diff --git a/configs.py b/configs.py index 527fdef..a4a933d 100644 --- a/configs.py +++ b/configs.py @@ -12,6 +12,7 @@ "prophet", "arima", "lstm", + "rfregressor_volatility" ] # Define the metrics to be used diff --git a/data/eth.csv b/data/eth.csv new file mode 100644 index 0000000..748042e --- /dev/null +++ b/data/eth.csv @@ -0,0 +1,400 @@ +timeOpen,timeClose,timeHigh,timeLow,name,open,high,low,close,volume,marketCap,date +2024-09-05T00:00:00.000Z,2024-09-05T23:59:59.999Z,2024-09-05T01:41:00.000Z,2024-09-05T19:46:00.000Z,2781,2448.9867379635,2465.3851633709,2348.8632710269,2367.7375740886,13632325040.12,284870243828.82,2024-09-05 23:59:59.999000+00:00 +2024-09-04T00:00:00.000Z,2024-09-04T23:59:59.999Z,2024-09-04T16:34:00.000Z,2024-09-04T01:05:00.000Z,2781,2420.1923594609,2488.9165297931,2313.2650229482,2448.976963363,16709600746.79,294644654909.23,2024-09-04 23:59:59.999000+00:00 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data + self.volatility_window = '5min' # Window for volatility calculation + self.n_lags = 5 # Number of lags for features + self.ma_window = 10 # Window for moving averages + self.target_shift = 5 # Prediction horizon + + # Data preprocessing + self.fillna_method = 'ffill' # Method for handling missing values + self.technical_indicators = [ # List of technical indicators to use + 'SMA_10', + 'EMA_10' + ] + + #Model parameters + self.n_estimators = 10 + self.random_state = 42 + self.n_jobs = 25 + + def display(self): + """Prints out the current configuration.""" + print("Random Forest Regressor Volatility Configuration:") + print(f" params: {self.params}") + print(f" candle_interval: {self.candle_interval}") + + print(f" volatility_window: {self.volatility_window}") + print(f" n_lags: {self.n_lags}") + print(f" ma_window: {self.ma_window}") + print(f" target_shift: {self.target_shift}") + print(f" fillna_method: {self.fillna_method}") + print(f" technical_indicators: {self.technical_indicators}") diff --git a/models/rfregressor_volatility/model.py b/models/rfregressor_volatility/model.py new file mode 100644 index 0000000..3a11664 --- /dev/null +++ b/models/rfregressor_volatility/model.py @@ -0,0 +1,113 @@ +import pandas as pd +import numpy as np +from sklearn.ensemble import RandomForestRegressor +from models.base_model import Model +from models.rfregressor_volatility.configs import RfregressorConfig + +class RfregressorVolatilityModel(Model): + """Random Forest Regressor model for volatility prediction.""" + + def __init__(self, model_name="rfregressor_volatility", config=RfregressorConfig(), debug=False): + super().__init__(model_name=model_name, debug=debug) + self.config = config + self.model = RandomForestRegressor( + n_estimators=self.config.n_estimators, + random_state=self.config.random_state, + n_jobs=self.config.n_jobs + ) + self.candle_interval = config.candle_interval + self.volatility_window = config.volatility_window + + + def _calculate_non_overlapping_volatility(self, series: pd.Series, window_points: int) -> pd.Series: + """Calculate volatility using non-overlapping windows""" + log_returns = np.log(series / series.shift(1)) + volatility = pd.Series(index=series.index, dtype=float) + + for start_idx in range(0, len(series), window_points): + end_idx = start_idx + window_points + if end_idx > len(series): + break + window_data = log_returns.iloc[start_idx:end_idx] + vol = window_data.std() + volatility.iloc[start_idx:end_idx] = vol + + return volatility + + def _calculate_volatility_features(self, df: pd.DataFrame): + """Calculate volatility features with non-overlapping windows""" + df = df.copy() + df = df.fillna(method='ffill').fillna(method='bfill') + + window_points = int(pd.Timedelta(self.volatility_window) / pd.Timedelta(self.candle_interval)) + list_of_features = [] + + df['log_returns'] = np.log(df['close'] / df['close'].shift(1)) + for i in range(1, 5): + log_return_lag_name = f'log_return_t-{i}' + list_of_features.append(log_return_lag_name) + df[log_return_lag_name] = df['log_returns'].shift(i) + volume_lag_name = f'volume_t-{i}' + list_of_features.append(volume_lag_name) + df[volume_lag_name] = df['volume'].shift(i) + + df['SMA_10'] = df['close'].rolling(window=10).mean() + df['EMA_10'] = df['close'].ewm(span=10, adjust=False).mean() + list_of_features.extend(['SMA_10', 'EMA_10']) + + df['current_volatility'] = self._calculate_non_overlapping_volatility(df['close'], window_points) + list_of_features.append('current_volatility') + + df['target_volatility'] = df['current_volatility'].shift(-5) + print(f"df.tail(): {df.tail()}") + df_sampled = df.iloc[::window_points].copy() + + # df_sampled = df.copy() + print(f"df_sampled.tail(): {df_sampled.tail()}") + return df_sampled, list_of_features + + def train(self, data: pd.DataFrame): + """Train the volatility prediction model.""" + df_sampled, list_of_features = self._calculate_volatility_features(data) + df_sampled = df_sampled.dropna() + print(f"df_sampled.tail() after dropna: {df_sampled.tail()}") + list_of_features.append('close') + features = df_sampled[list_of_features] + target = df_sampled['target_volatility'] + + self.model.fit(features, target) + self.save() + + def inference(self, input_data: pd.DataFrame) -> pd.DataFrame: + """Make predictions using the trained model.""" + df = input_data.copy() + df = df.fillna(method='ffill').fillna(method='bfill') + + window_points = int(pd.Timedelta(self.volatility_window) / pd.Timedelta(self.candle_interval)) + list_of_features = [] + + df['log_returns'] = np.log(df['close'] / df['close'].shift(1)) + for i in range(1, 5): + log_return_lag_name = f'log_return_t-{i}' + list_of_features.append(log_return_lag_name) + df[log_return_lag_name] = df['log_returns'].shift(i) + volume_lag_name = f'volume_t-{i}' + list_of_features.append(volume_lag_name) + df[volume_lag_name] = df['volume'].shift(i) + + df['SMA_10'] = df['close'].rolling(window=10).mean() + df['EMA_10'] = df['close'].ewm(span=10, adjust=False).mean() + list_of_features.extend(['SMA_10', 'EMA_10']) + + df['current_volatility'] = self._calculate_non_overlapping_volatility(df['close'], window_points) + list_of_features.append('current_volatility') + list_of_features.append('close') + + features = df[list_of_features].fillna(0) + predictions = self.model.predict(features) + + return pd.DataFrame({"prediction": predictions}, index=features.index) + + def forecast(self, steps: int) -> pd.DataFrame: + """Forecast future volatility.""" + return pd.DataFrame({"forecast": [0] * steps}) diff --git a/package_model_worker.py b/package_model_worker.py index 17d59c3..5fd200a 100644 --- a/package_model_worker.py +++ b/package_model_worker.py @@ -6,7 +6,6 @@ import shutil import sys import warnings - import pandas as pd from utils.common import print_colored, snake_to_camel