Rick Bandazian Jr.: Eleven Years Without a Losing Month, Five Minutes at a Time

Last reviewed: September 2026. Every figure below is drawn from chapter nine of Market Wizards: The Next Generation unless it is explicitly labelled as our own note. Complete Trader’s Edge has not audited any trading statement.

Read Bandazian in his own words

Market Wizards: The Next Generation

Jack Schwager & George Coyle · 2026 · Chapter 9: “Beating the Bots”

Everything below is our own analysis of the published record. The full interview, the trade walkthroughs in his own words and the closing notes from both authors are only in the book.

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Over the past eleven years, Rick Bandazian Jr. has not had a single down month. His average holding period is about five minutes on winners and shorter on losses. He funded the original account with $25,000 and has withdrawn profits amounting in the millions.

The number that makes the chapter is not the return. It is what he does with the machines. Bandazian makes his money by effectively beating algorithms, with very short-term trading around corporate events that require more nuance than a state-of-the-art machine can handle. He is also, by his own description, a technology dinosaur, which he demonstrated by conducting the interview on his phone because his PC has no camera, and then holding the camera backwards while trying to show the authors a chart on his screen.

The record, and the honest caveat attached to it

Schwager states the limits of the number himself, which is the right way to read it.

Bandazian has not accumulated tens of millions of dollars, because his approach does not scale. And even if it did scale, Schwager judges that his personal risk tolerance would likely prevent him from building a fortune. What he has done is support himself and his family on his trading profits for over a decade. His risk-adjusted returns are either off the charts or not even calculable, because there are no down months.

The percentage requires an assumption, and it is disclosed. His account size is constant and completely independent of his account balance, because it is a function of the timing of his last withdrawal. The authors conservatively assume a constant account size of $100,000, which is conservative in the sense that it exceeds what he needs to cover his daily trading. On that basis his average annual return is 316%. On rare occasions he may take a larger position, liquidated the same day. His required funds are low because his position size is relatively small and he rarely holds anything overnight.

Fordham, a bombed interview, and the LBO that gapped

Bandazian had no idea what career he wanted as a young man, only that making money was always important to him and that he always had some kind of job. Mostly blue-collar: landscaping, selling Christmas trees. He was not trading in high school and barely knew what trading was.

He went to Fordham University in the Bronx and majored in finance, likely because of his proximity to Manhattan and a belief a finance degree would help him get a job.

After college he was determined to become an investment banker, which around 2000 was the most coveted job on Wall Street. Fordham was not recruited by the investment banks at the time. That did not deter him. He describes himself as a grinder who goes after what he wants: he sent out hard copies of his resume and cold-called Bear Stearns, J.P. Morgan and Deutsche Bank.

It worked, with one instructive failure. He bombed the Bear Stearns interview. They asked him about amortisation and how a cash flow statement works, he did not know any of the answers, and he tried to bluff his way through, which he calls a mistake. They asked him to leave. In hindsight he should have told them he did not know but could learn and would work harder than anyone else.

He was not asked those questions at J.P. Morgan or Deutsche Bank, and got offers from both. He attributes it to work ethic and his ability to convey it: he told them he would work 120 hours a week or whatever it took to succeed, he meant it, and they believed it. He took J.P. Morgan’s syndicated finance role over Deutsche Bank’s asset management offer because the J.P. Morgan group was led by Jimmy Lee, and knowing what he knows now, he thinks he probably should have taken the Deutsche Bank job.

Syndicated finance was mostly boring cash flow work. But every tenth deal was an LBO financing, and those deals were a wake-up call. Nobody cares when Verizon gets a revolving line of credit. An LBO deal is on the tape and the stock gaps up. There is a lot of action in the stock, which he liked. He would call his parents, beaming with pride, asking whether they had seen the deal announced on CNBC.

Day to day he did a little financial statement analysis, but most entry-level banking work at that time was preparing presentations for senior bankers: head-down work with Excel and PowerPoint. He moved to the capital markets team after roughly eighteen months, which involved secondaries, convertible bonds and preferred securities: less modelling and more finding out who held specific securities, then approaching a company to argue why they should be hired to price a secondary rather than a competitor.

Approaching the three-year mark, he assessed his path forward. He no longer wanted to be an investment banker, and while he liked capital markets more, he did not want to be a capital markets guy either. Part of that was compensation being capped: even working twice as hard as the next person, his bonus did not reflect it, and it was not meritocratic. The other factor was that he wanted to be closer to the markets, which meant the buy side.

Learning event-driven trading the hard way

A high school friend introduced him to a portfolio manager at First New York Securities who needed an analyst. After several meetings he was offered the job as an event-driven analyst, a term he says he did not even understand at the time.

The work was typical buy-side analyst work: go to conferences, find out what people were saying about upcoming corporate events, run comps against similar companies, go to idea dinners. He was the idea guy; he conducted research and presented ideas, and the PM decided whether the idea went into the portfolio and at what size.

His definition of an event is worth holding onto, because it explains everything he does now. A simple example is a rumour that a company is going to break up. The stock might be trading at $50, and a sum-of-the-parts analysis says the parts are worth $120. But you also have to identify a catalyst that will trigger the realisation of that value, such as an activist taking a stake and pushing the board to break up the company. The key is to make sure there is both value and an imminent catalyst to unlock it, because otherwise you end up stuck in a value trap.

On whether banking prepared him: he thinks many people overemphasise the value of what they learn in investment banking for subsequent buy-side work. The PM trained him, but it was a crash course, baptism by fire. The PM would send him an analysis model for one company and ask him to replicate it for another, held his hand for about six months, and did not start getting real value out of him until about month seven.

The difference between what they were doing and an event-driven hedge fund is one line: a hedge fund might put on a billion-dollar position and wait a few years for the value to materialise. They were trading.

He liked the job more than banking, and the reason is telling. He got a report card from the market every day telling him whether he was doing a good job as an analyst. He was not the PM, so it was not his profit and loss directly, but he viewed them as a team and considered it his as well. He also liked being able to wear jeans and a t-shirt rather than suits and ties.

The fund, the bank, and being let go

After around three years they spun off and formed their own hedge fund with seed capital from First New York. The trading was the same, but the experience was different: he was the head analyst with three analysts under him, there was a learning curve in managing them, and the biggest difference was the investors. As head analyst, he was the one they all wanted to meet with, and he was heavily involved in the marketing effort.

The fund reached approximately $150 million and found it hard to get much beyond that, because most of the money went to large hedge funds such as Greenlight, Jana and Elliott, whose managers were frequently featured on CNBC. They were generating good returns and could not compete with those firms. The PM ultimately closed the fund and went back to prop trading, which Bandazian took as his cue to find the next job.

That was an analyst role on a merger arbitrage book at the National Bank of Canada. The salary was higher, the total compensation about the same, and the vast majority of the trading involved merger arbitrage spreads.

Then 2008 happened. They were making money, but the bank needed to plug holes in its balance sheet, so it shut down the prop trading desk. It was the first time he had ever been let go. His boss pulled him into a room and said they were pulling the plug. He asked when. The answer was right now. He walked back to his desk and his computer and keyboard were gone.

His first child was due in about a week. He had always saved money, so it was not catastrophic, but the timing added to the stress. A group of them went out that night to commiserate and he woke up the next morning with a bad hangover, called Steve Heinemann at First New York, with whom he had a strong rapport, told him the story, and was told he had a seat on Monday.

The two trades that changed everything

Back at First New York he was officially hired as the analyst for a merger arb portfolio manager, but he also got his own pad: a trading line of around $2.5 million. Not huge, but the first time he had his own profit and loss.

The merger arb business was slow in 2008, though they found things to trade: Dutch tenders, spinoffs, index imbalances. There is pretty much always a way to make money.

His own pad was a different story. Even though it was a small line, the pressure was on to generate revenue. He tried event-driven trades because he knew that style well. He was not losing money, but he was not making any either. Up a unit, then down a unit, up a unit, down a unit. He started toying with a momentum strategy, tried corporate actions, sometimes piggybacked other traders at the firm. Nothing was working. He was getting desperate.

Then came the turning point, and it was two trades.

One day he was watching headlines come across his Bloomberg. A headline said a company was in talks to be bought. He typed in the symbol, hit his hotkey, and bought a large number of shares. Within seconds he was up the equivalent of two months of his former salary at the bank. He sold the stock and booked the profit. He assumed it was a fluke, reverted to his old methods, and went back to one unit up and one unit down with no progress.

Shortly afterwards, another similar headline came across the newswire on a different company. He hit the hotkey, bought a large amount of shares, and was up almost the same amount within a few minutes. He booked that profit too.

He does not remember which companies. He remembers the outcome, which was that the results of those two trades led him to give up on all the other trades he was doing and focus on trading headlines. Outside his work as the merger arb analyst, he shifted his full focus to trading headlines, kept getting great results, and kept at it.

He is immediately clear about why that story is not a template.

The transition happened before bots were involved in trading headlines. To stay ahead of the algos, he has had to adapt his approach, which is what the rest of the chapter is about.

Why he never blew up

Schwager points out that most traders in the Market Wizards series experience some kind of life-altering loss or account blow-up in the early days, and asks why it never happened to him.

His answer: he is probably the most risk-averse person you will ever meet, and he cannot quite explain why. Maybe it relates to something from his childhood. When it comes to trading, he simply cannot take any pain. He gets out of trades that are not working within minutes, and rarely takes overnight positions because he does not want the associated gap risk, which is particularly relevant in event-driven trading where the outcome is often binary.

He also mentions, almost in passing, that in 2008 he was down $6,000 on one day, an insignificant amount compared with many of the other traders at First New York, and he felt horrible about it.

The same risk aversion caps his size. Some stocks do not scale well, but others do: in a stock like Intel he could buy 200,000 shares, but would not buy more than 20,000, because he is not comfortable trading that size. The firm tried to give him more capital at various points and he never wanted or needed it. Beyond the scaling problem, he is not comfortable taking bigger positions due to his risk aversion. He can count on two hands the number of times he has used his whole line, including intraday leverage.

The hourglass: why he exits on a clock

This is the single most transferable idea in the chapter, and almost nobody outside high-frequency event trading thinks about risk this way.

Bandazian typically only takes a trade when he is the first to see something. As soon as an announcement gets recognised by the masses, his odds of success go way down. So he uses that realisation as a signal to exit positions as well.

His own framing is an hourglass. If he is the first to see something, his edge might be 98%. As more and more people see the news, the edge drops to 70%, then 60%, then 50%. When he is early to spot a trade, 98% of the sand is at the top. The more time that passes, the smaller the percentage of sand in the top.

Almost all of his trades now exit using a time stop, whether he is profitable or not.

Schwager highlights the time stop in his closing note as a distinguishing feature: if a trade does not respond very quickly to a market development the way Bandazian thinks it should, he gets out even if his protective stop has not been reached. That response to the market not acting right, even when it has moved somewhat in his direction, helps him exit some trades as net profits before they turn into net losses.

The instinct behind it is twenty years of watching price. After doing this for over two decades he has developed a feel for what other people are doing based on watching the price. If a stock spikes a bit and then pauses, he generally thinks that someone else is likely in it for the same reason he is, and it becomes a game of either waiting for the next incremental buyer or trying to be the first one out. If a stock’s path does not meet his expectations, he moves on.

What the bots took, and what they left behind

The original method, seeing a simple headline and getting ahead of the masses, lasted several years. Then it stopped.

He dates the change precisely: it happened slowly between 2016 and 2018, and by 2018 the simple headline game was over. What made him realise it was his own record-keeping. He is meticulous about every trade he does. If he misses a trade, he looks into why. He examines the time and sales to see how the move played out relative to when the news broke, and whether somebody got it earlier, and how the stock moved before it hit Bloomberg’s newswire. He is very aware of where information comes from and how traders obtain it. After he got whipped a few times, he knew something was off.

His adaptation has two parts, and both are worth understanding because they define where a human still has an advantage.

He stopped competing on speed for simple news. It is impossible to beat the black boxes on speed when it comes to simple headlines. He does not place market orders, which he calls a recipe for disaster; he uses keyboard hotkeys with bid-plus components, for example current bid plus 20 cents, so a stock bid at $10 means he buys everything up to $10.20. But he is outmatched on simple headline trades and tends not to trade on those.

He moved to headlines that require interpretation. Because he does the preparatory work, he can still get an advantage on headlines that are more nuanced or require more complex analysis to correctly gauge the potential price impact. Whenever a deal is announced, the stock is added to his watch list and he begins conducting his research. The main questions he asks are: what will be the next catalyst, and how will he trade the stock based on subsequent potential headlines?

Where the edge actually comes from

Schwager challenges him directly: he is not a lawyer or a former M&A banker, so how does he outperform firms with teams of analysts and lawyers?

Bandazian says he loves the question. He has been reading merger agreements and proxy statements for almost 20 years, and worked in the space for so long that he understands how the game works. At any given time there are 75 to 100 announced deals, and he becomes familiar with what matters in each. As new information becomes available, he updates his files and adjusts his expectations. He also has a game plan for how he will respond to various announcements depending on how the stock reacts. If a stock does not respond to a headline the way he thinks it should, he takes a position in it.

In broad terms: he takes a position if the market gets something wrong and the price goes where he thinks it should not.

What does the market getting it wrong look like? Sometimes merger arb funds or home-gamers get things wrong, usually due to a lack of speed. Sometimes an algo trades a headline that appears to be new news but is not. A headline might read that a merger has received European Union approval, which is positive but not significant as long as United States approval remains outstanding. People not up to speed buy the EU headline, thinking it is important when it is not. At that point he might take a position on the short side, believing the stock will come back down.

His reason for trading outside market hours follows from the same logic. In the past few years, about two-thirds of his trading has been outside regular market hours, because the algos tend to be less active then, which creates numerous opportunities. Big news is often released in 30-minute increments early in the morning, at 6:30 a.m. or 7 a.m., and the machines are watching those headlines. It is the buried nuance that is not yet, in his phrase, algo’d.

Four trades, and what each one demonstrates

The chapter contains four worked examples, and together they define the niche better than any description of it.

Whitestone REIT: knowing the state law

Whitestone REIT (WSR) had attracted persistent activist interest over the years, including a specific attempt in early 2024. When that news hit the tape, WSR went on his list. A few months later, a separate REIT-focused private equity firm bid $14 a share for a 9% stake in the company. The stock surged on the news, and Bandazian shorted the rally and covered shortly afterwards for a quick profit.

Why short it? Because his research told him WSR is incorporated in Maryland, and the rules in Maryland allow companies to tell activist shareholders to beat it. In a state like Delaware, management has to entertain the bidder. Additionally, WSR has a history of telling activists to buzz off regardless of the price they bid. He figured that scenario would happen again and went short the rally.

The jump in the stock after the bid was announced was evidence that some traders were unfamiliar with WSR’s history of ignoring bids, nor did they likely know that WSR was based in Maryland, which gave it the right to disregard the bid. Twenty or thirty minutes after the announcement the market figured it out and the stock came back down. His summary: the larger point is that he made money by being prepared and acting fast, and he capitalised on someone else’s lack of knowledge, preparation, or both.

Twitter, 2022: the 13D edge

He calls this one of his best trades, and the licence plate on his truck is 13D.

When Elon Musk took a stake in Twitter, the stock was highly volatile due to considerable uncertainty about his ultimate intentions. Bandazian began his research, which primarily involved scenario analysis to determine the path forward. He asked himself whether Musk really wanted to buy Twitter, and if he made a bid, where the stock should go based on his analysis of similar historical deals. He notes that this kind of thinking is the important part of his process.

He concluded that Musk intended to buy the company, but that Musk is a bit of a loose cannon, so he had to factor that in as well. He figured the merger arb funds would be sceptical given Musk’s erratic personality. His conclusion was that the stock should trade at approximately a 10% discount to Musk’s proposed bid.

A few weeks later Musk filed a 13D on the stock at around 6 a.m., and Bandazian was able to buy the stock before the price moved too much. Because he had completed his analysis in advance, he had already concluded Musk was interested, which gave him more comfort buying on the 13D release. Then, once he bought the stock, based on his analysis of where it should trade, he immediately offered it at a 10% discount to the bid price contained in the 13D filing, which if his memory is correct was the day’s high.

Had he not done the analysis ahead of time, it would have taken him a few minutes to digest the news and walk through his scenario analysis. Those few minutes, he says, are where he makes his money. It might sound trivial, but having a game plan makes all the difference.

Foot Locker, May 2025: the sentence nobody read

This is the clearest demonstration of preparation beating speed.

In May 2025 a headline hit the tape that Dick’s Sporting Goods was looking to acquire Foot Locker for $24 per share. Bandazian began conducting research, including an examination of antitrust considerations and the potential timing of a deal.

The following morning he was at his screen, caffeinated, fingers on the keyboard, ready to strike. The stock was trading between $21.50 and $22. It remained at a discount to the rumoured price of $24 because there was a risk that the next headline would read “No deal”. At that point it could have gone either way, and he was ready for either headline.

At 6 a.m. a press release was issued with the headline: Dick’s Sporting Goods to Acquire Foot Locker to Create a Global Leader in the Sports Retail Industry. Buried in the press release was the line that the election is not subject to a minimum or maximum amount of cash or stock consideration.

In layman’s terms, that deal structure allowed Foot Locker to trade above $24. He knew that once the arbs figured this out, Foot Locker would trade higher than his generic model implied. He bought as much as he could and ended up selling the stock just shy of $24 once the easy money was made. His preparation and speed enabled him to get ahead of all the other arbs; by the time most of them realised there was even a release, he had already booked his profit.

Many of his trades in recent years look like this one.

Cigna and Humana: the trade he did not take, and the one he did

In October 2024 a headline hit the tape that Cigna was said to resume merger discussions with Humana. Both stocks went onto his list, his news scanners were trained on them, and he listened to every conference call.

Based on his interpretation of how the story was written, he had a negative bias about the deal happening. He did not trade on that bias, because he sees no edge in predicting deal outcomes. Instead he prepared for upcoming releases, ready to short Humana because it was trading above his estimate of fair value.

In November 2024 Cigna issued a press release. He dove in, saw that Cigna said there would not be a deal, combined his fair value estimate with the release, went short Humana and covered a few minutes later at a profit. It was not a huge trade, but many small wins like this start to add up.

His fair-value process is a database. He maintains a database of historical deals which he categorises and uses to determine his estimates of fair value. He categorises everything: a strategic buyer differs from an activist; is it a merger, a tender, does it require regulatory approval, where is the company domiciled, is this an announced deal? A multitude of factors contribute to his model, enabling him to arrive at a reasonable estimate of fair value. When the stock is trading away from that value, he places a trade.

He is candid that his analysis frequently turns out to be dead wrong, and that his tight risk management is what protects him.

The schedule behind the record

The cost of the record is the part of the chapter that deserves the most attention, and Bandazian does not hide it. He describes himself as pretty much married to his desk from 3:45 a.m. until 8 p.m. on weekdays.

Against those hours, the trading itself is sparse. Average holding period about five minutes; thirty minutes in a stock is a long time. He trades four out of every five days, usually one trade a day, sometimes three or four. He never takes positions ahead of a potential deal, because his edge comes from two sources only: being more prepared and being faster than other traders when an announcement comes out. He sees no edge in trying to predict a deal.

Asked whether being short a deal stock that receives a higher competitive bid worries him, the answer follows from the holding period: not really, because he trades on news releases and is usually out of his position within twenty minutes. Unless a second bidder emerges within twenty minutes, it is not a risk.

Schwager asks the counterfactual that hangs over the whole chapter: if he had the stomach of a riverboat gambler, how many multiples of his current position size could the market handle? Bandazian’s estimate is that his execution prices would not be as good, but he could probably make three or four times the amount he does now.

The firehouse

He has been a volunteer firefighter for about ten years, and his reason for it is the most human passage in the book. He believes there is no higher calling than risking yourself for the sake of your community. In those moments it is not about titles or backgrounds, it is about doing your part. He has never been a soldier, but instead of the enemy trying to kill you, the fire is trying to kill you and the people you are with are coming to help. Going through intense situations with a crew that shares that conviction creates a bond that is difficult to describe. He calls it the purest form of giving back, and says placing yourself in the path of danger taps into the ancient, primal instinct of protecting your own and stepping forward when others cannot.

Calls come whenever they come. As a volunteer he does not have to answer all of them, and it depends on the situation and severity. For a life-or-death matter he will always answer. If it is a cat up a tree and he has positions on, or is waiting for a release, he usually will not go. Because his holding periods are so short, he does not have to worry about walking away from the desk with a large position; he simply gets out. He tries to sleep at the station a few times a month, since they can always use the extra help, and ideally those days coincide with slow trading, but sometimes he misses a trade.

What he tells new traders, and what he is building next

He often gets asked by the guys at the firehouse how to trade, and invites them to watch him trade. But he tells them his way is not necessarily the right way. There are a million ways to trade, and his approach is unlikely to be a good fit for most other people.

His standing advice is to find a trading approach that suits your personality, and he recommends reading the Market Wizards series, because it covers a wide range of trading approaches. The biggest takeaway he thinks people will get from it is that it is all about a personality fit. He also tells people they should try to do something different from what everybody else is doing, because they are probably not going to gain much of an edge being the millionth person trying to figure out what the Federal Reserve is going to do next. Trading without an edge is fine if you are doing it for entertainment, but it is a losing proposition.

He is honest about his own consistency too: he is fortunate to be so consistent, but there have been moments along the way when he was not sure if it would last to this point.

And he is thinking hard about sustainability. He is 47. He has been able to adapt to the technological changes so far, but imagines a time will come when the algos take over everything, including his current niche. It is also becoming increasingly difficult to work from 3:45 in the morning until 8 at night, five days a week; to the extent those workday hours are suitable for anyone, they are probably most suited to single people in their twenties.

So he has been working on a semi-systematic macro strategy that is more sustainable and scalable, dealing with what he calls offsides positioning. The catalyst for exploring it was the Jason Shapiro interview in Unknown Market Wizards. He is using the concept in a unique way that he does not think anyone else is doing, and is still ironing out the kinks, but hopes it will eventually replace his need to live in front of the screens.

What Coyle and Schwager conclude

Coyle opens with a Twilight Zone framing, and the line he lands on is the summary of the chapter: picture a man who found a niche that enabled him to handily beat the markets to such a degree that he became a Market Wizard, and whose cost is being virtually chained to his desk for roughly sixteen hours a day, five days a week. He adapts John Philpot Curran’s phrase about eternal vigilance being the price of liberty into eternal vigilance being the price of market wizardry.

He also places Bandazian in a category. Throughout his career he has occasionally met traders who discover niche strategies that are too sophisticated for retail but do not scale sufficiently for large quant shops to dedicate the necessary resources, because the related costs would offset most of the potential profits. Bandazian is one of these traders.

His central risk point is the same one that runs through this entire cohort: the central tenet of cutting losses short very quickly is the largest driver of Bandazian’s risk management, and readers should take heed, because cutting losses rigorously is probably the primary trait that separates Market Wizards from most traders.

He notes the many similarities with Jason Berry in chapter seven: both possess supreme risk management skills, trade very quickly, use their market instincts in their trading, and spend a considerable amount of time glued to their screens. Another commonality is that their strategies do not scale in any meaningful way.

Then he makes the argument that most readers will find genuinely useful. At first glance their results are not as impressive as those of a trader who turns a small sum into a huge fortune, as is true for many of the traders in this volume, and he imagines most readers would prefer to have that fortune. However, having seen the granular data on what it takes to turn a small sum into a fortune, he can confidently say that at least 90%, and perhaps even 99%, of people would not be comfortable with the ride required to achieve exponential growth in their account balance. Comparatively, an equity curve similar to Bandazian’s or Berry’s would be a walk in the park. Both of these traders essentially print money, which he calls an amazing feat.

As a system designer and research coder, he closes by saying it would be a tremendous undertaking to transform Bandazian’s methodology into a mechanical process. The idiosyncrasies of each deal, the required nuanced interpretation of multiple factors, and the need to reevaluate the situation with each new filing or news release are all impediments to computerising his approach. Perhaps someday artificial intelligence will be able to replicate what Bandazian does, but for now it is pleasing to know there are trading areas where man can achieve what computers cannot.

Schwager approaches it through the efficient market hypothesis, and the argument is worth following.

The EMH says that at any given moment the market discounts all known information, so it is impossible for anyone to beat the market, with two exceptions: short-term outperformance from random fluctuation, and higher returns achieved by accepting higher risk. How, then, can the EMH be correct if someone like Bandazian can achieve over eleven years of all winning months with much higher returns than the market indexes? It cannot. If the EMH were correct, the probability of any trader having a track record like Bandazian’s would be infinitesimally small, even if there were trillions of traders.

The irony Schwager adds is the important part. Most people would still be better off making investment decisions based on the assumption that the EMH is true, because most people do not have any edge in trading or investing and would be better off buying a low-cost index fund than making their own trading decisions. The usefulness of the EMH as a guiding investment principle for most people does not change the fact that traders like Bandazian are walking embodiments of compelling evidence that the EMH is wrong. Skilled traders can and do outperform the market, sometimes by incredible margins, and significant outperformance is possible, albeit difficult to achieve and accomplished only by a small minority who attempt the task.

His other three points are: that successful trading is the byproduct of hard work, and he doubts many wannabe traders would be eager to put in the hours Bandazian does; that alongside protective stops, Bandazian employs time stops, exiting when a trade does not respond quickly even if the protective stop is not reached, which helps him exit some trades as net profits before they turn into net losses; and that he too was struck by the commonalities with Berry despite completely different markets and methodologies, adding one more to the list: both consider possible future events that might impact their markets and plan their response to those scenarios, which lets them avoid doing analysis and making trading decisions during the chaotic market environments that accompany major news.

He ends on a warning aimed squarely at anyone reading this series for career inspiration. Many people are attracted to the idea of trading for a living without fully considering the consequences. If you are dependent on trading profits to pay the mortgage, even a sideways period can become a crisis, let alone a period with a significant equity drawdown. Trading for a living requires extraordinary consistency, which is very difficult to achieve given that market opportunities for any trading approach are inconsistent.

Mind, Method, Money

Bandazian is the clearest case in the book of a trader whose Money pillar sets the size of everything else, deliberately.

Read across the cohort, the pattern is complete. Goedeker, Berry, Sharkness and Bandazian all arrived independently at the same reflexive exit before any of them had a method, and all four describe it as instinct rather than training. Our overview of the three pillars explains why that one habit does so much work.

◆ Further reading

Greatest Traders

Eighty-six lives read through Mind · Method · Money, from Livermore reading a chalkboard in 1892 to the traders still working from those ideas today. Told as they happened, with the losses left in, and every quotation traced to a source.

Read the Book Page →Buy on Amazon →

The counterweight

Four things belong beside the record, and the book supplies most of them itself.

The percentage rests on an assumed account size. The 316% is calculated on a conservatively assumed constant $100,000, because his real account size is a function of when he last withdrew. Profits are withdrawn, so nothing compounds. The unassailable statistic is the absence of losing months, not the return.

It does not scale, and that is structural. Schwager says it plainly, Coyle says it twice, and Bandazian agrees. He would not buy more than 20,000 shares of a stock where he could buy 200,000. The firm offered him more capital and he declined. A strategy where the edge dissipates within a minute cannot absorb size.

The niche is closing and he says so. The simple headline trade died between 2016 and 2018. He expects the algos eventually to take his current ground too, which is why he is building a semi-systematic macro strategy as a successor. Anyone reading this chapter as a blueprint is reading a description of a shrinking window.

The hours are the price. Sixteen-hour days, five days a week, in bed by 8:30 p.m. to be at the desk before four in the morning. He is 47 and says those hours are probably most suited to single people in their twenties. Coyle calls him virtually chained to his desk. Schwager’s closing warning about trading for a living requiring extraordinary consistency, in a business where opportunities are inconsistent, is aimed at readers who see the equity curve and not the schedule.

What actually transfers

Use a time stop, not only a price stop. If your edge decays with attention, then time is your real risk variable. Decide in advance how many minutes the trade has to work, and exit on the clock whether you are up or down.

Picture the hourglass. First to see it, edge near 98%. As the crowd arrives, 70%, then 60%, then 50%. The sand running down is the argument for getting out, and it applies to any edge that depends on being early.

Do the analysis before the news, not after. Bandazian’s entire advantage is a few minutes of pre-work. Know what each plausible headline means for the price before it lands, and you will not be reading and deciding at the same time. Both Berry and Bandazian do this, and both authors flag it as the commonality between them.

Build a fair-value database for whatever you trade. Categorise past cases, derive an expected value for the new one, and trade only when price is away from it. Without the reference point there is nothing to fade.

Do not trade the prediction. He explicitly sees no edge in forecasting whether a deal closes, so he does not take positions ahead of announcements. He prepares for what happens after. Our piece on risk of ruin covers why prediction-based sizing is the expensive kind.

Look where the machines are not. Two-thirds of his trading is outside regular hours. The algos handle one-line headlines; they struggle where the fact is buried in a filing and needs interpreting. Find the version of that in your own market.

Size to the temperament you actually have. He could make three or four times as much and will not, because he cannot tolerate the positions required. That is not a failure. A method you can run for eleven years beats one you abandon in the first bad month. Our position sizing guide covers how to set that honestly.

Frequently asked questions

Who is Rick Bandazian Jr.?

Rick Bandazian Jr. is the trader profiled in chapter nine of Market Wizards: The Next Generation (2026) by Jack Schwager and George Coyle, under the title “Beating the Bots”. A Fordham finance graduate, he worked at J.P. Morgan and then as a buy-side event-driven analyst before going solo in early 2015. He trades corporate events and merger news on holds of about five minutes, and has not had a losing month in eleven years. He is also a volunteer firefighter.

What are Rick Bandazian’s returns?

No down months over eleven years, and an average annual return of 316% calculated on a conservatively assumed constant account size of $100,000. He funded the original account with $25,000 and has withdrawn profits amounting in the millions, so the capital never compounds. The authors note his risk-adjusted returns are either off the charts or not calculable, because there are no down months to measure against.

How does Rick Bandazian beat trading algorithms?

Not on speed. He stopped competing for simple headlines when the bots took that ground between 2016 and 2018. His edge now is preparation on headlines that require interpretation: SEC filings such as 13Ds that have to be opened and read, deal terms buried in press releases, state incorporation rules, multi-jurisdiction regulatory conditions. He tracks 75 to 100 live deals, keeps a database of historical deals to estimate fair value, and plans his response to each plausible headline in advance. About two-thirds of his trading happens outside regular market hours, when the algorithms are less active.

What is Bandazian’s hourglass rule?

His edge decays with attention. If he is first to see something, his edge might be 98%; as more people see the news it drops to 70%, then 60%, then 50%. He pictures an hourglass where 98% of the sand is at the top when he is early, and less remains as time passes. Almost all of his trades now exit on a time stop of three or four minutes, whether profitable or not.

What is Rick Bandazian’s risk management?

A hard profit-and-loss stop of about one unit, which he defines as $2,500, plus a time stop of three or four minutes. Average holding period about five minutes, rarely anything overnight because of gap risk. He describes himself as probably the most risk-averse person you will ever meet, and estimates he could make three or four times as much with a higher risk tolerance. 85% of his trades are winners or breakeven.

Which Market Wizards book is Rick Bandazian in?

Market Wizards: The Next Generation (Harriman House, 2026), chapter nine, “Beating the Bots”. It is the final chapter. The book also profiles Kristjan Kullamägi, Lance Breitstein, Simon Russo, Lukas Fröhlich, Phil Goedeker, Kelvin Chiu, Jason Berry and Kenny Sharkness. Our review of the book covers the full cohort.

Sources and further reading

Primary source: Jack D. Schwager and George Coyle, Market Wizards: The Next Generation (Harriman House, 2026), chapter nine, “Beating the Bots”, including the closing notes from both authors. All figures, trades and dates above are drawn from that chapter. Notes explicitly labelled as ours are our own observations, not figures the book publishes. Complete Trader’s Edge has not audited any of the underlying trading statements.

Continue Learning

• Kenny Sharkness: 62.9% a Year From a Method That Never Stops Changing
• Jason Berry: Five Losing Months in Fourteen Years
• Phil Goedeker: $5,000 to $53 Million, and Exactly One Losing Year
• Simon Russo: $40,000 to $500 Million, and the Trades That Nearly Ended It
• Lance Breitstein: $46 Million at Trillium, $71 Million on His Own
• Market Wizards: The Next Generation Book Review (2026)
• Risk of Ruin: The Mathematics Every Trader Must Understand
• The Psychology of Losing
• Position Sizing: The Most Important Decision in Every Trade
• The Three Pillars: Mind, Method, Money
• The Complete Trader’s Edge – The Book

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Written by

Louw van Riet

Author · Trader · Coach

Louw is the author of The Complete Trader's Edge — a 70-chapter trading framework covering psychology, technical analysis, ICT concepts, and professional risk management. He has spent years studying institutional price action across forex, indices, and crypto, and built this platform to provide the complete, honest trading education he wished existed when he started.

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