Quantitative Trading refers to the utilization of modern statistical models, mathematical methods in combination of immensely complex computer program to track patterns or trends in trading behavior and create formulas to predict future market movements. These formulas are then fed into powerful computers that buy and sell automatically according to triggers generated by the algorithms.
Strictly speaking, the use of numbers and statistics to scope investment and trading opportunities and use powerful computers to automate the order flow in the financial markets began as early as the 1970s. Since then technological progression accelerated, exchanges began using trading programs to improve order execution. Simultaneously, computers allowed for the swift analysis of large data-sets, with the latter being made increasingly attainable with the advent of the Internet. A recent study estimated that between 80% - 80% of all futures trading in the US & Europe was triggered by computer-driven strategies. In China, quantitative trading is still in developing stage. With fewer competitors, while markets are also less efficient and mature than in the U.S. and Europe, it provide a better opportunity to outperform peers trading in developed markets. According to the report from Eurekahedge, Asian hedge funds outperformed global funds in each calendar year from 2012, returning an annualized 9.5 percent against 5.7 percent in the four years through 2015.
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- Strict discipline
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- Complete system
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- Properly using the arbitrage theory
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- probability to win
While the overall success rate is debatable, the reason some quant strategies work is that they are based on discipline. If the model is right, the discipline keeps the strategy working with lightning-speed computers to exploit inefficiencies in the markets based on quantitative data. Successful strategies can pick up on trends in their early stages as the computers constantly run scenarios to locate inefficiencies before others do.
Types of Quantitative Trading
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- Algorithmic Trading: This is the use of computer programs to follow a set of instructions and criteria for executing a trade in order to make a profit. The parameters that may be fed into the programming criteria are endless, with price and volume being two common components.
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- Statistical Arbitrage: This is the use of statistical models to predict movements in prices; for example that a difference in prices between two similar company equities is temporary and will revert back to a mean.
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- High-frequency Trading: Often used interchangeably with algorithmic trading, this is the use of high-speed computer programs to execute trades that are latency sensitive; i.e. – the profitability of the trade is highly dependent on the program’s ability to execute it before any other person/ program.
Quantitative trading, which uses sophisticated models and state-of-the-art scientific technologies to calculate the impact of various factors on stock and other asset prices. Different trading models would have different performances in various markets/instruments.
We believe that individual strategies will, over time, succeed or fail in different markets condition, and that the key to sustainable, attractive portfolio returns is to allocate capital between trading systems and markets dynamically and intelligently between different investment portfolio or investment strategies, in order to achieve the optimal risk adjusted return.
Our Dynamic Multiple Strategies (DMS) is a systematic asset and strategies allocation program, which apply a group of robust, non-correlated systems dynamically on a broad range of markets, with the consideration of different market environment factors.