SmartQuant: End-to-End Algo Trading Infrastructure

Algorithmic Trading is responsible for an ever-increasing share of trading volumes across all major markets. Having started in 1990s with the computerization of trading in equity and futures markets, and subsequently growing to cover all types of securities including fixed income, FX, options, and most recently cryptocurrencies, the Algorithmic Trading industry includes a variety of participants, from market makers and agency trading firms serving fundamental and institutional investors seeking efficient execution of large orders, to quantitative investors and proprietary traders seeking to implement various rules-based trading strategies. Quoting Arthur M. Berd, Partner at SmartQuant, “The main difference between the quantitative and the discretionary trading style is the systematic nature of the quant approach.” His firm, SmartQuant, develops end-to-end algo trading solutions for quantitative hedge funds, proprietary traders and institutional trading groups on the buy side, and brokers catering to such clients on the sell side.

Some algo trading strategies are geared towards the speed of execution, enabling the so-called high-frequency trading algos to access and act on information much faster than human traders. Many algorithmic trading servers are now located near major exchanges and data sources. It is a highly competitive segment of the global financial markets since the marginal profit per trade is narrow and the potential for profit is high. However, it is not sufficient to find a working algorithm once. There is a constant arms race for speed and accuracy in the high-frequency trading world, and HFT algorithms must be modified constantly to keep ahead of the competition. Having a great technology that can keep up with the pace of innovation is as important in this industry as having a great algorithm or a fast connection to the exchanges.

At the same time, many other algo strategies are not as sensitive to the latency of trading. The bulk of quant investment firms are actually focused on much longer time horizons than intraday trading, even though they care a lot about efficient execution which happens intraday. There are many hundreds of billions of dollars of capital invested with quant strategies that have typical holding horizons measured in weeks and months. But even on such longer time frames, it is critical that the corresponding algorithms are well tested, and that portfolio managers can estimate their ongoing profitability and risk characteristics.

With their solutions, SmartQuant provides emerging and midsize quant managers with an industrial strength strategy development, backtesting, optimization, and automation platform that fits their budgets, and scales up with the growth of their business and the complexity of their strategies while retaining the reliable modular architecture and not compromising on quality. SmartQuant algo solutions allow managers to focus on the development of investment strategies and benefit from a reliable framework to implement and deploy them in the market using a distributed trading environment. SmartQuant fills the gap between expensive high-end institutional systems and the lower-end systems, which do not have the requisite features and robustness necessary for professional investment managers. Tailored solutions are offered to clients, which comprises the set of products for each stage of their development.

SmartQuant Distributed Trading Platform™ consists of OpenQuant integrated development environment (IDE), QuantTrader, a production deployment engine for automated trading strategies developed in OpenQuant, QuantBroker,an algo execution server with feed replication, consolidation, aggregation, and smart order routing, QuantBase, a data server with real-time feed capture and optimized time series management capabilities, and QuantController, a central command and control module for the distributed trading environment.