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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): | |
| if series.shape[1] > 1: | |
| series = series['Close'].values | |
| if model == "ARIMA": | |
| model = ARIMA(series, order=(5, 1, 0)) | |
| model_fit = model.fit() | |
| forecast = model_fit.forecast(steps=forecast_period) | |
| # Get confidence intervals | |
| conf_int = model_fit.get_forecast(steps=forecast_period).conf_int() | |
| # Ensure all arrays have the same length | |
| min_length = min(len(forecast), conf_int.shape[0]) | |
| predictions = forecast[:min_length] | |
| lower_ci = conf_int.iloc[:min_length, 0] if isinstance(conf_int, pd.DataFrame) else conf_int[:min_length, 0] | |
| upper_ci = conf_int.iloc[:min_length, 1] if isinstance(conf_int, pd.DataFrame) else conf_int[:min_length, 1] | |
| elif model == "Prophet": | |
| # Implement Prophet forecasting method | |
| pass | |
| elif model == "LSTM": | |
| # Implement LSTM forecasting method | |
| pass | |
| else: | |
| raise ValueError(f"Unsupported model: {model}") | |
| # Ensure all arrays are of the same length | |
| min_length = min(len(predictions), len(lower_ci), len(upper_ci)) | |
| predictions = predictions[:min_length] | |
| lower_ci = lower_ci[:min_length] | |
| upper_ci = upper_ci[:min_length] | |
| return predictions, pd.DataFrame({'Lower_CI': lower_ci, 'Upper_CI': upper_ci}) | |
| 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=len(predictions)) | |
| forecast_dates = [date for date in forecast_dates if is_business_day(date)] | |
| # Ensure all data has the same length | |
| min_length = min(len(predictions), len(forecast_dates), len(confidence_intervals)) | |
| predictions = predictions[:min_length] | |
| forecast_dates = forecast_dates[:min_length] | |
| confidence_intervals = confidence_intervals.iloc[:min_length] | |
| forecast = pd.DataFrame({ | |
| "Date": forecast_dates, | |
| "Forecast": predictions, | |
| "Lower_CI": confidence_intervals['Lower_CI'], | |
| "Upper_CI": confidence_intervals['Upper_CI'] | |
| }) | |
| 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 |