A Python library for accessing historical stock data from major stock exchanges and equity markets. This library simplifies the process of fetching and formatting stock data using the Stocks Historical API, making it easy to retrieve and analyze stock data programmatically. Currently, it supports data for top 100 stocks from NYSE and NASDAQ markets, with plans to include data from additional markets in the future.

Library inspired by Yahoo Historical.

• Retrieve historical stock data for NASDAQ and NYSE.
• Supports specifying date ranges with start and optional end dates.
• Formats data into Pandas DataFrames for easy analysis.
• Fetch Open, High, Low, and Close data.

Install the library via pip:

pip install stocks-historical

You can use the Nasdaq or Nyse classes to fetch stock data:

from stocks_historical import Nasdaq, Nyse

# Fetch NASDAQ data
nasdaq_data = Nasdaq(symbol="AAPL", start="2023-01-03", end="2023-01-09")  # Start and end are dates (YYYY-MM-DD)
data = nasdaq_data.get_data()
print(data.head())

# Fetch NYSE data
nyse_data = Nyse(symbol="GE", start="2023-01-03")  # End is optional
data = nyse_data.get_data()
print(data.head())

Output: The data is returned as a Pandas DataFrame with columns:

• Date: Timestamp of the record (in human-readable format).
• Open: Opening price of the stock.
• High: Highest price of the stock.
• Low: Lowest price of the stock.
• Close: Closing price of the stock.

Example DataFrame:

Fetched Data:
                 Date     Open     High     Low   Close
0 2023-01-03 01:00:00  130.280  130.900  124.17  125.07
1 2023-01-04 01:00:00  126.890  128.656  125.08  126.36
2 2023-01-05 01:00:00  127.130  127.770  124.76  125.02
3 2023-01-06 01:00:00  126.010  130.290  124.89  129.62
4 2023-01-09 01:00:00  130.465  133.410  129.89  130.15

This library is built on top of the Stocks Historical API. If you prefer to work directly with the API, refer to the API repository for documentation: Stocks Historical API

The stock data used in this library is sourced from Kaggle Datasets:

1. Top 100 NYSE Daily Stock Prices by Steven Van Ingelgem

2. Top 100 NASDAQ daily stock prices by Steven Van Ingelgem

All credits goes to the Kaggle community for providing these datasets!

Contributions are welcome 🤗! If you'd like to improve this library, feel free to fork the repository and submit a pull request.