39 lines
1.6 KiB
Markdown
39 lines
1.6 KiB
Markdown
---
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title: "Getting Into Day Trading: Analyzing The Moving Average"
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date: 2017-11-04T14:11:54-04:00
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draft: true
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tags: ["day trading", "data analysis", "julia"]
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---
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Now that we have a Julia environment good to go, and a dataset available, time to start doing some real analysis.
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I know that I have this bit of data for the WLTW symbol, and what would be helpful is to see that data completely
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plotted in all of it's glory. Let's take a look at the closing costs (y) plotted against the date(x).
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Not bad, we can see an ok trend going from January to December 2016. This data isn't very useful yet but I can
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showcase some awesome Julia packages, and how I generated the graph.
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I used DataFrames.jl to store the data, Query.jl to grab a subset of the data, and Gadfly.jl to plot the data.
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All of these are excellent libraries for doing your thing when analyzing.
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```julia
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data = readtable("prices.csv", header=True)
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q = @from i in data begin
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@where i.symbol == "WLTW"
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@select {i.date, i.close}
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@collect DataFrame
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end
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p = (q, y=:close, Geom.Point, Guide.Title("Closing Costs: WLTW - 2016"))
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draw(PNG("wltw_closing_costs.png, 6inch, 4inch), p)
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```
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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
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are, are the average of either the last 3 days or the last 5 days for a single datapoint. I believe that
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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
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how close any given moving average is to the actual trend of the close costs for the WLTW security.
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