February 2018 has seen a tremendous spike in volatility, and the market went from being in a bubble to abruptly crashing in two massive days of relentless selling.
These are very strange market characteristics of every day up the stairs to falling off a cliff – it shows you how silly and irrational markets really are (there goes the efficient market hypothesis).
Even though algorithmic systems love this type of volatility, they will still have increased risk during such a period. When working with long-term statistical-based systems, sometimes bad luck can play out over a handful of trades. Therefore, traders must modify risk management and trade position size to ride through this potentially extended period of uncertainty. With any algorithmic trading systems, nobody knows when the strong trades will come; there is always a chance algorithmic trading systems suffer a couple of bad trades during volatility before the odds begin to play out (see metrics). Traders who trade a position size in excess of their comfort levels will always be forced sellers, and many cannot handle the undue stress of violent swings in the market.
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Trading During Volatility
- ES is trading in 3-4% range during a volatility spike, and we must consider that quant systems will have to increase the level of risk they take on each trade; otherwise, they will get chopped to death. Trying to make stops smaller during periods of violent swings is a recipe for disaster and one the markets love to expose.
- Assuming an algorithmic trading system takes a 3% daily range stop on a hypothetical trade, then this equates to 80 ES points per contract (each point is $50), which equals a whopping potential $4k drawdown per contract. Ordinarily, from a pure dollar value, this would be considered large but at current prices, it would be unrealistic to really consider a 3% move extensive. If the ES is moving 3 to 4% daily range, then the dollar value per trade is going to be far greater than the historic dollar value.
We find many investors look at past historical values as something set in stone but never heed advice to normalize drawdown values to current levels – please see the following blog post, which outlines: Why drawdown can be a very misleading metric.
The reason we advocate day trade-only algorithmic trading systems is because swing trading systems often give up a whole year’s gains during a powerful move down, especially in futures markets where the talking heads fail to mention the moves overnight, which can be just as brutal, for example, the Dow Jones futures were limit down Tuesday night before rallying up – this is another 4 to 5% decline which is ignored by the average investor not accustomed to 24-hour trading. The ES market has declined 12% from the peak, so pretty much most swing traders will be heavily underwater. In contrast, daytrading systems only need a couple of decent trades to make some good gains should the volatility continue.
Investing your own money is a very difficult thing to do. To properly invest, you need to emotionally detach yourself from your money. Many investors are incapable of removing their “gut feelings” from the equation even when using algorithmic trading systems. Those who hire advisors and buy-and-hold style money managers are also at the mercy of volatile markets. The investors often find themselves second-guessing their advisor and perhaps searching for another, usually with very little patience. As the market heads back up, the investor feels satisfied with their new advisor—until the next downturn. The whole process will begin again and later repeat itself. This behaviour makes it difficult to achieve personal investment goals and we find the average investor constantly succumbs to irrational exuberance and excess risk during an up period and then has no stomach to handle a drawdown. Many studies in behavioural finance have shown the average investor to consistently lose money even with very strong long-term funds.
Please see the following blog post, which outlines: Why You Need to Use Alternative Data