Some trading systems have prolonged periods of winning or losing trades. Long winning streaks may be followed by a prolonged period of drawdown. Wouldn’t it be nice if you could minimize those long drawdown periods? Here is one tip that might help you do just that. Try applying a simple moving average to your trading system’s equity curve and use that as a signal on when to stop and restart trading your system. This technique just might radically change your trading system’s performance.
Markets are dominated by a few large investors, creating problems of concentration. Similar portfolios and strategies exacerbate risk and the problems of illiquidity if a large number of participants or very large holders wish to exit positions at the same times.
Investors are frequently market following trading the momentum, buying when prices go up and selling when they fall. They are users rather than providers of liquidity. Their buying creates the illusion of active trading when markets are rising but suck liquidity out when prices fall.
On computer-driven, automatic trading strategies :
Cobras are revered in Indian culture, but the British Raj took a dimmer view of the poisonous snake. Officials promised a lucrative reward for every dead serpent — a scheme that, according to economic lore, backfired horribly.
Enterprising Indians began breeding cobras to collect the bounty, which forced the colonial government to abandon the plan. The frustrated breeders then released the worthless cobras, worsening the infestation. The story has never been fully confirmed by historians, but was seized on by German economist Horst Siebert, who in 2001 published The Cobra Effect on perverse incentives and unintended consequences. The book turned the anecdote into a potent example of how solutions to a problem can make it worse.
In the summer of 2014, Puzz had another puzzle to solve. From March to July, the frequency with which an IEX customer could have gotten a better price less than 10 milliseconds after a trade posted rose from about 3 percent to as much as 10 percent. This wasn’t meant to happen. IEX was supposed to protect investors from what’s known as stale quote arbitrage; that’s when a high-frequency trader takes advantage of milliseconds-long delays in how markets update prices to reflect movements on other exchanges. These tiny delays allow high-speed traders to see a price fluctuation on one exchange and then quickly send an order to another market—often a dark pool—that it knows updates its prices more slowly, hoping to pick off the orders resting there at stale prices. It’s a bit like betting on yesterday’s horse race against someone who doesn’t know the result.
IEX prevents stale quote arbitrage with its “magic shoe box,” a metal container in its data center in Weehawken, New Jersey. Crammed into it are 38 miles (61 kilometers) of coiled fiber-optic wire, creating IEX’s speed bump of 350 microseconds (about one one-thousandth of the time it takes to blink). The idea of countering super-fast traders by creating a slower market might seem like a paradox. It’s not. IEX uses the same high-speed data feeds as HFT firms do to monitor other exchanges for price changes. But because IEX didn’t want to be in a technological arms race with the high-frequency traders to process this information faster than they do, it uses the speed bump to slow down all new orders—just enough to ensure IEX has time to update its prices to reflect any movements on public exchanges. This prevents orders on IEX from being traded against at stale prices.
So how, Aisen wondered, could HFT firms be picking off IEX orders despite the magic shoe box? It didn’t take Puzz long to solve the riddle. He discovered that some HFT algorithms could predict price changes—like surfers sitting out past the break, scanning the swell for their next ride—and target orders before the magic shoe box’s speed bump could protect them.
While in Greenwich Ct. one afternoon I will never forget a conversation I had with a leading quantitative portfolio manager. He said to me that despite its obvious attributes “Black Box” trading was very tricky. The algorithms may work for a while [even a very long while] and then, inexplicably, they’ll just completely “BLOW-UP”. To him the most important component to quantitative trading was not the creation of a good model. To him, amazingly, that was a challenge but not especially difficult. The real challenge, for him, was to “sniff out” the degrading model prior to its inevitable “BLOW-UP”. And I quote his humble, resolute observation “because, you know, eventually they ALL blow-up“…as most did in August 2007.