Skip to content

Install Libraries

More on Darts library forecasting models can be found: https://unit8co.github.io/darts/generated_api/darts.models.forecasting.html

!pip install yfinance
!pip install darts
Hidden output

Import libraries

import yfinance as yf
import numpy as np
import pandas as pd
from datetime import datetime

Create sector dataframe

#Vanguard Sector ETF List for Trend Rotation
sectors = ['SPY', 'VGLT', 'VGIT', 'VGSH', 'VCSH', 'VCIT', 'VCLT', 'BND', 'VTI', 'VOO',	'VV', 'VIG', 'VFH',	'VGT', 'VCR', 'VHT', 'VIS', 'VAW', 'VDC', 'VPU', 'VOX', 'VDE', 'VNQ']

# Function to fetch and process data for all ETFs
def fetch_process_data(tickers):
    # Use yfinance to download data for all tickers at once
    data = yf.download(tickers, end=datetime.now().strftime('%Y-%m-%d'), period='max', 
                       progress=False, interval='1wk', auto_adjust=False, group_by='ticker')
    
    # Initialize an empty list to store processed data
    processed_data = []

    # Process data for each ticker
    for ticker in tickers:
        # Extract individual ticker data
        df = data[ticker].dropna()  # Drop NaN value rows that might appear during download
        df['Ticker'] = ticker  # Add ticker column
        # Append processed data to the list
        processed_data.append(df)

    # Concatenate all dataframes into a single dataframe
    final_data = pd.concat(processed_data)

    return final_data

# Function call
etf_data = fetch_process_data(sectors)

import plotly for quick EDA checks

import pyplot for more analysis

import darts for timeseries objects

import plotly.express as ple
import matplotlib.pyplot as plt
import darts

quick check of etf_data

display(etf_data.tail(100))

Choice of ETF to test along with log & percent change normalizations

sector_choice = 'SPY'
etf = etf_data[etf_data['Ticker']==sector_choice][['Adj Close']]
etf.index = pd.to_datetime(etf.index)
etf['Log_Price'] = np.log(etf['Adj Close'])
etf['Percent_Diff'] = etf['Adj Close'].pct_change() * 100
print(len(etf))
display(etf.tail(100))
etf_series = darts.TimeSeries.from_dataframe(etf, value_cols = 'Log_Price', fill_missing_dates=True, freq=None)
etf_series.plot()

Simple seasonality check

from darts.utils.statistics import check_seasonality

is_monthly_seasonal, monthly_period = darts.utils.statistics.check_seasonality(etf_series, m=4, max_lag=400, alpha=0.05)
is_quarterly_seasonal, quarter_period = darts.utils.statistics.check_seasonality(etf_series, m=12, max_lag=400, alpha=0.05)
is_annually_seasonal, annual_period = darts.utils.statistics.check_seasonality(etf_series, m=52, max_lag=400, alpha=0.05)

print(f'Montly seasonality: {is_monthly_seasonal} - period = {monthly_period}')
print(f'Quarterly seasonality: {is_quarterly_seasonal} - period = {quarter_period}')
print(f'Annually seasonality: {is_annually_seasonal} - period = {annual_period}')

Visualization of Train and Test datasets

‌
‌
‌