QuantConnect – where cloud computing meets open data for Algorithm Trading.

QuantConnect, a radical FinTech open data trading startup was established In 2011 with an ambitious goal: offer open trading data algorithm technology to mainstream audiences. QuantConnect follows the concept of open data philosophies that data should be freely available to use and republish as necessary, without restrictions from copyright, patents or other mechanisms of control. The founders aim to empower the general public with powerful tools to make trading and investment accessible through advanced technology to general audiences.

Jared Broad, QuantConnect co-founder and CEO won a Start Up Chile grant In 2011 and since that was invited to the TEDx Wall Street event and Battle of the Quants in New York (April 2012). In this last conference founded focusing exclusively on the growing quantitative hedge fund community and industry, by the hedge fund visionary Bartt C. Kellerman’s Global Capital Acquisition in 2006, Jared has been displaying his vision of a further advanced trading platform that can push the boundaries of algorithm trading, using the most advanced technologies to offer open data.

QuantConnect, offering: Equity, FX tick, Earning predictions, and Twitter Sentiment Analysis

Offering US equities tick data going back to January 1998, and updated daily with latest market data QuantConnect displays over 16,000 stocks, in an open library of data provided by QuantQuote. Besides this it offers an impressive FX tick data on 13 major currency pairs going back to April 2007, and updated daily with most recent market provided by FXCM.

With another deal with Estimize QuantConnect provides quarterly earnings predictions generated by a community of 13,000 traders and investors. Through the data from Estimize that goes back early 2011 and covers most stocks QuantConnect manages an impressive better than most of Wall Street’s predictions, such as 69% of the time better performance!

Last but not least through a partnership with StockPulse QuantConnect provides Twitter Sentiment Analysis to identify the most relevant capital market moods, rumors, and market-moving trends with it offering a valuable array of trading ideas and signals on every traded asset in the world.

QuantConnect startup seeded in Chile and New York

QuantConnect is a new trading and investment cloud-based algorithm service and community startup that was seeded between Chile and New York. The company aims to level the playing field for independent traders by providing the right technology tools to design and execute present and future proof trading strategies, and back-test their programs using historical market data.

The project is the brainchild of Jared Broad, serial entrepreneur born in New Zealand, founder of automated trading firm Stocktrack.org, and Shai Rosen, founder of Chilean auction website Ganeselo.com.

The service has been in the beta-testing stage for the past year with programmers including graduate students interested in pursuing a career in quant trading, and computer scientists from leading financial firms. The firm has been working quite hard in its disruptive strategy and has signed up thousand of prospective clients in its community platform and raise its profile at conferences such as Finovate in London and TechCrunch in San Francisco. During the process the firm has been creating an unique space in the algorithm trading industry, including the respect and support of employees of Google and Facebook. Tshe recent partnership with high profile trading global players such as FXCM and tech innovators such as Estimize and StockPulse gives them an unique outstanding spot.

The Plan

Looking further ahead, the firm has plans to set up a hedge fund based around the most successful managers, marketing individual strategies to retail investors via online brokerages, and packaging the best algorithms as an exchange-traded fund.

Although QuantConnect appears to have first-mover advantage in what could be a very lucrative market, they have at least one potential competitor in the form of Quantopian, which is working on a similar offering. The Boston-based technology shop has the backing of VC investor Spark Capital and high-frequency market maker Getco.

The Technical Nitty-Gritty

Unlike MT4, which uses the proprietary MQL4 programming language for its algorithms, Quantconnect uses the C# programming language, a general-purpose object-oriented language derived from the popular C/C+/C++ family of languages. Programmers can design and back-test their strategies for free, using vast cloud computing power leased from Amazon.com, and will only be charged when they want to make trades with their strategies by setting up trading accounts.

Instant access to this massive computing power means that a simulation based on 30 stocks in the Dow Jones Industrial Average could be run in a matter of minutes. By comparison, the same calculations on the average desktop computer would take several days to compute.

Applying principles of openness and transparency in all aspects QuantConnect is creating a spot in a high competitive and disruptive part of the global financial and trading world. For that QuantConnect defines themselves as an innovative company giving any advanced algorithm trader or a complete access to an enormous library of financial and capital markets data, thanks to a generous network of data partners for providing the right resources.

quantconnect.com/
twitter.com/QuantConnect

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Norway’s $860 billion sovereign wealth fund — the world’s largest — has decided to abandon algorithm-based High Frequency Trading (HFT). The electronic “trail” left by such trading allows traders elsewhere to profit on the HFT orders placed by the fund. But the decision comes at a very tumultuous time for HFT trading: Although the majority of stock market trade volume is by way of HFT, regulators continue to cry “foul” when HFT abuse crosses the line into market fraud.

The Emergence of HFT Stocks

HFTs have become controversial in large part due to the fact that they now account for at least 50 percent of all trades transacted by traders in United States equity markets — although in 2011, that figure was widely quoted as being as high as 70 percent. But it is not just the percentage of volume that has sparked debate over its implementation but also whether it places conventional, institutional investors at a distinct disadvantage. In addition, the technology has given rise to a category of stocks known as “HFT stocks” that are favored by HFT traders. These easily traded, highly liquid stocks in large companies put long-term investors at a disadvantage if they do not employ the fastest trading media utilizing the latest, most aggressive algorithms.

The Liquidity Debate

Defenders of HFT point out that it lends greater liquidity to the market. However, opponents cite instances of large orders being placed only to be immediately canceled, creating nothing more than “phantom liquidity.” The SEC recently investigated a New York HFT firm for the manipulative practice of placing rapid-fire, aggressive  trades in the last two seconds of almost every trading day during a six-month period from June through December 2009 that resulted in the manipulation of closing prices of thousands of NASDAQ-listed stocks. The tactic overwhelmed the market’s available liquidity and artificially pushed the closing price in the firm’s favor.

Crossing the Line

Although the defendant firm was relatively small in size, it dominated the market in the last few seconds of a trading day for stocks that it otherwise traded only minimally. During the period investigated by the SEC, the firm’s trades constituted more than 70 percent of the total trading volume of the stocks it manipulated. A statement released by SEC Chair Mary Jo White cautioned: “When high frequency traders cross the line and engage in fraud we will pursue them as we do with anyone who manipulates the markets.”

The condemnation of HFT abuse was echoed by Andrew J. Ceresney, director of the SEC’s Division of Enforcement:

“Traders today can certainly use complex algorithms and take advantage of cutting-edge technology, but what happened here was fraud.”

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The Full Binary Options Trading Guide</a> <p>Anyone can trade and profit from binary options trading, regardless of experience or prior knowledge. However, as a trader you will be even more successful if you arm yourself with the right knowledge and tools – that’s why anyoption created the “Zero to Hero” binary options trading guide. The “Zero to Hero” guide is designed to take you step by step from a complete beginner to an expert, pro trader. In our anyoption Zero to Hero guide you will learn not only the fundamentals of options trading, but also advanced trading techniques and pro tips

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MKTPlace is a leading digital and social media platform for traders and investors. MKTPlace offers premiere resources for trading and investing education, digital resources for personal finance, news about IoT, AI, Blockchain, Business, market analysis and education resources and guides.

At the beginning of last year, the Members of Congress in the US, Edward J. Markey, stated algorithm trading or high frequency trading clearly corresponds to a high level of risk to the safety and permanence of the capital market in the US and the fact it should be truncated as soon as possible. And he isn’t alone in saying HFT does indeed pose considerable risks to the trading infrastructure.

Many financial analysts claim to back this same assertion with the proof taken from recent market events, such as the Flash Crash along with the loss Knight Capital as a result of a software malfunction. Mentioned below is the summary of the overall risk associated with high frequency trading:

• The sheer speed of the trade puts most trading styles at a considerable disadvantage
• High frequency trading intensifies market volatility
• Most of the ‘other’ types of investors usually run away from it
• HFT volume has a risky high proportion of the total traded volume

But it is also important to understand the fact not everyone agrees with the analysis made in regards to the potential dangers of high frequency trading. It is true the machines have taken over when you talk about modern trading. They have taken the place of human specialists or the smart market makers and a majority of the trading quotes, offers and bids which come flowing in today flow in through high frequency trading computers and systems.

And it is also true the Flash Crash that occurred back in 2010 is a great example of the dangers posed by high frequency trading when Waddell & Reed incorrectly keyed a trading order which resulted in a terrible market dive. HFTs went scurrying out of the scene as the market fell for a short period of time and there were no bid being placed and there were significantly big price dislocations demonstrated by the 10% market freefall.

A Drop in High Frequency Trading

Despite the fact that several trading companies get to enjoy the high speed benefits of HFT, it has been seen that there has been a considerable drop in profitability using HFT. If you look at the reports from 2011 and 2012, you will see that those years saw considerable drops (7.8 billion shares each day to 6.5 billion shares). The drop was of 17%. Another thing to consider is the fact HFT is all about speed and co-location.

HFT providers such as Citadel, Virtu Financial and GETCO have to all constantly bring in new technology and upgrade processing units to match each other’s speed. According to a report, it was revealed GETCO spent a total of $37 million on upgrades.

Lower volumes are bad for HFT solely because of the fact the lower the amount of the orders that enter the market, the fewer the opportunities to make a bid/offer. Another problem that isn’t highlighted much is the fact the amount of traders who want to willingly use HFT is dropping. One of the major concerns with HFT today is the fact that it might blow out of proportion in the capital markets, just like what happened at Knight Capital. Companies employing HFT and the HFT vendors have both failed to display a successful effort to manage and control high frequency trading.

The only things you need to have are state of the art evaluation and tracking control systems to prevent events like the Flash Crash from happening again. Because if HFT isn’t controlled, the losses could easily keep piling up and then you would need to place a more drastic countermeasure: a ban on HFT, which most traders suggest should happen now. But if you talk about the future and improvements, then taking a drastic step like banning it now could overcomplicate the situation.

The science of HFT systems and processors enable the computers to analyze and predict your moves in the market which mean there could be landmines everywhere you look. It is no surprise the framework of modern trading has been forever altered. A human hand that just takes a couple of seconds to conduct a trade now is replaced by a machine which can do the same trade in a millisecond or a microsecond.

The fact is HFT can neither be called bad nor can it be called good and that’s the way it’s going to be in the future as well.

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“Algorithmic trading frees you from the drudgery, but do you have good ideas? There aren’t that many masterpieces out there.”  EquaMetrics’ Christopher Ivey

Several traders and investors in the global money markets have been for quite some time transforming their entry, exit and financial management and investment strategies into automated computerized trading which in turn allows the computers to trade for them. And you have to understand that one of the biggest advantages of using technology is the fact that it complete eliminates any emotion. It takes the emotion and any bias right out of the trade.

However, it is also important to note that when you let the machine do all your work, especially when you talk about trading, there are bound to be a few mistakes and these mistakes can be pretty much devastating for both traders and the markets. For example, let’s go back to the ‘flash crash’ which happened back in May 2010. And it happened in August 2012 as well when the trading software used by the traders and investors at Knight Capital Group Inc. broke down. What this crash did was cause a series of unintended stock trades which resulted in a $440 million loss for the company.

And the irony of all it was the fact that Knight Capital Inc. was known in the financial world as a market trendsetter and trailblazer which employed the services of experts and seasoned traders and investment specialists who had the ability to monitor trades on both sides of a particular security to make sure that the market functions smoothly. However, despite the probability of setbacks, almost all of Wall Street now heavily depends on algorithmic programs to conduct trades quickly and decisively.

There is no doubt about the fact that many traders have mixed feelings about using computerized programs to run the show and say too much algorithm trading might just destabilize the markets. And another thing, algorithm trading isn’t something that was invented a few years ago. The concept was implemented several decades ago.

Algorithm Trading – A Look Back in Time

Back in 1951, a student from the University of Chicago, Harry Markowitz, acted on the advice of his Ph.D. supervisor, Jacob Marschak, and proceeded to complete his dissertation on how to successfully apply complex mathematical algorithms and concepts and fuse them with the financial world, more specifically, the stock markets. The result, however, turned out to be quite fascinating and transformed into a modern portfolio explaining the difference and conflict between a security and how it may affect the profits risk-averse traders demand when dealing in potentially riskier securities.

Back then, the normal method to calculate and determine a variance in securities included a thorough evaluation approach which was designed by John Burr Williams in the 1930s. Investors used Burr’s price-to-earnings ratios and other factors pertaining to the overall statistical health of a company or organization. These methods helped traders at the time to determine whether or not the real price for a security became a standard tool for analysts.

An Enhanced Trading Portfolio 

Once Markowitz came up with his brilliant new method, he aided in the development of various algorithms that do all the important and necessary calculations to make an enhanced trading portfolio. With his intellectual contributions and the advent of the IBM System/360 central processing unit in 1960, strategy traders and investors as well as various financial economists had the power to methodically evaluate millions of information and data centres that have been produced since.

Around the same time, there was another trading methodology that became popular, named the Signal theory, a strategy that implemented to extract various patterns and information from a given set of data. Stock charting experts and analysts were never too worried about the price of a security. What they were concerned with is how much would the price fluctuate. The data extracted or collected from a fluctuating stock price drops in value far too quickly. So, when investment companies were emphasizing on a core and fundamental analysis that would aid in the execution of trades stretched over a period of several days or weeks, the signals experts detect have to be executed right then and there.

Investment companies since then have been trying not to rely too much on human trade executions and decisions and that is why most of them switched to using computerized programs as algorithms are designed to conduct instantaneous trades on the fleeting information given. Long-Term Capital Management, which was established in 1994 by John Meriwether, employed algorithms and computer programs to identify tiny fleeting variances in stocks and securities so they could make hefty profits.

On the other hand, the company was also experiencing a shortfall in yearly profits solely because of the fact there were other firms which began to use algorithm trading technologies. This led to LTCM devising other strategies that didn’t quite seem to pan out and resulted in the fund losing everything in 1998.

With the downfall of LTCM, there was little consideration to the potentially destabilizing effects of algorithm trading in the money markets. And within a decade or so, algorithm trading transformed into nano-trading. Nano-trading is all about catching the signal faster than others. Even a second seems like forever in the financial world. For example, an algorithm trading centre near the New York Stock Exchange would use its servers to detect and catch signals a millisecond faster than a nano-trading company which is further away from the stock exchange.

It is also important to understand that traders and investors, even small ones, would never attempt to rely on the basic fundamental evaluation methods. And according to reports by chartists, algorithm trading will rule the trading world and looking back at history, it is makes sense to expect more problems associated with algorithm trading in the future.

Although algorithm trading has its merits and can prove to be a money-making tool, if executed correctly, at the same time it is also imperative you never substitute the use of technology for cleverly thought-out and well-executed trading.

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Wall Street, along with the financial centres in London and Hong Kong, have become algorithm trading hubs where thousands of traders employ sophisticated algorithm programs to gauge the market trends and rely on the analytical superiority of these high powered super computer programs. Algorithm trading is carried out by mathematical robots and big data crawlers that still few people actually know about.

Believe it or not, algorithm trading is now done all over the globe which has led to the emergence of a new technological trend in financial industries of several developed nations. You have to admit that at times people fail to predict market volatilities, the bull and bear trends and a number of factors, which algorithm systems never overlook.

What is Algorithm Trading?

At its core, algorithm trading, which can also be referred to as high frequency or HF trading, is a trading process which is conducted by highly sophisticated computer programs. These programs determine various aspects of a trade, which include critical decisions like timing, trends and prices in the market and the all the factors associated with these factors that can either affect them positively or negatively.  These programs are also designed to function independently, which means they can if they choose to, execute an entire trade order without consulting the trader.

Who Uses Algorithm Trading Strategies?

High frequency or algorithm trading is most commonly conducted by traders belonging to mutual and pension funds and in some cases, institutional traders who aim to break down a big trade into smaller chunks solely to manage the impact and risks caused by it in the market. Algorithm systems are complex and are not meant for every trader. These programs are designed to search for crucial trading factors like the ups and downs in interest rates, minor fluctuation in the economy, important news and notification and a number other intricacies.

They look for areas where they can mark an existing opportunity that is before anyone else can mark them. The algorithm systems which are employed in today’s trading practices have the ability to disperse massive trading orders into manageable pieces so they could be used in a multitude of regions across the world and at a speed which will remain unmatched.

Why Use Algorithm Trading?

The central purpose of HF or algorithm trading is to reduce the risk involved in a trade as much as possible. They present traders with smaller deals which allow them to enter and exit the markets faster than any other trader, also allowing the HF trader to switch between different trading platforms and exchanges.

Moreover, all financial markets are now operated by various sophisticated and overly complex trading technologies which have given a considerable edge to most traders. At the moment in the financial markets, top companies like Goldman Sachs, Morgan Stanley, and Citi, along with Barclays have been using some complex computer programs and big data programming in the Forex markets, which are responsible for a majority of trading in worldwide markets.

75% of All World Trading Is Done Through Algorithms

According to statistics and analytical research done on high frequency trading, it has been identified that algorithm trading now accounts for 75% of all trading done in the world. And not only that, it was also discovered that market trends are established through not just the macroeconomic factors or data but they are also determined by the traders who vigorously compete against each other to see who comes out on top in terms of the fastest information processing and the most analytical business minds. These are backed by big numbers in financial algorithms which have the ability to evaluate massive amounts of data to determine top profits margins.

Algorithm Trading & the Forex Markets

Some of the best algorithm traders in the Forex market are interbank traders who have been incorporating algorithm trading for the past couple of years. However, it is important to realize that algorithm trading has not yet put most independent trades at a disadvantage because most traders are focused on the long-term and upon witnessing the increased liquidity in the financial markets along with stability in share prices, a majority of independent traders are now seeking to integrate their trading styles and strategies with algorithm trading techniques and technologies.

Forms of High Frequency Traders

There are varying forms of algorithm traders. There are many HF traders who are referred to as market swingers and employ trading strategies to trade mostly on signals to create a market through the provision of securities on every side of the buy and sell trade order.

Other algorithm traders use high frequency systems to try and get a fix on where the markets are headed in the short run. Irrespective of the trading strategies these HF traders use and implement, they all aim at one thing only: making massive amounts of money without increasing the risk involved in their trades.

In the past five years alone, there has been a considerable rise in the number of traders using algorithm trading systems and according to a statistical analysis report published by the Aite Group LLC, a Boston based firm, it was identified that a third of all trades that were conducted in Europe and the US in 2006 were carried forward through algorithmic programs. Keeping this number in mind, you can say that 2% of 20,000 US companies use algorithm trading, especially in the equity markets.

Matthew Rothman, an analyst at Barclays Capital, had this to say,

“Five years ago, ‘high-frequency’ traders, and few others considered the funds more than a niche strategy. However, the niche’s role now overshadows that of mainstream brokers mutual funds and hedge funds.”

All in all, it is safe to assume there is going to be a big change in trading trends now that most independent investors and traders have seen what algorithm trading can do for them. And the sole reason for its increase in popularity is its potential for making huge profits while minimizing risk.

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MKTPlace is a leading digital and social media platform for traders and investors. MKTPlace offers premiere resources for trading and investing education, digital resources for personal finance, news about IoT, AI, Blockchain, Business, market analysis and education resources and guides.