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The NASOSv4 strategy is a trading strategy implemented as a class that inherits from the IStrategy interface. It has several methods for populating indicators, determining buy signals, determining sell signals, and confirming trade exits. In the populate_indicators method, the strategy calculates various indicators using the provided dataframe and metadata.
It merges informative 1-hour indicators with the current timeframe indicators and returns the updated dataframe.
The populate_buy_trend method identifies buy conditions based on a set of criteria.
It checks for specific conditions such as the rolling maximum of the previous hour's close price being below a profit threshold, RSI values, close prices relative to moving averages, EWO (Elliott Wave Oscillator) values, and volume. If the conditions are met, it sets the 'buy' column in the dataframe to 1 and assigns a corresponding buy tag. The populate_sell_trend method determines sell conditions based on criteria related to close prices, moving averages, RSI values, and volume. If the conditions are satisfied, it sets the 'sell' column in the dataframe to 1. The strat_dca_nasos class is a subclass of NASOSv4 that overrides the populate_indicators method to add an additional RSI indicator to the dataframe. Lastly, there are additional methods related to stake calculation, determining when to initiate safety orders, and confirming trade exits. These methods involve calculations based on parameters such as stake amounts, profit triggers, safety order step scales, safety order volume scales, current rates, and trade data. Overall, the NASOSv4 strategy aims to generate buy and sell signals based on various technical indicators and criteria. It incorporates elements of trend following, moving averages, RSI, EWO, and volume analysis to make trading decisions.
It merges informative 1-hour indicators with the current timeframe indicators and returns the updated dataframe.
The populate_buy_trend method identifies buy conditions based on a set of criteria.
It checks for specific conditions such as the rolling maximum of the previous hour's close price being below a profit threshold, RSI values, close prices relative to moving averages, EWO (Elliott Wave Oscillator) values, and volume. If the conditions are met, it sets the 'buy' column in the dataframe to 1 and assigns a corresponding buy tag. The populate_sell_trend method determines sell conditions based on criteria related to close prices, moving averages, RSI values, and volume. If the conditions are satisfied, it sets the 'sell' column in the dataframe to 1. The strat_dca_nasos class is a subclass of NASOSv4 that overrides the populate_indicators method to add an additional RSI indicator to the dataframe. Lastly, there are additional methods related to stake calculation, determining when to initiate safety orders, and confirming trade exits. These methods involve calculations based on parameters such as stake amounts, profit triggers, safety order step scales, safety order volume scales, current rates, and trade data. Overall, the NASOSv4 strategy aims to generate buy and sell signals based on various technical indicators and criteria. It incorporates elements of trend following, moving averages, RSI, EWO, and volume analysis to make trading decisions.
stoploss: -0.15 timeframe: 5m hash(sha256): 0f5a125d99984a62614103493268d0b5af20310dcd88d982b0c887f91487d3ad indicators: rsi_buy EWO ewo_high safety_order_step_scale ewo_low high_offset_2 close ewo_high_2 dca_min_rsi sell_signal profit_threshold rsi_fast ma_sell_val ma_buy_val volume low_offset retries initial_safety_order_trigger high_offset low_offset_2 base_nb_candles_buy stake_amount ___retries base_nb_candles_sell hma_50 buy buy_tag sma_9 ema_100 lookback_candles max_slippage rsi_slow rsi safety_order_volume_scale max_safety_orders low close_1h
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last change: 2022-07-02 19:54:08