The MLStrategy2 is a machine learning-based trading strategy implemented as a class. Here's a short description of what the strategy does:
populate_indicators: This method calculates various technical indicators such as Simple Moving Average (SMA), Relative Strength Index (RSI), and Bollinger Bands for the given price data. The calculated indicators are added as additional columns to the DataFrame.
Data Preprocessing: The strategy performs data preprocessing by stacking the values of the 'open', 'high', 'low', 'close', and 'volume' columns into a single row, creating a window of historical data.
The window length is determined by self.window_length.
The resulting DataFrame is then rearranged, moving the indicator columns to the end. Model Prediction: The strategy uses machine learning models to make predictions. It iterates over different timeframes and generates predictions for each timeframe based on the preprocessed data. The predictions are stored in all_predictions. Majority Voting: The strategy combines the predictions from different timeframes using majority voting. It calculates whether more than half of the models predicted a positive outcome for each timestep, resulting in the majority_predictions. populate_buy_trend: This method populates the 'buy' column in the DataFrame based on the predictions made by the strategy. If the pair in the metadata is supported by the strategy, it predicts the 'buy' signal using the corresponding model. populate_sell_trend: This method populates the 'sell' column in the DataFrame based on the predictions made by the strategy. If the pair in the metadata is supported by the strategy, it predicts the 'sell' signal using the corresponding model. Stake Calculation: The strategy includes a stake calculation logic that determines the stake amount for a trade. The provided stake can be limited by a minimum and maximum stake amount. If the fastk_rsi_1h value is greater than the fastd_rsi_1h value, the stake amount is calculated based on the maximum stake amount or the total stake amount in the wallets, depending on the configuration. Overall, the strategy uses machine learning models to generate buy and sell signals based on technical indicators, and it includes stake management functionality to determine the stake amount for each trade.
Data Preprocessing: The strategy performs data preprocessing by stacking the values of the 'open', 'high', 'low', 'close', and 'volume' columns into a single row, creating a window of historical data.
The window length is determined by self.window_length.
The resulting DataFrame is then rearranged, moving the indicator columns to the end. Model Prediction: The strategy uses machine learning models to make predictions. It iterates over different timeframes and generates predictions for each timeframe based on the preprocessed data. The predictions are stored in all_predictions. Majority Voting: The strategy combines the predictions from different timeframes using majority voting. It calculates whether more than half of the models predicted a positive outcome for each timestep, resulting in the majority_predictions. populate_buy_trend: This method populates the 'buy' column in the DataFrame based on the predictions made by the strategy. If the pair in the metadata is supported by the strategy, it predicts the 'buy' signal using the corresponding model. populate_sell_trend: This method populates the 'sell' column in the DataFrame based on the predictions made by the strategy. If the pair in the metadata is supported by the strategy, it predicts the 'sell' signal using the corresponding model. Stake Calculation: The strategy includes a stake calculation logic that determines the stake amount for a trade. The provided stake can be limited by a minimum and maximum stake amount. If the fastk_rsi_1h value is greater than the fastd_rsi_1h value, the stake amount is calculated based on the maximum stake amount or the total stake amount in the wallets, depending on the configuration. Overall, the strategy uses machine learning models to generate buy and sell signals based on technical indicators, and it includes stake management functionality to determine the stake amount for each trade.
Traceback (most recent call last): File "/freqtrade/freqtrade/strategy/strategy_wrapper.py", line 27, in wrapper return f(*args, **kwargs) ^^^^^^^^^^^^^^^^^^ File "/freqtrade/user_data/strategies/MLStrategy2.py", line 29, in bot_start with open(f'model_{self.model_kind}_{pair.replace("/", "-")}_{timeframe}.pkl', 'rb') as file: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: 'model_RandomForest_DOGE-USDT_5m.pkl'
stoploss: -0.271 timeframe: 5m hash(sha256): 305b616fdaf4a909701e50cceb865b75bdbd902cc4325fe923786ea426b85713 indicators: fastk_rsi_1h rsi bband_lower bband_upper open high low close volume enter_long sell fastd_rsi_1h stake_amount _for_predictionself close rsi bband_lower sma_sma_period max_open_trades date buy bband_upper
Similar Strategies: (based on used indicators)
Strategy: MLStrategy, Similarity Score: 95.65%
last change: 2024-04-29 02:32:05