In this case, our institutional investor is willing to buy shares in a price range of $20.00 to
$20.05. The algo gets hit, and buys 100 shares at $20.00. Next, it shows it wants to buy
500 shares. It gets hit on that, and buys 500 more shares. Based on that information, a
rebate trading computer program can spot the institution as having an algo order. Then,
the rebate trading computer goes ahead of the algo by a penny, placing a bid to buy 100
shares at $20.01. Whoever had been selling to the institutional investor at $20.00 is likely
to sell to the rebate trading computer at $20.01. That happens, and the rebate trading
computer is now long 100 shares at $20.01 and has collected a rebate of ¼ penny a share.
Then, the computer immediately turns around and offers to sell its 100 shares at $20.01.
Chances are that the institutional algo will take them.

The rebate trading computer makes no money on the shares, but collects another ¼ penny
for making the second offer. Net, net, the rebate trading computer makes a ½ penny per
share, and has caused the institutional investor to pay a penny higher per share.

More than half of all institutional algo orders are “pegged” to the National Best Bid or
Offer (NBBO). The problem is, if one trader jumps ahead of another in price, it can cause
a second trader to go along side of the first one. Very quickly, every algo trading order in
a given stock is following each other up or down (or down and up), creating huge, whip
like price movements on relatively little volume.

This has led to the development of predatory algo trading strategies. These strategies are
designed to cause institutional algo orders to buy or sell shares at prices higher or lower
than where the stock had been trading, creating a situation where the predatory algo can
lock in a profit from the artificial increase or decrease in the price.

To illustrate, let’s use an institutional algo order pegged to the NBBO with discretion to
pay up to $20.10. First, the predatory algo uses methods similar to the liquidity rebate
trader to spot this as an institutional algo order. Next, with a bid of $20.01, the predatory
algo goes on the attack. The institutional algo immediately goes to $20.01. Then, the
predatory algo goes $20.02, and the institutional algo follows. In similar fashion, the
predatory algo runs up the institutional algo to its $20.10 limit. At that point, the predatory
algo sells the stock short at $20.10 to the institutional algo, knowing it is highly likely that
the price of the stock will fall. When it does, the predatory algo covers.

This is how a stock can move 10 or 15 cents on a handful of 100 or 500 share trades.

AMMs, however, often work counter to real investors. AMMs have the ability to “ping”
stocks to identify reserve book orders. In pinging, an AMM issues an order ultra fast, and
if nothing happens, it cancels it. But if it is successful, the AMM learns a tremendous
amount of hidden information that it can use to its advantage.

To show how this works, this time our institutional trader has input discretion into the algo
to buy shares up to $20.03, but nobody in the outside world knows that. First, the AMM
spots the institution as an algo order. Next, the AMM starts to ping the algo. The AMM
offers 100 shares at $20.05. Nothing happens, and it immediately cancels. It offers
$20.04. Nothing happens, and it immediately cancels.