Forex Trading And High-frequency Trading Regulation: Legal Considerations In Toronto

Forex Trading And High-frequency Trading Regulation: Legal Considerations In Toronto – Calls from regulators and the industry to slow down high-frequency forex trading have led to some firms offering their own solutions.

Since the frequency of forex trading is growing faster and faster, calls have been made to put restrictions at such high levels as a way to level the playing field.

Forex Trading And High-frequency Trading Regulation: Legal Considerations In Toronto

High frequency trading (HFT) has been criticized by regulators and trading platforms for the way in which investors compete using algorithms to get the smallest advantage. They tend to favor larger traders with the resources to use such algorithms, therefore preventing individual investors from getting the best value.

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To address this problem, a leading firm, EBS owned by ICAP, proposed earlier in the year a system in which trades are grouped before their place in the queue for confirmation is randomized. That way, the company says, smaller merchants will get as good a value as those with advanced computer systems that support them. Two other firms, ParFX and Thomson Reuters, both offer similar batching systems to counter high-frequency traders.

Gil Mandelzis, CEO of forex trading platform EBS, told the FT earlier this year: “It’s a technological arms race at the bottom, and a huge toll on the industry, as people he will make significant investments in speed without any connection to his business strategy.

“The speed has little to do with why many participants come to our markets. These are serious players who come to the market to exchange risk; they do not come to the race.”

He added: “The first twenty years of algorithmic trading added great transparency and led to the compression of spreads – all great things. But there is a line beyond which marginal speed and smaller trade sizes do not add up.” no value and really hurt the markets. At some point, we, the public markets in all asset classes, crossed the line. The “first in, first out” model sounds fair and plausible, but in the markets modern audiences implies “the winner takes all”.

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Other suggestions put forward by regulators in Europe and Australia include rest periods for smaller trades. While some don’t feel that HFT is such a bad thing for the industry, others think that slowing down the market will allow smaller traders a greater chance of success.

Larry Harris, professor of finance at the USC Marshall School of Business and a former chief economist at the SEC, also told the FT: “Where you see high-frequency trading, requiring a delay is a sensible thing to do. We’re talking delays of one-thirteenth of the time it takes to blink an eye. It hardly slows down the market, but it ensures that a smaller trader has a better chance of getting first in line.”

Latest headlines British airports rip off rent from forex operators UFX – Reimagining online trading and trader loyalty Staying in the competitive forex market Living in the era of negative interest rates OANDA acquires IBFX Latin America forex accounts seeking to protect against currency swings Banks cut currency traders as forex volatility continues Brexit spooks currency traders India and UAE sign currency exchange pact The former FX trader battles Citibank in court Sweden’s central bank cuts rates further below zero Could the end of big money stop crime? high frequency trading (HFT) have come to dominate the business world, especially HFT. During 2009-2010, more than 60% of US trade was attributed to HFT, although the percentage has decreased in recent years.

Here is a look at the world of algorithmic and high-frequency trading: how they are related, their benefits and challenges, their main users and their current and future status.

What Is High Frequency Trading (hft)? How It Works And Example

First, note that HFT is a subset of algorithmic trading and, in turn, HFT includes Ultra HFT trading. Algorithms essentially function as intermediaries between buyers and sellers, with HFT and Ultra HFT being a way for traders to capitalize on infinitesimal price discrepancies that might only exist for a tiny period.

Computer-assisted rule-based algorithmic trading uses dedicated programs that make automated trading decisions to place orders. AT splits large size orders and places these split orders at different times and also manages trade orders after their submission.

Large orders of magnitude, usually made by pension funds or insurance companies, can have a severe impact on price levels. AT aims to reduce the price impact by dividing large orders into several small orders, thus offering traders a price advantage.

Algorithms also dynamically control the timing of sending orders to the market. These algorithms read data feeds at high speed in real time, detect trading signals, identify appropriate price levels and then place trade orders once they identify a suitable opportunity. They can also detect arbitrage opportunities and can trade based on trend following, news events, and even speculation.

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High frequency trading is an extension of algorithmic trading. It handles small trade orders to be sent to the market at high speed, often in milliseconds or microseconds – a millisecond is one thousandth of a second and a microsecond is one thousandth of a millisecond.

These orders are handled by high-speed algorithms that replicate the role of a market maker. HFT algorithms usually involve two-sided (buy-low and sell-high) order placements in an attempt to take advantage of bid-ask spreads. HFT algorithms also try to “sense” any large pending orders by sending multiple small orders and analyzing the patterns and time taken in trade execution. If they sense an opportunity, the HFT algorithms then try to capitalize on large pending orders by adjusting prices to fill them and make profits.

Also, Ultra HFT is another specialized stream of HFT. By paying an additional exchange fee, trading firms have access to view pending orders a fraction of a second before the rest of the market.

Exploiting market conditions that cannot be detected by the human eye, HFT algorithms bank on finding profit potential in the ultra-short time span. An example is the arbitrage between futures and ETFs on the same underlying index.

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The following charts reveal what the HFT algorithms have to discover and capitalize on. These charts show the tick-by-tick price movements of E-mini S&P 500 futures (ES) and SPDR S&P 500 ETFs (SPY) at different time frequencies.

The deeper you zoom into the charts, the bigger price differences can be found between two securities that at first glance seem perfectly correlated.

Please note that the axis for the two instruments is different. The price differentials are significant, even if they appear at the same horizontal levels.

So what appears to be perfectly in sync to the naked eye turns out to have serious profit potential when viewed from the perspective of fast algorithms.

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In the US markets, the SEC authorized automated electronic exchanges in 1998. About a year later, HFT began, with the trade execution time, at that time, being a few seconds. In 2010, this was reduced to milliseconds – see Bank of England Andrew Haldane’s “Patience and finance” speech – and today, a hundredth of a microsecond is enough time for most HFT trading decisions and executions. Given the ever-increasing computing power, working at nanosecond and picosecond frequencies may be achievable via HFT in the relatively near future.

“Accounted for more than 60% of all US equity volume”, which proved to be a high water mark. By 2013, that percentage had dropped to about 50%. Bloomberg also noted that where, in 2009, “high-frequency traders. moved about 3.25 billion shares a day. In 2012, it was 1.6 billion a day” and “the average profit fell from about one-tenth of a cent to one-twentieth of a cent.”

HFT trading ideally needs to have the lowest possible data latency (time delays) and the maximum possible level of automation. So participants prefer to trade in markets with high levels of automation and integration capabilities in their trading platforms. These include NASDAQ, NYSE, Direct Edge and BATS.

HFT is dominated by proprietary trading firms and spreads across many securities, including stocks, derivatives, index funds, and ETFs, currencies and fixed income instruments. A 2011 Deutsche Bank report found that of then-current HFT participants, proprietary trading firms made up 48%, private trading desks of multi-service broker-dealers were 46% and hedge funds about 6%. Major names in the space include trading companies such as KWG Holdings (formed by the merger between Getco and Knight Capital) and large institutional trading banks such as Citigroup (C), JP Morgan (JPM) and Goldman Sachs (GS).

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HFT is beneficial for traders, but does it help the overall market? Some general market benefits that HFT proponents cite include:

Opponents of HFT argue that algorithms can be programmed to send hundreds of fake orders and cancel them in the next second. Such “spoofing” momentarily creates a false spike in demand/supply leading to price anomalies, which can be exploited by HFT traders to their advantage. In 2013, the SEC introduced the Market Information Data Analysis System (MIDAS), which screens multiple markets for data at millisecond frequencies to try to catch fraudulent activities such as “spoofing”.

The HFT market has also been crowded, with participants trying to gain an edge over their competitors by constantly improving algorithms and adding infrastructure. Because of this “arms race”, it has become more difficult for traders to capitalize on price anomalies, even if they have the best computers and cutting-edge networks.

And the prospect

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