| #### \*\*\* IMPORTANT LEGAL DISCLAIMER \*\*\* --- **Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of Yahoo, Inc.** yfinance is **not** affiliated, endorsed, or vetted by Yahoo, Inc. It's an open-source tool that uses Yahoo's publicly available APIs, and is intended for research and educational purposes. **You should refer to Yahoo!'s terms of use** ([here](https://policies.yahoo.com/us/en/yahoo/terms/product-atos/apiforydn/index.htm), [here](https://legal.yahoo.com/us/en/yahoo/terms/otos/index.html), and [here](https://policies.yahoo.com/us/en/yahoo/terms/index.htm)) **for details on your rights to use the actual data downloaded. Remember - the Yahoo! finance API is intended for personal use only.** |
**yfinance** offers a threaded and Pythonic way to download market data from [Yahoo!Ⓡ finance](https://finance.yahoo.com).
→ Check out this [Blog post](https://aroussi.com/#post/python-yahoo-finance) for a detailed tutorial with code examples.
[Changelog »](https://github.com/ranaroussi/yfinance/blob/main/CHANGELOG.rst)
---
- [Installation](#installation)
- [Quick start](#quick-start)
- [Advanced](#logging)
- [Wiki](https://github.com/ranaroussi/yfinance/wiki)
- [Contribute](#developers-want-to-contribute)
---
## Installation
Install `yfinance` using `pip`:
``` {.sourceCode .bash}
$ pip install yfinance --upgrade --no-cache-dir
```
[With Conda](https://anaconda.org/ranaroussi/yfinance).
To install with optional dependencies, replace `optional` with: `nospam` for [caching-requests](#smarter-scraping), `repair` for [price repair](https://github.com/ranaroussi/yfinance/wiki/Price-repair), or `nospam,repair` for both:
``` {.sourceCode .bash}
$ pip install "yfinance[optional]"
```
[Required dependencies](./requirements.txt) , [all dependencies](./setup.py#L62).
---
## Quick Start
### The Ticker module
The `Ticker` module, which allows you to access ticker data in a more Pythonic way:
```python
import yfinance as yf
msft = yf.Ticker("MSFT")
# get all stock info
msft.info
# get historical market data
hist = msft.history(period="1mo")
# show meta information about the history (requires history() to be called first)
msft.history_metadata
# show actions (dividends, splits, capital gains)
msft.actions
msft.dividends
msft.splits
msft.capital_gains # only for mutual funds & etfs
# show share count
msft.get_shares_full(start="2022-01-01", end=None)
# show financials:
# - income statement
msft.income_stmt
msft.quarterly_income_stmt
# - balance sheet
msft.balance_sheet
msft.quarterly_balance_sheet
# - cash flow statement
msft.cashflow
msft.quarterly_cashflow
# see `Ticker.get_income_stmt()` for more options
# show holders
msft.major_holders
msft.institutional_holders
msft.mutualfund_holders
msft.insider_transactions
msft.insider_purchases
msft.insider_roster_holders
# show recommendations
msft.recommendations
msft.recommendations_summary
msft.upgrades_downgrades
# Show future and historic earnings dates, returns at most next 4 quarters and last 8 quarters by default.
# Note: If more are needed use msft.get_earnings_dates(limit=XX) with increased limit argument.
msft.earnings_dates
# show ISIN code - *experimental*
# ISIN = International Securities Identification Number
msft.isin
# show options expirations
msft.options
# show news
msft.news
# get option chain for specific expiration
opt = msft.option_chain('YYYY-MM-DD')
# data available via: opt.calls, opt.puts
```
If you want to use a proxy server for downloading data, use:
```python
import yfinance as yf
msft = yf.Ticker("MSFT")
msft.history(..., proxy="PROXY_SERVER")
msft.get_actions(proxy="PROXY_SERVER")
msft.get_dividends(proxy="PROXY_SERVER")
msft.get_splits(proxy="PROXY_SERVER")
msft.get_capital_gains(proxy="PROXY_SERVER")
msft.get_balance_sheet(proxy="PROXY_SERVER")
msft.get_cashflow(proxy="PROXY_SERVER")
msft.option_chain(..., proxy="PROXY_SERVER")
...
```
### Multiple tickers
To initialize multiple `Ticker` objects, use
```python
import yfinance as yf
tickers = yf.Tickers('msft aapl goog')
# access each ticker using (example)
tickers.tickers['MSFT'].info
tickers.tickers['AAPL'].history(period="1mo")
tickers.tickers['GOOG'].actions
```
To download price history into one table:
```python
import yfinance as yf
data = yf.download("SPY AAPL", period="1mo")
```
#### `yf.download()` and `Ticker.history()` have many options for configuring fetching and processing. [Review the Wiki](https://github.com/ranaroussi/yfinance/wiki) for more options and detail.
### Logging
`yfinance` now uses the `logging` module to handle messages, default behaviour is only print errors. If debugging, use `yf.enable_debug_mode()` to switch logging to debug with custom formatting.
### Smarter scraping
Install the `nospam` packages for smarter scraping using `pip` (see [Installation](#installation)). These packages help cache calls such that Yahoo is not spammed with requests.
To use a custom `requests` session, pass a `session=` argument to
the Ticker constructor. This allows for caching calls to the API as well as a custom way to modify requests via the `User-agent` header.
```python
import requests_cache
session = requests_cache.CachedSession('yfinance.cache')
session.headers['User-agent'] = 'my-program/1.0'
ticker = yf.Ticker('msft', session=session)
# The scraped response will be stored in the cache
ticker.actions
```
Combine `requests_cache` with rate-limiting to avoid triggering Yahoo's rate-limiter/blocker that can corrupt data.
```python
from requests import Session
from requests_cache import CacheMixin, SQLiteCache
from requests_ratelimiter import LimiterMixin, MemoryQueueBucket
from pyrate_limiter import Duration, RequestRate, Limiter
class CachedLimiterSession(CacheMixin, LimiterMixin, Session):
pass
session = CachedLimiterSession(
limiter=Limiter(RequestRate(2, Duration.SECOND*5)), # max 2 requests per 5 seconds
bucket_class=MemoryQueueBucket,
backend=SQLiteCache("yfinance.cache"),
)
```
### Managing Multi-Level Columns
The following answer on Stack Overflow is for [How to deal with
multi-level column names downloaded with
yfinance?](https://stackoverflow.com/questions/63107801)
- `yfinance` returns a `pandas.DataFrame` with multi-level column
names, with a level for the ticker and a level for the stock price
data
- The answer discusses:
- How to correctly read the the multi-level columns after
saving the dataframe to a csv with `pandas.DataFrame.to_csv`
- How to download single or multiple tickers into a single
dataframe with single level column names and a ticker column
### Persistent cache store
To reduce Yahoo, yfinance store some data locally: timezones to localize dates, and cookie. Cache location is:
- Windows = C:/Users/\