yf_as_dataframe

YfAsDataframe

Download market data from Yahoo! Finance's API

*** IMPORTANT LEGAL DISCLAIMER *** Yahoo!, Y!Finance, and Yahoo! finance are registered trademarks of Yahoo, Inc. yf_as_dataframe is not affiliated, endorsed, or vetted by Yahoo, Inc. It is 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, here, and here) for details on your rights to use the actual data downloaded. Remember - the Yahoo! finance API is intended for personal use only.

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Purpose

This package provides for pulling data from Yahoo!'s unofficial API, and providing that data using using Polars dataframes in ruby. Data in those dataframes can then be easily post-processed using technical indicators provided by Tulip via Tulirb's ruby bindings, and visualized using Vega.

Quick Start: The Ticker module

The Ticker class, which allows you to access ticker data from Yahoo!'s unofficial API:

msft = YfAsDataframe::Ticker.new("MSFT")

# get all stock info
msft.info

# get historical market data as a dataframe 
hist = msft.history(period: "1mo")
hist2 = msft.history(start: '2020-01-01', fin: '2021-12-31')

# 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.shares_full(start: "2022-01-01", fin: nil)

# 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

# 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.
msft.earnings_dates

# show ISIN code
# 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('2026-12-18')
# data available via: opt.calls, opt.puts


# technical operations, using the Tulirb gem, which provides bindings to 
# the Tulip technical indicators library
h = msft.history(period: '2y', interval: '1d')
YfAsDataframe.ad(h)

# then
h.insert_at_idx(h.columns.length, YfAsDataframe.ad(h))
h['ad_results'] = YfAsDataframe.ad(h)

Most of the indicators are found here and here. Indicator parameters in Tulirb called, e.g., "period" or "short_period" are renamed as "window" or "short_window", respectively. There are a few other variants that are affected. Default values are shown below.

df = msft.history(period: '3y', interval: '1d') # for example

YfAsDataframe.ad(df)
YfAsDataframe.adosc(df, short_window: 2, long_window: 5)
YfAsDataframe.adx(df, column: 'Adj Close', window: 5)
YfAsDataframe.adxr(df, column: 'Adj Close', window: 5)
YfAsDataframe.avg_daily_trading_volume(df, window: 20)
YfAsDataframe.ao(df)
YfAsDataframe.apo(df, column: 'Adj Close', short_window: 12, long_window: 29)
YfAsDataframe.aroon(df, window: 20)
YfAsDataframe.aroonosc(df, window: 20)
YfAsDataframe.avg_price(df)
YfAsDataframe.atr(df, window: 20)
YfAsDataframe.bbands(df, column: 'Adj Close', window: 20, stddev: 1 )
YfAsDataframe.bop(df)
YfAsDataframe.cci(df, window: 20)
YfAsDataframe.cmo(df, column: 'Adj Close', window: 20)
YfAsDataframe.cvi(df, window: 20)
YfAsDataframe.dema(df, column: 'Adj Close', window: 20)
YfAsDataframe.di(df, window: 20)
YfAsDataframe.dm(df, window: 20)
YfAsDataframe.dpo(df, column: 'Adj Close', window: 20)
YfAsDataframe.dx(df, window: 20)
YfAsDataframe.ema(df, column: 'Adj Close', window: 5) 
YfAsDataframe.emv(df)
YfAsDataframe.fisher(df, window: 20) 
YfAsDataframe.fosc(df, window: 20) 
YfAsDataframe.hma(df, column: 'Adj Close', window: 5) 
YfAsDataframe.kama(df, column: 'Adj Close', window: 5)
YfAsDataframe.kvo(df, short_window: 5, long_window: 20)
YfAsDataframe.linreg(df, column: 'Adj Close', window: 20)
YfAsDataframe.linregintercept(df, column: 'Adj Close', window: 20)
YfAsDataframe.linregslope(df, column: 'Adj Close', window: 20)
YfAsDataframe.macd(df, column: 'Adj Close', short_window: 12, long_window: 26, signal_window: 9)
YfAsDataframe.marketfi(df)
YfAsDataframe.mass(df, window: 20)
YfAsDataframe.max(df, column: 'Adj Close', window: 20)
YfAsDataframe.md(df, column: 'Adj Close', window: 20)
YfAsDataframe.median_price(df)
YfAsDataframe.mfi(df, window: 20)
YfAsDataframe.min(df, column: 'Adj Close', window: 20)
YfAsDataframe.mom(df, column: 'Adj Close', window: 5)
YfAsDataframe.moving_avgs(df, window: 20)
YfAsDataframe.natr(df, window: 20)
YfAsDataframe.nvi(df)
YfAsDataframe.obv(df)
YfAsDataframe.ppo(df, column: 'Adj Close', short_window: 12, long_window: 26)
YfAsDataframe.psar(df, acceleration_factor_step: 0.2, acceleration_factor_maximum: 2)
YfAsDataframe.pvi(df)
YfAsDataframe.qstick(df, window: 20)
YfAsDataframe.roc(df, column: 'Adj Close', window: 20)
YfAsDataframe.rocr(df, column: 'Adj Close', window: 20)
YfAsDataframe.rsi(df, window: 20)
YfAsDataframe.sma(df, column: 'Adj Close', window: 20)
YfAsDataframe.stddev(df, column: 'Adj Close', window: 20)
YfAsDataframe.stderr(df, column: 'Adj Close', window: 20)
YfAsDataframe.stochrsi(df, column: 'Adj Close', window: 20)
YfAsDataframe.sum(df, column: 'Adj Close', window: 20)
YfAsDataframe.tema(df, column: 'Adj Close', window: 20)
YfAsDataframe.tr(df, column: 'Adj Close')
YfAsDataframe.trima(df, column: 'Adj Close', window: 20)
YfAsDataframe.trix(df, column: 'Adj Close', window: 20)
YfAsDataframe.trima(df, column: 'Adj Close', window: 20)
YfAsDataframe.tsf(df, column: 'Adj Close', window: 20)
YfAsDataframe.typical_price(df)
YfAsDataframe.ultosc(df, short_window: 5, medium_window: 12, long_window: 26)
YfAsDataframe.weighted_close_price(df)
YfAsDataframe.var(df, column: 'Adj Close', window: 20)
YfAsDataframe.vhf(df, column: 'Adj Close', window: 20)
YfAsDataframe.vidya(df, column: 'Adj Close', short_window: 5, long_window: 20, alpha: 0.2)
YfAsDataframe.volatility(df, column: 'Adj Close', window: 20)
YfAsDataframe.vosc(df, column: 'Adj Close', short_window: 5, long_window: 20)
YfAsDataframe.vol_weighted_moving_avg(df, window: 20)
YfAsDataframe.wad(df)
YfAsDataframe.wcprice(df)
YfAsDataframe.wilders(df, column: 'Adj Close', window: 20)
YfAsDataframe.willr(df, window: 20)
YfAsDataframe.wma(df, column: 'Adj Close', window: 5)
YfAsDataframe.zlema(df, column: 'Adj Close', window: 5)

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Graphing

To graph any of the series using Vega, per the information here, you will need to run

yarn add vega-cli vega-lite

Then, from within irb, you can generate charts, e.g.,

> msft = YfAsDataframe::Ticker.new("MSFT")
# => 
# #<YfAsDataframe::Ticker:0x000000011e6d50a0
# ...

> df = msft.history(period: '3y', interval: '1d')
# => 
# shape: (754, 10)
# ...

> df.insert_at_idx(df.columns.length, YfAsDataframe.ema(df, column: 'Adj Close', window: 5))
# => 
# shape: (753, 11)
# ┌────────────┬────────────┬────────────┬────────────┬───┬───────────┬───────────────┬──────────────┬──────────────────────┐
# │ Timestamps ┆ Open       ┆ High       ┆ Low        ┆ … ┆ Dividends ┆ Capital Gains ┆ Stock Splits ┆ EMA(5) for Adj Close │
# │ ---        ┆ ---        ┆ ---        ┆ ---        ┆   ┆ ---       ┆ ---           ┆ ---          ┆ ---                  │
# │ date       ┆ f64        ┆ f64        ┆ f64        ┆   ┆ f64       ┆ f64           ┆ f64          ┆ f64                  │
# ╞════════════╪════════════╪════════════╪════════════╪═══╪═══════════╪═══════════════╪══════════════╪══════════════════════╡
# │ 2021-07-12 ┆ 279.160004 ┆ 279.769989 ┆ 276.579987 ┆ … ┆ 0.0       ┆ 0.0           ┆ 0.0          ┆ 270.325745           │
# │ 2021-07-13 ┆ 277.519989 ┆ 282.850006 ┆ 277.390015 ┆ … ┆ 0.0       ┆ 0.0           ┆ 0.0          ┆ 271.514984           │
# │ 2021-07-14 ┆ 282.350006 ┆ 283.660004 ┆ 280.549988 ┆ … ┆ 0.0       ┆ 0.0           ┆ 0.0          ┆ 272.804932           │
# │ 2021-07-15 ┆ 282.0      ┆ 282.51001  ┆ 279.829987 ┆ … ┆ 0.0       ┆ 0.0           ┆ 0.0          ┆ 273.184001           │
# │ 2021-07-16 ┆ 282.070007 ┆ 284.100006 ┆ 279.459991 ┆ … ┆ 0.0       ┆ 0.0           ┆ 0.0          ┆ 273.345751           │
# │ …          ┆ …          ┆ …          ┆ …          ┆ … ┆ …         ┆ …             ┆ …            ┆ …                    │
# │ 2024-07-02 ┆ 453.200012 ┆ 459.589996 ┆ 453.109985 ┆ … ┆ 0.0       ┆ 0.0           ┆ 0.0          ┆ 454.288375           │
# │ 2024-07-03 ┆ 458.190002 ┆ 461.019989 ┆ 457.880005 ┆ … ┆ 0.0       ┆ 0.0           ┆ 0.0          ┆ 456.448913           │
# │ 2024-07-05 ┆ 459.609985 ┆ 468.350006 ┆ 458.970001 ┆ … ┆ 0.0       ┆ 0.0           ┆ 0.0          ┆ 460.152608           │
# │ 2024-07-08 ┆ 466.549988 ┆ 467.700012 ┆ 464.459991 ┆ … ┆ 0.0       ┆ 0.0           ┆ 0.0          ┆ 462.181735           │
# │ 2024-07-09 ┆ 467.0      ┆ 467.329987 ┆ 458.0      ┆ … ┆ 0.0       ┆ 0.0           ┆ 0.0          ┆ 461.30116            │
# └────────────┴────────────┴────────────┴────────────┴───┴───────────┴───────────────┴──────────────┴──────────────────────┘ 

> File.binwrite('/tmp/chart.png',df.plot("Timestamps", "EMA(5) for Adj Close", type: "line", width:800, height:500).to_png)
# => 44913 

Then the following image should be saved at the specified location.

PNG, SVG, and PDF output formats are supported directly. See this page for more information in constructing supported charts.

While it has not been tested yet, images should be able to be produced interactively using iruby operating in a Jupyter environment.

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Installation

Add this line to your application's Gemfile:

gem 'yf_as_dataframe'

And then execute:

$ bundle install

Or install it yourself as:

$ gem install yf_as_dataframe

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Development

To install this gem onto your local machine, run bundle exec rake install. To release a new version, update the version number in version.rb, and then run bundle exec rake release, which will create a git tag for the version, push git commits and the created tag, and push the .gem file to rubygems.org.

Contributing

Bug reports and pull requests are welcome on GitHub at https://github.com/bmck/yf_as_dataframe.

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Legal Stuff

The yf_as_dataframe gem is available as open source under the MIT Software License (https://opensource.org/licenses/MIT). See the LICENSE.txt file in the release for details.

AGAIN - yf_as_dataframe 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, here, and here) for details on your rights to use the actual data downloaded.

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