Automated trading system

An automated trading system (ATS), a subset of algorithmic trading, uses a computer program to create buy and sell orders and automatically submits the orders to a market center or exchange. The computer program will automatically generate orders based on predefined set of rules using a trading strategy which is based on technical analysis, advanced statistical and mathematical computations or input from other electronic sources. These automated trading systems are mostly employed by investment banks or hedge funds, but are also available to private investors using simple online tools. Automated trading systems are often used with electronic trading in automated market centers, including electronic communication networks, "dark pools", and automated exchanges. Automated trading systems and electronic trading platforms can execute repetitive tasks at speeds orders of magnitude greater than any human equivalent. Traditional risk controls and safeguards that relied on human judgment are not appropriate for automated trading and this has caused issues such as the 2010 Flash Crash. New controls such as trading curbs or 'circuit breakers' have been put in place in some electronic markets to deal with automated trading systems. The automated trading system determines whether an order should be submitted based on, for example, the current market price of an option and theoretical buy and sell prices. The theoretical buy and sell prices are derived from, among other things, the current market price of the security underlying the option. A look-up table stores a range of theoretical buy and sell prices for a given range of current market price of the underlying security. Accordingly, as the price of the underlying security changes, a new theoretical price may be indexed in the look-up table, thereby avoiding calculations that would otherwise slow automated trading decisions. A distributed processing on-line automated trading system uses structured messages to represent each stage in the negotiation between a market maker (quoter) and a potential buyer or seller (requestor).

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Concepts associés (7)

Cours associés (10)

Séances de cours associées (48)

Flash Crash Événement systémique

Explore le flash crash comme un événement systémique, en discutant de sa propagation et de ses implications réglementaires.

Flash Crash: analyse de trading à haute fréquence

Analyse le crash flash de 2010, en se concentrant sur l'impact du trading à haute fréquence.

Exécution algorithmique sur les marchés obligataires

Plongez dans l'exécution algorithmique sur les marchés obligataires, en soulignant la transition vers le trading électronique et l'importance d'algorithmes efficaces.

Automated trading system

Dark pool

Un dark pool est un système privé d'échange de valeurs mobilières exploité par un prestataire de services d'investissement ou un opérateur de marché, qui s'effectue donc de gré à gré, à l'inverse des échanges en bourse. Deux traits importants les distinguent des marchés boursiers : les prix des transactions ne sont pas affichés avant la transaction finale, ce qui permet l'anonymat des acteurs (acheteurs ou vendeurs). L'intérêt de ce système est d'éviter les trop grands impacts de marché lors d'échanges massifs.

Trading strategy

In finance, a trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets. The main reasons that a properly researched trading strategy helps are its verifiability, quantifiability, consistency, and objectivity. For every trading strategy one needs to define assets to trade, entry/exit points and money management rules. Bad money management can make a potentially profitable strategy unprofitable. Trading strategies are based on fundamental or technical analysis, or both.

FIN-525: Financial big data

The course introduces modern methods to acquire, clean, and analyze large quantities of financial data efficiently. The second part expands on how to apply these techniques and robust statistics to fi

FIN-413: Financial applications of blockchains and distributed ledgers

This course provides an introduction to Distributed Ledger Technology (DLT), blockchains and cryptocurrencies, and their applications in finance and banking and draws the analogies between Traditional

FIN-405: Investments

The course covers a wide range of topics in investment analysis