ProgrammaticTrading&Risk
inEnergyCommodities
Within this decade, we have seen programmatic (or algorithmic) trading expand to become a
significant proportion of trade flows within the securities markets. This technology is now well
establishedfromequitiesandfuturestobondsandforeignexchange.Theassetclasswheretake-up
has lagged is commodities. This article examines the challenges and opportunities in applying
Programmatic Trading and Risk Management(PTRM)techniquestotheenergymarkets.
By Mike Reilly
A
utomation comes in several guises with varying now due to the acceptance of PT by smaller trading entities
terminology. For the purpose of this article, andasset-backedbuy-sidefirms.Someequityexchangeshave
Programmatic Trading (PT) is a collective term that stated that more than half of their trade flow is generated
encompasses any trading activity that does not involve programmatically. Bond and FX markets typically quote
manual intervention between the inception of a trading between 25-40% of trades as being computer-generated. As
signalandtheexecutionofatradeorderwithinanelectronic Figure 1 indicates, some 35% of futures trades are now
market. This includes straightforward limit-orders and stop- generated algorithmically. Commodities account for roughly
losstradesthroughtomorecomplexalgorithmsusingevent one fifth of all futures trades, although algo-trading is
correlation for arbitrage trading. The terms Algorithmic, typicallyhigherthan35%inoilproductsandlowerinother
Quantitative and Automated Trading canallbeincludedwithin energyproducts.
thisdefinitionofPT.
Programmatictechniquesarenowbeingappliedtoportfolio
Figure 1: PTRM Adoption in Futures Trading
riskmanagement.Thisisanaturalprogressiontoensurethat Growth & estimates in percentage of orders
positions generated by PT are monitored in real-time.
Programmatic Trading and Risk Management (PTRM) can be
considered as “two sides of the same coin”. The exposures
created by PT are monitored and limits managed using the
samereal-timetechnology.
PTRM Evolution
Programmatic trading was pioneered in the early years of
the decade by vendors such as Apama who worked with
leading securities firms including JPMorgan and Deutsche
Bank.Atradingalgorithmisessentiallythelogicthatembeds
amathematicalmodelthatanalysesmarketparametersand
automaticallygeneratestradeexecutionordersintoelectronic
Source: AiteGroup,October2008
markets.PTRMcanalsobeappliedinasemi-automaticway The proliferation of new exchanges, multilateral trading
where trade signals are output to be authorised and/or facilities(MTFs)andalternativeOTCmatchingvenueshasled
executedbyahumantrader. toarangeoftradingactivities,especiallyinthesearchforso-
The initial proving grounds for PT were cash equities and called ‘dark pools’ of liquidity. This requires market
listedfutures.Havingproventhetechnology,therehasbeena participants to do more pre-trade processing, for example:
progressiveexpansionintoforeignexchangeandfixedincome aggregation, smart order routing and order slicing or
markets.Theassetclassthathasseenthemostrecenttake-up ‘iceberging’.Donemanually,thesetasksarelabour-intensive
of trade automation is commodities. Whilst there is an and prone to error. These Use Cases, discussed later, are a
established cadre of PTRM platforms being used in metals, natural fit for a programmatic trading tool which can work
freightandsoftcommoditymarkets,energyhasbeenslowerto dozensofstrategiesconcurrentlygeneratinghundredsoforders
adoptthistechnology.PTRMhasbeensuccessfullyappliedin acrossmultiplevenues.PTtoolscanprocessthesetasksmore
more liquid energy markets such as crude and refined oil quickly, accurately and effectively than the most productive
products, as well as natural gas in the US. However, broader humantraderortradingdesk.
application in other energy commodities such as gas and Thevolatilityexhibitedincommoditymarketsinthelast12-
electricity has yet to be observed. The opportunities and 18 months is well documented, as are the resultant business
inhibitorsforPTRMinenergymarketsarediscussedbelow. failures. Consequently, risk management policies, tools and
Initially the domain of larger institutional investors, an techniques are now under far greater scrutiny. The need for
increasing proportion of programmatically-derived trades is rigour in risk controls applied to programmatic trading has
worldPower2009 87
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