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High Liquidity

Ticker Fullname EWJ iShares MSCI Japan ETF FXI iShares China Large-Cap ETF EWC iShares MSCI Canada ETF EWZ iShares MSCI Brazil ETF EWU iShares MSCI United Kingdom ETF EWY iShares MSCI South Korea ETF EWT iShares MSCI Taiwan ETF EWA iShares MSCI-Australia ETF EWW iShares MSCI Mexico ETF EWL iShares MSCI Switzerland ETF EWQ iShares MSCI France ETF
 
Low liquidity Ticker Fullname JPXN iShares JPX-Nikkei 400 ETF PGJ Invesco Golden Dragon China ETF EWH iShares MSCI Hong Kong ETF EWS iShares MSCI Singapore ETF EWI iShares MSCI Italy ETF EWD iShares MSCI Sweden ETF EZA iShares MSCI...

import yfinance as yf
import pandas as pd
# Fetch U.S. Treasury yield data from yfinance
tickers = # 3-month, 5-year, 10-year, and 30-year Treasury yield indices
data = yf.download(tickers, auto_adjust=False, start='1980-01-01', end='2025-04-04')
# Rename columns for clarity
data.columns =
# Display the first few rows of data
print(data.tail())

import matplotlib.pyplot as plt
# Plot the yield curve
plt.figure(figsize=(10, 6))
for date in :
plt.plot( , data.loc , label=f'Yield Curve {date}')
plt.title('U.S. Treasury Yield Curve')
plt.xlabel('Maturity')
plt.ylabel('Yield (%)')
plt.legend()...

ETF

# Local Qlib classes
from qlib.data.data_connector import DataConnector

# Logger
from qlib.data.utils.data_logs import data_logger
logger = data_logger()

async def main():

dc = DataConnector()

# Define tickers and sectors
tickers =
sectors =

# Fetch data for different tickers
all_data = []
for ticker in tickers:
try:
output = await dc.etfs.get_etf_ohlcv(tickers= , exchanges= , granularity='1d', period_starts= , period_ends= )

data = output .set_index( datetime )
data = ticker
all_data.append(data)
logger.info(f Fetched data for {ticker} )

except Exception as e:...

import yfinance as yf google = yf.Ticker( GOOG )
df = google.history(period='1d', interval= 1m )
print(df.head())

df = google.history(period='1d', interval= 1m )
df = df[ ]
df.head()

df = pd.to_datetime(df.index).time
df.set_index('date', inplace=True)
df.head()

X = df.index.values
y = df .values # The split point is the 10% of the dataframe length
offset = int(0.10*len(df)) X_train = X
y_train = y
X_test = X
y_test = y If we plot it, we get: plt.plot(range(0,len(y_train)),y_train, label='Train')
plt.plot(range(len(y_train),len(y)),y_test,label='Test')
plt.legend()
plt.show()...

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use('Qt5Agg')
import matplotlib.pyplot as plt

#top 10 market value 20240614
top_10_companies =

#past 91 days price data
data = yf.download(top_10_companies, period='91d')
#adjusted close prices
adj_close = data

#total percentage change
total_change = ((adj_close.iloc - adj_close.iloc ) / adj_close.iloc ) * 100

#Plot bar
plt.figure(figsize=(10, 6))
bars = total_change.sort_values().plot(kind='bar', color='skyblue')
plt.title('Total Percentage Change in Past 91 Days')
plt.xlabel('Company')...

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