Update app.py

app.py

@@ -1,124 +1,85 @@
-import datetime
-import gradio as gr
-import pandas as pd
 import yfinance as yf
-import seaborn as sns
-sns.set()
-import matplotlib.pyplot as plt
+import pandas as pd
 import plotly.graph_objects as go
-
+import gradio as gr
 from datetime import date, timedelta
-from matplotlib import pyplot as plt
-from plotly.subplots import make_subplots
-from pytickersymbols import PyTickerSymbols
-from statsmodels.tsa.arima.model import ARIMA
-from pandas.plotting import autocorrelation_plot
 from dateutil.relativedelta import relativedelta
 
-index_options = ['FTSE 100(UK)', 'NASDAQ(USA)', 'CAC 40(FRANCE)']
-ticker_dict = {'FTSE 100(UK)': 'FTSE 100', 'NASDAQ(USA)': 'NASDAQ 100', 'CAC 40(FRANCE)': 'CAC 40'}
-time_intervals = ['1d', '1m', '5m', '15m', '60m']
+# ... (keep the existing imports and global variables)
 
-global START_DATE, END_DATE
+def get_company_info(ticker):
+    stock = yf.Ticker(ticker)
+    info = stock.info
+    
+    # Select relevant fundamental information
+    fundamentals = {
+        "Company Name": info.get("longName", "N/A"),
+        "Sector": info.get("sector", "N/A"),
+        "Industry": info.get("industry", "N/A"),
+        "Market Cap": f"${info.get('marketCap', 'N/A'):,}",
+        "P/E Ratio": round(info.get("trailingPE", 0), 2),
+        "EPS": round(info.get("trailingEps", 0), 2),
+        "52 Week High": f"${info.get('fiftyTwoWeekHigh', 'N/A'):,}",
+        "52 Week Low": f"${info.get('fiftyTwoWeekLow', 'N/A'):,}",
+        "Dividend Yield": f"{info.get('dividendYield', 0) * 100:.2f}%",
+        "Beta": round(info.get("beta", 0), 2),
+    }
+    
+    return pd.DataFrame(list(fundamentals.items()), columns=['Metric', 'Value'])
 
-END_DATE = date.today()
-START_DATE = END_DATE - relativedelta(years=1)
-FORECAST_PERIOD = 7
-demo = gr.Blocks()
-stock_names = []
+def get_stock_graph_and_info(idx, stock, interval, graph_type, forecast_method):
+    stock_name, ticker_name = stock.split(":")
+    
+    if ticker_dict[idx] == 'FTSE 100':
+        ticker_name += '.L' if ticker_name[-1] != '.' else 'L'
+    elif ticker_dict[idx] == 'CAC 40':
+        ticker_name += '.PA'
 
-with demo:
-    d1 = gr.Dropdown(index_options, label='Please select Index...', info='Will be adding more indices later on', interactive=True)
-    d2 = gr.Dropdown([], label='Please Select Stock from your selected index', interactive=True)
-    d3 = gr.Dropdown(time_intervals, label='Select Time Interval', value='1d', interactive=True)
-    d4 = gr.Radio(['Line Graph', 'Candlestick Graph'], label='Select Graph Type', value='Line Graph', interactive=True)
-    d5 = gr.Dropdown(['ARIMA', 'Prophet', 'LSTM'], label='Select Forecasting Method', value='ARIMA', interactive=True)
+    # Get stock price data
+    series = yf.download(tickers=ticker_name, start=START_DATE, end=END_DATE, interval=interval)
+    series = series.reset_index()
 
-    def forecast_series(series, model="ARIMA", forecast_period=7):
-        predictions = list()
-        if series.shape[1] > 1:
-            series = series['Close'].values.tolist()
-        
-        if model == "ARIMA":
-            for i in range(forecast_period):
-                model = ARIMA(series, order=(5, 1, 0))
-                model_fit = model.fit()
-                output = model_fit.forecast()
-                yhat = output[0]
-                predictions.append(yhat)
-                series.append(yhat)
-        elif model == "Prophet":
-            # Implement Prophet forecasting method
-            pass
-        elif model == "LSTM":
-            # Implement LSTM forecasting method
-            pass
+    # Generate forecast
+    predictions = forecast_series(series, model=forecast_method)
 
-        return predictions
+    # ... (keep the existing forecast date generation code)
 
-    def is_business_day(a_date):
-        return a_date.weekday() < 5
+    # Create graph
+    if graph_type == 'Line Graph':
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=series['Date'], y=series['Close'], mode='lines', name='Historical'))
+        fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast'))
+    else:  # Candlestick Graph
+        fig = go.Figure(data=[go.Candlestick(x=series['Date'],
+                                             open=series['Open'],
+                                             high=series['High'],
+                                             low=series['Low'],
+                                             close=series['Close'],
+                                             name='Historical')])
+        fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast'))
 
-    def get_stocks_from_index(idx):
-        stock_data = PyTickerSymbols()
-        index = ticker_dict[idx]
-        stocks = list(stock_data.get_stocks_by_index(index))
-        stock_names = [f"{stock['name']}:{stock['symbol']}" for stock in stocks]
-        return gr.Dropdown(choices=stock_names, label='Please Select Stock from your selected index', interactive=True)
+    fig.update_layout(title=f"Stock Price of {stock_name}",
+                      xaxis_title="Date",
+                      yaxis_title="Price")
+
+    # Get fundamental information
+    fundamentals = get_company_info(ticker_name)
+
+    return fig, fundamentals
+
+# Update the Gradio interface
+with demo:
+    # ... (keep the existing input components)
+
+    out_graph = gr.Plot()
+    out_fundamentals = gr.DataFrame()
 
-    d1.input(get_stocks_from_index, d1, d2)
-    
-    def get_stock_graph(idx, stock, interval, graph_type, forecast_method):
-        stock_name, ticker_name = stock.split(":")
-        
-        if ticker_dict[idx] == 'FTSE 100':
-            ticker_name += '.L' if ticker_name[-1] != '.' else 'L'
-        elif ticker_dict[idx] == 'CAC 40':
-            ticker_name += '.PA'
-    
-        series = yf.download(tickers=ticker_name, start=START_DATE, end=END_DATE, interval=interval)
-        series = series.reset_index()
-    
-        predictions = forecast_series(series, model=forecast_method)
-    
-        last_date = pd.to_datetime(series['Date'].values[-1])
-        forecast_week = []
-        i = 1
-        while len(forecast_week) < FORECAST_PERIOD:
-            next_date = last_date + timedelta(days=i)
-            if is_business_day(next_date):
-                forecast_week.append(next_date)
-            i += 1
-    
-        # Ensure predictions and forecast_week have the same length
-        predictions = predictions[:len(forecast_week)]
-        forecast_week = forecast_week[:len(predictions)]
-    
-        forecast = pd.DataFrame({"Date": forecast_week, "Forecast": predictions})
-    
-        if graph_type == 'Line Graph':
-            fig = go.Figure()
-            fig.add_trace(go.Scatter(x=series['Date'], y=series['Close'], mode='lines', name='Historical'))
-            fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast'))
-        else:  # Candlestick Graph
-            fig = go.Figure(data=[go.Candlestick(x=series['Date'],
-                                                 open=series['Open'],
-                                                 high=series['High'],
-                                                 low=series['Low'],
-                                                 close=series['Close'],
-                                                 name='Historical')])
-            fig.add_trace(go.Scatter(x=forecast['Date'], y=forecast['Forecast'], mode='lines', name='Forecast'))
-    
-        fig.update_layout(title=f"Stock Price of {stock_name}",
-                          xaxis_title="Date",
-                          yaxis_title="Price")
-    
-        return fig
-    out = gr.Plot()
     inputs = [d1, d2, d3, d4, d5]
-    d2.input(get_stock_graph, inputs, out)
-    d3.input(get_stock_graph, inputs, out)
-    d4.input(get_stock_graph, inputs, out)
-    d5.input(get_stock_graph, inputs, out)
+    outputs = [out_graph, out_fundamentals]
+
+    d2.input(get_stock_graph_and_info, inputs, outputs)
+    d3.input(get_stock_graph_and_info, inputs, outputs)
+    d4.input(get_stock_graph_and_info, inputs, outputs)
+    d5.input(get_stock_graph_and_info, inputs, outputs)
 
 demo.launch()