Update app.py

app.py

@@ -1,85 +1,128 @@
-import yfinance as yf
+ 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 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
 
-# ... (keep the existing imports and global variables)
+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']
 
-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'])
+global START_DATE, END_DATE
+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'
 
-    # Get stock price data
-    series = yf.download(tickers=ticker_name, start=START_DATE, end=END_DATE, interval=interval)
-    series = series.reset_index()
 
-    # Generate forecast
-    predictions = forecast_series(series, model=forecast_method)
-
-    # ... (keep the existing forecast date generation code)
+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)
 
-    # 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 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
 
-    fig.update_layout(title=f"Stock Price of {stock_name}",
-                      xaxis_title="Date",
-                      yaxis_title="Price")
+        return predictions
 
-    # Get fundamental information
-    fundamentals = get_company_info(ticker_name)
+    def is_business_day(a_date):
+        return a_date.weekday() < 5
 
-    return fig, fundamentals
 
-# Update the Gradio interface
-with demo:
-    # ... (keep the existing input components)
+    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)
 
-    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]
-    outputs = [out_graph, out_fundamentals]
+    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)
 
-    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()