Breaking Down Systematic Trading Methodology

Breaking Down Systematic Trading Methodology


What is Systematic Trading Methodology?

Systematic trading methodology (not to be confused with systematic risk) is based on the premise of automated rules-based strategies1 usually generated from a computerized model (algorithms). It offers a probability-based predictive view of the markets constructed from back-testing historical data. These systems are frequently called a “black-box” system due to their computerized methodology.

Rules-Based Trading Systems: A Little Background

The concept of rules-based trading systems are not recent concepts derived for computers, but instead, technology embracing an age-old trading methodology. The equity markets can easily trace rules-based trading back to the 1880’s original Dow Theory.2 Smart beta ETFs are an example of objective, rules-based trading systems for equities using various fundamental factors such as the Fama-French factors to determine equity allocations and rebalancing. 3

In the futures or global macro universe, rules-based trading can be traced back to at least 1948, when Richard Donchian began Futures Inc., one of the first known managed futures funds based on rules-based trading to identify trends.4


Systematic trading analyzes data to generate entry and exit signals. The signals could be a single moment of trade execution (the entire trade is executed at one time) or it could be multiple signals as the strategy enters or exits positions over a given period of time. The duration of holding positions may vary from intra-day to several months.

These systems frequently incorporate risk management controls that may include stop-loss orders, profit targets and/or adjusting position size. The trading signals may be derived from one or multiple inputs. Examples of market input factors may include price, volume, or volatility. Fundamental market-related factors examples are supply, demand, and USDA reports. Finally, examples of macro-related factors include interest rates, the unemployment rate, and forex pricing.

Systematic strategies are considered conditional models because they require specified conditions or criteria to appear in the input data for a signal to be generated. For example, if a model needs a market breakout above a previous high of the last 60 days to generate a buy signal, the model won’t go long (buy) until that condition occurs.

Systematic trading models may be based on technical analysis and/or quantitative or quantifying fundamental data sometimes known as “quantamental” to generate signals.5 The strategy input factors could be given equal weighting or the strategy may give greater weight to some factors relative to others. Technical analysis is defined as the study of market action to determine future price direction6 and can be traced back to at least 18th century rice trading with the use of Japanese Candlestick charting.7

A rules-based trading system may be applied to most asset classes and allows a fund manager to trade multiple markets simultaneously offering potential opportunities to diversify their fund’s portfolio. Managed futures funds are often perceived as systematic trading. However, some managed futures strategies are discretionary. Systematic traders have the largest proportion of assets under management in managed futures.8  Trend following is considered the primary strategy in managed futures.9-10 Today, 64% of fund managers identify as trend followers.11 Hedge funds are often considered discretionary, but not all hedge funds are discretionary.12 An estimated 20% of hedge fund assets are allocated to quant strategies.13

Systematic Trading: Advantages and Disadvantages

Some of the benefits of systematic trading involve the ability to backtest the system on past data to determine its potential robustness in various market environments. Systematic trading usually removes the day-to-day human interaction and behavioral biases that strengthen strategy discipline, but trading systems are designed by a person or group of people, integrating their risk management and market perspective. The disadvantage may include an increased frequency of false signals when the system does not function as designed in some market environments.



  1. Fung, W., & Hsieh, D. A. (1997). Empirical Characteristics of Dynamic Trading Strategies: The Case of Hedge Funds. Review of Financial Studies, 10(2), 275–302.
  2. Edwards, R. D., & Magee, J. (1997). Technical analysis of stock trends (7th ed.). Chicago: J. Magee.
  3. Draper, D. (2016, January 20). Everything you wanted to know about smart beta. Retrieved from
  4. 4 Covel, M. I. (n.d.). Richard Donchian: Lessons from a Trend Following Trading Legend. Retrieved February 13, 2020, from
  5. Molnar, M. (2012, December). Quantamental Investing: A Fuzzy Term That Describes An … Retrieved from
  6. Murphy, J. J. (1999). Technical analysis of the financial markets: a comprehensive guide to trading methods and applications. New York: New York Institute of Finance.
  7. Zaremba, A. (2015). The financialization of commodity markets: investing during times of transition. New York: Palgrave Macmillan.
  8. Backstop Solutions Group. (2020). CTA Industry Assets Under Management. Retrieved February 14, 2020, from
  9. 9 Fung, W., & Hsieh, D. A. (1997). Survivorship Bias and Investment Style in the Returns of CTAs. The Journal of Portfolio Management, 24(1), 30–41.
  10. Greyserman, A., & Kaminski, K. (2014, August 25). Trend Following with Managed Futures: The Search for Crisis Alpha. Retrieved from
  11. (2016). Preqin Global Hedge Fund Report. Fig 9.6
  12. Anson, M. J. P., Chambers, D. R., Black, K. H., & Kazeni, H. (2012). Caia Level I: an introduction to core topics in alternative investments. New Jersey: John Wiley & Sons.
  13. (2019). Crossing Currents 2019 Hedge Fund Industry Outlook.


To send a question to the author, or to learn more about this topic, click here.

For assistance with Managed Futures services, please click here.


Disclosure: The risk of loss in trading futures and/or options is substantial. Past performance is not indicative of future results. The information in this message derived from third-party sources is believed to be accurate and reliable; Coquest does not guarantee the accuracy or completeness of the information. Opinions expressed in this material are subject to change without notice. This report should not be interpreted as a request to engage in any transaction of futures, options, and/or OTC derivatives. The information contained in this material is not to be relied upon in substitution for the exercise of your independent judgment. Seek independent financial, tax, legal, and accounting advice from your own professional advisers, based upon your particular circumstances.