From 2a336b4ae713416f6c6dd4c5d84f0aa99a661aea Mon Sep 17 00:00:00 2001
From: palaknajawan <2021.palak.najawan@ves.ac.in>
Date: Sun, 6 Oct 2024 23:29:13 +0530
Subject: [PATCH] add

---
 FiFo.py | 63 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 63 insertions(+)
 create mode 100644 FiFo.py

diff --git a/FiFo.py b/FiFo.py
new file mode 100644
index 0000000..9345981
--- /dev/null
+++ b/FiFo.py
@@ -0,0 +1,63 @@
+import streamlit as st
+import yfinance as yf
+from sklearn.model_selection import train_test_split 
+from sklearn.linear_model import LinearRegression
+import numpy as np
+import datetime
+from art import text2art
+
+# Function to download historical data and make predictions
+def predict_prices(symbol):
+    today = datetime.date.today()
+    data = yf.download(symbol, start='2010-07-17', end=today)
+    predictions = {}
+
+    for price_type in ['High', 'Low', 'Close']:
+        temp_data = data.copy()
+        temp_data['Prediction'] = temp_data[price_type].shift(-1)
+        temp_data.dropna(inplace=True)
+        X = np.array(temp_data.drop(['Prediction'], axis=1))
+        Y = np.array(temp_data['Prediction'])
+
+        x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
+        model = LinearRegression()
+        model.fit(x_train, y_train)
+        temp_data['Prediction'] = model.predict(np.array(temp_data.drop(['Prediction'], axis=1)))
+        predictions[price_type] = temp_data['Prediction']
+
+    return predictions, data
+
+# Streamlit app
+def main():
+    st.set_page_config(
+        page_title="Stock Market Prediction",
+        page_icon=":chart_with_upwards_trend:",
+        layout="centered",
+        initial_sidebar_state="expanded",
+    )
+
+    st.title(":chart_with_upwards_trend: Stock Market Prediction")
+    st.image("stock.jpg")
+
+    st.sidebar.title("User Input")
+    symbol = st.sidebar.text_input("Enter the symbol of the stock (e.g., AAPL, META, etc)")
+
+    if st.sidebar.button("Predict", key="predict_button"):
+        if not symbol:
+            st.warning("Please enter a symbol.")
+        else:
+            try:
+                predictions, data = predict_prices(symbol.upper())
+
+                st.subheader("Predicted Prices")
+                for i in [1, 7, 30, 365, 3650]:
+                    if len(data) > i:
+                        with st.expander(f"{i} day(s) from now"):
+                            st.write(f"- High: {predictions['High'].iloc[-i]:.2f}")
+                            st.write(f"- Low: {predictions['Low'].iloc[-i]:.2f}")
+                            st.write(f"- Close: {predictions['Close'].iloc[-i]:.2f}")
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+
+if __name__ == "__main__":
+    main()