Algorithmic Trading

/Algorithmic Trading

Algorithmic Trading 2018-05-17T09:05:18+00:00

Algorithmic Trading – All You Need To Know

Quick inner navigation:

  1. What is Algorithmic trading?
  2. How algorithmic trading works?
  3. Technical Requirements for Algorithmic trading
  4. What are the benefits of Algo trading?
  5. Algorithmic Trading strategies
  6. High-frequency trading and cryptocurrency

Computation of a lot of sectors has led to a massive revolution that has made life easy in general.

The tasking of the computer to help with human jobs has not only made things faster and error-free, it has also saved cost and made it possible to remove some level of error and mistakes of these actions. A significant introduction of computation into an industry can be recorded with the financial industry. In the pre-computer age, what we naturally enjoy such as ease of opening a trade account, fast trade executions and access to past trading data amongst other things, are not possible for people with low capital or those who aren’t connected to large banks and funds manager.

With the advancement of computer and the heavy backing and investment of WallStreet banks and funds, trading computation became a must have integration for most funds and financial institutions. This led to the opening of discounted brokers with a minimal commission and no physical presence is needed to get a trade across. This also made it possible for anyone with an internet connected laptop and some financial capital to be able to invest in the financial market. The integration of computation into the trading ecosystem not only make execution and implementation of trading knowledge easy it also makes it quite possible to rise above human flaws such as greed and fear. Computation makes it possible for traders to trade based on commands and rules. This is referred to as Algorithmic trading.

What is Algorithmic trading?

Algo trading aka Algo trading is basically the implementation of computer robots usually referred to as Expert Advisers into trading. The Expert Advisors are designed to follow a set of preprogrammed rules and guides that take into consideration the effects of time, volume and the current market price of a trading instrument to help determines the validity of a trading action. Algorithmic trading systems use mathematical computational coding to trigger trading decisions, execute orders, and properly manage orders after execution.

Algorithmic trading makes it possible to execute a large volume of trade that normally can’t be placed at ones. The Algo-trading advisor helps to divide this large trading volume into small lots sizes and then execute them into the market over a set period of time, making use of pre-set configurations. This means if all the rules required before taking a trade are not in place, the Bots will not execute such a trade. This takes out any form of error from trading a financial instrument, and as long as the rules put in place actually work, the trading robot takes out any form of error or mistake that is quite common with human traders.

Algorithm trading started in the late 1980s. However, it was relatively underutilized until the later 90s. Several factors spurred the growth of Algorithm trading in the 1990s. The Decimalization of the US minimum tick rate from 1/16 to $0.01 is one of the factors that might have driven the need for Algo trading. IBM also added more traction to Algo trading through the paper that was released in 2001 at the International Joint Conference on Artificial Intelligence. A team of researchers also conducted a test of two algorithmic strategies developed by IBM and HP and found out that algorithm trading consistently outperformed human traders, with results in billions of dollars.

Since Algorithmic trading is all about making use of a preexisting rule to determine if a trade is worth executing or not, the rules need to be well refined and proven to perform. The defined set of rules when it comes to Algo trading usually follows a pattern set by the trader himself.

The rule usually follows a pattern of confirmation of conditions in a stepwise manner, that is to say, if a certain condition is not been met, the next condition will not be checked. So these conditions which are usually in chronological orders are being ticked before a trade is being executed into the market. If all these set rules are not checked the Algo trade will not trigger.

How algorithmic trading works?

Algo trading’s main principle is centered on making sure that trades are executed with the best speed and minimal market impact as possible and also making a profit from trading through the use of tested and cutting edge automated trading strategies. For Algo trading to be effective there needs to be a consensus in the set of rules and guidelines that is being put in place by the programmer. This is to say Algorithmic trade work in a stepwise manner whereby one action leads to another action. If one action is not completed the other actions will not be validated and no trade will be triggered.

For example, if there are three sets of rules (A, B and C)  that need to be confirmed before a trade is made and one out of these rules doesn’t pass confirmation and the others did,  the trade would never be triggered. Also, Algo trading works in response to an action being taken. Once an action has been taken, it signals the occurrence of consequential actions till the final result is gotten. This is evident with retail broker available for traders. These brokers make use of comprehensive computer coding to help transfer trades from client accounts directly to the dealer’s desk and liquidity provider in a matter of seconds. This can never be achieved through human actions. The speed of executing these trades make it possible for a trader entering the market at the original market price with little risk of order not being filled at the chosen rate.

Another significant example of computation and Algorithmic trading can be found in charting software and applications available on the market today. With charting software, a trader can now travel back in time to view price data and trends that took place in the past and project the likely future of a particular financial instrument. This charting application takes on different names but their basic essence is to present how the law of demand and supply is impacting the current direction of a trading Instrument. They make use of computation to assess data on the current price of a trading Instrument and display it for traders to view.

Algo trading is also used when it comes to making profitable trading systems that are not prone to human flaws. This usually involves the use of technical indicators that are developed using complex mathematical equations. This indicator makes use of past market data to predict the likely directions of the market. With Algorithmic trading, these indicators can be combined to create a set of advanced trading rules that will help to perfectly predict the likely direction of price action. This has been known to remove every form of human emotion from trading.

An example of this can be found in the use of moving average indicators cross as a signal for entry. Most popular being the 50 Simple moving average (SMA) crossing the 200 Simple Moving Average (SMA) upward or downward. Once this cross occurs, the direction of the cross is usually used to determine the current trend a market is. An expert advisor can be programmed to make trades ones a cross occurs between these two moving averages. Here the Algo trading is used to aid “profitable trading”.

Technical Requirements for Algorithmic trading

  • A coder

A computer programmer is needed for the coding of the software and tools needed to carry out an algorithmic trade.

  • Adequate and strong network connection.

In most cases to connect to all the resources and data needed for algo trading, a strong internet connection is needed.

  • A trading broker platform.

For trades to be executed, a broker’s platform is needed. Without this, the trade won’t make it to the dealing desk or the market.

  • Past market Data.

In cases where the Algo trading software is built for profit making, the need for an extensive backtesting is paramount, because without this testing if the system actually works will take more time and also quite risky.

  • Assess to accurate and current up-to-date market price data.

It’s only through an up-to-date data trading software can actually compute details needed to take actions that are accurate.

What are the benefits of Algo trading?

There are many benefits associated with Algorithmic trading which includes;

  • Reduced cost of making the trades

Algo trading has made it quite possible for a trader to send trade to the dealing desk with just a single click of a button. This takes away the cost of having to visit the broker’s office or putting a phone call across in order to make a trade and also the cost of running an actual physical old fashioned dealing desk with clerks is nonexistence. All these help to reduce the cost of running a brokerage service thus reducing the commission charged a trade.

  • Accurate and intended market entry price advantage

Before the introduction of Algo trading, the chances of entering a trade at the current market price is very low as in most cases the move you plan on capitalizing on might have reached its peak before you are able to get across to your broker and make a trade. Algo trading makes it possible to enter a trade as fast as possible with little to no chance of slippage in price.

  • Zero human mistake possibilities.

Human emotional factors such as greed, fear, and indecisiveness are known to be leading cause of loss amongst traders. With Algo trading, ones all the rule boxes are checked, the robot executes the trades into the market without any further thought.

  • Ability to make use of and document multiple market conditions at ones.

Most traders now make use of multiple trading technical and fundamental indicators at once to determine the likely direction of price. With Algorithmic trading, a trader can make use of as many indicators as possible and make trading decisions based on the signals and result given from the compilation of these trading indicators.

As good as the introduction of computation into financial trading is; some major negative effects have been attributed to the use of Algorithmic trading. A good example is the Flash Crash of 2010. It was reported that Algorithmic trading by mutual funds led to an influx of sell orders which led to a 998.5 points dip in the Dow Jones industrial average. This event is often described as one of the most turbulent times in financial market history. This has clearly shown that Algorithmic trading can affect the financial market significantly. Experts such as the UK Treasury minister (Lord Myners) forecasted that businesses could become the “playthings” of financial speculators as a result of automatic high-frequency trading. Lord Myners believes the system will destroy the relationship usually enjoyed by investors and companies in the past.

Algo trading can lead to volatile trading which can trigger market reactions such a short or long squeeze, bankruptcy, and market devaluation. Algorithm trading also has its downside in the fact that a single failure or a wrongly coded system can lead to a massive irreversible loss. One such case is that of Knight Capital Group, which experienced a technological malfunction in their automated trading system, which leads to a massive loss of $440 million.

This means a lot of backtesting and controlled experiments needs to be carried out before an Algo trading system is allowed into the real market environment. These downsides haven’t stopped people from believing in the use of Algorithmic trading. Robert GreiFeld (NASDAQ CEO) in April 2017 proclaimed the total death of manual trading methods and trading pits in general as the future is all about Algorithmic trading. With media companies like the Bloomberg Thomson Reuters and Dow Jones, using algorithmic software to create news and generate economic news based on companies earning results and economic stances to be read and traded on via algorithms, and large financial institution investing heavily into automated trading companies the confidence level for Algo trading as greatly appreciated.

Algorithmic Trading strategies

Since algorithmic trading is achieved by the use of programs and computers to generate and execute orders in markets with network access. This way, traders do not necessarily need to watch or monitor a stock. In the financial market, a trading strategy is a designed plan with the goal of a profitable return by either going long or short on a trade. In general, it is an automated computer-assisted trading. With the advancement and involvement of computer programs in the financial market, the creation of different variations of algorithmic trading strategies is now possible. Examples include;

  • Trend-following Strategies

Trend following is an investment strategy that takes advantage of long or short-term moves that seem to play out in various markets. The underlying idea behind this strategy is that you can move money back and forth between two currencies and make profits. Calculations to analyze data points are often achieved by making use of a moving average. This enables an investor to purchase assets that are rising in value and sell assets that are declining in value. This is a strategy that involves taking note of events.

  • Arbitrage opportunities

This is basically a trading strategy without any downside risk. Arbitrage is can simply be defined as the process of capitalizing on the difference in price of two securities of the financial market through selling and buying of assets. For example, a trader may not a stock on a foreign exchange where the price has not yet adjusted for the constantly fluctuating exchange rate. This leads to the stock price being undervalued on the foreign exchange in relation to the local exchange price of this particular stock of financial instrument. Upon this, arbitrage traders can now make a profit.

  • Index fund rebalancing

This refers to the methods of rearranging the sizes and weight of a portfolio to ascertain that the original desired or projected level of asset location is being maintained. For example, if the predetermined allocation of target asset was 40% bonds and also 60% stocks. If the stock performed well during the period, it could have increased the stock weighting of the portfolio to 70%. The investor may then decide to sell some stocks and buy Bonds to get the original target allocation of 40/60.

  • Scalping

Scalping is a trading strategy with the aim of attaining significant profits on minor price changes. Traders who implement this strategy are called “scalpers”. Here traders place anywhere from 10 to a 100 trades in a single trading day with the belief that small moves in stock price are easier to catch than larger move in price.

  • Mathematical Model Based Strategies

This refers to the creation of a computer-based mathematical algorithm that can predict the likelihood that a trade will result in a profit or loss. If done carefully, the mathematical expectation tools will help build systems that work across multiple currency pairs with a set of good quality indicators to generate signals across the four major currency pairs. The expert advisor then calculates mathematical expectations to see whether the trade is a likely profit or not.

  • Volume Weighted Average Price Strategy

A volume-weighted average Price (VWAP) strategy is a trading tool that can be used by all traders. It is used mostly by short-term traders in algorithm-based trading programs. It takes into account volume which provides a much more accurate value of the average price. It is calculated by multiplying the price by the total shared being trade and then dividing it all the overall number of shared that was traded on that particular trading day. If the price of a buy trade is lower than the Volume weighted average Price, it is a good trade and vice versa.

  • Trading Range (mean reversion)

Mean reversion formed a postulation that returns and prices usually project back towards the average level over a period of time. This signifies that average or mean can be simultaneous to the historical mean of the return or price or a much significant average, for example, the growth and increase in economic strength or the total average in return recorded for an industry. This basically involves selecting the trading sizes and range of a stock and using data computing and analytical methods to document the averages in price.

  • Time Weighted Average Price (TWAP)

Time-weighted average price is the average price of a security over a specified time. It is a strategy that will attempt to execute an order and achieve the time-weighted average price. High volume traders use this strategy to execute their orders over a specific time, so they trade to keep the price close to that which reflects the true market price. Unlike the VWAP which balances execution with volume, the TWAP balances execution with time.

  • Percentage of Volume (POV)

Percentage of volume (POV) is a trading strategy making use of an expert advisor robot to divide trade into smaller volumes in order to reduce the impact of such trades on the overall market trend. This trading tool allows you to participate in volume at a user-defined rate. Order quantity and volume distribution over the day are determined using the target percent of volume you entered along with continuously updated volume forecasts calculated from trader workstation (TWS) market data.

  • Implementation Shortfall

Implementation shortfall is the difference between the decision price (price of the stock that led to the decision to buy or sell) with regard to a security and the final execution price for a trade, commission, and tax inclusive. It is also known as the slippage. In order to maximize the potential for profit, investors aim to keep implementation shortfall as low as possible. Investors have been helped in these endeavors over the past 20 years by developments such as discount brokerages, online trading, and access to real-time information.

Now, moving beyond the usual algorithm-based trading strategies to high-frequency trading. What exactly is high-frequency trading is an often asked question? How does it connect to algorithm trading?

High-frequency trading is a program trading platform that uses powerful trading terminals to execute a buy or sell order that is a lot at a significantly high speed. It is connected to algorithm trading by the simple fact that this strategy also makes use of very advanced trading pattern and algorithms to trade many markets and execute the transactional order in accordance to the current market conditions. Basically, it is a well-known fact that trader who can boost of a faster connection and market entry speeds tends to be more profitable on the long run.

The introduction of the electronic computer and the need for a faster way to access and relay trading information led to the advent of high-frequency trading. With an investment from Merrill Lynch, Bloomberg designed and built the first computer system to use real-time market data to quote stock prices and relay information.

Later, the SEC ruled in favor of creating electronic stock exchanges which laid the groundwork for high-frequency trading. In the world of high-frequency trading, traders tend to flood the financial markets with orders through the speedy entry and exit approach they take to trading. This quote flooding leads to losing in processing time for competitors. This is called “Quote stuffing”. High-frequency traders use it to gain a pricing edge over competitors. It is made possible because high-frequency trading programs can execute market actions at incredibly high speeds. This is has been frowned upon by the SEC by the way.

Also, similar to quote stuffing is a practice by high-frequency traders in which they attempt to give an artificial impression of market conditions by entering and quickly canceling large buy or sell orders onto an exchange in an attempt to manipulate prices. This is called “spoofing”.

High-frequency trading and cryptocurrency

As we all know, the crypto market is highly volatile. Huge price changes measured against time is termed “volatility”. The instability of the crypto network is caused by nothing less than high-frequency trading and its development of complex mathematical computer-based algorithms. These are high-end trading bots; This bot crawls the financial market and they are designed to make trading orders based on the current market analysis and conditions, just like the new Application specific integrated circuits miners in the crypto world. These high-frequency trading bots are responsible for the high volatility nature of Cryptocurrencies.

In a Nutshell

Algorithmic trading has improved market liquidity. Algorithmic trading is here to stay because it is not illegal though it allows large companies to profit at the expense of the institutional and retail investors. The production of new trading bots which are highly effective and expensive might lead to the extinction of small time investors in the stock market and miners in the crypto world. Lastly, with the continuous production of trading bots, the volatility of cryptocurrency is not bound to improve anytime soon.

Thank you for reading our guide to Algorithm trading. Check out our site regularly for more guides about cryptocurrency trading.

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