### Quantmod Getting Started

Source: https://github.com/joshuaulrich/quantmod/blob/master/README.md

Basic usage of the quantmod package, including importing data with `getSymbols()` and visualizing with `chartSeries()`.

```APIDOC
## Getting Started

It is possible to import data from a variety of sources with one quantmod function: `getSymbols()`.

### Example 1: Importing data from Yahoo Finance and FRED

```r
> getSymbols("AAPL", src = "yahoo")    # from yahoo finance
[1] "AAPL"
> getSymbols("DEXJPUS", src = "FRED")  # FX rates from FRED
[1] "DEXJPUS"
```

### Example 2: Visualizing data and adding technical indicators

Once you've imported the data, you can use `chartSeries()` to visualize it and even add technical indicators from the [TTR](https://CRAN.R-project.org/package=TTR) package:

```r
> getSymbols("AAPL")
[1] "AAPL"
> chartSeries(AAPL)
> addMACD()
> addBBands()
```
```

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

### Install quantmod Package

Source: https://github.com/joshuaulrich/quantmod/blob/master/README.md

Instructions for installing the quantmod package from CRAN or the development version from GitHub.

```R
install.packages("quantmod")
remotes::install_github("joshuaulrich/quantmod")
devtools::install_github("joshuaulrich/quantmod")
```

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

### Quantmod Installation

Source: https://github.com/joshuaulrich/quantmod/blob/master/README.md

Instructions for installing the quantmod R package from CRAN or development versions from GitHub.

```APIDOC
## Installation

The current release is available on [CRAN](https://CRAN.R-project.org/package=quantmod), which you can install via:

```r
install.packages("quantmod")
```

To install the development version, you need to clone the repository and build from source, or run one of:

```r
# lightweight
remotes::install_github("joshuaulrich/quantmod")
# or
devtools::install_github("joshuaulrich/quantmod")
```

You may need tools to compile C, C++, or Fortran code. See the relevant appendix in the [R Installation and Administration manual](https://cran.r-project.org/doc/manuals/r-release/R-admin.html) for your operating system.
```

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

### Example of a Well-Structured Git Commit Message

Source: https://github.com/joshuaulrich/quantmod/wiki/Contributing-Guide

This snippet demonstrates the recommended format for a commit message, including a concise subject line, a detailed body explaining the 'what' and 'why', and a reference to the relevant issue tracker.

```text
Correct endpoints when index is before the epoch

The endpoints C code casts the double index to long, which truncates
it toward zero. This behavior is desired when the index is positive,
because it moves the endpoint *back* in time. But when the index is
negative, truncating toward zero moves the endpoint *forward* in time.

This is also an issue if the index value is stored as integer, since the
C99 specification states that integer division truncates toward zero.

If the first index value is less than zero, branch into a special case
to handle pre-epoch index values. This avoids performance degradation
if all index values are after the epoch.

If the index value is less than zero, simply add 1 to offset the
truncation toward zero. We also need to furthre adjust the potential
endpoint value if the index is exactly equal to zero.

Fixes #144.
```

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

### Convenience Charting Functions: barChart, candleChart, lineChart

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Provides examples of using convenience wrapper functions (barChart, candleChart, lineChart) that simplify the creation of specific financial chart types by calling chartSeries with predefined settings.

```R
getSymbols("AAPL")

# Bar chart (OHLC bars)
barChart(AAPL)
barChart(AAPL, subset = "2023")

# Candlestick chart
candleChart(AAPL)
candleChart(AAPL, theme = chartTheme("white"))

# Line chart
lineChart(AAPL)
lineChart(AAPL, subset = "last 6 months")
```

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

### GET /getOptionChain

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Retrieves option chain data including calls and puts for a specified underlying symbol.

```APIDOC
## GET /getOptionChain

### Description
Retrieves call and put option data for a symbol, supporting specific expiration dates and alternative data sources.

### Method
GET

### Endpoint
getOptionChain(symbol, Exp, src, api.key)

### Parameters
#### Path Parameters
- **symbol** (string) - Required - The ticker symbol

#### Query Parameters
- **Exp** (string) - Optional - Expiration date or range
- **src** (string) - Optional - Data source (e.g., "orats")
- **api.key** (string) - Optional - API key for external sources

### Request Example
getOptionChain("AAPL", Exp = "2024-01/2024-06")

### Response
#### Success Response (200)
- **calls** (data.frame) - Call options data
- **puts** (data.frame) - Put options data
```

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

### GET /tradeModel

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Applies a fitted model to generate trading signals based on predictions.

```APIDOC
## GET /tradeModel

### Description
Generates trading signals by applying a previously fitted model to the data.

### Method
GET

### Endpoint
tradeModel(model)

### Parameters
#### Query Parameters
- **model** (quantmodModel) - Required - The fitted model object.

### Request Example
{
  "model": "q.model"
}

### Response
#### Success Response (200)
- **signals** (xts) - Time series of generated trading signals.

#### Response Example
{
  "signals": "[...data...]"
}
```

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

### GET /Delt

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Calculates k-period percent differences or returns for time series data.

```APIDOC
## GET /Delt

### Description
Calculates arithmetic or logarithmic percent changes for a series or between two series.

### Method
GET

### Endpoint
Delt(x1, x2, k, type)

### Parameters
#### Query Parameters
- **x1** (numeric) - Required - First series
- **x2** (numeric) - Optional - Second series for comparison
- **k** (integer) - Optional - Number of periods to lag
- **type** (string) - Optional - "arithmetic" or "log"

### Request Example
Delt(Cl(AAPL), k = 1, type = "arithmetic")

### Response
#### Success Response (200)
- **result** (xts) - Time series of calculated percent changes
```

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

### GET /getDividends

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Retrieves dividend history for a given symbol with options for date ranges and split adjustment.

```APIDOC
## GET /getDividends

### Description
Fetches historical dividend data for a specified stock symbol.

### Method
GET

### Endpoint
getDividends(symbol, from, to, split.adjust)

### Parameters
#### Path Parameters
- **symbol** (string) - Required - The ticker symbol (e.g., "AAPL")

#### Query Parameters
- **from** (string) - Optional - Start date in YYYY-MM-DD format
- **to** (string) - Optional - End date in YYYY-MM-DD format
- **split.adjust** (boolean) - Optional - Whether to adjust dividends for stock splits

### Request Example
getDividends("AAPL", from = "2020-01-01", to = "2023-12-31")

### Response
#### Success Response (200)
- **data** (xts) - Time series object containing dividend history
```

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

### Quantmod Contributing and Support

Source: https://github.com/joshuaulrich/quantmod/blob/master/README.md

Information on how to contribute to quantmod development and where to seek support.

```APIDOC
## Contributing

Please see the [contributing guide](https://github.com/joshuaulrich/quantmod/wiki/Contributing-Guide).

## Supporting quantmod development

If you are interested in supporting the ongoing development and maintenance of quantmod, please consider [becoming a sponsor](https://github.com/sponsors/joshuaulrich).

## Have a question?

Ask your question on [Stack Overflow](http://stackoverflow.com/questions/tagged/r) or the [R-SIG-Finance](https://stat.ethz.ch/mailman/listinfo/r-sig-finance) mailing list (you must subscribe to post).
```

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

### Retrieve Financial Data with getSymbols

Source: https://github.com/joshuaulrich/quantmod/blob/master/README.md

Demonstrates how to import financial data from external sources like Yahoo Finance and FRED using the getSymbols function.

```R
getSymbols("AAPL", src = "yahoo")
getSymbols("DEXJPUS", src = "FRED")
```

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

### Visualize Financial Data and Indicators

Source: https://github.com/joshuaulrich/quantmod/blob/master/README.md

Shows how to create a chart series for imported data and overlay technical indicators such as MACD and Bollinger Bands.

```R
getSymbols("AAPL")
chartSeries(AAPL)
addMACD()
addBBands()
```

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

### periodReturn: Calculate Various Periodic Returns

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Demonstrates the calculation of financial returns at different time frequencies (daily, weekly, monthly, quarterly, yearly) using the periodReturn function and its wrappers. It supports arithmetic and logarithmic return types and allows subsetting by date.

```R
getSymbols("QQQ")

# Calculate returns at different frequencies
dailyReturn(QQQ)        # Daily returns
weeklyReturn(QQQ)       # Weekly returns
monthlyReturn(QQQ)      # Monthly returns
quarterlyReturn(QQQ)    # Quarterly returns
yearlyReturn(QQQ)       # Yearly returns (same as annualReturn)
annualReturn(QQQ)       # Annual returns

# Get all available return periods at once
allReturns(QQQ)

# Specify period using periodReturn
periodReturn(QQQ, period = "monthly")
periodReturn(QQQ, period = "quarterly")
periodReturn(QQQ, period = "yearly")

# Subset by date
periodReturn(QQQ, period = "yearly", subset = "2020::")  # 2020 onwards
periodReturn(QQQ, period = "yearly", subset = "2020")    # Only 2020

# Arithmetic vs logarithmic returns
dailyReturn(QQQ, type = "arithmetic")  # Default
dailyReturn(QQQ, type = "log")         # Continuous/log returns

# Exclude incomplete leading period
monthlyReturn(QQQ, leading = FALSE)

# Example: Performance analysis
monthly_rets <- monthlyReturn(QQQ)
annual_rets <- yearlyReturn(QQQ)

# Calculate cumulative returns
cumprod(1 + dailyReturn(QQQ))
```

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

### POST /buildModel

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Fits a specified trading model using various machine learning and statistical methods.

```APIDOC
## POST /buildModel

### Description
Fits a specified model using various machine learning and statistical methods such as lm, randomForest, or svm.

### Method
POST

### Endpoint
buildModel(model, method, training.per, ...)

### Parameters
#### Request Body
- **model** (quantmodModel) - Required - The model object created by specifyModel.
- **method** (string) - Required - The algorithm to use (e.g., 'lm', 'randomForest', 'svm').
- **training.per** (vector) - Required - Date range for training.

### Request Example
{
  "method": "randomForest",
  "training.per": ["2020-01-01", "2022-12-31"]
}

### Response
#### Success Response (200)
- **fitted_model** (object) - The trained model object.

#### Response Example
{
  "status": "success",
  "model_type": "randomForest"
}
```

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

### chartSeries: Create Financial Charts

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Illustrates the creation of professional financial charts using chartSeries, supporting various chart types (candlesticks, bars, lines) and customization options. It includes automatic volume display, technical indicators, date range subsetting, and theming.

```R
getSymbols("AAPL")

# Basic candlestick chart with volume
chartSeries(AAPL)

# Different chart types
chartSeries(AAPL, type = "candlesticks")  # Default
chartSeries(AAPL, type = "bars")          # OHLC bars
chartSeries(AAPL, type = "line")          # Line chart
chartSeries(AAPL, type = "matchsticks")   # 1-pixel candles

# Chart specific date range using xts-style subsetting
chartSeries(AAPL, subset = "2023")                  # Year 2023
chartSeries(AAPL, subset = "2023-06::")             # June 2023 onwards
chartSeries(AAPL, subset = "2023-01::2023-06")      # Jan-Jun 2023
chartSeries(AAPL, subset = "last 4 months")         # Last 4 months
chartSeries(AAPL, subset = "last 52 weeks")         # Last year

# Customize appearance
chartSeries(AAPL, theme = chartTheme("white"))      # White background
chartSeries(AAPL, theme = chartTheme("black"))      # Black background (default)

# Remove volume
chartSeries(AAPL, TA = NULL)

# Add technical indicators during chart creation
chartSeries(AAPL, TA = c(addVo(), addBBands(), addMACD()))

# Use logarithmic scale
chartSeries(AAPL, log.scale = TRUE)

# Multi-color candles (4 color coding scheme)
chartSeries(AAPL, multi.col = TRUE)

# Custom up/down colors
chartSeries(AAPL, up.col = "green", dn.col = "red")
```

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

### Fit Trading Models with buildModel

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Fits a specified model using various statistical and machine learning methods such as linear regression, random forests, or support vector machines.

```R
q.model <- buildModel(q.model, method = "lm", training.per = c("2020-01-01", "2022-12-31"))
q.model.rf <- buildModel(q.model, method = "randomForest", training.per = c("2020-01-01", "2022-12-31"), ntree = 500)
q.model.svm <- buildModel(q.model, method = "svm", training.per = c("2020-01-01", "2022-12-31"))
```

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

### Data Loading and Management

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Functions for loading financial data from various sources, configuring data sources, and downloading real-time quotes and exchange rates.

```APIDOC
## getSymbols - Load Financial Data from Multiple Sources

### Description
The `getSymbols` function is the primary data import mechanism in quantmod, supporting multiple data sources including Yahoo Finance, FRED, Alpha Vantage, Tiingo, MySQL databases, and CSV files. By default, it automatically assigns data to the calling environment with the symbol name as the variable name, though this can be disabled for explicit assignment.

### Method
`getSymbols(Symbols, src = "yahoo", ...)`

### Endpoint
N/A (R function)

### Parameters
#### Path Parameters
None

#### Query Parameters
- **Symbols** (character or character vector) - The stock symbol(s) or data identifier(s) to load.
- **src** (character) - The data source (e.g., "yahoo", "FRED", "av", "tiingo", "MySQL", "csv"). Defaults to "yahoo".
- **from** (character or Date) - The start date for the data.
- **to** (character or Date) - The end date for the data.
- **auto.assign** (logical) - If TRUE, data is automatically assigned to the environment. Defaults to TRUE.
- **env** (environment) - The environment to assign the data to if `auto.assign` is TRUE.
- **api.key** (character) - API key for sources like Alpha Vantage or Tiingo.
- **dir** (character) - Directory path for loading from CSV files.

### Request Example
```r
library(quantmod)

# Load stock data from Yahoo Finance (default source)
getSymbols("AAPL")

# Load multiple symbols at once
getSymbols(c("AAPL", "MSFT", "GOOG"))

# Load from FRED
getSymbols("CPIAUCNS", src = "FRED")

# Specify date range
getSymbols("AAPL", from = "2020-01-01", to = "2023-12-31")
```

### Response
#### Success Response (200)
Data is typically returned as an object of class 'xts' (eXtensible Time Series) and assigned to a variable named after the symbol, or returned directly if `auto.assign = FALSE`.

#### Response Example
```r
# Example output structure for AAPL
#            AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
# 2007-01-03    86.29    86.58   81.90     83.80    309579900         11.17
```
```

```APIDOC
## setSymbolLookup - Configure Default Data Sources

### Description
The `setSymbolLookup` function allows users to pre-configure default data sources and parameters for specific symbols, making subsequent `getSymbols` calls simpler and more consistent across an analysis session.

### Method
`setSymbolLookup(..., auto.assign = TRUE)`

### Endpoint
N/A (R function)

### Parameters
#### Path Parameters
None

#### Query Parameters
- **Symbol configurations**: A list or named arguments where each name is a symbol and the value specifies the source and parameters (e.g., `AAPL = "yahoo"`, `DJI = list(name = "^DJI", src = "yahoo")`).
- **auto.assign** (logical): If TRUE, applies the lookup configuration. Defaults to TRUE.

### Request Example
```r
# Set default sources for specific symbols
setSymbolLookup(AAPL = "yahoo", DIA = "MySQL", CPIAUCNS = "FRED")

# Set with additional parameters
setSymbolLookup(DJI = list(name = "^DJI", src = "yahoo"))

# Clear a symbol's defaults
setSymbolLookup(AAPL = NULL)
```

### Response
#### Success Response (200)
Modifies the global symbol lookup table. No direct return value, but subsequent `getSymbols` calls will use the configured defaults.
```

```APIDOC
## getQuote - Download Real-Time Stock Quotes

### Description
The `getQuote` function fetches current stock quotes from Yahoo Finance or Alpha Vantage, providing real-time or near-real-time market data for one or more symbols.

### Method
`getQuote(Symbols, src = "yahoo", ...)`

### Endpoint
N/A (R function)

### Parameters
#### Path Parameters
None

#### Query Parameters
- **Symbols** (character or character vector) - The stock symbol(s) to get quotes for.
- **src** (character) - The data source (e.g., "yahoo", "av"). Defaults to "yahoo".
- **what** (function or character) - Specifies the fields to retrieve (e.g., `yahooQF()`, `yahooQuote.EOD`).
- **api.key** (character) - API key for sources like Alpha Vantage.

### Request Example
```r
# Get current quote for a single symbol
getQuote("AAPL")

# Get quotes for multiple symbols
getQuote("AAPL;MSFT;GOOG")

# Request specific fields
getQuote("AAPL", what = yahooQF(c("Bid", "Ask", "Volume")))

# Get quote from Alpha Vantage
getQuote("AAPL", src = "av", api.key = "your_api_key")
```

### Response
#### Success Response (200)
A data frame containing the requested quote information for the specified symbols.

#### Response Example
```r
# Example output structure
#        Trade Time   Last Change  % Change   Open   High    Low Volume
# AAPL   2024-01-15  185.92  1.23    0.67%  184.50 186.74 184.22 52M
```
```

```APIDOC
## getFX - Download Exchange Rates

### Description
The `getFX` function is a convenience wrapper for downloading foreign exchange rate data from Oanda.

### Method
`getFX(Currencies, from = "1970-01-01", to = Sys.Date(), auto.assign = TRUE, ...)`

### Endpoint
N/A (R function)

### Parameters
#### Path Parameters
None

#### Query Parameters
- **Currencies** (character or character vector) - The currency pair(s) to download (e.g., "USD/JPY").
- **from** (character or Date) - The start date for the data.
- **to** (character or Date) - The end date for the data.
- **auto.assign** (logical) - If TRUE, data is automatically assigned to the environment. Defaults to TRUE.

### Request Example
```r
# Download USD/JPY exchange rate
getFX("USD/JPY")

# Download EUR/USD with date range
getFX("EUR/USD", from = "2023-01-01")

# Download multiple currency pairs
getFX(c("USD/JPY", "EUR/USD", "GBP/USD"))

# Return data directly without auto-assignment
equiv_eur_usd <- getFX("EUR/USD", auto.assign = FALSE)
```

### Response
#### Success Response (200)
Exchange rate data, typically returned as an 'xts' object named after the currency pair, or returned directly if `auto.assign = FALSE`.

#### Response Example
```r
# Example output structure for USDJPY
#            USD/JPY
# 2023-01-01 130.50
# 2023-01-02 131.20
```
```

```APIDOC
## getDividends and getSplits - Corporate Action Data

### Description
These functions download dividend and stock split history from Yahoo Finance for a given symbol.

### Method
`getDividends(Symbols, ...)`
`getSplits(Symbols, ...)`

### Endpoint
N/A (R function)

### Parameters
#### Path Parameters
None

#### Query Parameters
- **Symbols** (character or character vector) - The stock symbol(s) to get dividend or split history for.

### Request Example
```r
# Load stock data first (if not already loaded)
getSymbols("MSFT")

# Get dividend history
msft_div <- getDividends("MSFT")

# Get split history
msft_splits <- getSplits("MSFT")
```

### Response
#### Success Response (200)
A data frame or 'xts' object containing the dividend or split history for the specified symbol(s).

#### Response Example
```r
# Example output structure for dividends
#            MSFT.div
# 2003-02-19     0.08
# 2003-08-18     0.08
```
```

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

### Specify Trading Models with specifyModel

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Uses R formula syntax to define predictive trading models. It supports multi-asset inputs and integration with external economic data sources like FRED.

```R
q.model <- specifyModel(Next(OpCl(QQQ)) ~ Lag(OpHi(QQQ), 0:3))
setSymbolLookup(CPIAUCNS = "FRED")
q.model <- specifyModel(Next(Cl(SPY)) ~ Lag(Cl(SPY), 1:5) + CPIAUCNS)
model_data <- modelData(q.model)
```

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

### Retrieve Financial Symbols with getSymbols

Source: https://github.com/joshuaulrich/quantmod/blob/master/NEWS.md

Demonstrates the usage of the getSymbols function to fetch financial data. Note that symbols with a '^' prefix are handled by removing the prefix from the resulting object name.

```R
sym <- getSymbols("^IXIC")
```

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

### Retrieve Dividends and Splits with Quantmod

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Functions to fetch dividend history and stock split data for financial symbols, with options for split adjustments and automatic object updates.

```R
getDividends("AAPL", from = "2020-01-01", to = "2023-12-31")
getDividends("AAPL", split.adjust = TRUE)
getDividends("AAPL", split.adjust = FALSE)
msft_splits <- getSplits("MSFT")
getDividends("MSFT", auto.update = TRUE)
getSplits("MSFT", auto.update = TRUE)
```

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

### Manage Technical Indicators on Charts

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Demonstrates how to add, list, remove, swap, and move technical indicators on financial charts. It also shows how to set default indicators for all subsequent chartSeries calls.

```R
getSymbols("AAPL")
chartSeries(AAPL)
addMACD()
addRSI()
addBBands()
listTA()
dropTA(2)
swapTA(1, 2)
moveTA(from = 3, to = 1)
setTA(addVo(), addMACD(), addRSI())
chartSeries(AAPL)
```

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

### Generate Trading Signals with tradeModel

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Applies a previously fitted model to generate actionable trading signals based on model predictions.

```R
signals <- tradeModel(q.model)
head(signals)
```

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

### POST /specifyModel

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Defines a trading model specification using R formula syntax and data from various sources.

```APIDOC
## POST /specifyModel

### Description
Creates a reusable model specification using R formula syntax, automatically handling data retrieval from multiple sources.

### Method
POST

### Endpoint
specifyModel(formula, data, ...)

### Parameters
#### Request Body
- **formula** (formula) - Required - R formula defining the relationship between target and predictors.
- **data** (xts/zoo) - Optional - Data object containing the variables.

### Request Example
{
  "formula": "Next(OpCl(QQQ)) ~ Lag(OpHi(QQQ), 0:3)"
}

### Response
#### Success Response (200)
- **model** (quantmodModel) - A model specification object.

#### Response Example
{
  "model": "q.model"
}
```

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

### Calculate Price Changes and Returns

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Functions to calculate intraday, overnight, and daily price changes between OHLC components, and percentage changes using Delt.

```R
OpCl(AAPL)
ClCl(AAPL)
ClOp(AAPL)
HiCl(AAPL)
LoCl(AAPL)
LoHi(AAPL)
OpHi(AAPL)
OpLo(AAPL)
OpOp(AAPL)
Delt(Stock.Open, k = 1:3)
Delt(Stock.Open, type = "log")
Delt(Cl(AAPL), k = 5)
```

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

### Download Exchange Rates with getFX in R

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

The getFX function is a utility for downloading foreign exchange rate data, primarily from Oanda. It allows downloading rates for specific currency pairs, over specified date ranges, and supports multiple pairs simultaneously.

```r
# Download USD/JPY exchange rate
getFX("USD/JPY")
head(USDJPY)

# Download EUR/USD with date range
getFX("EUR/USD", from = "2023-01-01")

# Download multiple currency pairs
getFX(c("USD/JPY", "EUR/USD", "GBP/USD"))

# Return data directly without auto-assignment
eur_usd <- getFX("EUR/USD", auto.assign = FALSE)
```

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

### Download Option Chain Data

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Retrieves call and put option data for a symbol. Supports filtering by expiration dates and using external backends like ORATS.

```R
aapl_opts <- getOptionChain("AAPL")
aapl_all_opts <- getOptionChain("AAPL", Exp = NULL)
aapl_2024 <- getOptionChain("AAPL", Exp = "2024")
aapl_range <- getOptionChain("AAPL", Exp = "2024-01/2024-06")
opts <- getOptionChain(c("AAPL", "MSFT"), Exp = "2024", src = "orats", api.key = Sys.getenv("ORATS_API_KEY"))
```

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

### Quantmod Related Packages

Source: https://github.com/joshuaulrich/quantmod/blob/master/README.md

List of packages related to quantmod, including TTR and xts.

```APIDOC
## See Also

- [TTR](https://CRAN.R-project.org/package=TTR): functions for technical trading rules
- [xts](https://CRAN.R-project.org/package=xts): eXtensible Time Series based on [zoo](https://CRAN.R-project.org/package=zoo)
```

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

### Utilize Built-in Technical Indicators

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Quantmod provides a comprehensive set of functions for adding various pre-built technical indicators, often integrating with the TTR package. These include moving averages, trend indicators, momentum oscillators, volatility measures, and volume-based indicators.

```r
getSymbols("AAPL")
chartSeries(AAPL, subset = "last 6 months")
addSMA(n = 20)
addEMA(n = 20)
addWMA(n = 20)
addDEMA(n = 20)
addZLEMA(n = 20)
addEVWMA(n = 20)
addMACD(fast = 12, slow = 26, signal = 9)
addADX(n = 14)
addAroon(n = 20)
addAroonOsc(n = 20)
addSAR()
addTRIX(n = 20)
addKST()
addRSI(n = 14)
addCCI(n = 20)
addCMO(n = 14)
addROC(n = 10)
addMomentum(n = 10)
addWPR(n = 14)
addSMI()
addBBands(n = 20, sd = 2)
addATR(n = 14)
addVolatility(calc = "close")
addEnvelope(n = 20, p = 0.025)
addVo()
addOBV()
addMFI(n = 14)
addCMF(n = 20)
addChAD()
addEMV(n = 9)
addExpiry()
addZigZag()
addLines(h = 100)
addPoints(x, y)
```

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

### Lag and Next Functions for Time Series Data

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Demonstrates the use of Lag and Next functions to create lagged (past) and forward (future) shifted time series data. These are essential for creating features for predictive models.

```R
Lag(Stock.Close, k = 1)     # Same as default
Lag(Stock.Close, k = 2)     # 2-period lag
Lag(Stock.Close, k = 1:3)   # Multiple lags at once

Next(Stock.Close)           # 1-period forward
Next(Stock.Close, k = 2)    # 2-period forward

getSymbols("AAPL")

features <- cbind(
  Today = Cl(AAPL),
  Lag1 = Lag(Cl(AAPL), 1),
  Lag2 = Lag(Cl(AAPL), 2),
  Tomorrow = Next(Cl(AAPL), 1)
)
head(features, 5)
```

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

### Configure Default Data Sources with setSymbolLookup in R

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

The setSymbolLookup function allows users to define default data sources and parameters for specific financial symbols. This simplifies subsequent calls to getSymbols by pre-configuring source and other options, and the configuration can be viewed, saved, and loaded.

```r
# Set default sources for specific symbols
setSymbolLookup(AAPL = "yahoo", DIA = "MySQL", CPIAUCNS = "FRED")

# Now getSymbols will use the configured sources automatically
getSymbols(c("AAPL", "CPIAUCNS"))

# Set with additional parameters
setSymbolLookup(DJI = list(name = "^DJI", src = "yahoo"))
setSymbolLookup(MYSTOCK = list(src = "MySQL", dbname = "stocks", password = "secret"))

# View current symbol lookup configuration
getSymbolLookup("AAPL")
getSymbolLookup(c("AAPL", "DIA", "CPIAUCNS"))

# Save and load symbol lookup tables for persistence
saveSymbolLookup(file = "my_symbols.rda")
loadSymbolLookup(file = "my_symbols.rda")

# Clear a symbol's defaults
setSymbolLookup(AAPL = NULL)
```

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

### Download Corporate Actions Data with getDividends and getSplits in R

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

These functions, getDividends and getSplits, are used to retrieve historical dividend payments and stock split information, respectively, for a given stock symbol, typically from Yahoo Finance.

```r
# Get dividend history
getSymbols("MSFT")
msft_div <- getDividends("MSFT")
head(msft_div)
#            MSFT.div
# 2003-02-19     0.08
# 2003-08-18     0.08
```

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

### adjustOHLC: Adjust Stock Prices for Splits and Dividends

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Shows how to use the adjustOHLC function to correct historical OHLC (Open, High, Low, Close) stock prices for the effects of stock splits and dividend payouts. It allows adjustments for splits only, dividends only, or both, and can also use Yahoo's pre-adjusted data.

```R
getSymbols("AAPL", from = "2014-01-01")

# View unadjusted data
head(AAPL)

# Adjust for both splits and dividends (default)
AAPL.adjusted <- adjustOHLC(AAPL)
head(AAPL.adjusted)

# Adjust for splits only
AAPL.split.adj <- adjustOHLC(AAPL, adjust = "split")

# Adjust for dividends only
AAPL.div.adj <- adjustOHLC(AAPL, adjust = "dividend")

# Use Yahoo's pre-adjusted column (less precise but faster)
AAPL.yahoo.adj <- adjustOHLC(AAPL, use.Adjusted = TRUE)

# Compare intraday returns (should be identical)
head(cbind(
  Original = OpCl(AAPL),
  Adjusted = OpCl(AAPL.adjusted),
  YahooAdj = OpCl(AAPL.yahoo.adj)
))

# Compare close-to-close returns (may differ slightly)
head(cbind(
  Original = ClCl(AAPL),
  Adjusted = ClCl(AAPL.adjusted)
))
```

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

### Load Financial Data with getSymbols in R

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

The getSymbols function is the primary tool for importing financial data into R from multiple sources like Yahoo Finance, FRED, Alpha Vantage, Tiingo, MySQL, and CSV files. It can automatically assign data to variables or return it directly, and supports specifying date ranges and environments.

```r
library(quantmod)

# Load stock data from Yahoo Finance (default source)
getSymbols("AAPL")
head(AAPL)
#            AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
# 2007-01-03    86.29    86.58   81.90     83.80    309579900         11.17

# Load multiple symbols at once
getSymbols(c("AAPL", "MSFT", "GOOG"))

# Load data and return directly (no auto-assignment)
spy_data <- getSymbols("SPY", auto.assign = FALSE)

# Load into a specific environment
data_env <- new.env()
getSymbols("AAPL", env = data_env)
ls(data_env)  # [1] "AAPL"

# Load from FRED (Federal Reserve Economic Data)
getSymbols("CPIAUCNS", src = "FRED")  # Consumer Price Index

# Load from Alpha Vantage (requires API key)
getSymbols("AAPL", src = "av", api.key = "your_api_key")

# Load from Tiingo (requires API key)
getSymbols("AAPL", src = "tiingo", api.key = "your_api_key")

# Load from CSV file
getSymbols("MYDATA", src = "csv", dir = "/path/to/data/")

# Specify date range
getSymbols("AAPL", from = "2020-01-01", to = "2023-12-31")
```

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

### Download Real-Time Quotes with getQuote in R

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

The getQuote function retrieves current stock quotes from sources like Yahoo Finance and Alpha Vantage. It supports fetching quotes for single or multiple symbols, requesting specific data fields, and using different quote formats.

```r
# Get current quote for a single symbol
getQuote("AAPL")
#        Trade Time   Last Change  % Change   Open   High    Low Volume
# AAPL   2024-01-15  185.92  1.23    0.67%  184.50 186.74 184.22 52M

# Get quotes for multiple symbols (semicolon-separated)
getQuote("AAPL;MSFT;GOOG")

# Request specific fields
getQuote("AAPL", what = yahooQF(c("Bid", "Ask", "Volume")))

# Use end-of-day quote format
getQuote("AAPL", what = yahooQuote.EOD)

# Get quote from Alpha Vantage
getQuote("AAPL", src = "av", api.key = "your_api_key")

# Interactive field selection (opens selection menu)
yahooQF()

# View standard quote fields
standardQuote()
```

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

### Customize Chart Appearance with chartTheme

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

The chartTheme function allows users to create and modify chart theme objects to control the visual styling of financial charts, including colors for price movements, backgrounds, and grid lines. It supports pre-built themes and extensive customization options.

```r
str(chartTheme())
chartTheme("black")
chartTheme("white")
custom_theme <- chartTheme("white",
  up.col = "darkgreen",
  dn.col = "darkred",
  up.border = "darkgreen",
  dn.border = "darkred"
)
chartSeries(AAPL, theme = custom_theme)
chartSeries(AAPL, theme = chartTheme("white", up.col = "blue", dn.col = "red"))
chartTheme(addRSI.col = "purple")
my_theme <- chartTheme("black",
  fg.col = "white",
  bg.col = "#1a1a2e",
  grid.col = "#2d2d44",
  border = "#4a4a6a",
  up.col = "#00ff88",
  dn.col = "#ff4444",
  up.border = "#00ff88",
  dn.border = "#ff4444"
)
getSymbols("AAPL")
chartSeries(AAPL, theme = my_theme, subset = "last 3 months")
```

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

### Create Custom Technical Indicator with newTA

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Defines a custom technical indicator using the newTA function. It requires a processing function (FUN) and a pre-processing function (preFUN) to extract specific OHLC data columns.

```R
newSpread <- newTA(
  FUN = function(x) x[, 1] - x[, 2],
  preFUN = function(x) cbind(Hi(x), Lo(x)),
  legend.name = "Hi-Lo Spread"
)
newSpread()
```

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

### Extract OHLC Data Columns

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Helper functions to extract specific price components (Open, High, Low, Close, Volume, Adjusted) from OHLC time series objects.

```R
getSymbols("AAPL")
Op(AAPL)
Hi(AAPL)
Lo(AAPL)
Cl(AAPL)
Vo(AAPL)
Ad(AAPL)
HL(AAPL)
HLC(AAPL)
OHLC(AAPL)
OHLCV(AAPL)
seriesHi(AAPL)
seriesLo(AAPL)
getPrice(AAPL, prefer = "close")
```

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

### Add Custom Technical Indicators with addTA

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

The addTA function is used to overlay custom data series or technical indicators onto existing financial charts. Indicators can be plotted on the main price panel or in separate sub-panels, with options for customizing appearance and line styles.

```r
getSymbols("SBUX")
barChart(SBUX)
addTA(EMA(Cl(SBUX), n = 20), on = 1, col = "blue")
addTA(EMA(Cl(SBUX), n = 50), on = 1, col = "red")
addTA(OpCl(SBUX), col = "purple", type = "h")
addTA(EMA(Cl(SBUX), n = 10), on = 1, col = "green", lwd = 2, lty = 2)
bullish <- Cl(SBUX) > Op(SBUX)
addTA(bullish, on = 1, col = "lightgreen")
```

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

### Create Custom TA Functions with newTA

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

The newTA function simplifies the creation of reusable custom technical analysis indicator functions that adhere to the addTA convention. It allows users to define custom indicators based on existing functions, specifying how they should be plotted on charts.

```r
getSymbols("SBUX")
barChart(SBUX)
newEMA <- newTA(FUN = EMA, preFUN = Cl, on = 1, col = 7)
newEMA()
newEMA(n = 10, col = 5)
newEMA(on = NA, col = 3)
newRSI <- newTA(FUN = RSI, preFUN = Cl, yrange = c(0, 100))
newRSI()
```

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

### Shift Time Series with Lag and Next

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

Shifts time series data forward or backward by a specified number of periods, essential for creating predictive features.

```R
Lag(Stock.Close)
Next(Stock.Close)
```

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

### Change Chart Zoom Level with zoomChart

Source: https://context7.com/joshuaulrich/quantmod/llms.txt

The zoomChart function enables dynamic adjustment of the date range displayed on a financial chart without requiring a full redraw. It supports zooming to specific dates, ranges, or resetting to the full data view. An interactive zooming function, zooom(), is also available.

```r
getSymbols("AAPL")
chartSeries(AAPL)
zoomChart("2023-06::")
zoomChart("2023-01::2023-06")
zoomChart("2023")
zoomChart("::2023-03")
zoomChart()
zooom()
```

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