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RichardCox

Pros and Cons of Quant Trading

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Pros and Cons of Quant Trading

 

Forex traders using technical analysis strategies as the basis for their positions will often run into practictioners of Quantitative techniques. This can easily open up a “can of worms” as there are some significant similarities between classical technical analysis and Quantitative strategies. The primary difference is that Quantitative strategies use automation to remove most of the human element, but even this characterization is not entirely true because all quantitative strategies are simply human-constructed technical analysis techniques that are triggered by computer signals. Here, we will look at some of the strengths and weaknesses of the Quantitative approach, so that technical analysis traders can decide whether or not these strategies are an appropriate addition to daily trading.

 

Quantitative Trading Defined

 

Quantitative strategies often implement a high-frequency approach, trading algorithms, and arbitrage positioning that is based on statistical averages. Many market analysts argue that Quant trading artificially adds to price volatility, because of the quick in-and-out positioning that has little or nothing to do with economic fundamentals. Some might not know that indicators and oscillators (such as the MACD or RSI) can also be characterized as Quantitative analysis tools, and this essentially means that Quant strategies and technical analysis are very close cousins.

 

Trading opportunities are largely defined by probability -- mathematical computations that decide the chances prices will rise or fall based on current market events. Price, volume, and moving averages are common inputs used in trading models. In early computer trading Quant models were reserved for hedge funds and large financial institutions. But today, easily accessible applications like Trading Stations or MetaTrader are perfectly capable of executing Quant strategies -- and these methods can even be implemented from remote servers (meaning your personal computer doesn’t even need to be turned on to open and close trades).

 

Common Strategies

 

Quant strategies might seem new because modern computers can put incredible processing power in nearly every home. But the strategies behind even the most modern strategies have been in place for nearly a century. So just because you might see an advertisement for a “90% effective EA” doesn’t mean you are seeing a new and innovative strategy. If anything, it is probably the opposite. This can be true even for strategies that are back-tested. What worked in the past might give you no incite into what will happen next, which is a problem because this is what is truly needed to make profitable trades based on price behavior.

 

Arguments Against Quant Strategies

 

People selling Quant strategies as Expert Advisors (EAs) will, almost by definition, have techniques that have strong backtesting results. But if the markets were as simple as that, everyone would be a successful trader and the guy asking for change the street corner would have a penthouse apartment. Life -- and the financial markets -- don’t work that way. So, opponents of Quant strategies will argue that if you want to have confidence in your next trade you will need to monitor the actions of human beings, because human beings are what determine the net-worth of market assets.

 

This is one of the reasons why many traditionalists label Quant EAs as “black boxes,” and it is beyond debate that there are just as many losing EAs as there are winning EAs. And when EAs fail, they tend to fail on a massive scale. One of the main reasons this will occur happens when market dynamics change, and historical events have no way of including future events. For example, when the Swiss National Bank enacted a price floor in the EUR/CHF at 1.20, market values in the currency pair rose by nearly 1,000 pips in a matter of minutes. There would have been no way for an EA to predict this type of event, so it would have been very likely you would have accrued losses if you were using an EA to trade the EUR/CHF (or any highly correlated forex pair) at that moment.

 

Quantitative strategies can be constantly re-defined and streamlined to account for potential market occurrences, but the stark reality is that it would be impossible to account for all possible occurences in an EA. These strategies also tend to suffer when markets are experiencing above average volatility. In these cases, buy and sell signals are sent so often that rising transaction costs can significantly erode your potential for gains.

 

Arguments Supporting Quant Strategies

 

On the other side of the debate, the main strength of Quant strategies is that they operate using the highest level of discipline and objectivity. If your Quant model accurately forecasts what will happen in the market, your positions will use the available quantitative data to successfully exploit market inefficiencies and generate profits. Quant models can be composed of as little as one or two inputs (such as price activity relative to a moving average, or overbought/oversold readings on an indicator tool), or much more complex (which can mean thousands of inputs working in conjuntion with one another).

 

Another benefit is that EAs are able to pick-up on trend activity as it develops. A running EA is constantly monitoring price activity and market scenarios to identify opportunities. Human beings are simply not capable of this level of attention or awareness. Quant models can be set to analyze large groups of assets simultaneously, whereas a person could monitor only a few at any given moment. These models will then rate scenarios in all cases, often using numeric or alphabetical grade levels (such as A-F, or 1-5). Assets with the highest ratings trigger long positions, assets with the lowest grade levels trigger short positions. This simplifies the trading process and allows traders to position themselves only in the most extreme cases (which offer the highest probability for gains).

 

But Quant models are capable of doing much more than simply opening and closing positions. Trades can also be structured to account for proper risk levels (outlining stop losses, profit targets, and position sizing). Once trades are opened, it is also possible to limit exposure in correlated assets. For example, long positions in both the USD/JPY and USD/CHF would mean the account is taking on double exposure in the USD. Proper diversification is needed for any successful strategy, so it is important for those running Quant strategies through EAs to keep this in mind in order to avoid over-leveraging.

 

Conclusion: Quant Strategies Offer Objective, Disciplined Trading But Also Create Added Risks

 

Quantitative trading strategies have evolved significantly in the last few decades and have quickly changed from mysterious (potentially risky) entities to gain a much more accepted position in the markets. Gone are the time when these tools were available only to large hedge funds and financial institutions. Nowadays, anyone with a customizable trading station can run an EA and trade automatically with these signals.

 

When deciding whether or not to use these tools, there is a spirited debate on both ends of the spectrum. Proponents will argue that EAs offer the most disciplined, objective approach to trading -- and this allows positions to be entered the moment major trend information develops. Opponents of these tools will cite added risks created when market events create trading environments that have not been seen before. In any case, Quant EAs are powerful tools and are not likely to be leaving the market’s presence any time soon. As always, it is important to test these applications with a demo account first in order to avoid making critical trading mistakes.

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Good article. To me quant trading means having a solid measure of risk and reward for each trade. This can be done with statistical analysis (t-test, Monte Carlo, etc.) One drawback is that the future may not resemble the past and the statistics will not be valid. Using the most appropriate analysis to measure risk and reward can make the difference. IMO backtesting setups in many markets increases the significance of any statistics like it is done in this website that uses come concepts and a methodology useful to quant trading. .

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One drawback is that the future may not resemble the past and the statistics will not be valid.

 

Just because past futures resembled past pasts does not mean to say that future futures will . . . The problem of Inductive Reasoning . . .

 

I had Scottish porridge oats for breakfast this morning (as might have the Scottish philosopher David Hume, responsible for one of the most thorough treatments of induction). They've never poisoned me in the past, but who is to say that one day oats won't just suddenly become poisonous to the human body? Seems implausible, but it certainly isn't logically impossible.

 

We all act as though inductive principles are totally valid, even though we know they are not, because they are the best tool that we have for dealing with the world around us. Quant trading is no exception.

 

BlueHorseshoe

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Poisonous Oats is quite plausible and already affects people today. Well maybe not poisonous, but for those people they may as well treat it as poison because the effects can be lethal if prolonged. Those people are celiacs.

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Poisonous Oats is quite plausible and already affects people today. Well maybe not poisonous, but for those people they may as well treat it as poison because the effects can be lethal if prolonged. Those people are celiacs.

 

Are you certain? Are Oats and Wheat the same?

 

Actually, forget that - we don't want to derail the thread with a discussion about cereals . . . :)

 

BlueHorseshoe

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The real con to system trading is that if one studies the market then they can develop a "feel" for the market and can integrate multiple types of information, as well as learn rules to complex too fully code. This contrast with trading systems which are defined by rigid rules and rely on very specific and selective events, patterns, and conditions to profit from. Due to the infrequency of trades and lack of higher level abstraction inherent in most trading systems, it is much more difficult (though not impossible) for traders to get a "feel" for a trading system. This division between the trader and the system can cause internal conflict in the systems trader who has developed market expertise as the internal expertise may conflict with the systems information.

 

There are various ways to solve this conflict. One approach is to ignore the internal conflict. This prevents any development of higher level abstraction and integration from taking place. Another approach that I've used is to vary the position size based on my own confidence. A similar approach requires opening a system trade but leaves the closing to discretion.

 

As for pros, there are many pros. The first is having a stronger basis for a trade. The ability to compare the actual results to the hypothetical results also provides a yardstick for measuring performance. And, it is possible to come up with various expectations for maximize bet sizing with systems which is hard to do with discretionary trading. Furthermore, style drift is not as much of an issue. As you note, the possibility for the system to watch several markets is a huge benefit to systems traders for those with larger accounts.

 

Currently, we are researching graybox systems which we expect will combine better with discretionary trading. The goal with our graybox approach is different from traditional systems. The goal with our graybox approach is to give the trader the best insight, clarity,and intelligence into the market versus to come up with the most profitable backtest. The change in approach from "find most profitable backtest" to "generate best intelligence" opens up expanded potential for exploration and unique potential for synthesis.

 

---

Curtis

Home - OrderFlowDashPro

Edited by Predictor

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