Package 'RTL'

Title: Risk Tool Library - Trading, Risk, 'Analytics' for Commodities
Description: A toolkit for Commodities 'analytics', risk management and trading professionals. Includes functions for API calls to <https://commodities.morningstar.com/#/>, <https://developer.genscape.com/>, and <https://www.bankofcanada.ca/valet/docs>.
Authors: Philippe Cote [aut, cre], Nima Safaian [aut]
Maintainer: Philippe Cote <[email protected]>
License: MIT + file LICENSE
Version: 1.3.6
Built: 2024-11-23 05:35:06 UTC
Source: https://github.com/risktoollib/rtl

Help Index

Bond pricing

Description

Compute bond price, cash flow table or duration

Usage

bond(ytm = 0.05, C = 0.05, T2M = 1, m = 2, output = "price")

Arguments

ytm

Yield to Maturity. numeric
C

Coupon rate per annum. numeric
T2M

Time to maturity in years. numeric
m

Periods per year for coupon payments e.g semi-annual = 2. numeric
output

"price", "df" or "duration". character

Value

Returns price numeric, cash flows tibble, or duration numeric

Author(s)

Philippe Cote

Examples

bond(ytm = 0.05, C = 0.05, T2M = 1, m = 2, output = "price")
bond(ytm = 0.05, C = 0.05, T2M = 1, m = 2, output = "df")
bond(ytm = 0.05, C = 0.05, T2M = 1, m = 2, output = "duration")

EIA weekly supply-demand information by product group

Description

Given a product group extracts all information to create SD Balances.

Usage

chart_eia_sd(
  market = "mogas",
  key = "your EIA.gov API key",
  from = "2011-01-01",
  legend.pos = list(x = 0.4, y = 0.53),
  output = "chart"
)

Arguments

market

"mogas", "dist", "jet" or "resid". character
key

Your private EIA API token. character
from

Date as character "2020-07-01". Default to all dates available. character
legend.pos

Defaults to list(x = 0.4, y = 0.53). list
output

"chart" for plotly object or "data" for dataframe.

Value

A plotly chart htmlwidget or a tibble.

Author(s)

Philippe Cote

Examples

## Not run: 
chart_eia_sd(key = key, market = "mogas")

## End(Not run)

EIA Short Term Energy Outlook

Description

Extract data and either plots or renders dataframe.

Usage

chart_eia_steo(
  market = "globalOil",
  key = "your EIA.gov API key",
  from = "2018-07-01",
  fig.title = "EIA STEO Global Liquids SD Balance",
  fig.units = "million barrels per day",
  legend.pos = list(x = 0.4, y = 0.53),
  output = "chart"
)

Arguments

market

"globalOil" only currently implemented. character
key

Your private EIA API token. character
from

Date as character "2020-07-01". Default to all dates available. character
fig.title

Defaults to "EIA STEO Global Liquids SD Balance". character
fig.units

Defaults to "million barrels per day" character
legend.pos

Defaults to list(x = 0.4, y = 0.53) list
output

"chart" for plotly object or "data" for dataframe.

Value

A plotly chart htmlwidget or a tibble.

Author(s)

Philippe Cote

Examples

## Not run: 
chart_eia_steo(key = EIAkey, market = "globalOil")

## End(Not run)

Plots historical forward curves

Description

Returns a plot of forward curves through time

Usage

chart_fwd_curves(df = dfwide, cmdty = "cmewti", weekly = TRUE, ...)

Arguments

df

Wide dataframe with date column and multiple series columns (multivariate). tibble
cmdty

Futures contract code in expiry_table object: unique(expiry_table$cmdty). character
weekly

Defaults to TRUE for weekly forward curves. logical
...

other graphical parameters

Value

plot of forward curves through time. NULL

Author(s)

Philippe Cote

Examples

df <- dfwide %>%
  dplyr::select(date, dplyr::starts_with("CL")) %>%
  tidyr::drop_na()
chart_fwd_curves(
  df = df, cmdty = "cmewti", weekly = TRUE,
  main = "WTI Forward Curves", ylab = "$ per bbl", xlab = "", cex = 2
)

Pairwise scatter plots for timeseries

Description

Plots pairwise scatter plots with the time dimension. Useful when exploring structural changes in timeseries properties for modeling.

Usage

chart_pairs(df = df, title = "Time Series Pairs Plot")

Arguments

df

Wide data frame. tibble
title

Chart title. character

Value

A plotly object. htmlwidget

Author(s)

Philippe Cote

Examples

df <- dfwide %>%
  dplyr::select(date, CL01, NG01, HO01, RB01) %>%
  tidyr::drop_na()
chart_pairs(df = df, title = "example")

Cumulative performance and drawdown summary.

Description

Multi Asset Display of Cumulative Performance and Drawdowns

Usage

chart_PerfSummary(
  ret = ret,
  geometric = TRUE,
  main = "Cumulative Returns and Drawdowns",
  linesize = 1.25
)

Arguments

ret

Wide dataframe univariate or multivariate of percentage returns. tibble
geometric

Use geometric returns TRUE or FALSE. logical
main

Chart title. character
linesize

Size of lines in chart and legend. numeric

Value

Cumulative performance and drawdown charts. ggplot

Author(s)

Philippe Cote

Examples

ret <- data.frame(
  date = seq.Date(Sys.Date() - 60, Sys.Date(), 1),
  CL01 = rnorm(61, 0, .01), RB01 = rnorm(61, 0, 0.02)
)
chart_PerfSummary(ret = ret,
geometric = TRUE,
main = "Cumulative Returns and Drawdowns",
linesize = 1.25)

Futures contract spreads comparison across years

Description

Plots specific contract pairs across years with time being days from expiry.

Usage

chart_spreads(
  cpairs = cpairs,
  daysFromExpiry = 200,
  from = "2012-01-01",
  conversion = c(1, 1),
  feed = "CME_NymexFutures_EOD",
  iuser = "[email protected]",
  ipassword = "pass",
  title = "March/April ULSD Nymex Spreads",
  yaxis = "$ per bbl",
  output = "chart"
)

Arguments

cpairs

Tibble of contract pairs - see example for expiry when not expired yet. tibble
daysFromExpiry

Number of days from expiry to compute spreads. numeric
from

From date character
conversion

Defaults to c(1,1) first and second contracts. 42 from $ per gallons to bbls. numeric
feed

Morningstar Feed Table. character
iuser

Morningstar user name as character - sourced locally in examples. character
ipassword

Morningstar user password as character - sourced locally in examples. character
title

Title for chart. character
yaxis

y-axis label. character
output

"chart" for htmlwidget or "data" for tibble.

Value

A plotly object or a dataframe

Author(s)

Philippe Cote

Examples

## Not run: 
cpairs <- dplyr::tibble(
 year = c("2018", "2019", "2020","2021","2022","2023"),
 first = c("@HO8H", "@HO9H", "@HO0H","@HO21H","@HO22H","@HO23H"),
 second = c("@CL8H", "@CL9H", "@CL0H","@CL21H","@CL22H","@CL23H"),
 expiry = c(NA,NA,NA,NA,NA,"2023-02-23")
)
chart_spreads(
  cpairs = cpairs, daysFromExpiry = 200, from = "2012-01-01",
  conversion = c(42, 1), feed = "CME_NymexFutures_EOD",
  iuser = "[email protected]", ipassword = "pass",
  title = "March/April ULSD Nymex Spreads",
  yaxis = "$ per bbl",
  output = "data"
)

## End(Not run)

Z-Score applied to seasonal data divergence

Description

Supports analytics and display of seasonal data. Z-Score is computed on residuals conditional on their seasonal period. Beware that most seasonal charts in industry e.g. (NG Storage) is not de-trended so results once you apply an STL decomposition will vary from the unajusted seasonal plot.

Usage

chart_zscore(
  df = df,
  title = "NG Storage Z Score",
  per = "yearweek",
  output = "zscore",
  chart = "seasons"
)

Arguments

df

Long data frame with columns series, date and value. tibble
title

Default is a blank space returning the unique value in df$series. character
per

Frequency of seasonality "yearweek" (DEFAULT). "yearmonth", "yearquarter" character
output

"stl" for STL decomposition chart, "stats" for STL fitted statistics. "res" for STL fitted data. "zscore" for residuals Z-score, "seasonal" for standard seasonal chart.
chart

"seasons" for feasts::gg_season() (DEFAULT) "series" for feasts::gg_subseries()

Value

Time series of STL decomposition residuals Z-Scores, or standard seasonal chart with feast package.

Author(s)

Philippe Cote

Examples

## Not run: 
df <- eiaStocks %>% dplyr::filter(series == "NGLower48")
title <- "NGLower48"
chart_zscore(df = df, title = " ", per = "yearweek", output = "stl", chart = "seasons")
chart_zscore(df = df, title = " ", per = "yearweek", output = "stats", chart = "seasons")
chart_zscore(df = df, title = " ", per = "yearweek", output = "res", chart = "seasons")
chart_zscore(df = df, title = " ", per = "yearweek", output = "zscore", chart = "seasons")
chart_zscore(df = df, title = " ", per = "yearweek", output = "seasonal", chart = "seasons")

## End(Not run)

metadata for WTI CMA

Description

CME WTI Calendar Month Average swap information

Usage

cma

Format

data frame

Value

tibble

Source

cme

Cox-Ross-Rubinstein binomial option model

Description

European option binomial model on a stock without dividends.For academic purpose only. Use RTL::CRRoption for real-life usage.

Usage

CRReuro(S, X, sigma, r, T2M, N, type)

Arguments

S

Stock price. numeric
X

Strike price. numeric
sigma

Implied volatility e.g. 0.20 numeric
r

Risk-free rate. numeric
T2M

Time to maturity in years numeric
N

Number of time steps. Internally dt = T2M/N. numeric
type

"call" or "put" character

Value

List of asset price tree, option value tree and option price. list

Author(s)

Philippe Cote

Examples

CRReuro(S = 100, X = 100, sigma = 0.2, r = 0.1, T2M = 1, N = 5, type = "call")

Cox-Ross-Rubinstein Option Pricing Model

Description

Computes the price of European and American options using the Cox-Ross-Rubinstein binomial model. This function is optimized for performance and implemented in C++. Haug (2007) provides a detailed description of the model.

Usage

CRROption(S, X, sigma, r, b, T2M, N, type, optionStyle)

Arguments

S

Numeric, the current stock price (also known as the underlying asset price).
X

Numeric, the strike price of the option.
sigma

Numeric, the implied volatility of the underlying stock (annualized).
r

Numeric, the risk-free interest rate (annualized).
b

Numeric, the cost of carry, b = r - q for dividend paying assets, where q is the dividend yield rate.
T2M

Numeric, the time to maturity of the option (in years).
N

Integer, the number of time steps in the binomial tree.
type

Character, the type of option ("call" or "put").
optionStyle

Character, the style of the option ("european" or "american").

Value

A list containing the computed price of the option and a note indicating if the model is suitable for the provided parameters.

Examples

# CRROption(S = 100, X = 100, sigma = 0.25, r = 0.1, b = 0, T2M = 1, N = 500,
# type = "call", optionStyle = "european")
# CRROption(S = 100, X = 100, sigma = 0.25, r = 0.1, b = 0, T2M = 1, N = 500,
# type = "call", optionStyle = "american")

dataset: crude assays

Description

crude assays

Usage

crudeOil

Format

list

Value

list

dataset: WTI Cushing Futures and storage utilization

Description

c1, c2, c1c2 and Cushing storage utilization

Usage

cushing

Format

list

Value

list

Source

CME and EIA

dataset: commodity prices in a long dataframe format

Description

Futures settlement data set.

Usage

dflong

Format

data frame

Value

tibble

Source

Morningstar Commodities

dataset: commodity prices in a wide dataframe format

Description

Futures settlement data set.

Usage

dfwide

Format

data frame

Value

tibble

Source

Morningstar Commodities

Markowitz Efficient Frontier

Description

Generates random portfolio weights statistics based on absolute returns.

Usage

efficientFrontier(
  nsims = 5000,
  x = RTL::fizdiffs %>% dplyr::select(date, dplyr::contains("WCS")),
  expectedReturns = NULL
)

Arguments

nsims

Number of portfolio simulations. Defaults to 5000 numeric
x

List as provided by output of RTL::simMultivariates(). list
expectedReturns

Defaults to NULL using periodic returns means. numeric

Details

Commodities

Unlike traditional portfolio management, in commodities many transactions are with derivatives (futures and swaps) and have zero or low initial investments.

Return types

This function is used for commodities where returns are dollars per units for real assets e.g. storage tanks, pipelines...Here we measure directly the periodic return in dollars per contract unit.

Empirical Finance

I would encourage you to pick a commodity futures contract of your choice and draw a scatter plot of price level versus the daily dollar per unit change as measure of risk. As a trading analyst or risk manager, then ask yourself about the implications of using log returns that you then re-apply to current forward curve level to arrive at a dollar risk measure per units instead of measuring directly risk in dollars per unit.

Value

List of portfolios and chart of efficient frontier list

Author(s)

Philippe Cote

Examples

x =  RTL::fizdiffs %>% dplyr::select(date, dplyr::contains("WCS"))
efficientFrontier(nsims = 10, x = x, expectedReturns = NULL)
efficientFrontier(nsims = 10, x = x, expectedReturns = c(0.5,0.8,0.9))

EIA API call with tidy output

Description

Extracts data from the Energy Information Administration (EIA) API to tibble format with optional custom series name. Makes a clean wrapper for use with purrr for multiple series extraction. Query Browser at https://www.eia.gov/opendata/qb.php.

Usage

eia2tidy(ticker, key, name = " ")

Arguments

ticker

EIA series name. character
key

Your private EIA API token as character "yourapikey". character
name

Name you want to give the series. Defaults to ticker if set to " " character

Value

A tibble object with class date for weekly, monthly, quarterly or annual data and class POSIXct for hourly. tibble

Author(s)

Philippe Cote

Examples

## Not run: 
# Single Series
RTL::eia2tidy(ticker = "PET.MCRFPTX2.M", key = "yourapikey", name = "TexasProd")
# Multiple Series
# Use eia2tidy_all() or pivot_longer, drop_na and then pivot_wider to wrangled results.

## End(Not run)

EIA API multiple calls with tidy output

Description

Extracts data from the Energy Information Administration (EIA) API to tibble format with optional custom series name. Makes a clean wrapper for use with purrr for multiple series extraction. Query Browser at https://www.eia.gov/opendata/qb.php.

Usage

eia2tidy_all(
  tickers = tibble::tribble(~ticker, ~name, "PET.W_EPC0_SAX_YCUOK_MBBL.W",
    "CrudeCushing", "NG.NW2_EPG0_SWO_R48_BCF.W", "NGLower48"),
  key,
  long = TRUE
)

Arguments

tickers

tribble of EIA series and names you want to assign. character
key

Your private EIA API token as character "yourapikey". character
long

TRUE (default) to return a long data frame or FASLE for wide. logical

Value

A tibble object with class date for weekly, monthly, quarterly or annual data and class POSIXct for hourly. tibble

Author(s)

Philippe Cote

Examples

## Not run: 
eia2tidy_all(tickers = tibble::tribble(~ticker, ~name,
                         "PET.W_EPC0_SAX_YCUOK_MBBL.W", "CrudeCushing",
                         "NG.NW2_EPG0_SWO_R48_BCF.W", "NGLower48"),
            key = "your API key", long = TRUE)

## End(Not run)

dataset: EIA weekly stocks

Description

EIA weekly crude, NG, ULSD and RBOB stocks.

Usage

eiaStocks

Format

data frame

Value

tibble

dataset: EIA working storage capacity

Description

EIA working storage capacity in kbs except NG in bcf.

Usage

eiaStorageCap

Format

data frame

Value

tibble

dataset: Eurodollar futures contracts

Description

ED futures contract for December 2024

Usage

eurodollar

Format

data frame

Value

tibble

Source

Morningstar

dataset: expiry of common commodity futures contract.

Description

This dataframe provides detailed information on major futures contracts specifications pertaining to last settlement, notices and delivery dates. It also provides tickers in some data service.

Usage

expiry_table

Format

data frame

Value

tibble

Fits a Ornstein–Uhlenbeck process to a dataset

Description

Parameter estimation for Ornstein–Uhlenbeck process using OLS

Usage

fitOU(spread, dt = 1/252)

Arguments

spread

Spread time series. tibble
dt

Time step size in fractions of a year. Default is 1/252.

Value

List of theta, mu, annualized sigma estimates. It returns half life consistent with periodicity list

Author(s)

Philippe Cote

Examples

spread <- simOU(nsims = 1, mu = 5, theta = .5, sigma = 0.2, T = 5, dt = 1 / 252)
fitOU(spread = spread$sim1)

dataset: randomised physical crude differentials

Description

Randomized data set for education purpose of selected physical crude differentials to WTI.

Usage

fizdiffs

Format

data frame

Value

tibble

dataset: futures contracts metadata

Description

Exchange-traded contract month codes and specifications.

Usage

futuresRef

Format

data frame

Value

tibble

dataset: USDCAD FX forward rates

Description

USDCAD historicals and forward curve

Usage

fxfwd

Format

list

Value

list

Source

Morningstar and https://ca.investing.com/rates-bonds/forward-rates

Wrapper for a Garch(1,1) returning either a plot or data.

Description

Computes annualised Garch(1,1) volatilities using fGarch package.

Usage

garch(x = x, out = TRUE)

Arguments

x

Wide dataframe with date column and single series (univariate). tibble
out

"chart" to return replot_xts chart, "data" to return xts data or "fit" for uGARCHfit fit output

Value

replot_xts chart, xts data, or uGARCHfit fit

Author(s)

Philippe Cote

Examples

## Not run: 
x <- dflong %>% dplyr::filter(series == "CL01")
x <- returns(df = x, retType = "rel", period.return = 1, spread = TRUE)
x <- rolladjust(x = x, commodityname = c("cmewti"), rolltype = c("Last.Trade"))
summary(garch(x = x, out = "fit"))
garch(x = x, out = "chart")
garch(x = x, out = "data")

## End(Not run)

Generalized Black-Scholes (GBS) Option Pricing Model

Description

Computes the price and Greeks of European call and put options using the Generalized Black-Scholes model.

Usage

GBSOption(S, X, T2M, r, b, sigma, type = "call")

Arguments

S

numeric, the current stock price (also known as the underlying asset price).
X

numeric, the strike price of the option.
T2M

numeric, the time to maturity (in years). Previously denoted as T.
r

numeric, the risk-free interest rate (annualized).
b

numeric, the cost of carry, b = r - q for dividend paying assets, where q is the dividend yield rate.
sigma

numeric, the volatility of the underlying asset (annualized).
type

character, the type of option to evaluate, either "call" or "put". Default is "call".

Value

A list containing the following elements:

  • price: The price of the option.

  • delta: The sensitivity of the option's price to a change in the price of the underlying asset.

  • gamma: The rate of change in the delta with respect to changes in the underlying price.

  • vega: The sensitivity of the option's price to the volatility of the underlying asset.

  • theta: The sensitivity of the option's price to the passage of time.

  • rho: The sensitivity of the option's price to the interest rate.

Examples

GBSOption(S = 100, X = 100, T2M = 1, r = 0.05, b = 0.02, sigma = 0.2, type = "call")

Bank of Canada Valet API

Description

Extracts series from the Bank of Canada's Valet API. API documentation at https://www.bankofcanada.ca/valet/docs.

Usage

getBoC(series)

Arguments

series

Array of series name: c("FXCADUSD","BD.CDN.2YR.DQ.YLD"). character

Value

A long data frame. tibble

Author(s)

Philippe Cote

Examples

RTL::getBoC(series = c("FXCADUSD","BD.CDN.2YR.DQ.YLD"))

Morningstar Commodities API forward curves

Description

Returns forward curves from Morningstar API. See below for current feeds supported. You need your own credentials with Morningstar.

Usage

getCurve(
  feed = "CME_NymexFutures_EOD_continuous",
  contract = "CL",
  numOfcontracts = 12,
  date = "2023-08-24",
  fields =
    c("open_price, high_price, low_price, settlement_price, volume, open_interest"),
  iuser = "[email protected]",
  ipassword = "pass"
)

Arguments

feed

Morningstar Feed Table e.g "Crb_Futures_Price_Volume_And_Open_Interest". character
contract

Morningstar contract root e.g. "CL" for CME WTI and "BG" for ICE Brent. character
numOfcontracts

Number of listed contracts to retrieve. numeric
date

Date yyyy-mm-dd. character
fields

Defaults to c("open_price, high_price, low_price, settlement_price, volume, open_interest"). character
iuser

Morningstar user name as character - sourced locally in examples. character
ipassword

Morningstar user password as character - sourced locally in examples. character

Value

wide data frame. tibble

Current Feeds Supported

  • CME_NymexFutures_EOD_continuous

Author(s)

Philippe Cote

Examples

## Not run: 
# CME WTI Futures
getCurve(
  feed = "CME_NymexFutures_EOD_continuous", contract = "CL",
  date = "2023-08-24",
  fields = c("open_price, high_price, low_price, settlement_price, volume, open_interest"),
  iuser = "[email protected]", ipassword = "pass"
)

## End(Not run)

Genscape API call for oil pipelines

Description

Returns oil pipeline flows in barrels per day data from Genscape API.You need your own credentials. Refer to API documentation for argument values. It is assumed if you use this function that you know the pipelines you need to extract to build supply demand balances. Use the online API to identify the pipeline IDs. https://developer.genscape.com/docs/services/oil-transportation/operations/GetPipelineFlowValuesV2/

Usage

getGenscapePipeOil(
  frequency = "daily",
  regions = "Canada",
  pipelineIDs = c(97),
  revision = "revised",
  limit = 5000,
  offset = 0,
  startDate = "2015-01-01",
  endDate = as.character(Sys.Date()),
  apikey = "yourapikey"
)

Arguments

frequency

"daily" DEFAULT. character
regions

See API webpage. Multiple values separated by commas e.g. "Canada", "GulfCoast"). character
pipelineIDs

See API webpage. c(98,54...) for specific pipes. numeric
revision

See API webpage. character
limit

See API webpage. Max 5000. numeric
offset

See API webpage. numeric
startDate

"yyyy-mm-dd". character
endDate

"yyyy-mm-dd". character
apikey

Your API key. character

Value

wide data frame. tibble

Author(s)

Philippe Cote

Examples

## Not run: 
getGenscapePipeOil(
  frequency = "daily", regions = "Canada", pipelineIDs = c(97),
  revision = "revised", limit = 5000, offset = 0,
  startDate = "2015-01-01", endDate = as.character(Sys.Date()),
  apikey = "yourapikey"
)

## End(Not run)

Genscape API call for oil storage

Description

Returns oil storage data from Genscape API.You need your own credentials. Refer to API documentation for argument values. https://developer.genscape.com/docs/services/oil-storage/operations/StorageVolumeByOwnerGet

Usage

getGenscapeStorageOil(
  feed = "owner-volumes",
  regions = "Canada",
  products = "Crude",
  revision = "revised",
  limit = 5000,
  offset = 0,
  startDate = "2011-01-01",
  endDate = as.character(Sys.Date()),
  apikey = "yourapikey"
)

Arguments

feed

"owner-volumes" DEFAULT or "tank-volumes". character
regions

See API webpage. Multiple values separated by commas e.g. "Canada, Cushing"). character
products

See API webpage. Multiple values separated by commas e.g. "Crude, JetFuel"). character
revision

See API webpage. character
limit

See API webpage. Max 5000. numeric
offset

See API webpage. numeric
startDate

"yyyy-mm-dd". character
endDate

"yyyy-mm-dd". character
apikey

Your API key as a character string. character

Value

wide data frame tibble

Author(s)

Philippe Cote

Examples

## Not run: 
# where yourapikey = "yourapikey".
getGenscapeStorageOil(
  feed = "owner-volumes", regions = "Canada", products = "Crude",
  revision = "revised", limit = 5000, offset = 0,
  startDate = "2011-01-01", endDate = "2020-11-01", apikey = yourapikey
)

## End(Not run)

Extract and convert GIS data from a URL

Description

Returns a SpatialPointsDataFrame from a shapefile URL. @section Examples with EIA and Government of Alberta

  • from https://www.eia.gov/maps/layer_info-m.php :

  • crudepipelines <- getGIS(url = "https://www.eia.gov/maps/map_data/CrudeOil_Pipelines_US_EIA.zip")

  • refineries <- getGIS(url = "https://www.eia.gov/maps/map_data/Petroleum_Refineries_US_EIA.zip")

  • from https://gis.energy.gov.ab.ca/Geoview/OSPNG

  • AB <- getGIS(url = "https://gis.energy.gov.ab.ca/GeoviewData/OS_Agreements_Shape.zip")

Usage

getGIS(url = "https://www.eia.gov/maps/map_data/CrudeOil_Pipelines_US_EIA.zip")

Arguments

url

URL of the zipped shapefile. character

Value

SpatialPointsDataFrame. SpatialPolygonsDataFrame

Author(s)

Philippe Cote

Examples

## Not run: 
getGIS(url = "https://www.eia.gov/maps/map_data/CrudeOil_Pipelines_US_EIA.zip")

## End(Not run)

Morningstar Commodities API single call

Description

Returns data from Morningstar API. See below for current feeds supported. You need your own credentials with Morningstar. In examples sourced locally.

Usage

getPrice(
  feed = "CME_NymexFutures_EOD",
  contract = "@CL21Z",
  from = "2020-09-01",
  iuser = "[email protected]",
  ipassword = "pass"
)

Arguments

feed

Morningstar Feed Table. character
contract

Morningstar key. character
from

From date yyyy-mm-dd. character
iuser

Morningstar user name as character - sourced locally in examples. character
ipassword

Morningstar user password as character - sourced locally in examples. character

Value

wide data frame. tibble

Current Feeds Supported

  • CME_CbotFuturesEOD and CME_CbotFuturesEOD_continuous

  • CME_NymexFutures_EOD, CME_NymexFuturesFinal_EOD andCME_NymexFutures_EOD_continuous

  • CME_NymexOptionsFinal_EOD and CME_NymexOptions_EOD

  • CME_CmeFutures_EOD and CME_CmeFutures_EOD_continuous

  • CME_Comex_FuturesSettlement_EOD and CME_Comex_FuturesSettlement_EOD_continuous

  • LME_AskBidPrices_Delayed

  • SHFE_FuturesSettlement_RT

  • ICE_EuroFutures and ICE_EuroFutures_continuous

  • ICE_NybotCoffeeSugarCocoaFutures and ICE_NybotCoffeeSugarCocoaFutures_continuous

  • CME_STLCPC_Futures

  • CFTC_CommitmentsOfTradersCombined. Requires multiple keys. Separate them by a space e.g. "N10 06765A NYME 01".

  • Morningstar_FX_Forwards. Requires multiple keys. Separate them by a space e.g. "USDCAD 2M".

  • ERCOT_LmpsByResourceNodeAndElectricalBus.

  • PJM_Rt_Hourly_Lmp.

  • AESO_ForecastAndActualPoolPrice.

Author(s)

Philippe Cote

Examples

## Not run: 
getPrice(
  feed = "CME_NymexFuturesFinal_EOD", contract = "CL 2024 07",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "CME_NymexFutures_EOD", contract = "@CL21Z",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "CME_NymexFutures_EOD_continuous", contract = "CL_006_Month",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "CME_NymexOptionsFinal_EOD", contract = "06 2024 P LO 8000",
  from = "2020-03-15", iuser = username, ipassword = password
)
getPrice(
  feed = "CME_NymexOptions_EOD", contract = "@LO21ZP4000",
  from = "2020-03-15", iuser = username, ipassword = password
)
getPrice(
  feed = "CME_CbotFuturesEOD", contract = "C0Z",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "CME_CbotFuturesEOD_continuous", contract = "ZB_001_Month",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "CME_CmeFutures_EOD_continuous", contract = "HE_006_Month",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "Morningstar_FX_Forwards", contract = "USDCAD 2M",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "CME_CmeFutures_EOD", contract = "LH0N",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "CME_CmeFutures_EOD_continuous", contract = "HE_006_Month",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "ICE_EuroFutures", contract = "BRN0Z",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "ICE_EuroFutures_continuous", contract = "BRN_001_Month",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "ICE_NybotCoffeeSugarCocoaFutures", contract = "SB21H",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "ICE_NybotCoffeeSugarCocoaFutures_continuous", contract = "SF_001_Month",
  from = "2019-08-26", iuser = username, ipassword = password
)
getPrice(
  feed = "AESO_ForecastAndActualPoolPrice", contract = "Forecast_Pool_Price",
  from = "2021-04-01", iuser = username, ipassword = password
)
getPrice(
  feed = "LME_MonthlyDelayed_Derived", contract = "AHD 2021-12-01 2021-12-31",
  from = "2021-04-01", iuser = username, ipassword = password
)

## End(Not run)

Morningstar Commodities API multiple calls

Description

Multiple Morningstar API calls using getPrice functions. Refer to getPrices() for list of currently supported data feeds.

Usage

getPrices(
  feed = "CME_NymexFutures_EOD",
  contracts = c("CL9Z", "CL0F", "CL0M"),
  from = "2019-01-01",
  iuser = "[email protected]",
  ipassword = "pass"
)

Arguments

feed

Morningstar Feed Table. character
contracts

Symbols vector. character
from

From date yyyy-mm-dd. character
iuser

Morningstar user name as character - sourced locally in examples. character
ipassword

Morningstar user password as character - sourced locally in examples. character

Value

wide data frame. tibble

Author(s)

Philippe Cote

Examples

## Not run: 
getPrices(
  feed = "CME_NymexFutures_EOD", contracts = c("@CL0Z", "@CL1F", "@CL21H", "@CL21Z"),
  from = "2020-01-01", iuser = username, ipassword = password
)

## End(Not run)

dataset: NYMEX and ICE holiday calendars

Description

Holiday calendars for NYMEX and ICE Brent

Usage

holidaysOil

Format

data frame

Value

tibble

NPV

Description

Computes NPV with discount factor interpolation. This function is used for teaching NPV and NPV at Risk and needs to be customized.

Usage

npv(
  init.cost = -375,
  C = 50,
  cf.freq = 0.25,
  TV = 250,
  T2M = 2,
  disc.factors = us.df,
  BreakEven = FALSE,
  BE.yield = 0.01
)

Arguments

init.cost

Initial investment cost. numeric
C

Periodic cash flow. numeric
cf.freq

Cash flow frequency in year fraction e.g. quarterly = 0.25. numeric
TV

Terminal Value. numeric
T2M

Time to Maturity in years. numeric
disc.factors

Data frame of discount factors using ir.df.us() function. numeric
BreakEven

TRUE when using a flat discount rate assumption. logical
BE.yield

Set the flat IR rate when BreakEven = TRUE. logical

Value

List of NPV and NPV Data frame. list

Author(s)

Philippe Cote

Examples

npv(
  init.cost = -375, C = 50, cf.freq = .5, TV = 250, T2M = 2,
  disc.factors = RTL::usSwapCurves, BreakEven = FALSE, BE.yield = .0399
)$npv
npv(
  init.cost = -375, C = 50, cf.freq = .5, TV = 250, T2M = 2,
  disc.factors = RTL::usSwapCurves, BreakEven = FALSE, BE.yield = .0399
)$df

dataset: randomiser to convert settlement into OHLC

Description

OHLC profile using historical CL 1st Contract OHLC

Usage

ohlc

Format

data frame

Value

tibble

Source

CME

dataset: IR compounding

Description

Planet metrics from NASA

Usage

planets

Format

data frame

Value

tibble

Source

https://nssdc.gsfc.nasa.gov/planetary/factsheet/index.html

Computes betas of futures contracts with respect to the 1st line contract

Description

Returns betas of futures contracts versus front futures contract.

Usage

promptBeta(x = x, period = "all", betatype = "all", output = "chart")

Arguments

x

Wide dataframe with date column and multiple series columns (multivariate). tibble
period

"all" or numeric period of time in last n periods as character eg "100". character
betatype

"all" "bull" "bear". character
output

"betas" or "chart". character

Value

betas data frame tibble or plotly chart of betas htmlwidgets

Author(s)

Philippe Cote

Examples

## Not run: 
 x <- dflong %>%
 dplyr::filter(grepl("CL",series)) %>%
 dplyr::mutate(series = readr::parse_number(series)) %>% dplyr::group_by(series) %>%
 RTL::returns(df = ., retType = "abs",period.return = 1,spread = TRUE) %>%
 RTL::rolladjust(x = .,commodityname = c("cmewti"),rolltype = c("Last.Trade")) %>%
 # removing the day it prices went negative...
 dplyr::filter(!date %in% c(as.Date("2020-04-20"),as.Date("2020-04-21")))
promptBeta(x = x, period = "all", betatype = "all", output = "chart")
promptBeta(x = x, period = "all", betatype = "bull", output = "betas")
promptBeta(x = x, period = "100", betatype = "bear", output = "betas")

## End(Not run)

LP model for refinery optimization

Description

Plain vanilla refinery optimization LP model.

Usage

refineryLP(
  crudes = RTL::refineryLPdata$inputs,
  products = RTL::refineryLPdata$outputs
)

Arguments

crudes

Data frame of crude inputs. tibble
products

Data frame of product outputs and max outputs. tibble

Value

Optimal crude slate and profits. tibble

Author(s)

Philippe Cote

Examples

refineryLP(crudes = RTL::refineryLPdata$inputs, products = RTL::refineryLPdata$outputs)

dataset: refinery LP model sample inputs and outputs

Description

Simple refinery to be used in running LP modeling for education purposes.

Usage

refineryLPdata

Format

list

Value

list

Compute absolute, relative or log returns.

Description

Computes periodic returns from a dataframe ordered by date

Usage

returns(df = dflong, retType = "abs", period.return = 1, spread = FALSE)

Arguments

df

Long dataframe with colnames = c("date","value","series"). character
retType

"abs" for absolute, "rel" for relative, or "log" for log returns. character
period.return

Number of rows over which to compute returns. numeric
spread

TRUE if you want to spread into a long dataframe. logical

Value

A dataframe object of returns. tibble

Author(s)

Philippe Cote

Examples

x <- dflong %>% dplyr::filter(grepl("CL01", series))
returns(df = x, retType = "abs", period.return = 1, spread = TRUE)

Adjusts daily returns for futures contracts roll

Description

Returns a xts price or return object adjusted for contract roll. The methodology used to adjust returns is to remove the daily returns on the day after expiry and for prices to adjust historical rolling front month contracts by the size of the roll at each expiry. This is conducive to quantitative trading strategies as it reflects the PL of a financial trader.

Usage

rolladjust(x, commodityname = c("cmewti"), rolltype = c("Last.Trade"), ...)

Arguments

x

A df of returns.
commodityname

Name of commodity in expiry_table: unique(expiry_table$cmdty) or "cmecan" for WCW
rolltype

Type of contract roll: "Last.Trade" or "First.Notice".
...

Other parms

Value

Roll-adjusted xts object of returns

Author(s)

Philippe Cote

Examples

ret <- dplyr::tibble(date = seq.Date(Sys.Date() - 60, Sys.Date(), 1), CL01 = rnorm(61, 0, 1))
rolladjust(x = ret, commodityname = c("cmewti"), rolltype = c("Last.Trade"))

GBM process simulation

Description

Simulates a Geometric Brownian Motion process

Usage

simGBM(
  nsims = 1,
  S0 = 10,
  drift = 0,
  sigma = 0.2,
  T2M = 1,
  dt = 1/12,
  vec = TRUE
)

Arguments

nsims

number of simulations. Defaults to 1. numeric
S0

Spot price at t=0. numeric
drift

Drift term in percentage. numeric
sigma

Standard deviation. numeric
T2M

Maturity in years. numeric
dt

Time step in period e.g. 1/250 = 1 business day. numeric
vec

Vectorized implementation. Defaults to TRUE. logical

Value

A tibble of simulated values. tibble

Author(s)

Philippe Cote

Examples

simGBM(nsims = 2, S0 = 10, drift = 0, sigma = 0.2, T2M = 1, dt = 1 / 12, vec = TRUE)

Multivariate normal from historical dataset

Description

Generates multivariate random epsilons using absolute returns.

Usage

simMultivariates(nsims = 10, x, s0 = NULL)

Arguments

nsims

Number of simulations. Defaults to 10. numeric
x

Wide data frame of prices with date as first column. tibble
s0

Vector of starting value for each variables. Defaults to NULL with zero. numeric

Value

List of means, sds, covariance matrix, correlation matrix and simulated values. list

Author(s)

Philippe Cote

Examples

simMultivariates(nsims = 10, x = RTL::fizdiffs, s0 = NULL)

OU process simulation

Description

Simulates a Ornstein–Uhlenbeck process

Usage

simOU(
  nsims = 2,
  S0 = 5,
  mu = 5,
  theta = 0.5,
  sigma = 0.2,
  T2M = 1,
  dt = 1/12,
  epsilon = NULL
)

Arguments

nsims

number of simulations. Defaults to 2. numeric
S0

S at t=0. numeric
mu

Mean reversion level. numeric
theta

Mean reversion speed. numeric
sigma

Standard deviation. numeric
T2M

Maturity in years. numeric
dt

Time step size e.g. 1/250 = 1 business day. numeric
epsilon

Defaults to NULL function generates its own. numeric OPTIONAL: Array of epsilons for nsims = 1, if you want to feed your own e.g. in a multivariate context.

Value

Simulated values. tibble

Author(s)

Philippe Cote

Examples

simOU(nsims = 5, S0 = 5, mu = 5, theta = .5, sigma = 0.2, T2M = 1, dt = 1 / 12, epsilon = NULL)
simOU(nsims = 1, S0 = 5, mu = 5, theta = .5, sigma = 0.2, T2M = 1, dt = 1 / 12,
epsilon = matrix(rnorm(12,0,sqrt(1/12))))
simOU(nsims = 2, S0 = 5, mu = 5, theta = .5, sigma = 0.2, T2M = 1, dt = 1 / 12,
epsilon = replicate(2,rnorm(12,0,sqrt(1/12))))

OUJ process simulation

Description

Simulates a Ornstein–Uhlenbeck process with Jumps

Usage

simOUJ(
  nsims = 2,
  S0 = 5,
  mu = 5,
  theta = 10,
  sigma = 0.2,
  jump_prob = 0.05,
  jump_avesize = 2,
  jump_stdv = 0.05,
  T2M = 1,
  dt = 1/250
)

Arguments

nsims

number of simulations. Defaults to 2. numeric
S0

S at t=0. numeric
mu

Mean reversion level. numeric
theta

Mean reversion speed. numeric
sigma

Standard deviation. numeric
jump_prob

Probability of jumps. numeric
jump_avesize

Average size of jumps. numeric
jump_stdv

Standard deviation of jump average size. numeric
T2M

Maturity in years. numeric
dt

Time step size e.g. 1/250 = 1 business day. numeric

Value

Simulated values. tibble

Author(s)

Philippe Cote

Examples

simOUJ(nsims = 2, S0 = 5, mu = 5, theta = .5, sigma = 0.2,
jump_prob = 0.05, jump_avesize = 3, jump_stdv = 0.05,
T2M = 1, dt = 1 / 12)

OU process simulation

Description

Simulates a Ornstein–Uhlenbeck process with mu as a function of time

Usage

simOUt(
  nsims = 2,
  S0 = 0,
  mu = dplyr::tibble(t = 0:20, mr = c(rep(2, 7), rep(4, 14))),
  theta = 12,
  sigma = 0.2,
  T2M = 1,
  dt = 1/12
)

Arguments

nsims

number of simulations. Defaults to 2. numeric
S0

S at t=0. numeric
mu

data frame of mean reversion level as a function of time. tibble
theta

Mean reversion speed. numeric
sigma

Standard deviation. numeric
T2M

Maturity in years. numeric
dt

Time step size e.g. 1/250 = 1 business day. numeric

Value

Simulated values. tibble

Author(s)

Philippe Cote

Examples

mu = dplyr::tibble(t = 0:20,mr = c(rep(2,7),rep(4,14)))
simOUt(nsims = 2, S0 = 5, mu = mu, theta = .5, sigma = 0.2, T2M = 1, dt = 1 / 12)

dataset: spot to futures convergence

Description

Cash and futures

Usage

spot2futConvergence

Format

data frame

Value

tibble

Source

Morningstar, EIA

dataset: spot to futures convergence curve

Description

Forward Curve

Usage

spot2futCurve

Format

data frame

Value

tibble

Source

Morningstar, EIA

dataset: EIA Short Term Energy Outlook

Description

Short Term Energy Outlook from the EIA.

Usage

steo

Format

plotly object

Value

htmlwidget

Source

eia

dataset: Yahoo Finance data sets

Description

Traded equity prices and returns

Usage

stocks

Format

list

Value

list

Source

Yahoo Finance

Commodity Calendar Month Average Swaps

Description

Commodity swap pricing from exchange settlement

Usage

swapCOM(
  futures = futs,
  futuresNames = c("CL0M", "CL0N"),
  pricingDates = c("2020-05-01", "2020-05-30"),
  contract = "cmewti",
  exchange = "nymex"
)

Arguments

futures

Wide data frame of futures prices for the given swap pricing dates. tibble
futuresNames

Tickers of relevant futures contracts. character
pricingDates

Vector of start and end pricing dates. See example. character
contract

Contract code in data(expiry_table). sort(unique(expiry_table$cmdty)) for options. character
exchange

Exchange code in data(holidaysOil). Currently only "nymex" and "ice" supported. character

Value

Data frame of histocial swap prices. tibble

Author(s)

Philippe Cote

Examples

## Not run: 
c <- paste0("CL0", c("M", "N", "Q"))
futs <- getPrices(
  feed = "CME_NymexFutures_EOD", contracts = c, from = "2019-08-26",
  iuser = username, ipassword = password
)
swapCOM(
  futures = futs, futuresNames = c("CL0M", "CL0N"),
  pricingDates = c("2020-05-01", "2020-05-30"), contract = "cmewti", exchange = "nymex"
)

## End(Not run)

Commodity Calendar Month Average Swap futures weights

Description

Returns the percentage weight of the future in Calendar Month Average swaps

Usage

swapFutWeight(
  Month = "2020-09-01",
  contract = "cmewti",
  exchange = "nymex",
  output = "first.fut.weight"
)

Arguments

Month

First calendar day of the month. character
contract

Contract code in data(expiry_table). sort(unique(expiry_table$cmdty)) for options. character
exchange

Exchange code in data(holidaysOil). Currently only "nymex" and "ice" supported. character
output

Either "numDaysFut1", "numDaysFut2" or "first.fut.weight". character

Value

Depending on output setting. numeric If first.fut.weight, to compute swap 1 - first.fut.weight = % applied to 2nd line contract.

Author(s)

Philippe Cote

Examples

swapFutWeight(
  Month = "2020-09-01",
  contract = "cmewti", exchange = "nymex", output = "first.fut.weight"
)

Commodity Swap details to learn their pricing

Description

Returns dataframe required to price a WTI averaging instrument based on first line settlements.

Usage

swapInfo(
  date = "2023-08-24",
  feed = "CME_NymexFutures_EOD_continuous",
  ticker = "CL",
  contract = "cmewti",
  exchange = "nymex",
  iuser = "[email protected]",
  ipassword = "pass",
  output = "all"
)

Arguments

date

Character date as of which you want to extract daily settlement and forward values. character
feed

Feeds for Morningstar getCurve() and getPrice(). character
ticker

Nymex contract code. character
contract

Contract code in data(expiry_table). sort(unique(expiry_table$cmdty)) for options. character
exchange

Exchange code in data(holidaysOil). Defaults to "nymex". character
iuser

Morningstar user name as character - sourced locally in examples. character
ipassword

Morningstar user password as character - sourced locally in examples. character
output

"chart" or "all". character

Value

Plot or a list of data frame and plot if output = "all". htmlwidget or list

Author(s)

Philippe Cote

Examples

## Not run: 
swapInfo(
  date = "2020-05-06", feed = "CME_NymexFutures_EOD_continuous",
  ticker = "CL",
  contract = "cmewti", exchange = "nymex",
  iuser = "[email protected]", ipassword = "pass", output = "all"
)

## End(Not run)

Interest Rate Swap

Description

Computes the mark to market of an IRS

Usage

swapIRS(
  trade.date = lubridate::today(),
  eff.date = lubridate::today() + 2,
  mat.date = lubridate::today() + 2 + lubridate::years(2),
  notional = 1e+06,
  PayRec = "Rec",
  fixed.rate = 0.05,
  float.curve = usSwapCurves,
  reset.freq = 3,
  disc.curve = usSwapCurves,
  convention = c("act", 360),
  bus.calendar = "NY",
  output = "price"
)

Arguments

trade.date

Date object. Defaults to today(). Date
eff.date

Date object. Defaults to today() + 2 days. Date
mat.date

Date object. Defaults to today() + 2 years. Date
notional

Numeric value of notional. Defaults to 1,000,000. numeric
PayRec

"Pay" or "Rec" fixed. character
fixed.rate

Numeric fixed interest rate. Defaults to 0.05. Date
float.curve

List of interest rate curves. Defaults to data("usSwapCurves"). list
reset.freq

Numeric where 1 = "monthly", 3 = quarterly, 6 = Semi annual 12 = yearly. character
disc.curve

List of interest rate curves. Defaults to data("usSwapCurves"). list
convention

Vector of convention e.g. c("act",360) c(30,360),... character
bus.calendar

Banking day calendar. Not implemented.
output

"price" for swap price or "all" for price, cash flow data frame, duration. character

Value

List of swap price, cash flow data frame, duration. list

Author(s)

Philippe Cote

Examples

data("usSwapCurves")
swapIRS(
  trade.date = as.Date("2020-01-04"), eff.date = as.Date("2020-01-06"),
  mat.date = as.Date("2022-01-06"), notional = 1000000,
  PayRec = "Rec", fixed.rate = 0.05, float.curve = usSwapCurves, reset.freq = 3,
  disc.curve = usSwapCurves, convention = c("act", 360),
  bus.calendar = "NY", output = "all"
)

datasest: metadata of key EIA tickers grouped by products.

Description

Supports automated upload of EIA data through its API by categories. Data frame organized by Supply Demand categories and products.

Usage

tickers_eia

Format

data frame

Value

tibble

dataset: Canadian and US physical crude trading calendars

Description

Crude Trading Trade Cycles. Note that is uses NYMEX calendar (WIP)

Usage

tradeCycle

Format

data frame

Value

tibble

dataset: GIS locations for crude oil trading hubs

Description

Trading Hubs

Usage

tradeHubs

Format

data frame

Value

tibble

dataset: data for teaching the various ways to monetize a market call.

Description

Data set for explaining the various ways to monetize a market view.

Usage

tradeprocess

Format

data frame

Value

tibble

Risk-reward statistics for quant trading

Description

Compute list of risk reward metrics

Usage

tradeStats(x, Rf = 0)

Arguments

x

Univariate xts object of returns OR dataframe with date and return variables. xts
Rf

Risk-free rate. numeric

Value

List of risk/reward metrics. list

Author(s)

Philippe Cote

Examples

library(PerformanceAnalytics)
tradeStats(x = stocks$spy, Rf = 0)

Sample quantitative trading strategy

Description

Based on dividend yield.

Usage

tradeStrategyDY(data, par1value = 50, par2value = 200)

Arguments

data

Dataframe of OHLC data e.g. RTL::uso. tibble
par1value

Value of first parameter e.g. short MA. numeric
par2value

Value of second parameter e.g. long MA. numeric

Value

Dataframe with indicators, signals, trades and profit and loss. tibble

Author(s)

Philippe Cote

Examples

tradeStrategyDY(data = RTL::stocks$ry, par1value = 50, par2value = 200)

Sample quantitative trading strategy

Description

Moving average crossover strategy

Usage

tradeStrategySMA(data = RTL::stocks$uso, par1value = 50, par2value = 200)

Arguments

data

Dataframe of OHLC data e.g. RTL::uso. tibble
par1value

Value of first parameter e.g. short MA. numeric
par2value

Value of second parameter e.g. long MA. numeric

Value

Dataframe with indicators, signals, trades and profit and loss. tibble

Author(s)

Philippe Cote

Examples

tradeStrategySMA(data = RTL::stocks$uso, par1value = 50, par2value = 200)

dataset: interest rate curve data for RQuantlib .

Description

USD IR curve input for RQuantlib::DiscountCurve

Usage

tsQuotes

Format

data frame

Value

tibble

dataset: US bootstrapped interest rate curve.

Description

USD IR Discount, Forward and Zero curves from RQuantlib::DiscountCurve

Usage

usSwapCurves

Format

List

Value

list

Source

Morningstar and FRED

dataset: US bootstrapped interest rate curve parallel sample.

Description

USD IR Discount, Forward and Zero curves from RQuantlib::DiscountCurve - Parallel toy data set

Usage

usSwapCurvesPar

Format

data frame

Value

tibble

dataset: WTI Calendar Month Average Swap pricing data

Description

WTI Crude futures

Usage

wtiSwap

Format

data frame

Value

tibble

Source

Morningstar