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| 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 = [] | |
| 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)) | |
| # Check if forecast is a numpy array (newer statsmodels) or a ForecastResults object (older statsmodels) | |
| if isinstance(forecast, np.ndarray): | |
| predictions = forecast | |
| confidence_intervals = model_fit.get_forecast(steps=forecast_period).conf_int() | |
| else: | |
| 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 |