Articles related to quantitative trading and software tools aiding automated investment operations.
http://empirica.io/wp-content/uploads/2015/10/logo_empirica-300x80_new.png 0 0 empirica http://empirica.io/wp-content/uploads/2015/10/logo_empirica-300x80_new.png empirica2011-07-21 19:51:562016-07-21 20:01:39Some random thoughts on algorithmic trading
The future is most algorithmic. This really is a potential scenario for disintermediation. The human trader’s attention span, processing and endurance only cannot match the functionality demands of marketplace action and intricacy and the volatile developments in trade volume which is increasing exponentially. The Tier 1 firms will increasingly have to rely on algorithmic trading and will shrink their trader headcount in the act, as the avalanche of information to be processed and responded to goes from gigabytes to terabytes. The substantial investment in algorithmic trading will give you – ‘intelligent’ algorithms which make their ‘own’ decisions to optimize trades to many surprises. The area of artificial intelligence is not adequately explored and will finally yield some important advances in the near future. Predictive algorithms will emerge with a hit rate that is reasonable and the predictive horizon will stretch farther into the future. As Artificial Intelligence (AI) and Artificial Neural Networks and Genetic Networks develop to reach critical mass with the increased ability of predictive estimators we envisage a shoal of rapacious ALPHA ALGOS will be released into the algorithmic trading space. In our miniscule way we’re the vanguard supplying the first dose of ALPHA ALGOS for the individual trader. We will be studying P1: OTA/XYZ P2: ABC c10 JWBK496/ Leshik 2011 8:7 Printer Name: Yet to Come 38 Introduction to Trading Neural Networks to expand the trading toolbox of the individual dealer. Or even counseling on the choice of the Head Dealer. In the long run it seems like it will wind up as a conflict between supercomputers, with the person trader almost entirely out of the actual trading picture – but that may be a while. There may be enough time to turn or flatten out the tendency a bit. Ray Kurzweil’s vision of the singularity’ that is ‘ does, nevertheless, loom on the horizon. This type of supercomputer scenario is filled with potential disasters of most kinds – functional, regulatory and human. It’d not be frighteningly difficult to subvert this type of powerful system as the architectural complexity would be beyond the intellectual grasp of any single individual, regardless of how talented. The system could then succumb to your disciplined team approach. It’d be a fantastic accomplishment for the marketplaces if more and more people would enter the fray and take liability for trading their own hard earned funds. To them it is their lifeblood. To the professional dealers it’s a well paid job. We would like to give to this hoped for tendency by supplying applications of algorithmic trading, the requisite concepts and background information. Leveling the playing field from tipping over and keeping it, is a gigantic job, but in our opinion, a rewarding one.
To Err Is Human. Blunders are made by Individual. Thats why we make machines to overcome our errors.
Algorithmic trading of securities is now a staple of modern approaches to monetary investment.
Artificial Intelligence plays a massive part in our lives and will play a larger part.
Algo-trading has proven to be quite precise and can be a rewarding instrument for investors in determining profitable stock picks.
I Know First’s algorithm has a success rate that speaks for itself.
There’s tons of advantages of algorithmic trading : –
–Enhanced Order Entrance Rate
–Skill to Backtest
Disadvantages of Automated Trading
Judgment on algorithmic trading – although appealing for a number of factors, automated trading systems should not be considered a substitute for carefully carried out trading.
Given the evolution of the financial markets, many analysts and researchers today believe that algorithms that are only are the future of trading. But there are reasons. Within an attempt to estimate the future of algorithmic trading, your opinion is wanted by the Algorithmic Traders Association.
Market arrangement, and the markets, are always changing. Already largely technology dependent, the financial markets are becoming electronic, and researchers and quite several analysts now believe that algorithms that are only are the future of markets. Nevertheless, upon a consideration that is bit deeper, this statement doesn’t appear to be so obvious, and we can identify two important reasons for that.
If we take a look at the history of markets, we can see that progress within their construction and infrastructure never supplied any marketplace participants with a long term edge. Really, any such improvements may provide an advantage just until it becomes common. During the era of early computerization of markets, nearly anyone with a computer had a major advantage over those that traded within an old fashioned manner; when computers became part of regular life yet, this edge was partially levelled. One of the latest examples – latency arbitrage can be mentioned by us, high frequency trading and other similar techniques – that edge completely depends merely on the advantage in technology.
While just several market participants were not physically unable to trade on the sub-millisecond level, it indeed provided a fantastic border to them, but with more extensive distribution of this technology, this border is gradually reducing. Many former high frequency dealers turn to lower-frequency options, introducing elements of directional trading. The same issues now are associated with algorithms aiming to find the best performance, be they liquidity seekers, iceberg or others. Thus we can assume that algorithmic trading in general may provide an edge just for a certain limited time, finally asking for better trading decisions that are human.
Another reason to consider that the future may be not as algorithmic now as you can envision is the perpetual changes in regulations. All of us remember what happened in 2013 and specially 2014 when new requirements aiming for better transparency resulted in unprecedented low volatility and afterwards caused lots of trading algorithms to fail (see my previous article on the subject, “The Great Comeback of Unpredictability-Based Alpha-Generating Strategies”).
Lately I was encouraged by Algorithmic Traders Association to head their educational and research section, and as part of the project we decided to arrange a survey asking for opinions of various professionals more or less related to trading in general and algorithmic or systematic trading in particular: developers, risk managers, prop traders, institutional dealers, retail dealers. We consider that the investigation of the survey’s results may show the authentic public opinion, and we all know that in many cases public opinion is what determines the real future.
Please take part in this brief survey, which can be found here. We are going to release the results on our web site, but also here, so you may have easy access to it the moment it’s complete. We’re really intrigued to see the actual distribution of votes, and hope that it will not be uninteresting to you as well.
Societal benefits of HFT and Algo trading – regulators should embrace them
The enormous increase in fiscal trading volumes in the past number of decades continues to be made possible just with the usage of technology. Technology has helped in
– automating the dissemination of market data and other pertinent information by trading participants and exchanges
– capturing and consolidation of market data from various exchanges and other sources of related information
– making intricate calculations on data that is historical and live, leading to trade conclusions
– trading and handling danger
– lowering cost and the latency of trading
– various post-commerce procedures
Technology is heavily and usually economically used by brokerages (supplying various retail and institutional investors access to financial markets), high frequency traders (who trade in tremendous volumes aggregating little profits in as many trades as possible, this usually contains arbitrageurs and market makers), quant algo dealers (who use quantitative models to forecast the future prices etc.), exchanges (who are the liquidity aggregators forming marketplaces), and various other market participants. Each stake holder mentioned above has an important function in the orderly function of the financial markets. Complete Risk Management is the leading demand in handling these fiscal algorithmic trading companies.
There has been a major backlash against the fiscal firms (primarily the ones driven by technology including HFT and algorithmic trading companies) specially since the Flash Crash – US marketplaces in 2010, Knight Capital posting a major loss due to algo technology glitch in 2012, and various other occasions. According to some estimates, over 60% of US equity trading and over 30% of UK equity trading can be accounted to HFT firms. Politicians and regulators across the world have attempted to control high frequency trading and the algo in particular by coming up with some pretty interesting suggestions and arguments,
– European regulator needing Half-second delay before performance of a trade or cancelling an order in a bid to check motivators of trading fast.
It’s like introducing a speed breaker on a high way. Individuals will drive very fast up to the breaker, and speed up again after they cross it. It’s not that hard for HFT and algorithmic trading companies to adjust their algorithms to accommodate with this regulatory change and still do what they think to do.
– Indian regulator proposed that the exchanges to accept orders alternately from a co-location member (whose servers are located in exactly the same physical place as the exchange) and a non co-location member.
Depending upon the cost-benefit analysis, an HFT company can setup a colocation server and a server in proximity to the exchange and alternative sending through those 2 servers of these orders by fixing their strategy.
– Limiting the order to commerce ratios for all marketplace participants.
Technically the algos can work too. But this can surely help reduce some overfishing in the marketplace(i.e. algos sending too many smaller orders at extensive limit prices and then cancelling them to search for trading opportunities which are not easily transparent).
– the regulator of the algos or Particular regulators trying to have HFT and algorithmic players notify the exchange
Its part of Germany and India’s regulatory system, but does not control the automated trading related over- trading triggers as exchanges or the regulators will unable to understand the detailed operation of the algos. They are able to only try and ensure sound risk management is ensured by the HFT firms and follow the practices strictly at the time of audit or algo approval.
– Retail Investor has a disadvantage and cannot get the same arbitrage opportunities due to dearth of access to same technology as HFT and algorithmic businesses
Retail Investor should not be striving to do sub second arbitrage the market in the first place. It’s like saying a cyclist cannot ride as quickly as a car on a high way and car-drivers should be stopped or slowed down. Retail investors should look to take somewhat longer term perspective as they was doing historically.
– Institutional investors can additionally not compete with the HFT companies’ rise in technology
HFT and algorithmic trading firms was driving the use of algorithmic technology and promotion to low latency trading. In their constant RnD process, they’ve stepped up in the technology value chain and have empowered the Institutional investors with efficient and good trading tools at affordable costs. For example, performance algorithms like VWAP, Smart Routers were not available to Institutional Investors a decade or two ago, but HFT companies led RnD and trading styles have made performance algorithms accessible to Retail Investors in addition to other Institutional indirectly. Also, the object of the Institutional Investors is generally to manage portfolios over with longer horizon targets rather than intra-day small price movements.
– HFT companies lead to market catastrophe and failures like Flash Crash in 2010, Knight Capital’s loss in 2012, etc.
In the absence of HFT and low latency algorithmic trading, there had been considerable disasters in the financial business (and in various other industries also) including the LTCM Crisis in 1998, Subprime disaster in 2007, where the human processes have led to major catastrophe in the markets. The use of algorithmic technology if handled well and regulated economically can reduce the probability and scale of such occasions.
For example, If an airplane crashes, it does not make the Air Travel an awful company. Air travel brings lots of benefits to our societal and economical growth that it’s worth it despite unlucky that is uncommon crashes. The regulators in that sector focus on ensuring the safety of passengers and plans is an extreme priority for the airlines.
In the same way, manage and HFT firms recognize the risk management priority and generally target to mitigate risk first, and then focus on profits.
HFT and algorithmic trading businesses actually help in supplying better liquidity in the market, lower cost of trading, tighter spreads, that help institutional investors and retail investors in getting their executions at better costs.
– Visiting extra-taxes on HFT (like a regulatory proposal out there in Germany). Make it expensive for HFT players and reduce their net profits.
The technology developments that are constructive should be embraced by regulators across the world in financial markets that support growth of the economies and businesses. Their aim should be to understand the market behaviour, advancements in technology, bring transparency to the market, introduce risk management measures like a kill switch (for cancelling all trades when things go wrong), push for stronger pressure testing of automation tools, apply conformity, track patterns that can result in systemic risks, and cause a sound and complete risk management across the financial sector.