implement the prediction, add requirements.txt

app.py

@@ -1,4 +1,102 @@
 import streamlit as st
+import pandas as pd
+from neuralprophet import NeuralProphet
+import matplotlib.pyplot as plt
+import torch
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# Set the random seed for reproducibility
+torch.manual_seed(42)
+
+
+def preprocess_data(data, ticker):
+    # Convert 'Date' to datetime
+    data['Date'] = pd.to_datetime(data['Date'], utc=True)
+    #st.write(f"Data shape before proceeding: {data.shape}")
+
+    # Filter data for the selected ticker
+    ticker_data = data[data['Ticker'] == ticker]
+
+    # Remove duplicates by keeping the row with the highest 'Volume' for each 'Date'
+    ticker_data = ticker_data.sort_values(['Date', 'Volume'], ascending=[True, False]).drop_duplicates(subset=['Date'],
+                                                                                                       keep='first')
+    #st.write(f"Ticker data shape after removing duplicates: {ticker_data.shape}")
+
+    # Rename 'Date' to 'ds' and 'Close' to 'y' for NeuralProphet
+    ticker_data = ticker_data[['Date', 'Close']].rename(columns={'Date': 'ds', 'Close': 'y'})
+
+    if ticker == "ADDYY":
+        ticker_data = ticker_data[ticker_data['ds'] >= '2015-01-01']
+    return ticker_data
+
+
+def forecast(data, epochs):
+    # begin
+    n = len(data)
+    # Set the test period to the last 90 days
+    test_size = 90
+    # Define the test set as the last 90 days
+    test_data = data.iloc[-test_size:]
+
+    # Split the remaining data (excluding the test set) into training and validation
+    train_valid_data = data.iloc[:n - test_size]
+
+    # Determine the split for training and validation (80% training, 20% validation)
+    train_size = 0.80
+    train_end = int(train_size * len(train_valid_data))
+
+    train_data = train_valid_data.iloc[:train_end]
+    valid_data = train_valid_data.iloc[train_end:]
+    # end
+    model: NeuralProphet = NeuralProphet(trend_reg=0.0001, yearly_seasonality=True, weekly_seasonality=True,
+                                         daily_seasonality=False,
+                                         learning_rate=0.001, seasonality_mode='multiplicative')
+
+    # Train the model on training data and validate on validation data
+    # freq='B' tells NeuralProphet to expect data only on business days (excluding weekends)
+    metrics = model.fit(train_data, validation_df=valid_data, freq='B', epochs=epochs, early_stopping=True)
+
+    # After training, test the model on the test data
+    forecast_test = model.predict(test_data)  # Predict on the test set
+    return forecast_test, model, metrics
+
+
+def plot_training_loss(metrics):
+    # Extract training and validation loss from metrics
+    training_loss = metrics['Loss'].values
+    validation_loss = metrics['Loss_val'].values
+
+    # Plotting
+    fig, ax = plt.subplots(figsize=(10, 5))
+    ax.plot(training_loss, label="Training Loss")
+    ax.plot(validation_loss, label="Validation Loss")
+    ax.set_title("Training vs Validation Loss")
+    ax.set_xlabel("Epoch")
+    ax.set_ylabel("Loss")
+    ax.legend()
+    st.pyplot(fig)
+
+
+# Streamlit App
+st.title("Stock Prediction with NeuralProphet")
+st.write("Upload your stock data and select a ticker to forecast.")
+
+uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+if uploaded_file:
+    data = pd.read_csv(uploaded_file)
+    tickers = data['Ticker'].unique()
+    #get the index of Adidas ticker ADDYY
+    default_index = tickers.tolist().index("ADDYY")
+    ticker = st.selectbox("Select Ticker", tickers, index=default_index)
+
+    data_processed = preprocess_data(data, ticker)
+
+    epochs = st.slider("Select Epochs", 10, 200, 200, step=10)
+
+    if st.button("Train Model and Forecast"):
+        forecast_data, model, metrics = forecast(data_processed, epochs)
+        fig = model.plot(forecast_data)
+        st.plotly_chart(fig)
+        plot_training_loss(metrics)
+        fig_components = model.plot_components(forecast_data)
+        st.plotly_chart(fig_components)

requirements.txt

@@ -0,0 +1,5 @@
+matplotlib==3.8.4
+neuralprophet==0.9.0
+pandas==2.2.3
+streamlit==1.42.2
+torch==2.6.0