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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 only 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. |
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.
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)New in v0.4.0: This gem now includes built-in support for curl-impersonate. The curl-impersonate integration is enabled by default in v0.4.0+. Existing code will automatically use curl-impersonate to bypass TLS fingerprinting:
To use the TLS fingerprinting protection, you need to install curl-impersonate:
# macOS
brew tap shakacode/brew
brew install curl-impersonate
# Verify installation
ls -la /usr/local/bin/curl_*The codebase will look for the location of the curl-impersonate binaries per the CURL_IMPERSONATE_DIR environment variable;
if it is not assigned, the default location of the binaries is /usr/local/bin.
The code will randomly select one of the binaries (expected to be named "curl_chrome*", "curl_ff*", "curl_edge*", etc.) for its communications with the servers.
# Set custom directory
export CURL_IMPERSONATE_DIR="/opt/curl-impersonate/bin"
# Or set it for a single command
CURL_IMPERSONATE_DIR="/opt/curl-impersonate/bin" ruby your_script.rbYou can configure the curl-impersonate behavior if needed:
# Disable curl-impersonate (use HTTParty only)
YfAsDataframe::YfConnection.enable_curl_impersonate(false)
# Disable fallback (fail if curl-impersonate fails)
YfAsDataframe::YfConnection.enable_curl_impersonate_fallback(false)
# Set timeout
YfAsDataframe::YfConnection.set_curl_impersonate_timeout(45)
# Check available executables
executables = YfAsDataframe::YfConnection.get_available_curl_impersonate_executables
puts "Available: #{executables.length} executables"
# Check which directory is being used
puts "Using directory: #{YfAsDataframe::CurlImpersonateIntegration.executable_directory}"For more detailed information, see MINIMAL_INTEGRATION.md.
To graph any of the series using Vega, per the information here, you will need to run
yarn add vega-cli vega-liteThen, 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.
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
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.
Bug reports and pull requests are welcome on GitHub at https://github.com/bmck/yf_as_dataframe.
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.