CryptoMarketParser/BitcoinPricePredictor.py

import os
from datetime import datetime
import time

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.metrics import confusion_matrix, classification_report
from sqlalchemy import create_engine
from tensorflow.keras.models import Sequential
from tensorflow.keras.regularizers import l1_l2 
from tensorflow.keras.layers import LSTM, Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import RobustScaler
import gc
import matplotlib.pyplot as plt
from scipy.signal import find_peaks
from matplotlib.backends.backend_agg import FigureCanvasAgg
from matplotlib.figure import Figure
import matplotlib
from trend_detector_simple import TrendDetectorSimple

class BitcoinPricePredictor:
    def __init__(self, db_path, timeframe, model=None, timesteps=10, batch_size=32, learning_rate=0.001, epochs=50):
        self.db_path = db_path
        self.engine = create_engine(f'sqlite:///{self.db_path}')
        self.timesteps = timesteps
        self.batch_size = batch_size
        self.learning_rate = learning_rate
        self.epochs = epochs
        self.model = model
        self.X_train = None
        self.X_test = None
        self.y_train = None
        self.y_test = None
        self.history = None
        self.scaler = None
        self.timeframe = timeframe
        self.feature_columns = ['open', 'high', 'low', 'close', 'volume',
                                'hl_ratio', 'sma_7', 'sma_21', 'price_change']
        self.df = None

    @staticmethod
    def reduce_mem_usage(df):
        """Optimize memory usage of the dataframe by downcasting numeric types."""
        numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']
        start_mem = df.memory_usage().sum() / 1024**2
        
        for col in df.columns:
            col_type = df[col].dtypes
            if col_type in numerics:  # Only process numeric columns
                c_min = df[col].min()
                c_max = df[col].max()
                if str(col_type)[:3] == 'int':
                    if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:
                        df[col] = df[col].astype(np.int8)
                    elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:
                        df[col] = df[col].astype(np.int16)
                    elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:
                        df[col] = df[col].astype(np.int32)
                    elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:
                        df[col] = df[col].astype(np.int64)
                else:
                    if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:
                        df[col] = df[col].astype(np.float16)
                    elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:
                        df[col] = df[col].astype(np.float32)
                    else:
                        df[col] = df[col].astype(np.float64)
        
        end_mem = df.memory_usage().sum() / 1024**2
        print(f'Memory usage reduced from {start_mem:.2f} MB to {end_mem:.2f} MB ({(1 - end_mem/start_mem)*100:.1f}% reduction)')
        return df

    def add_essential_features(self, df):
        """Add technical indicators and features to the dataframe."""
        print("Adding technical indicators and features...")
        df = df.copy()
        
        # Price ratio features
        df['hl_ratio'] = (df['high'] / df['low']).clip(lower=0.8, upper=1.2)
        
        # Moving averages
        df['sma_7'] = df['close'].rolling(window=7, min_periods=1).mean()
        df['sma_21'] = df['close'].rolling(window=21, min_periods=1).mean()
        df['sma_50'] = df['close'].rolling(window=50, min_periods=1).mean()
        
        # Exponential moving averages
        df['ema_12'] = df['close'].ewm(span=12, adjust=False).mean()
        df['ema_26'] = df['close'].ewm(span=26, adjust=False).mean()
        
        # MACD
        df['macd'] = df['ema_12'] - df['ema_26']
        df['macd_signal'] = df['macd'].ewm(span=9, adjust=False).mean()
        
        # RSI
        delta = df['close'].diff()
        gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
        loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
        rs = gain / loss
        df['rsi'] = 100 - (100 / (1 + rs))
        
        # Bollinger Bands
        df['bb_middle'] = df['close'].rolling(window=20).mean()
        df['bb_std'] = df['close'].rolling(window=20).std()
        df['bb_upper'] = df['bb_middle'] + 2 * df['bb_std']
        df['bb_lower'] = df['bb_middle'] - 2 * df['bb_std']
        df['bb_width'] = (df['bb_upper'] - df['bb_lower']) / df['bb_middle']
        
        # Price changes
        df['price_change_1d'] = df['close'].pct_change(periods=1).clip(lower=-0.5, upper=0.5)
        df['price_change_3d'] = df['close'].pct_change(periods=3).clip(lower=-0.5, upper=0.5)
        df['price_change_7d'] = df['close'].pct_change(periods=7).clip(lower=-0.5, upper=0.5)
        
        # Volatility
        df['volatility'] = df['close'].rolling(window=14).std() / df['close'].rolling(window=14).mean()
        
        # Get trend indicators from TrendDetector 
        # TrendDetector already expects lowercase columns, so no need for conversion
        if 'datetime' not in df.columns:
            df['datetime'] = df.index
        
        trend_detector = TrendDetectorSimple(df)
        trend_data, trend_analysis = trend_detector.detect_trends()
        
        # Add supertrend signals
        for i, st in enumerate(trend_analysis['supertrend']):
            df[f'supertrend_{i+1}'] = st['results']['trend']
        
        # Add meta-supertrend consensus
        meta_results = trend_detector.calculate_metasupertrend(df, trend_analysis['supertrend'])
        df['metasupertrend'] = meta_results['meta_trends']
        
        # Add SMA crossover signals
        df['sma_cross'] = np.where(trend_analysis['sma']['7'] > trend_analysis['sma']['15'], 1, -1)
        
        # Clean up NaN or infinite values
        df = df.fillna(0)
        df = df.replace([np.inf, -np.inf], 0)
        
        # Update feature columns list - exclude non-numeric columns
        self.feature_columns = [col for col in df.columns if col not in ['next_period_return', 'next_period_up', 'datetime']]
        
        print(f"Shape after adding features: {df.shape}")
        return df

    def create_sequences(self, data, target):
        """Create sequences of data for LSTM input with corresponding targets."""
        x, y = [], []
        for i in range(len(data) - self.timesteps):
            x.append(data[i:i + self.timesteps])
            y.append(target[i + self.timesteps])
        return np.array(x, dtype=np.float32), np.array(y, dtype=np.float32)

    def create_sequences_for_prediction(self, data):
        """Create sequences of data for prediction without targets."""
        return np.array([data[i:i + self.timesteps] for i in range(len(data) - self.timesteps)], dtype=np.float32)

    def create_model(self, input_shape):
        """Create and compile the LSTM model architecture."""
        model = Sequential([
            LSTM(64, return_sequences=True, input_shape=input_shape, 
                 recurrent_dropout=0.2, kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)),
            Dropout(0.3),
            LSTM(32, return_sequences=True, recurrent_dropout=0.1, kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)),
            Dropout(0.2),
            LSTM(16),
            Dropout(0.2),
            Dense(8, activation='relu'),
            Dense(1, activation='sigmoid')
        ])
        
        optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)
        model.compile(optimizer=optimizer, 
                     loss='binary_crossentropy', 
                     metrics=['accuracy'])
        
        print(model.summary())
        return model

    def load_data_csv(self, file_path):
        """Load Bitcoin price data from a CSV file."""
        try:
            # Read the CSV file
            self.df = pd.read_csv(file_path)
            
            # Convert column names to lowercase
            self.df.columns = self.df.columns.str.lower()
            
            # Convert timestamp to datetime
            self.df['timestamp'] = pd.to_datetime(self.df['timestamp'])
            self.df.set_index('timestamp', inplace=True)
            
            if self.df is not None and not self.df.empty:
                print(f"Data loaded successfully from CSV. Shape: {self.df.shape}")
            else:
                print("Failed to load data. DataFrame is empty or None.")
                
        except Exception as e:
            print(f"Error loading CSV data: {str(e)}")
            self.df = None

    def load_data(self):
        """Load data from SQLite database."""
        try:
            import sqlite3
            conn = sqlite3.connect(self.db_path)
            
            self.df = pd.read_sql_query("SELECT * FROM bitcoin_data", conn)
            
            # Convert column names to lowercase
            self.df.columns = self.df.columns.str.lower()
            
            if self.df is not None and not self.df.empty:
                print(f"Data loaded successfully. Shape: {self.df.shape}")
            else:
                print("Failed to load data. DataFrame is empty or None.")
            
            conn.close()
        except Exception as e:
            print(f"Error loading database data: {str(e)}")
            self.df = None

    def prepare_data(self):
        """Prepare data for model training."""
        start_time = time.time()
        
        self.df = self.add_essential_features(self.df)

        # Define target variable - binary classification for price movement
        self.df['next_period_return'] = self.df['close'].pct_change(periods=1).shift(-1).clip(lower=-0.5, upper=0.5)
        self.df['next_period_up'] = (self.df['next_period_return'] > 0).astype(np.int8)
        self.df = self.df.dropna()

        # Scale features
        self.scaler = RobustScaler()
        # Ensure we're only scaling numeric features
        numeric_features = [col for col in self.feature_columns if col != 'datetime' and pd.api.types.is_numeric_dtype(self.df[col])]
        self.df[numeric_features] = self.scaler.fit_transform(self.df[numeric_features])

        # Create sequences for LSTM
        x, y = self.create_sequences(self.df[numeric_features].values, self.df['next_period_up'].values)
        print(f"Sequence shape: {x.shape}, Target shape: {y.shape}")
        
        # Class balance check
        class_distribution = np.bincount(y.astype(int))
        print(f"Class distribution - 0: {class_distribution[0]}, 1: {class_distribution[1]}")
        print(f"Positive class ratio: {class_distribution[1]/len(y):.2f}")

        # Train-test split (chronological)
        split_idx = int(len(x) * 0.8)
        self.X_train, self.X_test = x[:split_idx], x[split_idx:]
        self.y_train, self.y_test = y[:split_idx], y[split_idx:]
        
        print(f"Training data shape: {self.X_train.shape}, Test data shape: {self.X_test.shape}")
        print(f"Data preparation completed in {time.time() - start_time:.2f} seconds")

    def resample_data(self, df):
        """Resample data to specified timeframe."""
        print(f"Resampling data to {self.timeframe} timeframe...")
        df = df.resample(self.timeframe).agg({
            'open': 'first',
            'high': 'max',
            'low': 'min',
            'close': 'last',
            'volume': 'sum'
        })
        print(f"Shape after resampling: {df.shape}")
        return df

    def load_new_data_from_model(self):
        """Load new data and identify missing entries compared to the database."""
        new_data = pd.read_csv("./data/btcusd_1-min_data.csv")
        # Convert column names to lowercase
        new_data.columns = new_data.columns.str.lower()
        new_data['timestamp'] = pd.to_datetime(new_data['timestamp'], unit='s')
        
        existing_data = pd.read_sql('SELECT * FROM bitcoin_data', self.engine, index_col='timestamp',
                                    parse_dates=['timestamp'])
        # Convert column names to lowercase
        existing_data.columns = existing_data.columns.str.lower()
        
        # Show the most recent entries in the database
        last_entries = existing_data.sort_index(ascending=False).head(10)
        print("Most recent entries in database:")
        print(last_entries)

        # Find missing data
        latest_timestamp = existing_data.index.max()
        missing_data = new_data[new_data['timestamp'] > latest_timestamp]

        print(f"New data total length: {len(new_data)}")
        print(f"Missing data entries: {len(missing_data)}")
        print(f"Existing data entries: {len(existing_data)}")
        
        return missing_data

    def preprocess_data(self, data):
        """Preprocess new data with feature engineering and scaling."""
        # Add technical indicators
        data = self.add_essential_features(data)
        
        # Scale the features using the same scaler as training data
        if self.scaler is not None:
            data[self.feature_columns] = self.scaler.transform(data[self.feature_columns])
        else:
            # If no scaler exists, fit a new one
            scaler = RobustScaler()
            data[self.feature_columns] = scaler.fit_transform(data[self.feature_columns])
            
        return data

    def _prepare_prediction_data(self, new_data):
        """Helper method to prepare data for prediction."""
        # Ensure the 'timestamp' column is present
        if 'timestamp' not in new_data.columns:
            raise ValueError("Input data must contain a 'timestamp' column.")

        # Convert 'timestamp' to datetime and set as index
        new_data['timestamp'] = pd.to_datetime(new_data['timestamp'], errors='coerce')
        new_data = new_data.dropna(subset=['timestamp'])  # Drop rows where Timestamp is NaT
        new_data.set_index('timestamp', inplace=True)

        # Resample and aggregate data to the specified timeframe
        grouped_data = new_data.resample(self.timeframe).agg({
            'open': 'first',
            'high': 'max',
            'low': 'min',
            'close': 'last',
            'volume': 'sum'
        }).reset_index()  # Reset index to preserve 'timestamp' as a column

        if grouped_data.empty:
            print("No new data found.")
            return None

        # Preprocess the data
        grouped_data = self.preprocess_data(grouped_data)

        if grouped_data.empty:
            print("No new data after preprocessing.")
            return None

        # Create sequences for the model
        X = self.create_sequences_for_prediction(grouped_data[self.feature_columns].values)
        
        if len(X) == 0:
            print("Not enough data to create sequences.")
            return None, None
            
        return X, grouped_data

    def make_predictions_w_reality(self, new_data):
        """Make predictions and compare with actual outcomes."""
        # Convert column names to lowercase if needed
        new_data.columns = new_data.columns.str.lower()
        
        prepared_data = self._prepare_prediction_data(new_data)
        if prepared_data is None:
            return None, None
            
        X, grouped_data = prepared_data

        # Generate predictions
        predictions = self.model.predict(X)

        # Trim 'grouped_data' to align with sequence length
        grouped_data = grouped_data.iloc[self.timesteps:]  # Align with sequence length

        # Add predictions to the grouped_data DataFrame
        grouped_data['predictions'] = (predictions > 0.5).astype(int)
        grouped_data['prediction_probability'] = predictions

        # Calculate reality (actual price movement)
        grouped_data['reality'] = (grouped_data['close'].pct_change() > 0.005).astype(int)
        
        # Calculate accuracy
        grouped_data['correct'] = (grouped_data['predictions'] == grouped_data['reality']).astype(int)
        accuracy = grouped_data['correct'].mean()
        print(f"Prediction accuracy: {accuracy:.2f}")

        # Return predictions and reality
        return grouped_data[['timestamp', 'predictions', 'prediction_probability']], grouped_data['reality']

    def make_predictions(self, new_data):
        """Make predictions on new data."""
        # Convert column names to lowercase if needed
        new_data.columns = new_data.columns.str.lower()
        
        prepared_data = self._prepare_prediction_data(new_data)
        if prepared_data is None:
            return None
            
        X, grouped_data = prepared_data

        # Generate predictions
        predictions = self.model.predict(X)

        # Trim 'grouped_data' to align with sequence length
        grouped_data = grouped_data.iloc[self.timesteps:]

        # Add predictions to the grouped_data DataFrame
        grouped_data['predictions'] = (predictions > 0.5).astype(int)
        grouped_data['prediction_probability'] = predictions.flatten()

        # Return prediction results
        return grouped_data[['timestamp', 'predictions', 'prediction_probability']]

    def update_database(self, missing_data):
        """Update the database with the missing data."""
        if missing_data.empty:
            print("No new data to add to the database.")
            return
            
        missing_data.to_sql('bitcoin_data', self.engine, if_exists='append', index=False)
        print(f"Database updated with {len(missing_data)} new rows.")

    def train_model(self):
        """Train the LSTM model with early stopping and checkpointing."""
        if self.X_train is None or self.y_train is None:
            raise ValueError("Data not loaded. Call prepare_data() first.")
            
        # Create model directory if it doesn't exist
        os.makedirs("./models", exist_ok=True)
            
        # Configure TensorFlow for GPU memory
        self._configure_gpu_memory()
        
        # Create the model
        self.model = self.create_model(input_shape=(self.timesteps, len(self.feature_columns)))
        
        # Setup callbacks
        current_date = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
        checkpoint_path = f"./models/model_checkpoint_{current_date}.keras"
        
        callbacks = [
            EarlyStopping(
                monitor='val_loss',
                patience=10,
                restore_best_weights=True,
                verbose=1
            ),
            ModelCheckpoint(
                filepath=checkpoint_path,
                save_best_only=True,
                monitor='val_loss',
                verbose=1
            )
        ]

        # Train the model
        print(f"Training model with {self.epochs} epochs and batch size {self.batch_size}...")
        self.history = self.model.fit(
            self.X_train, self.y_train,
            validation_data=(self.X_test, self.y_test),
            epochs=self.epochs,
            batch_size=self.batch_size,
            callbacks=callbacks,
            verbose=1
        )
        
        # Save the final model
        final_model_path = f"./models/model_final_{current_date}.keras"
        self.model.save(final_model_path)
        print(f"Model saved to {final_model_path}")

    def _configure_gpu_memory(self):
        """Configure TensorFlow to use GPU memory efficiently."""
        gpus = tf.config.experimental.list_physical_devices('GPU')
        if gpus:
            try:
                # Limit TensorFlow to use only 80% of GPU memory
                for gpu in gpus:
                    tf.config.experimental.set_virtual_device_configuration(
                        gpu,
                        [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=2048)]
                    )
                print("GPU memory limit set")
            except RuntimeError as e:
                print(f"GPU memory limit setting failed: {e}")

    def evaluate_model(self):
        """Evaluate the trained model on test data."""
        if self.model is None:
            raise ValueError("Model not trained. Call train_model() first.")
            
        # Basic evaluation
        test_loss, test_accuracy = self.model.evaluate(self.X_test, self.y_test, verbose=1)
        print(f"\nTest Accuracy: {test_accuracy:.4f}")
        print(f"Test Loss: {test_loss:.4f}")
        
        # Make predictions on test data
        y_pred = (self.model.predict(self.X_test) > 0.5).astype(int)
        
        # Calculate confusion matrix
        cm = confusion_matrix(self.y_test, y_pred)
        print("\nConfusion Matrix:")
        print(cm)
        
        # Print classification report
        print("\nClassification Report:")
        print(classification_report(self.y_test, y_pred))

    def plot_history(self):
        """Plot the training history metrics."""
        if self.history is None:
            raise ValueError("No training history available. Train the model first.")
            
        plt.figure(figsize=(15, 6))
        
        # Plot accuracy
        plt.subplot(1, 2, 1)
        plt.plot(self.history.history['accuracy'], label='Training Accuracy')
        plt.plot(self.history.history['val_accuracy'], label='Validation Accuracy')
        plt.title('Model Accuracy')
        plt.xlabel('Epoch')
        plt.ylabel('Accuracy')
        plt.legend(loc='lower right')
        plt.grid(True)
        
        # Plot loss
        plt.subplot(1, 2, 2)
        plt.plot(self.history.history['loss'], label='Training Loss')
        plt.plot(self.history.history['val_loss'], label='Validation Loss')
        plt.title('Model Loss')
        plt.xlabel('Epoch')
        plt.ylabel('Loss')
        plt.legend(loc='upper right')
        plt.grid(True)
        
        plt.tight_layout()
        
        # Save the plot
        os.makedirs("./plots", exist_ok=True)
        current_date = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
        plt.savefig(f"./plots/training_history_{current_date}.png")
        
        plt.show()

    def analyze_market_trends(self, window_size=100, prominence=0.01, height=None, threshold=0.0, distance=None):
        """Analyze market trends by finding local minima and maxima in the price data."""
        matplotlib.use('TkAgg')  # Use TkAgg backend for interactive plotting
        
        # Make sure data is loaded
        if not hasattr(self, 'df') or self.df is None:
            print("Data not loaded. Call load_and_prepare_data() first.")
            return
        
        # Get the closing prices
        prices = self.df['close'].values
        
        # Calculate prominence as a percentage of price range if provided as a relative value
        price_range = np.max(prices) - np.min(prices)
        if prominence < 1:  # If prominence is provided as a relative value
            prominence_abs = prominence * price_range
        else:
            prominence_abs = prominence
        
        # Use provided distance or default to window_size
        if distance is None:
            distance = window_size
        
        # Find local maxima (peaks)
        peaks, peaks_props = find_peaks(
            prices, 
            height=height,
            threshold=threshold,
            distance=distance,
            prominence=prominence_abs
        )
        
        # Find local minima (valleys)
        valleys, valleys_props = find_peaks(
            -prices, 
            height=-height if height is not None else None,
            threshold=threshold,
            distance=distance,
            prominence=prominence_abs
        )
        
        # Create figure for trend analysis
        self._plot_trend_analysis(prices, peaks, valleys)
        
        # Print trend statistics
        self._print_trend_statistics(prices, peaks, valleys)
        
        return peaks, valleys
        
    def _plot_trend_analysis(self, prices, peaks, valleys):
        """Helper method to plot trend analysis."""
        plt.figure(figsize=(14, 7))
        
        # Plot the price data
        plt.plot(self.df.index, prices, label='Bitcoin Price')
        
        # Highlight the peaks and valleys
        plt.scatter(self.df.index[peaks], prices[peaks], color='green', s=100, marker='^', label='Local Maxima')
        plt.scatter(self.df.index[valleys], prices[valleys], color='red', s=100, marker='v', label='Local Minima')
        
        # Identify trends by connecting consecutive extrema
        all_points = np.sort(np.concatenate([peaks, valleys]))
        
        self.up_trends = []
        self.down_trends = []
        
        for i in range(len(all_points) - 1):
            start_idx = all_points[i]
            end_idx = all_points[i+1]
            
            # Determine if it's an uptrend or downtrend
            if start_idx in valleys and end_idx in peaks:
                # Uptrend
                plt.plot([self.df.index[start_idx], self.df.index[end_idx]], 
                         [prices[start_idx], prices[end_idx]], 
                         'g-', linewidth=2, alpha=0.7)
                
                duration = end_idx - start_idx
                magnitude = prices[end_idx] - prices[start_idx]
                percent_change = 100 * magnitude / prices[start_idx]
                self.up_trends.append((duration, magnitude, percent_change))
                
            elif start_idx in peaks and end_idx in valleys:
                # Downtrend
                plt.plot([self.df.index[start_idx], self.df.index[end_idx]], 
                         [prices[start_idx], prices[end_idx]], 
                         'r-', linewidth=2, alpha=0.7)
                
                duration = end_idx - start_idx
                magnitude = prices[start_idx] - prices[end_idx]
                percent_change = 100 * magnitude / prices[start_idx]
                self.down_trends.append((duration, magnitude, percent_change))
        
        plt.title('Bitcoin Price Trends Analysis')
        plt.xlabel('Date')
        plt.ylabel('Price')
        plt.legend()
        plt.grid(True)
        plt.tight_layout()
        plt.savefig('bitcoin_trends_analysis.png')
        plt.show(block=True)
        
    def _print_trend_statistics(self, prices, peaks, valleys):
        """Helper method to print trend statistics."""
        print(f"Found {len(peaks)} local maxima and {len(valleys)} local minima")
        
        # Calculate average trend durations and magnitudes
        if hasattr(self, 'up_trends') and self.up_trends:
            avg_up_duration = sum(t[0] for t in self.up_trends) / len(self.up_trends)
            avg_up_magnitude = sum(t[1] for t in self.up_trends) / len(self.up_trends)
            avg_up_percent = sum(t[2] for t in self.up_trends) / len(self.up_trends)
            print(f"Average uptrend: {avg_up_duration:.1f} periods, {avg_up_magnitude:.2f} price change ({avg_up_percent:.2f}%)")
        
        if hasattr(self, 'down_trends') and self.down_trends:
            avg_down_duration = sum(t[0] for t in self.down_trends) / len(self.down_trends)
            avg_down_magnitude = sum(t[1] for t in self.down_trends) / len(self.down_trends)
            avg_down_percent = sum(t[2] for t in self.down_trends) / len(self.down_trends)
            print(f"Average downtrend: {avg_down_duration:.1f} periods, {avg_down_magnitude:.2f} price change ({avg_down_percent:.2f}%)")