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Quant-in-R

Simple financial applications & analysis programs written in R language

SHY: The 'SHarpe Yield' Strategy // SHY strategy on the whole Taiwan Stock Market

R, as a programming language

The R programming language is designed for statistical analysis, and is widely used in many area recently. Here, I will focus on one of the most adopting domain, financial analysis, to demonstrate the strength of R. Besides, I shall pay extra attention to data visualizaiton in R.

The SHY 'SHarpe Yield' strategy

The introduced investment strategy, SHY 'SHarpe Yield', has a very simple computation model. It evaluates each stock by the SHY value, simply yield represents in a sharpe ratio way, i.e., its mean divided by its standard deviation. Given a list of sample stocks in folder csv/ (a dozen stock csv files reside), we can compute the SHY values and report the SHY value as follows:

    source("shy.R")
    shy_list = get_shy_suggestion()

Then, it shows

[1] "( 1) 個股：2382, ＳＨＹ：3.529709"
[1] "( 2) 個股：2912, ＳＨＹ：3.470507"
[1] "( 3) 個股：1101, ＳＨＹ：3.037783"
[1] "( 4) 個股：2330, ＳＨＹ：2.506207"
[1] "( 5) 個股：2207, ＳＨＹ：2.486131"
[1] "( 6) 個股：2801, ＳＨＹ：3.330069"
[1] "( 7) 個股：2701, ＳＨＹ：3.978262"
[1] "( 8) 個股：2357, ＳＨＹ：2.116582"
[1] "( 9) 個股：2325, ＳＨＹ：2.385228"
[1] "(10) 個股：1434, ＳＨＹ：3.511784"
[1] "(11) 個股：2354, ＳＨＹ：1.811173"
[1] "(12) 個股：0050, ＳＨＹ：NA"
[1] "(13) 個股：2408, ＳＨＹ：NA"

Based on the computed SHY value, we can form a strategy that selects stocks with better SHY, for example, buy a bunch of stocks with better SHY value periodically, just as the way as ETF (Exchange Traded Funds) does.

How good (or bad) is SHY?

Would you like to believe a strategy is good when someone throws it to you and claims that it is? I hope not.
Here, PerformanceAnalytics, a powerful module in finance evaluation in R is adopted, to express the quality of SHY:

    source("shy_eval.R")
    eval_res = evaluate_shy()

The result is then shown in your RStudio console:
(Note: here we buy 3 stocks based on SHY values periodically, among the candidates)
show multiple yield values

In case that the performance comparison between two targets is interested, we can put the two targets in same evaluation:

    eval_pair_res = evaluate_shy_stock('2330')

show multiple yield values

Another way to compare performance is to use a relative performance view:

    eval_rel_res = evaluate_shy_stock_relative('2357')

show multiple yield values

If some stocks, instead of SHY are interested, we can do the following:

    source('stock_eval.R')
    evaluate_stock('2330')
    evaluate_stock_pair('2330', '2357')
    evaluate_stock_pair_relative('2330', '2357')

Don't forget to try above yourself to see how the result is. :)

Note: have 'DEBUG=1' to show more message during computation

Web Visualization

Visualization is always a good idea for better comprehension, especially when it is presented by a web page. Shiny is yet one perfect web visualization framework to help R programmers present their work as a web page, in less effort without downgraded outcome. A page on how SHY performs, under the whole Taiwan Stock Market, can be found at:

SHY: The 'SHarpe Yield' Strategy // SHY strategy on the whole Taiwan Stock Market

There is also a page for simple data visualization, which you can open it locally as follows:

    library(shiny)
    setwd("SHY_draft")
    runApp()

Or visit the online page: SHY_draft: Simple demonstration for data visualization

More R programs

For future plan, I will present programs utilize some other R features in terms of financial analysis:

  • Probability Distributions
  • Linear Least Square Regression
  • Confidence Intervals
  • pValues
  • Power of a test