Code update: Stock Market Performance in January

This is a simple code update for Stock Market Performance in January, it is tested with the free Wolfram Cloud. 

The main change is that FinancialData now returns a TimeSeries object.

I haven't touched Mathematica 10,11 and 12, learning 13 on Wolfram Cloud right now. Mathematica has changed a lot!!

View stock market seasonality

A reader asks about the seasonal chart. Here is a simpler version.

The seasonality is defined as the percent change from the beginning of year to the end of each month.

monthlychange[stock_, year_] :=
Module[{data, firstdateprice, endofmonthprice},
  data = FinancialData[stock, "Close", {{year, 1, 1}, {year, 12, 31}}];
  data = Map[Flatten[#] &, data]; firstdateprice = data[[1, 4]];
  endofmonthprice = Table[Last@Select[data, #[[2]] == i &][[All, 4]], {i, 12}]; (endofmonthprice/firstdateprice - 1)*100]

This will give you the monthly change of AAPL in the year of 2008.

monthlychange["AAPL", 2008]

For multiple years, the average seasonality:

mc = monthlychange["AAPL", #] & /@ Range[2000, 2008];

Mean@mc

For APPL, the following graph shows the average seasonality from 2000 – 2008. According to this graph, it is probably a good idea to hold the stock through Oct and Nov before selling it.

This is a graph shows the seasonality year by year.

View stock market with heatmap

There is a heatmap tool which let you view the stock price change at a glance. It can be recreated with Mathematica in several lines of code.

Let’s pull the data by format {“symbol”, price change}

stockdata={#,(FinancialData[#]/FinancialData[#,"Close"]-1)*100 }& /@FinancialData["^DJI","Members"];

Then we need to represent the price change by color: Green means up, Red mean down, and the deeper the color, the bigger the change.

max = Max[Abs[stockdata[[All, 2]]]]; (* max change *)

GraphicsGrid[Partition[Graphics[{If[#[[2]]>=0.0,Blend[{White,Green},Rescale[#[[2]], {0,max}]], Blend[{Red,White}, Rescale[#[[2]],{-max,0}]]], Rectangle[], Black,Text[Style[#[[1]]<>"\n"<>ToString[NumberForm[#[[2]],{3,2}]]<>"%", Medium,Bold], {0.5,0.5}]}] &/@ stockdata, 6 ]]

The key function is if the price change is >0, then rescale the change in range (0, max) and get it’s color in Blend[{White, Green}]; if the price change is <0, then rescale the change in range (-max, 0) and use Blend[{Red, White}] to get the right color.

In the ascending order:

We can try other representations, too. For example, we can use the size of disk to represent the change.

Financial Data: AAPL vs MSFT

Technologically speaking, Apple and Microsoft are two quite different companies in many ways. We all know that in recently several years, Apple stock is very hot. Here is the stock price: APPL vs MSFT since 2004, the legend is not necessary for this graph.

DateListPlot[{FinancialData["AAPL",{2004}], FinancialData["MSFT",{2004}]}, Joined->True, Filling->Bottom]

Google Insights for Search gives another way to present it.

Google search volume index for "AAPL" since 2004

Google search volume index for "MSFT" since 2004

Understanding Finanical Market: Arbitrage

From Wikipedia, Arbitrage is the practice of taking advantage of a price differential between two or more markets, the profit being the difference between the markets prices.

Fro example, China Petro & Chem is traded in both US and HK stock markets. The price between these two markets are different, it seems US price follows the movements of HK's in recent several months.


After converting HK share price to US dollars, the price differences can be shown:
DateListPlot[{ussnp, hksnp}, Joined -> True, Filling -> {1 -> {2}}, FillingStyle -> {Green, Red}, DateTicksFormat -> {"Month", "/", "YearShort"}]
The trick is to use Filling -> {1 -> {2}}, FillingStyle -> {Green, Red}, Green indicates HK price is higher than US price, the red means the opposite.

Fun with Financial Data: Currency Exchange Rates

Someone posted some interesting photos from his trip to Japan in this January. One is delicious sushi sold at the roadside in somewhere, Kyoto, Japan.


In the top row, the one marked for 900 Yen looks exactly like the ones sold here in greocery store. I am wondering the price in US dollars.

900/FinancialData["USD/JPY"] gives $8.42, it is around 60% more expensive than the ones you can get here, however, it must taste much better.

One interesting thing is how much we can get if we convert 1 USD dollar to Japan Yen through the third currency.

FinancialData[{"USD",#}] / FinancialData[{"JPY",#}] &/@ {"HKD","EUR","GBP","CAD"}
{106.842, 106.625, 106., 106.947}

Direct exchange rate is 106.825.

USD->GBP->JPY: lost near 1% USD->CAD->JPY: gain tiny 0.1%

Is there a simple way to find a currency exchange chain "USD->..->... ->JPY" to maximize the gain over "USD->JPY"?

Play with Numbers: January Financial Market Performance

I am getting curious about the stock market during the lunch time. I like to check the January performance in the past.

(* define the function to get the January data *)
cc[stock_, year_] := FinancialData[stock, {{year, 1, 1}, {year, 1, 31}}]
(* plot multiyear graphs*)
Table[DateListPlot[cc["^DJI", year], PlotStyle -> {If[First[Last[#][[2]] - First[#] [[2]] >= 0 & /@ {cc["^DJI", year]}], Green, Red]}, Joined -> True, PlotLabel -> year], {year, 1991, 2006}]

Here is DOW and NASDAQ January performance from 1991 to 2006. By comparing the first and last trading day, if it gains, the line color is green, otherwise, it is red.

DOW January Performance (1991~2006)

NASDAQ January Performance (1991~2006)

You see, Mathematica does come in handy sometime!