Category Archive
for: ‘Day Trading’

Algorithmic trades require communicating considerably more parameters than traditional market and limit orders. A trader on one end (the “buy side”) must enable their trading system (often called an “Order Management System” or “Execution Management System”) to understand a constantly proliferating flow of new algorithmic order types. The R&D and other costs to construct complex …

Though its development may have been prompted by decreasing trade sizes caused by decimalization, algorithmic trading has reduced trade sizes further. Jobs once done by human traders are being switched to computers. The speeds of computer connections, measured in milliseconds and even microseconds, have become very important.

The technical designs of such systems are not standardized. Conceptually, the design can be divided into logical units: 1. The data stream unit (the part of the systems that receives data (e.g. quotes, news) from external sources). 2. The decision or strategy unit 3. The execution unit. With the wide use of social networks, some …

Financial market news is now being formatted by firms such as Need To Know News, Thomson Reuters, Dow Jones, and Bloomberg, to be read and traded on via algorithms. “Computers are now being used to generate news stories about company earnings results or economic statistics as they are released. And this almost instantaneous information forms …

Algorithmic trading has been shown to substantially improve market liquidity among other benefits. However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers.

Most of the algorithmic strategies are implemented using modern programming languages, although some still implement strategies designed in spreadsheets. Basic models can rely on as little as a linear regression, while more complex game-theoretic and pattern recognition or predictive models can also be used to initiate trading. Neural networks and genetic programming have been used …

HFT is often confused with low-latency trading that uses computers that execute trades within milliseconds, or “with extremely low latency” in the jargon of the trade. Low-latency trading is highly dependent on ultra-low latency networks. They profit by providing information, such as competing bids and offers, to their algorithms microseconds faster than their competitors. The …

In the U.S., high-frequency trading (HFT) firms represent 2% of the approximately 20,000 firms operating today, but account for 73% of all equity trading volume. As of the first quarter in 2009, total assets under management for hedge funds with HFT strategies were US$141 billion, down about 21% from their high. The HFT strategy was …

Recently, HFT, which comprises a broad set of buy-side as well as market making sell side traders, has become more prominent and controversial. These algorithms or techniques are commonly given names such as “Stealth” (developed by the Deutsche Bank), “Iceberg”, “Dagger”, “Guerrilla”, “Sniper”, “BASOR” (developed by Quod Financial) and “Sniffer”. Yet are at their core …

Most strategies referred to as Algorithmic Trading (as well as algorithmic liquidity seeking) fall into the cost-reduction category. Large orders are broken down into several smaller orders and entered into the market over time. This basic strategy is called “iceberging”. The success of this strategy may be measured by the average purchase price against the …