RTL: 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.

Author(s)

Maintainer: Philippe Cote pcote@ualberta.ca

Authors:

• Nima Safaian nima.safaian@gmail.com

See Also

Useful links:

• https://github.com/risktoollib/RTL

• Report bugs at https://github.com/risktoollib/RTL/issues

Pipe operator

Description

See ⁠magrittr::[\%>\%][magrittr::pipe]⁠ for details.

Usage

lhs %>% rhs

Value

Internal for use of pipe operator. No return value.

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

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")

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

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")

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

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")

Barrier Spread Option Pricing

Description

This model applies Kirk's (1995) closed-form approximation for pricing spread options, incorporating barrier adjustments for continuous and terminal monitoring.

Usage

barrierSpreadOption(
  F1 = -12,
  F2 = -3,
  X = 5.5,
  B = 9,
  sigma1 = 0.6,
  sigma2 = 0.6,
  rho = 0.3,
  T2M = 1/12,
  r = 0.045,
  type = "call",
  barrier_type = "uo",
  monitoring = "continuous"
)

Arguments

Value

A list containing option price and Greeks.

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

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")

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

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)

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

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

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

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

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")

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 = "x@xyz.com",
  ipassword = "pass",
  title = "March/April ULSD Nymex Spreads",
  yaxis = "$ per bbl",
  output = "chart"
)

Arguments

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 = "x@xyz.com", 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

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

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

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

## Not run: 
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))

## End(Not run)

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

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

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

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

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)

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

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 = "x@xyz.com",
  ipassword = "pass"
)

Arguments

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 = "x@xyz.com", ipassword = "pass"
)

## 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

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)

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

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

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)

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 = "x@xyz.com",
  ipassword = "pass"
)

Arguments

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 = "x@xyz.com",
  ipassword = "pass"
)

Arguments

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

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

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

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

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

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

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

Value

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

Author(s)

Philippe Cote

Examples

## Not run: 
simMultivariates(nsims = 10, x = RTL::fizdiffs, s0 = NULL)

## End(Not run)

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

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

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

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

Kirk's Approximation for Spread Option Pricing

Description

Computes the price and Greeks of European spread options using Kirk's 1995 approximation. The spread option gives the holder the right to receive the difference between two asset prices (F2 - F1) at maturity, if positive, in exchange for paying the strike price X.

Usage

spreadOption(F1, F2, X, sigma1, sigma2, rho, T2M, r, type = "call")

Arguments

Details

Kirk's approximation is particularly useful for spread options where the exercise price is zero or small relative to the asset prices. The approximation assumes that the ratio of the assets follows a lognormal distribution.

The implementation includes a small constant (epsilon) to avoid numerical instabilities that might arise from division by zero.

Value

A list containing the following elements:

• price: The price of the spread option

• delta_F1: The sensitivity of the option price to changes in F1

• delta_F2: The sensitivity of the option price to changes in F2

• gamma_F1: The second derivative of the option price with respect to F1

• gamma_F2: The second derivative of the option price with respect to F2

• gamma_cross: The mixed second derivative with respect to F1 and F2

• vega_1: The sensitivity of the option price to changes in sigma1

• vega_2: The sensitivity of the option price to changes in sigma2

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

• rho: The sensitivity of the option price to changes in the interest rate

References

Kirk, E. (1995) "Correlation in the Energy Markets." Managing Energy Price Risk, Risk Publications and Enron, London, pp. 71-78.

Examples

# Price a call spread option with the following parameters:
F1 <- 100  # Forward price of first asset
F2 <- 110  # Forward price of second asset
X <- 5     # Strike price
sigma1 <- 0.2  # Volatility of first asset
sigma2 <- 0.25 # Volatility of second asset
rho <- 0.5     # Correlation between assets
T2M <- 1       # One year to maturity
r <- 0.05      # Risk-free rate

result_call <- spreadOption(F1, F2, X, sigma1, sigma2, rho, T2M, r, type = "call")
result_put <- spreadOption(F1, F2, X, sigma1, sigma2, rho, T2M, r, type = "put")

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

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

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"
)

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

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"
)

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 = "x@xyz.com",
  ipassword = "pass",
  output = "all"
)

Arguments

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 = "x@xyz.com", ipassword = "pass", output = "all"
)

## End(Not run)

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

Risk-reward statistics for quant trading

Description

Compute list of risk reward metrics

Usage

tradeStats(x, Rf = 0)

Arguments

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

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

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: 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

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