Small-Cap

Gon describes his first year of real trading from mid-2021 to mid-2022. Working solo, he studied charts shared by day traders on social media — traders posting 1–2x daily returns on small-cap names — and tried to reverse-engineer their patterns. His cornerstone was a concept from mentor Bryce: risk exactly $20 per trade, size small, and focus on consistency over profits. Despite the small risk, he struggled with beginner problems: ignoring established setups in favor of his own ideas, and watching everyone on Twitter claim 2022 as a breakout year while his own results languished.

Gon shows his actual trade executions for the ICCT multi-day runner. He entered at $3.20 and peeled off a third as it pushed up — but admits he often cuts winners too early because he looks at his P&L and, especially when in drawdown, takes profits prematurely rather than letting the trade work. He uses no hard stop in the platform — only an emotional stop at his buy price. The moment the stock takes out his entry, he's out. He's OK with choking a trade by micromanaging it: 'I just want to have a good setup — as soon as I enter, it should go in my direction.' The stock never looked back, running exponentially — a signature of the small-cap squeeze world.

A question from host Ashley: what is Gon's process for selling? He always sells into strength — never waits for a fixed price target. His method: peel off 1/3 of the position as it pushes up, then if it confirms and continues, he may add back before peeling again. He never uses static targets in small-cap high-volatility names because the range of outcomes is too wide. His approach is reactive rather than predictive: collect the data the market is giving, and when momentum visibly slows, reduce — don't wait. Strength in price action is the signal.

Gon makes a key observation about small-cap squeezes: once a stock has run 100%+ in a few hours and then moves sideways on declining volume, forming another base, the chances of an even larger subsequent move are high. The sideways consolidation with drying volume shows that sellers are exhausted and the remaining holders are committed — the float is effectively even tighter than before. When the next catalyst or buyer wave hits, the move compounds. He admits that if he'd held a specific trade through the full continuation, his annual return would have been in four digits — a humbling lesson in letting winners run.

An audience question about post-market trading. Gon prefers post-market for small-cap squeeze plays: lower volume means the squeeze action is less noisy and more readable — fewer fakeouts, smoother price movement. The trade-off is wider spreads and slippage risk when exiting size. He also notes that panics toward market close, especially on large macro days (Fed, CPI), create a separate pool of intraday capitulation setups — the same playbook applies but the timing is different.

Host asks why Gon focuses exclusively on the long side. The answer is structural: shorting small-cap names requires locates from the broker, and by the time he calls, confirms availability, and places the order, the downward move has already started. Additionally, being wrong on a short in a small-float squeeze stock can be catastrophic — the stock can halt up multiple times in a row with no ability to exit. He tried shorting in 2022 but found the mechanical constraints removed whatever edge he might have had. For his setup and style, long-only is the only viable choice.

Gon closes with the study method that built his chart intuition: dump a category of charts without trying to understand them at first, study 30–40 examples until a pattern emerges, then form a thesis about why the move happens. He believes small-cap and low-float reversals will be the defining setup going forward — big explosive moves once they break structure. He credits his mentors: Mark Minervini, Lance Breitstein, SMB Capital, and TraderLion, and plans to compile his presentation into a shareable format to help other traders. His core message: observe everything. The answers are already out there; the work is in the looking.

The most striking feature of the upcoming Market Wizards: Next Generation is the age of its subjects — nearly all are under 40, with most in their 30s, the youngest cohort Schwager has ever profiled. The book includes a higher proportion of traders who leverage data sources unavailable to previous generations: social media sentiment, short-side small-cap strategies, and algorithmic pattern recognition. Schwager notes this is the first generation of traders who have grown up with tools and data that simply didn’t exist when the original Market Wizards were building their careers.

When asked how he scans for candidates, Kristjan is direct: scan for the strongest momentum stocks — those with high relative strength and significant recent price performance. The pattern itself cannot be automated; you have to learn to see it. What he looks for is linearity: how orderly is the pullback or consolidation after the previous leg higher? A disorderly, choppy base is a red flag; a clean, tight range that holds its structure signals institutional accumulation. He notes he now mostly trades large caps because of liquidity constraints at his size, but momentum trading in mid and small caps produced many of his best historical returns when the account was smaller.

Pradeep reflects on 26 years of market evolution: moves are far faster, information is exponentially more available, and today's beginner can access real traders on social media in ways impossible in 1999 — the playbooks that took him years to discover are now public. He identifies small-cap shorting as the dominant and well-documented edge in professional day trading, no longer a guarded secret. The structural insight that took him longest to grasp was the role of the Fed as the primary driver of secular bull and bear markets. Shorting into a Fed-accommodative environment is among the most dangerous mistakes a swing trader can make — when the Fed wants the market to go up, nothing stops it.

"Who is your daddy if you are in the stock market? That's the Fed. When the Fed decides that the market needs to go up, nothing is going to stop it."

When evaluating trading advice or strategies, Pradeep focuses on the content rather than the source's reputation, verifying every idea by checking it against historical data before accepting it. He debunks a widely repeated market rule — that stocks holding up best in corrections make the biggest post-correction moves — which he personally tested and found to be false. The same skeptical verification applies to discovering edges: by surveying what 20 or 30 well-known day traders publicly say and do, a motivated beginner can independently reach the conclusion that small-cap shorting is the dominant day trading edge. Ideas can come from anywhere; the filter is evidence, not authority.

"I don't go by what the person is. I look at what is the content. I don't trust any information but I verify by doing deep dive, by looking at — does this make sense?"

Strategy alone is never the differentiator — episodic pivots or small-cap shorting are well-known playbooks. The gap between a trader who makes a million dollars and one who does not is purely execution: minute entry and exit techniques, the small specific tactics that a new trader cannot even imagine. Pradeep illustrates with a personal example: for his first ten years, trades that made 20-30% would reverse to breakeven because he gave them room to run. The simple tactic of selling 80% into strength after a 10-20% gain and keeping only a small remainder — an idea he found from another trader and immediately adopted — would have saved him a decade of frustration. Successful trading is built on these small, specific execution edges accumulated over time, not on a single big idea.

"Execution is the edge. The difference between somebody who makes a million dollars in a trade versus somebody else is their execution — you can take a generic set of ideas and convert them into highly profitable trades by creating execution edges."

The most important first decision for a new trader is choosing a timeframe: day trading, swing trading, and position trading require fundamentally different skills, tools, and temperament. Once that decision is made, copy a proven strategy within that timeframe — for day traders, small-cap shorting and news-based stocks in play are the most documented edges. Pradeep reflects on the extreme difficulty of unlearning bad trading habits once formed: procedural memory makes wrong behavior automatic, just like a bad driving technique that persists despite conscious effort. The traders he has seen genuinely transform were often those who first hit absolute rock bottom — losing borrowed money, a relationship, or everything — before rebuilding with real discipline. The lesson: get the system right early, because a faulty framework that bakes in over years is very hard to rewire.

"It's very difficult once you build bad habits to change them because there's procedural memory — if you learn the wrong way to drive, it's very difficult to change. Same way in trading."

With the broader market in a neutral state, Weinstein identifies where genuine leadership is showing up: biotechnology has been almost universally strong, semiconductors and AI-related names have been outstanding, and the Russell 2000 has finally broken above its 200-day moving average after a prolonged period below it. The Russell breakout is particularly meaningful — when small-cap stocks join the large-cap leaders, the rally becomes broader and more credible as a sustained move rather than a narrow tech-driven spike. This broadening of stage 2 action across sectors is what Weinstein looks for to confirm a genuine change in market character.

Steven explains a major challenge of small-cap trading: historical data is often corrupted by reverse splits, dilutions, warrants, and ticker name changes. A stock that did 16 reverse splits will have completely distorted historical float and market cap data, making accurate backtesting nearly impossible with standard tools. His solution: he has manually tracked data for 10 years, recording live data on the date it occurs so he knows exactly what the float was. Even live, five different data sources (Finra, Bloomberg, Dilution Trackers) will give five different float numbers — a problem he still hasn't found a perfect tool to solve. For traders serious about small caps, building a clean, manually-tracked dataset is the only reliable foundation.

Steven breaks down the structural dynamics between hedge funds and retail traders in small caps. A hedge fund cannot take more than roughly 30% of a float without trapping itself — because if you own too much, there's no one to sell to without cratering the price. So hedge funds leave roughly 70% to retail. Retail has its own ceiling: once the total dollar block for a given market cap range is reached, buying power exhausts and the stock drops. Different market cap ranges have different thresholds — a $10M-$100M market cap stock might have a $600M block. Steven uses the dollar volume (volume × price) to track how close a ticker is to its ceiling. When it approaches the limit and momentum slows, the short opportunity crystallizes.

Steven explains that he is 98-99% short because the statistics don't lie: his short setups have a 90-95% win rate, while his long setups max out at 60-70%. Shorting also delivers higher rewards on a single-day basis — sometimes up to 70% in one day. He also maintains a permanent blacklist of biotech stocks. After 10 years of trading biotech purely on technical patterns, his net result was roughly +1% — losing $800K and making $850K. The irrational price action, multi-day runners without pullbacks, and the tendency of traders to irrationally hold biotech through collapses made the entire sector a negative-expected-value effort. Even when he won on biotech, he'd lose it back later. He finally learned to cut the sector entirely rather than keep fighting a losing statistical battle.

"I'm 98-99% short. Shorting has higher winning percentage especially in the small caps. Going long — the winning percentage is only about 60%. If you do really well, maybe 70. But there's no way you can hit 90-95%."

Steven walks through his account-sizing evolution. For pure day trading, he finds $300K is the most comfortable account equity — enough to capture meaningful returns without fighting for fills. The maximum for single-day trading in small caps is roughly $2 million in equity, after which liquidity issues become unavoidable. He resets his trading equity to roughly flat each year, withdrawing profits and keeping a separate account for multi-day swing positions. After the 2021 ego crash — making $20M in one month then taking an $800K loss on a 'stupidest ticker' the next — he built a 'perfect trader calculator' that models what a completely robotic, emotion-free version of himself would make. He compares his actual performance against this ceiling monthly and typically operates at only 25-30% of what the perfect version could achieve. The calculator serves dual purposes: keeping ego in check after wins, and maintaining motivation by revealing how much more is possible.

"I have this one calculator that supposed to be the perfect trader. So what I'm supposed to do, what I'm supposed to make and is this loss actually necessary? That sheet that I track is pure based on robotic trading and not emotional trading. My average performance is about 25-30% compared to the perfect trader."

Steven credits StarCraft 2 with directly improving his trading. The game's emphasis on APM (actions per minute) trained him to execute faster than other traders when shares became available — a critical edge in the days when small-cap share borrows were extremely limited and being 0.5 seconds faster meant getting the fill instead of watching someone else take it. More importantly, StarCraft is fundamentally about counter-strategies: you scout what your opponent is building and construct a counter, which is the exact same mental model he applies to trading — identifying what the crowd is doing and positioning against it. Combined with his engineering training (focus on failures, systematic testing), he developed a three-pillar mental framework: gaming (counter-strategy + speed), engineering (statistics + failure analysis), and trading psychology.

"StarCraft 2 actually helped a lot in terms of APM — actions per minute. You type a lot faster. Back in the days, shares are very hard to get. I'm always faster than other people. So I get all the shares and people don't get any shares."