Update app.py

app.py

@@ -1,15 +1,16 @@
 import streamlit as st
-import yfinance as yf
 import pandas as pd
+import numpy as np
 from prophet import Prophet
 import plotly.graph_objs as go
 import google.generativeai as genai
-import numpy as np
+import requests
+from datetime import datetime
 
 # Streamlit app details
 st.set_page_config(page_title="TechyTrade", layout="wide")
 
-# Custom CSS
+# Custom CSS from HF version
 st.markdown("""
     <style>
         @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;700&display=swap');
@@ -38,11 +39,11 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
-# Sidebar
+# Sidebar inputs
 with st.sidebar:
     st.title("📊 TechyTrade")
-    ticker = st.text_input("Enter a stock ticker (e.g. AAPL) 🏷️", "AAPL")
-    period = st.selectbox("Enter a time frame ⏳", ("1D", "5D", "1M", "6M", "YTD", "1Y", "5Y"), index=2)
+    ticker = st.text_input("Enter a stock ticker (e.g. AAPL) 🏷️", "AAPL").upper().strip()
+    period = st.selectbox("Enter a time frame ⏳", ("1M", "6M", "1Y", "5Y"), index=0)
     forecast_period = st.slider("Select forecast period (days) 🔮", min_value=1, max_value=365, value=30)
     st.write("Select Technical Indicators:")
     sma_checkbox = st.checkbox("Simple Moving Average (SMA)")
@@ -53,21 +54,27 @@ with st.sidebar:
     google_api_key = st.text_input("Enter your Google API Key 🔑", type="password")
     button = st.button("Submit 🚀")
 
-# Load generative model
+# Load generative model (cache)
 @st.cache_resource
 def load_model(api_key):
     genai.configure(api_key=api_key)
     return genai.GenerativeModel('gemini-1.5-flash')
 
-# Function to generate reasons using the generative model
-def generate_reasons(fig, stock_info, price_info, biz_metrics, api_key):
+# Generate AI-based reasons
+def generate_reasons(fig_desc, stock_info, price_info, biz_metrics, api_key):
     model = load_model(api_key)
-    prompt = f"Based on the following stock price graph description:\n\n{fig}\n\n and the tables:\n\n{stock_info}\n\n and\n\n{price_info}\n\n and\n\n{biz_metrics}\n\n and analyze the trends and give recommendations and insights."
+    prompt = f"Based on the following stock price graph description:\n\n{fig_desc}\n\n and the tables:\n\n{stock_info}\n\n and\n\n{price_info}\n\n and\n\n{biz_metrics}\n\n Analyze the trends and give recommendations and insights."
     response = model.generate_content(prompt)
     return response.text
 
-# Function to format large numbers
+# Format large numbers with suffix
 def format_value(value):
+    if value == "N/A" or value is None:
+        return "N/A"
+    try:
+        value = float(value)
+    except:
+        return "N/A"
     suffixes = ["", "K", "M", "B", "T"]
     suffix_index = 0
     while value >= 1000 and suffix_index < len(suffixes) - 1:
@@ -75,7 +82,7 @@ def format_value(value):
         suffix_index += 1
     return f"${value:.1f}{suffixes[suffix_index]}"
 
-# Technical Indicators Functions
+# Technical indicator calculations (same as HF version)
 def calculate_sma(data, window):
     return data.rolling(window=window).mean()
 
@@ -103,38 +110,105 @@ def calculate_bollinger_bands(data, window):
     lower_band = sma - (std * 2)
     return upper_band, lower_band
 
-# If Submit button is clicked
+# Alpha Vantage stock data retrieval function
+def get_stock_data_alpha_vantage(ticker, period):
+    # Use TIME_SERIES_DAILY or TIME_SERIES_WEEKLY depending on period
+    api_key = "656HG6SEB317EFAB"
+    function = "TIME_SERIES_DAILY"
+    interval_days = 1
+    if period == "1M":
+        outputsize = "compact"  # last 100 days approx
+    elif period == "6M":
+        outputsize = "full"
+    elif period == "1Y":
+        outputsize = "full"
+    elif period == "5Y":
+        # Alpha Vantage free API doesn't support 5Y directly, fallback to full daily
+        outputsize = "full"
+    else:
+        outputsize = "compact"
+
+    url = f"https://www.alphavantage.co/query?function={function}&symbol={ticker}&outputsize={outputsize}&apikey={api_key}"
+    response = requests.get(url)
+    data = response.json()
+
+    if "Time Series (Daily)" not in data:
+        raise ValueError(f"Error fetching data for ticker {ticker}: {data.get('Note') or data.get('Error Message') or 'Unknown error'}")
+
+    df = pd.DataFrame.from_dict(data["Time Series (Daily)"], orient='index')
+    df = df.rename(columns={
+        '1. open': 'Open',
+        '2. high': 'High',
+        '3. low': 'Low',
+        '4. close': 'Close',
+        '5. volume': 'Volume'
+    })
+    df.index = pd.to_datetime(df.index)
+    df = df.sort_index()
+
+    # Filter date range based on selected period
+    now = pd.Timestamp.now()
+    if period == "1M":
+        cutoff = now - pd.DateOffset(months=1)
+    elif period == "6M":
+        cutoff = now - pd.DateOffset(months=6)
+    elif period == "1Y":
+        cutoff = now - pd.DateOffset(years=1)
+    elif period == "5Y":
+        cutoff = now - pd.DateOffset(years=5)
+    else:
+        cutoff = df.index.min()
+    
+    df = df[df.index >= cutoff]
+
+    # Convert columns to float
+    for col in ['Open', 'High', 'Low', 'Close', 'Volume']:
+        df[col] = df[col].astype(float)
+
+    return df
+
+# Dummy function to simulate getting stock info (Alpha Vantage does not provide all info)
+def get_stock_info_alpha_vantage(ticker):
+    # Return dummy or partial info for demo
+    return {
+        "country": "N/A",
+        "sector": "N/A",
+        "industry": "N/A",
+        "marketCap": "N/A",
+        "enterpriseValue": "N/A",
+        "fullTimeEmployees": "N/A",
+        "currentPrice": "N/A",
+        "previousClose": "N/A",
+        "dayHigh": "N/A",
+        "dayLow": "N/A",
+        "fiftyTwoWeekHigh": "N/A",
+        "fiftyTwoWeekLow": "N/A",
+        "forwardEps": "N/A",
+        "forwardPE": "N/A",
+        "pegRatio": "N/A",
+        "dividendRate": "N/A",
+        "dividendYield": "N/A",
+        "recommendationKey": "N/A",
+        "longName": ticker
+    }
+
+# Main execution on button click
 if button:
-    if not ticker.strip():
+    if not ticker:
         st.error("Please provide a valid stock ticker.")
-    elif not google_api_key.strip():
+    elif not alpha_vantage_api_key:
+        st.error("Please provide a valid Alpha Vantage API Key.")
+    elif not google_api_key:
         st.error("Please provide a valid Google API Key.")
     else:
         try:
-            with st.spinner('Please wait...'):
-                # Retrieve stock data
-                stock = yf.Ticker(ticker)
-                info = stock.info
-
-                st.subheader(f"{ticker} - {info.get('longName', 'N/A')}")
-
-                # Plot historical stock price data
-                if period == "1D":
-                    history = stock.history(period="1d", interval="1h")
-                elif period == "5D":
-                    history = stock.history(period="5d", interval="1d")
-                elif period == "1M":
-                    history = stock.history(period="1mo", interval="1d")
-                elif period == "6M":
-                    history = stock.history(period="6mo", interval="1wk")
-                elif period == "YTD":
-                    history = stock.history(period="ytd", interval="1mo")
-                elif period == "1Y":
-                    history = stock.history(period="1y", interval="1mo")
-                elif period == "5Y":
-                    history = stock.history(period="5y", interval="3mo")
-
-                # Create a plotly figure
+            with st.spinner("Fetching and processing data..."):
+                # Get stock data from Alpha Vantage
+                history = get_stock_data_alpha_vantage(ticker, alpha_vantage_api_key, period)
+
+                st.subheader(f"{ticker} - {ticker}")
+
+                # Plot historical Close price
                 fig = go.Figure()
                 fig.add_trace(go.Scatter(x=history.index, y=history['Close'], mode='lines', name='Close Price'))
 
@@ -142,21 +216,17 @@ if button:
                 if sma_checkbox:
                     sma = calculate_sma(history['Close'], window=20)
                     fig.add_trace(go.Scatter(x=history.index, y=sma, mode='lines', name='SMA (20)'))
-                
                 if ema_checkbox:
                     ema = calculate_ema(history['Close'], window=20)
                     fig.add_trace(go.Scatter(x=history.index, y=ema, mode='lines', name='EMA (20)'))
-
                 if rsi_checkbox:
                     rsi = calculate_rsi(history['Close'], window=14)
                     fig.add_trace(go.Scatter(x=history.index, y=rsi, mode='lines', name='RSI (14)', yaxis='y2'))
                     fig.update_layout(yaxis2=dict(title='RSI', overlaying='y', side='right'))
-
                 if macd_checkbox:
                     macd, signal = calculate_macd(history['Close'])
                     fig.add_trace(go.Scatter(x=history.index, y=macd, mode='lines', name='MACD'))
                     fig.add_trace(go.Scatter(x=history.index, y=signal, mode='lines', name='Signal Line'))
-
                 if bollinger_checkbox:
                     upper_band, lower_band = calculate_bollinger_bands(history['Close'], window=20)
                     fig.add_trace(go.Scatter(x=history.index, y=upper_band, mode='lines', name='Upper Band'))
@@ -170,101 +240,79 @@ if button:
                 )
                 st.plotly_chart(fig, use_container_width=True)
 
-                col1, col2, col3 = st.columns(3)
+                # Get dummy stock info (replace with real API if available)
+                info = get_stock_info_alpha_vantage(ticker)
 
-                # Display stock information as a dataframe
-                country = info.get('country', 'N/A')
-                sector = info.get('sector', 'N/A')
-                industry = info.get('industry', 'N/A')
-                market_cap = info.get('marketCap', 'N/A')
-                ent_value = info.get('enterpriseValue', 'N/A')
-                employees = info.get('fullTimeEmployees', 'N/A')
+                col1, col2, col3 = st.columns(3)
 
+                # Stock info dataframe
                 stock_info = [
                     ("Stock Info", "Value"),
-                    ("Country ", country),
-                    ("Sector ", sector),
-                    ("Industry ", industry),
-                    ("Market Cap ", format_value(market_cap)),
-                    ("Enterprise Value ", format_value(ent_value)),
-                    ("Employees ", employees)
+                    ("Country", info["country"]),
+                    ("Sector", info["sector"]),
+                    ("Industry", info["industry"]),
+                    ("Market Cap", info["marketCap"]),
+                    ("Enterprise Value", info["enterpriseValue"]),
+                    ("Employees", info["fullTimeEmployees"])
                 ]
-                
-                df = pd.DataFrame(stock_info[1:], columns=stock_info[0])
-                col1.dataframe(df, width=400, hide_index=True)
-                
-                # Display price information as a dataframe
-                current_price = info.get('currentPrice', 'N/A')
-                prev_close = info.get('previousClose', 'N/A')
-                day_high = info.get('dayHigh', 'N/A')
-                day_low = info.get('dayLow', 'N/A')
-                ft_week_high = info.get('fiftyTwoWeekHigh', 'N/A')
-                ft_week_low = info.get('fiftyTwoWeekLow', 'N/A')
-                
+                df_stock_info = pd.DataFrame(stock_info[1:], columns=stock_info[0])
+                col1.dataframe(df_stock_info, width=400, hide_index=True)
+
+                # Price info dataframe (mostly N/A here)
                 price_info = [
                     ("Price Info", "Value"),
-                    ("Current Price ", f"${current_price:.2f}"),
-                    ("Previous Close ", f"${prev_close:.2f}"),
-                    ("Day High ", f"${day_high:.2f}"),
-                    ("Day Low ", f"${day_low:.2f}"),
-                    ("52 Week High ", f"${ft_week_high:.2f}"),
-                    ("52 Week Low ", f"${ft_week_low:.2f}")
+                    ("Current Price", info["currentPrice"]),
+                    ("Previous Close", info["previousClose"]),
+                    ("Day High", info["dayHigh"]),
+                    ("Day Low", info["dayLow"]),
+                    ("52 Week High", info["fiftyTwoWeekHigh"]),
+                    ("52 Week Low", info["fiftyTwoWeekLow"])
                 ]
-                
-                df = pd.DataFrame(price_info[1:], columns=price_info[0])
-                col2.dataframe(df, width=400, hide_index=True)
-
-                # Display business metrics as a dataframe
-                forward_eps = info.get('forwardEps', 'N/A')
-                forward_pe = info.get('forwardPE', 'N/A')
-                peg_ratio = info.get('pegRatio', 'N/A')
-                dividend_rate = info.get('dividendRate', 'N/A')
-                dividend_yield = info.get('dividendYield', 'N/A')
-                recommendation = info.get('recommendationKey', 'N/A')
-                
+                df_price_info = pd.DataFrame(price_info[1:], columns=price_info[0])
+                col2.dataframe(df_price_info, width=400, hide_index=True)
+
+                # Business metrics dataframe (mostly N/A here)
                 biz_metrics = [
                     ("Business Metrics", "Value"),
-                    ("EPS (FWD) ", f"{forward_eps:.2f}"),
-                    ("P/E (FWD) ", f"{forward_pe:.2f}"),
-                    ("PEG Ratio ", f"{peg_ratio:.2f}"),
-                    ("Div Rate (FWD) ", f"${dividend_rate:.2f}"),
-                    ("Div Yield (FWD) ", f"{dividend_yield * 100:.2f}%"),
-                    ("Recommendation ", recommendation.capitalize())
+                    ("EPS (FWD)", info["forwardEps"]),
+                    ("P/E (FWD)", info["forwardPE"]),
+                    ("PEG Ratio", info["pegRatio"]),
+                    ("Div Rate (FWD)", info["dividendRate"]),
+                    ("Div Yield (FWD)", info["dividendYield"]),
+                    ("Recommendation", info["recommendationKey"])
                 ]
-                
-                df = pd.DataFrame(biz_metrics[1:], columns=biz_metrics[0])
-                col3.dataframe(df, width=400, hide_index=True)
+                df_biz_metrics = pd.DataFrame(biz_metrics[1:], columns=biz_metrics[0])
+                col3.dataframe(df_biz_metrics, width=400, hide_index=True)
 
-                # Forecasting
-                st.subheader("Stock Price Forecast 🔮")
-                df_forecast = history.reset_index()[['Date', 'Close']]
-                df_forecast['Date'] = pd.to_datetime(df_forecast['Date']).dt.tz_localize(None)  # Remove timezone information
-                df_forecast.columns = ['ds', 'y']
+                # Prepare data for forecasting
+                forecast_df = history.reset_index()[['index', 'Close']].rename(columns={'index': 'ds', 'Close': 'y'})
 
                 m = Prophet(daily_seasonality=True)
-                m.fit(df_forecast)
+                m.fit(forecast_df)
 
                 future = m.make_future_dataframe(periods=forecast_period)
                 forecast = m.predict(future)
 
+                st.subheader("Forecasted Stock Price")
                 fig2 = go.Figure()
                 fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat'], mode='lines', name='Forecast'))
-                fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_upper'], mode='lines', name='Upper Confidence Interval', line=dict(dash='dash')))
-                fig2.add_trace(go.Scatter(x=forecast['ds'], y=forecast['yhat_lower'], mode='lines', name='Lower Confidence Interval', line=dict(dash='dash')))
+                fig2.add_trace(go.Scatter(x=forecast_df['ds'], y=forecast_df['y'], mode='lines', name='Actual'))
                 fig2.update_layout(
-                    title=f"Stock Price Forecast for {ticker}",
+                    title="Prophet Forecast",
                     xaxis_title="Date",
-                    yaxis_title="Predicted Close Price",
-                    hovermode="x unified"
+                    yaxis_title="Price"
                 )
                 st.plotly_chart(fig2, use_container_width=True)
 
-                # Generate reasons based on forecast
-                graph_description = f"The stock price forecast graph for {ticker} shows the predicted close prices along with the upper and lower confidence intervals for the next {forecast_period} days."
-                reasons = generate_reasons(fig, stock_info, price_info, biz_metrics, google_api_key)
+                # Generate AI-based reasons using Google Gemini
+                fig_description = "Line chart of stock closing prices and technical indicators as shown above."
+                reasons = generate_reasons(fig_description, df_stock_info.to_string(), df_price_info.to_string(), df_biz_metrics.to_string(), google_api_key)
 
-                st.subheader("Investment Analysis")
+                st.subheader("AI-based Stock Analysis and Recommendations")
                 st.write(reasons)
 
         except Exception as e:
-            st.exception(f"An error occurred: {e}")
+            st.error(f"Error: {e}")
+
+else:
+    st.info("Enter details and click Submit to start analyzing the stock.")