Create stock_analysis.py

stock_analysis.py

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+import pandas as pd
+import plotly.graph_objects as go
+from datetime import timedelta
+from statsmodels.tsa.arima.model import ARIMA
+from config import FORECAST_PERIOD, ticker_dict
+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 = list()
+    if series.shape[1] > 1:
+        series = series['Close'].values.tolist()
+    
+    if model == "ARIMA":
+        for _ 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
+
+    return predictions
+
+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'
+
+    series = get_stock_data(ticker_name, interval)
+    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
+
+    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")
+
+    fundamentals = get_company_info(ticker_name)
+
+    return fig, fundamentals