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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 |