Update app.py

app.py

@@ -1,38 +1,80 @@
-import streamlit as st
+# Import necessary libraries
+import pandas as pd
 import yfinance as yf
-from cuansignal import signals as cs
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+import streamlit as st
+from datetime import datetime, timedelta, timezone
+
+# Function to fetch historical stock data
+def fetch_stock_data(symbol, start_date, end_date):
+    data = yf.download(symbol, start=start_date, end=end_date)
+    return data
+
+# Function to create features for the model
+def create_features(data):
+    data['Date'] = data.index
+    data['Year'] = data['Date'].dt.year
+    data['Month'] = data['Date'].dt.month
+    data['Day'] = data['Date'].dt.day
+    data['Hour'] = data['Date'].dt.hour
+    data['Minute'] = data['Date'].dt.minute
+
+    return data
+
+# Function to train a machine learning model
+def train_model(data):
+    features = ['Year', 'Month', 'Day', 'Hour', 'Minute']
+    target = 'Close'
+
+    X = data[features]
+    y = data[target]
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    model = RandomForestRegressor()
+    model.fit(X_train, y_train)
+
+    # Evaluate the model
+    predictions = model.predict(X_test)
+    mse = mean_squared_error(y_test, predictions)
+    st.write(f"Mean Squared Error: {mse}")
 
-def fetch_yfinance_data(symbol, start, end):
-    try:
-        data = yf.download(symbol, start=start, end=end)
-        return data
-    except Exception as e:
-        st.error(f"Error fetching data: {e}")
-        return None
+    return model
 
+# Streamlit UI
 def main():
-    st.title("Streamlit App with cuansignal and yfinance")
+    st.title("Stock Price Prediction Tool")
 
-    # Input parameters
-    symbol = "AAPL"
-    start_date = "2024-02-04"
-    end_date = "2024-02-04"
+    symbol = st.text_input("Enter Stock Symbol (e.g., AAPL):")
+    start_date = st.date_input("Select Start Date:")
+    end_date = st.date_input("Select End Date:")
 
-    # Fetch data
-    data = fetch_yfinance_data(symbol, start_date, end_date)
+    if st.button("Predict Stock Price"):
+        # Fetch stock data
+        stock_data = fetch_stock_data(symbol, start_date, end_date)
 
-    if data is not None:
-        st.subheader("Original Data:")
-        st.write(data.head())
+        # Create features
+        stock_data = create_features(stock_data)
 
-        # Calculate dEMA
-        result = cs.dEMA(data, base='Close', short=10, long=100)
+        # Train the model
+        model = train_model(stock_data)
 
-        st.subheader("dEMA Result:")
-        st.write(result.head())
+        # Predict the stock price for a specific date (e.g., the last date in the dataset)
+        prediction_date = stock_data['Date'].iloc[-1]
+        prediction_features = [[
+            prediction_date.year,
+            prediction_date.month,
+            prediction_date.day,
+            prediction_date.hour,
+            prediction_date.minute
+        ]]
+        predicted_price = model.predict(prediction_features)[0]
 
-    else:
-        st.warning("Failed to fetch data. Check your input parameters.")
+        st.subheader(f"Predicted Stock Price on {prediction_date} (UTC):")
+        st.write(f"${predicted_price:.2f}")
 
-if __name__ == "__main__":
+# Run the Streamlit app
+if __name__ == '__main__':
     main()