Recently, I have been pondering whether there is still a future in algorithmic trading in the US markets for new players. Several months ago I feared and forecasted that we may have reached the algorithmic precipice. Last year the SEC passed Rule 13H-1 which requires large trader whose transaction in NMS securities equal to or exceeding 2 million shares or $20 million during any calendar day, or 20 million shares or $200 million during any calendar month, to identify itself to the Commission and make certain disclosures to the Commission on Form 13H. The question is why does the SEC need to identify large volume traders in the markets if not to later overregulate, fine and shutdown? Or do they see a new trend in this business and wants to make sure they get their hands on it before it spirals out of regulatory control? In the past, we have seen that new regulations and requirements typically herald the eventual end of certain business (see my blog on the CBSX Series 56 requirement ending prop trading: http://dastrading.blogspot.com/2011/08/future-of-prop-trading.html). Their white paper clearly stated their goal of the new rule in Release No. 34-64976; File No. S7-10-10: “The large trader reporting requirements are designed to provide the Commission with a valuable source of useful data to support its investigative and enforcement activities, as well as facilitate the Commission’s ability to assess the impact of large trader activity on the securities markets, to reconstruct trading activity following periods of unusual market volatility, and to analyze significant market events for regulatory purposes.”
Another thought that came to my mind is whether there is enough volume in the market for the survival of any black boxes to continue to be successful. When you see Goldman Sachs, Barclays and other large investment banks who have formerly dominated the algorithmic space begin to offer tools to retail clients, you have to wonder what this means to the markets. I read it to mean that they don’t have enough retail flow naturally because of an excessive saturation of black boxes and algorithms that have entered the markets. Now they have to compete against each other as well as with the smaller black boxes. You might be wondering how the average algorithmic trading strategy consistently makes money and what factor affects its longevity. The answer is simply mathematics (pun intended). If we all had the same formula for success then there would not be any successful people but just mediocre people. Success is measured based on someone winning or being ahead of the game. If we are all running in the same race, at the same speed, using the same technique and we all reach the finish line at the same time, are we all winners? For some to win, it seems they need to have prerequisite criteria in place to limit who can be allowed to run in the race and who can be allowed unfair advantages.
There does seem to be a correlation with the big banks’ decision to offer their strategies to retail clients and the US regulators’ new policies regarding algorithmic trading. Despite these indicators, algorithmic trading still sparks the interest of traders who have had some success at day trading or proprietary trading who want to transition into automation. Many of these algorithmic traders are looking for ways to enter the game via easy to program algorithmic systems. In response to that there has been a remarkable trend of the box trading automation firms emerging in the last two years, notably Trades Ideas, CoolTrade and most recently Equametrics. In the past it was really difficult for retail traders to transition to automation. There were so many variables in order to run an automated black box such as having the right collocation, coders, quants, data, equipment, capital invested, exchange connections, etc. Formerly most successful strategies needed to be collocated within the major exchanges in order to reduce latency on market data and order executions. The cost to collocate can be become very expensive and eats into the profit margin of this business model. However, firms, such as DAS|Inc., provide low latency solutions that reduce these costs for black boxes.
The exchanges have also decided to move in this direction by offering open source cloud based solutions. One such product is NYSE Euronext’s HD Tickerplant which was recently only offered to the European markets. We believe this product will be well received in the US Markets because of the change in the demographic of algo traders. The following are the key features of the product which will be offered through our networks:
- High density servers with at least 32 cores to be deployed at client site.
- NYSE Technologies Market Data Feed Handlers for ultra-low latency performance.
- Seamless integration with SuperFeed® for global market data.
- Data Fabric Middleware to publish data onto the client’s market data backbone.
- ITRS to monitor and manage the server and software.
- SFTI VPN for NYSE remote access and management.
In conclusion, I don’t think it is too late for the successful active trader to jump on the algo trading bandwagon. The keyword here is “successful.” The stock market is not a ride for the faint of heart or someone who may have an obscure strategy in his head that he believes will make money without a real history of success and back testing. This is a space where you must know that capital and human investment should not be discounted once a strategy is successfully implemented. Strategies needs to be as flexible and adjustable based on small changes in the market such as a new rule. And this can only be done by human interaction.