Great article on great definitions for Marketmaking, Dark Pools, Flash Trading, Quant, Algo trading strategies

(Last Updated On: August 6, 2010)

Great article on great definitions for Marketmaking, Dark Pools, Flash Trading, Quant, Algo trading strategies

This article was was very detailed on the pros and cons of each. The source is:
Quantitative Trading Strategies
Brought to life by the likes of blackjack aficionado and legendary investor Edward O. Thorpe and turned famously profitable by mathematician hedge fund kings like Renaissance Technologies’ James Simons, quantitative (or “quant”) strategies typically involve forms of statistical arbitrage and pairs trading. Quant computer models discern historical patterns and correlations between different securities, search for instances where those relationships go out of whack, and, finally, buy and short the affected securities to help push them back to their traditional correlations, collecting the spread along the way. The profits on any one trade tend to be small but, with enough speed and volume, they can create enormous profits.
The strategy most accurately defined as high-frequency trading—automated marketmaking—accounts for roughly half of the daily equity trade volume in the United States. Automated marketmakers are the computer-age equivalent of the screaming floor traders of a bygone era, providing liquidity by placing bids and offers onto exchanges, and seeing who will buy and sell at the quoted price.
Automated marketmaking would not exist without the recent advances in computing, but its ubiquity is actually more a product of regulatory changes than anything technology-based. The first shift came in the late 1990s, when the SEC approved the creation of alternative trading systems that ended the virtual duopoly of the NASDAQ and NYSE. With the new rules, Electronic Communication Networks (ECNs) could operate like the big exchanges—allowing marketmakers to post their bids and offers publicly—but without all the rules, regulations, and overhead costs of those big exchanges.
ECNs exploded after April 2001, when all exchanges were forced to switch from the old fraction pricing system to decimals. Suddenly the spread on a trade, which was almost always 1/16th of a dollar (6.25 cents), could go as low as a penny, bringing even more savings in the cheaper, alternative exchanges. To attract marketmakers, ECNs developed a new “maker-taker” payment model and, for providing liquidity, marketmakers were paid by the ECN for each deal they made. Because they took the liquidity provided by the marketmakers, firms on the other side of the trades paid for it. The sums involved were relatively small but, with enough volume, an automated marketmaker could all but print money. This incentive, along with the bounty of new exchanges and ultra-fast trades executed in fractions of a second, has helped double the average daily trading volume since 2003.
The industry received another regulatory B12 shot in 2007 when the SEC’s Regulation National Market System (“Reg NMS,” in industry parlance) went into effect. First, Reg NMS required that firms respond to all bids and offers within a second or so, intended to prevent a form of electronic “front-running” where firms would try to get ahead of a trade they were offered. The time requirement cut down on front-running, but also meant that all marketmakers and traders essentially were required to have lightning-fast computers, networks, and algorithms, a perfect environment for high-frequency trading.
Algorithmic Trading:
During World War II, one of the military’s biggest problems was prioritizing the infinite combinations of soldiers, weapons, supplies, and replacement parts that needed to be shipped to the front lines—until the arrival of a young Air Force officer named George Dantzig. In his first year at Berkeley, Dantzig had solved what he thought were homework assignments but were, in fact, two of the great unsolved problems in statistics. Over time, the details of the story changed and it was misattributed to a handful of famous mathematicians (and a recondite janitor in the movie Good Will Hunting) but Dantzig was the source. Almost 70 years later, variations on the “Simplex Method” that Dantzig devised for military supply-line problems are used to slice and dice large trades, spread them out over different exchanges, and either execute them with lightning speed or space them out over time, all in the hopes of minimizing price-slippage. While technically all quant strategies and forms of high-frequency trading employ algorithms, this is the sub-specialty most commonly labeled “algorithmic trading.”

Dark Pools:
Sometimes these algorithmic trades are done in “dark pools,” private exchanges that allow select participants to make big trades secretly (until the trades ultimately are reported on the consolidated tape) and anonymously to minimize market impact.

Flash Trading
Another feature of the SEC’s Reg NMS that catalyzed high-frequency trading was a new “trade through” rule that functions like an instantaneous equities version of retail stores’ “Best Price” guarantees. Before clearing a trade, exchanges must take the best share price available on all other exchanges. The trouble with this is that exchanges can only take share prices into account, not access fees that can drive the total price much higher. To cut access fees, and keep the trades on their own turf, exchanges like DirectEdge started letting customers “flash” their trades to fellow exchange members a fraction of a second before going out publicly, giving them a chance to match the best available price from other exchanges, and keep the trade on the original exchange. For the “flasher,” this means lower transaction costs; for the “flashee,” it means a chance to do more business; for the exchange, it means getting to keep the trade on their books

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