Update app.py

app.py

@@ -1,270 +1,270 @@
-import streamlit as st
-import yfinance as yf
-import pandas as pd
-from prophet import Prophet
-import plotly.graph_objs as go
-import google.generativeai as genai
-import numpy as np
-
-# Streamlit app details
-st.set_page_config(page_title="TechyTrade", layout="wide")
-
-# Custom CSS
-st.markdown("""
-    <style>
-        @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;700&display=swap');
-
-        body {
-            background-color: #f4f4f9;
-            color: #333;
-            font-family: 'Montserrat', sans-serif;
-        }
-        .sidebar .sidebar-content {
-            background-color: #2c3e50;
-            color: white;
-        }
-        h1, h2, h3 {
-            color: #2980b9;
-        }
-        .css-1v3fvcr {
-            color: #2980b9 !important;
-        }
-        .css-17eq0hr {
-            font-family: 'Montserrat', sans-serif !important;
-        }
-        .css-2trqyj {
-            font-family: 'Montserrat', sans-serif !important;
-        }
-    </style>
-""", unsafe_allow_html=True)
-
-# Sidebar
-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)
-    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)")
-    ema_checkbox = st.checkbox("Exponential Moving Average (EMA)")
-    rsi_checkbox = st.checkbox("Relative Strength Index (RSI)")
-    macd_checkbox = st.checkbox("Moving Average Convergence Divergence (MACD)")
-    bollinger_checkbox = st.checkbox("Bollinger Bands")
-    google_api_key = st.text_input("Enter your Google API Key 🔑", type="password")
-    button = st.button("Submit 🚀")
-
-# Load generative model
-@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):
-    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."
-    response = model.generate_content(prompt)
-    return response.text
-
-# Function to format large numbers
-def format_value(value):
-    suffixes = ["", "K", "M", "B", "T"]
-    suffix_index = 0
-    while value >= 1000 and suffix_index < len(suffixes) - 1:
-        value /= 1000
-        suffix_index += 1
-    return f"${value:.1f}{suffixes[suffix_index]}"
-
-# Technical Indicators Functions
-def calculate_sma(data, window):
-    return data.rolling(window=window).mean()
-
-def calculate_ema(data, window):
-    return data.ewm(span=window, adjust=False).mean()
-
-def calculate_rsi(data, window):
-    delta = data.diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
-    rs = gain / loss
-    return 100 - (100 / (1 + rs))
-
-def calculate_macd(data, short_window=12, long_window=26, signal_window=9):
-    short_ema = calculate_ema(data, short_window)
-    long_ema = calculate_ema(data, long_window)
-    macd = short_ema - long_ema
-    signal = calculate_ema(macd, signal_window)
-    return macd, signal
-
-def calculate_bollinger_bands(data, window):
-    sma = calculate_sma(data, window)
-    std = data.rolling(window=window).std()
-    upper_band = sma + (std * 2)
-    lower_band = sma - (std * 2)
-    return upper_band, lower_band
-
-# If Submit button is clicked
-if button:
-    if not ticker.strip():
-        st.error("Please provide a valid stock ticker.")
-    elif not google_api_key.strip():
-        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
-                fig = go.Figure()
-                fig.add_trace(go.Scatter(x=history.index, y=history['Close'], mode='lines', name='Close Price'))
-
-                # Add Technical Indicators
-                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'))
-                    fig.add_trace(go.Scatter(x=history.index, y=lower_band, mode='lines', name='Lower Band'))
-
-                fig.update_layout(
-                    title=f"Historical Stock Prices for {ticker}",
-                    xaxis_title="Date",
-                    yaxis_title="Close Price",
-                    hovermode="x unified"
-                )
-                st.plotly_chart(fig, use_container_width=True)
-
-                col1, col2, col3 = st.columns(3)
-
-                # 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 ", country),
-                    ("Sector ", sector),
-                    ("Industry ", industry),
-                    ("Market Cap ", format_value(market_cap)),
-                    ("Enterprise Value ", format_value(ent_value)),
-                    ("Employees ", employees)
-                ]
-                
-                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 ", 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"${ft_week_low:.2f}")
-                ]
-                
-                df = pd.DataFrame(price_info[1:], columns=price_info[0])
-                col2.dataframe(df, width=400, hide_index=True)
-
-                # Display business metrics as a dataframe
-                forward_eps = info.get('forwardEps', 'N/A')
-                forward_pe = info.get('forwardPE', 'N/A')
-                peg_ratio = info.get('pegRatio', 'N/A')
-                dividend_rate = info.get('dividendRate', 'N/A')
-                dividend_yield = info.get('dividendYield', 'N/A')
-                recommendation = info.get('recommendationKey', 'N/A')
-                
-                biz_metrics = [
-                    ("Business Metrics", "Value"),
-                    ("EPS (FWD) ", f"{forward_eps:.2f}"),
-                    ("P/E (FWD) ", f"{forward_pe:.2f}"),
-                    ("PEG Ratio ", f"{peg_ratio:.2f}"),
-                    ("Div Rate (FWD) ", f"${dividend_rate:.2f}"),
-                    ("Div Yield (FWD) ", f"{dividend_yield * 100:.2f}%"),
-                    ("Recommendation ", recommendation.capitalize())
-                ]
-                
-                df = pd.DataFrame(biz_metrics[1:], columns=biz_metrics[0])
-                col3.dataframe(df, 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']
-
-                m = Prophet(daily_seasonality=True)
-                m.fit(df_forecast)
-
-                future = m.make_future_dataframe(periods=forecast_period)
-                forecast = m.predict(future)
-
-                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.update_layout(
-                    title=f"Stock Price Forecast for {ticker}",
-                    xaxis_title="Date",
-                    yaxis_title="Predicted Close Price",
-                    hovermode="x unified"
-                )
-                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)
-
-                st.subheader("Investment Analysis")
-                st.write(reasons)
-
-        except Exception as e:
-            st.exception(f"An error occurred: {e}")
+import streamlit as st
+import yfinance as yf
+import pandas as pd
+from prophet import Prophet
+import plotly.graph_objs as go
+import google.generativeai as genai
+import numpy as np
+
+# Streamlit app details
+st.set_page_config(page_title="TechyTrade", layout="wide")
+
+# Custom CSS
+st.markdown("""
+    <style>
+        @import url('https://fonts.googleapis.com/css2?family=Montserrat:wght@300;400;700&display=swap');
+
+        body {
+            background-color: #f4f4f9;
+            color: #333;
+            font-family: 'Montserrat', sans-serif;
+        }
+        .sidebar .sidebar-content {
+            background-color: #2c3e50;
+            color: white;
+        }
+        h1, h2, h3 {
+            color: #2980b9;
+        }
+        .css-1v3fvcr {
+            color: #2980b9 !important;
+        }
+        .css-17eq0hr {
+            font-family: 'Montserrat', sans-serif !important;
+        }
+        .css-2trqyj {
+            font-family: 'Montserrat', sans-serif !important;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Sidebar
+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)
+    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)")
+    ema_checkbox = st.checkbox("Exponential Moving Average (EMA)")
+    rsi_checkbox = st.checkbox("Relative Strength Index (RSI)")
+    macd_checkbox = st.checkbox("Moving Average Convergence Divergence (MACD)")
+    bollinger_checkbox = st.checkbox("Bollinger Bands")
+    google_api_key = st.text_input("Enter your Google API Key 🔑", type="password")
+    button = st.button("Submit 🚀")
+
+# Load generative model
+@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):
+    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."
+    response = model.generate_content(prompt)
+    return response.text
+
+# Function to format large numbers
+def format_value(value):
+    suffixes = ["", "K", "M", "B", "T"]
+    suffix_index = 0
+    while value >= 1000 and suffix_index < len(suffixes) - 1:
+        value /= 1000
+        suffix_index += 1
+    return f"${value:.1f}{suffixes[suffix_index]}"
+
+# Technical Indicators Functions
+def calculate_sma(data, window):
+    return data.rolling(window=window).mean()
+
+def calculate_ema(data, window):
+    return data.ewm(span=window, adjust=False).mean()
+
+def calculate_rsi(data, window):
+    delta = data.diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
+    rs = gain / loss
+    return 100 - (100 / (1 + rs))
+
+def calculate_macd(data, short_window=12, long_window=26, signal_window=9):
+    short_ema = calculate_ema(data, short_window)
+    long_ema = calculate_ema(data, long_window)
+    macd = short_ema - long_ema
+    signal = calculate_ema(macd, signal_window)
+    return macd, signal
+
+def calculate_bollinger_bands(data, window):
+    sma = calculate_sma(data, window)
+    std = data.rolling(window=window).std()
+    upper_band = sma + (std * 2)
+    lower_band = sma - (std * 2)
+    return upper_band, lower_band
+
+# If Submit button is clicked
+if button:
+    if not ticker.strip():
+        st.error("Please provide a valid stock ticker.")
+    elif not google_api_key.strip():
+        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
+                fig = go.Figure()
+                fig.add_trace(go.Scatter(x=history.index, y=history['Close'], mode='lines', name='Close Price'))
+
+                # Add Technical Indicators
+                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'))
+                    fig.add_trace(go.Scatter(x=history.index, y=lower_band, mode='lines', name='Lower Band'))
+
+                fig.update_layout(
+                    title=f"Historical Stock Prices for {ticker}",
+                    xaxis_title="Date",
+                    yaxis_title="Close Price",
+                    hovermode="x unified"
+                )
+                st.plotly_chart(fig, use_container_width=True)
+
+                col1, col2, col3 = st.columns(3)
+
+                # 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 ", country),
+                    ("Sector ", sector),
+                    ("Industry ", industry),
+                    ("Market Cap ", format_value(market_cap)),
+                    ("Enterprise Value ", format_value(ent_value)),
+                    ("Employees ", employees)
+                ]
+                
+                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 ", 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"${ft_week_low:.2f}")
+                ]
+                
+                df = pd.DataFrame(price_info[1:], columns=price_info[0])
+                col2.dataframe(df, width=400, hide_index=True)
+
+                # Display business metrics as a dataframe
+                forward_eps = info.get('forwardEps', 'N/A')
+                forward_pe = info.get('forwardPE', 'N/A')
+                peg_ratio = info.get('pegRatio', 'N/A')
+                dividend_rate = info.get('dividendRate', 'N/A')
+                dividend_yield = info.get('dividendYield', 'N/A')
+                recommendation = info.get('recommendationKey', 'N/A')
+                
+                biz_metrics = [
+                    ("Business Metrics", "Value"),
+                    ("EPS (FWD) ", f"{forward_eps:.2f}"),
+                    ("P/E (FWD) ", f"{forward_pe:.2f}"),
+                    ("PEG Ratio ", f"{peg_ratio:.2f}"),
+                    ("Div Rate (FWD) ", f"${dividend_rate:.2f}"),
+                    ("Div Yield (FWD) ", f"{dividend_yield * 100:.2f}%"),
+                    ("Recommendation ", recommendation.capitalize())
+                ]
+                
+                df = pd.DataFrame(biz_metrics[1:], columns=biz_metrics[0])
+                col3.dataframe(df, 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']
+
+                m = Prophet(daily_seasonality=True)
+                m.fit(df_forecast)
+
+                future = m.make_future_dataframe(periods=forecast_period)
+                forecast = m.predict(future)
+
+                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.update_layout(
+                    title=f"Stock Price Forecast for {ticker}",
+                    xaxis_title="Date",
+                    yaxis_title="Predicted Close Price",
+                    hovermode="x unified"
+                )
+                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)
+
+                st.subheader("Investment Analysis")
+                st.write(reasons)
+
+        except Exception as e:
+            st.exception(f"An error occurred: {e}")