Algorithmic trading techniques and (HFT) high frequency trading have
dramatically altered the behavior of U.S. equity markets. If you are an active,
short term trader, you have probably noticed that the markets have radically
changed, and have become increasingly more difficult to trade. The development
of AT was initially intended to be a solution for the reduced liquidity in the
markets (as a result of decimalization), and then as a tool to more effectively
arbitrage disparities in the markets. However, people often find alternative
ways to profit from technological solutions, by bending the rules and
exploiting the system, and algorithmic trading is no exception to the rule.
Before traders can begin to adapt to the new trading environment, traders need
to understand how algorithmic trading techniques work, and the effects they
have on the markets. While algorithmic trading was not developed originally for
the purpose of implementing the toxic or predatory strategies described below,
these techniques dramatically affect the way the market trades in the short
term, and if not recognized will place the individual trader at a substantial
disadvantage.
Liquidity-rebate traders take advantage of volume rebates of about 0.25 cents
per share offered by exchanges to brokers who post orders, providing liquidity
to the market. When they spot a large order, they fill parts of it, then
re-offer the shares at the same price, collecting the exchange fee for
providing liquidity to the market. This strategy doesn't require the trader to make
a profit on the trade. If the trader breaks even, or even loses a small amount
on the trade, the rebates he receives for providing liquidity, will generate a
profit.
Predatory algorithmic traders take advantage of the institutional computers
that chop up large orders into many small ones. By placing small buy orders
that are quickly canceled, the predatory algo trader, fools the institutional
computer into bidding up the price of the stock. After the price of the stock,
is forced up to an artificially inflated level, the "predatory algo"
shorts the stock.
Automated market makers "ping" stocks to identify large reserve book
orders by issuing an order very quickly, then withdrawing it. By doing this,
they obtain information on a large buyer's limits. They use this information to
"front -run" orders by buying shares elsewhere and selling them
to the institution they traded in front of.
Program traders buy large numbers of stocks at the same time to fool
institutional computers into triggering large orders, creating volatile market
moves. The program traders race the large orders, taking profits against, the
very orders they triggered to move higher.
Finally, flash traders expose an order to only one exchange. They execute the
order only if it can be carried out on that exchange without going through the
"best price" procedure intended to give sellers on all exchanges a
chance at best price execution. The SEC has now promised to ban this technique.
My personal trading strategy includes, looking for technical setups,
interpreting price action, analyzing capital flows, and utilizing a disciplined
money management methodology. The previously mentioned practices have led to
the following changes in the market's behavior, and forced me to adapt
accordingly.
Buying new highs, and selling new lows, rarely works now. Chasing momentum can
be like chasing your own tail, as the market rarely follows through. Passive
algorithms designed to sell-the-new-high, or buy-the-new low, dictate that
traders need to take into account this phenomena, and make adjustments.
As a result of HFT, stocks touch more price points and may cause you to
be stopped out of more positions. Algorithms are configured to "hunt
stops", forcing the market to reach prices, that would not be reached
under normal conditions, and elect stops. In addition, false buy and sell
signals are more prevalent now, as competing algos battle one another.
Large institutional buy or sell orders, that used to move the market
dramatically, are now executed more efficiently, and have less impact on the
market. This makes momentum trading less profitable, and more difficult.
Limit orders may be difficult or impossible to get filled as HFT programs
"step in front" of your orders, and slippage is greater, as programs
drop the bids for lower prices, when they sense a sell order, or raise their
offers, when they sense a buy order.
Traders' percentage of winning trades is likely to drop, and their
risk-reward ratio may be less favorable now, so it is important, that money
management remains the most important part of their trading strategy. In the
"new" market dominated by HFT , setups that might have previously
worked extremely well, either no longer work, or work marginally. However,
variations on these setups might work extremely well, if adapted properly to
the HFT environment. In other words, if you know what the HFTs are doing, and
the effect they are having on the market, you can wait for the algos to do
their thing, and react accordingly.
If traders want to remain successful, they must periodically remake
themselves and their trading, by learning to adapt to changing market
conditions. They first need to recognize why and how the markets have changed,
and then they need to adjust their trading style so they can successfully adapt
to how the markets currently trade. Loopholes in market rules give high-speed
traders an unfair advantage, by allowing them to see what others are doing, or
intending to do. Their computers can essentially trick or fool slower traders'
computers, into giving up profits, and can do so in milliseconds. The
repercussions of these actions have irrevocably changed the way the market
trades, and has forced successful traders to adapt to a new market dynamic.
In Part Four we will discuss algorithmic trading
platforms available for individual traders, and algorithmic trading strategies.
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Gary Phillips is a contributing columnist to www.TheSmallCapInvestor.com. Mr. Phillips is the Founder of GAP
Capital and is a 30-year veteran in trading equities, bonds,
commodities and currencies.
