The FreqGymRScaler strategy is a trading strategy that involves backtesting various technical analysis indicators on a given DataFrame of market data. The strategy aims to populate indicators and generate buy and sell signals based on the indicators. The populate_indicators function adds multiple technical analysis indicators to the DataFrame, including PLUS_DM, PLUS_DI, MINUS_DM, MINUS_DI, awesome_oscillator, ULTOSC, EWO, ADX, Williams %R, CCI, RSI, Fisher RSI, AROON, AROONOSC, CMO, and MFI.
These indicators provide insights into different aspects of the market, such as trend strength, momentum, and volatility.
The populate_buy_trend function uses the indicators in the DataFrame to generate buy signals.
It makes use of a reinforcement learning model (rl_model_predict) to determine the action to take, and sets the 'buy' column in the DataFrame accordingly. The populate_sell_trend function is similar to populate_buy_trend, but it generates sell signals based on the indicators and the reinforcement learning model. The output function calculates a buy/sell output based on a trained model and a window of indicator observations. It predicts the outcome and assigns it to the 'output' DataFrame. The sma_difference function calculates the difference between two exponential moving averages (EMAs) of the closing price, normalized by the closing price itself, and multiplied by 100. This calculation provides insight into the relative position of the short-term and long-term EMAs, indicating potential trends in the market. Finally, there is a description of the Williams %R indicator, which is a technical analysis oscillator that measures the current closing price relative to the high and low prices over a specified period. It helps determine whether a market is trading near the high or low of its recent range. Overall, the strategy utilizes a combination of indicators to generate buy and sell signals, allowing for backtesting and evaluation of trading strategies.
These indicators provide insights into different aspects of the market, such as trend strength, momentum, and volatility.
The populate_buy_trend function uses the indicators in the DataFrame to generate buy signals.
It makes use of a reinforcement learning model (rl_model_predict) to determine the action to take, and sets the 'buy' column in the DataFrame accordingly. The populate_sell_trend function is similar to populate_buy_trend, but it generates sell signals based on the indicators and the reinforcement learning model. The output function calculates a buy/sell output based on a trained model and a window of indicator observations. It predicts the outcome and assigns it to the 'output' DataFrame. The sma_difference function calculates the difference between two exponential moving averages (EMAs) of the closing price, normalized by the closing price itself, and multiplied by 100. This calculation provides insight into the relative position of the short-term and long-term EMAs, indicating potential trends in the market. Finally, there is a description of the Williams %R indicator, which is a technical analysis oscillator that measures the current closing price relative to the high and low prices over a specified period. It helps determine whether a market is trading near the high or low of its recent range. Overall, the strategy utilizes a combination of indicators to generate buy and sell signals, allowing for backtesting and evaluation of trading strategies.
stoploss: -0.99 timeframe: 5m hash(sha256): 7efabd7911a9faf3c9f59ccb44d741cdaab49abfcb2c75c94a246ebeb718d529 indicators: sell minus_dm plus_dm mfi_period close aroonup_period aroondown_period uo rsi_period aroondown ewo date open close high low volume buy sell buy_tag plus_di aroonosc_period cmo_period wr_period adx_period fisher_rsi_period buy cci_period aroonup minus_di fisher_rsi_norma_period ao
No similar strategies found. (based on used indicators)
last change: 2024-04-29 13:11:10