trading_analysis/Yahoo_Finance_DL_COPPER_EXCEL.ipynb

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%pip install "yfinance[optional]"=="0.2.37"

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import yfinance as yf

hg_f = yf.Ticker("HG=F")

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import pandas as pd
import numpy as np

# get historical market data
hist = hg_f.history(period="100y")

# Replace 0 values with NaN in specific columns
hist.replace({'Volume': {0: pd.NA}, 'Open': {0: pd.NA}}, inplace=True)
hist.replace(0, np.nan, inplace=True)  # Replace 0 with NaN

columns = ['Open', 'High', 'Low', 'Close', 'Volume']

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hist.head()

Out [9]:

	Open	High	Low	Close	Volume	Dividends	Stock Splits
Date
2000-08-30 00:00:00-04:00	0.879	0.887	0.8770	0.8850	2886	NaN	NaN
2000-08-31 00:00:00-04:00	0.885	0.888	0.8800	0.8850	1095	NaN	NaN
2000-09-01 00:00:00-04:00	0.878	0.889	0.8780	0.8890	3449	NaN	NaN
2000-09-05 00:00:00-04:00	0.896	0.907	0.8950	0.9060	1397	NaN	NaN
2000-09-06 00:00:00-04:00	0.905	0.906	0.8975	0.9015	1195	NaN	NaN

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# Convert the index to datetime if it's not already
hist.index = pd.to_datetime(hist.index)

# Strip the time and timezone, keeping only the date for the index
hist.index = hist.index.date

hist.head()

Out [11]:

	Open	High	Low	Close	Volume	Dividends	Stock Splits
2000-08-30	0.879	0.887	0.8770	0.8850	2886	NaN	NaN
2000-08-31	0.885	0.888	0.8800	0.8850	1095	NaN	NaN
2000-09-01	0.878	0.889	0.8780	0.8890	3449	NaN	NaN
2000-09-05	0.896	0.907	0.8950	0.9060	1397	NaN	NaN
2000-09-06	0.905	0.906	0.8975	0.9015	1195	NaN	NaN

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from datetime import datetime

# Get the current date in 'YYYY-MM-DD' format
current_date = datetime.now().strftime('%Y-%m-%d')

# Create the file name with the current date
file_name = f'./Data_COPPER_{current_date}.csv'

hist = hist.round(3)

# Save the DataFrame to CSV with the dynamic file name
hist[["Open", "High", "Low", "Close"]].to_csv(file_name)

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