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The basic and the most commonly used algorithms are: arrival price,
time weighted average price (TWAP), volume weighted average price
(VWAP), market-on-close (MOC), and implementation shortfall (the
difference between the share-weighted average execution price and the
mid-quote at the point of first entry for market or discretionary
orders). Arrival price is the midpoint of the bid-offer spread at
order-receipt time, and it also notes the speed of the execution. VWAP
is calculated by adding the dollars traded for every transaction in
terms of price and multiplying that by shares traded, and then dividing
that by the total shares traded for the day. MOC measures the last
price obtained by a trader at the end of the day against the last price
reported by the exchange. Implementation shortfall is a model that
weighs the urgency of executing a trade against the risk of moving the
stock.
Popular algorithmic trading strategies -
- Iceberging - the common
strategy to slice orders into smaller sizes with the intention of
hiding, a large order. The maximum amount of shares to be bought at any
one time and during a certain sub-period will be specified by the fund
manager. For fund managers to build a stake in a particular company and
hide the extent of his accumulation, such a technique is useful. - Volatility Limit algorithm
will take the user's assumptions on volatility, interest rates and
dividends to monitor the market and sweep all liquidity when marketable. - Pegging - An order is sent out at the best bid (ask) if buying (selling) and if the price moves the order is modified accordingly.
- Simple time slicing - The order is split up and market orders are sent at regular time intervals.
- Guerilla
- Slicing orders into smaller sizes can also be done with the intention
of minimizing market impact. "Guerrilla", an algorithm, developed by
Credit Suisse, for example, attempts to determine in real time which
publicly displayed (that is those on an exchange or trading platform)
bids or offers can be hit or taken without a high likelihood of causing
jumps or a displacement in the stock's trading patterns. The technique
is useful for fund managers wanting to avoid moving prices against
themselves. - Participating strategies can be used to
ensure that a certain proportion of the trading volume in a particular
stock is captured. The algorithm then assures that the required
proportion of trading volume is achieved. Such strategies may appeal to
momentum-based investors and fund managers who placing an emphasis on
trends in volume as an indicator that often corroborate price trends. - Benchmark algorithms
can be used to achieve a specific benchmark, such as the volume
weighted average price over a certain time period. For such investors,
the shorter latency (that is, the lag between placing an order and it
being implemented) of algorithmic trades compared with those using more
traditional methods will help avoid any slippage between the price
movements of an index and the constituent components. - Market Making - Market
making involves placing a limit order to sell (or offer) above the
current market price or a buy limit order (or bid) below the current
price in order to benefit from the bid-ask spread. - Smart order routing
- With such algorithms, liquidity from many different sources
(conventional trading platforms and dark pools) is aggregated and
orders are sent out to the destination offering the best price or
liquidity. "Sniper" and "Sharks" are algorithms, developed to detect
such hidden sources of liquidity. They detect large orders by putting
small market orders to buy and sell.
Many of the algorithms used in the market have been developed by
investment banks and are supplied to their fund manager clients. This
raises the risk of users of algorithms "gaming" the system. For
example, an algorithm may trigger a buy order on a certain percentage
upward movement in a share price. But if such systems become widely
used, then triggering such an algorithm can be a useful way of
generating a better market price into which to sell.
There are option algorithms as well along with the ability to auto-
hedge that is automatically execute equity hedges in real time as the
option order is filled.
Leading algorithms
Dagger from Citi, Guerilla and Sniper algorithms from Credit Suisse,
Sonar from Goldman Sachs, the Raider algorithm from ITG and the Tap
algorithm from UBS. Most of these algorithms focus on liquidity
opportunities in both displayed venues and dark pools.
Implications of Algorithmic trading
- Volatility in the markets has
increased since algorithmic trading allows even the smallest of trades
to influence stock prices. This has been quite evident lately as volume
and diversity of buyers has materially decreased even though stock
prices are significantly increasing. - One of the key
implications of algorithmic trading is the proliferation of dark pools,
a type of alternative trading systems or electronic trading venues
where money managers trade large blocks of shares anonymously. These
dark pools had grown because they're faster, cheaper and open to
algorithms especially during volatile periods.
Dark pools have less-stringent requirements and don't have to report
monthly volumes or print bids and offers. In response to potential
investor protection and market integrity concerns raised by exchanges,
the SEC Chairman recently announced better oversight of these dark
pools which might include reporting of monthly volumes. According to
Goldman Sachs, these dark pools represent 10% of the total stock
volume. Some of the largest dark pools include Goldman's Sigma X,
GETCO's Execution Services, and Credit Suisse's CrossFinder.
In the news of algo trading today: A Goldman Trading Scandal?
Did someone try to steal
Goldman Sachs' secret sauce?
While most in the United States
were celebrating the Fourth of July holiday, a Russian immigrant living in New
Jersey was being held on federal charges of stealing secret computer trading
codes from a major New York-based financial institution. Authorities did not
identify the firm, but sources say that institution is none other than Goldman
Sachs
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The charges, if proven, are
significant because the codes that the accused, Sergey Aleynikov, tried to steal
are the secret sauce to Goldman's automated stock and commodities trading
business. Federal authorities contend the computer codes and related-trading
files that Aleynikov uploaded to a German-based website help this major
financial institution generate millions of dollars in profits each year.
The platform is one of the things
that gives Goldman an advantage over the competition when it comes to the
rapid-fire trading of stocks and commodities. Federal authorities say the
platform quickly processes rapid developments in the markets and using secret
mathematical formulas, allows the firm to make highly-profitable automated
trades.
The criminal case has the
potential to shed a light on the inner workings of an important profit center
for Goldman and other Wall Street firms. The charges also raise serious
questions about the safeguards that Wall Street firms deploy to protect these
costly-to-build proprietary trading systems.
The criminal case began to unfold
on the evening of July 3,
