Why Quantitative? – Serious Algorithmic Trading Series (Part 2)
Many individual traders will often hear the term “Quant” and may visualize either:
- A sharply dressed City trader working in a hedge fund, sat in front of his Bloomberg or Thomson Reuters Eikon terminals, or
- A stocky chap wearing a Batman t-shirt, with telescopes for specs (and possibly a doctorate in Nuclear Physics), sat in his basement trading room pouring over mathematical models, following a staple fast food diet.
For the avoidance of doubt: you do NOT need a university degree to become a Quantitative Trader (though having one does come in very handy, IF you were paying attention in class!)
Now, both these individuals above likely followed a similar academic (or independent learning) path to their current disposition.
It’s even possible that the City trader was once a frequent visitor to his own basement trading room, before being spotted by a financial recruiter and beamed up to the 39th floor.
This prompts a few questions:
Why do Quants become Quants in the first place?
There are numerous advantages to quantitative trading, advantages that not only reduce the financial impact of discretionary decision-making going wrong, but also help measure performance in a manner that permits efficient future learning.
For the purpose of distinction, it must be noted that unlike algorithmic trading that employs technical analysis, the quantitative approach to algorithmic trading involves primarily Bayesian Statistics and Time Series Analysis.
These are more effective methods of generating uncorrelated risk-adjusted returns, all too often ignored by the retail fundamental or technical analyst. Those who recognize their advantages, naturally progress down a path to becoming Quants.
Is Quantitative Trading reserved for just institutions?
In short, no it isn’t. Find out why in part 1 of this series here.
Why should this interest retail traders?
The world of quantitative trading extends far beyond that of technical moving average crossovers, candlestick patterns, martingales, grids, and off-the-shelf trading systems celebrating their latest and greatest “trading secrets” that resulted in their most sensationally overfitted backtests to date.
It does so because the markets are a much more complex phenomenon than we think, and undergo several micro-structure and regime changes frequently.
Note: To protect investors from overfitted systems, all strategies listed as DARWINS on the Darwin Exchange are analyzed for proficiency in 12 investment attributes. Investors therefore have the ability to immediately assess whether a strategy’s current equity curve is likely to persist in future or not, something standard backtest results cannot reveal.
Indeed one could go as far as to say that the market is a stochastic process in and of itself.
Generating uncorrelated risk-adjusted returns in as seemingly stochastic an environment as this, is therefore a mammoth task.
Why more retail traders haven’t then embraced quantitative methods (that enable the discovery and subsequent exploitation of inefficiencies in stochastic processes) can only be attributed to a lack of awareness and “approachable” learning resources.
We will aim to address this in Part 3 of this series, “The Quantitative Approach to Algorithmic Trading”, and all subsequent posts that fall in the same category.
Advantages of Quantitative Trading
Alpha is an elusive beast.
Alpha is difficult to discover, gradually loses its profitability as more capital is allocated to its exploitation, and inevitably transitions from a source of risk-adjusted profitability to a source of risk.
Quantitative methods allow traders to not only discover and exploit alpha, but to do so in a manner that maximises its profitability horizon and minimizes risk of decay.
Quantitative analysis also facilitates the creation of statistical testing frameworks that can forecast risk of alpha decay in advance.
Note: DARWIN investors benefit from a Scalability/Capacity investment attribute displayed on each asset listed on the Darwin Exchange. It is a score ranging from 1 to 10 that indicates how much capital the underlying strategy can support without decay. The higher the score, the more assets under management (AuM) it can attract.
Statistically Robust vs. Regular Backtesting
Quantitative traders can make use of statistical tools that add another layer of validity to backtests.
In simple terms, if you’re currently examining a backtest and taking its results with a “grain of salt”, substitute the latter with some rigorous quantitative testing and you have a much more credible (or not) backtest on your hands.
Quantitative Trading encourages thinking in “Portfolios”
Common behaviours among a large segment of individual traders include deploying a single trading strategy on a single asset, or deploying the same strategy on a mix of assets, without careful consideration given to weighted allocation as would be the case in an institutional setting.
In the former’s case, the trader has put all his/her eggs in one basket.
In the latter’s case, the trader has employed logic that goes something like “if it’s a robust strategy, it will perform well in all markets, under all market conditions.”
Unfortunately, this logic is prone to leading traders into intractable situations where the lack of an appropriate risk-adjusted allocation matrix may lead to unexplained underperformance.
Quantitative analysis allows traders to construct portfolios of assets or strategies (or both), such as to maximize their aggregate profitability while minimizing their aggregate risk. This is simply not possible to achieve without quantitative methods being employed.
Note: Investors on the Darwin Exchange can construct uncorrelated portfolios of DARWINS using our proprietary diagnostics toolkit. Twelve investment attributes (as of Darwinex Reloaded) allow investors to not only select DARWINS that match their criteria for investment, but also reduce overall portfolio risk through adequate diversification. As of Darwinex Reloaded, investors are even rewarded for diversifying their DARWIN portfolios, through the introduction of Diversification Rebates.
One of the most commonly voiced disadvantages of Quantitative Trading is that it’s a complex discipline to master.
So is learning to drive a car for the very first time. The right instructor can be the difference between a confident future driver and a jittery one.
Quantitative trading is no different. Over the course of these posts (that aim to make the complex simple), the best most approachable educational material will always be referenced for the reader’s benefit.
The fruits of a Quant’s labour are far sweeter than one can imagine. You will be well served by visiting the Darwin Exchange Leaderboard for inspiration.
The next post will begin our journey into the Quantitative Approach to Algorithmic Trading.
The Darwinex Team
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