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refs #181, fixing documentation issues #198

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Jun 11, 2024
Merged

refs #181, fixing documentation issues #198

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23a3c66
Use Artifacts.jl for sample data in README.md.
san-ath Apr 25, 2024
e40cc4e
Fix dependencies and other errors in demo_finance.md
san-ath Apr 25, 2024
68d5c01
Fixing error in GLM UUID.
san-ath Apr 25, 2024
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6 changes: 6 additions & 0 deletions Artifacts.toml
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@@ -0,0 +1,6 @@
[ibm]
git-tree-sha1 = "c8f3ebdaa31904fdd971537498f85bf9e61801cc"

[[ibm.download]]
sha256 = "31dd579169751f728b9aaf6dcc1fd9d32be4513bdb462bf5edd0df9a083d4704"
url = "https://gist.github.com/san-ath/9acea52b988e752b375617266f981d20/raw/c8f3ebdaa31904fdd971537498f85bf9e61801cc.tar.gz"
1 change: 1 addition & 0 deletions Project.toml
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Expand Up @@ -13,6 +13,7 @@ ShiftedArrays = "1277b4bf-5013-50f5-be3d-901d8477a67a"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
Artifacts = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"

[compat]
DataFrames = "1.3.2"
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4 changes: 2 additions & 2 deletions README.md
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Expand Up @@ -25,9 +25,9 @@ julia> Pkg.add("TSFrames")
TSFrames is a [Tables.jl](https://github.com/JuliaData/Tables.jl) compatible package. This helps in easy conversion between `TSFrame` objects and other [Tables.jl](https://github.com/JuliaData/Tables.jl) compatible types. For example, to load a `CSV` into a `TSFrame` object, we do the following.

```julia
julia> using CSV, Dates, DataFrames, TSFrames
julia> using CSV, Dates, DataFrames, TSFrames, Artifacts

julia> ts = CSV.read("IBM.csv", TSFrame)
julia> ts = CSV.read(joinpath(artifact"ibm", "IBM.csv"), TSFrame)
252x6 TSFrame with Date Index

Index Open High Low Close Adj Close Volume
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2 changes: 2 additions & 0 deletions docs/Project.toml
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Expand Up @@ -6,6 +6,8 @@ MarketData = "945b72a4-3b13-509d-9b46-1525bb5c06de"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
RecipesBase = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
Impute = "f7bf1975-0170-51b9-8c5f-a992d46b9575"
GLM = "38e38edf-8417-5370-95a0-9cbb8c7f171a"
TSFrames = "9f90e835-9451-4aaa-bcb1-743a1b8d2f84"

[compat]
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5 changes: 3 additions & 2 deletions docs/src/demo_finance.md
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Expand Up @@ -5,7 +5,7 @@
To load the IBM historical data, we will use the `MarketData.yahoo` function from [MarketData.jl](https://github.com/JuliaQuant/MarketData.jl), which returns the data in the form of a `TimeArray`. We just simply pass this on to the `TSFrame` constructor.

```@repl e1
using TSFrames, MarketData, Plots, Statistics, Impute
using TSFrames, MarketData, Plots, Statistics, Impute, GLM
ibm_ts = TSFrame(MarketData.yahoo(:IBM))
```

Expand Down Expand Up @@ -145,13 +145,14 @@ within the window.
```@repl e1
sp500 = TSFrame(MarketData.yahoo("^GSPC"));
sp500_adjclose = TSFrames.subset(sp500, date_from, date_to)[:, ["AdjClose"]]
ibm_adjclose = ibm[:, [:Index, :AdjClose]]

sp500_ibm = join(sp500_adjclose, ibm_adjclose, jointype=:JoinBoth)
sp500_ibm_returns = diff(log.(sp500_ibm))
TSFrames.rename!(sp500_ibm_returns, ["SP500", "IBM"]);

function regress(data)
ll = lm(@formula(IBM ~ SP500), data)
ll = lm(@formula(SP500 ~ IBM), data)
co::Real = coef(ll)[coefnames(ll) .== "IBM"][1]
sd::Real = Statistics.std(residuals(ll))
return (co, sd)
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