Return Home

Return to Python Projects Page

Top ETF Components NewYork Stock Exchange

ETFs or "exchange-traded funds" are exactly as the name implies: funds that trade on exchanges, generally tracking a specific index. When you invest in an ETF, you get a bundle of assets you can buy and sell during market hours—potentially lowering your risk and exposure, while helping to diversify your portfolio.

1 . U.S. Global GO GOLD and Precious Metal Miners ETF

The U.S. Global GO GOLD and Precious Metal Miners ETF (NYSE Arca: GOAU) provides investors access to companies engaged in the production of precious metals either through active (mining or production) or passive (owning royalties or production streams) means.

2.iShares Semiconductor ETF

The iShares Semiconductor ETF seeks to track the investment results of an index composed of U.S.-listed equities in the semiconductor sector.

3.First Trust Nasdaq Semiconductor ETF

The investment seeks investment results that correspond generally to the price and yield (before the fund's fees and expenses) of the Nasdaq US Smart Semiconductor TM Index.

4 VanEck Semiconductor ETF

The fund normally invests at least 80% of its total assets in securities that comprise the fund's benchmark index. The index includes common stocks and depositary receipts of U.S. exchange-listed companies in the semiconductor industry. Such companies may include medium-capitalization companies and foreign companies that are listed on a U.S. exchange. The fund is non-diversified.

5 Defiance 5G Next Gen Connectivity ETF

The fund uses a passive management approach to track the total return performance, before fees and expenses, of the index. The index is a rules-based index that consists of a tiered, modified market capitalization-weighted portfolio of the U.S.-listed equity securities, including depositary receipts, of companies whose products or services are predominantly tied to the development of 5G networking and communication technologies

6.Invesco China Technology ETF

The fund generally will invest at least 90% of its total assets in securities that comprise the underlying index, as well as ADRs and GDRs that represent securities in the underlying index. Strictly in accordance with its guidelines and mandated procedures, the index provider compiles, maintains, and calculates the underlying index. The underlying index may include China A-Shares, B Shares, H Shares, N Shares, Red Chips, P Chips and S Chips. The fund is non-diversified.

7.Siren Nasdaq NexGen Economy ETF

Under normal circumstances, at least 80% of the fund's assets, will be invested in component securities of the index. The index is designed to measure the returns of companies that are committing material resources to developing, researching, supporting, innovating or utilizing blockchain technology for their proprietary use or for use by others ("Blockchain Companies").

8.ProShares Ultra QQQ

The fund invests in financial instruments that ProShare Advisors believes, in combination, should produce daily returns consistent with the Daily Target. The index includes 100 of the largest domestic and international non-financial companies listed on The Nasdaq Stock Market based on market capitalization. The fund is non-diversified.

9.SPDR S&P Semiconductor ETF

In seeking to track the performance of the S&P Semiconductor Select Industry Index (the "index"), the fund employs a sampling strategy. It generally invests substantially all, but at least 80%, of its total assets in the securities comprising the index. The index represents the semiconductors segment of the S&P Total Market Index ("S&P TMI").

10.Global X MSCI China Consumer Discretionary ETF

The fund invests at least 80% of its total assets in the securities of the underlying index and in ADRs and GDRs based on the securities in the underlying index. The underlying index tracks the performance of companies in the MSCI China Index (the "parent index") that are classified in the consumer discretionary sector, as defined by the index provider. The fund is non-diversified

import yfinance as yf
import pandas as pd
import matplotlib.pyplot as plt

C:\Users\jki\anaconda3\Lib\site-packages\yfinance\base.py:48: FutureWarning: The default dtype for empty Series will be 'object' instead of 'float64' in a future version. Specify a dtype explicitly to silence this warning.
  _empty_series = pd.Series()

import yfinance as yf

# Define the stock symbols for Top Ten ETFs
Global_symbol = "GOAU"
iShares_symbol = "SOXX"
First_symbol = "FTXL"
VanEck_symbol = "SMH"
Defiance_symbol = "FIVG"
Invesco_symbol = "PSI"
Siren_symbol = "BLCN"
ProShares_symbol = "QLD"
SPDR_symbol = "XSD"
MSCI_symbol = "CHIQ"

# Define start and end dates
start_date = '2019-01-01'
end_date = '2024-02-29'

# Fetch stock price data using yfinance
Global_data = yf.download(Global_symbol, start=start_date, end=end_date)
iShares_data = yf.download(iShares_symbol, start=start_date, end=end_date)
First_data = yf.download(First_symbol, start=start_date, end=end_date)
VanEck_data = yf.download(VanEck_symbol, start=start_date, end=end_date)
Defiance_data = yf.download(Defiance_symbol, start=start_date, end=end_date)
Invesco_data = yf.download(Invesco_symbol, start=start_date, end=end_date)
Siren_data = yf.download(Siren_symbol, start=start_date, end=end_date)
ProShares_data = yf.download(ProShares_symbol, start=start_date, end=end_date)
SPDR_data = yf.download(SPDR_symbol, start=start_date, end=end_date)
MSCI_data = yf.download(MSCI_symbol, start=start_date, end=end_date)

[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed
[*********************100%%**********************]  1 of 1 completed

# Extract the 'Close' prices
Global_close_prices = Global_data['Close']
iShares_close_prices = iShares_data['Close']
First_close_prices = First_data['Close']
VanEck_close_prices = VanEck_data['Close']
Defiance_close_prices = Defiance_data['Close']
Invesco_close_prices = Invesco_data['Close']
Siren_Hathaway_close_prices = Siren_data['Close']
ProShares_close_prices = ProShares_data['Close']
SPDR_close_prices = SPDR_data['Close']
MSCI_close_prices = MSCI_data['Close']

# Create a DataFrame to store the ETF prices
ETF_prices_df = pd.DataFrame({
    'Global': Global_close_prices,
    'iShares': iShares_close_prices,
    'First': First_close_prices,
    'VanEck': VanEck_close_prices,
    'Defiance Platforms': Defiance_close_prices,
    'Invesco': Invesco_close_prices,
    'Siren': Siren_Hathaway_close_prices,
    'ProShares': ProShares_close_prices,
    'SPDR': SPDR_close_prices,
    'MSCI': MSCI_close_prices
})

# Plot the ETF prices
ETF_prices_df.plot(title='ETF Prices', ylabel='ETF Prices (USD)', xlabel='Date')
plt.show()