Update app.py

app.py

@@ -2,6 +2,9 @@ import streamlit as st
 import requests
 from Pandas_Market_Predictor import Pandas_Market_Predictor
 import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error, r2_score
 
 # Hard-coded API key for demonstration purposes
 API_KEY = "QR8F9B7T6R2SWTAT"
@@ -13,16 +16,23 @@ def fetch_alpha_vantage_data(api_key):
     return alpha_vantage_data
 
 def calculate_indicators(data):
-    # Convert all columns to numeric
     data = data.apply(pd.to_numeric, errors='coerce')
-
-    # Example: Simple condition for doji and inside
-    data['Doji'] = abs(data['Close'] - data['open']) <= 0.01 * (data['high'] - data['low'])
-    data['Inside'] = (data['high'] < data['high'].shift(1)) & (data['low'] > data['low'].shift(1))
+    data['Doji'] = abs(data['Close'] - data['Open']) <= 0.01 * (data['High'] - data['Low'])
+    data['Inside'] = (data['High'] < data['High'].shift(1)) & (data['Low'] > data['Low'].shift(1))
     return data
 
+def prepare_data(data, target_column='Close'):
+    X = data.drop(target_column, axis=1)
+    y = data[target_column]
+    return X, y
+
+def train_linear_regression(X_train, y_train):
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+    return model
+
 def main():
-    st.title("Stock Trend Predictor")
+    st.title("Stock Price Predictor")
 
     # Use the hard-coded API key
     api_key = API_KEY
@@ -37,7 +47,7 @@ def main():
     df = df.dropna(axis=0)
 
     # Rename columns
-    df = df.rename(columns={'1. open': 'open', '2. high': 'high', '3. low': 'low', '4. close': 'Close', '5. volume': 'volume'})
+    df = df.rename(columns={'1. open': 'Open', '2. high': 'High', '3. low': 'Low', '4. close': 'Close', '5. volume': 'Volume'})
 
     # Calculate indicators
     df = calculate_indicators(df)
@@ -45,21 +55,22 @@ def main():
     # Create predictor
     my_market_predictor = Pandas_Market_Predictor(df)
 
-    # Predict Trend
-    indicators = ["Doji", "Inside"]
-    trend = my_market_predictor.Trend_Detection(indicators, 10)
-
-    # Display results
-    st.subheader("Predicted Trend:")
-    st.write("Buy Trend :", trend['BUY'])
-    st.write("Sell Trend :", trend['SELL'])
-
-    # Calculate and display Support and Resistance Levels for the latest data point
-    latest_data_point = df.iloc[-1]
-    levels = my_market_predictor.Support_Resistance_Estimation_Tool(indicators, latest_data_point)
-    st.subheader("Support and Resistance Levels:")
-    st.write("Support Level:", levels.get('Support', 'Not Available'))
-    st.write("Resistance Level:", levels.get('Resistance', 'Not Available'))
+    # Prepare data for linear regression
+    X, y = prepare_data(df)
+
+    # Split data into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Train linear regression model
+    model = train_linear_regression(X_train, y_train)
+
+    # Make predictions on the test set
+    y_pred = model.predict(X_test)
+
+    # Display linear regression results
+    st.subheader("Linear Regression Results:")
+    st.write("Mean Squared Error:", mean_squared_error(y_test, y_pred))
+    st.write("R-squared Score:", r2_score(y_test, y_pred))
 
     # Delete the DataFrame to release memory
     del df