Is a 90% win rate trading strategy realistic?

Is a 90% win rate trading strategy realistic?

Yes, for a short stretch inside one account, but not as a permanent guarantee. Rihari says his own $100 account went 29 wins from 32 trades, a profit factor of 47, over roughly a month of disciplined trading, a real but small and time-limited sample, not proof of a repeatable system.

The figures come from an episode of the Big Work Energy podcast, where Rihari and his co-host Slade discuss the founding of Ambitious Investing Inc alongside this specific account update. The video is a casual, unscripted conversation rather than a formal performance disclosure, and it should be read that way: as Rihari's own recap of a recent run, shared to his co-hosts and audience, rather than an audited trading record.

That distinction matters more than it sounds. A 90% win rate over 32 trades is a statement about one short run, recorded on one account, under one trading plan, during one stretch of market conditions. It is not the same claim as "this strategy wins 90% of the time forever," and the video itself frames it that way: Rihari says the number followed weeks of losses, a rebuilt trading plan, and a narrower focus on fewer setups. The honest reading is that a 90% win rate is achievable in bursts, especially once overtrading and off-plan entries are removed, but no single run of 32 trades tells you what happens over the next 320. ASIC Report 828 records that 68% of Australian retail CFD investors lost money in the 2024 financial year, a reminder that most retail accounts do not sustain high win rates over full financial years, whatever a short sample shows.

What do Rihari's numbers from the video actually show?

Rihari says his $100 account, started on 4 November, produced 29 winning trades out of 32, a profit factor of 47, a TradeZella score of 100, and roughly 580% net profit at the point the video was recorded. These are self-reported figures from one account over a few weeks.

The video also gives day-by-day detail. Rihari says daily returns on that account ranged from around 29% on the lowest day up to 230% on the best, across a run of consecutive trading days he showed on screen from his TradeZella dashboard. He attributes the run to a specific, narrow trading plan built with his co-host Slade: trading only the New York session, focusing on gold, US30 and Nasdaq, and refusing to take ideas from outside that plan. None of these figures are independently audited in the video, and the transcript does not describe any third-party verification, so they should be read as Rihari's own account of his own results, not as a verified performance record or a claim about what any other trader using the same course could expect. A profit factor of 47 on 32 trades is also mathematically fragile: it can swing enormously with a single large loss, which is a feature of small samples rather than evidence of a stable edge.

It also matters that this was a $100 account, which Rihari frames in the video as a documented experiment rather than his main trading capital. A small account lets a trader show a percentage return that looks dramatic, such as the 580% figure mentioned, while the dollar amounts involved stay tiny. Turning $100 into several hundred dollars is a very different achievement, in absolute terms, from producing the same percentage on a funded account of $50,000 or more, and the video does not present the $100 account as equivalent to Rihari's broader trading activity, which he describes separately in terms of funded evaluation accounts and live capital.

What is profit factor, and why does the video treat it as more telling than win rate?

Profit factor is gross profit divided by gross loss, and it captures win size against loss size, something a win rate alone cannot show. A strategy can lose most trades and stay profitable if wins are large enough, or win most trades and still lose money.

Rihari says his own prior losing period, which he places in July, August and September, happened despite him "sticking to his stats" in his own mind, because many of the trades were not actually his own plan. He says he was pulling ideas from other people in his trading circle that did not fit his setup, so even when his win rate looked reasonable on individual sessions, the compounding effect of session-by-session losses across Asian, London and New York wore the account down. A high win rate built on small wins and one uncontrolled loss can still be a net loser, while a lower win rate with tightly controlled losses and occasional large wins can be a net winner. This is why a 90% win rate headline, on its own, tells a reader very little about whether a strategy is sound; profit factor, average win size against average loss size, and the discipline behind entries matter more than the win rate percentage by itself.

How does win rate interact with reward-to-risk in practice?

Win rate cannot be judged alone because a strategy's profitability depends on the size of wins relative to losses, not just how often it wins. The table below is a simplified, illustrative comparison, not a description of any specific account, to show how different win rate and reward-to-risk combinations can reach the same or different outcomes.

| Approach type | Typical win rate | Typical reward-to-risk | Net expectancy over 100 trades (illustrative) | | --- | --- | --- | --- | | High-frequency scalping, tight stops | 80 to 90% | 1:0.5 (small wins, small losses) | Profitable only if losses stay small and consistent | | Balanced trend-following with a fixed plan | 45 to 60% | 1:2 or better | Profitable if losses are cut early and wins are allowed to run | | Wide-net, unfiltered entries (what Rihari says he did in his losing period) | Variable, often looks fine session by session | Inconsistent, undefined | Prone to compounding losses across multiple sessions | | One documented 32-trade sample (what Rihari reports for his $100 account) | 90% (29 of 32) | Reported profit factor of 47 | Strong over this sample; not evidence of long-run expectancy |

The point of the table is not to rank one row as "the" correct approach. It is to show that a 90% win rate sits in the same table as strategies with far lower win rates that can be just as profitable, or more profitable, once reward-to-risk and consistency are accounted for. Judging any strategy, including the one Rihari describes, by win rate alone skips the two variables that actually decide whether an account grows over time: how large losses are allowed to get, and how consistently the plan is followed trade to trade.

What changed in the trading plan between the losing streak and the 90% run?

Rihari says the shift came from narrowing his trading down to a written plan he built with Slade on 4 November: trade the New York session only, focus on gold, US30 and Nasdaq, and stop taking trades outside that plan. He also says he began using ChatGPT as a trading journal to review his own stats and flag repeated mistakes.

According to Rihari, the specific problems he identified and corrected were overtrading across Asian and London sessions before New York even opened, taking entries based on other traders' ideas that did not match his own setup, entering positions without marking up the chart first, and oversizing positions on low-volume pairs. He says the AI journal process surfaced these patterns from his own trade history and told him, in his words, what he "shouldn't do," which he credits as much as the plan itself. He frames the underlying cause of the losing period as psychological rather than purely technical, describing several months where he considered stepping away from trading altogether, and ties his recovery partly to reading about habit and subconscious behaviour and applying the same discipline to trading, fitness and diet at the same time. None of this is presented in the video as a formula that produces the same result for another person; it is one trader's account of what changed for him.

Rihari also links the psychological side of this recovery to a specific book, Breaking the Habit of Being Yourself, and to a podcast interview with Dr Joe Dispenza about the subconscious mind. He says he applied the idea that repeated self-talk becomes automatic behaviour to more than just trading, including fitness and diet, at the same time as he rebuilt his trading plan. That timing detail matters for anyone reading the 90% win rate as a purely technical result: by Rihari's own account, the number followed a deliberate reset of routine, sleep, exercise and mental state alongside the narrower trading rules, not the trading rules in isolation.

Why doesn't a 32-trade sample prove a strategy works long term?

A 32-trade sample is too small to distinguish genuine skill from a favourable run of market conditions, because short samples carry wide statistical variance. The same underlying strategy can show a 90% win rate in one 32-trade stretch and a much lower one in the next, purely from normal variation, not because the plan changed.

Rihari himself makes a version of this point when discussing other traders in the video. He references a trader known as Alex G, noting that Alex G failed twice at growing a small account aggressively before succeeding on a third attempt, despite being, in Rihari's words, "a very, very skilled trader" with years of experience. Rihari uses this to argue that most people watching a short winning run online do not see the losing attempts that came before it. The same logic applies to any 90% win rate headline, including his own: a short sample that looks exceptional can sit on either side of a longer, more ordinary average once enough trades accumulate. Before treating any short run as proof, it helps to ask a consistent set of questions.

1. How many trades make up the sample, and does that number even out short-term variance, or is it small enough that one or two trades would change the headline number substantially.
2. Is the win rate reported alongside profit factor, average win size and average loss size, or only as a standalone percentage.
3. Does the source disclose losing periods or failed attempts that came before the winning run, the way Rihari discusses his own July to September losing stretch.
4. Was the account real money, a funded evaluation account, or a demo, and does that change what is actually at risk.
5. Is the same trading plan described in enough detail to be tested independently, or is the result attributed to a vague "strategy" without the specific rules.
6. Would the person reporting the result have shared it if the following month had gone the other way.

Rihari offers his own version of a more sustainable framing in the video, aimed at the mentees he describes coaching. Rather than chasing a 90% win rate or trying to turn $100 into a large sum quickly, he describes the math of taking a smaller, steadier target on a funded account: making 1% a day on a $300,000 funded account, in his example, works out to roughly $3,000 a day, or around $15,000 USD a week if repeated across a trading week. He presents this as a more realistic goal than a headline win rate, precisely because it does not depend on an exceptional short run continuing indefinitely. The 90% win rate and the 1% a day example come from the same conversation, but they are different claims: one is a description of a past 32-trade stretch, the other is a target Rihari says he uses to set expectations with newer traders.

What do independent statistics say about retail trading and market scale?

Independent data, separate from any individual trader's results, shows that most Australian retail CFD traders lose money over a full financial year, and that the foreign exchange market they trade in is enormous relative to any single account. Both facts sit alongside, not instead of, whatever a personal 32-trade sample shows.

• ASIC Report 828: Risky business records that 68% of Australian retail CFD investors lost money in the 2024 financial year. That figure covers an entire population of retail accounts over a full year, not a 32-trade stretch on one account, which is why a short winning sample and a poor population-wide outcome are not contradictory; both can be true for different traders, or even for the same trader in different periods.
• BIS 2025 Triennial Survey reported average daily OTC foreign-exchange turnover of US$9.6 trillion in April 2025. That scale is a useful reminder that retail participants, including anyone trading gold, US30 or Nasdaq off the back of a personal trading plan, are a small part of a market dominated by institutions, central banks and large financial firms whose activity around scheduled economic data can move price sharply in either direction.

This is relevant to the specific trading plan Rihari describes, because he says his approach leans on fundamental analysis timed around scheduled economic releases, on the reasoning that price action between one major release and the next reflects the market positioning itself ahead of that data. With average daily turnover measured in trillions of dollars, the release itself, and the large institutional flows that respond to it, will typically move price far more than any individual retail order. A trading plan built around anticipating that flow is a coherent idea, but the trillions-of-dollars scale of the market also means no individual trader, however disciplined, controls or can reliably predict the outcome of any single release.

Neither figure says anything about Rihari's account specifically, and neither proves or disproves the 90% win rate he reports. They exist here as context: the ASIC figure shows what typically happens to retail CFD traders over a full year, and the BIS figure shows the scale of the market any individual strategy operates inside. Reading a short personal win-rate claim against that backdrop is a more grounded way to weigh it than reading the claim alone.

Should a 90% win rate claim influence a decision to buy a trading course?

A single trader's short-term win rate reflects one account's recent past, not a guarantee for anyone else's future. Rihari frames Ambitious Investing Inc as something that grew from teaching friends and mentees, not a promise built on this one 32-trade result — so it shouldn't drive a course purchase alone.

In the video, Rihari describes the business's origin as informal: he first taught friends and family for free, then wrote a free beginner guide after running out of time to teach individually, then moved to paid one-on-one mentoring when a mentee offered to pay him, and eventually turned mentoring sessions into recorded videos because he could not scale one-on-one time to a growing waiting list. He is explicit that the underlying material, in his description, focuses on market structure, key levels and fundamental analysis timed around scheduled economic releases, rather than chart pattern recognition alone, and he says he charges a fraction of what other educators charge for similar content. None of that history, and none of the 90% win rate figures discussed in the video, are independent verification that a course purchase leads to similar results, and this article does not make that claim. Anyone weighing a claim like this should treat the reported win rate, profit factor and account growth as one trader's disclosed personal results, weigh them against the retail loss statistics above, and make a decision based on their own risk tolerance rather than a short highlight reel.