YStockData.jl

Fetch Data from Yahoo Finance
Popularity
13 Stars
Updated Last
2 Years Ago
Started In
December 2015

Download securities data from Yahoo Finance

Build Status

Installation

This package is not yet in the official package repository. Therefore, to install, use the following invocation Pkg.clone("https://github.com/Algocircle/YStockData.jl")

Usage

Yahoo Finance generally supports two different mechanisms for querying security data. The first returns current values for a set of fields, for a given ticker. In this package these can be retrieved by using a series of get_xxx methods, which take a ticker as an argument, and returns a Nullable value as a result. Each field supported by Yahoo has a corresponding get... method. For example:

julia> using YStockData

julia> YStockData.get_previous_close("IBM")
Nullable(136.75)

julia> YStockData.get_company_name("IBM")
Nullable("International Business Machines")

A higher level function, get retrieves all available fields, and returns the result as a Dictionary.

julia> YStockData.get("IBM")
Dict{ASCIIString,Nullable{T}} with 82 entries:
  "eps_estimate_next_year"          => Nullable(15.11)
  "todays_range_realtime"           => Nullable{Float64}()
  "commission"                      => Nullable{Float64}()
  "book_value"                      => Nullable(13.7)
  "eps_estimate_next_quarter"       => Nullable(2.95)
  "ask_size"                        => Nullable(200.0)
  "bid_size"                        => Nullable(200.0)
  "percent_change_from_52_week_low" => Nullable("+3.87%")
  "last_trade_date"                 => Nullable("12/18/2015")
  "percent_change_200_sma"          => Nullable("-9.55%")
  "last_trade_time_plus"            => Nullable("11:23am - <b>136.75</b>")
  "price_book"                      => Nullable(9.98)
  "stock_exchange"                  => Nullable("NYQ")
  "ex_dividend_date"                => Nullable("11/6/2015")
  "last_trade_realtime_time"        => Nullable{Float64}()
  "last_trade_size"                 => Nullable(100.0)
  "change_200_sma"                  => Nullable(-14.43)
  "peg"                             => Nullable(1.26)
  "dividend_pay_date"               => Nullable("12/10/2015")
  "eps_estimate_current_year"       => Nullable(14.93)
  "today_open"                      => Nullable(136.41)
  "holdings_gain_percent_realtime"  => Nullable{Float64}()
  "ebitda"                          => Nullable("21.41B")
  "previous_close"                  => Nullable(136.75)
  "revenue"                         => Nullable("83.80B")
  "1_year_target"                   => Nullable(148.85)
  "annualized_gain"                 => Nullable{Float64}()
  "ticker_trend"                    => Nullable{Float64}()
  ⋮                                 =>

The second mechanism returns OHLC time series values for a ticker and given time period. This data is available via the get_historical function, which takes the ticker, a start data and an end date as arguments. This function returns a DataFrame as its result.

YStockData.get_historical("IBM", now()-Year(1), now())
252x7 DataFrames.DataFrame
| Row | Date         | Open   | High   | Low    | Close  | Volume  | Adj_Close |
|-----|--------------|--------|--------|--------|--------|---------|-----------|
| 1   | "2015-12-17" | 139.35 | 139.5  | 136.31 | 136.75 | 4048600 | 136.75    |
| 2   | "2015-12-16" | 139.12 | 139.65 | 137.79 | 139.29 | 4313300 | 139.29    |
| 3   | "2015-12-15" | 137.4  | 138.97 | 137.28 | 137.79 | 4207900 | 137.79    |
| 4   | "2015-12-14" | 135.31 | 136.14 | 134.02 | 135.93 | 5103800 | 135.93    |
| 5   | "2015-12-11" | 135.23 | 135.44 | 133.91 | 134.57 | 5315200 | 134.57    |
| 6   | "2015-12-10" | 137.03 | 137.85 | 135.72 | 136.78 | 4754800 | 136.78    |
| 7   | "2015-12-09" | 137.38 | 139.84 | 136.23 | 136.61 | 4546600 | 136.61    |
| 8   | "2015-12-08" | 138.28 | 139.06 | 137.53 | 138.05 | 3859100 | 138.05    |
| 9   | "2015-12-07" | 140.16 | 140.41 | 138.81 | 139.55 | 3272000 | 139.55    |
| 10  | "2015-12-04" | 138.09 | 141.02 | 137.99 | 140.43 | 4504700 | 140.43    |
| 11  | "2015-12-03" | 140.1  | 140.73 | 138.19 | 138.92 | 5900900 | 138.92    |
| 12  | "2015-12-02" | 140.93 | 141.21 | 139.5  | 139.7  | 3707800 | 139.7     |
| 13  | "2015-12-01" | 139.58 | 141.4  | 139.58 | 141.28 | 4187400 | 141.28    || 239 | "2015-01-08" | 156.24 | 159.04 | 155.55 | 158.42 | 4236800 | 153.41    |
| 240 | "2015-01-07" | 157.2  | 157.2  | 154.03 | 155.05 | 4701800 | 150.147   |
| 241 | "2015-01-06" | 159.67 | 159.96 | 155.17 | 156.07 | 6146700 | 151.135   |
| 242 | "2015-01-05" | 161.27 | 161.27 | 159.19 | 159.51 | 4880400 | 154.466   |
| 243 | "2015-01-02" | 161.31 | 163.31 | 161.0  | 162.06 | 5525500 | 156.935   |
| 244 | "2014-12-31" | 160.41 | 161.5  | 160.38 | 160.44 | 4011900 | 155.366   |
| 245 | "2014-12-30" | 160.02 | 160.82 | 159.79 | 160.05 | 2829900 | 154.989   |
| 246 | "2014-12-29" | 162.0  | 162.34 | 159.45 | 160.51 | 3331800 | 155.434   |
| 247 | "2014-12-26" | 162.27 | 163.09 | 162.01 | 162.34 | 1912200 | 157.206   |
| 248 | "2014-12-24" | 162.88 | 162.99 | 161.61 | 161.82 | 1868100 | 156.703   |
| 249 | "2014-12-23" | 162.23 | 162.9  | 161.61 | 162.24 | 4043300 | 157.11    |
| 250 | "2014-12-22" | 158.33 | 161.91 | 158.33 | 161.44 | 4682500 | 156.335   |
| 251 | "2014-12-19" | 157.49 | 160.41 | 157.49 | 158.51 | 8864900 | 153.498   |
| 252 | "2014-12-18" | 153.58 | 157.68 | 153.3  | 157.68 | 7302400 | 152.694   |

The get_historical function also takes a Vector of tickers as input. The return value in this case is also a dataframe, with the ticker as an additional column.

julia> YStockData.get_historical(["IBM", "YHOO", "GOOG"], now()-Year(1), now())
Fetching historical data from Yahoo
from 2014-12-18T16:41:53 to 2015-12-18T16:41:53 for 3 tickers
1 of 3 : IBM
2 of 3 : YHOO
3 of 3 : GOOG
756x8 DataFrames.DataFrame
| Row | Date         | Open    | High    | Low     | Close   | Volume  | Adj_Close | Ticker |
|-----|--------------|---------|---------|---------|---------|---------|-----------|--------|
| 1   | "2015-12-17" | 139.35  | 139.5   | 136.31  | 136.75  | 4048600 | 136.75    | "IBM"  |
| 2   | "2015-12-16" | 139.12  | 139.65  | 137.79  | 139.29  | 4313300 | 139.29    | "IBM"  |
| 3   | "2015-12-15" | 137.4   | 138.97  | 137.28  | 137.79  | 4207900 | 137.79    | "IBM"  |
| 4   | "2015-12-14" | 135.31  | 136.14  | 134.02  | 135.93  | 5103800 | 135.93    | "IBM"  |
| 5   | "2015-12-11" | 135.23  | 135.44  | 133.91  | 134.57  | 5315200 | 134.57    | "IBM"  |
| 6   | "2015-12-10" | 137.03  | 137.85  | 135.72  | 136.78  | 4754800 | 136.78    | "IBM"  |
| 7   | "2015-12-09" | 137.38  | 139.84  | 136.23  | 136.61  | 4546600 | 136.61    | "IBM"  |
| 8   | "2015-12-08" | 138.28  | 139.06  | 137.53  | 138.05  | 3859100 | 138.05    | "IBM"  |
| 9   | "2015-12-07" | 140.16  | 140.41  | 138.81  | 139.55  | 3272000 | 139.55    | "IBM"  |
| 10  | "2015-12-04" | 138.09  | 141.02  | 137.99  | 140.43  | 4504700 | 140.43    | "IBM"  |
| 11  | "2015-12-03" | 140.1   | 140.73  | 138.19  | 138.92  | 5900900 | 138.92    | "IBM"  |
| 12  | "2015-12-02" | 140.93  | 141.21  | 139.5   | 139.7   | 3707800 | 139.7     | "IBM"  |
| 13  | "2015-12-01" | 139.58  | 141.4   | 139.58  | 141.28  | 4187400 | 141.28    | "IBM"  || 743 | "2015-01-08" | 497.992 | 503.482 | 491.002 | 502.682 | 3353600 | 502.682   | "GOOG" |
| 744 | "2015-01-07" | 507.002 | 507.246 | 499.652 | 501.102 | 2065100 | 501.102   | "GOOG" |
| 745 | "2015-01-06" | 515.002 | 516.177 | 501.052 | 501.962 | 2899900 | 501.962   | "GOOG" |
| 746 | "2015-01-05" | 523.262 | 524.332 | 513.062 | 513.872 | 2059800 | 513.872   | "GOOG" |
| 747 | "2015-01-02" | 529.012 | 531.272 | 524.102 | 524.812 | 1447600 | 524.812   | "GOOG" |
| 748 | "2014-12-31" | 531.252 | 532.602 | 525.802 | 526.402 | 1368200 | 526.402   | "GOOG" |
| 749 | "2014-12-30" | 528.092 | 531.152 | 527.132 | 530.422 | 876300  | 530.422   | "GOOG" |
| 750 | "2014-12-29" | 532.192 | 535.482 | 530.013 | 530.332 | 2278500 | 530.332   | "GOOG" |
| 751 | "2014-12-26" | 528.772 | 534.252 | 527.312 | 534.032 | 1036000 | 534.032   | "GOOG" |
| 752 | "2014-12-24" | 530.512 | 531.761 | 527.022 | 528.772 | 705900  | 528.772   | "GOOG" |
| 753 | "2014-12-23" | 527.002 | 534.562 | 526.292 | 530.592 | 2197600 | 530.592   | "GOOG" |
| 754 | "2014-12-22" | 516.082 | 526.462 | 516.082 | 524.872 | 2723800 | 524.872   | "GOOG" |
| 755 | "2014-12-19" | 511.512 | 517.722 | 506.913 | 516.352 | 3690200 | 516.352   | "GOOG" |
| 756 | "2014-12-18" | 512.952 | 513.872 | 504.702 | 511.102 | 2926700 | 511.102   | "GOOG" |

Disclaimer

Yahoo is a registered trademark of Yahoo Inc. This codebase is provided as is, without any warranties, or claims of fitness. It is not affiliated with or endorsed by Yahoo in any way.

You are responsible for adhering to all usage terms and restrictions for Yahoo Finance.