Algorithmic Trading strategies, examples, companies, books, software options in India
Only one in five-day traders is profitable. Algorithmic trading improves these odds through better strategy design, testing, and execution.
What is Algorithmic Trading?
Algorithmic trading is a process for executing orders utilizing automated and pre-programmed trading instructions to account for variables such as price, timing and volume. An algorithm is a set of directions for solving a problem. Computer algorithms send small portions of the full order to the market over time.
Algorithmic trading makes use of complex formulas, combined with mathematical models and human oversight, to make decisions to buy or sell financial securities on an exchange. Algorithmic traders often make use of high-frequency trading technology, which can enable a firm to make tens of thousands of trades per second. Algorithmic trading can be used in a wide variety of situations including order execution, arbitrage, and trend trading strategies.
Basically, algorithmic trading uses computer programs to place buy and sell orders automatically according to a specified set of rules. These rules are collectively referred to as the trading algorithm.
The use of algorithms in trading increased after computerized trading systems were introduced in American financial markets during the 1970s. In 1976, the New York Stock Exchange introduced the Designated Order Turnaround (DOT) system for routing orders from traders to specialists on the exchange floor. In the following decades, exchanges enhanced their abilities to accept electronic trading, and by 2009, upwards of 60 percent of all trades in the U.S. were executed by computers.
Author Michael Lewis brought high-frequency, algorithmic trading to the public’s attention when he published the best-selling book Flash Boys, which documented the lives of Wall Street traders and entrepreneurs who helped build the companies that came to define the structure of electronic trading in America. His book argued that these companies were engaged in an arms race to build ever-faster computers, which could communicate with exchanges ever more quickly, to gain an advantage on competitors with speed, using order types that benefited them to the detriment of average investors.
An Algorithmic Trading Strategy Example
The classic dual moving average (DMA) trading strategy executed by computer code is an example of an algorithmic trading system using a trend-following strategy. There are only two rules:
1)When the 50-day moving average crosses above the 200-day moving average, the trend is up and we buy.
2)When the 50-day moving average crosses below the 200-day moving average, the trend is down and we sell.
Using these two simple instructions, a computer program will automatically monitor the stock price (and the moving average indicators) and place the buy and sell orders when the defined conditions are met. The trader no longer needs to monitor live prices and graphs or put in the orders manually. The algorithmic trading system does this automatically by correctly identifying the trading opportunity.
Most algo-trading today is high-frequency trading (HFT), which attempts to capitalize on placing a large number of orders at rapid speeds across multiple markets and multiple decision parameters based on pre-programmed instructions.
Algo-trading is used in many forms of trading and investment activities including:
Mid- to long-term investors or buy-side firms—pension funds, mutual funds, insurance companies—use algo-trading to purchase stocks in large quantities when they do not want to influence stock prices with discrete, large-volume investments.
Short-term traders and sell-side participants—market makers (such as brokerage houses), speculators, and arbitrageurs—benefit from automated trade execution; in addition, algo-trading aids in creating sufficient liquidity for sellers in the market.
Systematic traders—trend followers, hedge funds, or pairs traders (a market-neutral trading strategy that matches a long position with a short position in a pair of highly correlated instruments such as two stocks, exchange-traded funds (ETFs) or currencies)—find it much more efficient to program their trading rules and let the program trade automatically.
Algorithmic trading provides a more systematic approach to active trading than methods based on trader intuition or instinct.
Technical Requirements for Algorithmic Trading
Implementing the algorithm using a computer program is the final component of algorithmic trading, accompanied by backtesting (trying out the algorithm on historical periods of past stock-market performance to see if using it would have been profitable). The challenge is to transform the identified strategy into an integrated computerized process that has access to a trading account for placing orders. The following are the requirements for algorithmic trading:
Computer-programming knowledge to program the required trading strategy, hired programmers, or pre-made trading software.
Network connectivity and access to trading platforms to place orders.
Access to market data feeds that will be monitored by the algorithm for opportunities to place orders.
The ability and infrastructure to backtest the system once it is built before it goes live on real markets.
Available historical data for backtesting depending on the complexity of rules implemented in the algorithm.
Advantages and Disadvantages of Algorithmic Trading
Algorithmic trading is mainly used by institutional investors and big brokerage houses to cut down on costs associated with trading. According to research, algorithmic trading is especially beneficial for large order sizes that may comprise as much as 10% of overall trading volume.3 Typically market makers use algorithmic trades to create liquidity.
Algorithmic trading also allows for faster and easier execution of orders, making it attractive for exchanges. In turn, this means that traders and investors can quickly book profits off small changes in price. The scalping trading strategy commonly employs algorithms because it involves rapid buying and selling of securities at small price increments.
The speed of order execution, an advantage in ordinary circumstances, can become a problem when several orders are executed simultaneously without human intervention. The flash crash of 2010 has been blamed on algorithmic trading.
The major disadvantage of algorithmic trading is that one mistake in your code can be catastrophic. An algorithm can trigger hundreds of transactions in a short period costing the trader their entire account. When performed en masse, they are called a flash crash.
These mistakes can cause major headaches at best and an empty account at worst
Another disadvantage of algorithmic trades is that liquidity, which is created through rapid buy and sell orders, can disappear in a moment, eliminating the change for traders to profit off price changes. It can also lead to instant loss of liquidity. Research has uncovered that algorithmic trading was a major factor in causing a loss of liquidity in currency markets after the Swiss franc discontinued its Euro peg in 2015.
There are additional risks and challenges such as system failure risks, network connectivity errors, time-lags between trade orders and execution, and, most important of all, imperfect algorithms. The more complex an algorithm, the more stringent backtesting is needed before it is put into action.
Algorithmic Trading Strategies
Any strategy for algorithmic trading requires an identified opportunity that is profitable in terms of improved earnings or cost reduction. The following are common trading strategies used in algo-trading:
Trend-following Strategies
The most common algorithmic trading strategies follow trends in moving averages, channel breakouts, price level movements, and related technical indicators. These are the easiest and simplest strategies to implement through algorithmic trading because these strategies do not involve making any predictions or price forecasts. Trades are initiated based on the occurrence of desirable trends, which are easy and straightforward to implement through algorithms without getting into the complexity of predictive analysis. Using 50- and 200-day moving averages is a popular trend-following strategy.
Arbitrage Opportunities
Buying a dual-listed stock at a lower price in one market and simultaneously selling it at a higher price in another market offers the price differential as risk-free profit or arbitrage. The same operation can be replicated for stocks vs. futures instruments as price differentials do exist from time to time. Implementing an algorithm to identify such price differentials and placing the orders efficiently allows profitable opportunities.
Index Fund Rebalancing
Index funds have defined periods of rebalancing to bring their holdings to par with their respective benchmark indices. This creates profitable opportunities for algorithmic traders, who capitalize on expected trades that offer 20 to 80 basis points profits depending on the number of stocks in the index fund just before index fund rebalancing. Such trades are initiated via algorithmic trading systems for timely execution and the best prices.
Mathematical Model-based Strategies
Proven mathematical models, like the delta-neutral trading strategy, allow trading on a combination of options and the underlying security. (Delta neutral is a portfolio strategy consisting of multiple positions with offsetting positive and negative deltas—a ratio comparing the change in the price of an asset, usually a marketable security, to the corresponding change in the price of its derivative—so that the overall delta of the assets in question totals zero.)
Trading Range (Mean Reversion)
Mean reversion strategy is based on the concept that the high and low prices of an asset are a temporary phenomenon that revert to their mean value (average value) periodically. Identifying and defining a price range and implementing an algorithm based on it allows trades to be placed automatically when the price of an asset breaks in and out of its defined range.
Time Weighted Average Price (TWAP)
Time-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using evenly divided time slots between a start and end time. The aim is to execute the order close to the average price between the start and end times thereby minimizing market impact.
Percentage of Volume (POV)
Until the trade order is fully filled, this algorithm continues sending partial orders according to the defined participation ratio and according to the volume traded in the markets. The related “steps strategy” sends orders at a user-defined percentage of market volumes and increases or decreases this participation rate when the stock price reaches user-defined levels.
Implementation Shortfall
The implementation shortfall strategy aims at minimizing the execution cost of an order by trading off the real-time market, thereby saving on the cost of the order and benefiting from the opportunity cost of delayed execution. The strategy will increase the targeted participation rate when the stock price moves favorably and decrease it when the stock price moves adversely.
Beyond the Usual Trading Algorithms
There are a few special classes of algorithms that attempt to identify “happenings” on the other side. These “sniffing algorithms”—used, for example, by a sell-side market maker—have the built-in intelligence to identify the existence of any algorithms on the buy side of a large order. Such detection through algorithms will help the market maker identify large order opportunities and enable them to benefit by filling the orders at a higher price. This is sometimes identified as high-tech front-running.
Is Algorithmic Trading the Future?
The odds of succeeding as an individual discretionary trader are getting worse by the minute. Like many other industries, the companies embracing technology are succeeding much more than those being disrupted. The same goes for trading. Traders that use these exciting new technologies when investing increase their chances of success significantly; however, while the path to profits is easier, the learning curve is steep.
We see two areas where algorithmic trading can improve a retail investor’s performance:
- Data Science enables better strategy development and testing
- Algorithmic Execution improves trade execution and reduces behavioral investing mistakes
In conclusion, this kind of trading is not suitable for all. You need to have a decent size of capital to be deployed for investment and trading. Investors also need to have a fair amount of knowledge on different trading strategies and a fair deal of knowledge about how to develop algos and test it. Execution and speed is the key for any kind of Algos and clients need to have pretty fast computer hardware to be able to trade fast.
To conclude we can say that Algorithmic trading needs to be undertaken under supervision only and do check our best intraday tips or Bank Nifty option tips which produce profit every day.
Algorithmic trading is a method of executing trades using automated, pre-programmed trading instructions, rather than making decisions based on human intuition or experience. Algorithmic trading is often used by large institutional investors and high-frequency traders to execute trades at high speeds and with great precision. These trades are executed using complex mathematical algorithms and are based on a variety of factors such as market conditions, pricing data, and other relevant information.
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