A look into Algorithmic and Electronic Trading
Algorithmic and electronic trading have become a significant focus for financial institutions, securities regulators, and various exchanges, with new automation technologies transforming trading dramatically in the past few years. Financial engineering, coupled with higher networking speed, has altered the manner in which electronic markets function thereby making it possible to create completely automated trading systems.
Developments in technology have spurred the replacement of the traditional trader manually typing in parameters of an order, with algorithms that allow reduction of trading systems into strict rules, or heuristics. Introduction of these programs, or trading algorithms, has revolutionized the trading of financial markets. The power to make financial trading decisions has shifted to quantitative analysts, or quants, who are quickly dominating the trading landscape.
Types of Algorithmic Trading
In today’s cost-conscious and hyper competitive trading market, fund managers and traders may rely heavily on computerized trading algorithms that are offered by brokers. Without these algorithms, brokers may find it difficult to retain their current clientele or create new business opportunities Algorithmic(Algo) Trading requires traders to enter the order of defined quantity within models that are quantitative – the model automatically generates the order timing and size based on the parameters of the algo trading strategy. The rules are built into the model from which the optimal time to place an entry of exit order is determined. This results in the generation of an impact algorithm. Algorithmic trading strategies not only help the broker in the execution of trading strategies, but also reduces the costs involved in trading transactions.
Trading versus Investing
To better understand algorithmic trading, the distinction between “trading” and “investing” must first be made. Many market participants use the two terms interchangeably and sometimes this causes confusion. For the purpose of “Investing,” an investor that decides to invest in stocks will analyze the strength of the underlying corporation’s FUNDAMENTAL data (earnings, revenues, financial ratios, etc.) in order to identify stocks that are priced below market value, and therefore perceive it to be a good investment. The investor will then take a long position (buy the stock) and hold it for the long-term until such time that the stock price reaches and/or exceeds its perceived market value; at which time the investor may choose to liquidate the stock to realize the profit from the favorable movement in price resulting from the strength of the corporation. By contrast, traders analyze an asset’s historical PRICE DATA using technical analysis methods to forecast price movements, up or down, that result from disparities in the asset’s supply and demand of the moment. A trader will attempt to capitalize on these price movements by initiating long and/or short positions with a short-term time horizon for taking profits.
Several kinds of trading algorithms are in use today in the electronic markets. These include:
1. Time weighted average price [TWAP]
2. Volume weighted average price [VWAP]
3. Market-on-close [MOC]
4. Implementation shortfall
With regard to Time Weighted Average Price [TWAP], arrival price refers to the midpoint at order-receipt time of the bid-offer spread. This price also refers to the speed of the execution. The calculation of VWAP is done by adding the dollars traded per every transaction and the multiplication of this value by the shares that have been traded. This is then divided with the total shares traded during the day. Market-on-close [MOC] refers to the value of the last price that has been traded at the end of the day by the trader against the last price that has been reported at the exchange. Implementation shortfall refers to a model that weighs the urgency of execution of a trade against the risk associated with the stock moving. Several trading algorithms permit customers to alter the execution timing, the rate at which the order fills. Attempts are made at the end or beginning of the trading day. A few algorithms offer the slippage tolerance of a particular stock under consideration relative to a set benchmark.
Evolution in trading methodologies
These innovations have resulted in considerable operational improvements in the marketplace, resulting in high market stability, and lower execution costs for investors and traders. As well, better market transparency has been seen in the securities market over the past few years. The demand for computer technology in the world consumer market has resulted in a significant drop in the prices of hardware across the board, making technology-enabled trading quite cost-effective. Algorithmic trading software also helps in saving the trader from errors in the data input and message transmission. The models are highly reliable when it comes to the order execution.
During the 1970s, the participants of the market were normally institutional organizations. Asset managers employed by mutual funds, hedge funds and pension funds used to dominate the markets. Manual market makers or the broker dealers played a significant role in taking short-term inventory risk. The financial landscape was highly manual, characterized in part with final transaction codes. The trading platforms of the day were associated with high risk as the trading process relied heavily on intuition and the experience of the brokers.
In today's market, new entrants can compete successfully against institutions with the use of technology. Utilizing precise investing models has helped to reshape the market. Managers with precise knowledge of finance, economics and the latest mathematical tools make it possible to forecast securities trajectories and improve the probability of profit. Technical analysis came into picture only during the nineteenth century, but automated pattern recognition capability has increased by leaps and bounds with the latest developments.
High Frequency Trading [HFT] and Applications
High frequency trading refers to a kind of algorithmic trading that is characterized by high rates of turnover, order-to-trade ratios that are high and high speeds that leverage both trading tools and financial data. The attributes of this type of trading includes specialized order types, highly refined algorithms, high cancellation rate of orders, and very short-term trading horizons. This can be viewed as a primary type of trading in the world of finance. It involves the use of computer algorithms and sophisticated technological tools to the trade securities.
HFT is used extensively in proprietary trading. During the year 2010, 50%-70% of all stock trades were done employing an HFT platform. The firms trading with HFT platforms make up for the low margins on trades with high volume. There has been significant growth in the high frequency liquidity providers.
HFT employs a powerful platform which utilizes computers operating at a very high speed. Complex algorithms are employed for analysis of multiple markets. The orders executed are dependent on market conditions. This form of trading became quite popular when incentives were offered to the firms to add liquidity to the market.
Time horizon of trades from fastest to slowest
Wall Street is quite quick when to comes to tapping opportunities that can offer a competitive edge in the world of trading. If the market behavior for the past thirty-five years is studied, a natural evolution of this phenomenon can be clearly observed. Computational power, which has enhanced the stability and the reliability in the trading process, is one of the key components that speeds up the entire process. Performance has received an immense boost with the power of trading algorithms.
There are two broad segments: Fast and slow. Short term informational advantage goes to the fastest traders who can trade based on data that is yet to be processed by the slow traders.
Day trading or swing trading
For active traders, swing trading and day trading terms are quite familiar. Short-term profits are sought in both trading methodologies.
Ø Level of effort required
Day trading requires placing trades during market hours. This makes it even hard to take a break as you are regularly analyzing the market conditions so that you can make quick decisions. Swing trading permits a trader more time for analysis, although the stocks must be managed continuously.
Ø Profit expectations per trade
Day trading is typically done over a short time frame, which can be from one to fifteen minute charts. The profit percentage varies from a few ticks to fractions of a percentage, to 1/2/3 %. The risk on each trade should be kept to a minimum.
Ø Number of trades
Day trading generally means that trades are opened and close within the same day. From one trade to few hundred trades per day can be done in day trading. Swing trading on the other hand does not require the closing of all positions before the market close, but are instead held open for up to two or three weeks. Although, intraday swing trading can include holding positions for hours instead of days.
Long term trading/Short term trading
Short term trading presents more opportunity (and more risk) due to the compounding principle. However, trading short term does not permit scaling up to a “billion dollar” account due to liquidity constraints in markets. Although position sizes are smaller, the risk is more tolerable when they are held overnight. Long term refers to a form of trading where an asset is held for a longer duration of time. Depending on the kind of security, an asset can be held from a year, or even up to thirty years or more. Investors can take advantage of long-term market trends if they can tolerate the short-term gyrations.
When a comparison of HFT and long-term trading is made, benefits can be seen with both types of trading. HFT begins and ends in the market with zero position. The idea is to purchase and sell on a small margin and profits earned are also small. HFT deploys small positions. Long term investors on the other hand begin with a large amount of capital from which they make larger profits over longer duration. The trader needs to invest substantial capital for deriving high profit margins from trading.
Stock market investors and traders are probably familiar with bears and bulls. However, there are other metaphoric animals as well.
Ø Bulls and Bears
The investors/traders depicted as bulls are optimistic about thefuture prospects of the stock market. The bear is the polar and believes that the market will decline. It is not clear as to how these terms originated. These refer to long term climbs and declines respectively.
The market participants that fall under this category are least interested in the bull or bear markets. They purchase shares of companies that are available via Initial Public Offerings (IPO), with the intention of capitalizing on the sharp upward price movement caused by inflated demand for the stock when public trading for it initially commences.
Ø Chickens and pigs
Chicken traders are afraid of the stock market and are timid when it comes to making investment/trading decisions. They stick to conservative methods such as bank deposits, company deposits, or bonds. Their tolerance to risk is quite low when it comes to investment. However, pigs embrace risk and they are quite impatient. They invest in hot tips and intend to make quick buck. These traders usually end up losing money in the market. Pigs usually end up getting slaughtered in the investing/trading world. Bears and bulls end up making money, if not too greedy.
These animals are used to represent powerful traders who employ unethical means for making money and are involved in scams. The case of Jordan Belfort is a classic example. His lifestyle and crimes have been depicted in the movie “The Wolf of Wall Street”. When groups try to manipulate the market, it’s referred to as a wolf market.
Ø Dead Cat Bounce
This market terminology refers to a recovery from a declining market that is temporary. It could refer to an upswing in the market that is temporary. This could also be limited to selective stocks.
The term Ostrich Effect was coined by George Loewenstein, a behavioral economist. This effect describes the way investors bury their head in sand when the markets are not functioning well in the hope of their stocks are not being affected by adverse market fluctuations.
Institutional investors refer to institutions where individuals pool funds together to form large investment consortiums. They invest funds on behalf of others in variety of asset classes and financial instruments. These investors have control over significant amount of assets and have high influence in the market. The influence of these investors has grown significantly and can be seen in the concentration of corporate equity ownership.
Institutional investors are proficient when it comes to investing. Because of greater access to management teams and companies, these investors have several advantages. Their investments include private and public pension funds, savings institutions, insurance companies, open and closed-end investment companies.
Typically, 40% of assets are the allocated in the standard way and 20% of assets are allocated to cash and the real estate segment. Institutional investors have experienced great growth over the last generation.
Significant investment in pension funds, to the tune of $10 trillion, has been made by the institutional investment community. About 40% is contributed to professionally managed assets. Payments are received from sponsors and a promise is made to pay the retirement benefits to fund beneficiaries in the future. Investment companies account for the second largest class of institutional investment and offer professional services to both individuals and banks. Most of the investment companies are open or closed-end mutual funds.
Insurance companies are also a part of the community of institutional investment. These organizations cover casualty insurers and properties which protect policy holders from probable risks. Institutional investors are an integral part of the world of investment and have a considerable impact on the asset classes in the market.
Volume weighted average price [VWAP]
VWAP refers to a trading tool that can be utilized by all traders. These tools are utilized in algorithmic trading programs, mostly by short term traders. It represents an accurate snapshot of average price and takes volume into consideration. The indicator also acts as a benchmark for institutions and individuals who need to gauge whether they received a good or poor execution.
Scaling large orders
There are several scaling algorithms designed to help traders in buying a weak, declining market or for averaging down. It also helps in selling into a market that has reached unstable highs. These can be expected to scale down or to scale out of a position. Several benefits are offered by scaling algorithms:
Ø Multiple product support.
Scale trading can be done in forex, stocks, futures, options, and bonds.
Ø Combos and trade pairs.
Set up and monitoring is made easy by custom tabbed interfaces for stocks and stock pairs.
Ø Helps in benefiting from fluctuating market conditions.
Scaling algorithms can be programmed for purchase and selling of stocks within fluctuating market. This can often be done with a single click.
Ø Liquidity beneficial rebates.
The scaling algorithms can help a trader benefit from liquidity rebates offered by exchanges.
“Dark Pools” is a term used to refer to forums or private exchanges for trading securities which are not accessible to the investing/trading public. These are referred to as dark as there is no transparency associated with the process. These were used primarily by institutional investors/traders for facilitating block trading. Participants do not want to create an adverse impact in the market with large orders and serve a valuable purpose. Although they have been portrayed in an unfavorable light and are susceptible to conflicts due to the lack of transparency.
An iceberg order is used to refer to a single large order that has been fragmented into smaller orders with the aid of an automated program. This is usually done for hiding the actual quantity of the order. A large amount can be divided by institutional investors when they intend to purchase or sell securities for their portfolios. This makes the public view an order that is small at any given instant of time. The “tip of the iceberg” is the only portion that is visible in comparison to a huge mass of ice. Price movements are reduced by masking the large size of the order.
Accumulation or distribution is a momentum indicator that tries to gauge supply and demand. It determines whether a stock is being distributed or being accumulated. The divergences are identified in the volume flow and the stock price. The following formula is used during the calculation process:
Accumulate/Distribute = ((Close – Low) – (High – Close)) / (High – Low) * Period's volume
Advantages offered by automated algorithmic trading systems
1. Eliminating emotional impact on decisions pertaining to trading
The role of emotions that impact the process of trading is minimized with the employment of algorithmic trading. It is crucial to keep emotions in check and adhere to the trading plan. As the trade orders are executed automatically, there is also less stress. Automated systems help traders who might be anxious about “pulling the trigger” on entries and exits and can also keep excessive trading in check. Algorithmic trading systems also favors trading at times when market conditions favor profitability.
2. Back testing
Back testing refers to applying the rules of the algorithm to historical market data to determine whether a system is viable or not. Over fitting, and forward testing also need to be considered when determining whether a system is potentially profitable.
3. Maintaining discipline
Since trades get executed automatically, it helps in establishing discipline with respect to the trading system and reduces stress related to missing a trade when focus may not be at an optimum level.
Proper planning is crucial when it comes to trade execution. Sometimes traders neglect the rules that have been set in the plan. Losses are an integral part within a trading system. However, these can create difficulty for a trader psychologically. Stability is assured by automating trading systems which will always follow the rules.
5. Improvements in entry speed
After the position is entered, the stop or exit orders will also get entered automatically. This covers protective stop losses and the profit targets. Markets may trade frenetically one day and barely move on others. However, automated trading systems can adjust to the speed at which the markets are trading.
6. Diversifying Trading
Multiple trading accounts and multiple strategies can be automated with trading systems. While diversifying trading seems incredibly challenging for a human to accomplish, it is relatively simple for a computer. The software or automated platform used will scan for trading opportunities across a diverse range of markets and generate orders when predefined criteria are met.
Disadvantages offered by automated algorithmic trading systems
Algorithmic/Automated trading systems have a number of advantages, but not everyone is successful. The following are the biggest reasons why some people fail to see promising results with automated trading systems.
1. Traders don’t know what they’re doing.
People who try to profit from automated trading without knowing or understanding the market are less likely to do well.
Knowing how a system truly functions is vital for success since knowing the expert’s inner mechanism allows traders to know what market conditions good and which market conditions are are bad.
2. Traders fail to identify the risks.
Most new traders look to make quick riches, and this comes with a hefty price tag. People who set unrealistic targets without analyzing the risk are most likely to end up with account wipe outs.
3. You get disappointed too soon.
Again, many new traders believe that automated trading is an automatic cash machine and they quickly get disappointed when they realize that the trading system is not risk free. Expecting the platform to be risk-free is not realistic. However, traders can minimize their risk and increase your chances of success by understanding the nature of trading and how profits can be generated.
4. Using ineffective trading tactics.
This is mainly due to lack of understanding of market and trading systems. Most traders resort to risky tactics with the hopes of generating high returns but this strategy can easily backfire.
5. Lack of risk analysis.
This is another reason why people end up with their accounts wiped out. In order to be successful, you need to correctly analyze the risk levels of the trading system you are using. Also, you must assume that worst can happen and you have to set your risk level accordingly.
6. You don’t think long-term.
It is a sad thing that most traders focus on short-term gains and this is one reason why people are left with nothing. It is important to have realistic profit and risk targets to be able to make gain with automated trading.
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Technological Innovations in Trading
The trading world has seen many transformations. Old systems have given way to more efficient and newer transparent systems. Price reporters and clerks are no longer needed to manage the flow of customer orders. The bid and ask prices are captured and matched automatically. Brokerage firms and exchanges have shifted from antiquated methods to software and hardware systems that are screen based. Traders have moved to a desk from the trading floor and are surrounded by multiple screens, a mouse, and keyboard. Traders have become black box babysitters who merely keep tabs on the sophisticated programming code which govern the trading decisions. With growing volume, the exchanges are struggling to increase the bandwidth and to improve their architecture.
Trading in its essence is a simple concept that has given rise to a complex process. Trading has been carried out for thousands of years in one form or another. There was a time when people used to meet to trade equities on a trading floor. Now this task can be accomplished with a single click from the computer screen at home. Technology now powers the sellers and the buyers in the world to negotiate a price in a reliable and quick manner from anywhere in the world.
Trading has transformed into a system that is fast, complex, and requires complex regulations and infrastructure to bring together every market. Coffee shops, town halls, and churches were once prime places for traders to meet and negotiate, but this fragmented the process due to separate and multiple meeting places. As the demand for trading increased, the gathering places became too crowded. These factors propelled the adoption of a primary place for carrying out trade in a structured manner, which led to the birth of modern exchanges.
Exchanges have transformed into complex organizations which play a critical role in the global economy. It was a novel idea developed for addressing the need that arose in early trading. Exchanges also protect the dealer and the brokers engaged in managing assets. The development of exchanges all over the world has a colorful and interesting history.
Although the exchanges have transformed into complex organizations, the cornerstone of trading still remains the physical floor. The world economy was revived during 1990s and was referred to as the “dot com” era. This era resulted in unparalleled growth in technology. Companies like Cisco, Dell and Microsoft came into existence during the 1980s and started offering operating systems and personal computers to people. Thousands of tech companies were created and went public in 1990s.
The first electronic market
The growth of computing and technology innovation gave rise to the first electronic market: The National Association of Securities Dealers Automated Quotations [Nasdaq]. For the first time, the floor trading model was not adopted by an exchange; no physical space was needed for this market to carry out trading. It employed telecommunications and computers to connect the sellers and buyers and adopted new technologies for remaining in the limelight. Nasdaq became a home for start-ups trying to raise the capital to compete in the technology sector. Without these groundbreaking innovations, electronic trading would not have been possible. Financial markets and exchanges today thrive on liquidity and rely on timely data and information for carrying out trading decisions.
An electronic trading platform, also referred to as an online trading platform, is nothing but a software program with abilities to place orders over a network for financial products. The products include bonds, stocks, commodities, currencies, cryptocurrencies, and derivatives provided through a financial intermediary such as stock exchanges, investment banks, market makers, or brokers. These trading platforms permit electronic trading by users from anywhere, and at any time.
Streaming of live market prices/data is done by electronic trading platforms. This permits the trader to use tools such as account management, news feeds and charting features. These platforms have been designed to permit individuals to gain access to the markets and have been designed to carry out trading strategies in a specific manner based on technical analysis.
Evolution of the trading systems
Previously, trading was done manually between counterparties or brokers. In the 1970s, a major portion of transactions shifted to electronic trading platforms. These include dark pools, alternative trading systems and electronic communication networks. The first trading platforms were linked to stock exchanges that allowed the brokers to place orders with “dumb”
terminals and dedicated networks. Instant live streaming of price quotes was not offered by these earlier systems.
The evolution of trading systems permitted live streaming of quotes with the use of the internet. Some platforms come with built-in scripting tools. Not long after robots and automatic trading systems were also developed. The graphical user interface (GUI) can be employed for trading options, futures, equities and currencies. The period during 2001 to 2005 marked a veritable proliferation and development of trading platforms.
Standards adopted by online trading platforms
The NFA [National Futures Association] lists the following requirements for any forex electronic trading systems.
Ø Recording of transactions
Ø Slippage and pricing standards
Today’s modern equity markets on which stocks are traded include the American Stock Exchange [Amex], New York Stock Exchange [NYSE], the National Association of Securities Dealers Automated Quotation System, and Pacific Stock Exchange.
Competition and greater transparency have been promoted by the regulators globally over the past few decades. The advent of fast and highly sophisticated computer technology has given rise to a class of trading that is popularly referred to as High Frequency Trading [HFT]. By leveraging advanced technology, the security positions can be turned over quite quickly. HFT is associated with low rates of latency and its popularity has grown exponentially ever since its inception. This has brought about major changes in the way trading is being carried out by capital market firms.
The floor-based trading style has been rooted out completely. More and more firms are adopting automated trading systems. Numerous advantages are offered by HFT, which has its own set of challenges. HFT has come under a great deal of criticism owing to the May 6th,2010 Flash Crash that occurred. Several proposals have been put forward by the regulators to curb the practices linked to HFT. The regulatory proposals and recent controversies that surround HFT have made some people question this type of trading.
Algorithmic trading has gained popularity due to a number of factors that have contributed to its birth including:
Ø Narrowing spread
The U.S. stock exchange in the year 2001 started quoting prices in decimals. This brought down the minimum spread between the bid and ask prices to one cent from 1/6th of a dollar. There were some market participants who benefitted from the earlier spread, which was in fractions. This made them seek new alternatives and offered an immense boost to the growth of algorithmic trading.
Ø Changes in regulations
The Regulation National Market System was passed during 2005. This updated the trade rules and promoted more competition and transparency in the markets. Trade orders must be posted nationally permitting traders to reap the benefits of more liquidity. But with the tradeoff being that quick action was required. These factors acted as a catalyst for the development of high-speed technology.
High frequency trading [HFT] today
HFT refers to a specialized case of algorithmic trading which consists of high turnover of less popular securities or other financial instruments (such as futures or forex) in a frequent manner. Specific characteristics are attributed to this method of trading although there is no formal definition for this kind of algorithmic trading.
Characteristics of HFT:
Ø Utilization of high-speed and sophisticated computer programs of routing, generation and execution of orders.
Ø The utilization of data feed from co-located servers and exchanges for minimizing the network orders and latencies of other kinds.
Ø Maintenance of short timeframes for liquidating and establishing positions.
Ø Submission of numerous orders that get cancelled after submission
Ø Maintaining few overnight positions, if any
Several strategies are employed by HFT traders. This makes it possible for many trades to be placed within a single day. They leverage trading algorithms and technology based on research and development of the firm or 3rd parties. The operations cover market data analysis to execution of orders. HFT relies highly on low latency connections and on the system speed. Three kinds of HFT firms currently exist.
Ø The biggest and the first segment consists of independent, proprietary trading firms. These firms carry out HFT utilizing different strategies and private equity. Most of them prefer maintaining secrecy regarding their operations. These systems generate sell and buy orders automatically throughout the day.
Ø The second segment covers the broker-dealer proprietary desks. Traditional brokerage firms come with separate trading desks which have no relation to the client businesses. Large investment banks are classic examples of these HFT firms.
Ø The third kind is comprised of hedge funds. These focus on taking the benefits of statistical arbitrage and pricing inefficiencies that persists among asset securities and classes.
Impact of HFT on markets
HFT has managed to capture a broad segment in the European and the U.S. equity trading volume in a short span of time. It has also gained immense popularity in the Asia-Pacific region.
There has been enormous growth when it comes to the trade volumes of HFT. Easy access to technology is the key driving force for total HFT trade value. HFT accounted for 56% of equity by volume during the year 2010.
In Europe, HFT amounted to 38% by volume in equities. A compounded annual growth of 106% has been noted in HFT from 2005 to 2010 which is remarkable. A mixed response has been offered to HFT in the Asia Pacific market which is quite heterogeneous. When it comes to HFT in Asia, Tokyo is the core market. HFT accounts to 45% of equities trading volume. The Arrowhead trading platform was launched in 2010 by the Tokyo Stock Exchange with revisions of existing rules to boost HFT volumes. These regulations favor HFT.
Singapore is another market in Asia where HFT is steadily increasing. Its new platform may prove out to be the fastest in the world. India as well is a good segment for HFT traders because of the use of sophisticated technology and co-location facilities. China has not been as receptive to HFT. The latency levels and the regulations do not favor the growth of HFT there.
The popularity and growth of HFT markets will continue to increase in the global markets with the growth in the proprietary trading firms that are independent and employ hedge fund strategies. HFT is playing an important role in asset classes as a result of improvements in technology. More and more exchanges are adopting sophisticated, modern technologies for carrying out trading, which will propel the adoption of HFT in other asset classes in the coming years.
Features of the trading systems
1. The traders do not have to rely on technical aspects when it comes to algorithms.
2. The software functions automatically upon entering the initial parameters.
3. The trend trade can be forecast by the trading algorithm(s).
4. Trading algorithms can factor in fluctuations in market conditions to adjust in trading.
An automated trading system refers to programs that help in the creation of orders which are submitted automatically to the exchange or the market. These systems are used with electronic trading in the market such as dark pools and electronic communication networks. Automated trading systems can execute tasks at high speeds with high volume orders. Traditional safeguards and risk controls that rely on human judgment can now be automated for controlling and evaluating automated trading.
Simulation of strategies
Automated trading systems have now become very important as each millisecond is valuable in today’s markets. The Radar Screen and TradeStation Chart Analysis make it possible for the traders to set their indicators to strategies for automating the entire process of trading. This reduces delays that may exist when trading the market manually.
Ø Helps in reacting to opportunities in a quick manner
Ø Greater confidence is maintained during the trade process
The propagation of automated trading systems allows order execution to happen at a greater frequency and at a faster pace than previously. Although opportunity exists in this world of trading, there is a certain amount of risk that comes with it. Faulty implementation, unanticipated market conditions and imperfect algorithms can result in malfunctioning of automated trading systems. Defects in the software are inevitable. There can be unexpected market conditions. Hence, there is a need for protective risk control.
Risk management systems entirely rely on post trade checks which in turn are traced back to Black Box algorithm issues. This is only possible after the trade is completed. Although this model is practical and cost-effective, there are noticeable flaws. The main issue lies in the post-trade event function. Serious losses can occur if post-trade risk management is relied upon heavily. There is a chance of the danger being amplified in HFT systems where trades get executed in a second. A trader can get wiped out and brokerages can be jeopardized before the problem is recognized. This cannot altogether be termed as risk management. Post-trade risk management cannot alone protect market contributors in an environment that attracts high trade volume.
Utilizing pre-trade controls can help in overcoming the issues that have been described above. There is a need however, to create a broad protection system during dangerous situations. To minimize the risk, simple and multiple trade-controls need to be kept in place. For safeguarding the trader’s interest, there is a need to bring down the latency impacts which is an acute factor in an industry that is ultra-competitive. Effective and simple tools should include basics on position and order size. Time-based limits should also be included in effective trade risk management strategies.
Traders get protection from the safety measures that have been tailored for individual capacity and tolerance. These controls exert a minimum burden on the speed of trade execution and processing time. Risk management pre-trade strategies impart added value to the trading system by reducing errors that can prove to be problematic later. This allows traders the opportunity to debug and examine the algorithms and helps in the creation of improved tools for trading during the process.
The value of pre-trade protection can be understood in a situation where trading of one system occurs in a second or multiple markets. This enhances the complications to the traders who seek liquidity and employ automated systems over several markets. When the conditions are dynamic, control strategies that have been implemented incorrectly can result in a heavy burdening on the system. This issue can be tackled by setting up a trade risk management in the last phase. However, it should always happen before order routing to the exchange occurs.
A certain amount of reluctance persists in the trading community when it comes to embracing risk management. This is because of perceptions associated with complexity in development of a system and added costs. When executed in an appropriate manner, however, it can prove to be quite effective. The costs associated with risk management offer insurance against future losses due to flaws in the software.
These orders are designed to limit the losses and lock the profits by developing a bracket around the order with side orders that are opposite. A high sell limit order can bracket a buy order. A high buy stop order can bracket a sell order. The quantity for low and high side bracket orders always matches the quantity of the original order. From the current price, the bracket order is offset by 1.0. This amount of offset can be varied for specific order in line.
Bracket orders are a special kind of order which offers an extra advantage to the trader. It protects the traders through a profit objective and a stop loss order. Three orders are placed by the system simultaneously: a limit order, a profit objective and a specified objective price. The combination of these three orders is together referred to as the Bracket Order which can help in limiting the potential losses.
The benefits offered are incredible. A trader can place an order simultaneously. Placing three trades to one reduces the exposure and the trade can be executed in a disciplined manner. The downside risks are minimized with these.
This refers to a trade order where simultaneous purchasing and selling of securities occurs. Basket trading is quite important for investment funds and institutional investors that seek to retain securities in specific proportions. As cash moves into and out of funds, there is a need to purchase large baskets of securities. This is done to avoid causing adverse price movements so that the price movements do not alter the allocation of the portfolio. For a trade to be termed as a basket trade, it may consist of a purchase or sale of 15 or more securities.
For example, the target size is tracked by an index fund by holding all or most of the securities of the index. The value of the fund increases as new cash comes in. Large number of securities must be purchased by the management in the same proportions that governs the index.
Trailing stop/Stop loss
Various kinds of orders are offered by online brokers. These have been designed to offer protection to the investors/traders from losses. A stop loss order is commonly used to avoid excessive losses. The trailing stop order is another type of stop order that helps protect trade profits. Both are frequently used methods for restricting the amount of drawdown or loss. The trader can fix a value which is based on the maximum loss that the trader can absorb. If the price drops below this value, the position is automatically closed at the current inside market price.
The trailing stop is primarily utilized to protect the unrealized profit gains for an open trade that is in-the-money. Once a trailing stop is triggered at a predetermined profit floor amount, a stop order is placed at a price whereby only a specified percentage amount of the unrealized profit is allowed to be risked by price retracement. If price action continues in a favorable direction with respect to the open trade, the stop order follows price and is continuously reset to “lock-in” more of the unrealized profit, hence the name “trailing stop.” When price action retraces to the set stop order, the position automatically exits.
Risk management tools include built-in sliding stop, scale-out, exit criteria, adaptive stops, basket trading, and position sizing. Stormchaser Technologies offers leading-edge trading system solutions.
KairosTM Platform/Automated Trading
· Automated Trading Platform – design and implement automated trading systems with futures, forex or equities. Systems are easily configured by selecting the timeframe/indicator, making conditions that are triggered causing the position to be entered. Positions can be exited with pre-configured stops/targets and/or exit criteria.
· 21 types of automated stops – built-in money management saves you time and optimizes your trading
· 5 types of automated targets – exit positions when a pre-determined target is hit – very flexible
· Order Scaling – Scale out of positions, letting your profitable positions run for the best results!
· Market Profile/Money Zone indicators – 5 modes of a very powerful indicator can be configured.
· Position Sizing – equalize your opportunity and risk management on equity trades
· Predefined Indicators – high/low of first 15,30,60 minutes of trading – and much more
· User Defined Indicators – easily automate a trading system with custom buy/sell levels that you change daily
· Basket Money Management – additional money management tools to manage baskets of futures/equities and set targets or max losses for a day
· Harmony of Discord – Nodal Trading System finds turning points from balance on multiple timeframes –works with futures markets with 24-hour price charts. Based on the book “The Harmony of Discord”. You will not find this indicator anywhere else.
· Fractal Detection Module Configurable on multiple timeframes and markets, this module finds repeating time-price patterns from days to months prior.
· Cycles Library – Martin Armstrong Pi Cycle, Earthtides and Sunspot data – correlate markets with these phenomena in order to project the future direction.
· Premarket Gaps - filter above/below daily ranges or moving averages
· Gann Analysis - Transits to Transits or Transits to Natal/Progressions/Directions, display time projections easily on the price chart. Includes ecliptic intercepts, Declination/Latitude Parallels, Inflections or min/max. Filter by Declination or Latitude Strength to find the strongest aspects. Phases of the moon, eclipses, Generate Ephemeris.
· Planetary Price Modules – used to plot harmonics of Planetary Longitude and Declination/Latitude. Lines and reversed lines can be displayed in the Geocentric/Heliocentric in 2 different modes
· Time Projection Analysis - finds the best planet/degree movement for a market. Analyzes natals, planets, planetary pairs, declination, latitude, eclipses and more. Display the results easily on the price chart. Helio and Geo Price Lines/Harmonics.
· Planet/Cycle Optimizer - The most powerful module in Kairos, it lets you test all possible planets and degree movements, helio or geo longitude, latitude, declination to determine the best results.
· Gann Angles - 6 types of configurable Gann Angles. A Geometric Square-outs feature is included in this module.
Data Management Systems
Analytics can be employed for managing large data sets in the markets. If the right tools are employed, date sets can be managed more easily. Data management helps in consolidating systems that are dependent on them. Efficient data delivery permits organizations to analyze, share, and assess critical market information.
Automate your Trading System with the Kairos Platform
The complexities in technology and the vast proliferation of data available continue to influence how participants in electronic markets function and compete. Up to 90 percent of the world’s data has been created in the previous two years due to the daily generation of 2.5 quintillion bytes of data in the same period. This is referred to as ‘Big Data’. Many opportunities are being offered for the capture, processing, and analysis of unstructured and structured data.
Data capture and analysis is performed by organizations in order to get better insight into various processes for making better decisions. The marketing, technology, healthcare, and financial sectors are avid consumers big data services. The use of Big Data is redefining is intended to gain a competitive edge amongst industries. Similarly, the financial services/investment sector also uses Big Data analytics, in conjunction with algorithmic trading, in order to make better investment decisions. Complex mathematical models and historical data are combined for maximizing returns. However, there are still several challenges that exist when it comes to data capture.
Velocity, volume, and variety play key roles when it comes to big data. Firms are now leveraging technology because of customer needs, regulatory constraints and increasing competition. Velocity refers to the speed at which data capture and analysis is done. As an example, 1 terabyte of information is captured by the New York Stock Exchange each day.
Due to growth in the capabilities of computer systems, algorithmic trading and big data have in many ways become enmeshed. Trading applications help in execution of trades at high frequencies and speeds because much of the process has been automated, but this would not be possible if fast data crunching of algorithms were not possible. Algorithmic trading not only executes trade in a timely manner and at the best possible prices, it also reduces errors caused by manual trading. Trading results can be made less risky by employing huge amounts of data and by back testing strategies, but this would not be possible without the help of big data. Algorithms are developed based on unstructured and structured data with the incorporation of both historical and real-time data for generating better trading decisions.
Low processing latency is a key goal for real-time data. This refers to the time of arrival of new data from an exchange to the trading application from which trading decisions are made. Low latency can often be the difference that provides a company with a competitive advantage over a competitor that runs similar algorithmic trading strategies. The measurement here lies in the speed at which data is generated and absorbed into a system. The number of steps involved in routing of data is the key.
Ø Storage of real-time data in memory
Modern high-tech memory intensive computer systems offer the fastest performance possible for queries and analytics.
Ø Efficient utilization of resources
Utilizing a single database and merging data from multiple sources
Ø Offloading processing
This can be done when the main server has been chained to several servers running in parallel.
The problem associated with historical data is its sheer size. It should be stored in a manner which allows efficient access for monitoring and trading applications. It is not possible to store this volume of data in a system’s RAM. Disk storage is a much more efficient method for this. The performance of a historical database can be enhanced with the use of several strategies.
Partitioning of historical data is done by drives, which allows running parallel queries. Queries can also be farmed out to servers that access specific disk drives and can be further indexed by date. Storage requirements can be further reduced with the use of data compression.
Market Data Capture
Before the first trade takes place, and before crunching of any numbers occurs in algorithms, a market book must be built by the system to permit the algorithms and the traders to view relevant market information. Updating and building of books demands high throughput. Data capture comprises the following:
Ø Viewing trade traffic from various exchanges
Ø Sorting the information that can be analyzed by the algorithms for determining anomalies and trends.
Capturing data plays a crucial role in electronic exchanges, as it’s the data which plays a significant role in trading. Without it, no trade can be executed. In a world where high frequency trading is creating waves by processing several trades per second, accurate data is critical.
Requirements for data management and analysis in algorithmic trading
The challenge lies in transforming the strategy into a process that can access the accounts of traders in order to place the orders. The following parameters are essential for data capture and analysis.
Ø Knowledge of software development that can be applied to a trading strategy. Pre-made trading software can be employed for the process or programmers can be hired for trade system development.
Ø Access to trading platforms and network connectivity for order placement.
Ø Access to a market data feed that can supply the algorithm with data.
Ø The infrastructure with the ability to carry out back-testing before trading a system live.
Ø Back testing of historical data based on the complexity in the rules of the algorithm implementation
Ø Reading/Monitoring current market prices.
Ø Feeds from stock exchanges such as the London Stock Exchange or the New York Stock Exchange.
Ø A forex data feed.
Ø Capability for placing an order which can be routed to an exchange.
Ø Using historical price data feeds for carrying out back testing.
The execution of algorithmic trading is not easy, especially when it involves a huge amount of data for processing and execution. There are several types of firms that are employing algorithmic trading.
The level of sophistication in the design of trading algorithms, coupled with the minute nature of price fluctuations and massive volume in today’s financial markets places a demand on financial institutions to bring to bear the most state-of-the-art technology in order to maintain a high level of dependability and accuracy of trading transactions.
There are several risks and challenges associated with the process such as errors in network connectivity, system failure, time lag between execution and order placement. An imperfect algorithm may pose more risk than anything else. Stringent back testing is also a requirement when the algorithm is more complex.
Quantitative analysis of the data impacts the performance of an algorithm. Hence, critical examination is required. Although switching to automated trading sounds exciting and may seem as if money can be made effortlessly, the system needs to be tested thoroughly and the limits have to be set. Analytical traders should consider building systems and learning software languages to be confident of strategy implementation. There are several tools available to traders that are powered by algorithms which are effective, and are easy to use. Only the parameters need to be set by the trader manually and profitable opportunities are created through testing of the data base management systems that feed the algorithmic trading models. At its core, a trading system merely employs a set of rules which are used to determine the entries and exits for a particular market position. Consistent returns can be generated with minimum risks using even a simple strategy applied with discipline allowed by algorithmic/automated trading.
How are automated trading systems developed?
Automated trading systems are developed by conversion of the trading systems rules with code that can be comprehended by software application. The rules are then applied by the application which places trades automatically with the broker.
The design phase is the first step in any coding process whether a trading system is being coded or an off-the-shelf software application is used. Careful planning in the design will help to accomplish the task in the shortest amount of time, saving development costs. Three simple steps can be employed for designing a trading system: Identification of the trade rules, trade component identification, and putting things into action. No coding task can be accomplished without a basic understanding of the trading system. Based on this, the appropriate mathematical models can be employed on applications such as MATLAB, C++ or R to analyze big data, and for making trading decisions in an accurate and profitable manner.
Tools employed for modeling
Algorithmic trading is a multidimensional and complex process. The process will require the data gathering, visualization, preparation, development of models, back testing, integration with other systems, calibration, and finally the deployment of the model. Challenges will be encountered in the process of developing an algorithmic trading system which will have to be addressed and overcome. Once fully developed, a successful algorithmic trading system can make trader very successful.
MATLAB and R for financial applications
Algorithmic trading utilizes heuristics which help in driving the decisions-making process as it pertains to trading in the financial markets. These are applicable in sell-side and buy-side institutions. Algorithms form the basis of FOREX trading, execution analytics, and high frequency trading. Users and builders of algorithmic trading applications must develop the algorithm that can employ mathematical models for detection and exploitation of price movements in the market. The workflow includes:
Ø Development of strategies with the use of machine learning, technical time-series, and methods of linear time-series.
Ø Application of GPU and parallel computing for parameter identification and time-efficient back testing.
Ø Calculation of loss or profit from carrying out risk analysis.
Ø Execution analytics such as iceberg detection and market impact modeling.
Ø Incorporation of analytics and strategies into the trade environment.
Ø Determining which is the best programming language to employ in algorithmic trading.
There is no “best language” when it comes to algorithmic trading. When it comes to selecting a language, several parameters must be considered including performance, strategy parameters, development, modularity, cost, and resiliency. Major components of an algorithmic trading system should also be considered including risk management, execution engine, portfolio optimizer, and research tools. There is as well a need to examine different trading strategies and how they are impacted by the design of the system. The likely trading volume and frequency of trading must also be considered before choosing a programming language.
Requirements must be defined before deciding which language is best suited for an automated trading system. Should the system be purely execution based? Will there be a need for portfolio or risk management construction module? Will the system require a high-performance back testing tool? These are just a few of the questions that need to be considered.
The trading system can be divided into two categories: signal generation and research. Research deals with the evaluation of the performance of a strategy. The process of evaluation of a strategy over data is referred to as back testing. The algorithm complexity and data size can have a major impact on the intensity of the computation. Signal generation deals with generation of trading signals from an algorithm. This includes sending orders to the exchanges.
Frequency, strategy, volume and type
The type of strategy employed can have a significant impact on the system design. It is necessary to consider the market that will be traded, the external data, connectivity, the volume, and the trading frequency of the strategy. The frequency can be a key driver in choosing the language. For processing high volumes of data required for high frequency trading applications, an optimized execution system, and a back tester must be employed. For this, C or C++ can be considered beneficial.
Research systems employ a mixture of automated scripting and interactive development. For this, R Studio, MATLAB or Visual studio can be employed. Extensive numerical calculations can be carried out with R Studio. A straightforward environment is offered for testing which enhances the evaluation of strategies. Python employs high performance libraries which are helpful with the back-testing process.
Use the Kairos Operating System to develop automated, closed-system trading systems with no programming required
Proprietary or open source?
A difficult choice of deciding between open source and commercial options in the technology world must be made. There are several factors that must be considered for deciding which option to go with. These factors include documentation, customer support, complexity of use, and much more.
When it comes to proprietary choices, MATLAB by Mathworks and Microsoft.Net are great options for developers. Both platforms have great support networks that come from online and documentation from the source. A heavy disadvantage is the costs associated with extra features. For example, a user must pay extra if they wish to utilize the bioinformatics toolbar on MATLAB. These fees could deter many away from using products like MATLAB.
A developer might choose open source if they want more flexibility and computing power. Python is a powerful coding language that gives developers the ability to have faster programs. For most of the world, speed is important. The power and speed of coding languages like Python might give open source an edge over proprietary. Another advantage is the functionality of open source. Python requires less lines of code making your potential program extremely efficient.
Open source technology also has the benefit of a great support system. Anyone can search online and find thousands of articles that pertain to open source. Whatever problem you may be experiencing, someone has been there before. While online support may not be an official support system like what proprietary languages offers, it should be sufficient.
Choosing open source or proprietary ultimately falls under personal needs. Developers should realistically reflect on their own capabilities and what can truly help the most.
MATLAB and R
MATLAB is primarily employed for numerical computation. It has received wide acceptance in the fields of finance, engineering, and academic sectors. Numerical libraries are offered by MATLAB for scientific computation. If the developed algorithm is subjected to parallelization or vectorization, then MATLAB can offer rapid execution speed. As it is expensive, it is considered to be less appealing to the traders who have budget constraints. Traders employ MATLAB for direct execution through a brokerage.
R is a dedicated scripting tool for statistics. It is open-source and free. It is comprised of free statistical packages for carrying out advanced analysis. R can be connected to brokerages; however, it is not suited for the task. It can be treated as a research tool, but lacks execution speed.
Engineering fields employ MATLAB while R is mainly used by statistics students. Available funds are a factor in driving people towards a particular programming language. MATLAB is an official programming tool offered by Mathworks. Official updates are released twice a year, and this offers advantage to those who are looking for software that is continually improving. The toolboxes of MATLAB can be expensive based on the number of toolboxes needed and the number of concurrent users.
R on another hand is an open source language. The capabilities and popularity of R has increased over time. Enhanced functionality is offered by the Comprehensive R Archive Network [CRAN]. More than 6,000 packages are available which have undergone review in the community and these are offered for free.
MATLAB is an ideal tool for designing algorithms, prototyping, and for simulations. MATLAB is comprised of specially developed libraries for matrix operations. This offers speed of execution. R does not offer out of box functionality. The best debugging tools are offered by MATLAB. This makes the entire process, as well as prototyping, quicker. The Simulink extension, optimization tools and parallel computing make this an ideal tool for data crunching.
R is ideal for statistics and data analysis
Formatting raw data is easy in R, as free and easy data transformation facilities are available. It also possesses analysis capabilities. Plots and graphics were not easy to customize in earlier versions of R and were visually unimpressive, however the addition of a graphics packages has made it possible to customize plots. This has made the tool friendlier than MATLAB. R is employed heavily for the purpose of dealing with statistical data and running predictive models, carrying out experimental result analysis, and analyzing random forests and logistic regressions.
Use the Kairos Operating System(KOS) to develop automated, closed-system trading systems with no programming required, R and Matlab are not required
Algorithmic Trading: At a Glance
Investors prefer to purchase shares when they are cheap, however capital gains will be reaped only during a longer run. When it comes to dealing with fluctuations in the market, patience is an invaluable trait. The volatility and variations in the market may be hard to endure and as well pose a threat to investors waiting for the companies to produce results in an uncertain global environment. When recession sets it, portfolios will inevitably decline .
Stock market declines can be described as nothing less than painful. The decline in the oil sector and lagging of emerging markets sector can weigh heavily on returns for the long-term investor.
When times are turbulent, it requires investors to be patient as well as careful. When seeing indications of slight growth, investors may be tempted to dive into a market segment. With the ongoing changes in the world of trading, it has become important for investors and traders to employ cutting-edge trading tools so that they don’t fall behind the technology curve and use these tools for their benefit. Algorithmic trading is a paradigm changing technology that has swept the market and will eventually dominate the world of trading. Users of algorithmic trading can reap immense benefits and it has the potential to impact the future state of the market as well. Algorithmic trading is undoubtedly a powerful tool that can be put to great use in a global market.
Algorithmic trading employs mathematical models for making predictions as well as transaction decisions in the market. The use of rules within the model is governed by mathematical principles which determine the optimal time for an order placement and minimized impact on price. An example of this is purchasing a block of shares by fragmenting it instead of making a single trade. Algorithms allow real-time decisions pertaining to the purchase of fragments of a large chunk of shares.
Algorithmic trading is known by several names; Algo trading, quant trading, or High Frequency Trading (HFT). It provides a plethora of benefits to offer end users. It makes possible the execution of trades at optimal prices and can be done instantly and accurately. It reduces transaction costs and minimizes the errors associated with manual calculations. It can be back tested based on the data available, which permits evaluating the performance.
A high percentage of trades in the market today are high frequency trading. Many strategies can be used, including technical indicators; moving averages and channel breakouts are just a few of the strategies that can be used.
Algorithmic trading has several risks associated with it. This includes fluctuations in the network connectivity. There can be issues in the order placement and execution of the trade or the algorithm itself can be faulty due to mistakes made in its development. When there is complexity in the algorithm, there is a requirement for algorithm testing. The risks, however, are outweighed by the numerous benefits offered by the algorithmic systems. Appropriate measures can be taken in minimizing issues within the algorithmic system.
Automated systems are essential when algorithmic trading systems are used. These follow strict risk management rules in executing the trade from start to finish. The world of algorithmic trading can be entered without carrying out any sort of laborious manual research which used to be the case. The data crunching is done by complex algorithms performing pattern recognition in the market. Hence, the barrier for entering the algorithmic trading world is much lower than most would expect. Having a basic knowledge, however, offers great advantage.
Previously tested algorithms can help traders reap benefits and provide a starting point without having to build an algorithm from scratch. Good habits can be developed by adhering to a set of risk management rules, enforced by automated trading. Less emotional impact on the trader is the result.
Fear, personal preferences, and greed can influence manual trading. Elimination of these negative influences is possible by adhering to the risk management of algorithmic trading. This also cuts down the guesswork, if unforeseen conditions occur. Algorithmic trading offers professional strategies, which allows you to shift focus to money management rather than investment research. Algorithms encapsulate market patterns and take into consideration key indicators that are market predictors. The ability to scan for many trade opportunities programmatically, is something that is not possible to do manually. This is a major advantage when utilizing algorithmic trading systems.
Once tested and implemented, automated/algorithmic trading systems work around the clock. This allows the trader more opportunities. In addition, Algo systems offer an edge to the traders by permitting them to run multiple systems in a single account.
KairosTM Automated Trading Platform
Stormchaser Technologies offers a scanner in its package which generates real-time algorithmic alerts which can be used as a standalone tool to alert for potential trades. This can be used in combination with the Kairos Automated Trading Platform [KATP]. KATP positions are initiated by any of the modules configured by the users and it manages trade right from the point of entry to the exit(Closed-System Automated Trading System). Multiple trading systems can be configured with the aid of the Configure Modules window. The Configure Modules window sets:
· Price strategy
· Order route
· Order type
· Entry parameters
· Long entry criteria
· 21 types of stops
· Scale out of positions
· Scale out and stops
· Automatic position sizing
· Position quantity
· Target exit price strategy
· Order scaling
· Entry and exit times
· Max capital
The Kairos Platform includes several starter automated trading systems that have been configured. These systems demonstrate how to utilize the features for designing a fully automated system. The STX series has been designed for equities while the PVTX is a series of futures trading systems. Click here for further information about the KairosTM Automated Trading Platform.
Developing Custom Indicators for KairosTM
If you require custom indicators, they can be developed either in C or C#. They can be imported into Kairos and used as an indicator. The custom indicator DLLs can either be unsecured or secured. Unsecured DLLs can be distributed to any user. Secured ones can be imported only if the user has been assigned the credentials for doing so. This enables 3rd party developers to turn off or on the access to DLL. Kairos users can import custom indicators easily with the aid of the menu. You can obtain a Custom Indicator Development Kit by contacting the support team.