Trading System Arkitekt

Algoritmisk Trading System Architecture. Tidigare på den här bloggen har jag skrivit om den konceptuella arkitekturen i ett intelligent algoritmiskt handelssystem, liksom de funktionella och icke-funktionella kraven i ett produktionsalgoritmiskt handelssystem. Sedan dess har jag utformat en systemarkitektur som jag tror kunde uppfylla dessa arkitektoniska krav I det här inlägget kommer jag att beskriva arkitekturen enligt riktlinjerna för ISO IEC IEEE 42010-system och programvaruarkitekturens beskrivningsstandard Enligt denna standard måste en arkitekturbeskrivning innehålla. Konfigurera flera standardiserade arkitektoniska synpunkter, t. ex. i UML och. Maintain spårbarhet mellan design beslut och arkitektoniska krav. Software arkitektur definition. Det finns fortfarande ingen överenskommelse om vad en systems arkitektur är. I denna artikel definieras den infrastruktur inom vilken applikationskomponenter som uppfyller funktionella krav kan specificeras, implementeras och exekveras Funktionskrav är systemets förväntade funktioner och dess komponenter. Icke-funktionella krav är åtgärder genom vilka systemets kvalitet kan mätas. Ett system som fullt ut uppfyller sina funktionskrav kan fortfarande misslyckas att uppfylla förväntningarna om icke-funktionella krav lämnas otillfredsställd För att illustrera detta koncept överväga följande scenario ett algoritmiskt handelssystem som du just köpt byggt gör bra handelsbeslut, men är helt oföränderligt med organisationerna riskhantering och redovisningssystem Skulle detta system uppfylla dina förväntningar. Konceptuell arkitektur. En konceptuell visa beskriver högnivåkoncept och mekanismer som finns i systemet på högsta nivå av granularitet På den här nivån följer det algoritmiska handelssystemet en händelsesdriven arkitektur EDA uppdelad i fyra lager och två arkitektoniska aspekter För varje lager och aspektreferensarkitekturer och mönster ar e används Arkitektoniska mönster är beprövade, generiska strukturer för att uppnå specifika krav Arkitektoniska aspekter är tvärgående problem som spänner över flera komponenter. Eventyrad arkitektur - en arkitektur som producerar, upptäcker, konsumerar och reagerar på händelser Händelser inkluderar realtidsmarknadsrörelser, komplexa händelser eller trender och handelshändelser, t. ex. att skicka en beställning. Detta diagram illustrerar den konceptuella arkitekturen i det algoritmiska handelssystemet. Referensarkitekturer. För att använda en analogi, liknar en referensarkitektur en ritning för en bärande vägg. Detta blåtryck kan återanvändas för flera byggnadsdesigner oberoende av vilken byggnad som byggs, eftersom den uppfyller en uppsättning vanliga krav. På liknande sätt definierar en referensarkitektur en mall som innehåller generiska strukturer och mekanismer som kan användas för att konstruera en konkret mjukvaruarkitektur som uppfyller specifika krav Arkitekturen för den algoritmiska tr addering-systemet använder en rymdbaserad arkitektur SBA och en modellvisningskontroll MVC som referenser Goda metoder såsom operativdatabutiken ODS, extraktransformationen och laddningen ETL-mönstret och ett datalager DW används också. Modelleringsregulator - ett mönster som separerar representationen av information från användarens interaktion med det. Spacebaserad arkitektur - specificerar en infrastruktur där löst kopplade behandlingsenheter interagerar med varandra genom ett gemensamt associativt minne som kallas utrymme som visas nedan. Spacebaserad arkitektonisk konceptuell vy Modell Visa Controller original bild. Strukturvyn. Strukturvyn av en arkitektur visar komponenterna och delkomponenterna i det algoritmiska handelssystemet. Det visar också hur dessa komponenter distribueras på fysisk infrastruktur. UML-diagrammen som används i denna vy inkluderar komponentdiagram och installationsdiagram installationsdiagrammen för det övergripande algoritmiska handelssystemet och p processorer i SBA-referensarkitekturen samt relaterade komponentdiagram för var och en av skikten. Algoritmiskt handelssystem med hög nivåutbyggnadsdiagram SBA-bearbetningsenheter Distributionsschema Orderbehandlingsskiktskomponentdiagram Automatiserad komponentdiagram för databehandlingshantering Datakälla och förbehandlingsskikt komponentdiagram MVC-baserad användargränssnittskomponentdiagram. Architectural Tactics. According to software engineering institute är en arkitektonisk taktik ett sätt att uppfylla ett kvalitetskrav genom att manipulera en del aspekter av en kvalitetsattributmodell genom arkitektoniska designbeslut. Ett enkelt exempel som används i den algoritmiska handeln systemarkitektur manipulerar en operativ datalager ODS med en kontinuerlig frågekomponent Denna komponent skulle kontinuerligt analysera ODS för att identifiera och extrahera komplexa händelser Följande taktik används i arkitekturen. Disruptormönstret i händelse - och beställningskön. Delat minne för händelse - och beställningskön. Kontinuerligt frågande språk CQL på ODS. Data som filtrerar med filterdesignmönstret på inkommande data. Konstruktion undviker algoritmer på alla inkommande och utgående anslutningar. Aktivköhantering AQM och explicit information om överbelastning av modifieringsmoditet med kapacitet för uppgradering skalbar. Active redundans för alla enskilda punkter av misslyckande. Indexation och optimerade persistensstrukturer i ODS. Schedule regelbunden säkerhetskopiering av data och rengöringsskript för ODS. Transaction-historier på alla databaser. Checksums för alla beställningar för att upptäcka fel. Anteckna händelser med tidsstämplar till Hoppa över vanliga händelser. Ordervalideringsregler, t. ex. maximala handelsmängder. Automatiserade handlarkomponenter använder en in-memory-databas för analys. Tvåstegsautentisering för användargränssnitt som ansluter till ATs. Encryption på användargränssnitt och anslutningar till ATs. Observer-designmönstret för MVC för att hantera visningar. Ovanstående lista är bara några designbeslut som jag identifierade under arkitekturens design Det är inte en komplett lista över taktik När systemet utvecklas bör ytterligare taktik användas över flera nivåer av granularitet för att uppfylla funktionella och icke-funktionella krav Nedan finns tre diagram som beskriver disruptor designmönstret, filterdesignmönster, och den kontinuerliga frågekomponenten. Kontinuerlig Querying-komponentdiagram Disruptor-designmönster Klassdiagramkälla Filterdesignmönsterklassdiagram. Behavioural View. This syn på en arkitektur visar hur komponenterna och lagren ska interagera med varandra Detta är användbart vid skapande av scenarier för testning av arkitektur mönster och för att förstå systemet från slutet till slutet Denna uppfattning består av sekvensdiagram och aktivitetsdiagram Aktivitetsdiagram som visar det interna processen för algoritmiska handelssystemets interna process och hur handlarna ska interagera med det algoritmiska handelssystemet visas nedan. End-to-end-algoritmisk handel process. Teknik och ramverk. Det sista steget i utformningen av en programvaruarkitektur är att identifiera potentiella teknologier och ramar som kan användas för att realisera arkitekturen. Som en allmän princip är det bättre att utnyttja befintlig teknik, förutsatt att de tillräckligt uppfyller både funktionella och icke-funktionella krav En ram är en realiserad referensarkitektur, t. ex. JBoss är en ram som realiserar JEE-referensarkitekturen. Följande tekniker och ramar är intressanta och bör beaktas vid implementering av ett algoritmiskt handelssystem. CUDA - NVidia har ett antal produkter som stöder högt prestanda beräkningsmodellmodellering Det går att uppnå upp till 50x prestandaförbättringar när det gäller att köra Monte Carlo-simuleringar på GPU istället för CPU. Apache River - River är ett verktygssats som används för att utveckla distribuerade system. Det har använts som ramverk för att bygga applikationsbaserade på SBA-mönstret. Apache Hadoop - i e utlopp som en genomgripande loggning är ett krav, då användningen av Hadoop erbjuder en intressant lösning på det stora dataproblemet Hadoop kan distribueras i en grupperad miljö som stöder CUDA-teknologier. AlgoTrader - en öppen källalgoritmisk handelsplattform AlgoTrader kan eventuellt utnyttjas i plats för de automatiserade handlaren komponenter. FIX Engine - en fristående applikation som stöder Financial Information Exchange FIX-protokollet, inklusive FIX, FAST och FIXatdl. Även om ingen teknik eller ramverk, bör komponenter byggas med API för applikationsprogrammeringsgränssnitt för att förbättra driftskompatibiliteten av systemet och dess komponenter. Den föreslagna arkitekturen har utformats för att tillfredsställa mycket generiska krav som identifieras för algoritmiska handelssystem. Generellt sett kompliceras algoritmiska handelssystem av tre faktorer som varierar med varje implementering. Dämpningar på externa företag och utbytessystem. Utgående icke-funktionella krav and. Ev oljande arkitektoniska begränsningar. Den föreslagna mjukvaruarkitekturen skulle därför behöva anpassas från fall till fall för att uppfylla specifika organisatoriska och regelbundna krav samt att övervinna regionala hinder. Den algoritmiska handelssystemarkitekturen bör ses som enbart en referenspunkt för individer och organisationer som vill utforma sina egna algoritmiska handelssystem. För en fullständig kopia och källor används, ladda ner en kopia av min rapport tack. Bäst programmeringsspråk för algoritmiska handelssystem. En av de vanligaste frågorna jag får i QS-brevlådan är Vad är det bästa programmeringsspråket för algoritmisk handel Det korta svaret är att det inte finns något bra språk Strategiparametrar, prestanda, modularitet, utveckling, elasticitet och kostnad måste alla övervägas. I denna artikel beskrivs de nödvändiga komponenterna i en algoritmisk handel systemarkitektur och hur beslut om implementering påverkar valet av språk. Först kommer huvudkomponenterna i ett algoritmiskt handelssystem att övervägas, såsom forskningsverktygen, portföljoptimeraren, riskhanteraren och genomförandemotorn. Därefter kommer olika handelsstrategier att undersökas och hur de påverkar systemets utformning. handelsvolymen och den sannolika handelsvolymen kommer båda att diskuteras. När handelsstrategin har valts är det nödvändigt att arkitektera hela systemet. Detta inkluderar val av hårdvara, operativsystem s och systemlöshet mot sällsynta, potentiellt katastrofala händelser. Medan arkitektur övervägas måste hänsyn tas till prestanda både för forskningsverktygen och för levande exekveringsmiljö. Vad är handelssystemet försök att göra? Innan du bestämmer dig för det bästa språket som du ska skriva ett automatiserat handelssystem på är nödvändigt för att definiera kraven Om systemet ska vara rent exekveringsbaserat Kommer systemet att kräva riskhantering nt eller portföljkonstruktionsmodul Ska systemet kräva en högpresterande backtester För de flesta strategier kan handelssystemet delas upp i två kategorier Forskning och signalgenerering. Forskning handlar om utvärdering av en strategisk prestanda över historiska data Processen att utvärdera en handelsstrategi över tidigare marknadsdata kallas backtesting. Datastorleken och den algoritmiska komplexiteten kommer att ha stor inverkan på beräkningsintensiteten hos backtestorns CPU-hastighet och samtidighet är ofta de begränsande faktorerna för att optimera forskningsexekveringshastigheten. Signalgenerering handlar om att generera en uppsättning av handel signaler från en algoritm och skicka sådana order till marknaden, vanligtvis via en mäklare För vissa strategier krävs en hög prestationsnivå IO-frågor som nätverksbandbredd och latens är ofta den begränsande faktorn för optimering av exekveringssystem. Således val av språk för varje komponent i hela ditt system kan vara ganska diffe hyra. Typ, frekvens och volym av Strategy. The typ av algoritmisk strategi som används kommer att ha en betydande inverkan på systemets utformning. Det kommer att vara nödvändigt att överväga att marknaderna handlas, anslutningen till externa dataleverantörer, frekvensen och volymen av strategin, avvägningen mellan enkel utveckling och prestandaoptimering, samt anpassad hårdvara, inklusive samplade anpassade servrar, GPU eller FPGA som kan vara nödvändiga. Teknologierna för en lågfrekvent amerikanska aktiestrategi kommer att vara väldigt annorlunda än en högfrekvent statistisk arbitragestrategihandel på terminsmarknaden Före språkvalet måste många dataleverantörer utvärderas som avser en strategi för hand. Det kommer att vara nödvändigt att överväga anslutning till säljaren, strukturen av alla API: er, aktuell data, lagringskrav och elasticitet i ansiktet av en leverantör som går offline. Det är också klokt att ha snabb tillgång till flera leverantörer Va riösa instrument har alla sina egna lagringsegenskaper, exempel på vilka inkluderar flera tickersymboler för aktier och utgångsdatum för terminer för att inte nämna några specifika OTC-data. Detta måste ingå i plattformens design. Sannolikheten för strategin är sannolikt en av De största drivkrafterna för hur teknikstacken kommer att definieras Strategier som använder data oftare än minutiöst eller andra staplar kräver väsentligt hänsyn till prestanda. En strategi som överstiger andra stavar, dvs kryssdata leder till en prestationsdriven design som det primära kravet För hög frekvens strategier en betydande mängd marknadsdata måste lagras och utvärderas Programvara som HDF5 eller kdb används vanligtvis för dessa roller. För att kunna bearbeta de omfattande datamängderna som behövs för HFT-applikationer måste ett omfattande optimerat backtester - och exekveringssystem vara använt CC eventuellt med någon assembler är sannolikt den starkaste språkkandidaten Ultra-high frekvensstrategier kommer nästan säkert att kräva anpassad hårdvara som FPGA, utbyte av samlokalisering och kärnanätverksgränssnitt. Sökningssystem. Resursystem involverar vanligtvis en blandning av interaktiv utveckling och automatiserad skript. Den tidigare sker ofta inom en IDE som Visual Studio, MatLab eller R Studio Det senare innebär omfattande numeriska beräkningar över många parametrar och datapunkter. Det leder till ett språkval som ger en enkel miljö för att testa koden, men ger också tillräcklig prestanda för att utvärdera strategier över flera parameterdimensioner. Typiska IDE i detta utrymme inkluderar Microsoft Visual CC, som innehåller omfattande felsökningsverktyg, kodkompletteringsfunktioner via Intellisense och genomsynliga översikter över hela projektstapeln via databasen ORM, LINQ MatLab, som är konstruerad för omfattande numerisk linjär algebra och vektoriserade operationer men på ett interaktivt konsol sätt R Studio som wraps R statistiska språkkonsolen i en fulländig IDE Eclipse IDE för Linux Java och C och semi-proprietary IDEs som Enthought Canopy for Python, som inkluderar databehandlingsbibliotek som NumPy SciPy scikit-lär och pandor i en enda interaktiv konsolmiljö . För numerisk backtesting är alla ovannämnda språk lämpliga, även om det inte är nödvändigt att använda en GUI IDE, eftersom koden kommer att utföras i bakgrunden. Den primära övervägandet i detta skede är det för exekveringshastigheten. Ett sammanställt språk som C är ofta användbar om parametrarna för backtesting-parametrar är stora. Kom ihåg att det är nödvändigt att vara försiktig med sådana system om så är fallet. Interpreterade språk som Python använder ofta högpresterande bibliotek som NumPy-pandor för backtesting-steget i syfte att för att upprätthålla en rimlig grad av konkurrenskraft med kompilerade ekvivalenter I slutändan kommer det språk som valts för backtesting att bestämmas av specifika algoritmiska behov såväl som utbudet av bibliotek tillgängliga på språket mer på det nedan. Språket som används för backtester och forskningsmiljöer kan dock vara helt oberoende av dem som används i portföljkonstruktion, riskhantering och exekveringskomponenter, vilket kommer att ses. Portfölj Konstruktion och riskhantering. Portföljkonstruktion och riskhanteringskomponenter är ofta förbisedda av detaljhandeln algoritmiska handlare. Det här är nästan alltid ett misstag. Dessa verktyg ger den mekanism med vilket kapital som ska bevaras. De försöker inte bara minska antalet riskabla satsningar, utan också Minska transaktionskostnaderna, vilket minskar transaktionskostnaderna. Snygga versioner av dessa komponenter kan ha en betydande inverkan på lönsamhetens kvalitet och konsistens Det är enkelt att skapa stabila strategier, eftersom portföljbyggnadsmekanismen och riskhanteraren lätt kan ändras till hantera flera system Således bör de betraktas som nödvändiga komponenter i början av utformningen av ett algoritmiskt handelssystem. Arbetet med portföljkonstruktionssystemet är att ta en uppsättning av önskade affärer och producera uppsättningen av verkliga verksamheter som minimerar churn, behåller exponeringar mot olika faktorer som sektorer, tillgångsklasser , volatilitet mm och optimera kapitaltilldelningen till olika strategier i en portfölj. Portföljkonstruktion reduceras ofta till ett linjärt algebraproblem, såsom en matrisfaktorisering och följaktligen är prestationen mycket beroende av effektiviteten hos den numeriska linjära algebraimplementationen. Gemensamma bibliotek innehåller uBLAS LAPACK och NAG för C MatLab har också omfattande optimerade matrisoperationer Python använder NumPy SciPy för sådana beräkningar. En ofta återbalanserad portfölj kräver ett kompilerat och väloptimerat matrisbibliotek för att utföra detta steg för att inte flaskhalsa handelssystemet. Riskhantering är En annan extremt viktig del av ett algoritmiskt handelssystem Ris k kan komma i många former Ökad volatilitet, även om det här kan ses som önskvärt för vissa strategier, ökade korrelationer mellan tillgångsklasser, motpartsstandard, serveravbrott, svarta svanhändelser och oupptäckta fel i handelskoden, för att nämna några. Risk ledningskomponenter försöker förutse effekterna av överdriven volatilitet och korrelation mellan tillgångsklasser och deras efterföljande effekter s på handelskapital. Detta minskar ofta till en uppsättning statistiska beräkningar som Monte Carlo stresstest. Detta liknar mycket de beräkningsbehov av en derivatprissättning motor och som sådan kommer att vara CPU-bundna Dessa simuleringar är mycket parallelliserbara se nedan och i viss mån är det möjligt att kasta hårdvara vid problemet. Exekveringssystem. Arbetet med exekveringssystemet är att ta emot filtrerade handelssignaler från portföljkonstruktion och riskhanteringskomponenter och vidarebefordra dem till mäklare eller annat sätt att få tillgång till marknaden För majoriteten av detaljhandeln algoritmiska handelsstrategier innebär detta en API eller FIX-anslutning till en mäklare som interaktiv mäklare. De primära övervägandena när man bestämmer sig för ett språk inkluderar kvaliteten på API: et, språkpaketets tillgänglighet för ett API, exekveringsfrekvens och förväntad glidning. Kvaliteten på API: n refererar till hur väl dokumenterad det är, vilken typ av prestanda det ger, oavsett om det behöver fristående programvara som ska nås eller om en gateway kan etableras på ett huvudlöst sätt, dvs ingen GUI. När det gäller interaktiva mäklare kan Trader WorkStation-verktyget måste köras i en GUI-miljö för att kunna komma åt deras API Jag hade en gång varit tvungen att installera en Desktop Ubuntu-utgåva på en Amazon Cloud-server för att få tillgång till Interactive Brokers på distans, helt av den anledningen. De flesta API-er kommer att ge ett C - eller Java-gränssnitt är vanligtvis upp till samhället för att utveckla språkspecifika wrappers för C, Python, R, Excel och MatLab. Observera att med varje extra plugin används espe API-wrappers finns det utrymme för buggar att krypa in i systemet. Testa alltid pluggar av detta slag och se till att de är aktivt underhållna. En värdefull mätare är att se hur många nya uppdateringar av en kodbas har gjorts under de senaste månaderna. Expeditionsfrekvensen är av yttersta vikt i exekveringsalgoritmen Observera att hundratals beställningar kan skickas varje minut och sålunda prestanda är kritisk. Slippage kommer att uppstå genom ett dåligt fungerande exekveringssystem och detta kommer att ha en dramatisk inverkan på lönsamheten. Statsvisa språk ses nedan eftersom C Java i allmänhet är optimalt för utförande men det finns ett kompromiss i utvecklingstiden, testning och enkel underhåll. Dynamiskt typade språk, som Python och Perl, är nu i allmänhet snabba nog. Se alltid till att komponenterna är modulerade se nedan så att de kan bytas ut som systemet vågar. Arkitektur planering och utveckling Process. The komponenter i ett handelssystem, dess freq oklarhet och volymkrav har diskuterats ovan men systeminfrastruktur har ännu inte täcks. De som handlar som detaljhandlare eller arbetar i en liten fond kommer sannolikt att ha på sig många hattar. Det kommer att vara nödvändigt att täcka alfamodellen, riskhantering och genomförande parametrar och även den slutliga implementeringen av systemet Innan du går in i specifika språk kommer designen av en optimal systemarkitektur att diskuteras. Avskiljning av bekymmer. En av de viktigaste besluten som måste fattas i början är hur man separerar bekymmerna för ett handelssystem I mjukvaruutveckling innebär detta i huvudsak hur man bryter upp de olika aspekterna av handelssystemet i separata modulära komponenter. Genom att exponera gränssnitt för var och en av komponenterna är det enkelt att byta ut delar av systemet för andra versioner som stöder prestanda , tillförlitlighet eller underhåll, utan att ändra externt beroendeskod. Det här är den bästa praxis för sådana system. För strategier vid lägre frekvenser sådana metoder rekommenderas För ultrahögfrekvenshandel kan regelboken ignoreras på bekostnad av att tweaking systemet för ännu mer prestanda Ett mer tätt kopplat system kan vara önskvärt. Att skapa en komponentkarta av ett algoritmiskt handelssystem är värt en artikel I själva verket är dock ett optimalt tillvägagångssätt att se till att det finns separata komponenter för de historiska och realtidiga datainmatningarna, datalagring, dataåtkomst-API, backtester, strategiparametrar, portföljkonstruktion, riskhantering och automatiserade exekveringssystem. Om den datalagring som används är för närvarande underpresterande, även vid betydande optimeringsnivåer, kan den bytas ut med minimala omskrivningar till datainnehållet eller dataåtkomst-API. Såvitt som backtesteren och efterföljande komponenter är det ingen skillnad . En annan fördel med separerade komponenter är att det tillåter en mängd olika programmeringsspråk att användas i det övergripande systemet där behöver inte begränsas till ett enda språk om kommunikationsmetoden för komponenterna är språkoberoende Detta kommer att vara fallet om de kommunicerar via TCP IP, ZeroMQ eller något annat språkoberoende protokoll. Som ett konkret exempel, överväga fallet av ett backtesting system skrivs i C för antal crunching prestanda, medan portföljhanteraren och exekveringssystemen är skrivna i Python med SciPy och IBPy. Performance Considerations. Performance är ett viktigt övervägande för de flesta handelsstrategier. För högre frekvensstrategier är det den viktigaste faktor Prestanda täcker ett brett spektrum av problem, såsom algoritmisk exekveringshastighet, nätverksfördröjning, bandbredd, data IO, parallell parallellitet och skalning. Var och en av dessa områden omfattas individuellt av stora läroböcker, så den här artikeln kommer bara att skrapa ytan av varje ämne Arkitektur och språkvalet kommer nu att diskuteras med avseende på deras effekter på performance. The rådande visdom som framgår av Donald Knuth, en av fäderna för datavetenskap, är att för tidig optimering är grunden till allt ont. Detta är nästan alltid fallet - förutom när man bygger en högfrekvent handelsalgoritm För dem som är intresserade av lägre frekvensstrategier, är en vanlig tillvägagångssätt är att bygga ett system på det enklaste sättet och bara optimera när flaskhalsar börjar dyka upp. Profilverktyg används för att bestämma var flaskhalsar uppstår Profiler kan göras för alla ovan nämnda faktorer, antingen i en MS Windows - eller Linux-miljö där finns många operativsystem och språkverktyg tillgängliga för det, liksom tredjepartsverktyg. Språkvalet kommer nu att diskuteras i samband med performance. C, Java, Python, R och MatLab innehåller alla högpresterande bibliotek antingen som en del av deras standard eller externt för grundläggande datastruktur och algoritmiskt arbete C-fartyg med standardmallabiblioteket, medan Python innehåller NumPy SciPy Vanliga matematiska uppgifter ska vara finns i dessa bibliotek och det är sällan bra att skriva en ny implementering. Ett undantag är om det krävs mycket anpassad hårdvaruarkitektur och en algoritm gör omfattande användning av proprietära tillägg, såsom anpassade cachar. Men ofta återuppfinning av hjulavfallet som kunde vara bättre spenderat utveckla och optimera andra delar av handelsinfrastrukturen. Utvecklingstiden är extremt värdefull, särskilt i samband med ensamutvecklare. Latency är ofta ett problem med exekveringssystemet, eftersom forskningsverktygen oftast ligger på samma maskin. För det första är latens kan inträffa vid flera punkter längs exekveringsvägen Databaser måste konsulteras med disknätets latentitet, signaler måste genereras operativsystem, kernal messaging latency, handelssignaler skickade NIC-latens och order interna latens för bearbetade växlingssystem. För högre frekvensoperationer är det nödvändigt att bli Intimt bekant med kärnoptimering samt optimering av nätverksöverföring Detta är ett djupt område och ligger betydligt längre än artikelns räckvidd, men om en UHFT-algoritm är önskad, var då medveten om det djup av kunskap som krävs. Caching är mycket användbar i verktyget för en kvantitativ handelsutvecklare. Caching hänvisar till Begreppet lagring av ofta tillgång till data på ett sätt som möjliggör högre prestanda på bekostnad av dataens potentiella stavhet. Ett gemensamt användningsfall uppstår i webbutveckling när data tas från en diskbaserad relationsdatabas och sätts i minnet. förfrågningar om uppgifterna behöver inte slå databasen och så kan prestationsvinsterna vara betydande. För handelssituationer kan caching vara mycket fördelaktig. Till exempel kan nuvarande status för en strategiportfölj lagras i en cache tills den är ombalanserad, så att listan behöver inte regenereras på varje slinga i handelsalgoritmen. Sådan regenerering är sannolikt att vara en hög CPU eller disk IO-operation. Dock, cachi ng är inte utan egna problem Regenerering av cacherdata på en gång, på grund av cachelagrings volatilitet, kan innebära en stor efterfrågan på infrastruktur Ett annat problem är hundpiling där flera generationer av en ny cache-kopia utförs under extremt höga belastning, vilket leder till kaskadfel. Dynamisk minnesallokering är en dyr operation vid programkörning. Det är därför viktigt att applikationer med högre prestandahandel är väl medvetna om hur minnet fördelas och fördelas under programflödet. Nya språkstandarder som Java, C och Python utför alla automatiska skräpsamlingar som hänför sig till deallokering av dynamiskt allokerat minne när objekt går utanför räckhåll. Säkerhetssamling är extremt användbar under utveckling eftersom det minskar fel och hjälpmedelläsbarhet. Det är dock ofta suboptimalt för vissa högfristiga handelsstrategier Anpassad skräpsamling är ofta önskad för dessa fall I Java, till exempel, genom att ställa in skräp åldersamlare och högkonfiguration är det möjligt att få hög prestanda för HFT-strategier. C tillhandahåller inte en inbyggd sopsamlare och så är det nödvändigt att hantera all allokering av minnesallokering som en del av en s s implementering. Medan potentiellt felproblem kan leda till danglingpekare är det extremt användbart att ha finkorrigerad kontroll över hur föremål förekommer i högen för vissa applikationer. När du väljer ett språk, se till att du studerar hur sopsamlaren fungerar och huruvida den kan modifieras för att optimera för ett visst användningsfall. Många Operationer i algoritmiska handelssystem är acceptabla för parallellisering Det här hänvisar till begreppet att utföra flera programmatiska operationer samtidigt, dvs parallellt. S kallade embarassingly parallella algoritmer inkluderar steg som kan beräknas helt oberoende av andra steg Vissa statistiska operationer, såsom som Monte Carlo-simuleringar, är ett bra exempel på embarassingly parallella algoritmer eftersom varje slumpmässig teckning och efterföljande banoperation kan beräknas utan kännedom om andra vägar. Övriga algoritmer är endast delvis parallella. Fluiddynamik simuleringar är ett exempel där beräkningsdomänen kan delas upp, men i slutändan måste dessa domäner kommunicera med varandra och sålunda är operationerna delvis sekventiella. Parallelliserbara algoritmer är föremål för Amdahls lag som ger en teoretisk övre gräns för prestationsökningen av en parallelliserad algoritm när den är föremål för N separata processer, t. ex. på en CPU-kärna eller tråd. Parallellisering har blivit allt viktigare som ett medel av optimering eftersom processorns klockhastigheter har stagnerat, eftersom nyare processorer innehåller många kärnor för att utföra parallella beräkningar. Stigningen av konsumentgrafikhårdvara som främst för videospel har lett till utvecklingen av grafiska processenheter GPU: er, som innehåller hundratals kärnor för högt samtidiga operationer Sådana GPU är nu v prisvärda ramar på hög nivå, till exempel Nvidia s CUDA, har lett till omfattande adoption i akademier och finanser. Såsom GPU-hårdvara är i allmänhet endast lämplig för forskningsaspekten för kvantitativ finansiering, medan andra mer specialiserade hårdvaror inklusive Field-Programmable Gate Arrays - FPGAs används för U HFT Nuförtiden stödjer de flesta moderna långauges en grad av samtidighet multithreading Således är det enkelt att optimera en backtester eftersom alla beräkningar är generellt oberoende av de andra. Skalning i mjukvaruutveckling och operationer avser systemets förmåga att hantera consistently increasing loads in the form of greater requests, higher processor usage and more memory allocation In algorithmic trading a strategy is able to scale if it can accept larger quantities of capital and still produce consistent returns The trading technology stack scales if it can endure larger trade volumes and increased latency, without bottlenecking. While systems must be desig ned to scale, it is often hard to predict beforehand where a bottleneck will occur Rigourous logging, testing, profiling and monitoring will aid greatly in allowing a system to scale Languages themselves are often described as unscalable This is usually the result of misinformation, rather than hard fact It is the total technology stack that should be ascertained for scalability, not the language Clearly certain languages have greater performance than others in particular use cases, but one language is never better than another in every sense. One means of managing scale is to separate concerns, as stated above In order to further introduce the ability to handle spikes in the system i e sudden volatility which triggers a raft of trades , it is useful to create a message queuing architecture This simply means placing a message queue system between components so that orders are stacked up if a certain component is unable to process many requests. Rather than requests being lost they are si mply kept in a stack until the message is handled This is particularly useful for sending trades to an execution engine If the engine is suffering under heavy latency then it will back up trades A queue between the trade signal generator and the execution API will alleviate this issue at the expense of potential trade slippage A well-respected open source message queue broker is RabbitMQ. Hardware and Operating Systems. The hardware running your strategy can have a significant impact on the profitability of your algorithm This is not an issue restricted to high frequency traders either A poor choice in hardware and operating system can lead to a machine crash or reboot at the most inopportune moment Thus it is necessary to consider where your application will reside The choice is generally between a personal desktop machine, a remote server, a cloud provider or an exchange co-located server. Desktop machines are simple to install and administer, especially with newer user friendly operati ng systems such as Windows 7 8, Mac OSX and Ubuntu Desktop systems do possess some significant drawbacks, however The foremost is that the versions of operating systems designed for desktop machines are likely to require reboots patching and often at the worst of times They also use up more computational resources by the virtue of requiring a graphical user interface GUI. Utilising hardware in a home or local office environment can lead to internet connectivity and power uptime problems The main benefit of a desktop system is that significant computational horsepower can be purchased for the fraction of the cost of a remote dedicated server or cloud based system of comparable speed. A dedicated server or cloud-based machine, while often more expensive than a desktop option, allows for more significant redundancy infrastructure, such as automated data backups, the ability to more straightforwardly ensure uptime and remote monitoring They are harder to administer since they require the abi lity to use remote login capabilities of the operating system. In Windows this is generally via the GUI Remote Desktop Protocol RDP In Unix-based systems the command-line Secure SHell SSH is used Unix-based server infrastructure is almost always command-line based which immediately renders GUI-based programming tools such as MatLab or Excel to be unusable. A co-located server, as the phrase is used in the capital markets, is simply a dedicated server that resides within an exchange in order to reduce latency of the trading algorithm This is absolutely necessary for certain high frequency trading strategies, which rely on low latency in order to generate alpha. The final aspect to hardware choice and the choice of programming language is platform-independence Is there a need for the code to run across multiple different operating systems Is the code designed to be run on a particular type of processor architecture, such as the Intel x86 x64 or will it be possible to execute on RISC process ors such as those manufactured by ARM These issues will be highly dependent upon the frequency and type of strategy being implemented. Resilience and Testing. One of the best ways to lose a lot of money on algorithmic trading is to create a system with no resiliency This refers to the durability of the sytem when subject to rare events, such as brokerage bankruptcies, sudden excess volatility, region-wide downtime for a cloud server provider or the accidental deletion of an entire trading database Years of profits can be eliminated within seconds with a poorly-designed architecture It is absolutely essential to consider issues such as debuggng, testing, logging, backups, high-availability and monitoring as core components of your system. It is likely that in any reasonably complicated custom quantitative trading application at least 50 of development time will be spent on debugging, testing and maintenance. Nearly all programming languages either ship with an associated debugger or possess well-respected third-party alternatives In essence, a debugger allows execution of a program with insertion of arbitrary break points in the code path, which temporarily halt execution in order to investigate the state of the system The main benefit of debugging is that it is possible to investigate the behaviour of code prior to a known crash point. Debugging is an essential component in the toolbox for analysing programming errors However, they are more widely used in compiled languages such as C or Java, as interpreted languages such as Python are often easier to debug due to fewer LOC and less verbose statements Despite this tendency Python does ship with the pdb which is a sophisticated debugging tool The Microsoft Visual C IDE possesses extensive GUI debugging utilities, while for the command line Linux C programmer, the gdb debugger exists. Testing in software development refers to the process of applying known parameters and results to specific functions, methods and objects wit hin a codebase, in order to simulate behaviour and evaluate multiple code-paths, helping to ensure that a system behaves as it should A more recent paradigm is known as Test Driven Development TDD , where test code is developed against a specified interface with no implementation Prior to the completion of the actual codebase all tests will fail As code is written to fill in the blanks , the tests will eventually all pass, at which point development should cease. TDD requires extensive upfront specification design as well as a healthy degree of discipline in order to carry out successfully In C , Boost provides a unit testing framework In Java, the JUnit library exists to fulfill the same purpose Python also has the unittest module as part of the standard library Many other languages possess unit testing frameworks and often there are multiple options. In a production environment, sophisticated logging is absolutely essential Logging refers to the process of outputting messages, with var ious degrees of severity, regarding execution behaviour of a system to a flat file or database Logs are a first line of attack when hunting for unexpected program runtime behaviour Unfortunately the shortcomings of a logging system tend only to be discovered after the fact As with backups discussed below, a logging system should be given due consideration BEFORE a system is designed. Both Microsoft Windows and Linux come with extensive system logging capability and programming languages tend to ship with standard logging libraries that cover most use cases It is often wise to centralise logging information in order to analyse it at a later date, since it can often lead to ideas about improving performance or error reduction, which will almost certainly have a positive impact on your trading returns. While logging of a system will provide information about what has transpired in the past, monitoring of an application will provide insight into what is happening right now All aspects of the system should be considered for monitoring System level metrics such as disk usage, available memory, network bandwidth and CPU usage provide basic load information. Trading metrics such as abnormal prices volume, sudden rapid drawdowns and account exposure for different sectors markets should also be continuously monitored Further, a threshold system should be instigated that provides notification when certain metrics are breached, elevating the notification method email, SMS, automated phone call depending upon the severity of the metric. System monitoring is often the domain of the system administrator or operations manager However, as a sole trading developer, these metrics must be established as part of the larger design Many solutions for monitoring exist proprietary, hosted and open source, which allow extensive customisation of metrics for a particular use case. Backups and high availability should be prime concerns of a trading system Consider the following two questions 1 If an entire production database of market data and trading history was deleted without backups how would the research and execution algorithm be affected 2 If the trading system suffers an outage for an extended period with open positions how would account equity and ongoing profitability be affected The answers to both of these questions are often sobering. It is imperative to put in place a system for backing up data and also for testing the restoration of such data Many individuals do not test a restore strategy If recovery from a crash has not been tested in a safe environment, what guarantees exist that restoration will be available at the worst possible moment. Similarly, high availability needs to be baked in from the start Redundant infrastructure even at additional expense must always be considered, as the cost of downtime is likely to far outweigh the ongoing maintenance cost of such systems I won t delve too deeply into this topic as it is a large area, but make sure it is one of the first considerations given to your trading system. Choosing a Language. Considerable detail has now been provided on the various factors that arise when developing a custom high-performance algorithmic trading system The next stage is to discuss how programming languages are generally categorised. Type Systems. When choosing a language for a trading stack it is necessary to consider the type system The languages which are of interest for algorithmic trading are either statically - or dynamically-typed A statically-typed language performs checks of the types e g integers, floats, custom classes etc during the compilation process Such languages include C and Java A dynamically-typed language performs the majority of its type-checking at runtime Such languages include Python, Perl and JavaScript. For a highly numerical system such as an algorithmic trading engine, type-checking at compile time can be extremely beneficial, as it can eliminate many bugs that would otherwise lead to numerical errors However, type-checking doesn t catch everything, and this is where exception handling comes in due to the necessity of having to handle unexpected operations Dynamic languages i e those that are dynamically-typed can often lead to run-time errors that would otherwise be caught with a compilation-time type-check For this reason, the concept of TDD see above and unit testing arose which, when carried out correctly, often provides more safety than compile-time checking alone. Another benefit of statically-typed languages is that the compiler is able to make many optimisations that are otherwise unavailable to the dynamically - typed language, simply because the type and thus memory requirements are known at compile-time In fact, part of the inefficiency of many dynamically-typed languages stems from the fact that certain objects must be type-inspected at run-time and this carries a performance hit Libraries for dynamic languages, such as NumPy SciPy alleviate this issue due to enforc ing a type within arrays. Open Source or Proprietary. One of the biggest choices available to an algorithmic trading developer is whether to use proprietary commercial or open source technologies There are advantages and disadvantages to both approaches It is necessary to consider how well a language is supported, the activity of the community surrounding a language, ease of installation and maintenance, quality of the documentation and any licensing maintenance costs. The Microsoft stack including Visual C , Visual C and MathWorks MatLab are two of the larger proprietary choices for developing custom algorithmic trading software Both tools have had significant battle testing in the financial space, with the former making up the predominant software stack for investment banking trading infrastructure and the latter being heavily used for quantitative trading research within investment funds. Microsoft and MathWorks both provide extensive high quality documentation for their products Furthe r, the communities surrounding each tool are very large with active web forums for both The software allows cohesive integration with multiple languages such as C , C and VB, as well as easy linkage to other Microsoft products such as the SQL Server database via LINQ MatLab also has many plugins libraries some free, some commercial for nearly any quantitative research domain. There are also drawbacks With either piece of software the costs are not insignificant for a lone trader although Microsoft does provide entry-level version of Visual Studio for free Microsoft tools play well with each other, but integrate less well with external code Visual Studio must also be executed on Microsoft Windows, which is arguably far less performant than an equivalent Linux server which is optimally tuned. MatLab also lacks a few key plugins such as a good wrapper around the Interactive Brokers API, one of the few brokers amenable to high-performance algorithmic trading The main issue with proprietary p roducts is the lack of availability of the source code This means that if ultra performance is truly required, both of these tools will be far less attractive. Open source tools have been industry grade for sometime Much of the alternative asset space makes extensive use of open-source Linux, MySQL PostgreSQL, Python, R, C and Java in high-performance production roles However, they are far from restricted to this domain Python and R, in particular, contain a wealth of extensive numerical libraries for performing nearly any type of data analysis imaginable, often at execution speeds comparable to compiled languages, with certain caveats. The main benefit of using interpreted languages is the speed of development time Python and R require far fewer lines of code LOC to achieve similar functionality, principally due to the extensive libraries Further, they often allow interactive console based development, rapidly reducing the iterative development process. Given that time as a developer is extremely valuable, and execution speed often less so unless in the HFT space , it is worth giving extensive consideration to an open source technology stack Python and R possess significant development communities and are extremely well supported, due to their popularity Documentation is excellent and bugs at least for core libraries remain scarce. Open source tools often suffer from a lack of a dedicated commercial support contract and run optimally on systems with less-forgiving user interfaces A typical Linux server such as Ubuntu will often be fully command-line oriented In addition, Python and R can be slow for certain execution tasks There are mechanisms for integrating with C in order to improve execution speeds, but it requires some experience in multi-language programming. While proprietary software is not immune from dependency versioning issues it is far less common to have to deal with incorrect library versions in such environments Open source operating systems such as Linu x can be trickier to administer. I will venture my personal opinion here and state that I build all of my trading tools with open source technologies In particular I use Ubuntu, MySQL, Python, C and R The maturity, community size, ability to dig deep if problems occur and lower total cost ownership TCO far outweigh the simplicity of proprietary GUIs and easier installations Having said that, Microsoft Visual Studio especially for C is a fantastic Integrated Development Environment IDE which I would also highly recommend. Batteries Included. The header of this section refers to the out of the box capabilities of the language - what libraries does it contain and how good are they This is where mature languages have an advantage over newer variants C , Java and Python all now possess extensive libraries for network programming, operating system interaction, GUIs, regular expressions regex , iteration and basic algorithms. C is famed for its Standard Template Library STL which contains a wealt h of high performance data structures and algorithms for free Python is known for being able to communicate with nearly any other type of system protocol especially the web , mostly through its own standard library R has a wealth of statistical and econometric tools built in, while MatLab is extremely optimised for any numerical linear algebra code which can be found in portfolio optimisation and derivatives pricing, for instance. Outside of the standard libraries, C makes use of the Boost library, which fills in the missing parts of the standard library In fact, many parts of Boost made it into the TR1 standard and subsequently are available in the C 11 spec, including native support for lambda expressions and concurrency. Python has the high performance NumPy SciPy Pandas data analysis library combination, which has gained widespread acceptance for algorithmic trading research Further, high-performance plugins exist for access to the main relational databases, such as MySQL MySQL C , J DBC Java MatLab , MySQLdb MySQL Python and psychopg2 PostgreSQL Python Python can even communicate with R via the RPy plugin. An often overlooked aspect of a trading system while in the initial research and design stage is the connectivity to a broker API Most APIs natively support C and Java, but some also support C and Python, either directly or with community-provided wrapper code to the C APIs In particular, Interactive Brokers can be connected to via the IBPy plugin If high-performance is required, brokerages will support the FIX protocol. As is now evident, the choice of programming language s for an algorithmic trading system is not straightforward and requires deep thought The main considerations are performance, ease of development, resiliency and testing, separation of concerns, familiarity, maintenance, source code availability, licensing costs and maturity of libraries. The benefit of a separated architecture is that it allows languages to be plugged in for different aspects o f a trading stack, as and when requirements change A trading system is an evolving tool and it is likely that any language choices will evolve along with it. Just Getting Started with Quantitative Trading. Trading Floor Architecture. Trading Floor Architecture. Executive Overview. Increased competition, higher market data volume, and new regulatory demands are some of the driving forces behind industry changes Firms are trying to maintain their competitive edge by constantly changing their trading strategies and increasing the speed of trading. A viable architecture has to include the latest technologies from both network and application domains It has to be modular to provide a manageable path to evolve each component with minimal disruption to the overall system Therefore the architecture proposed by this paper is based on a services framework We examine services such as ultra-low latency messaging, latency monitoring, multicast, computing, storage, data and application virtualization, tra ding resiliency, trading mobility, and thin client. The solution to the complex requirements of the next-generation trading platform must be built with a holistic mindset, crossing the boundaries of traditional silos like business and technology or applications and networking. This document s main goal is to provide guidelines for building an ultra-low latency trading platform while optimizing the raw throughput and message rate for both market data and FIX trading orders. To achieve this, we are proposing the following latency reduction technologies. High speed inter-connect InfiniBand or 10 Gbps connectivity for the trading cluster. High-speed messaging bus. Application acceleration via RDMA without application re-code. Real-time latency monitoring and re-direction of trading traffic to the path with minimum latency. Industry Trends and Challenges. Next-generation trading architectures have to respond to increased demands for speed, volume, and efficiency For example, the volume of options market data is expected to double after the introduction of options penny trading in 2007 There are also regulatory demands for best execution, which require handling price updates at rates that approach 1M msg sec for exchanges They also require visibility into the freshness of the data and proof that the client got the best possible execution. In the short term, speed of trading and innovation are key differentiators An increasing number of trades are handled by algorithmic trading applications placed as close as possible to the trade execution venue A challenge with these black-box trading engines is that they compound the volume increase by issuing orders only to cancel them and re-submit them The cause of this beh avior is lack of visibility into which venue offers best execution The human trader is now a financial engineer, a quant quantitative analyst with programming skills, who can adjust trading models on the fly Firms develop new financial instruments like weather derivatives or cross-asset class trades and they need to deploy the new applications quickly and in a scalable fashion. In the long term, competitive differentiation should come from analysis, not just knowledge The star traders of tomorrow assume risk, achieve true client insight, and consistently beat the market source IBM. Business resilience has been one main concern of trading firms since September 11, 2001 Solutions in this area range from redundant data centers situated in different geographies and connected to multiple trading venues to virtual trader solutions offering power traders most of the functionality of a trading floor in a remote location. The financial services industry is one of the most demanding in terms of IT requirements The industry is experiencing an architectural shift towards Services-Oriented Architecture SOA , Web services, and virtualization of IT resources SOA takes advantage of the increase in network speed to enable dynamic binding and virtualization of software components This allows the creation of new applications without losing the investment in existing systems and infrastructure The concept has the potential to revolutionize the way integration is done, enabling significant reductions in the complexity and cost of such integration. Another trend is the consolidation of servers into data center server farms, while trader desks have only KVM extensions and ultra-thin clients e g SunRay and HP blade solutions High-speed Metro Area Networks enable market data to be multicast between different locations, enabling the virtualization of the trading floor. High-Level Architecture. Figure 1 depicts the high-level architecture of a trading environment The ticker plant and the algorithmi c trading engines are located in the high performance trading cluster in the firm s data center or at the exchange The human traders are located in the end-user applications area. Functionally there are two application components in the enterprise trading environment, publishers and subscribers The messaging bus provides the communication path between publishers and subscribers. There are two types of traffic specific to a trading environment. Market Data Carries pricing information for financial instruments, news, and other value-added information such as analytics It is unidirectional and very latency sensitive, typically delivered over UDP multicast It is measured in updates sec and in Mbps Market data flows from one or multiple external feeds, coming from market data providers like stock exchanges, data aggregators, and ECNs Each provider has their own market data format The data is received by feed handlers, specialized applications which normalize and clean the data and then send it to data consumers, such as pricing engines, algorithmic trading applications, or human traders Sell-side firms also send the market data to their clients, buy-side firms such as mutual funds, hedge funds, and other asset managers Some buy-side firms may opt to receive direct feeds from exchanges, reducing latency. Figure 1 Trading Architecture for a Buy Side Sell Side Firm. There is no industry standard for market data formats Each exchange h as their proprietary format Financial content providers such as Reuters and Bloomberg aggregate different sources of market data, normalize it, and add news or analytics Examples of consolidated feeds are RDF Reuters Data Feed , RWF Reuters Wire Format , and Bloomberg Professional Services Data. To deliver lower latency market data, both vendors have released real-time market data feeds which are less processed and have less analytics. Bloomberg B-Pipe With B-Pipe, Bloomberg de-couples their market data feed from their distribution platform because a Bloomberg terminal is not required for get B-Pipe Wombat and Reuters Feed Handlers have announced support for B-Pipe. A firm may decide to receive feeds directly from an exchange to reduce latency The gains in transmission speed can be between 150 milliseconds to 500 milliseconds These feeds are more complex and more expensive and the firm has to build and maintain their own ticker plant. Trading Orders This type of traffic carries the actual trades It is bi-directional and very latency sensitive It is measured in messages sec and Mbps The orders originate from a buy side or sell side firm and are sent to trading venues like an Exchange or ECN for execution The most common format for order transport is FIX Financial Information The applications which handle FIX messages are called FIX engines and they interface with order management systems OMS. An optimization to FIX is called FAST Fix Adapted for Streaming , which uses a compression schema to reduce message length and, in effect, reduce latency FAST is targeted more to the delivery of market data and has the potential to become a standard FAST can also be used as a compression schema for proprietary market data formats. To reduce latency, firms may opt to establish Direct Market Access DMA. DMA is the automated process of routing a securities order directly to an execution venue, therefore avoiding the intervention by a third-party glossaryId 383 DMA requires a direct connection to the execution venue. The messaging bus is middleware software from vendors such as Tibco, 29West, Reuters RMDS, or an open source platform such as AMQP The messaging bus uses a reliable mechanism to deliver messages The transport can be done over TCP IP TibcoEMS, 29West, RMDS, and AMQP or UDP multicast TibcoRV, 29West, and RMDS One important concept in message distribution is the topic stream, which is a subset of market data defined by criteria such as ticker symbol, industry, or a certain basket of financial instruments Subscribers join topic groups mapped to one or multiple sub-topics in order to receive only the relevant information In the past, all traders received all market data At the current volumes of traffic, this would be sub-optimal. The network plays a critical role in the trading environment Market data is carried to the trading floor where the human traders are located via a Campus or Metro Area high-speed net work High availability and low latency, as well as high throughput, are the most important metrics. The high performance trading environment has most of its components in the Data Center server farm To minimize latency, the algorithmic trading engines need to be located in the proximity of the feed handlers, FIX engines, and order management systems An alternate deployment model has the algorithmic trading systems located at an exchange or a service provider with fast connectivity to multiple exchanges. Deployment Models. There are two deployment models for a high performance trading platform Firms may chose to have a mix of the two. Data Center of the trading firm Figure 2 This is the traditional model, where a full-fledged trading platform is developed and maintained by the firm with communication links to all the trading venues Latency varies with the speed of the links and the number of hops between the firm and the venues. Figure 2 Traditional Deployment Model. Co-location at the trading venue exchanges, financial service providers FSP Figure 3.The trading firm deploys its automated trading platform as close as possible to the execution venues to minimize latency. Figure 3 Hosted Deployment Model. Services-Oriented Trading Architecture. We are proposing a services-oriented framework for building the next-generation trading architecture This approach provides a conceptual framework and an implementation path based on modularization and minimization of inter-dependencies. This framework provides firms with a methodology to. Evaluate their current state in terms of services. Prioritize services based on their value to the business. Evolve the trading platform to the desired state using a modular approach. The high performance trading architecture relies on the following services, as defined by the services architecture framework represented in Figure 4.Figure 4 Service Architecture Framework for High Performance Trading. Ultra-Low Latency Messaging Service. This service is provided by the messaging bus, which is a software system that solves the problem of connecting many-to-many applications The system consists of. A set of pre-defined message schemas. A set of common command messages. A shared application infrastructure for sending the messages to recipients The shared infrastructure can be based on a message broker or on a publish subscribe model. The key requirements for the next-generation messaging bus are source 29West. Lowest possible latency e g less than 100 microseconds. Stability under heavy load e g more than 1 4 million msg sec. Control and flexibility rate control and configurable transports. There are efforts in the industry to standardize the messaging bus Advanced Message Queueing Protocol AMQP is an example of an open standard championed by J P Morgan Chase and supported by a group of vendors such as Cisco, Envoy Technologies, Red Hat, TWIST Process Innovations, Iona, 29West, and iMatix Two of the main goals are to provide a more simple path to inter-operability for applications written on different platforms and modularity so that the middleware can be easily evolved. In very general terms, an AMQP server is analogous to an E-mail server with each exchange acting as a message transfer agent and each message queue as a mailbox The bindings define the routing tables in each transfer agent Publishers send messages to individual transfer agents, which then route the messages into mailboxes Consumers take messages from mailboxes, which creates a powerful and flexible model that is simple source. Latency Monitori ng Service. The main requirements for this service are. Sub-millisecond granularity of measurements. Near-real time visibility without adding latency to the trading traffic. Ability to differentiate application processing latency from network transit latency. Ability to handle high message rates. Provide a programmatic interface for trading applications to receive latency data, thus enabling algorithmic trading engines to adapt to changing conditions. Correlate network events with application events for troubleshooting purposes. Latency can be defined as the time interval between when a trade order is sent and when the same order is acknowledged and acted upon by the receiving party. Addressing the latency issue is a complex problem, requiring a holistic approach that identifies all sources of latency and applies different technologies at different layers of the system. Figure 5 depicts the variety of components that can introduce latency at each layer of the OSI stack It also maps each source of latency with a possible solution and a monitoring solution This layered approach can give firms a more structured way of attacking the latency issue, whereby each component can be thought of as a service and treated consistently across the firm. Maintaining an accurate measure of the dynamic state of this time interval across alternative routes and destinations can be of great assistance in tactical trading decisions The ability to identify the exact location of delays, whether in the customer s edge network, the central processing hub, or the transaction application level, significantly determines the ability of service providers to meet their trading service-level agreements SLAs For buy-side and sell-side forms, as well as for market-data syndicators, the quick identification and removal of bottlenecks translates directly into enhanced trade opportunities and revenue. Figure 5 Latency Management Architecture. Cisco Low-Latency Monitoring Tools. Traditional network monitoring tools operate with minutes or seconds granularity Next-generation trading platforms, especially those supporting algorithmic trading, require latencies less than 5 ms and extremely low levels of packet loss On a Gigabit LAN, a 100 ms microburst can cause 10,000 transactions to be lost or excessively delayed. Cisco offers its customers a choice of tools to measure latency in a trading environment. Bandwidth Quality Manager BQM OEM from Corvil. Cisco AON-based Financial Services Latency Monitoring Solution FSMS. Bandwidth Quality Manager. Bandwidth Quality Manager BQM 4 0 is a next-generation network application performance management product that enables customers to monitor and provision their network for controlled levels of latency and loss performance While BQM is not exclusively targeted at trading networks, its microsecond visibility combined with intelligent bandwidth provisioning features make it ideal for these demanding environments. Cisco BQM 4 0 implements a broad set of patented and patent-pending traffic measurement and network analysis technologies that give the user unprecedented visibility and understanding of how to optimize the network for maximum application performance. Cisco BQM is now supported on the product family of Cisco Application Deployment Engine ADE The Cisco ADE product family is the platform of choice for Cisco network management applications. BQM Benefits. Cisco BQM micro-visibility is the abilit y to detect, measure, and analyze latency, jitter, and loss inducing traffic events down to microsecond levels of granularity with per packet resolution This enables Cisco BQM to detect and determine the impact of traffic events on network latency, jitter, and loss Critical for trading environments is that BQM can support latency, loss, and jitter measurements one-way for both TCP and UDP multicast traffic This means it reports seamlessly for both trading traffic and market data feeds. BQM allows the user to specify a comprehensive set of thresholds against microburst activity, latency, loss, jitter, utilization, etc on all interfaces BQM then operates a background rolling packet capture Whenever a threshold violation or other potential performance degradation event occurs, it triggers Cisco BQM to store the packet capture to disk for later analysis This allows the user to examine in full detail both the application traffic that was affected by performance degradation the victims and th e traffic that caused the performance degradation the culprits This can significantly reduce the time spent diagnosing and resolving network performance issues. BQM is also able to provide detailed bandwidth and quality of service QoS policy provisioning recommendations, which the user can directly apply to achieve desired network performance. BQM Measurements Illustrated. To understand the difference between some of the more conventional measurement techniques and the visibility provided by BQM, we can look at some comparison graphs In the first set of graphs Figure 6 and Figure 7 , we see the difference between the latency measured by BQM s Passive Network Quality Monitor PNQM and the latency measured by injecting ping packets every 1 second into the traffic stream. In Figure 6 we see the latency reported by 1-second ICMP ping packets for real network traffic it is divided by 2 to give an estimate for the one-way delay It shows the delay comfortably below about 5ms for almost all of the time. Figure 6 Latency Reported by 1-Second ICMP Ping Packets for Real Network Traffic. In Figure 7 we see the latency reported by PNQM for the same traffic at the same time Here we see that by measuring the one-way latency of the actual application packets, we get a radically different picture Here the latency is seen to be hovering around 20 ms, with occasional bursts far higher The explanation is that because ping is sending packets only every second, it is completely missing most of the application traffic latency In fact, ping results typically only indicate round trip propagation delay rather than realistic application latency across the network. Figure 7 Latency Reported by PNQM for Real Network Traffic. In the second example Figure 8 , we see the difference in reported link load or saturation levels between a 5-minute average view and a 5 ms microburst view BQM can report on microbursts down to about 10-100 nanosecond accuracy The green line shows the average utilization at 5-minut e averages to be low, maybe up to 5 Mbits s The dark blue plot shows the 5ms microburst activity reaching between 75 Mbits s and 100 Mbits s, the LAN speed effectively BQM shows this level of granularity for all applications and it also gives clear provisioning rules to enable the user to control or neutralize these microbursts. Figure 8 Difference in Reported Link Load Between a 5-Minute Average View and a 5 ms Microburst View. BQM Deployment in the Trading Network. Figure 9 shows a typical BQM deployment in a trading network. Figure 9 Typical BQM Deployment in a Trading Network. BQM can then be used to answer these types of questions. Are any of my Gigabit LAN core links saturated for more than X milliseconds Is this causing loss Which links would most benefit from an upgrade to Etherchannel or 10 Gigabit speeds. What application traffic is causing the saturation of my 1 Gigabit links. Is any of the market data experiencing end-to-end loss. How much additional latency does the failover data center experience Is this link sized correctly to deal with microbursts. Are my traders getting low latency updates from the market data distribution layer Are they seeing any delays greater than X milliseconds. Being able to answer these questions simply and effectively saves time and money in running the trading network. BQM is an essential tool for gaining visibility in market data and trading environments It provides granular end-to-end latency measurements in complex infrastructures that experience high-volume data movement Effectively detecting microbursts in sub-millisecond levels and receiving expert analysis on a particular event is invaluable to trading floor architects Smart bandwidth provisioning recommendations, such as sizing and what-if analysis, provide greater agility to respond to volatile market conditions As the explosion of algorithmic trading and increasing message rates continues, BQM, combined with its QoS tool, provides the capability of implementing QoS policies that can protect critical trading applications. Cisco Financial Services Latency Monitoring Solution. Cisco and Trading Metrics have collaborated on latency monitoring solutions for FIX order flow and market data monitoring Cisco AON technology is the foundation for a new class of network-embedded products and solutions that help merge intelligent networks with application infrastructure, based on either service-oriented or traditional architectures Trading Metrics is a leading provider of analytics software for network infrastructure and application latency monitoring purposes. The Cisco AON Financial Services Latency Monitoring Solution FSMS correlated two kinds of events at the point of observation. Network events correlated directly with coincident application message handling. Trade order flow and matching market update events. Using time stamps asserted at the point of capture in the network, real-time analysis of these correlated data streams permits precise identification of bottlenecks across the infrastructure while a trade is being executed or market data is being distributed By monitoring and measuring latency early in the cycle, financial companies can make better decisions about which network service and which intermediary, market, or counterparty to select for routing trade orders Likewise, this knowledge allows more streamlined access to updated market data stock quotes, economic news, etc , which is an important basis for initiating, withdrawing from, or pursuing market opportunities. The components of the solution are. AON hardware in three form factors. AON Network Module for Cisco 2600 2800 3700 3800 routers. AON Blade for the Cisco Catalyst 6500 series. AON 8340 Appliance. Trading Metrics M A 2 0 software, which provides the monitoring and alerting application, displays latency graphs on a dashboard, and issues alerts when slowdowns occur. Figure 10 AON-Based FIX Latency Monitoring. Cisco IP SLA. Cisco IP SLA is an embedded network management tool in Cisco IOS which allows routers and switches to generate synthetic traffic streams which can be measured for latency, jitter, packet loss, and other criteria. Two key concepts are the source of the generated traffic and the target Both of these run an IP SLA responder, which has the responsibility to timestamp the control traffic before it is sourced and returned by the target for a round trip measurement Various traffic types can be sourced within IP SLA and they are aimed at different metrics and target different services and applications The UDP jitter operation is used to measure one-way and round-trip delay and report variations As the traffic is time stamped on both sending and target devices using the resp onder capability, the round trip delay is characterized as the delta between the two timestamps. A new feature was introduced in IOS 12 3 14 T, IP SLA Sub Millisecond Reporting, which allows for timestamps to be displayed with a resolution in microseconds, thus providing a level of granularity not previously available This new feature has now made IP SLA relevant to campus networks where network latency is typically in the range of 300-800 microseconds and the ability to detect trends and spikes brief trends based on microsecond granularity counters is a requirement for customers engaged in time-sensitive electronic trading environments. As a result, IP SLA is now being considered by significant numbers of financial organizations as they are all faced with requirements to. Report baseline latency to their users. Trend baseline latency over time. Respond quickly to traffic bursts that cause changes in the reported latency. Sub-millisecond reporting is necessary for these customers, since many campus and backbones are currently delivering under a second of latency across several switch hops Electronic trading environments have generally worked to eliminate or minimize all areas of device and network latency to deliver rapid order fulfillment to the business Reporting that network response times are just under one millisecond is no longer sufficient the granularity of latency measurements reported across a network segment or backbone need to be closer to 300-800 micro-seconds with a degree of resolution of 100 seconds. IP SLA recently added support for IP multicast test streams, which can measure market data latency. A typical network topology is shown in Figure 11 with the IP SLA shadow routers, sources, and responders. Figure 11 IP SLA Deploymentputing Servicesputing services cover a wide range of technologies with the goal of elim inating memory and CPU bottlenecks created by the processing of network packets Trading applications consume high volumes of market data and the servers have to dedicate resources to processing network traffic instead of application processing. Transport processing At high speeds, network packet processing can consume a significant amount of server CPU cycles and memory An established rule of thumb states that 1Gbps of network bandwidth requires 1 GHz of processor capacity source Intel white paper on I O acceleration. Intermediate buffer copying In a conventional network stack implementation, data needs to be copied by the CPU between network buffers and application buffers This overhead is worsened by the fact that memory speeds have not kept up with increases in CPU speeds For example, processors like the Intel Xeon are approaching 4 GHz, while RAM chips hover around 400MHz for DDR 3200 memory source Intel. Context switching Every time an individual packet needs to be processed, the CPU performs a context switch from application context to network traffic context This overhead could be reduced if the switch would occur only when the whole application buffer is complete. Figure 12 Sources of Overhead in Data Center Servers. TCP Offload Engine TOE Offloads transport processor cycles to the NIC Moves TCP IP protocol stack buffer copies from system memory to NIC memory. Remote Direct Memory Access RDMA Enables a network adapter to transfer data directly from application to application without involving the operating system Eliminates intermediate and application buffer copies memory bandwidth consumption. Kernel bypass Direct user-level access to hardware Dramatically reduces application context switches. Figure 13 RDMA and Kernel Bypass. InfiniBand is a point-to-point switched fabric bidirectional serial communication link which implements RDMA, among other features Cisco offers an InfiniBand switch, the Server Fabric Switch SFS. Figure 14 Typical SFS Deployment. Trading applications benefit from the reduction in latency and latency variability, as proved by a test performed with the Cisco SFS and Wombat Feed Handlers by Stac Research. Application Virtualization Service. De-coupling the application from the underlying OS and server hardware enables them to run as network services One application can be run in parallel on multiple servers, or multiple applications can be run on the same server, as the best resource allocation dictates This decoupling enables better load balancing and disaster recovery for business continuance strategies The process of re-allocating computing resources to an a pplication is dynamic Using an application virtualization system like Data Synapse s GridServer, applications can migrate, using pre-configured policies, to under-utilized servers in a supply-matches-demand process. There are many business advantages for financial firms who adopt application virtualization. Faster time to market for new products and services. Faster integration of firms following merger and acquisition activity. Increased application availability. Better workload distribution, which creates more head room for processing spikes in trading volume. Operational efficiency and control. Reduction in IT complexity. Currently, application virtualization is not used in the trading front-office One use-case is risk modeling, like Monte Carlo simulations As the technology evolves, it is conceivable that some the trading platforms will adopt it. Data Virtualization Service. To effectively share resources across distributed enterprise applications, firms must be able to leverage data across multiple sources in real-time while ensuring data integrity With solutions from data virtualization software vendors such as Gemstone or Tangosol now Oracle , financial firms can access heterogeneous sources of data as a single system image that enables connectivity between business processes and unrestrained application access to distributed caching The net result is that all users have instant access to these data resources across a distributed network. This is called a data grid and is the first step in the process of creating what Gartner calls Extreme Transaction Processing XTP id 500947 Technologies such as data and applications virtualization enable financial firms to perform real-time complex analytics, event-driven applications, and dynamic resource allocation. One example of data virtualization in action is a global order book application An order book is the repository of active orders that is published by the exchange or other market makers A global order book aggregates orders from around the world from markets that operate independently The biggest challenge for the application is scalability over WAN connectivity because it has to maintain state Today s data grids are localized in data centers connected by Metro Area Networks MAN This is mainly because the applications themselves have limits they have been developed without the WAN in mind. Figure 15 GemStone GemFire Distributed Caching. Before data virtualization, applications used database clustering for failover and scalability This solution is limited by the performance of the underlying database Failover i s slower because the data is committed to disc With data grids, the data which is part of the active state is cached in memory, which reduces drastically the failover time Scaling the data grid means just adding more distributed resources, providing a more deterministic performance compared to a database cluster. Multicast Service. Market data delivery is a perfect example of an application that needs to deliver the same data stream to hundreds and potentially thousands of end users Market data services have been implemented with TCP or UDP broadcast as the network layer, but those implementations have limited scalability Using TCP requires a separate socket and sliding window on the server for each recipient UDP broadcast requires a separate copy of the stream for each destination subnet Both of these methods exhaust the resources of the servers and the network The server side must transmit and service each of the streams individually, which requires larger and larger server farms On th e network side, the required bandwidth for the application increases in a linear fashion For example, to send a 1 Mbps stream to 1000recipients using TCP requires 1 Gbps of bandwidth. IP multicast is the only way to scale market data delivery To deliver a 1 Mbps stream to 1000 recipients, IP multicast would require 1 Mbps The stream can be delivered by as few as two servers one primary and one backup for redundancy. There are two main phases of market data delivery to the end user In the first phase, the data stream must be brought from the exchange into the brokerage s network Typically the feeds are terminated in a data center on the customer premise The feeds are then processed by a feed handler, which may normalize the data stream into a common format and then republish into the application messaging servers in the data center. The second phase involves injecting the data stream into the application messaging bus which feeds the core infrastructure of the trading applications The larg e brokerage houses have thousands of applications that use the market data streams for various purposes, such as live trades, long term trending, arbitrage, etc Many of these applications listen to the feeds and then republish their own analytical and derivative information For example, a brokerage may compare the prices of CSCO to the option prices of CSCO on another exchange and then publish ratings which a different application may monitor to determine how much they are out of synchronization. Figure 16 Market Data Distribution Players. The delivery of these data streams is typically over a reliable multicast transport protocol, traditionally Tibco Rendezvous Tibco RV operates in a publish and subscribe environment Each financial instrument is given a subject name, such as Each application server can request the individual instruments of interest by their subject name and receive just a that subset of the information This is called subject-based forwarding or filtering Subject-based f iltering is patented by Tibco. A distinction should be made between the first and second phases of market data delivery The delivery of market data from the exchange to the brokerage is mostly a one-to-many application The only exception to the unidirectional nature of market data may be retransmission requests, which are usually sent using unicast The trading applications, however, are definitely many-to-many applications and may interact with the exchanges to place orders. Figure 17 Market Data Architecture. Design Issues. Number of Groups Channels to Use. Many application developers consider using thousand of multicast groups to give them the ability to divide up products or instruments into small buckets Normally these applications send many small messages as part of their information bus Usually several messages are sent in each packet that are received by many users Sending fewer messages in each packet increases the overhead necessary for each message. In the extreme case, sending onl y one message in each packet quickly reaches the point of diminishing returns there is more overhead sent than actual data Application developers must find a reasonable compromise between the number of groups and breaking up their products into logical buckets. Consider, for example, the Nasdaq Quotation Dissemination Service NQDS The instruments are broken up alphabetically. This approach allows for straight forward network application management, but does not necessarily allow for optimized bandwidth utilization for most users A user of NQDS that is interested in technology stocks, and would like to subscribe to just CSCO and INTL, would have to pull down all the data for the first two groups of NQDS Understanding the way users pull down the data and then organize it into appropriate logical groups optimizes the bandwidth for each user. In many market data applications, optimizing the data organization would be of limited value Typically customers bring in all data into a few machines a nd filter the instruments Using more groups is just more overhead for the stack and does not help the customers conserve bandwidth Another approach might be to keep the groups down to a minimum level and use UDP port numbers to further differentiate if necessary The other extreme would be to use just one multicast group for the entire application and then have the end user filter the data In some situations this may be sufficient. Intermittent Sources. A common issue with market data applications are servers that send data to a multicast group and then go silent for more than 3 5 minutes These intermittent sources may cause trashing of state on the network and can introduce packet loss during the window of time when soft state and then hardware shorts are being created. PIM-Bidir or PIM-SSM. The first and best solution for intermittent sources is to use PIM-Bidir for many-to-many applications and PIM-SSM for one-to-many applications. Both of these optimizations of the PIM protocol do not ha ve any data-driven events in creating forwarding state That means that as long as the receivers are subscribed to the streams, the network has the forwarding state created in the hardware switching path. Intermittent sources are not an issue with PIM-Bidir and PIM-SSM. Null Packets. In PIM-SM environments a common method to make sure forwarding state is created is to send a burst of null packets to the multicast group before the actual data stream The application must efficiently ignore these null data packets to ensure it does not affect performance The sources must only send the burst of packets if they have been silent for more than 3 minutes A good practice is to send the burst if the source is silent for more than a minute Many financials send out an initial burst of traffic in the morning and then all well-behaved sources do not have problems. Periodic Keepalives or Heartbeats. An alternative approach for PIM-SM environments is for sources to send periodic heartbeat messages to the mu lticast groups This is a similar approach to the null packets, but the packets can be sent on a regular timer so that the forwarding state never expires. S,G Expiry Timer. Finally, Cisco has made a modification to the operation of the S, G expiry timer in IOS There is now a CLI knob to allow the state for a S, G to stay alive for hours without any traffic being sent The S, G expiry timer is configurable This approach should be considered a workaround until PIM-Bidir or PIM-SSM is deployed or the application is fixed. RTCP Feedback. A common issue with real time voice and video applications that use RTP is the use of RTCP feedback traffic Unnecessary use of the feedback option can create excessive multicast state in the network If the RTCP traffic is not required by the application it should be avoided. Fast Producers and Slow Consumers. Today many servers providing market data are attached at Gigabit speeds, while the receivers are attached at different speeds, usually 100Mbps This creates the potential for receivers to drop packets and request re-transmissions, which creates more traffic that the slowest consumers cannot handle, continuing the vicious circle. The solution needs to be some type of access control in the application that limits the amount of data that one host can request QoS and other network functions can mitigate the problem, but ultimately the subscriptions need to be managed in the application. Tibco Heartbeats. TibcoRV has had the ability to use IP multicast for the heartbeat between the TICs for many years However, there are some brokerage houses that are still using very old versions of TibcoRV that use UDP broadcast support for the resiliency This limitation is often cited as a reason to maintain a Layer 2 infrastructure between TICs located in different data centers These older versions of TibcoRV should be phased out in favor of the IP multicast supported versions. Multicast Forwarding Options. PIM Sparse Mode. The standard IP multicast forwarding protoco l used today for market data delivery is PIM Sparse Mode It is supported on all Cisco routers and switches and is well understood PIM-SM can be used in all the network components from the exchange, FSP, and brokerage. There are, however, some long-standing issues and unnecessary complexity associated with a PIM-SM deployment that could be avoided by using PIM-Bidir and PIM-SSM These are covered in the next sections. The main components of the PIM-SM implementation are. PIM Sparse Mode v2. Shared Tree spt-threshold infinity. A design option in the brokerage or in the exchange.