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.vscode/settings.json

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+{
+    "python.linting.enabled": true,
+    "python.linting.pylintEnabled": true,
+    "files.exclude": {
+        "**/*.pyc": {"when": "$(basename).py"},
+        "**/__pycache__": true,
+        "**/*.pytest_cache": true,
+    }
+}

README.md

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----
-title: Real Time Stock Forecasting
-emoji: 📈
-colorFrom: green
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.36.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Stock Predict Lstm
+emoji: 👁
+colorFrom: blue
+colorTo: gray
+sdk: streamlit
+sdk_version: 1.36.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+from view_page import StockDashboard
+from model_page import StockModelPage
+
+def main():
+    st.set_page_config(layout='wide', page_title='Stock Analysis', page_icon=':dollar:')
+    page = st.sidebar.radio('Pages', ['View Page', 'Model Page'])
+    if page == 'View Page':
+       StockDashboard().run()
+    elif page == 'Model Page':
+       StockModelPage().run()
+
+if __name__ == '__main__':
+    main()

model.py

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+
+import numpy as np
+from sklearn.preprocessing import MinMaxScaler
+from keras.models import Sequential
+from keras.layers import LSTM, Dense
+import warnings
+warnings.filterwarnings("ignore")
+
+class Model:
+    def __init__(self, data):
+        self.data = data
+        self.scaler = MinMaxScaler(feature_range=(0, 1))
+        self.model = None
+
+    def prepare_data(self, look_back=1):
+        scaled_data = self.scaler.fit_transform(self.data['Close'].values.reshape(-1, 1))
+        def create_dataset(dataset):
+            X, Y = [], []
+            for i in range(len(dataset) - look_back):
+                a = dataset[i:(i + look_back), 0]
+                X.append(a)
+                Y.append(dataset[i + look_back, 0])
+            return np.array(X), np.array(Y)
+        
+        X, Y = create_dataset(scaled_data)
+        X = np.reshape(X, (X.shape[0], 1, X.shape[1]))
+        return X, Y
+
+    def train_lstm(self, epochs=5, batch_size=1):
+        X, Y = self.prepare_data()
+        self.model = Sequential()
+        self.model.add(LSTM(50, input_shape=(1, 1)))
+        self.model.add(Dense(1))
+        self.model.compile(loss='mean_squared_error', optimizer='adam')
+        self.model.fit(X, Y, epochs=epochs, batch_size=batch_size, verbose=0)
+
+    def make_predictions(self):
+        X, _ = self.prepare_data()
+        predictions = self.model.predict(X)
+        predictions = self.scaler.inverse_transform(predictions)
+        return predictions
+
+    def forecast_future(self, days=5):
+        last_value = self.data['Close'].values[-1:].reshape(-1, 1)
+        last_scaled = self.scaler.transform(last_value)
+        future_predictions = []
+        for _ in range(days):
+            prediction = self.model.predict(last_scaled.reshape(1, 1, 1))[0]
+            future_predictions.append(prediction)
+            last_scaled = prediction  
+        future_predictions = self.scaler.inverse_transform(future_predictions)
+        return future_predictions

model_page.py

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+import pandas as pd
+import streamlit as st
+from model import Model
+from plots import Plots
+from stock_data_loader import StockDataLoader
+
+class StockModelPage:
+    def __init__(self):
+        self.tickers = ['NVDA', 'AAPL', 'GOOGL', 'MSFT', 'AMZN']
+        self.setup_sidebar()
+
+    def setup_sidebar(self):
+        self.ticker = st.sidebar.selectbox('Choose Stock Ticker', self.tickers)
+        self.start_date = st.sidebar.date_input('Start Date', value=pd.to_datetime('2010-01-01'))
+        self.end_date = st.sidebar.date_input('End Date', value=pd.to_datetime('today'))
+        self.load_button_clicked = st.sidebar.button('Load Data')
+
+    def load_data(self):
+        if self.load_button_clicked:
+            loader = StockDataLoader(self.ticker, self.start_date, self.end_date)
+            st.session_state['stock_data'] = loader.get_stock_data()
+            st.write("--------------------------------------------")
+            st.write(f"Data for {self.ticker} from {self.start_date} to {self.end_date} loaded successfully!")
+
+    def handle_model_training(self):
+        if 'stock_data' in st.session_state:
+            stock_data = st.session_state['stock_data']
+            if st.button('Train Model'):
+                st.write("Training Model...")
+                model = Model(stock_data)
+                model.train_lstm()
+                predictions = model.make_predictions()
+                future_predictions = model.forecast_future(days=5)
+                self.plot_predictions(stock_data, predictions, future_predictions)
+            else:
+                st.write("Click the button above to train the model.")
+        else:
+            st.write("--------------------------------------------")
+            st.write("Please load data before training the model.")
+
+    def plot_predictions(self, stock_data, predictions, future_predictions):
+        plot_instance = Plots(stock_data)
+        plot_instance.plot_predictions(predictions, future_predictions)
+
+    def run(self):
+        st.write("--------------------------------------------")
+        st.write(f'<div style="font-size:50px">🤖 Real-Time Stock Prediction', unsafe_allow_html=True)
+        self.load_data()
+        self.handle_model_training()

plots.py

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+import pandas as pd 
+import streamlit as st
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+
+
+class StockChart:
+    def __init__(self, data):
+        self.data = data
+        self.fig = make_subplots(rows=2, cols=1, vertical_spacing=0.01, shared_xaxes=True)
+
+    def add_price_chart(self):
+        self.fig.add_trace(go.Scatter(x=self.data.index, y=self.data['Open'], name='Open Price', marker_color='#1F77B4'), row=1, col=1)
+        self.fig.add_trace(go.Scatter(x=self.data.index, y=self.data['High'], name='High Price', marker_color='#9467BD'), row=1, col=1)
+        self.fig.add_trace(go.Scatter(x=self.data.index, y=self.data['Low'], name='Low Price', marker_color='#D62728'), row=1, col=1)
+        self.fig.add_trace(go.Scatter(x=self.data.index, y=self.data['Close'], name='Close Price', marker_color='#76B900'), row=1, col=1)
+
+    
+    def add_oversold_overbought_lines(self):
+        self.fig.add_hline(y=30, line_dash='dash', line_color='limegreen', line_width=1, row=1, col=1)
+        self.fig.add_hline(y=70, line_dash='dash', line_color='red', line_width=1, row=1, col=1)
+        self.fig.update_yaxes(title_text='RSI Score', row=1, col=1)
+
+    def add_volume_chart(self):
+        colors = ['#9C1F0B' if row['Open'] - row['Close'] >= 0 else '#2B8308' for index, row in self.data.iterrows()]
+        self.fig.add_trace(go.Bar(x=self.data.index, y=self.data['Volume'], showlegend=False, marker_color=colors), row=2, col=1)
+
+    def render_chart(self):
+        self.fig.update_layout(title='Historical Price and Volume', height=500, margin=dict(l=0, r=10, b=10, t=25))
+        st.plotly_chart(self.fig, use_container_width=True)        
+
+class Plots:
+    def __init__(self, data):
+        self.data = data
+
+    def plot_predictions(self, predictions, future_predictions):
+
+        predicted_dates = self.data.index[-len(predictions):]  
+        future_dates = pd.date_range(start=self.data.index[-1] + pd.Timedelta(days=1), periods=len(future_predictions), freq='B')
+        predictions = [float(val) for val in predictions if pd.notna(val)]
+        future_predictions = [float(val) for val in future_predictions if pd.notna(val)]
+
+        fig = make_subplots(rows=1, cols=1)
+        fig.add_trace(go.Scatter(x=self.data.index, y=self.data['Close'], mode='lines', name='Actual Stock Prices', marker_color='blue'))
+        fig.add_trace(go.Scatter(x=predicted_dates, y=predictions, mode='lines', name='LSTM Predicted Prices', marker_color='red', line=dict(dash='dash')))
+        fig.add_trace(go.Scatter(x=future_dates, y=future_predictions, mode='lines', name='Future Predictions', marker_color='green', line=dict(dash='dot')))
+
+        fig.update_layout(title='Comparison of Actual, Predicted, and Future Stock Prices', xaxis_title='Date', yaxis_title='Price', legend_title='Legend', height=500)
+        st.plotly_chart(fig, use_container_width=True)
+
+
+
+    

requirements.txt

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+numpy
+pandas
+seaborn
+matplotlib
+keras
+tensorflow
+scikit-learn
+yfinance 
+plotly

stock_data_loader.py

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+import pandas as pd
+import yfinance as yf
+
+import warnings
+warnings.filterwarnings("ignore")
+
+class StockDataLoader:
+    def __init__(self, ticker, start_date, end_date):
+        self.ticker = ticker
+        self.start_date = start_date
+        self.end_date = end_date
+
+    def get_stock_data(self):
+        stock = yf.Ticker(self.ticker)
+        stock_data = stock.history(start=self.start_date, end=self.end_date)
+        stock_data.reset_index(inplace=True)
+        stock_data['Date'] = pd.to_datetime(stock_data['Date'])
+        stock_data.set_index('Date', inplace=True)
+        return stock_data

view_page.py

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+from stock_data_loader import StockDataLoader
+import streamlit as st
+import pandas as pd
+import yfinance as yf
+from datetime import datetime
+from plots import Plots, StockChart
+
+class StockDashboard:
+    def __init__(self):
+        self.tickers = ['NVDA', 'AAPL', 'GOOGL', 'MSFT', 'AMZN']
+        self.period_map = {'all': 'max','1m': '1mo', '6m': '6mo', '1y': '1y'}
+
+    def render_sidebar(self):
+        st.sidebar.header("Choose your filter:")
+        self.ticker = st.sidebar.selectbox('Choose Ticker', options=self.tickers, help='Select a ticker')
+        self.selected_range = st.sidebar.selectbox('Select Period', options=list(self.period_map.keys()))
+
+    def load_data(self):
+        self.yf_data = yf.Ticker(self.ticker)
+        self.df_history = self.yf_data.history(period=self.period_map[self.selected_range])
+        self.current_price = self.yf_data.info.get('currentPrice', 'N/A')
+        self.previous_close = self.yf_data.info.get('previousClose', 'N/A')
+          
+    def display_header(self):
+        company_name = self.yf_data.info['shortName']
+        symbol = self.yf_data.info['symbol']
+        st.subheader(f'{company_name} ({symbol}) 💰')
+        st.divider()
+        if self.current_price != 'N/A' and self.previous_close != 'N/A':
+            price_change = self.current_price - self.previous_close
+            price_change_ratio = (abs(price_change) / self.previous_close * 100)
+            price_change_direction = "+" if price_change > 0 else "-"
+            st.metric(label='Current Price', value=f"{self.current_price:.2f}",
+                      delta=f"{price_change:.2f} ({price_change_direction}{price_change_ratio:.2f}%)")
+
+    def plot_data(self):
+        chart = StockChart(self.df_history)
+        chart.add_price_chart()
+        chart.add_oversold_overbought_lines()
+        chart.add_volume_chart()
+        chart.render_chart()
+
+    def run(self):
+        st.write("--------------------------------------------")
+        self.render_sidebar()
+        self.load_data()
+        self.display_header()
+        self.plot_data()
+
+