blog/content/post/day-trading-generating-training-data.md

---
title: "Getting Into Day Trading: Analyzing The Moving Average"
date: 2017-11-04T14:11:54-04:00
draft: true
tags: ["day trading", "data analysis", "julia"]
---

Now that we have a Julia environment good to go, and a dataset available, time to start doing some real analysis.

I know that I have this bit of data for the WLTW symbol, and what would be helpful is to see that data completely
plotted in all of it's glory. Let's take a look at the closing costs (y) plotted against the date(x).

![Image](/img/post/WLTW_CLOSING_COSTS.png)

Not bad, we can see an ok trend going from January to December 2016. This data isn't very useful yet but I can
showcase some awesome Julia packages, and how I generated the graph.

I used DataFrames.jl to store the data, Query.jl to grab a subset of the data, and Gadfly.jl to plot the data.
All of these are excellent libraries for doing your thing when analyzing.

```julia
data = readtable("prices.csv", header=True)
q = @from i in data begin
    @where i.symbol == "WLTW"
    @select {i.date, i.close}
    @collect DataFrame
end

p = (q, y=:close, Geom.Point, Guide.Title("Closing Costs: WLTW - 2016"))
draw(PNG("wltw_closing_costs.png, 6inch, 4inch), p)
```

Now I'd like to add the plots for the 3-day SMA, and the 5-day SMA to the plot of WLTW closing costs. What these
are, are the average of either the last 3 days or the last 5 days for a single datapoint. I believe that
by doing so, we may be able to visualize if either datapoint is adequate in predicting trends in this data. I'll be looking for
how close any given moving average is to the actual trend of the close costs for the WLTW security.