The FreqGymNormalized strategy is a trading strategy that aims to backtest various trading strategies using technical analysis (TA) indicators. The strategy consists of three main methods: populate_indicators, populate_buy_trend, and populate_sell_trend. In the populate_indicators method, the strategy calculates multiple TA indicators and adds them to the given DataFrame.
These indicators include: PLUS_DI: Normalized value of the Plus Directional Indicator.
MINUS_DI: Normalized value of the Minus Directional Indicator.
UO: Normalized value of the Ultimate Oscillator. HT_SINE: Normalized values of the Hilbert Transform - Sine and Hilbert Transform - Lead Sine. BOP: Normalized value of the Balance of Power. STOCH: Normalized values of the Stochastic Oscillator (Slow %K and Slow %D). STOCHF: Normalized value of the Fast Stochastic Oscillator %K. Bollinger Bands: Two sets of Bollinger Bands (with different standard deviations) indicating whether the lower band is greater than the closing price. ADX: Normalized values of the Average Directional Index for different time periods. AROON: Normalized values of the Aroon Up, Aroon Down, and Aroon Oscillator for different time periods. CMO: Normalized value of the Chande Momentum Oscillator for different time periods. DX: Normalized value of the Directional Movement Index for different time periods. MFI: Normalized value of the Money Flow Index for different time periods. WILLR: Normalized value of the Williams %R for different time periods. RSI: Normalized value of the Relative Strength Index for different time periods. Fisher RSI: Normalized value of the Fisher Transform of RSI for different time periods. STOCHRSI: Normalized values of the Stochastic RSI (Fast %K and Fast %D). LINEARREG_ANGLE: Normalized value of the Linear Regression Angle for different time periods. After calculating these indicators, the method returns the DataFrame with all the mandatory indicators. In the populate_buy_trend method, the strategy generates a buy signal based on the TA indicators calculated in the previous step. It uses an RL (Reinforcement Learning) model to predict the action, and the buy signal is set to 1 if the predicted action is a buy. In the populate_sell_trend method, similar to the populate_buy_trend method, the strategy generates a sell signal based on the TA indicators and the RL model's predictions. The sell signal is set to 1 if the predicted action is a sell. Overall, this strategy aims to populate a DataFrame with TA indicators, generate buy and sell signals based on those indicators using an RL model, and provide the backtesting capability for various trading strategies.
These indicators include: PLUS_DI: Normalized value of the Plus Directional Indicator.
MINUS_DI: Normalized value of the Minus Directional Indicator.
UO: Normalized value of the Ultimate Oscillator. HT_SINE: Normalized values of the Hilbert Transform - Sine and Hilbert Transform - Lead Sine. BOP: Normalized value of the Balance of Power. STOCH: Normalized values of the Stochastic Oscillator (Slow %K and Slow %D). STOCHF: Normalized value of the Fast Stochastic Oscillator %K. Bollinger Bands: Two sets of Bollinger Bands (with different standard deviations) indicating whether the lower band is greater than the closing price. ADX: Normalized values of the Average Directional Index for different time periods. AROON: Normalized values of the Aroon Up, Aroon Down, and Aroon Oscillator for different time periods. CMO: Normalized value of the Chande Momentum Oscillator for different time periods. DX: Normalized value of the Directional Movement Index for different time periods. MFI: Normalized value of the Money Flow Index for different time periods. WILLR: Normalized value of the Williams %R for different time periods. RSI: Normalized value of the Relative Strength Index for different time periods. Fisher RSI: Normalized value of the Fisher Transform of RSI for different time periods. STOCHRSI: Normalized values of the Stochastic RSI (Fast %K and Fast %D). LINEARREG_ANGLE: Normalized value of the Linear Regression Angle for different time periods. After calculating these indicators, the method returns the DataFrame with all the mandatory indicators. In the populate_buy_trend method, the strategy generates a buy signal based on the TA indicators calculated in the previous step. It uses an RL (Reinforcement Learning) model to predict the action, and the buy signal is set to 1 if the predicted action is a buy. In the populate_sell_trend method, similar to the populate_buy_trend method, the strategy generates a sell signal based on the TA indicators and the RL model's predictions. The sell signal is set to 1 if the predicted action is a sell. Overall, this strategy aims to populate a DataFrame with TA indicators, generate buy and sell signals based on those indicators using an RL model, and provide the backtesting capability for various trading strategies.
stoploss: -0.99 timeframe: 5m hash(sha256): ba649dbb83a49a597a1dc9bd57b42329964d0016aa2ae4ee27aae38c1275eedf indicators: sell htsine f"aroondown_period uo f"aroonosc_period f"dx_period fastk htleadsine f"linangle_period slowd f"rsi_period bb3_lower_gt_close plus_di f"mfi_period f"plus_di_period slowk bb2_lower_gt_close f"adx_period f"willr_period buy f"stochrsi_k_period f"aroonup_period f"stochrsi_d_period minus_di f"cmo_period f"minus_di_period bop f"fisher_rsi_period
Similar Strategies: (based on used indicators)
Strategy: FreqGym_normalized_0, Similarity Score: 96.55%
Strategy: FreqGym_normalized, Similarity Score: 93.1%
last change: 2024-04-28 08:37:01