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The NASOSv4HO strategy is a trading strategy implemented as a class that inherits from the IStrategy class. It has two main methods: populate_indicators and populate_buy_trend, as well as a populate_sell_trend method. The populate_indicators method takes a DataFrame and metadata as input and adds additional indicators to the DataFrame.
It merges informative 1-hour data with the current timeframe data, fills any missing values, and updates the DataFrame with the indicators.
The populate_buy_trend method populates the buy signals in the DataFrame based on certain conditions.
It checks for various conditions such as the RSI (Relative Strength Index) being below a certain threshold, the close price being below a moving average multiplied by an offset value, the EWO (Elliott Wave Oscillator) being above a certain value, and the volume being greater than zero. It assigns a buy signal and a buy tag to the DataFrame rows that meet these conditions. The populate_sell_trend method populates the sell signals in the DataFrame based on certain conditions. It checks for conditions such as the close price being above a simple moving average (SMA) and a moving average multiplied by an offset value, the RSI being above 50, the volume being greater than zero, and the fast RSI being greater than the slow RSI. If any of these conditions are met, a sell signal is assigned to the corresponding rows in the DataFrame. Overall, the strategy combines various technical indicators such as RSI, moving averages, and Elliott Wave Oscillator to generate buy and sell signals based on specific conditions.
It merges informative 1-hour data with the current timeframe data, fills any missing values, and updates the DataFrame with the indicators.
The populate_buy_trend method populates the buy signals in the DataFrame based on certain conditions.
It checks for various conditions such as the RSI (Relative Strength Index) being below a certain threshold, the close price being below a moving average multiplied by an offset value, the EWO (Elliott Wave Oscillator) being above a certain value, and the volume being greater than zero. It assigns a buy signal and a buy tag to the DataFrame rows that meet these conditions. The populate_sell_trend method populates the sell signals in the DataFrame based on certain conditions. It checks for conditions such as the close price being above a simple moving average (SMA) and a moving average multiplied by an offset value, the RSI being above 50, the volume being greater than zero, and the fast RSI being greater than the slow RSI. If any of these conditions are met, a sell signal is assigned to the corresponding rows in the DataFrame. Overall, the strategy combines various technical indicators such as RSI, moving averages, and Elliott Wave Oscillator to generate buy and sell signals based on specific conditions.
stoploss: -0.15 timeframe: 5m hash(sha256): 7421d450bc97ab92aca4b11c25e53cd25572701df1e24ee77d4d43795656c2f9 indicators: rsi_buy EWO ewo_high ewo_low high_offset_2 close ewo_high_2 sell_signal profit_threshold rsi_fast ma_sell_val ma_buy_val volume low_offset retries high_offset low_offset_2 base_nb_candles_buy ___retries base_nb_candles_sell hma_50 buy buy_tag sma_9 ema_100 lookback_candles max_slippage rsi_slow rsi low close_1h
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last change: 2022-07-02 19:54:08