Trading Algorithms: Areas of Concern – Lack of Visibility
We know what a specific algorithm is supposed to do, measure its pre-trade analytics and see how the post-trade results match up to that expectation. But if the trader did not select the most optimal algorithm for that trade little can be done. This problem is caused by a lack of visibility and transparency into the algorithm while it is executing orders.
Algorithms Acting on Other trading Algorithms
If a fund managers’ trading pattern is spotted and tracked with the use of algorithms, then these algorithms are liable to be ‘reverse engineered.’ This implies that their buy and sell orders are pre-empted and used to the maximum effect by their competitors. Here, algorithms are acting on other algorithms.
You will not believe what the new trading algorithms can do… WATCH VIDEO
Which Algorithm to Use?
With brokers and independent firms offering algorithmic strategies, one concern is that buy-side institutions lack the tools to understand which algorithm to use for a particular investment or market condition. The lack of a standard benchmark has made it almost impossible to assess the quality of algorithms. Buy-side firms are having difficulty evaluating when to use a particular algorithm. For example, if a portfolio manager tells a trader to sell a mid-cap, semi-illiquid stock within five hours—because the manager has to raise cash—the trader may be confused about which algorithm would be the best solution, given the constraints on liquidity and time. They need a certain level of sophistication and understanding to use it.
Algorithmic trading requires careful real-time performance monitoring as well as pre and post-trade analysis to ensure it is appropriately applied. Traders must calibrate the algorithms to suit portfolio strategy. Algorithmic trading represents an additional tool in a trader’s expanding toolbox for trading, but they are not an easy tool to master.
Also, it is important to align execution choices with the level of order difficulty involved in terms of order size, liquidity, and trade urgency. Low-touch venues such as algorithmic trading lend themselves best to easier types of orders such as low-urgency and small orders for large-cap stocks. But urgent orders for a large volume of small-cap stocks would require a higher-touch approach of pushing shares out to the market.
Missing Ingredient—The Trader’s Gut Feel
Algorithms are simply advanced trading tools, and they cannot replace the human element or make inter¬action redundant. Algorithms fail to capture a trader’s “gut feel.” The intraday trading characteristics of a stock assist a trader in determining the right strategy, whether to back off or be more aggressive. To allow their guts to play a proper role, the traders need to see precisely what actions their algorithms are taking, what venue the orders are being sent to, and where they get filled. It is early in the development of trading software to think that an algorithm can mimic the thought process of a human trader.
Algorithms cannot compete with the ability of the human brain to react to unanticipated changes and op¬portunities. Some algorithm providers are trying to address this issue by offering instant messaging (IM) services that work with the algorithm. As trades go on, a trader is alerted of issues that arise, and the trader can alter the strategy depending on the nature of the news.
At the end of the day, it’s the clients who drive the demand and innovation necessitating next-generation algorithms. The next generation of algorithms will be able to “speak” to the trader to let the trader know what is going on and allow the trader to interact with the algorithm. Soon we will have adaptive algorithms that adjust their execution at each moment in time in response to what they see happening in the market just as a human trader.
Conclusion
Algorithms are widely recognized as one of the fastest moving bandwagons in the capital markets. Rule-based strategies have enabled buy-side firms to increase productivity, lower commission costs, and reduce implementation shortfall.
Algorithmic trading cuts down transaction costs and allows investment managers to take control of their own trading processes. By breaking large orders into smaller chunks, buy-side institutions are able to dis¬guise their orders and participate in a stock’s trading volume across an entire day or for a few hours. More sophisticated algorithms allow buy-side firms to fine-tune the trading parameters in terms of the start time, end time, and aggressiveness. In today’s hyper-competitive, cost-conscious trading environment, being the first to innovate can give an individual or larger firm a significant advantage over the competition both in capturing the order flow of early adopters and building a reputation as a thought leader.
While algorithmic trading may look and sound complex, it is in some cases depending on the amount of money you manage and the speed at which you demand orders to be executed. Remember that our algo trading system at AlgoTrades is nothing like these high frequency/big firm trading systems. Instead, we run a low/slow automated investing system that captures intermediate cycle highs and lows within the broad market. Our system only trades 34 times a year and trades only the SP500 index. Low volatility coupled with high liquidity means stable, consistent investing results.