The SagesFreqGym strategy is a trading strategy that utilizes technical analysis (TA) indicators to generate buy and sell signals for a given financial instrument. The strategy consists of three main components:
populate_indicators: This function calculates various TA indicators for the given DataFrame, such as the weighted moving average (WMA), relative strength index (RSI), and relative volatility index (RVI). The indicators are calculated for different periods (10, 20, 40, 80, 160) and added as new columns to the DataFrame.
populate_buy_trend: Using the populated indicators, this function generates the buy signal for the DataFrame.
It applies a reinforcement learning (RL) model (not shown in the code) to make predictions and sets the "buy" column to 1 if the predicted action is a buy, otherwise 0.
populate_sell_trend: Similar to the previous function, this one populates the sell signal for the DataFrame using the RL model. The "sell" column is set to 1 if the predicted action is a sell, otherwise 0. Additionally, there is a part of the code that calculates the output of the strategy, which involves creating an output DataFrame and performing predictions using a separate model. The exact details of this process are not provided in the given code snippet. Overall, the SagesFreqGym strategy combines TA indicators, reinforcement learning, and prediction models to generate buy and sell signals for backtesting trading strategies on a website.
populate_buy_trend: Using the populated indicators, this function generates the buy signal for the DataFrame.
It applies a reinforcement learning (RL) model (not shown in the code) to make predictions and sets the "buy" column to 1 if the predicted action is a buy, otherwise 0.
populate_sell_trend: Similar to the previous function, this one populates the sell signal for the DataFrame using the RL model. The "sell" column is set to 1 if the predicted action is a sell, otherwise 0. Additionally, there is a part of the code that calculates the output of the strategy, which involves creating an output DataFrame and performing predictions using a separate model. The exact details of this process are not provided in the given code snippet. Overall, the SagesFreqGym strategy combines TA indicators, reinforcement learning, and prediction models to generate buy and sell signals for backtesting trading strategies on a website.
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.99 timeframe: 5m hash(sha256): 6248a9a752a24eaf5d0f41ab399248a94fa5d6046464d163624f36244a2b9a51 indicators: sell volume f"rsi_period f"wma_period f"rvi_period buy volume_roc
No similar strategies found. (based on used indicators)
last change: 2024-07-27 04:12:56