Here's an attempt to describe the Algo Trading business in layman's terms. Online trading platforms: There is a large number of online trading platforms that provide easy, standardized access to historical data (via RESTful APIs) and real-time data (via socket streaming APIs), and also offer trading and portfolio features (via programmatic APIs).
Algorithms essentially work as middlemen between buyers and sellers, with high-frequency-trading and ultra high-frequency-trading being a way for traders to capitalize on infinitesimal price discrepancies that might exist only for a miniscule period of time.
The success of market making basically is sustained through (p. 323) real-time market price observation, since dealers with more timely information about the present market price can set up quotes in a more exact manner and so generate a thinner bid-ask spread through an increased number of executed trades.
Richard Balarkas, CEO of Instinet Europe, an institutional brokerage firm, draws a dark future for human intermediaries: It algorithmic trading signaled the death of the dealer that just outsourced all risk and responsibility for the trade to the broker and heralded the arrival of the buy-side trader that could take full control of the trade and be a more discerning buyer of sell-side services” ( Trade News 2009 ).
Further, there are also a lot of proven mathematical models, like the delta-neutral trading strategy, which allows trading on combination of options and its underlying security, where trades are placed to offset positive and negative deltas so that the portfolio delta is maintained at zero.
Gradually, old-school, high latency architecture of algorithmic systems is being replaced by newer, state-of-the-art, high infrastructure, low-latency networks The complex event processing engine (CEP), which is the heart of decision making in algo-based trading systems, is used for order routing and risk management.
In addition to these models, there are a number of other decision making models which can be used in the context of algorithmic trading (and markets in general) to make predictions regarding the direction of security prices or, for quantitative readers, to make predictions regarding the probability of any given move in a securities price.
Taking a similar view, Chris Jackson, head of the execution and quantitative services group at Liquidnet EMEA, believes some MiFID II requirements on algo trading equate to basic best practice, while the more broad-ranging demands will require future debate and discussion for clarification.
This, the advent of personal computers, and the proliferation of the internet, was what catalyzed the concept of electronic trading, which allows everyday users to trade from anywhere around the world using their computers, spearheading a new trading format: algorithmic trading (algo trading”).
High frequency trading is typically done by the traders using their own capital to trade and rather than being a strategy in itself is usually the use of sophisticated technology to implement more traditional trading strategies such as market algo trading python making or arbitrage” ( European Commission 2011, p. 25 ). Most academic and regulatory papers agree that HFT should be classified as technology rather than a specific trading strategy and therefore demarcate HFT from algorithmic trading.
The Aite Group (2006) estimated algorithm usage from a starting point near zero around 2000, thought to be responsible for over 50 percent of trading volume in the United States in 2010 ( Aite Group 2006 ). Hendershott and Riordan (2011) reached about the same number on the basis of a data set of Deutsche Boerse's DAX 30 instruments traded on XETRA in 2008.