adding metasupertrend data WIP

BitcoinPricePredictor.py

@@ -19,10 +19,10 @@ 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=8, learning_rate=0.001, epochs=50):
+    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
@@ -37,8 +37,9 @@ class BitcoinPricePredictor:
         self.history = None
         self.scaler = None
         self.timeframe = timeframe
-        self.feature_columns = ['Open', 'High', 'Low', 'Close', 'Volume',
+        self.feature_columns = ['open', 'high', 'low', 'close', 'volume',
-                                'HL_Ratio', 'SMA_7', 'SMA_21', 'Price_Change']
+                                'hl_ratio', 'sma_7', 'sma_21', 'price_change']
+        self.df = None
 
     @staticmethod
     def reduce_mem_usage(df):
@@ -73,54 +74,73 @@ class BitcoinPricePredictor:
         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)
+        df['hl_ratio'] = (df['high'] / df['low']).clip(lower=0.8, upper=1.2)
         
-        # Moving averages with different timeframes
+        # Moving averages
-        df['SMA_7'] = df['Close'].rolling(window=7, min_periods=1).mean()
+        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_21'] = df['close'].rolling(window=21, min_periods=1).mean()
-        df['SMA_50'] = df['Close'].rolling(window=50, 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_12'] = df['close'].ewm(span=12, adjust=False).mean()
-        df['EMA_26'] = df['Close'].ewm(span=26, 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'] = df['ema_12'] - df['ema_26']
-        df['MACD_Signal'] = df['MACD'].ewm(span=9, adjust=False).mean()
+        df['macd_signal'] = df['macd'].ewm(span=9, adjust=False).mean()
         
-        # Relative Strength Index (RSI)
+        # RSI
-        delta = df['Close'].diff()
+        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))
+        df['rsi'] = 100 - (100 / (1 + rs))
         
         # Bollinger Bands
-        df['BB_Middle'] = df['Close'].rolling(window=20).mean()
+        df['bb_middle'] = df['close'].rolling(window=20).mean()
-        df['BB_Std'] = df['Close'].rolling(window=20).std()
+        df['bb_std'] = df['close'].rolling(window=20).std()
-        df['BB_Upper'] = df['BB_Middle'] + 2 * df['BB_Std']
+        df['bb_upper'] = df['bb_middle'] + 2 * df['bb_std']
-        df['BB_Lower'] = 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']
+        df['bb_width'] = (df['bb_upper'] - df['bb_lower']) / df['bb_middle']
         
-        # Price changes at different timeframes
+        # Price changes
-        df['Price_Change_1d'] = df['Close'].pct_change(periods=1).clip(lower=-0.5, upper=0.5)
+        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_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)
+        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()
+        df['volatility'] = df['close'].rolling(window=14).std() / df['close'].rolling(window=14).mean()
         
-        # Clean up any NaN or infinite values
+        # 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
+        # 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']]
+        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
@@ -135,18 +155,15 @@ class BitcoinPricePredictor:
 
     def create_sequences_for_prediction(self, data):
         """Create sequences of data for prediction without targets."""
-        x = []
+        return np.array([data[i:i + self.timesteps] for i in range(len(data) - self.timesteps)], dtype=np.float32)
-        for i in range(len(data) - self.timesteps):
-            x.append(data[i:i + self.timesteps])
-        return np.array(x, 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=tf.keras.regularizers.l1_l2(l1=1e-5, l2=1e-4)),
+                 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=tf.keras.regularizers.l1_l2(l1=1e-5, l2=1e-4)),
+            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),
@@ -162,39 +179,68 @@ class BitcoinPricePredictor:
         print(model.summary())
         return model
 
-    def load_data(self):
+    def load_data_csv(self, file_path):
-        import pandas as pd
+        """Load Bitcoin price data from a CSV file."""
-        import sqlite3
+        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()
         
-        #df = self.resample_data(df)
-
         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_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['next_period_up'] = (self.df['next_period_return'] > 0).astype(np.int8)
         self.df = self.df.dropna()
 
         # Scale features
         self.scaler = RobustScaler()
-        self.df[self.feature_columns] = self.scaler.fit_transform(self.df[self.feature_columns])
+        # 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[self.feature_columns].values, self.df['Next_Period_Up'].values)
+        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
@@ -204,31 +250,21 @@ class BitcoinPricePredictor:
 
         # Train-test split (chronological)
         split_idx = int(len(x) * 0.8)
-        x_train, x_test = x[:split_idx], x[split_idx:]
+        self.X_train, self.X_test = x[:split_idx], x[split_idx:]
-        y_train, y_test = y[:split_idx], y[split_idx:]
+        self.y_train, self.y_test = y[:split_idx], y[split_idx:]
-
-        # Free memory
-        # del self.df
-        # gc.collect()
-
-        self.X_train, self.X_test = x_train, x_test
-        self.y_train, self.y_test = y_train, y_test
         
         print(f"Training data shape: {self.X_train.shape}, Test data shape: {self.X_test.shape}")
-
-        class_counts = np.bincount(self.y_train.astype(int))
-        print(f"Class distribution in training data: 0={class_counts[0]}, 1={class_counts[1]}")
-
         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',
+            'open': 'first',
-            'High': 'max',
+            'high': 'max',
-            'Low': 'min',
+            'low': 'min',
-            'Close': 'last',
+            'close': 'last',
-            'Volume': 'sum'
+            'volume': 'sum'
         })
         print(f"Shape after resampling: {df.shape}")
         return df
@@ -236,10 +272,14 @@ class BitcoinPricePredictor:
     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")
-        new_data['Timestamp'] = pd.to_datetime(new_data['Timestamp'], unit='s')
+        # 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',
+        existing_data = pd.read_sql('SELECT * FROM bitcoin_data', self.engine, index_col='timestamp',
-                                    parse_dates=['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)
@@ -248,7 +288,7 @@ class BitcoinPricePredictor:
 
         # Find missing data
         latest_timestamp = existing_data.index.max()
-        missing_data = new_data[new_data['Timestamp'] > latest_timestamp]
+        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)}")
@@ -271,36 +311,36 @@ class BitcoinPricePredictor:
             
         return data
 
-    def make_predictions_w_reality(self, new_data):
+    def _prepare_prediction_data(self, new_data):
-        """Make predictions and compare with actual outcomes."""
+        """Helper method to prepare data for prediction."""
-        # Ensure the 'Timestamp' column is present
+        # Ensure the 'timestamp' column is present
-        if 'Timestamp' not in new_data.columns:
+        if 'timestamp' not in new_data.columns:
-            raise ValueError("Input data must contain a 'Timestamp' column.")
+            raise ValueError("Input data must contain a 'timestamp' column.")
 
-        # Convert 'Timestamp' to datetime and set as index
+        # Convert 'timestamp' to datetime and set as index
-        new_data['Timestamp'] = pd.to_datetime(new_data['Timestamp'], errors='coerce')
+        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 = new_data.dropna(subset=['timestamp'])  # Drop rows where Timestamp is NaT
-        new_data.set_index('Timestamp', inplace=True)
+        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',
+            'open': 'first',
-            'High': 'max',
+            'high': 'max',
-            'Low': 'min',
+            'low': 'min',
-            'Close': 'last',
+            'close': 'last',
-            'Volume': 'sum'
+            'volume': 'sum'
-        }).reset_index()  # Reset index to preserve 'Timestamp' as a column
+        }).reset_index()  # Reset index to preserve 'timestamp' as a column
 
         if grouped_data.empty:
             print("No new data found.")
-            return None, None
+            return None
 
         # Preprocess the data
         grouped_data = self.preprocess_data(grouped_data)
 
         if grouped_data.empty:
             print("No new data after preprocessing.")
-            return None, None
+            return None
 
         # Create sequences for the model
         X = self.create_sequences_for_prediction(grouped_data[self.feature_columns].values)
@@ -309,6 +349,19 @@ class BitcoinPricePredictor:
             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)
 
@@ -316,57 +369,30 @@ class BitcoinPricePredictor:
         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['predictions'] = (predictions > 0.5).astype(int)
-        grouped_data['Prediction_Probability'] = predictions
+        grouped_data['prediction_probability'] = predictions
 
         # Calculate reality (actual price movement)
-        grouped_data['Reality'] = (grouped_data['Close'].pct_change() > 0.005).astype(int)
+        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)
+        grouped_data['correct'] = (grouped_data['predictions'] == grouped_data['reality']).astype(int)
-        accuracy = grouped_data['Correct'].mean()
+        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']
+        return grouped_data[['timestamp', 'predictions', 'prediction_probability']], grouped_data['reality']
 
     def make_predictions(self, new_data):
         """Make predictions on new data."""
-        # Ensure the 'Timestamp' column is present
+        # Convert column names to lowercase if needed
-        if 'Timestamp' not in new_data.columns:
+        new_data.columns = new_data.columns.str.lower()
-            raise ValueError("Input data must contain a 'Timestamp' column.")
         
-        # Convert 'Timestamp' to datetime and set as index
+        prepared_data = self._prepare_prediction_data(new_data)
-        new_data['Timestamp'] = pd.to_datetime(new_data['Timestamp'], errors='coerce')
+        if prepared_data is None:
-        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
+        X, grouped_data = prepared_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
 
         # Generate predictions
         predictions = self.model.predict(X)
@@ -375,11 +401,11 @@ class BitcoinPricePredictor:
         grouped_data = grouped_data.iloc[self.timesteps:]
 
         # Add predictions to the grouped_data DataFrame
-        grouped_data['Predictions'] = (predictions > 0.5).astype(int)
+        grouped_data['predictions'] = (predictions > 0.5).astype(int)
-        grouped_data['Prediction_Probability'] = predictions.flatten()
+        grouped_data['prediction_probability'] = predictions.flatten()
 
         # Return prediction results
-        return grouped_data[['Timestamp', 'Predictions', 'Prediction_Probability']]
+        return grouped_data[['timestamp', 'predictions', 'prediction_probability']]
 
     def update_database(self, missing_data):
         """Update the database with the missing data."""
@@ -393,24 +419,13 @@ class BitcoinPricePredictor:
     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 load_and_prepare_data() first.")
+            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 to use memory growth
+        # Configure TensorFlow for GPU memory
-        gpus = tf.config.experimental.list_physical_devices('GPU')
+        self._configure_gpu_memory()
-        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)]  # Set to 2GB or adjust as needed
-                    )
-                print("GPU memory limit set")
-            except RuntimeError as e:
-                print(f"GPU memory limit setting failed: {e}")
         
         # Create the model
         self.model = self.create_model(input_shape=(self.timesteps, len(self.feature_columns)))
@@ -450,6 +465,21 @@ class BitcoinPricePredictor:
         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:
@@ -509,16 +539,7 @@ class BitcoinPricePredictor:
         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."""
-        Analyze market trends by finding local minima and maxima in the price data.
-        
-        Args:
-            window_size (int): Default distance between peaks if distance is not provided
-            prominence (float): Minimum prominence of peaks (relative to price range)
-            height (float): Minimum height of peaks (absolute value)
-            threshold (float): Required threshold of peaks relative to neighbors
-            distance (int): Minimum distance between peaks in number of data points
-        """
         matplotlib.use('TkAgg')  # Use TkAgg backend for interactive plotting
         
         # Make sure data is loaded
@@ -527,7 +548,7 @@ class BitcoinPricePredictor:
             return
         
         # Get the closing prices
-        prices = self.df['Close'].values
+        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)
@@ -540,7 +561,7 @@ class BitcoinPricePredictor:
         if distance is None:
             distance = window_size
         
-        # Find local maxima (peaks) with adjustable parameters
+        # Find local maxima (peaks)
         peaks, peaks_props = find_peaks(
             prices, 
             height=height,
@@ -549,7 +570,7 @@ class BitcoinPricePredictor:
             prominence=prominence_abs
         )
         
-        # Find local minima (valleys) by inverting the signal
+        # Find local minima (valleys)
         valleys, valleys_props = find_peaks(
             -prices, 
             height=-height if height is not None else None,
@@ -558,7 +579,16 @@ class BitcoinPricePredictor:
             prominence=prominence_abs
         )
         
-        # Create a new figure for trend analysis
+        # 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
@@ -571,8 +601,8 @@ class BitcoinPricePredictor:
         # Identify trends by connecting consecutive extrema
         all_points = np.sort(np.concatenate([peaks, valleys]))
         
-        up_trends = []
+        self.up_trends = []
-        down_trends = []
+        self.down_trends = []
         
         for i in range(len(all_points) - 1):
             start_idx = all_points[i]
@@ -588,7 +618,7 @@ class BitcoinPricePredictor:
                 duration = end_idx - start_idx
                 magnitude = prices[end_idx] - prices[start_idx]
                 percent_change = 100 * magnitude / prices[start_idx]
-                up_trends.append((duration, magnitude, percent_change))
+                self.up_trends.append((duration, magnitude, percent_change))
                 
             elif start_idx in peaks and end_idx in valleys:
                 # Downtrend
@@ -599,33 +629,30 @@ class BitcoinPricePredictor:
                 duration = end_idx - start_idx
                 magnitude = prices[start_idx] - prices[end_idx]
                 percent_change = 100 * magnitude / prices[start_idx]
-                down_trends.append((duration, magnitude, percent_change))
+                self.down_trends.append((duration, magnitude, percent_change))
         
-        plt.title(f'Bitcoin Price Trends Analysis\nParameters: prominence={prominence}, distance={distance}')
+        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)
         
-        # Print some statistics about the trends
+    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 up_trends:
+        if hasattr(self, 'up_trends') and self.up_trends:
-            avg_up_duration = sum(t[0] for t in up_trends) / len(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 up_trends) / len(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 up_trends) / len(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 down_trends:
+        if hasattr(self, 'down_trends') and self.down_trends:
-            avg_down_duration = sum(t[0] for t in down_trends) / len(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 down_trends) / len(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 down_trends) / len(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}%)")
-        
-        # Show the plot interactively
-        plt.show(block=True)  # block=True ensures the plot window stays open
-        
-        return peaks, valleys

main_price_predictor.py

@@ -2,7 +2,8 @@ from BitcoinPricePredictor import BitcoinPricePredictor
 
 if __name__ == "__main__":
     predictor = BitcoinPricePredictor(db_path='databases/bitcoin_historical_data.db', timeframe='H')
-    predictor.load_data()
+    predictor.load_data_csv('./data/btcusd_daily_data.csv')
+    predictor.prepare_data()
     predictor.train_model()
     predictor.evaluate_model()
     predictor.plot_history()

poetry.toml

@@ -1,2 +0,0 @@
-[virtualenvs]
-in-project = false

pyproject.toml

@@ -1,32 +0,0 @@
-[project]
-name = "cryptomarketparser"
-version = "0.1.0"
-description = ""
-authors = [
-    {name = "Simon Moisy",email = "simon.moisy@tutanota.com"}
-]
-readme = "README.md"
-requires-python = ">=3.10,<4.0"
-dependencies = [
-    "numpy (>=1.26.0,<2.2.0)",
-    "pandas (>=2.2.3,<3.0.0)",
-    "sqlalchemy (>=2.0.39,<3.0.0)",
-    "scipy (>=1.15.2,<2.0.0)",
-    "matplotlib (>=3.10.1,<4.0.0)",
-    "scikit-learn (>=1.6.1,<2.0.0)",
-    "ollama (>=0.4.7,<0.5.0)",
-    "transformers (>=4.49.0,<5.0.0)",
-    "markdownify (>=1.1.0,<2.0.0)",
-    "ccxt (>=4.4.69,<5.0.0)",
-    "mplfinance (>=0.12.10b0,<0.13.0)",
-]
-
-
-[build-system]
-requires = ["poetry-core>=2.0.0,<3.0.0"]
-build-backend = "poetry.core.masonry.api"
-
-[[tool.poetry.source]]
-name = "pytorch"
-url = "https://download.pytorch.org/whl/cu121"
-priority = "explicit"

trend_detector_simple.py

@@ -0,0 +1,676 @@
+import pandas as pd
+import numpy as np
+import logging
+from scipy.signal import find_peaks
+from matplotlib.patches import Rectangle
+from scipy import stats
+from scipy import stats
+
+# Color configuration
+# Plot colors
+DARK_BG_COLOR = '#181C27'
+LEGEND_BG_COLOR = '#333333'
+TITLE_COLOR = 'white'
+AXIS_LABEL_COLOR = 'white'
+
+# Candlestick colors
+CANDLE_UP_COLOR = '#089981'  # Green
+CANDLE_DOWN_COLOR = '#F23645'  # Red
+
+# Marker colors
+MIN_COLOR = 'red'
+MAX_COLOR = 'green'
+
+# Line style colors
+MIN_LINE_STYLE = 'g--'  # Green dashed
+MAX_LINE_STYLE = 'r--'  # Red dashed
+SMA7_LINE_STYLE = 'y-'  # Yellow solid
+SMA15_LINE_STYLE = 'm-'  # Magenta solid
+
+# SuperTrend colors
+ST_COLOR_UP = 'g-'
+ST_COLOR_DOWN = 'r-'
+
+class TrendDetectorSimple:
+    def __init__(self, data, verbose=False):
+        """
+        Initialize the TrendDetectorSimple class.
+        
+        Parameters:
+        - data: pandas DataFrame containing price data
+        - verbose: boolean, whether to display detailed logging information
+        """
+        
+        self.data = data
+        self.verbose = verbose
+        
+        # Plot style configuration
+        self.plot_style = 'dark_background' 
+        self.bg_color = DARK_BG_COLOR
+        self.plot_size = (12, 8)
+        
+        # Candlestick configuration
+        self.candle_width = 0.6
+        self.candle_up_color = CANDLE_UP_COLOR
+        self.candle_down_color = CANDLE_DOWN_COLOR
+        self.candle_alpha = 0.8
+        self.wick_width = 1
+        
+        # Marker configuration
+        self.min_marker = '^'
+        self.min_color = MIN_COLOR
+        self.min_size = 100
+        self.max_marker = 'v'
+        self.max_color = MAX_COLOR
+        self.max_size = 100
+        self.marker_zorder = 100
+        
+        # Line configuration
+        self.line_width = 1
+        self.min_line_style = MIN_LINE_STYLE
+        self.max_line_style = MAX_LINE_STYLE
+        self.sma7_line_style = SMA7_LINE_STYLE
+        self.sma15_line_style = SMA15_LINE_STYLE
+        
+        # Text configuration
+        self.title_size = 14
+        self.title_color = TITLE_COLOR
+        self.axis_label_size = 12
+        self.axis_label_color = AXIS_LABEL_COLOR
+        
+        # Legend configuration
+        self.legend_loc = 'best'
+        self.legend_bg_color = LEGEND_BG_COLOR
+        
+        # Configure logging
+        logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
+                           format='%(asctime)s - %(levelname)s - %(message)s')
+        self.logger = logging.getLogger('TrendDetectorSimple')
+        
+        # Convert data to pandas DataFrame if it's not already
+        if not isinstance(self.data, pd.DataFrame):
+            if isinstance(self.data, list):
+                self.data = pd.DataFrame({'close': self.data})
+            else:
+                raise ValueError("Data must be a pandas DataFrame or a list")
+        
+        self.logger.info(f"Initialized TrendDetectorSimple with {len(self.data)} data points")
+
+    def calculate_tr(self):
+        """
+        Calculate True Range (TR) for the price data.
+        
+        True Range is the greatest of:
+        1. Current high - current low
+        2. |Current high - previous close|
+        3. |Current low - previous close|
+        
+        Returns:
+        - Numpy array of TR values
+        """
+        df = self.data.copy()
+        high = df['high'].values
+        low = df['low'].values
+        close = df['close'].values
+        
+        tr = np.zeros_like(close)
+        tr[0] = high[0] - low[0]  # First TR is just the first day's range
+        
+        for i in range(1, len(close)):
+            # Current high - current low
+            hl_range = high[i] - low[i]
+            # |Current high - previous close|
+            hc_range = abs(high[i] - close[i-1])
+            # |Current low - previous close|
+            lc_range = abs(low[i] - close[i-1])
+            
+            # TR is the maximum of these three values
+            tr[i] = max(hl_range, hc_range, lc_range)
+            
+        return tr
+    
+    def calculate_atr(self, period=14):
+        """
+        Calculate Average True Range (ATR) for the price data.
+        
+        ATR is the exponential moving average of the True Range over a specified period.
+        
+        Parameters:
+        - period: int, the period for the ATR calculation (default: 14)
+        
+        Returns:
+        - Numpy array of ATR values
+        """
+        
+        tr = self.calculate_tr()
+        atr = np.zeros_like(tr)
+        
+        # First ATR value is just the first TR
+        atr[0] = tr[0]
+        
+        # Calculate exponential moving average (EMA) of TR
+        multiplier = 2.0 / (period + 1)
+        
+        for i in range(1, len(tr)):
+            atr[i] = (tr[i] * multiplier) + (atr[i-1] * (1 - multiplier))
+            
+        return atr
+    
+    def detect_trends(self):
+        """
+        Detect trends by identifying local minima and maxima in the price data
+        using scipy.signal.find_peaks.
+        
+        Parameters:
+        - prominence: float, required prominence of peaks (relative to the price range)
+        - width: int, required width of peaks in data points
+        
+        Returns:
+        - DataFrame with columns for timestamps, prices, and trend indicators
+        - Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators
+        """
+        df = self.data.copy()
+        close_prices = df['close'].values
+        
+        # Find peaks in the price data
+        max_peaks, _ = find_peaks(close_prices)
+        min_peaks, _ = find_peaks(-close_prices)
+        
+        # Create boolean columns for min and max peaks using vectorized operations
+        df['is_max'] = False
+        df['is_min'] = False
+        df.iloc[max_peaks, df.columns.get_loc('is_max')] = True
+        df.iloc[min_peaks, df.columns.get_loc('is_min')] = True
+        
+        result = df[['datetime', 'close', 'is_min', 'is_max']].copy()
+        
+        # Perform linear regression on min_peaks and max_peaks
+        min_prices = df['close'].iloc[min_peaks].values
+        max_prices = df['close'].iloc[max_peaks].values
+        
+        # Linear regression for min peaks if we have at least 2 points
+        min_slope, min_intercept, min_r_value, _, _ = stats.linregress(min_peaks, min_prices)
+        # Linear regression for max peaks if we have at least 2 points
+        max_slope, max_intercept, max_r_value, _, _ = stats.linregress(max_peaks, max_prices)
+
+        # Calculate Simple Moving Averages (SMA) for 7 and 15 periods        
+        sma_7 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
+        sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
+        
+        analysis_results = {}
+        analysis_results['linear_regression'] = {
+            'min': {
+                'slope': min_slope,
+                'intercept': min_intercept,
+                'r_squared': min_r_value ** 2
+            },
+            'max': {
+                'slope': max_slope,
+                'intercept': max_intercept,
+                'r_squared': max_r_value ** 2
+            }
+        }
+        analysis_results['sma'] = {
+            '7': sma_7,
+            '15': sma_15
+        }
+        
+        # Calculate SuperTrend indicators
+        supertrend_results_list = self._calculate_supertrend_indicators()
+        meta_results = self.calculate_metasupertrend(df, supertrend_results_list)
+        analysis_results['supertrend'] = supertrend_results_list
+        analysis_results['metasupertrend'] = meta_results
+        
+        return result, analysis_results
+        
+    def _calculate_supertrend_indicators(self):
+        """
+        Calculate SuperTrend indicators with different parameter sets.
+        
+        Returns:
+        - list, the SuperTrend results
+        """
+        supertrend_params = [
+            {"period": 12, "multiplier": 3.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
+            {"period": 10, "multiplier": 1.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
+            {"period": 11, "multiplier": 2.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN}
+        ]
+        
+        supertrend_results_list = []
+        for params in supertrend_params:
+            supertrend_results = self.calculate_supertrend(
+                period=params["period"], 
+                multiplier=params["multiplier"]
+            )
+            supertrend_results_list.append({
+                "results": supertrend_results,
+                "params": params
+            })
+        
+        return supertrend_results_list
+
+    def calculate_supertrend(self, period, multiplier):
+        """
+        Calculate SuperTrend indicator for the price data.
+        
+        SuperTrend is a trend-following indicator that uses ATR to determine the trend direction.
+        
+        Parameters:
+        - period: int, the period for the ATR calculation (default: 10)
+        - multiplier: float, the multiplier for the ATR (default: 3.0)
+        
+        Returns:
+        - Dictionary containing SuperTrend values, trend direction, and upper/lower bands
+        """
+        df = self.data.copy()
+        high = df['high'].values
+        low = df['low'].values
+        close = df['close'].values
+        
+        # Calculate ATR
+        atr = self.calculate_atr(period)
+        
+        # Calculate basic upper and lower bands
+        upper_band = np.zeros_like(close)
+        lower_band = np.zeros_like(close)
+        
+        for i in range(len(close)):
+            # Calculate the basic bands
+            hl_avg = (high[i] + low[i]) / 2
+            upper_band[i] = hl_avg + (multiplier * atr[i])
+            lower_band[i] = hl_avg - (multiplier * atr[i])
+        
+        # Calculate final upper and lower bands with trend logic
+        final_upper = np.zeros_like(close)
+        final_lower = np.zeros_like(close)
+        supertrend = np.zeros_like(close)
+        trend = np.zeros_like(close)  # 1 for uptrend, -1 for downtrend
+        
+        # Initialize first values
+        final_upper[0] = upper_band[0]
+        final_lower[0] = lower_band[0]
+        
+        # If close price is above upper band, we're in a downtrend (ST = upper band)
+        # If close price is below lower band, we're in an uptrend (ST = lower band)
+        if close[0] <= upper_band[0]:
+            supertrend[0] = upper_band[0]
+            trend[0] = -1  # Downtrend
+        else:
+            supertrend[0] = lower_band[0]
+            trend[0] = 1   # Uptrend
+        
+        # Calculate SuperTrend for the rest of the data
+        for i in range(1, len(close)):
+            # Calculate final upper band
+            if (upper_band[i] < final_upper[i-1]) or (close[i-1] > final_upper[i-1]):
+                final_upper[i] = upper_band[i]
+            else:
+                final_upper[i] = final_upper[i-1]
+                
+            # Calculate final lower band
+            if (lower_band[i] > final_lower[i-1]) or (close[i-1] < final_lower[i-1]):
+                final_lower[i] = lower_band[i]
+            else:
+                final_lower[i] = final_lower[i-1]
+                
+            # Determine trend and SuperTrend value
+            if supertrend[i-1] == final_upper[i-1] and close[i] <= final_upper[i]:
+                # Continuing downtrend
+                supertrend[i] = final_upper[i]
+                trend[i] = -1
+            elif supertrend[i-1] == final_upper[i-1] and close[i] > final_upper[i]:
+                # Switching to uptrend
+                supertrend[i] = final_lower[i]
+                trend[i] = 1
+            elif supertrend[i-1] == final_lower[i-1] and close[i] >= final_lower[i]:
+                # Continuing uptrend
+                supertrend[i] = final_lower[i]
+                trend[i] = 1
+            elif supertrend[i-1] == final_lower[i-1] and close[i] < final_lower[i]:
+                # Switching to downtrend
+                supertrend[i] = final_upper[i]
+                trend[i] = -1
+                
+        # Prepare result
+        supertrend_results = {
+            'supertrend': supertrend,
+            'trend': trend,
+            'upper_band': final_upper,
+            'lower_band': final_lower
+        }
+        
+        return supertrend_results
+
+    def plot_trends(self, trend_data, analysis_results, view="both"):
+        """
+        Plot the price data with detected trends using a candlestick chart.
+        Also plots SuperTrend indicators with three different parameter sets.
+
+        Parameters:
+        - trend_data: DataFrame, the output from detect_trends()
+        - analysis_results: Dictionary containing analysis results from detect_trends()
+        - view: str, one of 'both', 'trend', 'supertrend'; determines which plot(s) to display
+
+        Returns:
+        - None (displays the plot)
+        """
+        import matplotlib.pyplot as plt
+        from matplotlib.patches import Rectangle
+
+        plt.style.use(self.plot_style)
+
+        if view == "both":
+            fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(self.plot_size[0]*2, self.plot_size[1]))
+        else:
+            fig, ax = plt.subplots(figsize=self.plot_size)
+            ax1 = ax2 = None
+            if view == "trend":
+                ax1 = ax
+            elif view == "supertrend":
+                ax2 = ax
+
+        fig.patch.set_facecolor(self.bg_color)
+        if ax1: ax1.set_facecolor(self.bg_color)
+        if ax2: ax2.set_facecolor(self.bg_color)
+
+        df = self.data.copy()
+
+        if ax1:
+            self._plot_trend_analysis(ax1, df, trend_data, analysis_results)
+
+        if ax2:
+            self._plot_supertrend_analysis(ax2, df, analysis_results)
+
+        plt.tight_layout()
+        plt.show()
+    
+    def _plot_candlesticks(self, ax, df):
+        """
+        Plot candlesticks on the given axis.
+        
+        Parameters:
+        - ax: matplotlib.axes.Axes, the axis to plot on
+        - df: pandas.DataFrame, the data to plot
+        """
+        from matplotlib.patches import Rectangle
+        
+        for i in range(len(df)):
+            # Get OHLC values for this candle
+            open_val = df['open'].iloc[i]
+            close_val = df['close'].iloc[i]
+            high_val = df['high'].iloc[i]
+            low_val = df['low'].iloc[i]
+            
+            # Determine candle color
+            color = self.candle_up_color if close_val >= open_val else self.candle_down_color
+            
+            # Plot candle body
+            body_height = abs(close_val - open_val)
+            bottom = min(open_val, close_val)
+            rect = Rectangle((i - self.candle_width/2, bottom), self.candle_width, body_height, 
+                            color=color, alpha=self.candle_alpha)
+            ax.add_patch(rect)
+            
+            # Plot candle wicks
+            ax.plot([i, i], [low_val, high_val], color=color, linewidth=self.wick_width)
+    
+    def _plot_trend_analysis(self, ax, df, trend_data, analysis_results):
+        """
+        Plot trend analysis on the given axis.
+        
+        Parameters:
+        - ax: matplotlib.axes.Axes, the axis to plot on
+        - df: pandas.DataFrame, the data to plot
+        - trend_data: pandas.DataFrame, the trend data
+        - analysis_results: dict, the analysis results
+        """
+        # Draw candlesticks
+        self._plot_candlesticks(ax, df)
+        
+        # Plot minima and maxima points
+        self._plot_min_max_points(ax, df, trend_data)
+        
+        # Plot trend lines and moving averages
+        if analysis_results:
+            self._plot_trend_lines(ax, df, analysis_results)
+        
+        # Configure the subplot
+        self._configure_subplot(ax, 'Price Chart with Trend Analysis', len(df))
+    
+    def _plot_min_max_points(self, ax, df, trend_data):
+        """
+        Plot minimum and maximum points on the given axis.
+        
+        Parameters:
+        - ax: matplotlib.axes.Axes, the axis to plot on
+        - df: pandas.DataFrame, the data to plot
+        - trend_data: pandas.DataFrame, the trend data
+        """
+        min_indices = trend_data.index[trend_data['is_min'] == True].tolist()
+        if min_indices:
+            min_y = [df['close'].iloc[i] for i in min_indices]
+            ax.scatter(min_indices, min_y, color=self.min_color, s=self.min_size, 
+                      marker=self.min_marker, label='Local Minima', zorder=self.marker_zorder)
+
+        max_indices = trend_data.index[trend_data['is_max'] == True].tolist()
+        if max_indices:
+            max_y = [df['close'].iloc[i] for i in max_indices]
+            ax.scatter(max_indices, max_y, color=self.max_color, s=self.max_size, 
+                      marker=self.max_marker, label='Local Maxima', zorder=self.marker_zorder)
+    
+    def _plot_trend_lines(self, ax, df, analysis_results):
+        """
+        Plot trend lines on the given axis.
+        
+        Parameters:
+        - ax: matplotlib.axes.Axes, the axis to plot on
+        - df: pandas.DataFrame, the data to plot
+        - analysis_results: dict, the analysis results
+        """
+        x_vals = np.arange(len(df))
+        
+        # Minima regression line (support)
+        min_slope = analysis_results['linear_regression']['min']['slope']
+        min_intercept = analysis_results['linear_regression']['min']['intercept']
+        min_line = min_slope * x_vals + min_intercept
+        ax.plot(x_vals, min_line, self.min_line_style, linewidth=self.line_width, 
+               label='Minima Regression')
+
+        # Maxima regression line (resistance)
+        max_slope = analysis_results['linear_regression']['max']['slope']
+        max_intercept = analysis_results['linear_regression']['max']['intercept']
+        max_line = max_slope * x_vals + max_intercept
+        ax.plot(x_vals, max_line, self.max_line_style, linewidth=self.line_width, 
+               label='Maxima Regression')
+
+        # SMA-7 line
+        sma_7 = analysis_results['sma']['7']
+        ax.plot(x_vals, sma_7, self.sma7_line_style, linewidth=self.line_width, 
+               label='SMA-7')
+
+        # SMA-15 line
+        sma_15 = analysis_results['sma']['15']
+        valid_idx_15 = ~np.isnan(sma_15)
+        ax.plot(x_vals[valid_idx_15], sma_15[valid_idx_15], self.sma15_line_style, 
+               linewidth=self.line_width, label='SMA-15')
+    
+    def _configure_subplot(self, ax, title, data_length):
+        """
+        Configure the subplot with title, labels, limits, and legend.
+        
+        Parameters:
+        - ax: matplotlib.axes.Axes, the axis to configure
+        - title: str, the title of the subplot
+        - data_length: int, the length of the data
+        """
+        # Set title and labels
+        ax.set_title(title, fontsize=self.title_size, color=self.title_color)
+        ax.set_xlabel('Date', fontsize=self.axis_label_size, color=self.axis_label_color)
+        ax.set_ylabel('Price', fontsize=self.axis_label_size, color=self.axis_label_color)
+        
+        # Set appropriate x-axis limits
+        ax.set_xlim(-0.5, data_length - 0.5)
+        
+        # Add a legend
+        ax.legend(loc=self.legend_loc, facecolor=self.legend_bg_color)
+    
+    def _plot_supertrend_analysis(self, ax, df, analysis_results=None):
+        """
+        Plot SuperTrend analysis on the given axis.
+        
+        Parameters:
+        - ax: matplotlib.axes.Axes, the axis to plot on
+        - df: pandas.DataFrame, the data to plot
+        - supertrend_results_list: list, the SuperTrend results (optional)
+        """
+        self._plot_candlesticks(ax, df)
+        self._plot_supertrend_lines(ax, df, analysis_results['supertrend'], style='Both')
+        self._configure_subplot(ax, 'Multiple SuperTrend Indicators', len(df))
+        
+    def _plot_supertrend_lines(self, ax, df, analysis_results, style="Horizontal"):
+        """
+        Plot SuperTrend lines on the given axis.
+        
+        Parameters:
+        - ax: matplotlib.axes.Axes, the axis to plot on
+        - df: pandas.DataFrame, the data to plot
+        - supertrend_results_list: list, the SuperTrend results
+        """
+        x_vals = np.arange(len(df))
+
+        if style == 'Horizontal' or style == 'Both':
+            if len(analysis_results) != 3:
+                raise ValueError("Expected exactly 3 SuperTrend results for meta calculation")
+
+            trends = [st["results"]["trend"] for st in analysis_results]
+
+            band_height = 0.02 * (df["high"].max() - df["low"].min())
+            y_base = df["low"].min() - band_height * 1.5
+            
+            prev_color = None
+            for i in range(1, len(x_vals)):
+                t_vals = [t[i] for t in trends]
+                up_count = t_vals.count(1)
+                down_count = t_vals.count(-1)
+
+                if down_count == 3:
+                    color = "red"
+                elif down_count == 2 and up_count == 1:
+                    color = "orange"
+                elif down_count == 1 and up_count == 2:
+                    color = "yellow"
+                elif up_count == 3:
+                    color = "green"
+                else:
+                    continue  # skip if unknown or inconsistent values
+
+                ax.add_patch(Rectangle(
+                    (x_vals[i-1], y_base),
+                    1,
+                    band_height,
+                    color=color,
+                    linewidth=0,
+                    alpha=0.6
+                ))
+                # Draw a vertical line at the change of color
+                if prev_color and prev_color != color:
+                    ax.axvline(x_vals[i-1], color="grey", alpha=0.3, linewidth=1)
+                prev_color = color
+
+            ax.set_ylim(bottom=y_base - band_height * 0.5)
+        if style == 'Curves' or style == 'Both':
+            for st in analysis_results:
+                params = st["params"]
+                results = st["results"]
+                supertrend = results["supertrend"]
+                trend = results["trend"]
+                
+                # Plot SuperTrend line with color based on trend
+                for i in range(1, len(x_vals)):
+                    if trend[i] == 1:  # Uptrend
+                        ax.plot(x_vals[i-1:i+1], supertrend[i-1:i+1], params["color_up"], linewidth=self.line_width)
+                    else:  # Downtrend
+                        ax.plot(x_vals[i-1:i+1], supertrend[i-1:i+1], params["color_down"], linewidth=self.line_width)
+            self._plot_metasupertrend_lines(ax, df, analysis_results)
+            self._add_supertrend_legend(ax, analysis_results)
+
+    def _plot_metasupertrend_lines(self, ax, df, analysis_results):
+        """
+        Plot a Meta SuperTrend line where all individual SuperTrends agree on trend.
+
+        Parameters:
+        - ax: matplotlib.axes.Axes, the axis to plot on
+        - df: pandas.DataFrame, the data to plot
+        - supertrend_results_list: list, each item contains SuperTrend 'results' and 'params'
+        """
+        x_vals = np.arange(len(df))
+        meta_results = self.calculate_metasupertrend(df, analysis_results)
+        params = analysis_results[0]["params"]  # Use first config for styling
+        
+        for i in meta_results['consensus_points']:
+            if i > 0:  # Skip first point as we need a previous point to draw a line
+                if i-1 in meta_results['consensus_points']:  # Only draw if previous point was also a consensus
+                    meta_trend = meta_results['meta_trends'][i]
+                    color = params["color_up"] if meta_trend == 1 else params["color_down"]
+                    ax.plot(x_vals[i-1:i+1], 
+                           [meta_results['meta_values'][i-1], meta_results['meta_values'][i]], 
+                           color, linewidth=self.line_width)
+
+    def _add_supertrend_legend(self, ax, supertrend_results_list):
+        """
+        Add SuperTrend legend entries to the given axis.
+        
+        Parameters:
+        - ax: matplotlib.axes.Axes, the axis to add legend entries to
+        - supertrend_results_list: list, the SuperTrend results
+        """
+        for st in supertrend_results_list:
+            params = st["params"]
+            period = params["period"]
+            multiplier = params["multiplier"]
+            color_up = params["color_up"]
+            color_down = params["color_down"]
+            
+            ax.plot([], [], color_up, linewidth=self.line_width, 
+                    label=f'ST (P:{period}, M:{multiplier}) Up')
+            ax.plot([], [], color_down, linewidth=self.line_width, 
+                    label=f'ST (P:{period}, M:{multiplier}) Down')
+    
+    def calculate_metasupertrend(self, df, supertrend_results_list):
+        """
+        Calculate Meta SuperTrend values where all individual SuperTrends agree on trend.
+        
+        Parameters:
+        - df: pandas.DataFrame, the data containing price information
+        - supertrend_results_list: list, each item contains SuperTrend 'results' and 'params'
+        
+        Returns:
+        - dict containing:
+            - meta_trends: list of trend values (1 for uptrend, -1 for downtrend, 0 for no consensus)
+            - meta_values: list of averaged supertrend values where trends agree
+            - consensus_points: list of indices where all trends agree
+        """
+        if len(supertrend_results_list) != 3:
+            raise ValueError("Expected exactly 3 SuperTrend results for meta calculation")
+
+        trends = [st["results"]["trend"] for st in supertrend_results_list]
+        supertrends = [st["results"]["supertrend"] for st in supertrend_results_list]
+        
+        data_length = len(df)
+        meta_trends = np.zeros(data_length)  # 0 means no consensus
+        meta_values = np.zeros(data_length)
+        consensus_points = []
+        
+        for i in range(1, data_length):
+            t1, t2, t3 = trends[0][i], trends[1][i], trends[2][i]
+            if t1 == t2 == t3:
+                meta_trends[i] = t1
+                meta_values[i] = np.mean([s[i] for s in supertrends])
+                consensus_points.append(i)
+        
+        return {
+            'meta_trends': meta_trends,
+            'meta_values': meta_values,
+            'consensus_points': consensus_points
+        }
+    

utils/converty_csv_to_day.py

@@ -0,0 +1,48 @@
+import pandas as pd
+from datetime import datetime
+
+def aggregate_to_daily(input_csv, output_csv):
+    """
+    Load a CSV file with Bitcoin price data, aggregate to daily values, and save to a new CSV.
+    
+    Args:
+        input_csv (str): Path to the input CSV file
+        output_csv (str): Path to save the output CSV file
+    """
+    try:
+        # Read the CSV file
+        print(f"Loading data from {input_csv}...")
+        df = pd.read_csv(input_csv)
+        
+        # Convert timestamp to datetime
+        df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
+        df.set_index('Timestamp', inplace=True)
+        
+        # Aggregate to daily values
+        print("Aggregating data to daily values...")
+        daily_df = df.resample('D').agg({
+            'Open': 'first',
+            'High': 'max',
+            'Low': 'min',
+            'Close': 'last',
+            'Volume': 'sum'
+        })
+        
+        # Reset index to make Timestamp a column
+        daily_df.reset_index(inplace=True)
+        
+        # Save to new CSV
+        print(f"Saving daily data to {output_csv}...")
+        daily_df.to_csv(output_csv, index=False)
+        
+        print(f"Successfully processed {len(df)} records into {len(daily_df)} daily records")
+        
+    except Exception as e:
+        print(f"Error processing data: {str(e)}")
+
+if __name__ == "__main__":
+    # Example usage
+    input_file = "../data/btcusd_1-min_data.csv"  # Update this path to your input file
+    output_file = "../data/btcusd_daily_data.csv"  # Update this path to your desired output file
+    
+    aggregate_to_daily(input_file, output_file)