It is no secret what trading algorithms are built for, and we are well versed on how to keep tabs on the analytics done as well as the results generated by algorithms. Still, much of the responsibility we want algorithms to own up to rests on the supervising trader who has to ensure that optimal parameters are always selected. Failure to do so will almost certainly lead to disparaging results.

The conundrum with this setup is amplified by the reality that there is an apparent lack of visibility and transparency of varying degree in most algorithms during the process of carrying out order executions.

 

Handing the baton: Algorithms acting on feedback from other trading algorithms

In a novel approach to always upping the competition, the trading pattern of a successful trader is spotted and tracked with the use of trading algorithms, providing more than enough data to work on so these algorithms are “reverse-engineered.” Thus, a competitor is able to pre-empt the positions to be taken by a poached competitor, and use this information to achieve a higher rate of success.

 

Making the hard choice: Choosing which trading algorithm to use

It is a complicated case of information overload for buy-side firms as brokers and independent institutions throw up a barrage of algorithmic strategies, leading to difficulty in having a well-founded understanding of which trading algorithm will be best for a specific investment or market condition.

With a standard benchmark for aligned assessment of algorithms ominously missing, it has become almost impossible for the quality of algorithms to be independently assessed.

Efficient algorithmic trading involves pedantic real-time performance monitoring as well as accurate pre- and post-trade analyses. For optimum returns, the trading algorithms have to be well calibrated to model the adopted portfolio strategy. Accordingly, algorithmic trading are important tools in a trader’s ever-expanding toolbox for trading.

Your portfolio strategy is not the only factor you have to worry about when optimizing your algorithm, the level of order difficulty is another important factor. The degree of order difficulty can be delineated into the liquidity, order size, trade urgency of an order; all of which informs the execution choices to be made.

Algorithm trading like other low-touch venues exhibit better use in easier order types such as small and low-urgency orders for large cap stocks. Contrastingly, higher-touch venues are necessary to push shares into the markets and so are appropriate for urgent orders of a large volume of small cap stocks.

 

The missing touch: A trader’s gut feel

Over the years, algorithms have become more sophisticated trading tools. However, its efficiency does not eliminate the usefulness of human elements or eliminate the need for interaction. Algorithms quite simply do not have the trader’s “gut feel.” During trading, expert traders note the intraday trading characteristics of a stock, and use these characteristics to decide on which strategy to use. Traders cannot be effective at what they do, if they are in the blind of what their algorithms do. Traders have to understand the precise actions of their algorithms, where their orders are sent to, and where their orders are fulfilled.

Currently, algorithms still exhibit obvious lack of the capacity to react accurately to sudden changes and prospects, an ability the human brain boasts of. Establishing a link is thus important for improved performance of a trading algorithm. Algorithm developers know this, and are trying to leverage the unique ability of the human brain, by providing instant messaging (IM) services that permit communication between the trader and a trading algorithm. Thus, traders are alerted of pressing issues as they arise during active trades, and have the latitude to alter the trading strategy depending on the nature of the issue.

With current advancements in technology, it is possible that one of the core features of next-generation algorithms will be direct communication between trading algorithms and traders. Another is that due to outstanding progress in the field of Artificial Intelligence, trading algorithms may one day be able to adjust their executions at any point in time as timely response to prevailing market sentiments and changes.

 

Trading Algorithms Lack of Visibility Roundup

Algorithms no doubt are one of the fast-rising trends in the capital markets; valuably leading to improved productivity, decreased shortfall in implementation and reduced commission costs.

The cost-effectiveness and increased control over trading activities are key selling points of trading algorithms. Buy-side firms are able to mask their transactions and stay part of a stock’s trading volume for any length of time, by breaking up large orders into smaller bits. Start time, end time and degree of aggressiveness are parameters present in sophisticated algorithms that permit buy-side institutions to polish up their trading strategy.

The complexity of trading algorithms does not come as a surprise because of the technicality involved, however, performance is influenced a great deal by the amount of money in management and the speed of order executions. Our algorithm trading system at AlgoTrades has no similarity with these high frequency (HF) trading systems. In contrast, we run a low frequency automated investing system that makes decisions based on intermediate cycle highs and lows within the broad market to better protect traders’ funds and increase the success rate. Going by the numbers, our system makes 34 traders per year, and is specialized for SP500 Index trades.