Quantitative Investment

A guide to systematic trading

5 min

17 Aug 2022

Alex Urse

What is Systematic Trading?

Systematic trading (also known as algorithmic trading) is a disciplined, rules-based approach to investment. Utilizing the suite of analytical tools made available with the arrival of machine learning, artificial intelligence and big data, the systematic approach allows for the construction of consistent, cost-efficient, and customizable trading strategies, as well as the operationalization of factors which until now had defied quantification. Furthermore, with trades executed according to a set of predetermined trading rules, systematic trading is able to control for the distortionary effects of emotion. Investment banks and hedge funds are increasingly utilizing systematic trading approaches in response to rising demand from investors.

Systematic trading can be contrasted with discretionary trading, which relies on the active engagement of the trader in not only building a strategy but in anticipating and responding to market fluctuations.This is not to suggest that algorithmic approaches prevent direct involvement with the operation of a strategy; systematic trading strategies aren’t necessarily fully automated. Rather, systematic traders can choose to automate their trading decisions, whilst retaining the ability to override the process if market conditions necessitate intervention.

Managing Risk in Systematic Trading

Whether making short term, reactionary trading decisions, pursuing a long term strategy or implementing a tactical asset allocation overlay to actively manage the allocation between e.g. fixed income and equity, coherent investment management demands the quantification of market risk. As such, risk management is a central consideration when designing a trading strategy. Systematic trading is well suited to the assessment and management of financial risk due its ability to monitor subtle variations in financial markets. To assess the performance of a fund’s trading and investing, its performance should be referenced against its risk level. The most commonly used variable for such an analysis is the Sharpe ratio, which measures risk-adjusted performance.

Risk Limits

The management of risk requires the imposition of limits which set a threshold for the exposure a strategy or portfolio can take on. Limits can be both endogenous (set by the trader or firm itself) or exogenous (imposed by regulators on various fund structures) and are defined in the trading strategy’s investment guidelines.

A selection of common risk limits are outlined below:

  • Position Limits

Position limits are an example of limits imposed by both internal and external actors. They seek to restrict the size of the position any one trader or portfolio manager can take in a particular financial instrument. An example of a position limit is a sector or country exposure limit; efforts to contain financial exposure to any particular sector or country. The performance of a sector or country can often be attributed to the performance of particular risk factors. As such, these limits seek to enforce diversification of a portfolio and reduce its cluster risk to specific factors.

  • Exposure limits

In addition to position limits at a security level, hedge funds – among others – impose limits on net and gross exposure. Gross exposure is the sum of the absolute value of all open positions (long and short) relative to a fund’s equity value (i.e. net asset value). Net exposure is the difference between the long and short positions.

  • Stop Losses

Stop losses are predefined orders to sell a particular asset at a specific price so as to avoid incurring unacceptable losses.

Common Risk Metrics

How we understand risk is contingent upon the metric we adopt to measure and express risk. Yet, due to the inherent complexity of risk, no single metric can ever provide a complete view of the actual risk associated with a trading action or position.

The principal metrics of financial risk are value-at-risk and expected shortfall. Of these, VaR is the most widely used. VaR is a statistical measure of the possible financial losses for a portfolio or strategy within a given time period. It seeks to determine both the extent of a loss and the probability that such a loss will occur. VaR modelling allows for the determination of aggregate risk across an entire fund or firm and the assessment of whether it has the necessary capital to cover its positions in the case of adverse market movements.

Expected shortfall or conditional value-at-risk (CVaR) is a measure of tail risk within an investment portfolio. By accounting for the weighted average of losses falling outside of the confidence interval used to calculate VaR, CVaR seeks to determine the extent of losses possible in extreme cases.

Executing a Systematic Trading Strategy

Execution is the completion – or filling – of a buy or sell order for a tradable financial instrument. This process comprises two steps; order placement and trade execution. These steps can be taken directly or via engagement with an order management system (OMS) or execution management system (EMS).

It is worth noting that even this process carries risk. In the time between the placement of an order and the execution of a trade, a price differential can emerge. This slippage represents an execution risk. For traders executing orders frequently, this risk can have real consequences for profitability.


An order management system (OMS) is a system designed to manage trade orders. It is generally used by portfolio managers to gain a high level overview of a portfolio and generate orders accordingly. Once an order is placed, an OMS will translate it into an actionable transaction (buy x or sell y) and send it to a trader for execution.


Once a trader receives an order to buy or sell a particular quantity of a financial instrument, they must execute the trade. An EMS provides the available trading avenues through which an order can be executed.

Systematic Trading with SigTech

SigTech facilitates the construction of automated trading systems. Once a strategy has been built and backtested it can be released into a live production environment. Within this production environment a strategy’s performance can be tested with respect to live market data without taking on actual exposure. From there, you can extract the results from the platform and deploy it into production to execute a strategy and take on actual market exposure.


  1. SigTech (2022): Hedge Fund Research Report 2022

This document is not, and should not be construed as financial advice or an invitation to purchase financial products. It is provided for information purposes only and is subject to the terms and conditions of our disclaimer which can be accessed at: https://www.sigtech.com/legal/general-disclaimer

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