Algorithmic Trading: Definition and Strategies

Algorithmic Trading: Definition and Strategies

What is Algorithmic trading?

Algorithmic trading is a kind of trading that utilizes powerful computers to run complex mathematical formulas for trading. An algorithm is a set of instructions for solving a problem. An example of an algorithm is an arithmetical equation, combined with the proper rules of algebra. With these two elements, a computer can derive the answer to that equation always.

Algorithmic trading concept on white background.

Algorithmic trading makes utilization of substantially more intricate formulas, combined with mathematical models and human oversight, to make choices to purchase or sell financial securities on an exchange. Moreover, algorithmic traders often make use of high-frequency trading technology, which can allow a firm to make tens of thousands of trades every second. Algorithmic trading can be used in an extensive variety of situations including order execution, arbitrage, and trend trading strategies.

The term is also utilized to mean automated trading system. These do for sure have the objective of making a profit. Also known as black box trading, these cover trading strategies that are heavily dependent on complex mathematical formulas and high-speed computer programs.

Also known as algo trading, algorithmic trading is a method of stock trading that utilizes complex mathematical models and formulas to initiate high-speed, automated financial transactions. The objective of algorithmic trading is to help investors execute on particular financial strategies as fast as possible to bring in higher benefits. While there are various important benefits to algorithmic trading, there are also some risks to consider.

Basics of Algorithmic trading

The utilization of algorithms in trading expanded after online trading systems were presented in American financial markets during the 1970s. In 1976, the New York Stock Exchange presented the Designated Order Turnaround (DOT) system for routing orders from traders to experts on the exchange floor. In the next decades, exchanges improved their skills to accept electric trading, and by 2010, upwards of 60 percent of all trades were executed by computers.

Algorithmic Trading

Suppose a trader follows these simple trade standards:

Purchase 50 shares of a stock when its 50-day moving average goes over the 200-day moving average. Sell shares of the stock when its 50-day moving average goes lower the 200-day moving average. Utilizing these two easy instructions, a computer program will automatically monitor the stock price (and the moving average indicators) and place the purchase and sell orders when the defined conditions are met.

How it works?

An algorithm is a procedure or set of defined rules intended to do a certain procedure. Algorithmic trading utilizes computer programs to trade at high speeds and volume based on various preset criteria, such as stock prices and particular market conditions.

For instance, a trader may utilize algorithmic trading to execute orders fast when a certain stock reaches or falls lower than a particular value. The algorithm may dictate how many shares to purchase or sell based on such conditions. When a program is put in place, that trader would then be able to take it easy, knowing that trades will automatically happen once those preset conditions are met.

See also: Investing, Trading, Speculating Stocks: The Differences

BUY and SELL options 3D illustration.

Benefits of Algorithmic trading

One major benefit of algorithmic trading is that it systematizes the trading procedure ensure that orders are executed at what are regarded to be ideal buying or selling conditions. Because orders are set immediately, investors can rest certain that they won’t miss a chance on important opportunities. Manual orders, on the other hand, can’t come close to copying the speed of algorithmic trading. In addition, because everything is done automatically by computer, human miscalculation is almost taken out of the equation (assuming, of course, that the algorithm is developed properly).

Furthermore, algorithmic trading often limits or lessens transaction prices, thus enabling investors to hold extra of their profits. Finally, algorithmic trading removes the risks of acting on emotion rather than logic, which investors are known to do.

Algo-trading is used in many forms of trading and investment activities

Mid- to long-term investors or buy-side firms – pension funds, mutual funds, insurance companies – use algo-trading to buy stocks in big 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 – profit from automated trade execution; in addition, algo-trading aids in making 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.

Algorithmic Trading Strategies

Every strategy for algorithmic trading requires a distinguished chance that is profitable in terms of enhanced income or price decrease. The following are basic trading strategies used in algorithmic trading:

Magnifying lens over background with text Pair trading.

Pairs trading

Pairs trading is a long-short, preferably market-neutral strategy allowing traders to profit from temporary discrepancies in relative value of close substitutes. Not like in the case of classic arbitrage, in case of pairs trading, the rule of one price cannot ensure merging of prices. This is particularly true once the strategy is tried to individual stocks – these imperfect substitutes can in fact diverge indefinitely.

Arbitrage

Buying a dual-listed stock at a lower cost in one market and contemporarily selling it at a more expensive rate in another market offers the value differential as risk-free profit or arbitrage. The same operation can be simulated for stocks against futures instruments as value differentials do exist an occasionally. Executing an algorithm to recognize such value differentials and placing the orders efficiently enables profitable opportunities.

Trend-following strategies

The most basic 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 execute through algorithmic trading because these strategies do not involve making any predictions or price forecasts.

Delta-neutral Strategies

Delta-neutral depicts a portfolio of related monetary securities, in which the portfolio value stays unaltered because of small changes in the price of the underlying security. Such a portfolio usually contains options and their corresponding underlying securities such that positive and negative delta components counterbalance, resulting in the portfolio’s value being relatively insensitive to changes in the value of the underlying security.

Mean reversion

Mean reversion strategy is based on the concept that the high and low costs of an asset are a brief phenomenon that return to their mean value (average value) sometimes. Identifying and defining a value range and executing an algorithm based on that enable trades to be placed automatically when the value of asset breaks in and out of its defined range.

Scalping

Scalping is liquidity provision by non-traditional market makers, whereby traders try to earn the bid-ask spread. This process enables for income for so long as price moves are less than this spread and usually involves establishing and liquidating a position rapidly, commonly in minutes or less.

Index Fund Rebalancing

Index funds have defined periods of rebalancing to bring their assets to parity with their respective benchmark indices. This makes profitable chances for algorithmic traders, who capitalize on expected trades that offer 20 to 80 basis points revenues relying on the quantity of stocks in the index fund just before index fund rebalancing. Such trades are started via algorithmic trading systems for timely implementation and best prices.

Mathematical Model-based Strategies

Demonstrated mathematical models, similar to the delta-neutral trading strategy, enable trading on a merger of options and the underlying security.

Conceptual business illustration with the words volume-weighted average price.

Volume-weighted Average Price (VWAP)

Volume-weighted average price strategy breaks up a large order and releases dynamically determined smaller chunks of the order to the market using stock-specific historical volume profiles. The aim is to execute the order close to the volume-weighted average price (VWAP).

Time Weighted Average Price (TWAP)

Time-weighted average price strategy separates a big order and releases dynamically determined smaller amounts of the order to the market using equally divided time slots between a start and end time. The goal is to implement the order near to the average price between the start and end times thus minimizing market impact.

Implementation Shortfall

The implementation shortfall strategy goals at minimizing the implementation price of an order by trading off the real-time market thereby saving on the price of the order and profiting from the opportunity price of late implementation. The strategy will increase the targeted participation rate when the stock price moves positively and decline it when the stock price moves unfavorably.

Market Timing

Strategies intended to generate alpha are considered market timing strategies. These types of strategies are intended using a methodology that includes back testing, forward testing and live testing. Market timing algorithms will usually use technical indicators such as moving averages but can also include pattern recognition logic executed using Finite State Machines.

Conclusion

Algorithmic trading is a very inexpensive field in which tools is a key factor. By the help of the algorithmic trading system the trade activity becomes quicker. Nonetheless, after all it is totally depends on the technology.  There is many example of crashing in the market because of algorithmic trade system.  Therefore one has to not depend completely on the algorithmic system.

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