Update app.py

app.py

@@ -1,16 +1,15 @@
 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 requests
-from datetime import datetime
+import numpy as np
 
 # Streamlit app details
 st.set_page_config(page_title="TechyTrade", layout="wide")
 
-# Custom CSS from HF version
+# Custom CSS
 st.markdown("""
     <style>
         @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;700&display=swap');
@@ -39,11 +38,11 @@ st.markdown("""
     </style>
 """, unsafe_allow_html=True)
 
-# Sidebar inputs
+# Sidebar
 with st.sidebar:
     st.title("📊 TechyTrade")
-    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)
+    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)
     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)")
@@ -54,27 +53,21 @@ with st.sidebar:
     google_api_key = st.text_input("Enter your Google API Key 🔑", type="password")
     button = st.button("Submit 🚀")
 
-# Load generative model (cache)
+# Load generative model
 @st.cache_resource
 def load_model(api_key):
     genai.configure(api_key=api_key)
     return genai.GenerativeModel('gemini-1.5-flash')
 
-# Generate AI-based reasons
-def generate_reasons(fig_desc, stock_info, price_info, biz_metrics, api_key):
+# Function to generate reasons using the generative model
+def generate_reasons(fig, 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_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."
+    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."
     response = model.generate_content(prompt)
     return response.text
 
-# Format large numbers with suffix
+# Function to format large numbers
 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:
@@ -82,7 +75,7 @@ def format_value(value):
         suffix_index += 1
     return f"${value:.1f}{suffixes[suffix_index]}"
 
-# Technical indicator calculations (same as HF version)
+# Technical Indicators Functions
 def calculate_sma(data, window):
     return data.rolling(window=window).mean()
 
@@ -110,103 +103,38 @@ def calculate_bollinger_bands(data, window):
     lower_band = sma - (std * 2)
     return upper_band, lower_band
 
-# 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 Submit button is clicked
 if button:
-    if not ticker:
+    if not ticker.strip():
         st.error("Please provide a valid stock ticker.")
-    elif not google_api_key:
+    elif not google_api_key.strip():
         st.error("Please provide a valid Google API Key.")
     else:
         try:
-            with st.spinner("Fetching and processing data..."):
-                # Get stock data from Alpha Vantage
-                history = get_stock_data_alpha_vantage(ticker, "656HG6SEB317EFAB", period)
-
-                st.subheader(f"{ticker} - {ticker}")
-
-                # Plot historical Close price
+            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
                 fig = go.Figure()
                 fig.add_trace(go.Scatter(x=history.index, y=history['Close'], mode='lines', name='Close Price'))
 
@@ -214,17 +142,21 @@ 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'))
@@ -238,79 +170,101 @@ if button:
                 )
                 st.plotly_chart(fig, use_container_width=True)
 
-                # Get dummy stock info (replace with real API if available)
-                info = get_stock_info_alpha_vantage(ticker)
-
                 col1, col2, col3 = st.columns(3)
 
-                # Stock info dataframe
+                # 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')
+
                 stock_info = [
                     ("Stock Info", "Value"),
-                    ("Country", info["country"]),
-                    ("Sector", info["sector"]),
-                    ("Industry", info["industry"]),
-                    ("Market Cap", info["marketCap"]),
-                    ("Enterprise Value", info["enterpriseValue"]),
-                    ("Employees", info["fullTimeEmployees"])
+                    ("Country ", country),
+                    ("Sector ", sector),
+                    ("Industry ", industry),
+                    ("Market Cap ", format_value(market_cap)),
+                    ("Enterprise Value ", format_value(ent_value)),
+                    ("Employees ", employees)
                 ]
-                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)
+                
+                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')
+                
                 price_info = [
                     ("Price Info", "Value"),
-                    ("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"])
+                    ("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}")
                 ]
-                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)
+                
+                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')
+                
                 biz_metrics = [
                     ("Business Metrics", "Value"),
-                    ("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"])
+                    ("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())
                 ]
-                df_biz_metrics = pd.DataFrame(biz_metrics[1:], columns=biz_metrics[0])
-                col3.dataframe(df_biz_metrics, width=400, hide_index=True)
+                
+                df = pd.DataFrame(biz_metrics[1:], columns=biz_metrics[0])
+                col3.dataframe(df, width=400, hide_index=True)
 
-                # Prepare data for forecasting
-                forecast_df = history.reset_index()[['index', 'Close']].rename(columns={'index': 'ds', 'Close': 'y'})
+                # 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']
 
                 m = Prophet(daily_seasonality=True)
-                m.fit(forecast_df)
+                m.fit(df_forecast)
 
                 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_df['ds'], y=forecast_df['y'], mode='lines', name='Actual'))
+                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.update_layout(
-                    title="Prophet Forecast",
+                    title=f"Stock Price Forecast for {ticker}",
                     xaxis_title="Date",
-                    yaxis_title="Price"
+                    yaxis_title="Predicted Close Price",
+                    hovermode="x unified"
                 )
                 st.plotly_chart(fig2, use_container_width=True)
 
-                # 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)
+                # 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)
 
-                st.subheader("AI-based Stock Analysis and Recommendations")
+                st.subheader("Investment Analysis")
                 st.write(reasons)
 
         except Exception as e:
-            st.error(f"Error: {e}")
-
-else:
-    st.info("Enter details and click Submit to start analyzing the stock.")
+            st.exception(f"An error occurred: {e}")