### Setup Plotting Environment

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/plotting.md

Initializes the plotting environment and loads necessary packages. This is a prerequisite for all plotting examples.

```julia
using Plots, MarketData, TimeSeries
default(; show=false)  # hide
ENV["GKSwstype"] = "100" # hide
gr()
t = yahoo(:GOOG, YahooOpt(; period1=now() - Month(1)))

```

--------------------------------

### Add MarketData Package

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/getting_started.md

Install the MarketData package, which provides historical financial data.

```julia
julia> Pkg.add("MarketData")

```

--------------------------------

### Add TimeSeries Package

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/getting_started.md

Install the TimeSeries package using the Julia package manager.

```julia
julia> Pkg.add("TimeSeries")

```

--------------------------------

### Create a TimeArray

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/retime.md

Initializes a TimeArray with hourly timestamps and random data for demonstration purposes. This setup is required before using the `retime` function.

```julia
using Plots, Dates, TimeSeries
default(show = false)  # hide
ENV["GKSwstype"] = "100" # hide
gr()
timestamps = range(DateTime(2020, 1, 1), length = 7*24, step = Hour(1))
ta = TimeArray(timestamps, cumsum(randn(7*24)), [:a])
```

--------------------------------

### Install TimeSeries.jl Package

Source: https://github.com/juliastats/timeseries.jl/blob/master/README.md

Use this snippet to add the TimeSeries.jl package to your Julia environment. Ensure Julia and the Pkg manager are set up.

```julia
using Pkg
Pkg.add("TimeSeries")
```

--------------------------------

### Create TimeArray with Date Timestamps

Source: https://github.com/juliastats/timeseries.jl/blob/master/README.md

Example demonstrating how to create a TimeArray using a range of Dates as timestamps and random data. Requires TimeSeries and Dates packages.

```julia
using TimeSeries
using Dates

dates = Date(2018, 1, 1):Day(1):Date(2018, 12, 31)
ta = TimeArray(dates, rand(length(dates)))
```

--------------------------------

### Create TimeArray with DateTime Timestamps

Source: https://github.com/juliastats/timeseries.jl/blob/master/README.md

Example demonstrating how to create a TimeArray using a range of DateTimes as timestamps and random data. Requires TimeSeries and Dates packages.

```julia
using TimeSeries
using Dates

timestamps = DateTime(2018, 1, 1):Hour(1):DateTime(2018, 12, 31)
ta = TimeArray(timestamps, rand(length(timestamps)))
```

--------------------------------

### Optimized Aggregation with Basecall

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Uses a base method for optimized performance when an aggregation function is available. This example uses `cumsum` for cumulative summation.

```julia
using TimeSeries
using MarketData
basecall(cl, cumsum)
```

--------------------------------

### Calculating Moving Average

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Performs a moving window calculation, such as a moving average. This example calculates a 10-period moving average using the `mean` function.

```julia
using TimeSeries
using MarketData
using Statistics
moving(mean, cl, 10)
```

--------------------------------

### Aggregating Values Up To Present

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Aggregates values from the beginning of the time series up to the present timestamp using a specified function. This example calculates the cumulative sum.

```julia
using TimeSeries
using MarketData
upto(sum, cl)
```

--------------------------------

### Leading Time Series Data by an Arbitrary Distance

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Leads the time series data by a specified number of days. This example demonstrates leading by 499 days, placing the last observation into the first date's value slot.

```julia
lead(cl, 499)
```

--------------------------------

### TimeArray constructor with existing TimeArray in TimeSeries.jl

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Shows how to create a new `TimeArray` by copying an existing one, with options to override timestamp, values, column names, and metadata. The example demonstrates updating the metadata.

```julia
TimeArray(ta::TimeArray; timestamp=..., values=..., colnames=..., meta=...)
```

```julia
julia> meta(cl)
"AAPL"

julia> cl′ = TimeArray(cl; meta=:AAPL);

julia> meta(cl′)
:AAPL
```

--------------------------------

### Get First N Elements using `head`

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

The `head` method returns the first value in a TimeArray by default. The number of elements to return can be specified as the second argument.

```julia
using TimeSeries
using MarketData

head(cl)
```

--------------------------------

### moving

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Applies a function over a sliding window of a specified size. For example, a 10-period moving average.

```APIDOC
## moving

### Description
Applies a function over a sliding window of a specified size. For example, a 10-period moving average.

### Method
moving(func, time_array, window_size)

### Parameters
- **func** (Function) - The function to apply to the window (e.g., `mean`, `sum`).
- **time_array** (TimeArray) - The input time series.
- **window_size** (Integer) - The size of the sliding window.

### Request Example
```julia
moving(mean, cl, 10)
```
```

--------------------------------

### Truncate TimeArray from a Specific Date using `from`

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

The `from` method truncates a TimeArray starting from the specified date.

```julia
using TimeSeries
using MarketData

from(cl, Date(2001, 12, 27))
```

--------------------------------

### from

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Truncates a TimeArray starting from the specified date.

```APIDOC
## `from`

The `from` method truncates a `TimeArray` starting with the date passed to
the method:

```@repl
using TimeSeries
using MarketData

from(cl, Date(2001, 12, 27))
```
```

--------------------------------

### 2D getindex for TimeArray in TimeSeries.jl

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Examples of using 2D indexing with `getindex` on a `TimeArray` to select specific rows and columns. Supports slicing by row range and specific column names.

```julia
ohlc[1:42, [:High, :Low]]
```

```julia
ohlc[42:end, [:High, :Low]]
```

```julia
ohlc[:, [:High, :Low]]
```

```julia
ohlc[42, [:High, :Low]]
```

--------------------------------

### Inner Join TimeArrays

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/combine.md

Performs an inner join on two TimeArrays, keeping only timestamps common to both. This example merges AAPL and CAT stock data.

```julia
using TimeSeries
using MarketData
AppleCat = merge(AAPL, CAT);
length(AppleCat)
```

--------------------------------

### Get Last N Elements using `tail`

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

The `tail` method returns the last value in a TimeArray by default. The number of elements to return can be specified as the second argument.

```julia
using TimeSeries
using MarketData

tail(cl)
```

```julia
using TimeSeries
using MarketData

tail(cl, 3)
```

--------------------------------

### Write TimeArray to CSV

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/readwrite.md

Use `writetimearray` to save a TimeArray object to a CSV file. The example shows writing the first 5 elements of a TimeArray named 'cl'.

```julia
writetimearray(cl[1:5], "close.csv")
```

--------------------------------

### Compress Daily Data to Monthly with Last Value

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/combine.md

Compresses daily closing prices to monthly, using the last known value for each month. Requires `TimeSeries` and `MarketData` packages.

```julia
using TimeSeries
using MarketData
collapse(cl, month, last)
```

--------------------------------

### Create Dummy TimeArray

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/getting_started.md

Generate a TimeArray with random data for a full year. This requires the Dates and TimeSeries packages.

```julia
using Dates
dates = Date(2018, 1, 1):Day(1):Date(2018, 12, 31)
ta = TimeArray(dates, rand(length(dates)))

```

--------------------------------

### Calculating Percent Change (Log Returns)

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Calculates the log returns of a time series. Log returns are useful for downstream calculations as they can be added instead of multiplied.

```julia
percentchange(cl, :log)
```

--------------------------------

### Calculating Percent Change (Simple Returns)

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Calculates the percent change between consecutive timestamps, also known as simple returns. This is the default behavior.

```julia
using MarketData
percentchange(cl)
```

--------------------------------

### Apply Reduction Functions to TimeArray

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Demonstrates applying standard reduction functions like `sum`, `mean`, `std`, and `var` to `TimeArray` objects. These can be applied to the entire array or specific dimensions.

```julia
sum(ta)
sum(ta, 2)
```

--------------------------------

### Visualize Downsampling

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/retime.md

Visualizes the original TimeArray alongside the downsampled version, showing the effect of the mean aggregation over 6-hour intervals.

```julia
plot(ta)
plot!(ta_)
savefig("retime-downsample.svg"); nothing # hide
```

--------------------------------

### Date and DateTime Row Indexing for Time Series

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/indexing.md

Index time series data using specific Date objects or ranges of Dates. Requires the Dates package.

```julia
using MarketData
using Dates
ohlc[Date(2000, 1, 3)]
```

```julia
using MarketData
using Dates
ohlc[Date(2000, 1, 3):Day(1):Date(2000, 2, 4)]
```

--------------------------------

### upto

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Applies an aggregation function cumulatively from the beginning of the time series to the present timestamp.

```APIDOC
## upto

### Description
Applies an aggregation function cumulatively from the beginning of the time series to the present timestamp.

### Method
upto(func, time_array)

### Parameters
- **func** (Function) - The aggregation function to apply (e.g., `sum`, `prod`).
- **time_array** (TimeArray) - The input time series.

### Request Example
```julia
upto(sum, cl)
```
```

--------------------------------

### Displaying Time Series Data

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Shows the first three values of the cl time series. This is a common way to inspect time series data.

```julia
using MarketData
cl[1:3]
```

--------------------------------

### Use TimeArray as Dictionary Key

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Demonstrates using a `TimeArray` as a key in a `Dict`. `isequal` and `hash` are now supported, allowing `TimeArray` objects to be correctly hashed and compared within dictionaries.

```julia
d = Dict(cl => 42)

d[cl]
d[copy(cl)]
```

--------------------------------

### Find Dates Meeting a Condition using `findwhen`

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Use `findwhen` to test a condition and return a vector of Dates or DateTimes where the condition is true.

```julia
using TimeSeries
using MarketData

green = findwhen(ohlc[:Close] .> ohlc[:Open]);
typel(green)
```

```julia
using TimeSeries
using MarketData

ohlc[green]
```

--------------------------------

### Moving function with multi-column input in TimeSeries.jl

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Demonstrates using the `moving` function with multi-column input for user-defined functions. The input `ohlc` is a 500x4 `TimeArray`, and `A` will be a 10x4 `TimeArray` within the closure.

```julia
moving(ohlc, 10; dims=2, colnames=[:A, ...]) do
    # given that `ohlc` is a 500x4 `TimeArray`,
    # size(A) is (10, 4)
    ...
end
```

--------------------------------

### Load TimeArray from CSV

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/tables.md

Load a TimeArray from a CSV file using `CSV.File` and the TimeArray constructor. Specify the timestamp column using the `timestamp` keyword argument.

```julia
using CSV
TimeArray(CSV.File(filename); timestamp=:timestamp)
```

--------------------------------

### Visualize Upsampling

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/retime.md

Plots the original TimeArray and the upsampled TimeArray to visually compare the results of linear interpolation.

```julia
plot(ta)
plot!(ta_)
savefig("retime-upsampling.svg"); nothing # hide
```

--------------------------------

### Aggregate by Time Period using `when`

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Use `when` to aggregate elements from a TimeArray into specific time periods like Mondays or October. The period argument must be a valid Date method.

```julia
using TimeSeries
using MarketData
when(cl, dayofweek, 1)
```

```julia
using TimeSeries
using MarketData
when(cl, dayname, "Monday")
```

--------------------------------

### Retime with Irregular Timestamps

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/retime.md

Demonstrates retime on a TimeArray with irregular timestamps, or using a vector of irregular timestamps. The function automatically handles upsampling or downsampling based on the input.

```julia
new_timestamps = vcat(
    range(DateTime(2020, 1, 1), DateTime(2020, 1, 2)-Minute(15), step = Minute(15)), 
    range(DateTime(2020, 1, 2), DateTime(2020, 1, 3), step = Hour(1)),
)
retime(ta, new_timestamps)
```

--------------------------------

### percentchange

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Calculates the percent change (simple or log returns) between consecutive points in a time series. Defaults to simple returns.

```APIDOC
## percentchange

### Description
Calculates the percent change (simple or log returns) between consecutive points in a time series. Defaults to simple returns.

### Method
percentchange(time_array, type=:simple)

### Parameters
- **time_array** (TimeArray) - The input time series.
- **type** (Symbol) - The type of return to calculate. Can be `:simple` or `:log`. Defaults to `:simple`.

### Request Example
```julia
percentchange(cl)
percentchange(cl, :log)
```
```

--------------------------------

### split

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Splits data by a given function, e.g. Dates.day, into periods.

```APIDOC
## Splitting by period

Splitting data by a given function, e.g. `Dates.day` into periods.

```@repl
using TimeSeries
using MarketData

split(cl, Dates.day)
```
```

--------------------------------

### Compress Data with Independent Timestamp and Value Functions

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/combine.md

Compresses time series data by independently specifying the function to determine the timestamp and the function to determine the value. Requires the `Statistics` package.

```julia
using Statistics
collapse(cl, month, last, mean)
```

--------------------------------

### basecall

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

A performance-optimized method for applying cumulative functions, often used as an alternative to `upto` for specific operations like summation.

```APIDOC
## basecall

### Description
A performance-optimized method for applying cumulative functions, often used as an alternative to `upto` for specific operations like summation.

### Method
basecall(time_array, func)

### Parameters
- **time_array** (TimeArray) - The input time series.
- **func** (Function) - The cumulative function to apply (e.g., `cumsum`).

### Request Example
```julia
basecall(cl, cumsum)
```
```

--------------------------------

### Base Method Extensions for TimeArray

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/api.md

TimeSeries.jl extends several Base methods to provide enhanced functionality when working with TimeArray objects. These extensions allow for seamless integration with existing Julia workflows.

```APIDOC
## Base Method Extensions

TimeSeries.jl extends several Base methods to work with `TimeArray` objects:

- `Base.hcat(::TimeArray, ::TimeArray)`
- `Base.hcat(::TimeArray, ::TimeArray, ::Vararg{TimeArray})`
- `Base.vcat(::TimeArray...)`
- `Base.map`
- `Base.split(::TimeSeries.TimeArray, ::Function)`
- `Base.:+`
- `Base.:-`
- `Base.:(==)`
- `Base.eachrow`
- `Base.eachcol`
```

--------------------------------

### when

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Aggregates elements from a TimeArray into specific time periods based on a provided Date method and value.

```APIDOC
## `when`

The `when` methods allows aggregating elements from a `TimeArray` into
specific time periods, such as Mondays or the month of October:

```@repl
using TimeSeries
using MarketData
when(cl, dayofweek, 1)
when(cl, dayname, "Monday")
```

The period argument holds a valid `Date` method. Below are currently
available alternatives.

| Dates method       | Example                  |
|:------------------ |:------------------------ |
| `day`              | Jan 3, 2000 = 3          |
| `dayname`          | Jan 3, 2000 = "Monday"   |
| `week`             | Jan 3, 2000 = 1          |
| `month`            | Jan 3, 2000 = 1          |
| `monthname`        | Jan 3, 2000 = "January"  |
| `year`             | Jan 3, 2000 = 2000       |
| `dayofweek`        | Monday = 1               |
| `dayofweekofmonth` | Fourth Monday in Jan = 4 |
| `dayofyear`        | Dec 31, 2000 = 366       |
| `quarterofyear`    | Dec 31, 2000 = 4         |
| `dayofquarter`     | Dec 31, 2000 = 93        |
```

--------------------------------

### head

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Returns the first value(s) in a TimeArray. Defaults to the first value, but can be modified to return a specified number of elements from the top.

```APIDOC
## `head`

The `head` method defaults to returning only the first value in a
`TimeArray`. By selecting the second positional argument to a different
value, the user can modify how many from the top are selected:

```@repl
using TimeSeries
using MarketData

head(cl)
```
```

--------------------------------

### findall

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Tests a condition and returns a vector of Int representing the row in the array where the condition is true.

```APIDOC
## `findall`

The `findall` method tests a condition and returns a vector of `Int`
representing the row in the array where the condition is `true`:

```@repl
using TimeSeries
using MarketData

red = findall(ohlc[:Close] .< ohlc[:Open]);
typeof(red)
ohlc[red]
```

The following example won't create a temporary `Bool` vector, and gains better
performance.

```@setup findall
using TimeSeries
using MarketData
```

```@repl findall
findall(>(100), cl)
```
```

--------------------------------

### findwhen

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Tests a condition and returns a vector of Date or DateTime where the condition is true.

```APIDOC
## `findwhen`

The `findwhen` method test a condition and returns a vector of `Date` or
`DateTime` where the condition is `true`:

```@repl
using TimeSeries
using MarketData

green = findwhen(ohlc[:Close] .> ohlc[:Open]);
typeof(green)
ohlc[green]
```
```

--------------------------------

### Find Row Indices Meeting a Condition using `findall`

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

The `findall` method tests a condition and returns a vector of Int representing the row indices where the condition is true. This method can be more performant as it avoids creating temporary boolean vectors.

```julia
using TimeSeries
using MarketData

red = findall(ohlc[:Close] .< ohlc[:Open]);
typel(red)
```

```julia
using TimeSeries
using MarketData

ohlc[red]
```

```julia
findall(>(100), cl)
```

--------------------------------

### Leading Time Series Data

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Applies a lead of one time unit to the time series data. The last observation is omitted as there is no subsequent value to lead.

```julia
using TimeSeries
using MarketData
lead(cl[1:3])
```

--------------------------------

### Left Join TimeArrays

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/combine.md

Performs a left join on two TimeArrays, including all timestamps from the first TimeArray and matching timestamps from the second, introducing NaN for non-matches.

```julia
merge(op[1:3], cl[2:4]; method=:left)
```

--------------------------------

### Mixed Indexing for Time Series

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/indexing.md

Combine row and column indexing for more specific data retrieval. Supports combinations like integer ranges with symbols, or symbols with Date objects.

```julia
using MarketData
using Dates
ohlc[1:3, :Open]
```

```julia
using MarketData
using Dates
ohlc[:Open][Date(2000, 1, 3)]
```

--------------------------------

### Define Custom Mapping Function for TimeArray

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Illustrates how to use `map` with a callable object (struct with a call method) to transform `TimeArray` data. This allows for flexible, custom data manipulation based on timestamps and values.

```julia
struct T end
(::T)(timestamp, x) = (timestamp, x + 42)

t = T()

map(t, ta)
```

--------------------------------

### Integer Row Indexing for Time Series

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/indexing.md

Index time series data using single integers, ranges, ranges with steps, or combinations of ranges and integers. Supports indexing the last row with 'end'.

```julia
using MarketData
ohlc[1]
```

```julia
using MarketData
ohlc[1:3]
```

```julia
using MarketData
ohlc[1:2:10]
```

```julia
using MarketData
ohlc[[1:3; 8]]
```

```julia
using MarketData
ohlc[end]
```

--------------------------------

### Symbol Column Indexing for Time Series

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/indexing.md

Index time series data by column name using symbols. Supports single symbols, multiple symbols, or a collection of symbols.

```julia
using MarketData
using Dates
ohlc[:Open]
```

```julia
using MarketData
using Dates
ohlc[:Open, :Close]
```

```julia
using MarketData
using Dates
cols = [:Open, :Close]
ohlc[cols]
```

--------------------------------

### Retime Using a New Timestamp Vector

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/retime.md

Applies retime using a custom vector of timestamps, allowing for irregular or specific timestamp alignments. Ensure the new timestamps cover the desired range.

```julia
new_timestamps = range(DateTime(2020, 1, 1), DateTime(2020, 1, 2), step = Minute(15))
retime(ta, new_timestamps)
```

--------------------------------

### Downsampling with Mean Aggregation

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/retime.md

Applies downsampling to the TimeArray, aggregating data within each interval using the mean. The `downsample` argument should be set to `Mean()` or `:mean`.

```julia
ta_ = retime(ta, Hour(6), downsample=Mean())
```

--------------------------------

### Extrapolation with Missing Values

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/retime.md

Handles extrapolation at the beginning and end of the time series by filling with missing values. Use `MissingExtrapolate()` or `:missing` for the `extrapolate` argument.

```julia
new_timestamps = range(DateTime(2019, 12, 31), DateTime(2020, 1, 2), step = Minute(15))
ta_ = retime(ta, new_timestamps, extrapolate=MissingExtrapolate())
```

--------------------------------

### Apply `all()` to TimeArray Columns

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Use `all()` with a specified dimension (e.g., `2` for columns) to check if all elements in a `TimeArray` satisfy a condition. This is useful for data validation and filtering.

```julia
all(ta .> 3, 2)
```

--------------------------------

### Lagging Time Series Data with Padding

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Applies a lag of one time unit and pads the result with NaN values for omitted observations. Use this when preserving the date index is important.

```julia
lag(cl[1:3]; padding=true)
```

--------------------------------

### Split TimeArray by a Function using `split`

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Splits data by a given function, such as `Dates.day`, into periods.

```julia
using TimeSeries
using MarketData

split(cl, Dates.day)
```

--------------------------------

### Symbol column indexing in TimeSeries.jl

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Demonstrates accessing columns of a `TimeArray` using symbols, which is the preferred method since version 0.14.0. String indexing is deprecated.

```julia
using MarketData
ohlc[:Close]  # and cl["Close"] is deprecated
```

--------------------------------

### uniformspaced and uniformspace

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Methods to check or enforce uniform spacing in time series data.

```APIDOC
## uniformspaced and uniformspace

### Description
Methods to check or enforce uniform spacing in time series data.

### Method
uniformspaced(time_array)
uniformspace(time_array)

### Parameters
- **time_array** (TimeArray) - The input time series.

### Request Example
```julia
uniformspaced(cl)
uniformspace(cl)
```
```

--------------------------------

### Merge function with variable length input in TimeSeries.jl

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Illustrates the `merge` function's support for variable length input, allowing multiple `TimeArray` objects to be merged. The `method` argument remains a keyword argument.

```julia
merge(x, y, [zs...]; method=:outer)
```

--------------------------------

### Upsampling with Linear Interpolation

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/retime.md

Performs upsampling of the TimeArray using linear interpolation when no exact data points align with the new timestamps. Requires `Linear()` or `:linear` as the `upsample` argument.

```julia
ta_ = retime(ta, Minute(15), upsample=Linear())
```

--------------------------------

### Right Join TimeArrays

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/combine.md

Performs a right join on two TimeArrays, including all timestamps from the second TimeArray and matching timestamps from the first, introducing NaN for non-matches.

```julia
merge(op[1:3], cl[2:4]; method=:right)
```

--------------------------------

### Merge TimeArrays with Custom Meta

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/combine.md

Merges two TimeArrays and explicitly sets the meta field value for the resulting TimeArray. If not provided and meta fields are strings, they are concatenated.

```julia
meta(AppleCat)
```

```julia
CatApple = merge(CAT, AAPL; meta=47);
meta(CatApple)
```

```julia
meta(merge(AppleCat, CatApple))
```

--------------------------------

### Retime to a New Time Step

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/retime.md

Resamples the TimeArray to a new, regular time step of 15 minutes. This is a common use case for aligning data to a different frequency.

```julia
retime(ta, Minute(15))
```

--------------------------------

### diff

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Calculates the finite difference between consecutive points in a time series. The resulting series has fewer points. Use `padding=true` to fill initial missing values with NaN.

```APIDOC
## diff

### Description
Calculates the finite difference between consecutive points in a time series. The resulting series has fewer points. Use `padding=true` to fill initial missing values with NaN. Higher order differences can be calculated using the `differences` parameter.

### Method
diff(time_array; differences=1, padding=false)

### Parameters
- **time_array** (TimeArray) - The input time series.
- **differences** (Integer) - The order of the difference. Defaults to 1.
- **padding** (Boolean) - If true, pads the beginning with NaN. Defaults to false.

### Request Example
```julia
diff(cl)
```
```

--------------------------------

### Base.getproperty for TimeArray columns in TimeSeries.jl

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Illustrates accessing `TimeArray` columns using dot notation (e.g., `ta.column_name`), a feature supported since version 0.14.0. Accessing original fields like `values(ohlc)` requires using the function form.

```julia
using MarketData
ohlc.Open
```

```julia
ohlc.values  # this is unavailable due to Base.getproperty support
values(ohlc)  # change to this
```

--------------------------------

### Enforcing Uniform Spacing

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Enforces uniform spacing in a time series, potentially by resampling or interpolating. This is useful for preparing data for certain analyses.

```julia
using TimeSeries
uniformspace(cl)
```

--------------------------------

### lead

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Shifts time series values forward by a specified number of periods. Similar to lag but in the opposite direction. Arbitrary time distances are supported.

```APIDOC
## lead

### Description
Shifts time series values forward by a specified number of periods. Similar to lag but in the opposite direction. Arbitrary time distances are supported.

### Method
lead(time_array, lead_distance=1)

### Parameters
- **time_array** (TimeArray) - The input time series.
- **lead_distance** (Integer) - The number of periods to lead. Defaults to 1.

### Request Example
```julia
lead(cl[1:3])
lead(cl, 499)
```
```

--------------------------------

### Calculate Higher Order Differences with Lag

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

The `diff` function now supports calculating higher-order differences and specifying a lag in time steps. This is useful for analyzing trends and rates of change over extended periods.

```julia
diff(cl, 5)
```

--------------------------------

### Rename columns of a TimeArray

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/modify.md

Use `rename` to change column names. It accepts a single new name, an array of names, or a dictionary mapping old names to new names. Can also accept a function to transform existing names.

```julia
first(rename(cl, :Close′))
```

```julia
first(rename(cl, [:Close′]))
```

```julia
first(rename(ohlc, [:Open′, :High′, :Low′, :Close′]))
```

```julia
first(rename(ohlc, :Open => :Open′))
```

```julia
first(rename(ohlc, :Open => :Open′, :Close => :Close′))
```

```julia
first(rename(ohlc, Dict(:Open => :Open′, :Close => :Close′)...))
```

```julia
first(rename(Symbol ∘ uppercase ∘ string, ohlc))
```

```julia
first(rename(uppercase, ohlc, String))
```

--------------------------------

### Calculating Finite Differences

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Calculates the finite difference between consecutive points in a time series. The resulting series will have fewer points, with NaNs for padding if specified.

```julia
using TimeSeries
using MarketData
diff(cl)
```

--------------------------------

### Plot Candlestick Chart

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/plotting.md

Generates a candlestick chart, which requires the TimeArray to have 'open', 'high', 'low', and 'close' columns. Ideal for financial data visualization.

```julia
plot(ta; seriestype=:candlestick)
savefig("cs.svg");
nothing; # hide

```

--------------------------------

### to

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Truncates a TimeArray after the specified date.

```APIDOC
## `to`

The `to` method truncates a `TimeArray` after the date passed to the
method:

```@repl
using TimeSeries
using MarketData

to(cl, Date(2000, 1, 5))
```
```

--------------------------------

### Save TimeArray to CSV

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/tables.md

Save a TimeArray object to a CSV file using the `CSV.write` function from the CSV.jl package. Ensure the CSV.jl package is imported.

```julia
using CSV
CSV.write(filename, ta)
```

--------------------------------

### Checking for Uniform Spacing

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Checks if the time series has uniform spacing between timestamps. This is a utility function for data validation.

```julia
using TimeSeries
uniformspaced(cl)
```

--------------------------------

### Merge function with keyword argument in TimeSeries.jl

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Shows the updated `merge` function signature where the `method` argument is now a keyword argument. This change was introduced in version 0.16.0.

```julia
merge(x, y; method=:outer)
```

--------------------------------

### Calculating Higher Order Differences

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Calculates higher order finite differences by specifying the 'differences' keyword argument. `differences=2` is equivalent to applying `diff` twice.

```julia
diff(cl, differences=2)
```

--------------------------------

### Outer Join TimeArrays

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/combine.md

Performs an outer join on two TimeArrays, including all timestamps from both TimeArrays and introducing NaN for timestamps present in only one.

```julia
merge(op[1:3], cl[2:4]; method=:outer)
```

--------------------------------

### Compare TimeArrays for Equality

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Use `==` to compare two `TimeArray` objects for equality, checking all fields including dimensions. This is useful for verifying data integrity after operations.

```julia
cl == copy(cl)
```

--------------------------------

### BoundsError for TimeArray indexing in TimeSeries.jl

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Demonstrates the expected `BoundsError` when attempting to access an out-of-bounds index using `[]` on a `TimeArray`. This behavior was standardized in version 0.16.0.

```julia
julia> cl[]
ERROR: BoundsError: attempt to access TimeArray{Float64,1,Date,Array{Float64,1}}
  at index []
```

--------------------------------

### Convert TimeArray to DataFrame

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/tables.md

Convert a TimeArray object to a DataFrame using the DataFrame constructor. This is useful for further analysis or manipulation with DataFrame-specific functions.

```julia
using MarketData, DataFrames
df = DataFrame(ohlc)
```

--------------------------------

### TimeArray Struct Definition

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/timearray.md

Defines the structure of the TimeArray, including its fields for timestamps, values, column names, and metadata. The inner constructor code is omitted for brevity.

```julia
struct TimeArray{T,N,D<:TimeType,A<:AbstractArray{T,N}}
    timestamp::Vector{D}
    values::A # some kind of AbstractArray{T,N}
    colnames::Vector{Symbol}
    meta::Any

    # inner constructor code enforcing invariants
end
```

--------------------------------

### Truncate TimeArray to a Specific Date using `to`

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

The `to` method truncates a TimeArray after the specified date.

```julia
using TimeSeries
using MarketData

to(cl, Date(2000, 1, 5))
```

--------------------------------

### Map TimeSeries Data

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/combine.md

Use this to transform both timestamps and values in a TimeArray. The provided function receives the timestamp and values, returning new timestamp and values.

```julia
using TimeSeries
using Dates
ta = TimeArray([Date(2015, 10, 01), Date(2015, 11, 01)], [15, 16])
map((timestamp, values) -> (timestamp + Year(1), values), ta)
```

--------------------------------

### Iterate Rows of TimeArray

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/tables.md

Iterate over each row of a TimeArray using the `eachrow` function. Access columns by name, such as 'timestamp' and 'Close'.

```julia
using MarketData
for row in eachrow(ohlc)
    time = row.timestamp
    c = row.Close
    # ...
end
```

--------------------------------

### tail

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/split.md

Returns the last value(s) in a TimeArray. Defaults to the last value, but can be modified to return a specified number of elements from the bottom.

```APIDOC
## `tail`

The `tail` method defaults to returning only the last value in a
`TimeArray`. By selecting the second positional argument to a different
value, the user can modify how many from the bottom are selected:

```@repl
using TimeSeries
using MarketData

tail(cl)
tail(cl, 3)
```
```

--------------------------------

### Convert DataFrame to TimeArray

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/tables.md

Convert a DataFrame to a TimeArray, specifying the timestamp column using the `timestamp` keyword argument. The column name in the DataFrame will be used as the timestamp index.

```julia
df′ = DataFrames.rename(df, :timestamp => :A)
first(df′)
TimeArray(df′; timestamp=:A)
```

--------------------------------

### lag

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Shifts time series values by a specified number of periods. By default, it shifts by 1 period. Use `padding=true` to fill initial missing values with NaN.

```APIDOC
## lag

### Description
Shifts time series values by a specified number of periods. By default, it shifts by 1 period. Use `padding=true` to fill initial missing values with NaN.

### Method
lag(time_array, lag_distance=1; padding=false)

### Parameters
- **time_array** (TimeArray) - The input time series.
- **lag_distance** (Integer) - The number of periods to lag. Defaults to 1.
- **padding** (Boolean) - If true, pads the beginning with NaN. Defaults to false.

### Request Example
```julia
lag(cl[1:3])
lag(cl[1:3]; padding=true)
```
```

--------------------------------

### Customize Candlestick X-axis Ticks

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/plotting.md

Controls the density of x-axis labels on a candlestick chart using the `xticks` attribute and rotates them with `xrotation` for better readability.

```julia
plot(ta; seriestype=:candlestick, xticks=3, xrotation=60)
savefig("xticks.svg");
nothing; # hide

```

--------------------------------

### Lagging Time Series Data

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Applies a lag of one time unit to the time series data. The first observation is omitted as there is no prior value to lag.

```julia
lag(cl[1:3])
```

--------------------------------

### Horizontally Concatenate TimeArrays

Source: https://github.com/juliastats/timeseries.jl/blob/master/NEWS.md

Use the `hcat` operator `[...]` for efficient horizontal concatenation of `TimeArray` objects that share the same timestamps. This is faster than `merge` for this specific case.

```julia
[ta1 ta2]
```

--------------------------------

### Plot Multiple Series

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/plotting.md

Plots multiple variables from a TimeArray as individual lines on the same y-axis. Useful for comparing related time series data.

```julia
plot(ta[:Open, :High, :Low, :Close])
savefig("multi-series.svg");
nothing; # hide

```

--------------------------------

### Read CSV into TimeArray

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/readwrite.md

Use `readtimearray` to parse a CSV file into a TimeArray object. Supports custom delimiters and date formats.

```julia
readtimearray(fname; delim=',', meta=nothing, format="")
```

```julia
ta = readtimearray("close.csv"; format="dd/mm/yyyy HH:MM", delim=';')
```

--------------------------------

### Customize Candlestick Bar Width

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/plotting.md

Adjusts the width of the bars in a candlestick chart. The `bar_width` attribute accepts values between 0 and 1.

```julia
plot(ta; seriestype=:candlestick, bar_width=0.7)
savefig("bw.svg");
nothing; # hide

```

--------------------------------

### dropnan

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Removes rows containing NaN (Not a Number) values from a TimeArray.

```APIDOC
## dropnan

### Description
Removes rows containing NaN (Not a Number) values from a TimeArray.

### Method
dropnan(time_array)

### Parameters
- **time_array** (TimeArray) - The input time series.

### Request Example
```julia
dropnan(cl)
```
```

--------------------------------

### Removing NaN Values

Source: https://github.com/juliastats/timeseries.jl/blob/master/docs/src/apply.md

Removes rows containing NaN values from a TimeArray. This is a common data cleaning step.

```julia
using TimeSeries
dropnan(cl)
```

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