How to “Front-Run” Triangle Breakouts

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How Traders Use Front-Running to Profit From Client Orders

About $5.3 trillion changes hands daily in the foreign exchange market. When banks make trades in currencies on behalf of customers, similar to when they trade other securities like stocks or bonds, they are supposed to put the client’s interests ahead of their own. But that’s not how it always works out. In a case announced on Wednesday, prosecutors say two HSBC employees used information they had gotten about a pending client transaction to trade ahead of it, turning a profit for the bank in the process.

This is called “front running,” a practice in which a trader places orders on a security for the firm’s own account, taking advantage of advance knowledge of orders coming from its customers.

In the case in question, the customer was looking to exchange about $3.5 billion for British pounds, a fairly large transaction, says John Halligan, the president of Global Trading Analytics in Rutherford, N.J. “It would have made the radar.”

A trade that big has the potential to move the price of that security. Under normal circumstances, the transaction would be handled to minimize that risk, saving the client money.

This is where the front-running came into play. The bank employees bought British pounds before placing the client’s order, federal prosecutors in Brooklyn say, expecting that the large transaction would raise the price of the pound. The client trade was set up to be done in two stages roughly 40 minutes apart in the early afternoon on Dec. 7, 2020. The HSBC employees had already bought pounds in the days leading up to the transaction and, while they were executing it, they traded in a way that pushed up the price of the pound even more, something the prosecutors call “ramping.”

Front-running is not an uncommon practice. In 2009, regulators cracked down on 14 Wall Street firms for front-running customer stock trades.

Traders with potentially market-moving information are likely to use it, according to a working paper by a group of academics for the European Central Bank. Mutual funds have long complained that their big trades are front-run as word leaks out, leaving them with an inferior price. Regulators have focused on whether big banks use customer information to glean such advantages, either through their own trading activities or by tipping favored clients.

Prosecutors on Wednesday cited a phone call between the defendants during which they discussed how high they could ramp the price of the pound “before the victim company would ‘squeal,’” the complaint said. The price was the highest that day for that type of currency trade, allowing the traders to generate significant profits. How significant? About $3 million when the price of the pound went up, plus another $5 million in fees they charged for placing the trade for their client.

How To Front Run The Next Big Takeover For 40%+ Gains

If you’re like me, few things get your blood pumping faster than when one of your stocks gets bought out—sending its price skyrocketing.

I’m writing you about this now because it’s fresh in my mind: subscribers to my Hidden Yields service recently bagged a 58% total return in less than two years on reinsurer Validus after it was snapped up by insurance giant American International Group in a deal announced in January.

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A big part of that gain came literally overnight—the stock popped 45% from its previous closing price when news of the deal broke.

I’ll share the name of an insurer that’s set to be the next Validus in a second. First, let me tell you a little more about our 1-click “buy the takeover” strategy—and how you can use it yourself.

How We Beat AIG to the Punch

A big part of our double-digit gain on VR hinged on its price-to-book-value ratio, which we watched closely, striking at two points when the stock had dropped to a level even with book value—or what Validus’s assets would be worth if it were to be broken up and sold off.

So because VR was trading at book value in February 2020, we were paying full value for its assets. But the key is that we got its actual business for free!

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And what a business—VR sells insurance and reinsurance (or insurance bought by insurance companies to manage their own risk exposure).

Insurance, when done responsibly, is a huge moneymaker. Firms collect cash up front from their customers, in the form of premiums, and may not have to pay it out in claims for a long time, if ever. They then invest that money—called the “float”—and earn income on it.

Validus has been great at leveraging its float and handing the spoils back to shareholders: between its founding in 2005 and my 2020 buy call, VR had put $3.57 billion in investors’ pockets in the form of “regular” dividends, special dividends and share buybacks.

Put another way, Validus’s market cap at the time of its IPO was $1.84 billion, so folks who were in at the beginning had already received their investment back twice over, just in dividends and buybacks.

VR then went on to hand us a nice total return over the next 19 months—until Hurricanes Harvey, Irma and Maria sideswiped the stock, driving its price-to-book ratio back to 1x, giving us another chance to buy.

Here’s what people forget about disasters: while they do force insurers to pay out higher insurance claims in the short term, they let them charge more for insurance and reinsurance in those areas afterward, goosing cash flow in the long run.

The result was a 44% gain in just 4 months—from the time we bought the dip till the takeover was announced.

The takeaway: when bargain-hunting insurance stocks, keep a close eye on price-to-book. When it dips to 1x—or better yet below—it’s time to make your move.

Another Cheap Insurer to Buy Now

As I write, there’s another reinsurer that’s showing the same signs VR did.

Lincoln Financial Group trades at less than book value today—just 92%—a level it hasn’t seen since November 2020. That’s ridiculous when you consider that LNC grew earnings, sales and book value per share at double-digit rates last year.

You may be wondering how book value can go down while book value per share soars. It’s because management knows how to bargain-shop its own stock: it’s been buying back shares hand over fist in the last five years, slashing the total outstanding by 17.3%, thereby driving per-share book value upward.

Lincoln’s buybacks are currently on hold but will resume in Q3, now that it’s closed its $3.3-billion deal for the group-benefits business of Liberty Mutual, which will start chipping in to the bottom line in 2020.

Buying now also gets you in on one of the most explosive dividends in the space: even though the payout yields just 2.0%, it has exploded 3,200% since LNC resumed dividend hikes following the financial crisis, in late 2020.

Lincoln’s $14.7-billion market cap could make it a little tough for a buyer to acquire. But even if you don’t get the adrenaline hit our Validus owners did, you’re still getting a history of 3,200% payout growth, with more to come: just 33% of LNC’s free cash flow heads out the door as dividends.

Further upside—and payout growth—will come from rising interest rates (boosting the income LNC collects on its float) and profits from the Liberty Mutual buy. Snap up this world-class business now, while we can still get it for “free.”

High-frequency traders can’t front-run anyone

I am not a high frequency trader. But I have been involved in the capital markets for almost 20 years and I’ve specialized in algorithmic trading, so I know about HFT and there are a few things in the recent debate that warrant clarifying — first and foremost, this idea of front-running.

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Some allege that HFTs front-run other players’ orders because HFTs have access to direct feeds from various exchanges, while “regular” investors generally rely on the SIP-provided national best bid-offer (NBBO), which aggregates all the various exchanges’ order books, but which also arrives with considerable latency (due not to conspiracy, but to outdated technology). As a recent University of Michigan report claims, “[b]y anticipating future NBBO, an HFT algorithm can capitalize on cross-market disparities before they are reflected in the public price quote, in effect jumping ahead of incoming orders[. ]”. This is blatantly false.

What’s actually happening behind the scenes may be frustratingly complicated, but it’s not immoral, unethical, harmful or illegal. Nor does it cost the non-HFT anything. HFTs generally use direct connections to exchanges in order to post bids and offers and collect market data, rather than relying on the centralized SIP feed. This is because the SIP feed is unacceptably slow. Surely we cannot expect market makers to make markets without actually knowing what the current market is. When an order is placed, it takes some time to be reflected in the NBBO. But that order is already in the market before the HFT can see it, even on the direct feed, by definition. HFTs never know what a customer’s order is before it’s in the market. HFTs have no customers.

HFTs cannot front-run anyone.

HFTs can, by virtue of having invested in superior infrastructure, react faster to the information embedded in a new order, but let us not confuse speed with front-running. This information is public and available to anyone willing to overcome the challenges of acquiring and processing it very quickly. If there were no computers trading, it is easy to imagine that some human traders would be able to get and process information more quickly than others. In fact, we don’t have to imagine. We can simply recall. Decades ago, by being physically closer to the action (co-location, anyone?) was a major advantage for those who invested in it.

Even simpler: what of the difference simply between paying for a conventional real-time data feed and using freely available market data from the Internet with its standard 20-minute delay? Can the real-time data subscriber front-run the delayed data user? Obviously not. We correctly attribute the speed advantage of the real-time data subscriber to his investment in the data stream. But even with a difference in speed of 20 minutes, not 20 microseconds, we understand that the advantage is in reaction time. In this way, speed always has been and will always be an advantage.

In the mid-1800s, Paul Reuter created a network of carrier pigeons to more quickly disseminate news. His biggest customers? Financial firms, who predictably abandoned pigeons with the advent of the direct telegraph. Just so today, except that the value of speed has diminished over time, and with it, the advantage of being fastest.

Thus, much of the confusion around HFTs derives from a complicated market structure that makes perfectly legitimate behavior seem predatory to the uninitiated. Most of the rest is attributable to a problem of convoluting the types of players involved in our complex market ecosystem and misunderstanding how they interact.

Retail investors have accounts at brokerage firms. Brokerage firms generally get their customers’ orders executed by sending them to market makers known as order flow internalizers (e.g., Knight Trading). The internalizers usually pay the brokers for this”order flow.” Brokers do this for a variety of reasons, which mostly boil down to lower operating costs and higher profit margins. Note that sophisticated brokers (e.g., Goldman Sachs) build internalization capabilities for themselves — it is profitable enough to warrant the investment.

The internalizer, having acquired inventory from the retail customer, now turns to the exchanges to unload this inventory. Internalizers needn’t be particularly fast in providing liquidity: they have plenty of time to deal with customer orders. Internalizers care about being fast only when they are trying to unload those positions to HFTs and other market participants on the exchanges. Thus, the orders that are done on the exchanges, where HFTs trade, are almost entirely devoid of any retail participation. Traders who face off with HFTs, then, are neither moms nor pops. They’re professional traders working for Wall Street, in all likelihood.

I do not believe that the U.S. equity market is perfect. But it’s the most cost-effective equity market in the world for a retail or institutional investor, and it’s a better market today than it’s ever been before.The kinds of improvements that need to be implemented are mostly incremental: making the NBBO real-time, enforcing rigorously against any unfair advantage given to any participant, getting rid of the ban on zero-spread (i.e., locked) markets. These changes will improve transparency and reduce complexity.

Internalization of order flow, and other off-exchange transactions (such as those done in dark pools) also warrant close inspection. There are legitimate and interesting concerns about conflicts of interest, free rider problems and market integrity that need addressing. But these issues have nothing to do with HFT. So, even as we strive for improvements, we should try to understand that we have never in our history seen a more level playing field in any equity market.

And tell me this: Why are we OK with the myriad advantages Warren Buffett enjoys over every retail investor (and most professionals)? Is this not evidence of a two-tiered system, not at a microsecond timescale, but on a much grander and more impactful one?

Rishi K Narang is the founding principal of T2AM LLC, a hedge-fund advisory firm located in Los Angeles. He was also co-founder, with his brother, Manoj Narang, of the high-frequency trading firm Tradeworx. He is the author of “Inside the Black Box: A Simple Guide to Quantitative and High Frequency Trading.” Follow him on Twitter at @rishiknarang.

STL list.push_front run times longer then what O(1) suggests [closed]

Want to improve this question? Update the question so it’s on-topic for Stack Overflow.

Closed 3 years ago .

I am testing for runtimes with visual studio 2020’s Perftips and I noticed that when I use the STL “list” and access the push_front() function, it is running a 1000x’s longer than it should and is also in O(n) time when it should be O(1).

For example: I am creating a doubly linked list with a million random integers and it takes about 2000ms to add a single int value to the front of that million long list when I know it should be around 3ms. I also noticed that the run times expand linearly with the size of the list I make, which is not what big-O predicts.

Here is the line of code I am testing:

list.push_front(10); // 10 is an arbitrary number

I am also getting the same problem for singly linked lists and for O(1) vector functions. Does anyone know what is going on? Thanks for your time.

Side note: I’m not sure if my hardware has anything to do with it but, I have a 2.4Ghz laptop i7 and half empty 700 gb hard drive with 8Gb of ram

Edit: I am also running microsoft Excel, Adobe Reader, and chrome simultaneously while testing. I closed the programs and it actually bumped up my run times by about 100ms oddly enough.

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