add.signal(strategy.st, name=”sigFormula”, arguments=list(formula=”(Cl(mktdata) > TrendFilter) & (Oversold < 20)”, cross=TRUE), label=”longEntry”)

results <- apply.paramset(strategy.st, paramset.label=”breakout”, portfolio.st=portfolio.st)

Quantitative Edge

We trade with the most advanced quantitative feature framework architecture in digital assets.

mkt_regime <- function(x) {
rets <- na.omit(Return.calculate(x))
spec <- MSGARCH::CreateSpec(variance.spec = list(model = c(“sGARCH”, “sGARCH”)))
fit <- MSGARCH::FitML(spec = spec, y = rets) prob <- MSGARCH::State(fit)$Pstate[, 1, drop = FALSE] # Prob of Regime 1 (Low Vol) return(reclass(prob, x))
}

Signal Mastery

We use this differentiated capability to identify opportunities, drive returns, and outperform by optimizing signal extraction in a robust high frequency infrastructure.

require(xts)
require(PerformanceAnalytics)
require(PortforlioAnalytics)
require(MSGARCH)
require(blotter)
require(quantstrat)

R Innovators

We are one of the largest contributors to the open-sourced R statistical language and the most significant contributor to the quantStrat research package.

require(blotter)
port<- readRDS(portfolio.rds)
updatePortf(Portfolio=”port”)
stats <- tradeStats(Portfolios=”port”)
View(t(stats))

Institutional Backbone

Many of the leading global investment banks and trading firms rely on our code.