculate slippage. Returns true if the slippage is below 0.038, indicating a valid buy signal. Logs a warning and returns false if the slippage is above the threshold. Additional helper functions: reduce function: Performs logical AND or OR operations on a list of conditions. sma1 and sma2 calculations: Calculate the Simple Moving Average (SMA) for the given lengths. Overall, the strategy combines multiple indicators and conditions to identify potential buying opportunities in the market.
stoploss: -0.99 timeframe: 5m hash(sha256): 61292c9b66e366cb9192b35c78133213cafd2c17d84f504b18c23c3b514fbafb indicators: sma_200_1h res_level upper bt_agefilter_ok rsi_14_4h res_hlevel ema_200 ema_50 res3_1d close_s close high open close res1 high_s low_s btc_info_ pivot USDTBUSDUSDCDAITUSDPAXUSDEURGBP sma_200_4h xe5 r_14 bb20_2_upp_1h rsi_14_1h volume sma_50_1h sup3 live_data_ok sup_level_4h sup_level_1h xe1 sma_50 date open sup2 5m15m1h4h1d xe4 open_s rsi_14 bb20_2_low bb20_2_upp high sma_200 low_s open_s close_s mid low open close r_480 sup1 sma_50_4h 15m1h4h1d sup3_1d not_downtrend crossed_below_ema_12_26 xe6
Similar Strategies: (based on used indicators)
Strategy: NostalgiaForInfinityX2_238, Similarity Score: 96.72%
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last change: 2024-05-02 14:03:37