Update stock_analysis.py

stock_analysis.py

@@ -1,93 +1,59 @@
-import pandas as pd
-import numpy as np
-import plotly.graph_objects as go
-from datetime import timedelta
-from statsmodels.tsa.arima.model import ARIMA
-from config import FORECAST_PERIOD, ticker_dict, CONFIDENCE_INTERVAL
-from data_fetcher import get_stock_data, get_company_info
-
-def is_business_day(a_date):
-    return a_date.weekday() < 5
-
-def forecast_series(series, model="ARIMA", forecast_period=FORECAST_PERIOD):
-    predictions = []
-    confidence_intervals = []
+import gradio as gr
+from datetime import datetime, date, timedelta
+from config import index_options, time_intervals, START_DATE, END_DATE, FORECAST_PERIOD
+from data_fetcher import get_stocks_from_index
+from stock_analysis import get_stock_graph_and_info
+
+def validate_date(date_string):
+    try:
+        return datetime.strptime(date_string, "%Y-%m-%d").date()
+    except ValueError:
+        return None
+
+demo = gr.Blocks()
+
+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)
     
-    if series.shape[1] > 1:
-        series = series['Close'].values.tolist()
-    
-    if model == "ARIMA":
-        model = ARIMA(series, order=(5, 1, 0))
-        model_fit = model.fit()
-        forecast = model_fit.forecast(steps=forecast_period, alpha=(1 - CONFIDENCE_INTERVAL))
-        predictions = forecast.predicted_mean
-        confidence_intervals = forecast.conf_int()
-    elif model == "Prophet":
-        # Implement Prophet forecasting method
-        pass
-    elif model == "LSTM":
-        # Implement LSTM forecasting method
-        pass
-
-    return predictions, confidence_intervals
-
-def get_stock_graph_and_info(idx, stock, interval, graph_type, forecast_method, start_date, end_date):
-    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 = get_stock_data(ticker_name, interval, start_date, end_date)
-    predictions, confidence_intervals = forecast_series(series, model=forecast_method)
-
-    last_date = pd.to_datetime(series['Date'].values[-1])
-    forecast_dates = pd.date_range(start=last_date + timedelta(days=1), periods=FORECAST_PERIOD)
-    forecast_dates = [date for date in forecast_dates if is_business_day(date)]
-
-    forecast = pd.DataFrame({
-        "Date": forecast_dates,
-        "Forecast": predictions,
-        "Lower_CI": confidence_intervals.iloc[:, 0],
-        "Upper_CI": confidence_intervals.iloc[:, 1]
-    })
-
-    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'))
-        fig.add_trace(go.Scatter(
-            x=forecast['Date'].tolist() + forecast['Date'].tolist()[::-1],
-            y=forecast['Upper_CI'].tolist() + forecast['Lower_CI'].tolist()[::-1],
-            fill='toself',
-            fillcolor='rgba(0,100,80,0.2)',
-            line=dict(color='rgba(255,255,255,0)'),
-            hoverinfo="skip",
-            showlegend=False
-        ))
-    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.add_trace(go.Scatter(
-            x=forecast['Date'].tolist() + forecast['Date'].tolist()[::-1],
-            y=forecast['Upper_CI'].tolist() + forecast['Lower_CI'].tolist()[::-1],
-            fill='toself',
-            fillcolor='rgba(0,100,80,0.2)',
-            line=dict(color='rgba(255,255,255,0)'),
-            hoverinfo="skip",
-            showlegend=False
-        ))
-
-    fig.update_layout(title=f"Stock Price of {stock_name}",
-                      xaxis_title="Date",
-                      yaxis_title="Price")
-
-    fundamentals = get_company_info(ticker_name)
-
-    return fig, fundamentals
+    # New date inputs using Textbox
+    date_start = gr.Textbox(label="Start Date (YYYY-MM-DD)", value=START_DATE.strftime("%Y-%m-%d"))
+    date_end = gr.Textbox(label="End Date (YYYY-MM-DD)", value=END_DATE.strftime("%Y-%m-%d"))
+
+    out_graph = gr.Plot()
+    out_fundamentals = gr.DataFrame()
+
+    inputs = [d1, d2, d3, d4, d5, date_start, date_end]
+    outputs = [out_graph, out_fundamentals]
+
+    def update_stock_options(index):
+        stocks = get_stocks_from_index(index)
+        return gr.Dropdown(choices=stocks)
+
+    def process_inputs(*args):
+        idx, stock, interval, graph_type, forecast_method, start_date, end_date = args
+        
+        start = validate_date(start_date)
+        end = validate_date(end_date)
+        
+        if start is None or end is None:
+            return gr.Textbox(value="Invalid date format. Please use YYYY-MM-DD."), None
+        
+        if start > end:
+            return gr.Textbox(value="Start date must be before end date."), None
+        
+        return get_stock_graph_and_info(idx, stock, interval, graph_type, forecast_method, start, end)
+
+    d1.change(update_stock_options, d1, d2)
+    d2.change(process_inputs, inputs, outputs)
+    d3.change(process_inputs, inputs, outputs)
+    d4.change(process_inputs, inputs, outputs)
+    d5.change(process_inputs, inputs, outputs)
+    date_start.change(process_inputs, inputs, outputs)
+    date_end.change(process_inputs, inputs, outputs)
+
+if __name__ == "__main__":
+    demo.launch()