Today Markets offers algorithmic and high frequency traders the best attributes of both RFQ and ECN platforms in one unique DMA trading venue. The firm’s technology and pricing solutions ensure that traders experience all the benefits of a fully anonymous, low latency trading environment.
Today Markets enables direct access to consistent, reliable interbank and non-bank price liquidity covering a wide range of instruments including FX, Metals, Energies and CFDs.
Full Order eXecution
Full Order eXecution, a true block-trading mechanism, is an available option for strategies that rely on larger trade size for their optimization.
Direct FIX API
Direct FIX API connections are readily facilitated with our servers located at Equinix’s NY4 data center and Hong Kong’s HK3 data center.
MetaTrader 5 (MT5)
Today Markets offers two front-end platforms to those algorithmic traders seeking to employ their own trading scripts. The popular Metatrader 5 (MT5) platform enables traders to run their MQL scripts (EAs – Expert Advisors) directly on our DMA liquidity with high quote update rates and the stability of pricing from at least ten of the top FX banks in the world.
The Difference between HFT and Algorithmic Trading
The use of High-Frequency and Algorithmic trading in finance, also known as algo-trading, is the application of automated electronic systems for trading strategy execution. They are slightly different terms but similar and go together.
The core difference between them is that algorithmic trading is designed for long-term trading, while high-frequency trading (HFT) allows to buy and sell at a very fast rate. The use of these methods became very common since they beat the human trading capacity making it a far superior option.
The electronic style trading first surfaced in the seventies with the creation of Nasdaq. It was a system that used electronic bulletin board without computer commands. Later in 1987, the Chicago Mercantile Exchange implemented a more widespread platform, Globex, that became fully established in 1992. This system traded several assets such as treasuries, foreign exchange, and commodities.
Lower prices and faster execution time drove other exchanges to become electronic. Humans can’t compute giant volumes of trading data like a computer can. This lead to the inspiration for high-frequency and algorithmic trading, just like every other drive for automation such as improved service, cost effect, and speed in execution time. It made the whole trading process to be cheaper and less cumbersome. Today, even people who are not trained professionals can be traders.
High-Frequency Trading is a subset of algorithmic trading. Its major characteristics are high speed, a huge turnover rate, co-location, and high order-to-order ratios. It operates by using complex algorithms and sophisticated technological tools to trade securities.
HFT solutions manage small scale trade orders sending them to a market or exchange at great speed. It benefits from bid-ask spreads. The height of the speed involved in the transaction process makes high-frequency trading a market maker.
Real-time data feeds are needed to reduce microseconds delay and avoid profit loss. Usually, the latency should be between 300 – 800 nanoseconds. This is achieved with a high-performance software, low-latency networks, and FPGA-based hardware acceleration.
Opportunities are noted through sensing large size orders that are pending by placing small-sized multiple orders and analyzing the pending and execution time. The successfully noted opportunities in the form of pending orders are then capitalized by adjusting prices to cover them and make profits.
Algorithm trading is also known as automated trading or black box trading. It’s a trading solution that uses coded sets of algorithms and execution strategies to submit orders to a market or exchange automatically after a technical analysis.
In other words, algorithm trading involves the use of predefined sets of variables such as price, time, and volume by automated pre-programmed trading instructions. These instructions, known as an execution algorithm, send child orders (small slices) to make up for larger orders too big to send at once.
Slicing into small orders helps to attain good pricing within a specified time. Reduction in the size of orders is good for an aggressive market. Thus, making algorithmic trading widely applicable to trading with high market volumes such as mutual funds, investment banks, hedge funds, etc.
The main objective of algorithmic trading is not just to profit by trading but to save cost, minimize market impact and the execution risk of a trading order. Traders don’t need to watch stocks or send slices manually. Algorithmic trading enables the execution strategies of a seller side to get a good order and monitor the trade chart simultaneously.