Quantitative traders, or “quants” for short, use mathematical models to identify trading opportunities and buy and sell assets, one in-house example is our CYBO.

The word "quant" is derived from quantitative, which essentially means working with numbers. The advancement of computer-aided algorithmic trading and high-frequency trading means there is a huge amount of data to be analyzed. Quants mine and research the available price and quote data, identify profitable trading opportunities, develop relevant trading strategies and capitalize on opportunities with lightning-fast speed using self-developed computer programs.

In essence, a quant trader needs a balanced mix of in-depth mathematics knowledge, practical trading exposure, and computer skills. Quant traders can work for investment firms and banks, or they can be proprietary traders, using their own money for investment.

“(If you're looking for a self-paced video course to help you learn about quantitative analysis and CYBO, NAGA Webinars has several courses taught by experienced market analysts. Learn more about the NAGA Webinars on our NAGA Feed )”.

BASICS OF QUANTITATIVE ANALYSIS

A quantitative trading system consists of four major components:

  • Strategy Identification
    Finding a strategy, exploiting an edge and deciding on the trading frequency.
  • Strategy Backtesting
    Obtaining data, analyzing strategy performance and adjust the strategy if needed.
  • Execution System
    Linking to a brokerage, automating the trading and minimizing transaction costs.
  • Risk Management
    Optimal capital allocation, "bet size". 

STRATEGY IDENTIFICATION 

All quantitative trading processes begin with an initial period of research. This research process encompasses finding a strategy, seeing whether the strategy fits into a portfolio of other strategies you may be running, obtaining any data necessary to test the strategy and trying to optimize the strategy for higher returns and/or lower risk. You will need to optimize in your own capital requirements if running the strategy as a "retail" trader and how any transaction costs will affect the strategy, the optimizations are the key to turning a relatively poor strategy into a highly profitable one. In fact, one of the best ways to create your own unique strategies is to find similar methods and then carry out your own optimization procedure.

Another hugely important aspect of quantitative trading is the frequency of the trading strategy. Low-frequency trading (LFT) generally refers to any strategy which holds assets longer than a trading day. Correspondingly, high-frequency trading (HFT) generally refers to a strategy which holds assets intraday. Ultra-high frequency trading (UHFT) refers to strategies that hold assets on the order of seconds and milliseconds.

Once a strategy, or set of strategies, has been identified it now needs to be tested for profitability on historical data. That is the domain of backtesting.

STRATEGY BACKTESTING

The goal of backtesting is to provide evidence that the strategy identified via the above process is profitable when applied to both historical and out-of-sample data. This sets the expectation of how the strategy will perform in the "real world". However, backtesting is NOT a guarantee of success. Other areas of importance within backtesting include availability and cleanliness of historical data, factoring in realistic transaction costs and deciding upon a robust backtesting platform.

The main concerns with historical data include accuracy/cleanliness, survivorship bias and adjustment for corporate actions such as dividends and stock splits:

  • Accuracy pertains to the overall quality of the data - whether it contains any errors. Errors can sometimes be easy to identify, such as with a spike filter, which will pick out incorrect "spikes" in time series data and correct for them. At other times they can be very difficult to spot. It is often necessary to have two or more providers and then check all of their data against each other.
  • Survivorship bias is often a "feature" of free or cheap datasets. A dataset with survivorship bias means that it does not contain assets which are no longer trading. In the case of equities, this means delisted/bankrupt stocks. This bias means that any stock trading strategy tested on such a dataset will likely perform better than in the "real world" as the historical "winners" have already been preselected.
  • Corporate actions include "logistical" activities carried out by the company that usually cause a step-function change in the raw price, that should not be included in the calculation of returns of the price. A process known as the back adjustment is necessary to be carried out at each one of these actions. One must is to be very careful and not to confuse a stock split with a true returns adjustment. Many a trader has been caught out by a corporate action! 

When backtesting a system one must is to be able to quantify how well it is performing. The "industry standard" metrics for quantitative strategies are the maximum drawdown and the Sharpe Ratio. The maximum drawdown etc.

EXECUTION SYSTEMS

An execution system is the means by which the list of trades generated by the strategy are sent and executed by the broker. Despite the fact that the trade generation can be semi- or even fully-automated, the execution mechanism can be manual, semi-manual (i.e. "one-click") or fully automated. For LFT strategies, manual and semi-manual techniques are common. For HFT strategies it is necessary to create a fully automated execution mechanism. 

RISK MANAGEMENT

The final piece to the quantitative trading puzzle is the process of risk management. "Risk" includes all of the previous points we have discussed.Risk management also encompasses capital allocation to a set of different strategies and to the trades within those strategies. It is a complex area and relies on some non-trivial mathematics.Another key component of risk management is in dealing with one's own psychological profile. There are many cognitive biases that can creep trading. Although this is admittedly less problematic with algorithmic trading if the strategy is left alone! Then, of course, there are the classic pair of emotional biases - fear and greed. These can often lead to under- or over-leveraging, which can cause reduced profits or a margin call


We hope this guide was helpful. Read more articles in the Help Center. If you still have questions contact Support Center directly via [email protected]

Did this answer your question?