High Frequency Trading
is a type of algorithmic trading characterized by high speeds, high
turnover rates, and high order to-trade ratios that leverages
high-frequency financial data.
The two essential elements of
high-frequency trading are an algorithm that can accurately identify a
pricing mismatch or trading opportunity, and trading systems that are
lightening fast-trading speeds are measured in milliseconds.
High Frequency Trading, or popularly
known as HFT, is a set of trading algorithms which aim to profit by
lightning fast trade executions in electronic trading markets. More than
30% of currency trading in world markets, about 90% of stock trading in
exchanges like NASDAQ and about 60% trading in NIFTY is carried out by
computers using HFT algorithms HFT algorithms average stock-holding
period is just about 30-50 seconds!
High frequency trading firms have some
450-500 microseconds’ advantage for stock quote data over any firm that
doesn’t use a direct feed from the exchanges to quote prices
This analysis is made possible by new
software changes implemented by stock exchanges which measure the
difference in speed between data transmitted by exchanges to their
direct feeds and data transmitted to the Security Information Processor
(SIP). which links the equity markets by processing and consolidating
all bid/ask quotes and trades from every trading venue into a single,
easily consumed data feed.
Most commonly used algorithms in the
market place 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-weight
average execution price and the mid-quote at the point of first entry
for market or discretionary orders). Most algorithms already allow
customers to change the timing of executions, the rate of order-filling
attempts at the beginning or end of the trading day, and the tolerance
for the slippage of a stock from certain benchmarks.
Recently algorithmic trading is being
explored in the fixed-income market. It is happening slower than in
foreign exchange and stocks. The reason for the slow uptake is due to a
different market structure in terms of how it functions and operates and
algorithmic trading takes off fastest where there is an order driven
environment and greater price transparency.
Automated trading helps ensure that
discipline is maintained because the trading plan will be followed
exactly. Because the trade rules are established and trade execution is
performed automatically, discipline is preserved even in volatile
markets. Discipline is often lost due to emotional factors such as fear
of taking a loss, or the desire to eke out a little more profit from a
trade.
Technology Cons
With brokers offering many algorithmic
strategies, one concern is that buy-side institutions lack the tools to
understand which algorithm to use for a particular stock. The lack of a
standard benchmark has made it almost impossible to assess the quality
of algorithms. Buy-side firms are having a hard time evaluating when to
use a particular algorithm.
If the merchant didn’t choose the most
ideal calculation for that exchange little should be possible. This
issue is brought on by an absence of transparency and
straightforwardness into the calculation while it is executing requests.
Algorithms Acting on Other Algorithms
On the off chance that fund managers’
trading pattern is spotted and standard; followed with the utilization
of calculations, then these calculations are subject to be ‘reverse
engineered’. This infers that their purchase and offer requests are
pre-empted and used to the greatest impact by their competitors.
Concerns
It adds no real “economic value.
People buy and sell stocks
for short-term advantage, with no interest in long-term. HFT just moves
the time frame up to fractions of a second.
Certain trading strategies are a form of market manipulation or may otherwise harm long-term investors.
