Most traders lose money chasing patterns that don’t exist.
Dr. Samir Varma discovered this truth the hard way—after making catastrophic emotional decisions during the dot-com bubble despite identifying the right stock at the right time. But that failure launched a 30-year journey that transformed him from particle physicist to systematic trader. His secret? Treating markets like quantum physics experiments—reactive, not predictive.
And what he’s learned may completely change how you think about trading.
From Supercolliders to Stock Markets
In 1993, when the U.S. Congress canceled the Superconducting Super Collider project, Samir faced unemployment. A physicist without a physics job. That’s when he read Matt Ridley’s article in The Economist about mathematicians making short-term predictions on Wall Street using chaos theory.
“I said this sounds like nonsense,” Varma recalls. “We know about efficient market hypothesis. This can’t possibly be true.”
But here’s what most people miss…
His skepticism drove him to test the theory himself. Within months, he began algorithmic trading of S&P 500 futures using chaos theory—possibly among the first to do so with advanced mathematics beyond simple moving averages.
“It worked very nicely for quite some time,” he notes.
Three decades later, his approach has evolved into something far more sophisticated than those early chaos theory models.
The $550 Mistake That Changed Everything
Varma bought Siebel Systems (SEBL) at $5 split-adjusted. He watched it climb to approximately $120. Then greed and regret took over—he held through the crash and finally sold at $550 split-adjusted.
“I made 10% on my very worst trade,” he says. “How can that be your worst trade?“
Here’s why this matters:
Despite the gain, this was his worst trade because of massive opportunity cost. He had identified both the correct entry and the correct exit point around $120, but failed to execute.
“I identified the correct stock. I identified the correct time to sell it. I didn’t pull the trigger. Then I had regret over the fact that I didn’t pull the trigger to sell it. And as it plummeted down, I kept saying ‘No, I’m going to wait for it to bounce.’ And it didn’t.”
The surprising part? This wasn’t about market prediction. It was about psychology versus systems.
And that distinction separates profitable traders from permanent losers.
Why Your Stop Losses Keep Getting Hit (It’s Not Manipulation)
If you’ve ever watched price sweep your stop loss before reversing in your predicted direction, you’re not alone. Varma spent years studying this phenomenon.
The answer isn’t market manipulation—it’s iceberg orders and algorithmic execution strategies.
“When there’s an institutional order, what you see is some number of shares, but there’s a giant iceberg below them that triggers upon the first one,” Varma explains. “That’s why you get the drift in that direction.”
The Liquidity Illusion
Here’s the counterintuitive truth: Liquidity exists over time, not at specific moments.
“The liquidity of the stock market at any given instant in time is not very big,” he reveals. “An actual retail stop order can move the market even though you wouldn’t expect it to because at that moment in time there isn’t much liquidity. Liquidity exists over periods of time. At instance in time it doesn’t really exist.”
VWAP (Volume Weighted Average Price) algorithms may exploit this by:
- Identifying areas where stops are likely clustered
- Adjusting order execution timing
- Acquiring shares during temporary price dips
- Resuming normal buying as price stabilizes
The defensive solution? Never use round numbers for entries or exits.
“Use prime numbers. Put an order for 96 shares or 221—some really crazy number,” Varma suggests. “Use fractional shares. Make yourself look like you’re not an institutional order.”
The “Leech on a Whale” Strategy
“You’re basically saying, ‘I know there’s a whale out here trading in this direction the rest of the day. I want to catch a ride whenever I can.'” – Dr. Samir Varma
Varma’s explanation of intraday trading centers on one principle: identify institutional flow and position accordingly.
For scalpers and day traders, this may involve:
- Opening range breakouts (10-20 minute consolidation followed by directional move)
- Post-sweep confirmations (price takes out predictable stops, then reverses)
- Protected lows after initial sweeps (areas where institutional buying may resume)
When asked about a trader’s specific approach of watching for sweeps at predictable levels followed by confirmations during market open hours, Varma validated: “It’s funny. In two questions, you’ve described my approach.”
But he emphasizes understanding the underlying order flow mechanics, not just pattern recognition.
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Why Hedge Funds May Not Have All The Answers
This may surprise you: Varma believes many hedge funds operate with fundamentally flawed approaches.
His research paper (expected in the Journal of Portfolio Management) examines standard drawdown limits of 7-10% used by multi-manager platforms.
“I demonstrably show that the reason they’re successful is in spite of their risk management, not because of their risk management,” he states.
His research tested pairs of ETFs during their most profitable historical periods. “I showed that if you imposed any kind of a drawdown cutoff on it, you turned what was a certain profit because you know it’s going to be profitable into a loss more often than not.”
The Problem with Cookie-Cutter Rules
“They hire the same people, trained by the same professors, at the same schools, on the same strategies, at the same time and then they complain that they don’t have anything unique,” Varma observes.
The Warren Buffett Bet Validates This
Varma references Warren Buffett’s famous wager with a hedge fund manager that the S&P 500 would outperform actively managed funds over 10 years.
“The S&P beat it by some ridiculous margin,” he notes. “And this just keeps happening over and over and over.”
For retail traders, the implication: “All you have to do to beat essentially all these hedge funds is buy the SPY and sit at home. So why even bother investing in them?”
But if you’re going to actively trade, you need a genuinely different approach.
The Physics of Market Unpredictability
Varma’s book, The Science of Free Will, introduces a concept critical to understanding markets: computational irreducibility.
In simple terms: The simplest possible rules can produce outputs that can never be predicted, even though they’re deterministic.
“A lot of market movement genuinely is random—even though it’s coming from a deterministic process, it’s not predictable,” he states. “It can’t be predicted.”
This changes the trader’s objective.
Your job isn’t to outthink the market. It’s to find consistency, not predictability.
The Pattern Recognition Trap
Varma gives two contrasting examples:
Noise masquerading as pattern: “For the longest time, stocks whose symbols began with a vowel outperformed the market. That’s not a pattern.”
Genuine pattern (though weakened): “The Congressional Effect—almost the entire return of the S&P 500 took place when Congress was not in session. That was a real pattern, though it has weakened over the years.”
The difference? Economic rationale versus statistical coincidence.
Even genuine patterns can decay over time, which is why continuous testing and adaptation matter.
The Two Things You Must Know Before Trading
According to Varma, beginners need to understand two critical elements before choosing a trading approach:
1. How You Handle Losses and Overnight Risk
Two simple questions determine your optimal trading style:
“If you’re holding a position overnight, do you get nervous? Can you sleep?”
If no, you may need to focus on day trading or scalping.
“If you’ve had a bunch of losses in a row, do you freak out?”
This determines whether you can handle trend-following strategies (potentially 25-30% win rate with large winners) versus counter-trend strategies (potentially 65-70% win rate with occasional large losses).
When asked these two questions, the podcast host realized: “It’s funny. In two questions, you’ve described my approach.”
Varma’s response: “Just those two questions already suggest you could probably do intraday trend following… most likely the place you would probably want to do this is opening range breakouts.”
2. Bad Luck vs. Bad Process
“You need to understand what it is about your logic that went wrong that caused you to lose money and be able to distinguish bad luck from bad process.”
Why this matters critically:
“It’s always better to have a good process and a bad result than a bad process with a good result. Always.”
The reason? Good outcomes from bad processes create false confidence. “You didn’t learn anything from it. In fact, you learned the wrong thing from it. And undoing things is much harder than doing them in the first place.”
Building an Unbreakable Trading System
“You have to try to break your system in the nastiest way you can think of until you throw your hands up and say, ‘I can’t break this anymore.'” – Dr. Samir Varma
Varma’s stress-testing methodology for systematic strategies:
The Noise Addition Test
“Take the inputs which are presumably sitting in CSV files or something like that. Write a little program that adds noise to each day, random noise from some reasonable distribution.”
What to expect: “Your returns should be unaffected with small amounts of noise and should start to degrade with large amounts of noise. What you should see is that there’s a curve that degrades as you add more and more noise.”
Red flag: “What you don’t want to see is something where some amounts of noise produce a good result and other amounts of noise produce a bad result. That doesn’t fly.”
Why? “You fitted noise, you didn’t fit data. Which means you never really had an advantage.”
The Moving Average Optimization Trap
“The classic example which people still do, beginning traders particularly, is they’ll take say some agricultural commodity and they’ll say I’m going to trade a moving average crossover system, and then they’ll try every single combination of the two moving averages until they find the quote optimal moving average. That is almost never going to work.”
Real-World Stress Test: 2022
Varma’s personal example of systematic discipline under pressure:
“In 2022, I had seven straight trades where it looked to the system—I say me, but it’s the system—that it’s time to get back into the market. We took a small position and then the small position would get hit by a 7% drop the next day or 5% drop the next day, and it happened seven straight times.”
“I was tearing my hair out by the end. But it wasn’t like I was not going to take the next trade.”
That’s systematic discipline. Following the process even when emotionally exhausted.
The Risk Classification Revolution
Most traders try to predict risk. Varma classifies it instead.
The critical difference:
Prediction approach: “The S&P will have 23% annualized volatility next month.”
Classification approach: “Risk is high right now. Reduce exposure.”
Varma uses an analogy: “Was Tiger Woods twice as good as Phil Mickelson or eight times as good? Does it matter? He was just a hell of a lot better than Phil Mickelson.”
Applied to risk: You don’t need to know if volatility will be 26% or 28%. You need to know if risk is high or low relative to historical norms.
The 2/3 Rule for Position Sizing
This statistical observation should influence your position sizing:
When markets trade above long-term moving averages (200-day example):
- Approximately 2/3 of returns occur
- Approximately 1/3 of risk exists
When markets trade below these levels:
- Approximately 1/3 of returns occur
- Approximately 2/3 of risk exists
Practical application: “Even if you have a positive expected value trade below that long-term line, if you’re trying to keep your drawdowns limited, you should actually be limiting the size of your position when you’re below that long-term line.”
Example: “If you were going to invest say 50% above the line, you should be investing I don’t know 25% below the line because what you’re trying to do is to keep the risk roughly speaking constant.”
Why Standard Deviation May Mislead
Varma challenges the standard risk measurement approach used throughout the industry.
“The problem is that assumes the returns are normally distributed, and they’re not. They are most certainly not. They are what’s called in the industry leptokurtic.”
What leptokurtic means: “If you overlay the distribution of returns over a Gaussian distribution, the peak will be thinner and the tails will be fatter.”
Translation: Extreme events happen more often than normal distribution predicts. Small movements happen less often.
“There’s also some good evidence to suggest that the standard deviation of the stock market is not defined. It might be infinite.”
His example: “A stock can only go down 100%, but it can go up infinitely. If you were short Tesla all this time, you’re crying unless you got the timing exactly right. So what good would standard deviation have been to you when trying to measure the risk in Tesla? The answer is it wouldn’t have done you any good at all.”
The Trader’s Personality Equation
Varma emphasizes that successful trading requires alignment between strategy and personality, not just technical edge.
“You can find more than one edge. But that edge then has to be congruent with your personality. If the edge is not congruent with your personality, you will never be successful with the trading strategy even if it works.”
Counter-Trend vs. Trend-Following Psychology
Counter-trend strategies:
- Typically 65-70% winning trades
- Occasional large losses
- Many small gains
Trend-following strategies:
- Typically 25-30% winning trades
- Large gains when correct
- Many small losses
“Some people can’t deal with being wrong five times out of six or four times out of five. So it wouldn’t work for them. They’d get whipsawed in a moving average strategy for example and they just say ‘no the seventh time I’m not going to take the loss’ and that’s the time it goes up 200%.”
His personal preference: “My personality is that I actually hate having to make decisions, which is a strange thing for a trader to say. I decided years ago that I’m a physicist, I like systematic stuff, so I’d need to be a systematic trader.”
Why Varma Stopped Short-Term Trading
Despite initial success with chaos theory and short-term trading, Varma made a strategic shift in 2003.
“I realized that alpha from short-term trading is going to become harder and harder to achieve because there were more and more people trying to do it.”
Efficient markets hypothesis at work: More competition equals less opportunity.
The capital problem: “The shorter term your trade is, the less money you can run through it because you create the alpha decay by just trying to take advantage of it.”
His solution: “I would try to become a longer-term trader. And then I said I hate having a majority opinion on anything. So what is it that a systematic or quantitative equities trader would not do?”
Answer: “Increase their time frame to beyond a year. Nobody does that. And the second is stop looking for alpha.”
Current approach: Holding periods that can extend beyond one year, focused entirely on risk classification rather than alpha generation.
The Overnight vs. Intraday Return Pattern
Varma shares a fascinating statistical pattern that has persisted in U.S. markets:
“If you take the S&P 500 ETF SPY and you divide it up into its intraday return open to close and its overnight return close to open, you will find that more than 100% of the return of the SPY takes place overnight.”
Translation: “On average, the SPY goes down during the day.”
Why this pattern exists: “You’re getting paid for taking overnight risk.”
The arbitrage challenge: “There’s only a limited amount that you can do before you start to move the market too much, and second, you are then subject to the reason it’s positive, which is that you’re getting paid for taking overnight risk.”
Note: Varma presents this as an example of an edge that exists but is difficult to arbitrage effectively. Indian market traders would need to independently verify if similar patterns exist in NSE/BSE markets.
Market Experience: The Necessary Losses
“You need to understand how to lose money and what your reaction will be to the loss and how you’re going to act.” – Dr. Samir Varma
Varma emphasizes that losing money is a necessary part of developing as a trader.
“You need to start actually trading with real money in the market. There’s no substitute for that. And expect to lose money. Period.”
What you’re learning through losses:
- Your psychological response: “How to lose money and what your reaction will be to the loss and how you’re going to act.”
- Process evaluation: “What it is about your logic that went wrong that caused you to lose money and be able to distinguish bad luck from bad process.”
The critical learning: “You never really learn anything from making money. You only learn it from losing money.”
The dangerous shortcut: “The stupidest things I’ve ever done in my life is when I’ve read an economic theory and said that makes a lot of sense, got it, I’m going to use that in the market. And it blows up in your face every single time.”
Why Economics Matters (And Doesn’t)
Varma’s relationship with economic theory is nuanced:
What helps: Understanding different schools of economic thought (Austrian, Keynesian, Neo-classical) to identify which insights apply to specific market conditions.
What hurts: Blindly applying economic theories without understanding their limitations.
“You really have to use economics to figure out whether something is real or whether something is not real, because finance will not tell you.”
His approach: “You need to read a lot of books on economics and finance. But then the most important thing is you need to read them with a very skeptical eye to see where they’re wrong.”
The Risk Model Critique
Varma strongly criticizes standard risk forecasting models (GARCH, EGARCH, ARMA, ARIMA):
“They work until they don’t. That is to say, until the hits the fan, they work great. When it hits the fan, they blow up and they don’t react fast enough.”
The fundamental problem: “They are mistaking an exact prediction for reality.”
He attributes this observation to Keynes: “These models would prefer to be exactly wrong than approximately right. It’s a lot more important to be approximately right than it is to be exactly wrong.”
Technical Analysis: When It Works (And When It Doesn’t)
When asked if technical patterns can drive price movement through self-fulfilling prophecy, Varma’s answer is nuanced:
Short-term validity: “Yes, it can. Studies suggest that it does, but it is as best one can tell a fairly short-lived effect.”
Example that works: “If you find a channel and there’s been a lot of times when the price has bounced off a certain level, there’s a very good chance that there’s going to be a bunch of stops below. So if the market drives through those stops, it’s a nice short-term scalp to short here, wait for it to fall a bit, and then buy it back.”
What doesn’t work long-term: “It’s much harder to justify that as being a long-term statement.”
The Round Number Effect
“People like to place trades at round numbers. You’ll find more trades at zeros or fives or 2.5s than you will at 2.17 or 99.17. That’s something you can exploit. No question.”
The mechanism: Predictable human psychology creates clustered liquidity at psychologically significant price levels.
The Yield Curve Mystery
Varma discusses one current market puzzle that challenges his framework:
“Whenever the yield curve is inverted, a recession invariably follows, and also because a recession invariably follows, the S&P invariably goes down.”
The current anomaly: “We’ve had an inverted yield curve now more or less for two years. Not a whole hell of a lot has happened.”
He mentions recent market declines but notes they were “identifiably because of the Trump tariffs. Nothing to do with the recession.”
His approach to uncertain indicators: “The safest thing to do is to not ignore it. So we don’t ignore it, but that is one where there’s been some change in the market for which I can give you a hypothesis but it’s merely a hypothesis.”
The lesson: Even historically reliable indicators can fail or change. Rigid adherence to any single signal can be dangerous.
The Institutional Advantage Myth
Varma challenges the notion that institutional traders have inherent advantages:
Institutional disadvantages:
- Forced to maintain market exposure regardless of risk environment
- Risk committee constraints
- Career risk (tracking error concerns)
- Cookie-cutter processes that limit adaptability
Retail advantages:
- Complete flexibility in position sizing
- No mandate to stay invested
- Ability to sit in cash during high-risk periods
- No career risk from short-term underperformance
“Many times they know better, but they’re forced by the risk management committees to do it anyway. They are forced to take positions regardless of what the risk outlook is.”
The mutual fund manager problem: “They can’t do things to actually actively manage the risk in that way because they’re constantly frightened of being behind the index.”
Using AI in Trading (The Right Way)
Varma uses AI extensively but with critical caveats:
Where AI helps: “Once you get started, AI is a massive help” for data analysis and pattern recognition.
Where AI fails without human guidance:
“You have to have some direction in which to point the AI. What are you going to ask the AI? Number one. And number two, how are you going to judge if the AI’s answers make sense?”
The prerequisite knowledge: “To be able to even ask the AI questions and judge the answers, you actually need to know something.”
How to build that knowledge:
- “Read lots and lots of books” (economics, finance, trading psychology)
- “Start actually trading with real money in the market”
- Develop ability to distinguish signal from noise through experience
Bottom line: AI accelerates analysis for those with foundational knowledge. It cannot replace market experience or judgment.
The Long-Term Capital Management Lesson
Varma uses the famous LTCM collapse as a case study in institutional failure:
The setup: Nobel Prize winners arbitraging on-the-run versus off-the-run Treasury bonds
The thesis: “These are two identical assets. Financial theory tells us they’re exactly the same. They’re trading at different prices. We should arbitrage this.”
The leverage: 40-to-1
What they missed: “There’s a liquidity risk. Because no one’s trading the off-the-run bond, it can go off to some other random price and there’s nothing you can do about it. And if you’re short it, you’re dead.”
The institutional problem: “Cookie-cutter processes… are not able to deal with the nuances of actually how you would handle real life risk situations.”
Applicable Lessons for Indian Stock Market Traders
While Varma’s primary experience is in U.S. markets, several principles apply universally to NSE/BSE trading:
1. Personality-Strategy Alignment
The need to match trading style to psychological makeup is universal. Indian traders must honestly assess their overnight risk tolerance and losing streak resilience.
2. Order Placement Psychology
The round number effect applies across markets. Avoid placing orders at ₹100, ₹500, ₹1,000. Consider using ₹487, ₹997, or other “ugly” numbers.
3. Opening Range Breakouts
Varma mentions trading this strategy with Joe Richie in Chicago on individual stocks. Indian traders could test similar approaches on Nifty 50 constituents during the first 15-30 minutes of trading.
4. Institutional Flow Recognition
While specifics differ, large institutional orders exist in Indian markets too. Block deals, bulk deals, and FII/DII activity create similar flow dynamics.
5. Risk Classification Over Prediction
The principle of classifying market conditions as high/low risk rather than forecasting exact volatility levels applies equally to Indian indices.
6. System Testing Methodology
The noise addition test, avoiding curve-fitting, and stress testing approaches work for any market.
7. Position Sizing Based on Market Regime
The 2/3 rule concept (returns vs. risk above/below long-term moving averages) can be independently tested on Nifty 50, Bank Nifty, and Indian stocks.
Important note: Indian traders must independently verify these patterns in local markets rather than assuming U.S. market statistics automatically apply.
The Physics Paper That Applied Finance Thinking
In a fascinating reversal, Varma applied his “finance eye” to physics:
“I have a physics paper coming out in a proper physics journal, European Physical Journal C, Particles and Fields, where I put my finance eye looking at a very peculiar set of data which is the mass of quarks.”
The pattern no one explained: “The heaviest quark is I think 13,000 times heavier than the lightest one. No one knows why.”
His insight: “I looked at the pattern and I said I think I get this. And it turned out… the paper is now being published and it’s being peer-reviewed, I was right.”
The lesson: Pattern recognition skills developed in one complex system (markets) can transfer to another (particle physics). The key is distinguishing genuine patterns from noise—a skill honed through decades of market experience.
What “Boring” Trading Actually Looks Like
Varma’s philosophy on trading as a business rather than entertainment:
“I want my life to be as boring as possible. I hate excitement. I don’t want anything. I don’t want to be happy. I don’t want to be sad. I don’t want anything. I just want to essentially be able to ignore everything as much as I can for mental equanimity.”
His ideal state: “On any day, you should not be able to tell whether I’m making a loss or a profit. I shouldn’t even be able to tell whether they’re making a loss or a profit, and I should just be able to ignore what’s going on completely.”
The systematic trader’s mindset: “If you’re going to make trades, forget about whether they made a profit or a loss. Just make the trade because it made sense.”
Why this matters: Trading is meant to be a business that generates consistent returns, not an emotional roller coaster that provides excitement or identity validation.
The Trader’s Journey Timeline
Understanding Varma’s evolution helps set realistic expectations:
- 1993: Started with chaos theory on S&P 500 futures
- Late 1990s: Experienced the Siebel Systems emotional disaster despite technical correctness
- 2003: Pivoted from short-term to long-term trading as alpha decay accelerated
- 2020s: Operates lean hedge funds focused entirely on risk classification
- 2022: Survived seven consecutive false signals through systematic discipline
- 2025: Publishing peer-reviewed research in both physics and finance journals
The timeline lesson: Mastery took 30+ years of continuous learning, adaptation, and painful lessons. There are no shortcuts.
Key Principles Summary
On Edge:
“You need to find an area of the market where you can be reasonably confident that you have some edge, and you need to be able to identify what that edge is, and you need to be able to say something sensible about the edge before you ever trade that edge.”
On Consistency:
“Find a process, find an edge, find where it is that in the market there is some element of consistency. Something tends to consistently happen. Find that and trade that and you can exploit that.”
On Prediction:
“A lot of market movement genuinely is random, i.e., it is unpredictable. Even though it’s coming from a deterministic process, it’s not predictable. Your job as a trader is not to try to outthink the market or outfox the market.”
On Process:
“It is always better to have a good process and a bad result than a bad process with a good result. Always.”
On Risk:
“What cannot be arbitraged away is risk because risk is generally a pylon. A sells so B gets a margin call. B sells so C gets a margin call. You can’t arbitrage that away. All you want to do really is figure out periods of time when that’s likely to happen and be out of the market if you can.”
The Bottom Line
After three decades of systematic trading and research published in peer-reviewed journals, Dr. Samir Varma’s conclusion is both humbling and empowering:
Markets are fundamentally unpredictable in detail. But they exhibit statistical consistency that can be exploited through disciplined process.
The difference between winning and losing isn’t intelligence, capital, or access to information.
It’s knowing when to react instead of predict. When to classify instead of forecast. When to follow your tested system despite emotional resistance. When to reduce position size even with positive expected value. When to take the eighth trade after seven consecutive losses.
“You need to find the edge and then you need to find an edge that is congruent with whatever it is that you can live with. The reason the trading journey is so hard is that you have to lose a lot of money to begin with to learn what works and what works with your personality.”
The question isn’t whether you’re smart enough or funded enough.
The question is: Are you disciplined enough to build a system, test it ruthlessly, and follow it without deviation even when every emotional instinct screams otherwise?
That’s the difference between a physicist who cracked the market and the 95% who keep searching for the next “sure thing.”
Frequently Asked Questions
Q1: What is the main difference between Dr. Samir Varma’s approach and traditional trading strategies?
A: Varma emphasizes reactive trading rather than predictive trading. Instead of forecasting where markets will go, he classifies current risk environments and adjusts position sizing accordingly. He focuses on finding statistical consistency rather than trying to predict specific price movements. His evolution from short-term chaos theory models to long-term risk classification demonstrates this shift from prediction to reaction.
Q2: How does the “iceberg order” concept explain apparent stop loss hunting?
A: Iceberg orders are large institutional orders where only a small portion is visible in the order book. “When there’s an institutional order, what you see is some number of shares, but there’s a giant iceberg below them that triggers upon the first one,” Varma explains. VWAP algorithms may use knowledge of clustered stops to optimize execution timing, creating the appearance of “hunting” when it’s actually sophisticated order execution strategy. This isn’t manipulation—it’s algorithmic efficiency taking advantage of predictable retail behavior.
Q3: Why does Varma recommend avoiding round numbers for stop losses and entries?
A: Round numbers attract clustered orders due to human psychological bias. “People like to place trades at round numbers. You’ll find more trades at zeros or fives,” Varma notes. Institutional algorithms are programmed to recognize these clusters as liquidity sources. By using “ugly” numbers like 96 shares or prime numbers, you avoid advertising yourself as a predictable retail trader. “Make yourself look like you’re not an institutional order” that can be picked off.
Q4: What is the biggest mistake hedge funds make according to Varma’s research?
A: Rigid drawdown limits (7-10% stop outs) without nuanced analysis. Varma’s forthcoming Journal of Portfolio Management paper demonstrates that “even in perfect hindsight scenarios,” these cookie-cutter rules “turned what was a certain profit into a loss more often than not.” The problem is treating all drawdowns identically without understanding causation—whether from bad process, external shocks, or temporary adverse conditions. “The reason they’re successful is in spite of their risk management, not because of their risk management.”
Q5: Can retail traders in India apply Varma’s strategies to NSE/BSE markets?
A: The principles are universal, but specific patterns must be independently verified. Applicable concepts include:
- Personality-strategy congruence (works everywhere)
- Avoiding round numbers (₹100, ₹500, ₹1,000 in Indian markets)
- Opening range breakouts (testable on Nifty 50 stocks)
- Position sizing based on risk regime (above/below 200-day MA on Indian indices)
- Distinguishing bad process from bad luck (psychological truth)
However, statistical patterns like overnight vs. intraday returns or the Congressional Effect are U.S.-specific and require independent testing in Indian markets. Never assume patterns transfer without verification.
Q6: What’s the “2/3 rule” and how should it affect position sizing?
A: This is Varma’s observation about risk-return distribution relative to long-term trend indicators:
Above long-term moving averages:
- Approximately 2/3 of returns
- Approximately 1/3 of risk
Below these levels:
- Approximately 1/3 of returns
- Approximately 2/3 of risk
Application: “Even if you have a positive expected value trade below that long-term line, if you’re trying to keep your drawdowns limited, you should actually be limiting the size of your position.” Example: If you’d invest 50% above the line, consider 25% below it to maintain consistent risk exposure.
Note: These are approximate ratios, not precise predictions, and should be verified in your specific market.
Q7: How did Varma handle seven consecutive losing trades in 2022?
A: He took the eighth trade. “I was tearing my hair out by the end. But it wasn’t like I was not going to take the next trade.” This exemplifies systematic discipline—trusting thoroughly tested processes despite emotional exhaustion. Each trade represented his system correctly identifying entry conditions; the losses resulted from 2022’s unusual market behavior, not process failure. This distinction between bad luck and bad process allowed him to continue executing without abandoning his strategy.
Q8: What does “computational irreducibility” mean for traders?
A: From Varma’s book, The Science of Free Will: Even the simplest deterministic rules can produce completely unpredictable outputs. “A lot of market movement genuinely is random, i.e., it is unpredictable. Even though it’s coming from a deterministic process, it’s not predictable. It can’t be predicted.”
Practical implication: Stop trying to predict every market movement. “Your job as a trader is not to try to outthink the market or outfox the market. It’s to find an area of the market where you can be reasonably confident that you have some edge… find where it is that there is some element of consistency. Something tends to consistently happen.”
Q9: Should traders use AI and algorithms in 2025?
A: Yes, but with critical prerequisites. Varma uses AI extensively but warns: “You have to have some direction in which to point the AI. What are you going to ask the AI? And how are you going to judge if the AI’s answers make sense?”
Requirements before using AI effectively:
- Read extensively (economics, finance, trading psychology)
- Trade with real money and expect losses
- Develop ability to distinguish signal from noise
- Build foundational understanding of market mechanics
AI accelerates analysis for knowledgeable traders but cannot replace market experience or judgment. “To be able to even ask the AI questions and judge the answers, you actually need to know something.”
Q10: What’s the most important personality trait for trading success?
A: Congruence between personality and trading strategy.
“You can find more than one edge. But that edge then has to be congruent with your personality. If the edge is not congruent with your personality, you will never be successful with the trading strategy even if it works.”
Examples:
- Can’t sleep with overnight positions? → Must day trade
- Can’t handle 70% losing trades? → Avoid trend-following
- Hate making decisions? → Need systematic approach
- Love analysis? → Might suit longer-term strategies
“The reason the trading journey is so hard is that you have to lose a lot of money to begin with to learn what works and what works with your personality.” Self-awareness beats sophistication.
IMPORTANT DISCLAIMER:
This article is based on a podcast interview and presents Dr. Samir Varma’s personal experiences and opinions. It is intended for educational purposes only and does not constitute investment advice, recommendation, or solicitation. Past performance does not guarantee future results. Trading and investing in securities involves substantial risk of loss and may not be suitable for all investors. Readers should conduct their own research and consult SEBI-registered investment advisors before making any trading or investment decisions.
RISK WARNING:
Trading and investing in securities, derivatives, futures, and options involves substantial risk of loss and may not be suitable for all investors. Leverage can work against you. Past performance is not indicative of future results. The strategies and concepts discussed are based on one individual’s experience and opinions and may not work in all market conditions or for all personality types. Readers should conduct thorough independent research, paper trade extensively, and consult SEBI-registered investment advisors before implementing any trading strategy with real capital. Never trade with money you cannot afford to lose.
