algo trading in India

Algorithmic trading Issues and Recent developments

Algorithmic trading has been shown to substantially improve market liquidity among other benefits. However, improvements in productivity brought by algorithmic trading have been opposed by human brokers and traders facing stiff competition from computers.

Cyborg finance

Technological advances in finance, particularly those relating to algorithmic trading, has increased financial speed, connectivity, reach, and complexity while simultaneously reducing its humanity. Computers running software based on complex algorithms have replaced humans in many functions in the financial industry. Finance is essentially becoming an industry where machines and humans share the dominant roles – transforming modern finance into what one scholar has called, “cyborg finance.”

Concerns

While many experts laud the benefits of innovation in computerized algorithmic trading, other analysts have expressed concern with specific aspects of computerized trading.

“The downside with these systems is their black box-ness,” Mr. Williams said. “Traders have intuitive senses of how the world works. But with these systems you pour in a bunch of numbers, and something comes out the other end, and it’s not always intuitive or clear why the black box latched onto certain data or relationships.”

“The Financial Services Authority has been keeping a watchful eye on the development of black box trading. In its annual report the regulator remarked on the great benefits of efficiency that new technology is bringing to the market. But it also pointed out that ‘greater reliance on sophisticated technology and modelling brings with it a greater risk that systems failure can result in business interruption’.”

UK Treasury minister Lord Myners has warned that companies could become the “playthings” of speculators because of automatic high-frequency trading. Lord Myners said the process risked destroying the relationship between an investor and a company.

Other issues include the technical problem of latency or the delay in getting quotes to traders,security and the possibility of a complete system breakdown leading to a market crash.

“Goldman spends tens of millions of dollars on this stuff. They have more people working in their technology area than people on the trading desk…The nature of the markets has changed dramatically.”

On August 1, 2012 Knight Capital Group experienced a technology issue in their automated trading system, causing a loss of $440 million.

This issue was related to Knight’s installation of trading software and resulted in Knight sending numerous erroneous orders in NYSE-listed securities into the market. This software has been removed from the company’s systems. … Clients were not negatively affected by the erroneous orders, and the software issue was limited to the routing of certain listed stocks to NYSE. Knight has traded out of its entire erroneous trade position, which has resulted in a realized pre-tax loss of approximately $440 million.

Algorithmic and high-frequency trading were shown to have contributed to volatility during the May 6, 2010 Flash Crash,when the Dow Jones Industrial Average plunged about 600 points only to recover those losses within minutes. At the time, it was the second largest point swing, 1,010.14 points, and the biggest one-day point decline, 998.5 points, on an intraday basis in Dow Jones Industrial Average history.

Recent developments

Financial market news is now being formatted by firms such as Need To Know News, Thomson Reuters, Dow Jones, and Bloomberg, to be read and traded on via algorithms.

“Computers are now being used to generate news stories about company earnings results or economic statistics as they are released. And this almost instantaneous information forms a direct feed into other computers which trade on the news.”

The algorithms do not simply trade on simple news stories but also interpret more difficult to understand news. Some firms are also attempting to automatically assign sentiment (deciding if the news is good or bad) to news stories so that automated trading can work directly on the news story.

“Increasingly, people are looking at all forms of news and building their own indicators around it in a semi-structured way,” as they constantly seek out new trading advantages said Rob Passarella, global director of strategy at Dow Jones Enterprise Media Group. His firm provides both a low latency news feed and news analytics for traders. Passarella also pointed to new academic research being conducted on the degree to which frequent Google searches on various stocks can serve as trading indicators, the potential impact of various phrases and words that may appear in Securities and Exchange Commission statements and the latest wave of online communities devoted to stock trading topics.

“Markets are by their very nature conversations, having grown out of coffee houses and taverns,” he said. So the way conversations get created in a digital society will be used to convert news into trades, as well, Passarella said.

“There is a real interest in moving the process of interpreting news from the humans to the machines” says Kirsti Suutari, global business manager of algorithmic trading at Reuters. “More of our customers are finding ways to use news content to make money.”

An example of the importance of news reporting speed to algorithmic traders was an advertising campaign by Dow Jones (appearances included page W15 of The Wall Street Journal, on March 1, 2008) claiming that their service had beaten other news services by two seconds in reporting an interest rate cut by the Bank of England.

In July 2007, Citigroup, which had already developed its own trading algorithms, paid $680 million for Automated Trading Desk, a 19-year-old firm that trades about 200 million shares a day. Citigroup had previously bought Lava Trading and OnTrade Inc.

In late 2010, The UK Government Office for Science initiated a Foresight project investigating the future of computer trading in the financial markets,led by Dame Clara Furse, ex-CEO of the London Stock Exchange and in September 2011 the project published its initial findings in the form of a three-chapter working paper available in three languages, along with 16 additional papers that provide supporting evidence. All of these findings are authored or co-authored by leading academics and practitioners, and were subjected to anonymous peer-review. Released in 2012, the Foresight study acknowledged issues related to periodic illiquidity, new forms of manipulation and potential threats to market stability due to errant algorithms or excessive message traffic. However, the report was also criticized for adopting “standard pro-HFT arguments” and advisory panel members being linked to the HFT industry.

algo trading in India

How Algorithmic Trading Strategies used and different types Algorithmic Trading Strategies

Trading ahead of index fund rebalancing

Most retirement savings, such as private pension funds or 401(k) and individual retirement accounts in the US, are invested in mutual funds, the most popular of which are index funds which must periodically “rebalance” or adjust their portfolio to match the new prices and market capitalization of the underlying securities in the stock or other index that they track.Profits are transferred from passive index investors to active investors, some of whom are algorithmic traders specifically exploiting the index rebalance effect. The magnitude of these losses incurred by passive investors has been estimated at 21–28bp per year for the S&P 500 and 38–77bp per year for the Russell 2000. John Montgomery of Bridgeway Capital Management says that the resulting “poor investor returns” from trading ahead of mutual funds is “the elephant in the room” that “shockingly, people are not talking about.”

Pairs trading

Pairs trading or pair trading is a long-short, ideally market-neutral strategy enabling traders to profit from transient discrepancies in relative value of close substitutes. Unlike in the case of classic arbitrage, in case of pairs trading, the law of one price cannot guarantee convergence of prices. This is especially true when the strategy is applied to individual stocks – these imperfect substitutes can in fact diverge indefinitely. In theory the long-short nature of the strategy should make it work regardless of the stock market direction. In practice, execution risk, persistent and large divergences, as well as a decline in volatility can make this strategy unprofitable for long periods of time (e.g. 2004-2007). It belongs to wider categories of statistical arbitrage, convergence trading, and relative value strategies.

Delta-neutral strategies 

In finance, delta-neutral describes a portfolio of related financial securities, in which the portfolio value remains unchanged due to small changes in the value of the underlying security. Such a portfolio typically contains options and their corresponding underlying securities such that positive and negative delta components offset, resulting in the portfolio’s value being relatively insensitive to changes in the value of the underlying security.

Arbitrage

In economics and finance, arbitrage the practice of taking advantage of a price difference between two or more markets: striking a combination of matching deals that capitalize upon the imbalance, the profit being the difference between the market prices. When used by academics, an arbitrage is a transaction that involves no negative cash flow at any probabilistic or temporal state and a positive cash flow in at least one state; in simple terms, it is the possibility of a risk-free profit at zero cost. Example: One of the most popular Arbitrage trading opportunities is played with the S&P futures and the S&P 500 stocks. During most trading days these two will develop disparity in the pricing between the two of them. This happens when the price of the stocks which are mostly traded on the NYSE and NASDAQ markets either get ahead or behind the S&P Futures which are traded in the CME market.

Conditions for arbitrage

Arbitrage is possible when one of three conditions is met:

The same asset does not trade at the same price on all markets (the “law of one price” is temporarily violated).

1.Two assets with identical cash flows do not trade at the same price.

2.An asset with a known price in the future does not today trade at its future price discounted at the risk-free interest rate (or, the asset does not have negligible costs of storage; as such, for example, this condition holds for grain but not for securities).

3.Arbitrage is not simply the act of buying a product in one market and selling it in another for a higher price at some later time. The long and short transactions should ideally occur simultaneously to minimize the exposure to market risk, or the risk that prices may change on one market before both transactions are complete. In practical terms, this is generally only possible with securities and financial products which can be traded electronically, and even then, when first leg(s) of the trade is executed, the prices in the other legs may have worsened, locking in a guaranteed loss. Missing one of the legs of the trade (and subsequently having to open it at a worse price) is called ‘execution risk’ or more specifically ‘leg-in and leg-out risk’.

In the simplest example, any good sold in one market should sell for the same price in another. Traders may, for example, find that the price of wheat is lower in agricultural regions than in cities, purchase the good, and transport it to another region to sell at a higher price. This type of price arbitrage is the most common, but this simple example ignores the cost of transport, storage, risk, and other factors. “True” arbitrage requires that there be no market risk involved. Where securities are traded on more than one exchange, arbitrage occurs by simultaneously buying in one and selling on the other. Such simultaneous execution, if perfect substitutes are involved, minimizes capital requirements, but in practice never creates a “self-financing” (free) position, as many sources incorrectly assume following the theory. As long as there is some difference in the market value and riskiness of the two legs, capital would have to be put up in order to carry the long-short arbitrage position.

Mean reversion

Mean reversion is a mathematical methodology sometimes used for stock investing, but it can be applied to other processes. In general terms the idea is that both a stock’s high and low prices are temporary, and that a stock’s price tends to have an average price over time. An example of a mean-reverting process is the Ornstein-Uhlenbeck stochastic equation.

Mean reversion involves first identifying the trading range for a stock, and then computing the average price using analytical techniques as it relates to assets, earnings, etc.

When the current market price is less than the average price, the stock is considered attractive for purchase, with the expectation that the price will rise. When the current market price is above the average price, the market price is expected to fall. In other words, deviations from the average price are expected to revert to the average.

The standard deviation of the most recent prices (e.g., the last 20) is often used as a buy or sell indicator.

Stock reporting services (such as Yahoo! Finance, MS Investor, Morningstar, etc.), commonly offer moving averages for periods such as 50 and 100 days. While reporting services provide the averages, identifying the high and low prices for the study period is still necessary.

Scalping

Scalping is liquidity provision by non-traditional market makers, whereby traders attempt to earn (or make) the bid-ask spread. This procedure allows for profit for so long as price moves are less than this spread and normally involves establishing and liquidating a position quickly, usually within minutes or less.

A market maker is basically a specialized scalper. The volume a market maker trades is many times more than the average individual scalper and would make use of more sophisticated trading systems and technology. However, registered market makers are bound by exchange rules stipulating their minimum quote obligations. For instance, NASDAQ requires each market maker to post at least one bid and one ask at some price level, so as to maintain a two-sided market for each stock represented.

Transaction cost reduction

Most strategies referred to as algorithmic trading (as well as algorithmic liquidity-seeking) fall into the cost-reduction category. The basic idea is to break down a large order into small orders and place them in the market over time. The choice of algorithm depends on various factors, with the most important being volatility and liquidity of the stock. For example, for a highly liquid stock, matching a certain percentage of the overall orders of stock (called volume inline algorithms) is usually a good strategy, but for a highly illiquid stock, algorithms try to match every order that has a favorable price (called liquidity-seeking algorithms).

The success of these strategies is usually measured by comparing the average price at which the entire order was executed with the average price achieved through a benchmark execution for the same duration. Usually, the volume-weighted average price is used as the benchmark. At times, the execution price is also compared with the price of the instrument at the time of placing the order.

A special class of these algorithms attempts to detect algorithmic or iceberg orders on the other side (i.e. if you are trying to buy, the algorithm will try to detect orders for the sell side). These algorithms are called sniffing algorithms. A typical example is “Stealth.”

Some examples of algorithms are VWAP, TWAP, Implementation shortfall, POV, Display size, Liquidity seeker, and Stealth. Modern algorithms are often optimally constructed via either static or dynamic programming .

Strategies that only pertain to dark pools

Recently, HFT, which comprises a broad set of buy-side as well as market making sell side traders, has become more prominent and controversial.These algorithms or techniques are commonly given names such as “Stealth” (developed by the Deutsche Bank), “Iceberg”, “Dagger”, “Guerrilla”, “Sniper”, “BASOR” (developed by Quod Financial) and “Sniffer”. Dark pools are alternative trading systems that are private in nature—and thus do not interact with public order flow—and seek instead to provide undisplayed liquidity to large blocks of securities. In dark pools trading takes place anonymously, with most orders hidden or “iceberged.” Gamers or “sharks” sniff out large orders by “pinging” small market orders to buy and sell. When several small orders are filled the sharks may have discovered the presence of a large iceberged order.

“Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.” 

Market timing

Strategies designed to generate alpha are considered market timing strategies. These types of strategies are designed using a methodology that includes backtesting, forward testing and live testing. Market timing algorithms will typically use technical indicators such as moving averages but can also include pattern recognition logic implemented using Finite State Machines.[citation needed]

Backtesting the algorithm is typically the first stage and involves simulating the hypothetical trades through an in-sample data period. Optimization is performed in order to determine the most optimal inputs. Steps taken to reduce the chance of over optimization can include modifying the inputs +/- 10%, schmooing the inputs in large steps, running monte carlo simulations and ensuring slippage and commission is accounted for.

Forward testing the algorithm is the next stage and involves running the algorithm through an out of sample data set to ensure the algorithm performs within backtested expectations.

Live testing is the final stage of development and requires the developer to compare actual live trades with both the backtested and forward tested models. Metrics compared include percent profitable, profit factor, maximum drawdown and average gain per trade.