Algo trading systems need the right approach

November 30, 2010 06:00 PM

Today’s markets are dominated by professional traders employing high-speed algorithmic trading systems. Individual traders need to equip themselves with equally strong analytical muscle and risk management procedures. Traders can design algorithms with these elements at their core.

Computer automation and human intelligence are complementary. Traders need computers to execute trading strategies at ultra-fast speeds in a disciplined manner. Computers need humans to mitigate model risk. Performance is delivered through human traders controlling computer programs. We will discuss how to find trading strategies best adapted to the current environment and executing those strategies through timing triggers.

Darwinian strategy

Key to the success of computer-based trading is selecting the right strategy. Many algorithm-based systems implement their own proprietary trading strategy in a static manner. Unfortunately, there is no mathematical proof that any one strategy is profiting all the time. Model risk can be great when an incorrect trading strategy is automated in large scale.

To create the best strategy, modelers and programmers work to implement as many diverse strategies as possible. The best strategy is selected similar to the evolution process, where the system best suited to the current environment thrives. One approach puts all strategies in a dry run, or theoretical, state to select the best adapted system. This also can determine the optimum holding period.

Say you are trading a large block of stock or futures positions. First, the block is broken into a number of small units. The size of unit is determined by volatility of the market and trading cost. Each trade is conducted at a triggering event. Each strategy is conducted in a separate computer process.

In a real-time trading system, each trading process interacts with the brokerage to finish the transaction. In a dry run state, the national best bid and offer (NBBO) prices are logged and the trade is conducted in a simulated fashion. Alternatively, data can be purchased so that dry run is performed off trading hours without human intervention.

Both quantitative criteria and qualitative assessment can be applied to determine the best trading strategy. For example, a daily score based on profit and loss can be kept for each system. By combining daily scores, we can determine both the best holding period and best performer for the period.

You can devise as many trading strategies as you need, which can be based on fundamental as well as technical inputs. Some strategies work for a certain period of time and then break down. This is reflected in academic studies of past decades, many of which are devoted to the efficacy of these strategies. No conclusions have been reached about correctness of any one strategy all the time.

However, what you can do is dynamically and efficiently discover the strategy best adapted to the current market sentiment. Through constant dry runs and frequent comparisons, the winning strategy can be identified and employed in a timely manner (see "Testing and review," below).


In practice, there are two types of performance results you should be concerned with when consuming the data log. One is action related. The other is performance result without action. The former corresponds to buy or sell action. The latter is just for scoring. Triggers determine buying and selling.

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About the Author
Yong Liu holds masters degrees in physics and computer science and has worked at Nortel networks and Nav Canada Inc. Liu consults on trading automation for financial institutions in China. Reach him at email