In 1602, Amsterdam Stock Exchange, world’s first stock market, formally began trading in securities. In the beginning of 17th century, Rothschilds started using carrier pigeons to arbitrage prices of the same security by relaying information ahead of their competitors. In 1983 – Bloomberg – world’s first computerized system to provide real-time price feed and analytics to Wall Street firms – officially kicked off. In 1998, SEC authorized computerized high frequency trading, capable of executing trades 1000 times faster than humans. In 2000, HFT accounted for less than 10% of total equity trading but at the turn of the 21st Century execution time of HFT trades reduced from several seconds to microseconds in 2010 and then further reduced to nanoseconds in 2012
In 2005, HFT share rose to 35% of total equity trades across the United States. Then in 2010, HFT share in total equity trading increased further to 56%. On May 6th 2010, $1 trillion was wiped off the market as dow plunged 1000 points in a single day because of computer-driven selling of over $4b. HFT firms were blamed for the crash. In 2011, nano trading technology allowed execution of trade in just few nanoseconds. One nanosecond equals one billionth of a second.
In Sep. 2012, the launch of determiner turns social media news into actionable trading signals and helps reporting the latest business news 54 minutes faster than conventional news mediums. In late 2012, the share of HFT increased to 70% of the total US equity trading. During the same year, HFT industry attracted investment worth millions. A custom-made chip developed for HFT allowed trade execution in 0.000000074 second. A special cable worth $300 was built just to save 0.006 second off the transaction time.
In Nov. 2012, FBI began investigation about social media frauds amid its high impact on stock markets. In April 2, 2013, CFTC and SEC banned financial announcements relating to public companies on social media. In April 4, 2013, Bloomberg terminals included live tweets into its data service. April 23, 2013, a false tweet about white house bombing caused Tsunami in financial markets, Dow plunges 1% in just 3 minutes. Later on, a server based in Washington DC archived capability to transmit data to New Jersey at the speed of light thanks to superfast microwave transmission technology
In September 18, 2013, around 2pm, Fed surprised Wall Street by announcing a delay in QE tapering. Assets worth $600m had changed hands in milliseconds before the news reached Chicago. Then on September 2013, Italy became first country to impose tax on HFT. A 0.002% tax was imposed on equity trades closing before 0.5 seconds. In 2013, economists debate risks of HFT trading amid 2010 crash; some economists demanded a complete ban on HFT. In Oct 7, 2016, Pound nosedived more than 800 pips in just few minutes; economists blamed HFT for the cable’s mysterious crash.
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High Frequency Trading (HFT) is a type of algorithmic trading in which large volumes of assets are bought and sold automatically at very high speed. HFT is a very popular form of trading. It can generate quick profits with steady win-rate on certain market conditions.
High frequency trading strategies describe an algorithm that is trading thousands of times a day, to capture inefficiencies in the exchange rate of a currency pair or some other financial instrument. The concept is a relative term, describing how market participants use technology to gain information, and act upon it, in advance of the rest of the market. In essence, high frequency traders are front running your order. If the price of a currency pair is off by even half of a pip, the high frequency trader will attempt to capture this inefficiency.
High frequency traders initially appeared onto the equity market scene. New regulation allowed electronic exchanges to compete with one another, which left the door open for high frequency traders to step in and search for discrepancies in prices. High frequency traders rely on extremely low latencies and use high speed connections in conjunction with trading algorithms to exploit inefficiencies created by these exchanges.
Many HFT strategies revolve around searching for and sniffing out institutional order flows, by going through the multitude of electronic exchanges available to trade securities. These algorithms would detect a trade and attempt to transact the same trade before the order was filled at another electronic exchange. These algorithms are front running many securities orders and are predicated on the idea that speed was of the essence.
Speed has become so important to the success of a high frequency operation, that these businesses invest enormous sums of money into building their low latency infrastructure. High frequency traders target low latency machinery in an effort to find the fastest computers available. For a high frequency trader, finding the path of least resistance in communication is the key to successful arbitrage. Additionally, the proximity of a high frequency trader’s black box to an exchange will reduce or increase the speed at which a transaction is recognized. So, a proximity war, among high frequency firms, has emerged and created competition for real-estate around a physical exchange location, especially in the equity space.
• HFT is a great way to make quick profits. It is highly risky but very effective trading strategy.
• HFT is executed via robots so there is no risk that you can lose your money due to emotional trading.
• Robots place all trades on the basis of predefined trading strategies; there is no way they can deviate from predefined trading plan which is a key to success in forex trading.
There are many advantages associated with HF Algo Trading. For example, all trades are executed at the best possible prices. Instant and accurate trade order placement is another key advantage of HF Algo Trading that consequently increases chances of execution at desired levels. Reduced transaction cost is another main pro of algo trading. Similarly, simultaneous automated checks can be implemented on multiple market conditions. HF Algo trading also reduces risk of manual errors in placing the trades. Last but not the least; HF algo trading reduces possibility of mistakes by human traders based on emotional and psychological behavior.
There are many strategies employed by high frequency traders to make money; some are quite common, some are more controversial. For instance, some HF traders trade from both sides i.e. they place orders to buy as well as sell using limit orders that are above the current market place (in the case of selling) and slightly below the current market price (in the case of buying). The difference between the two is the profit they pocket. Thus these traders indulge in “market making” only to make profits from the difference between the bid-ask spread. These transactions are carried out by high-speed computers using algorithms. Another way traders make money is by looking for price discrepancies between securities on different exchanges or asset classes. This strategy is called statistical arbitrage, wherein a proprietary trader is on the lookout for temporary inconsistencies in prices across different exchanges. With the help of ultra fast transactions, they capitalize on these minor fluctuations which many don’t even get to notice.
There are many algorithms that are common in High Frequency Trading, let’s pick up and describe three of them;
Pair Trading - Trade two currencies which naturally track each other an example could be Euro and US Dollar, make money when they fall out of line on the idea that they will have to revert back to tracking each other.
Volume-Weighted Average Price - VWAP is used to execute large orders at a better average price. It is the ratio of the value traded to the total volume traded over a time period
Time-Weighted Average Price - TWAP like VWAP is another sophisticated strategy for buying or selling large blocks of currencies without affecting the price.