| Type: | Package |
| Title: | Trade Objects, Advanced Correlation & Beta Estimates, Betting Strategies |
| Version: | 3.2 |
| Date: | 2025-04-13 |
| Author: | Tasos Grivas [aut, cre] |
| Maintainer: | Tasos Grivas <info@openriskcalculator.com> |
| Description: | Contains performance analysis metrics of track records including entropy-based correlation and dynamic beta based on a state/space algorithm. The normalized sample entropy method has been implemented which produces accurate entropy estimation even on smaller datasets. On a separate stream, trades from the five major assets classes and also functionality to use pricing curves, rating tables, Credit Support Annex and add-on tables. The implementation follows an object oriented logic whereby each trade inherits from more abstract classes while also the curves/tables are objects. Furthermore, odds calculators and P&L back-testing functionality has been implemented for the most widely used betting/trading strategies including martingale, 'DAlembert', 'Labouchere' and Fibonacci. Back testing has also been included for the 'EuroMillions', the 'EuroJackpot', the UK Lotto, the Set For Life and the UK 'ThunderBall' lotteries. Furthermore, some basic functionality about climate risk has been included. |
| Imports: | methods, reticulate, PerformanceAnalytics, data.table,ggplot2,readxl, RcppAlgos |
| URL: | https://openriskcalculator.com/ |
| License: | GPL-3 |
| Collate: | 'AngularDistance.R' 'Future.R' 'Swap.R' 'Vol.R' 'Option.R' 'Trade.R' 'IRD.R' 'Bond.R' 'CSA.R' 'CalcEuroLotteryPnL.R' 'CalcSetForLifePnL.R' 'CalcUKLotteryPnL.R' 'CalcUKThunderBallPnL.R' 'Chebyshev_distance.R' 'Collateral.R' 'Commodity.R' 'Credit.R' 'CrossSampleEntropy.R' 'Curve.R' 'DynamicBeta.R' 'Equity.R' 'EuroJackpotResults.R' 'EuroLotteryAllCombinations.R' 'EuroLotteryBacktesting.R' 'EuroMillionsResults.R' 'FX.R' 'GetTradeDetails.R' 'HashTable.R' 'InformationAdjustedBeta.R' 'InformationAdjustedCorr.R' 'NormXASampEn.R' 'Other.R' 'OuterJoinMerge.R' 'ParseTrades.R' 'SampleEntropy.R' 'SelectDerivatives.R' 'SetForLifeBacktesting.R' 'SetForLifeExample.R' 'SetForLifeResults.R' 'UKLotteryBacktesting.R' 'UKLotteryExample.R' 'UKLotteryResults.R' 'UKThunderBallBacktesting.R' 'UKThunderBallExample.R' 'UKThunderBallResults.R' 'VariationOfInformation.R' 'capped_fibonacci_seq.R' 'cf.R' 'ci.R' 'eurojackpotExample.R' 'euromillionsExample.R' 'globals.R' 'martingale_strategy_calculator.R' 'onLoad.R' 'roulette_pl_calculator_Labouchere.R' 'roulette_pl_calculator_dalembert.R' 'roulette_pl_calculator_fibonacci.R' 'roulette_pl_calculator_martingale.R' 'roulette_pl_calculator_specific_number.R' 'tce.R' 'top5.R' 'waci.R' |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Repository: | CRAN |
| Packaged: | 2025-04-13 12:36:14 UTC; user1 |
| Date/Publication: | 2025-04-13 12:50:02 UTC |
Angular distance metrics
Description
Calculates the angular distance between a matrix of the track records of various assets/strategies. The sign of the correlation can be ignored for long/short portfolios.
Usage
AngularDistance(returns_matrix, long_short = FALSE)
Arguments
returns_matrix |
a matrix containing the track records of the underlying assets/strategies. |
long_short |
a boolean value which results in the sign of the correlation being ignored, default value is FALSE |
Value
A matrix containing the angular distance values.
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Lopez de Prado, Marcos, Codependence (Presentation Slides) (January 2, 2020). Available at SSRN: https://ssrn.com/abstract=3512994
Examples
## calling AngularDistance() without an argument loads the historical edhec data
## for the "Short Selling" and "Convertible Arbitrage" strategies
returns_matrix = PerformanceAnalytics::edhec[,c("Short Selling","Convertible Arbitrage")]
angular_distance = AngularDistance(returns_matrix, long_short=FALSE)
Bond Class
Description
Creates a Bond object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
yield |
The yield of the Bond |
ISIN |
The ISIN of the Bond, |
payment_frequency |
the frequency that the bond pays coupon (Quarter, SA etc) |
maturity_date |
the maturity date of the bond |
coupon_type |
The coupon type of the bond (fixed, floating, flipper etc) |
credit_risk_weight |
The percentage weight of the exposure of the bond that should be attributed to the 'Credit' asset class |
Issuer |
The issuer of the bond |
Value
An object of type Bond
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
tr1 = Bond(Notional=10000,MtM=30,Currency="EUR",Si=0,maturity_date="2026-04-04",
BuySell='Buy',payment_frequency="SA",
credit_risk_weight=0.2,coupon_type="Fixed",Issuer="FirmA",ISIN = "XS0943423")
Bond Future Class
Description
Creates a Bond Future object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
yield |
The yield of the Underlying Bond |
isin |
The ISIN of the Underlying Bond, |
payment_frequency |
the frequency that the bond pays coupon (Quarter, SA etc) |
maturity_date |
the maturity date of the bond |
coupon_type |
The coupon type of the bond (fixed, floating, flipper etc) |
Issuer |
The issuer of the bond |
Value
An object of type Bond
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
example_trades = ParseTrades()
bondfuture_trade = example_trades[[17]]
tr1 = BondFuture(Notional=10000,MtM=30,Currency="EUR",Si=0,Ei=10,BuySell='Buy',
payment_frequency="SA",coupon_type="Fixed",Issuer="CountryA",ISIN = "XS0943423")
CDO tranche Class
Description
Creates a CDO tranche Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the belongs |
Si |
The number of years after which the trade will start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
attach_point |
The attachment point of the tranche |
detach_point |
The detachment point of the tranche |
Value
An object of type CDOTrance
Examples
## a CDO trance object
tr3 = CDOTranche(Notional=10000,MtM=0,Currency="USD",Si=0,Ei=5,
BuySell='Buy',SubClass='IG',RefEntity='CDX.IG',cdo_attach_point=0.3 ,cdo_detach_point=0.5)
CDS Class
Description
Creates a CDS Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
SubClass |
Specifies the rating of the underlying entity (possible values are A, AA, BB etc) |
RefEntity |
The name of the underlying entity |
Value
An object of type CDS
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Basel Committee: The standardised approach for measuring counterparty credit risk exposures http://www.bis.org/publ/bcbs279.htm
Examples
## the CDS trade given in the Basel regulation Credit example
tr1 = CDS(Notional=10000,MtM=20,Currency="USD",Si=0,Ei=3,BuySell='Buy',
SubClass='AA',RefEntity='FirmA')
CDX Class
Description
Creates a Credit Index Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the belongs |
Si |
The number of years after which the trade will start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
SubClass |
Specifies if the underlying Index is investment grade or not (possible values are IG & SG) |
RefEntity |
The name of the underlying Index |
Value
An object of type CDX
Examples
## the CDX trade given in the Basel regulation Credit example
tr3 = CDX(Notional=10000,MtM=0,Currency="USD",Si=0,Ei=5,
BuySell='Buy',SubClass='IG',RefEntity='Portfolio_1')
CSA Class
Description
Creates a collateral agreement Object containing all the relevant data and methods regarding the maturity factor and the calculation of the exposures after applying the relevant threshold
Arguments
ID |
The ID of the CSA ID |
Counterparty |
The counterparty the CSA is linked to |
Currency |
The currency that the CSA applies to (can be a list of different currencies) |
TradeGroups |
The trade groups that the CSA applies to |
Values_type |
The type of the numerical values (can be "Actual" or "Perc" whereby the values are percentages of the MtM) |
thres_cpty |
The maximum exposure that the counterparty can generate before collateral will need to be posted |
thres_PO |
The maximum exposure that the processing organization can generate before collateral will need to be posted |
MTA_cpty |
The minimum transfer amount for the counterparty |
MTA_PO |
The minimum transfer amount for the processing organization |
IM_cpty |
The initial margin that is posted by the counterparty |
IM_PO |
The initial margin that is posted by the processing organization |
mpor_days |
The margin period of risk in days |
remargin_freq |
The frequency of re-margining the exposure in days |
rounding |
The rounding amount of the transfers |
Value
An object of type CSA
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Basel Committee: The standardised approach for measuring counterparty credit risk exposures http://www.bis.org/publ/bcbs279.htm
Examples
csa_raw = read.csv(system.file("extdata", "CSA.csv", package = "Trading"),
header=TRUE,stringsAsFactors = FALSE)
csas = list()
for(i in 1:nrow(csa_raw))
{
csas[[i]] = CSA()
csas[[i]]$PopulateViaCSV(csa_raw[i,])
}
PnL calculation for EuroMillions/EuroJackpot backtesting
Description
Calculates the PnL for a pay out structure created during backtesting
Usage
CalcEuroLotteryPnL(backtested_results, plot_results = FALSE)
Arguments
backtested_results |
The EuroMillions/EuroJackpot results backtested against the user input |
plot_results |
(Optional) If TRUE, the P&L historical graphs are plotted, default FALSE |
Value
PnL figures
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
euromillions_results = EuroMillionsResults()
user_input = c(10,20,30,40,50,5,10)
backtested_results = EuroLotteryBacktesting(euromillions_results, '2005-01-01', user_input)
pnl_result = CalcEuroLotteryPnL(backtested_results, plot_results = TRUE)
PnL calculation for Set For Life backtesting
Description
Calculates the PnL for a pay out structure created during backtesting
Usage
CalcSetForLifePnL(backtested_results, plot_results = FALSE)
Arguments
backtested_results |
The Set For Life results backtested against the user input |
plot_results |
(Optional) If TRUE, the P&L historical graphs are plotted, default FALSE |
Value
PnL figures
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
setforlife_results = SetForLifeResults()
user_input = c(10,20,30,40,50,5)
backtested_results = SetForLifeBacktesting(setforlife_results, date_since='2005-01-01', user_input)
pnl_result = CalcSetForLifePnL(backtested_results, plot_results = FALSE)
PnL calculation for UKLottery backtesting
Description
Calculates the PnL for a pay out structure created during backtesting
Usage
CalcUKLotteryPnL(backtested_results, plot_results = FALSE)
Arguments
backtested_results |
The UKLottery results backtested against the user input |
plot_results |
(Optional) If TRUE, the P&L historical graphs are plotted, default FALSE |
Value
PnL figures
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
uklottery_results = UKLotteryResults()
user_input = c(5,10,20,30,40,50)
backtested_results = UKLotteryBacktesting(uklottery_results,, user_input)
pnl_result = CalcUKLotteryPnL(backtested_results, plot_results = FALSE)
PnL calculation for UKThunderBall backtesting
Description
Calculates the PnL for a pay out structure created during backtesting
Usage
CalcUKThunderBallPnL(backtested_results, plot_results = FALSE)
Arguments
backtested_results |
The UKThunderBall results backtested against the user input |
plot_results |
(Optional) If TRUE, the P&L historical graphs are plotted, default FALSE |
Value
PnL figures
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
ukthunderball_results = UKThunderBallResults()
user_input = c(10,20,30,31,32,5)
backtested_results = UKThunderballBacktesting(ukthunderball_results, user_input = user_input)
pnl_result = CalcUKThunderBallPnL(backtested_results, plot_results = FALSE)
Carbon Footprint
Description
Returns the Total carbon emissions for a portfolio normalized by the market value of the portfolio, expressed in tons CO2e / $M invested.Scope 1 and Scope 2 GHG emissions are allocated to investors based on an equity
Usage
Carbon_Footprint(portfolio_exposure, emissions_capitalization_data)
Arguments
portfolio_exposure |
The exposure per issuer in the portfolio |
emissions_capitalization_data |
The capitalization and the Scope 1 & 2 GHG emissions per issuer |
Value
Total carbon emissions for a portfolio normalized by the market value of the portfolio, expressed in tons CO2e / $M invested.
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://www.tcfdhub.org/Downloads/pdfs/E09
Examples
portfolio_exposure = data.table::data.table(Issuers = c('A','B','C'),
exposures = c(100, 200, 50))
emissions_capitalization_data = data.table::data.table(Issuers = c('A','B','C'),
emissions = c(1000, 5000, 6000), Capitalization = c(20000, 10000, 30000))
Carbon_Footprint(portfolio_exposure, emissions_capitalization_data)
Carbon Intensity
Description
Returns the Volume of carbon emissions per million dollars of revenue expressed in tons CO2e / $M revenue. Scope 1 and Scope 2 GHG emissions are allocated to investors based on an equity ownership approach. The company's (or issuer's) revenue is used to adjust for company size to provide a measurement of the efficiency of output.
Usage
Carbon_Intensity(portfolio_exposure, emissions_capitalization_revenue_data)
Arguments
portfolio_exposure |
The exposure per issuer in the portfolio |
emissions_capitalization_revenue_data |
The capitalization, revenue and the Scope 1 & 2 GHG emissions per issuer |
Value
Volume of carbon emissions per million dollars of revenue expressed in tons CO2e / $M revenue.
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://www.tcfdhub.org/Downloads/pdfs/E09
Examples
portfolio_exposure = data.table::data.table(Issuers = c('A','B','C'),
exposures = c(100, 200, 50))
emissions_capitalization_revenue_data = data.table::data.table(Issuers = c('A','B','C'),
emissions = c(1000, 5000, 6000), revenue = c(2000, 5000, 3000),Capitalization =
c(20000, 10000, 15000))
Carbon_Intensity (portfolio_exposure, emissions_capitalization_revenue_data)
Chebyshev distance
Description
Calculates the Chebyshev distance
Usage
Chebyshev_distance(x, y)
Arguments
x |
a vector containing the track record of the underlying asset/strategy |
y |
a vector containing the track record of the underlying asset/strategy |
Value
The Chebyshev distance of the two vectors
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://en.wikipedia.org/wiki/Chebyshev_distance
Examples
x = rnorm(1000)
y = rnorm(1000)
chebyshev_dist = Chebyshev_distance(x, y)
Collateral Class
Description
Creates a Collateral amount object which needs to be linked with a CSA ID
Arguments
ID |
The ID of each object |
Amount |
The collateral amount |
csa_id |
The csa_id that this object is linked with |
type |
Describes the type of the collateral: can be "ICA", "VariationMargin" etc |
Value
An object of type Collateral
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Basel Committee: The standardised approach for measuring counterparty credit risk exposures http://www.bis.org/publ/bcbs279.htm
Examples
colls = list()
coll_raw = read.csv(system.file("extdata", "coll.csv", package = "Trading"),header=TRUE,
stringsAsFactors = FALSE)
for(i in 1:nrow(coll_raw))
{
colls[[i]] = Collateral()
colls[[i]]$PopulateViaCSV(coll_raw[i,])
}
Commodity Swap Class
Description
Creates a Commodity Swap Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Value
An object of type CommSwap
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Basel Committee: The standardised approach for measuring counterparty credit risk exposures http://www.bis.org/publ/bcbs279.htm
Commodity Class
Description
Creates a Commodity Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
commodity_type |
Takes the values of 'Oil/Gas','Silver','Electricity' etc. |
Value
An object of type Commodity
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Basel Committee: The standardised approach for measuring counterparty credit risk exposures http://www.bis.org/publ/bcbs279.htm
Examples
tr1 = Commodity(Notional=10000,MtM=-50,
BuySell='Buy',SubClass='Energy',commodity_type='Oil')
Commodity Forward Class
Description
Creates a Commodity Forward Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
commodity_type |
Takes the values of 'Oil','Gas','Silver','Electricity' etc. |
SubClass |
Defines the relevant hedging set. Possible values: 'Energy','Agriculture','Metal','Other','Climatic' |
Value
An object of type Commodity Forward
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Regulation (EU) 2019/876 of the European Parliament and of the Council of 20 May 2019 http://data.europa.eu/eli/reg/2019/876/oj
Examples
## the Commodity Forward trade given in the Basel regulation Commodity example
tr1 = CommodityForward(Notional=10000,MtM=-50,Si=0,Ei=0.75,
BuySell='Buy',SubClass='Energy',commodity_type='Oil')
Angular distance metrics
Description
Calculates the cross sample entropy between two track records of various assets/strategies.
Usage
CrossSampleEntropy(returns_matrix, m = 2, r = 0.2)
Arguments
returns_matrix |
a matrix containing the track records of the underlying assets/strategies. These will be normalized during the algorithm |
m |
an integer value defining the embedding dimension , default value is 2 |
r |
a double value defining the tolerance, default value is 0.2 |
Value
The value of cross sample entropy
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://physoc.onlinelibrary.wiley.com/doi/epdf/10.1113/expphysiol.2007.037150
Examples
## calling CrossSampleEntropy() without an argument loads the historical edhec data
## for the "Short Selling" and "Convertible Arbitrage" strategies
returns_matrix = PerformanceAnalytics::edhec[,c("Short Selling","Convertible Arbitrage")]
Cross_Sample_Entropy = CrossSampleEntropy(returns_matrix,m=2,r=0.2)
Curve Class
Description
Creates a Curve Object containing pairs of Tenors with relevant rates and the interpolation function. Also, methods for populating the object via a .csv file and the generation of the interpolation function via cubic splines are included.
Arguments
Tenors |
The Tenors of the curve |
Rates |
The rates on the corresponding tenors |
interp_function |
(Optional) The interpolation function of the curve. Can be populated via the 'CalcInterpPoints' method |
Value
An object of type Curve
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
## generating a curve either directly or through a csv -
## the spot_rates.csv file can be found on the extdata folder in the installation library path
funding_curve = Curve(Tenors=c(1,2,3,4,5,6,10),Rates=c(4,17,43,47,76,90,110))
spot_rates = Curve()
spot_rates$PopulateViaCSV('spot_rates.csv')
time_points = seq(0,5,0.01)
spot_curve = spot_rates$CalcInterpPoints(time_points)
Time Varying Beta via Kalman filter & smoother
Description
Calculates the beta of an investment strategy or stock by applying the Kalman filter & smoother. Apart from the beta timeseries, the state covariances are also returned so as to provide an estimate of the uncertainty of the results. The python package "Pykalman" is used for the calculations given its proven stability.
Usage
DynamicBeta(csvfilename, do_not_set_to_true = FALSE)
Arguments
csvfilename |
the name of csv file containing the track record of the fund & the benchmark |
do_not_set_to_true |
function returns zero when TRUE - used only so as to pass the CRAN tests where pykalman couldn't be installed |
Value
A list of beta values based on Kalman Filter & smoother and the respective covariance matrices
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
## calling DynamicBeta() without an argument loads a test file containing a sample track
## record and a benchmark index
## ATTENTION!!: set do_not_set_to_true to FALSE when running the example
##-- this is only used to pass CRAN tests whereby
## pykalman was not installable!
dyn_beta_values = DynamicBeta(do_not_set_to_true = TRUE)
Equity Class
Description
Creates an Equity object
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
ISIN |
the ISIN of the Equity |
traded_price |
the price that trade was done |
Issuer |
the issuer of the stock |
Value
An object of type Equity
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
tr1 = Equity(external_id="ext1",Notional=10000,MtM=30,Currency="EUR",BuySell='Buy',
traded_price = 10,ISIN = "XS04340432",Issuer='FirmA')
Equity Index Future Class
Description
Creates an Equity Index Future object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
traded_price |
the price that trade was done |
Value
An object of type EquityIndexFuture
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
example_trades = ParseTrades()
Equity_Index_Future_trade = example_trades[[18]]
Equity Option Index Class
Description
Creates an Equity Option Index object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
traded_price |
the price that trade was done |
Value
An object of type EquityOption
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Equity Option Single Class
Description
Creates an Equity Option Single object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
traded_price |
the price that trade was done |
Value
An object of type EquityOption
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Eurojackpot analysis example
Description
Displays how the functionality related to the eurojackpot analysis can be utilized
Usage
EuroJackpotExample()
Value
The final results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment/betting decisions should be taken based on this)
final_results = EuroJackpotExample()
Returns all the EuroJackpot results until the end of 2023
Description
Returns all the EuroJackpot results since the first draw on Feb 2004 until the end of 2023
Usage
EuroJackpotResults()
Value
A dataframe with all the EuroJackpot results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
eurojackpot_results = EuroJackpotResults()
Returns all the possible number combinations for EuroMillions/EuroJackpot
Description
Returns all the possible number combinations for EuroMillions/EuroJackpot
Usage
EuroLotteryAllCombinations()
Value
PnL figures
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
# returns all the 139,838,160 possible combinations, can create memory issues.
# all_combinations = EuroLotteryAllCombinations()
Euromillions/EuroJackpot Backtesting
Description
Backtests the numbers the user has selected against the full (or the specified) history of Euromillions/EuroJackpot results
Usage
EuroLotteryBacktesting(euroLottery_results, date_since, user_input)
Arguments
euroLottery_results |
The full list of EuroMillions/EuroJackpot results |
date_since |
The date after which the analysis is to be performed, i.e. 2022-12-22 |
user_input |
The seven numbers the user has selected |
Value
The backtested results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
euromillions_results = EuroMillionsResults()
user_input = c(10,20,30,40,50,5,10)
backtested_results = EuroLotteryBacktesting(euromillions_results, '2005-01-01', user_input)
Euromillions analysis example
Description
Displays how the functionality related to the euromillions analysis can be utilized
Usage
EuroMillionsExample()
Value
The final results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment/betting decisions should be taken based on this)
final_results = EuroMillionsExample()
Returns all the EuroMillions results until the end of 2023
Description
Returns all the EuroMillions results since the first draw on Feb 2004 until the end of 2023
Usage
EuroMillionsResults()
Value
A dataframe with all the EuroMillions results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
euromillions_results = EuroMillionsResults()
FX Forward Class
Description
Creates a FX Forward Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency that the input amounts are in |
ccyPair |
The currency Pair of the trade |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
traded_price |
the price that trade was done |
Value
An object of type FX Forward
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Basel Committee: The standardised approach for measuring counterparty credit risk exposures http://www.bis.org/publ/bcbs279.htm
Examples
## an FX Forward trade
tr1 = FxForward(Notional=10000,MtM=-50,Si=0,Ei=0.75,BuySell='Buy',ccyPair="EUR/USD")
## a dynamic version of the same trade
tr2 = FxForward(MtM=-50,Si=0,Ei=0.75,ccy_paying="USD",amount_paying=10000,
ccy_receiving="EUR",amount_receiving=9900)
tr2$base_ccy="EUR"
tr2$setFXDynamic()
Fx Swap Class
Description
Creates an FX Swap object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency that the input amounts are in |
ccyPair |
The currency Pair of the trade |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
traded_price |
the price that trade was done |
fx_near_leg_fields |
(Optional) In case the near leg hasn't settled yet, its notional, MtM, settlement date should be provided separated via a semicolon |
Value
An object of type FXSwap
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Basel Committee: The standardised approach for measuring counterparty credit risk exposures http://www.bis.org/publ/bcbs279.htm
Examples
tr1 = FxSwap(Notional=10000,MtM=30,ccyPair="EUR/USD",Si=0,Ei=10,
BuySell='Buy',fx_near_leg_fields='1000;-20;2020-02-11')
Returns a list with the populated fields of a Trade Object
Description
Returns a list with the populated fields of a Trade Object
Usage
GetTradeDetails(trade)
Arguments
trade |
A trade Object |
Value
A list of fields
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
example_trades = ParseTrades()
Equity_Index_Future_trade = example_trades[[18]]
populated_fields = GetTradeDetails(Equity_Index_Future_trade)
Hashtable Class
Description
Creates a hashtable-like object so as to represent data with a key structure (for example addon tables, rating-based factors etc). Also, it includes methods for populating the object via a .csv file and finding a value based on a specific key on an interval of keys For examples of the format of the CSVs files, please view RatingsMapping.csv or AddonTable.csv on the extdata folder in the installation folder of the library
Arguments
keys |
A vector of keys |
values |
A vector of values mapping to the keys |
keys_type |
The type of the keys |
values_type |
The type of the values |
Value
An object of type HashTable
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
## loading a ratings' mapping matrix from the extdata folder
rating_table = HashTable('RatingsMapping.csv',"character","numeric")
reg_weight =rating_table$FindValue("AAA")
IRD Future Class
Description
Creates an IRD Future Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
Value
An object of type IRDFuture
IRD Swap Class
Description
Creates an IRD Swap Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
Value
An object of type IRDSwap
Examples
# the IRD Swap trade given in the Basel regulation IRD example
tr1 = IRDSwap(Notional=10000,MtM=30,Currency="USD",Si=0,Ei=10,BuySell='Buy')
IRD Swap Volatility Class
Description
Creates an IRD Swap Volatility-based Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Value
An object of type IRDSwapVol
IRD Swaption Class
Description
Creates an IRD Swaption Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
OptionType |
Takes the values of either 'Put' or 'Call' |
UnderlyingPrice |
The current price of the underlying |
StrikePrice |
The strike price of the option |
Value
An object of type IRDSwaption
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Basel Committee: The standardised approach for measuring counterparty credit risk exposures http://www.bis.org/publ/bcbs279.htm
Examples
# the Swaption trade given in the Basel regulation IRD example
tr3 = IRDSwaption(Notional=5000,MtM=50,Currency="EUR",Si=1,Ei=11,BuySell='Sell',
OptionType='Put',UnderlyingPrice=0.06,StrikePrice=0.05)
Information Adjusted Beta
Description
Calculates the Information-Adjusted Beta between the track records of two assets/strategies which covers for cases whereby the 'typical' linearity and Gaussian I.I.D assumptions do not hold. The normalized cross sample entropy has been utilized for the mutual information estimation.
Usage
InformationAdjustedBeta(x, y, m = 2, r = 0.2)
Arguments
x |
a vector containing the track record of the underlying asset/strategy (can be a data.table, data.frame, vector etc) |
y |
a vector containing the track record of the underlying asset/strategy (can be a data.table, data.frame, vector etc) |
m |
an integer value defining the embedding dimension for the sample entropy calculation, default value is 2 |
r |
a double value defining the tolerance for the sample entropy calculation, default value is 0.2 |
Value
The information adjusted Beta
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://github.com/devisechain/Devise/blob/master/yellow_paper.pdf
Examples
x = PerformanceAnalytics::edhec[,c("Short Selling")]
y = PerformanceAnalytics::edhec[,c("Convertible Arbitrage")]
Information_Adjusted_Beta = InformationAdjustedBeta(x, y, m=2, r=0.2)
Information Adjusted Correlation
Description
Calculates the Information-Adjusted Correlation between the track records of various assets/strategies which covers for cases whereby the 'typical' Pearson's correlation assumptions do not hold. The normalized cross sample entropy has been utilized for the mutual information estimation.
Usage
InformationAdjustedCorr(x, y, m = 2, r = 0.2)
Arguments
x |
a vector containing the track record of the underlying asset/strategy (can be a data.table, data.frame, vector etc) |
y |
a vector containing the track record of the underlying asset/strategy (can be a data.table, data.frame, vector etc) |
m |
an integer value defining the embedding dimension for the sample entropy calculation, default value is 2 |
r |
a double value defining the tolerance for the sample entropy calculation, default value is 0.2 |
Value
The information adjusted correlation
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://github.com/devisechain/Devise/blob/master/yellow_paper.pdf
Examples
x = PerformanceAnalytics::edhec[,c("Short Selling")]
y = PerformanceAnalytics::edhec[,c("Convertible Arbitrage")]
Information_Adjusted_Corr = InformationAdjustedCorr(x, y, m=2, r=0.2)
Normalized Cross Sample Entropy
Description
Calculates the Normalized Cross Sample Entropy of the track records of two assets/strategies based on the sample entropy.
Usage
NormXASampEn(x, y, m = 2, r = 0.2)
Arguments
x |
a vector containing the track record of the underlying asset/strategy, this will be normalized during the algorithm |
y |
a vector containing the track record of the underlying asset/strategy, this will be normalized during the algorithm |
m |
an integer value defining the embedding dimension , default value is 2 |
r |
a double value defining the tolerance, default value is 0.2 |
Value
A value containing the NormXASampEn
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Lopez de Prado, Marcos, Codependence (Presentation Slides) (January 2, 2020). Available at SSRN: https://ssrn.com/abstract=3512994
Examples
x = PerformanceAnalytics::edhec[,c("Short Selling")]
y = PerformanceAnalytics::edhec[,c("Convertible Arbitrage")]
Normalized_Cross_Sample_Entropy = NormXASampEn(x, y, m=2, r=0.2)
OtherExposure Class
Description
Creates a OtherExposure Object with the relevant info needed to calculate the Exposure-at-Default (EAD)
Arguments
Notional |
The notional amount of the trade |
MTM |
The mark-to-market valuation of the trade |
Currency |
The currency set that the trade belongs to |
Si |
The number of years that the trade will take to start (zero if already started) |
Ei |
The number of years that the trade will expire |
BuySell |
Takes the values of either 'Buy' or 'Sell' |
SubClass |
Defines the hedging set the relevant trade will belong to |
Value
An object of type OtherExposure
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Regulation (EU) 2019/876 of the European Parliament and of the Council of 20 May 2019 http://data.europa.eu/eli/reg/2019/876/oj
Examples
tr1 = OtherExposure(Notional=10000,MtM=-50,Si=0,Ei=10,
BuySell='Buy',SubClass='Other_1')
Returns all possible combinations of two dataframes
Description
Returns all possible combinations of two dataframes
Usage
OuterJoinMerge(df_a, df_b)
Arguments
df_a |
The first dataframe |
df_b |
The second dataframe |
Value
A dataframe with all combinations
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
df_a = data.frame(matrix(seq(1,20),nrow = 5, ncol = 4))
df_b = data.frame(matrix(seq(21,40),nrow = 5, ncol = 4))
joined_df = OuterJoinMerge(df_a, df_b)
Parse trades through a .csv file.
Description
Parse trades through a .csv file. In case no file name is given, an example file is automatically loaded containing trades corresponding to Basel's SA-CCR regulation (the example trades file can be found on the extdata folder in the installation library path)
Usage
ParseTrades(csvfilename)
Arguments
csvfilename |
the name of csv file containing the trades |
Value
A list of trades
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
## calling ParseTrades() without an argument loads a test file containing all
## the different trade types supported
example_trades = ParseTrades()
Sample Entropy
Description
Calculates the sample entropy of a track record. Sample entropy is an improvement of the approximate entropy and should produce accurate results for timeseries of smaller length like historical returns of strategies
Usage
SampleEntropy(returns, m = 2, r = 0.2)
Arguments
returns |
a vector containing the track record of the underlying asset/strategy, these will be normalized during the algorithm |
m |
an integer value defining the embedding dimension , default value is 2 |
r |
a double value defining the tolerance, default value is 0.2 |
Value
The sample Entropy of the input returns
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://en.wikipedia.org/wiki/Sample_entropy
Examples
## calling SampleEntropy() without an argument loads the historical edhec
## data for the "Short Selling" strategy
returns = PerformanceAnalytics::edhec[,c("Short Selling")]
Sample_Entropy = SampleEntropy(returns,m=2,r=0.2)
Select the derivatives out of a trades' list
Description
Select the derivatives out of a trades' list which will be utilized to calculate the CCR Exposure.
Usage
SelectDerivatives(trades_list)
Arguments
trades_list |
the file holding the trades of the portfolio |
Value
The derivatives out of a trades' list
Author(s)
Tasos Grivas <info@openriskcalculator.com>
References
Regulation (EU) 2019/876 of the European Parliament and of the Council of 20 May 2019 http://data.europa.eu/eli/reg/2019/876/oj
Set For Life Backtesting
Description
Backtests the numbers the user has selected against the full (or the specified) history of Set For Life results
Usage
SetForLifeBacktesting(setforlife_results, date_since, user_input)
Arguments
setforlife_results |
The full list of Set For Life results |
date_since |
The date after which the analysis is to be performed, i.e. 2022-12-22 |
user_input |
The seven numbers the user has selected |
Value
The backtested results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
setforlife_results = SetForLifeResults()
user_input = c(10,20,30,40,50,5,10)
backtested_results = SetForLifeBacktesting(setforlife_results, '2005-01-01', user_input)
Set For Life analysis example
Description
Displays how the functionality related to the Set For Life analysis can be utilized
Usage
SetForLifeExample()
Value
The final results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment/betting decisions should be taken based on this)
final_results = SetForLifeExample()
Returns all the EuroJackpot results until the end of 2023
Description
Returns all the SetForLifeResults results since the first draw on Feb 2004 until the end of 2023
Usage
SetForLifeResults()
Value
A dataframe with all the EuroJackpot results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
eurojackpot_results = EuroJackpotResults()
Total Carbon Emissions
Description
Returns the absolute greenhouse gas emissions associated with a portfolio, expressed in tons CO2e. Under this approach, if an investor owns 5 percent of a company's total market capitalization, then the investor owns 5 percent of the company as well as 5 percent of the company's GHG (or carbon) emissions.
Usage
Total_Carbon_Emissions(portfolio_exposure, emissions_capitalization_data)
Arguments
portfolio_exposure |
The exposure per issuer in the portfolio |
emissions_capitalization_data |
The capitalization and the Scope 1 & 2 GHG emissions per issuer |
Value
The absolute greenhouse gas emissions associated with a portfolio, expressed in tons CO2e
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://www.tcfdhub.org/Downloads/pdfs/E09
Examples
portfolio_exposure = data.table::data.table(Issuers = c('A','B','C'),
exposures = c(100, 200, 50))
emissions_capitalization_data = data.table::data.table(Issuers = c('A','B','C'),
emissions = c(1000, 5000, 6000),
Capitalization = c(20000, 10000, 30000))
Total_Carbon_Emissions(portfolio_exposure, emissions_capitalization_data)
UKLottery Backtesting
Description
Backtests the numbers the user has selected against the full (or the specified) history of UKLottery results
Usage
UKLotteryBacktesting(uklottery_results, date_since, user_input)
Arguments
uklottery_results |
The full list of UKLottery results |
date_since |
The date after which the analysis is to be performed, i.e. 2022-12-22 |
user_input |
The seven numbers the user has selected |
Value
The backtested results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
uklottery_results = UKLotteryResults()
user_input = c(5,10,20,30,40,50)
backtested_results = UKLotteryBacktesting(uklottery_results, '2005-01-01', user_input)
UK Lottery analysis example
Description
Displays how the functionality related to the UK Lottery analysis can be utilized
Usage
UKLotteryExample()
Value
The final results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment/betting decisions should be taken based on this)
final_results = UKLotteryExample()
Returns all the UKLottery results until the beginning of 2025
Description
Returns all the UKLottery results since the first draw on Nov 1994 until the beginning of 2025
Usage
UKLotteryResults()
Value
A dataframe with all the UKLottery results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
UKLotto_results = UKLotteryResults()
Returns all the UK ThunderBall results until the beginning of 2025
Description
Returns all the UK ThunderBall results until the beginning of 2025
Usage
UKThunderBallResults()
Value
A dataframe with all the UK ThunderBall results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
UKThunderBall_Results = UKThunderBallResults()
UK ThunderBall Backtesting
Description
Backtests the numbers the user has selected against the full (or the specified) history of UK ThunderBall results
Usage
UKThunderballBacktesting(ukthunderball_results, date_since, user_input)
Arguments
ukthunderball_results |
The full list of UK ThunderBall results |
date_since |
The date after which the analysis is to be performed, i.e. 2022-12-22 |
user_input |
The seven numbers the user has selected |
Value
The backtested results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
ukthunderball_results = UKThunderBallResults()
user_input = c(10,20,30,31,32,5)
backtested_results = UKThunderballBacktesting(ukthunderball_results, user_input = user_input)
UK ThunderBall analysis example
Description
Displays how the functionality related to the UK ThunderBall analysis can be utilized
Usage
UKThunderballExample()
Value
The final results
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment/betting decisions should be taken based on this)
final_results = EuroJackpotExample()
Variation of Information
Description
Calculates the variation of information of the track records of two assets/strategies based on the sample entropy.
Usage
VariationOfInformation(x, y, m = 2, r = 0.2, normalized = TRUE)
Arguments
x |
a vector containing the track record of the underlying asset/strategy, this will be normalized during the algorithm |
y |
a vector containing the track record of the underlying asset/strategy, this will be normalized during the algorithm |
m |
an integer value defining the embedding dimension , default value is 2 |
r |
a double value defining the tolerance, default value is 0.2 |
normalized |
a boolean value so as to bound the return value between 0 and 1, default value is TRUE |
Value
A value containing the variation of information
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
Lopez de Prado, Marcos, Codependence (Presentation Slides) (January 2, 2020). Available at SSRN: https://ssrn.com/abstract=3512994
Examples
x = PerformanceAnalytics::edhec[,c("Short Selling")]
y = PerformanceAnalytics::edhec[,c("Convertible Arbitrage")]
variation_of_information = VariationOfInformation(x, y, m=2, r=0.2, normalized = TRUE)
Weighted Average Carbon Intensity
Description
Returns the portfolio's exposure to each issuer expressed in tons CO2e / $M revenue. Scope 1 and Scope 2 GHG emissions are allocated based on portfolio weights (the current value of the investment relative to the current portfolio value), rather than the equity ownership approach
Usage
Weighted_Average_Carbon_Intensity(portfolio_exposure, emissions_revenue_data)
Arguments
portfolio_exposure |
The exposure per issuer in the portfolio |
emissions_revenue_data |
The capitalization, revenue and the Scope 1 & 2 GHG emissions per issuer |
Value
Total carbon emissions for a portfolio normalized by the market value of the portfolio, expressed in tons CO2e / $M invested.
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://www.tcfdhub.org/Downloads/pdfs/E09
Examples
portfolio_exposure = data.table::data.table(Issuers = c('A','B','C'),
exposures = c(100, 200, 50))
emissions_revenue_data = data.table::data.table(Issuers = c('A','B','C'),
emissions = c(1000, 5000, 2000),
revenue = c(2000, 5000, 3000))
Weighted_Average_Carbon_Intensity(portfolio_exposure, emissions_revenue_data)
Fibonacci sequence up to a specified maximum number
Description
Generates the Fibonacci sequence up to a specified maximum number
Usage
capped_fibonacci_seq(max_number)
Arguments
max_number |
The maximum number up to which the sequence should be generated |
Value
A vector containing the Fibonacci sequence
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://en.wikipedia.org/wiki/Fibonacci_number
Examples
fibonacci_seq = capped_fibonacci_seq(max_number = 6000)
Martingale Strategy Repetitions
Description
Calculates the number of repetitions needed for a specific number of consequtive failed trades/bet to appear. This can apply to roulette betting but also trading algorithms which use the same logic on doubling down after a failed trade.
Usage
martingale_strategy_repetitions(
length_of_targeted_sequence,
prob_of_success = 18/37,
simulations_num,
trials_per_sim,
quantile_perc
)
Arguments
length_of_targeted_sequence |
The number of consecutive failed trades/bets that we try to calculate the expected number of repetitions for |
prob_of_success |
The probability of a sucessful trade/bet |
simulations_num |
The number of simulations to be run |
trials_per_sim |
The number of trials in each simulation |
quantile_perc |
(Optional) When set, the number of repetitions expected with such probability is returned. |
Value
A list containing the number of repetitions needed to reach the targeted sequence for the first time in each simulation (will be zero if the sequence is not found) and, when the quantile_perc is set, the above number of repetitions.
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://en.wikipedia.org/wiki/Roulette#Betting_strategies_and_tactics
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment or betting decisions should be taken based on this)
# On top of these, the below example contains a tiny number of simulations and
# trials just to pass CRAN tests - the user would have to highly increase both
# variables when running these.
repetitions_for_failed_sequence = martingale_strategy_repetitions(length_of_targeted_sequence = 8,
prob_of_success = 18/37, simulations_num = 1000, trials_per_sim = 10000, quantile_perc = 0.1)
repetitions_for_failed_sequence$relevant_quantile
summary(repetitions_for_failed_sequence$num_of_trials_needed)
Roulette P&L betting based on the D'Alembert Betting System
Description
Calculates the potential profit or loss when someone is betting in the roulette based on the D'Alembert Betting System
Usage
roulette_pl_calculator_dalembert(
bet_minimum,
bet_maximum,
initial_capital,
simulations_num,
trials_per_sim
)
Arguments
bet_minimum |
The minimum betting amount that the casino allows |
bet_maximum |
The maximum betting amount that the casino allows |
initial_capital |
The initial capital to be used |
simulations_num |
The number of simulations to be run |
trials_per_sim |
The number of trials in each simulation |
Value
A list containing the minimum, the maximum and the final balance for each simulation. Also the P&L graph for the last simulation will be plotted.
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://en.wikipedia.org/wiki/Roulette#Betting_strategies_and_tactics
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment/betting decisions should be taken based on this)
# On top of these, the below example contains a tiny number of simulations and
# trials just to pass CRAN tests - the user would have to highly increase both
# variables when running these.
pl_results = roulette_pl_calculator_dalembert(bet_minimum = 0.1 , bet_maximum = 3276.8,
initial_capital = 20000, simulations_num = 100, trials_per_sim = 100)
summary(pl_results$min_capital)
summary(pl_results$max_capital)
summary(pl_results$final_capital)
Roulette P&L betting based on the Fibonacci Betting System
Description
Calculates the potential profit or loss when someone is betting in the roulette based on the Fibonacci Betting System.
Usage
roulette_pl_calculator_fibonacci(
bet_minimum,
bet_maximum,
initial_capital,
simulations_num,
trials_per_sim
)
Arguments
bet_minimum |
The minimum betting amount that the casino allows |
bet_maximum |
The maximum betting amount that the casino allows |
initial_capital |
The initial capital to be used |
simulations_num |
The number of simulations to be run |
trials_per_sim |
The number of trials in each simulation |
Value
A list containing the minimum, the maximum and the final balance for each simulation. Also the P&L graph for the last simulation will be plotted.
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://en.wikipedia.org/wiki/Roulette#Betting_strategies_and_tactics
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment or betting decisions should be taken based on this)
# On top of these, the below example contains a tiny number of simulations and
# trials just to pass CRAN tests - the user would have to highly increase both
# variables when running these.
pl_results = roulette_pl_calculator_fibonacci(bet_minimum = 0.1 , bet_maximum = 6000,
initial_capital = 20000, simulations_num = 100, trials_per_sim = 100)
summary(pl_results$min_capital)
summary(pl_results$max_capital)
summary(pl_results$final_capital)
Roulette P&L betting based on the Labouchere Betting System
Description
Calculates the potential profit or loss when someone is betting in the roulette based on the Labouchere Betting System.
Usage
roulette_pl_calculator_labouchere(
bet_minimum,
bet_maximum,
initial_capital,
profit_target,
profit_sequence,
simulations_num,
trials_per_sim
)
Arguments
bet_minimum |
The minimum betting amount that the casino allows |
bet_maximum |
The maximum betting amount that the casino allows |
initial_capital |
The initial capital to be used |
profit_target |
The profit amount to be earned |
profit_sequence |
(Optional) the amounts of the bets to reach this profit amount. If omitted, the minimum betting amount will be used |
simulations_num |
The number of simulations to be run |
trials_per_sim |
The number of trials in each simulation |
Value
A list containing the minimum, the maximum and the final balance for each simulation. Also the P&L graph for the last simulation will be plotted.
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://en.wikipedia.org/wiki/Roulette#Betting_strategies_and_tactics
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment/betting decisions should be taken based on this)
# On top of these, the below example contains a tiny number of simulations and
# trials just to pass CRAN tests - the user would have to highly increase both
# variables when running these.
pl_results = roulette_pl_calculator_labouchere(bet_minimum = 0.1 , bet_maximum = 3276.8,
initial_capital = 20000, profit_target = 100, profit_sequence = rep(10,10),
simulations_num = 100, trials_per_sim = 100)
summary(pl_results$min_capital)
summary(pl_results$max_capital)
summary(pl_results$final_capital)
Roulette P&L betting based on a modified martingale strategy
Description
Calculates the potential profit or loss when someone is betting in the roulette based on the martingale system while trying to reduce the risk by 1. Starting to double after the first loss 2. Not doubling if the second number is zero.
Usage
roulette_pl_calculator_martingale(
bet_minimum,
bet_maximum,
initial_capital,
simulations_num,
trials_per_sim
)
Arguments
bet_minimum |
The minimum betting amount that the casino allows |
bet_maximum |
The maximum betting amount that the casino allows |
initial_capital |
The initial capital to be used |
simulations_num |
The number of simulations to be run |
trials_per_sim |
The number of trials in each simulation |
Value
A list containing the minimum, the maximum and the final balance for each simulation. Also the P&L graph for the last simulation will be plotted.
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://en.wikipedia.org/wiki/Roulette#Betting_strategies_and_tactics
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment/betting decisions should be taken based on this)
# On top of these, the below example contains a tiny number of simulations and
# trials just to pass CRAN tests - the user would have to highly increase both
# variables when running these.
pl_results = roulette_pl_calculator_martingale(bet_minimum = 0.1 , bet_maximum = 3276.8,
initial_capital = 20000, simulations_num = 100, trials_per_sim = 100)
summary(pl_results$min_capital)
summary(pl_results$max_capital)
summary(pl_results$final_capital)
Roulette P&L betting on a specific number
Description
Calculates the potential profit or loss when someone is betting on a specific number in the roulette and keeps doubling every eighteen spins if the number hasn't appeared yet.
Usage
roulette_pl_calculator_specific_number(
bet_minimum,
bet_maximum,
initial_capital,
targeted_number,
simulations_num,
trials_per_sim,
stop_loss
)
Arguments
bet_minimum |
The minimum betting amount that the casino allows |
bet_maximum |
The maximum betting amount that the casino allows |
initial_capital |
The initial capital to be used |
targeted_number |
The specific number that we expect to be drawn (statistically speaking, this should have zero effect on the results) |
simulations_num |
The number of simulations to be run |
trials_per_sim |
The number of trials in each simulation |
stop_loss |
(Optional) The number of spins after which the betting amount will go back to the minimum if the targeted number hasn't appeared. |
Value
A list containing the minimum, the maximum and the final balance for each simulation. Also the P&L graph for the last simulation will be plotted.
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
References
https://en.wikipedia.org/wiki/Roulette#Betting_strategies_and_tactics
Examples
# This software is covered by GPL license and provided strictly for educational
# reasons (no actual investment or betting decisions should be taken based on this)
# On top of these, the below example contains a tiny number of simulations and
# trials just to pass CRAN tests - the user would have to highly increase both
# variables when running these.
pl_results = roulette_pl_calculator_specific_number(bet_minimum =0.1 , bet_maximum = 3276.8,
initial_capital = 20000, targeted_number = 0, simulations_num = 100,
trials_per_sim = 100, stop_loss = 180)
summary(pl_results$min_capital)
summary(pl_results$max_capital)
summary(pl_results$final_capital)
Top 5 most or least lucky numbers for EuroMillions/EuroJackpot
Description
Returns the top 5 most or least lucky euromillion numbers
Usage
top5(eurolottery_results, date_since, least_lucky = FALSE)
Arguments
eurolottery_results |
The full list of EuroMillions/EuroJackpot results |
date_since |
The date after which the analysis is to be performed, i.e. 2022-12-22 |
least_lucky |
If TRUE, the least lucky numbers will be returned (default FALSE) |
Value
Top 5 numbers
Author(s)
Tasos Grivas <tasos@openriskcalculator.com>
Examples
euromillions_results = EuroMillionsResults()
top_5 = top5(euromillions_results, '2022-12-22', least_lucky=TRUE)