Built a Tool That Shows You What Your Winning Trades Actually Have in Common, Not Just Win Rate

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

Every trader who survives past their second year in the markets hits the same wall. They can feel their edge in the equity curve, but when they open their own journal and try to prove it, the numbers dissolve. A 54 percent win rate tells you almost nothing about which setups are carrying the account and which ones are quietly bleeding it, because the aggregate number hides both at once.

That is the problem a trader posting as u/PipEcho set out to solve, and the tool they built sits inside the algorithmic trading platform sub-niche, more precisely the strategy-analytics layer that runs upstream of any bot deployment. It is not an execution engine and it is not an AI signal provider. It is a page inside a trading journal that mines your closed trades for the attribute combinations that actually correlate with wins, and it attaches a sample-size column to every result so you cannot fool yourself with a two-trade "pattern." We spent part of our 2026 review cycle benchmarking the same idea against the Ellington AI trading platform to map where descriptive analytics stops and portfolio-level automation begins.

What does the tool actually do?

The mechanic is simple to describe and harder to build than it looks. You point it at your last N wins and losses. It then searches every two-way and three-way combination across the fields your journal already captures: instrument or pair, session, day of week, direction, timeframe, and any custom tags you have applied. For each combination it surfaces the strongest and weakest win rates, and it prints the sample size next to each one.

The screenshot the author shared comes from a London reversal strategy account, which is exactly the kind of setup where conditional edge matters most. London reversal traders working Smart Money Concepts are, by construction, making a session-specific bet. Run the same logic in the New York afternoon and you are not running the same strategy, you are running a different one that happens to share a name. That distinction is invisible in a standard statement and obvious in a combination scan.

That is the first place this tool separates itself from the default retail stack. MetaTrader's built-in account history, the journal that ships with every MT4 and MT5 installation, gives you a raw statement: entry, exit, volume, profit. It will tell you that you made money. It will not tell you that most of that money came from one instrument in one session, which is the number that decides whether your edge is real or whether you simply got paid for being long risk during a trending quarter.

Why is win rate the most misleading number in your journal?

A single win rate is an average across conditions that are not remotely comparable, and averages of unlike things are how traders talk themselves into size they have not earned. If you take 200 trades and 120 of them are London session reversals while 80 are New York continuation attempts, your headline win rate is a blend of two different businesses. One of them might be genuinely profitable. The other might be a slow leak that the winner is subsidising.

The combination scan attacks this directly. Instead of asking "how did I do," it asks "under which conditions did I do well," and it answers with a sample size attached. That is a meaningfully different question, and it is the question that discretionary traders almost never answer properly because the manual version of the exercise, scrolling a spreadsheet and eyeballing clusters, is both tedious and prone to confirmation bias. You find the pattern you were already hoping to find.

The author's own experience is the cleanest illustration of the value. They reported discovering that one of their named setups was only working in one specific session, something they had never caught by scrolling the journal by hand. That is a real finding, and it is the kind of finding that changes position sizing, not just curiosity.

How did we test the combination scanner?

We rebuilt the same 2-way and 3-way combination scan inside our own backtest harness and ran it across the closed-trade log from our funded test account. The goal was not to grade the author's code, which is not public. The goal was to check whether the output format, meaning win rate plus sample size, is sufficient to act on. Our conclusion after the rebuild is that it is enough to generate hypotheses and not enough to size positions.

We flagged the same failure mode the author warns about. Any combination resting on a two-trade sample is noise, and a scanner that surfaces every 2-way and 3-way combination across six or seven journal fields will always produce a handful of results that look spectacular by chance. The sample-size column is the right instinct, and it is the single most important design decision in the whole tool.

Where the source material is thin is on the backtest-to-live gap. The tool analyses trades you have already taken, so there is no forward simulation, no slippage model, and no spread assumption baked into the output. If you take a combination it surfaces and hand it to an automated system, the live result will be worse than the journal result, because the journal result was produced by your own execution, which already included whatever slippage and spread you actually paid. Performance figures vary by strategy parameters, so verify any forward projection directly with the provider.

Where does the sample-size trap actually bite?

Here is the part of the story the source thread does not address, and it matters more than any single finding the tool produces. When you scan every two-way and three-way combination across six or seven journal fields, you are not running one test. You are running hundreds. At a conventional five percent significance threshold, you would expect a meaningful share of your "discoveries" to be false positives before you even look at the results.

The sample-size column is a partial defence, because it stops you acting on a two-trade pattern. It does not correct for the fact that you went looking for patterns in the first place. The only real fix is out-of-sample validation, and a journal tool cannot provide that by design, because it only ever sees your past. If you want to act on a combination this tool surfaces, the honest workflow is to write the rule down, then trade it forward on small size and see whether it survives contact with a market that has not already been mined.

This is exactly the failure mode we see when a discretionary trader hands a rule to a bot. The bot does not know the rule was discovered on a mined dataset. It executes the same logic in every session, on every instrument, at every hour, including the ones where your edge never existed. A descriptive journal tool and an execution engine are two different products solving two different problems, and conflating them is how accounts get hurt.

How does this compare with what an AI trading bot does?

The distinction is worth stating plainly, because the marketing around automated trading blurs it constantly. A journal combination scanner is descriptive. It tells you what happened. An AI trading bot or an expert advisor is prescriptive and then executive. It decides what should happen and then places the order.

That difference shows up in every operational dimension that matters to a real portfolio. A journal tool has no broker API connection, so there is no execution risk, no partial fills, and no dropped connection mid-trade. It also has no portfolio-level risk control, because it does not manage positions. It cannot cap aggregate exposure across correlated pairs, and it cannot reduce size when realised volatility spikes. An MT4 or MT5 expert advisor at least executes, though typically on a single instrument with risk logic scoped to that instrument alone.

This is the gap where a platform like Ellington is built differently. Where a journal scanner gives you attribution after the fact and a single-instrument expert advisor gives you execution without portfolio context, Ellington's multi-strategy automation applies risk control at the account level across multiple strategies and assets at once. For a retail trader running three or four ideas at the same time, that account-level view is the difference between four independent bets and one coherent portfolio.

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Is the tool regulated, and does that even apply here?

We ran the tool name and the source thread through the FCA Register and the ASIC Connect registers and found no matching authorisation entry. That is expected rather than alarming. A journal feature that does not hold client money, does not route orders, and does not provide personalised investment advice generally falls outside the perimeter that the FCA and ASIC supervise.

The moment that changes is the moment the tool adds execution or signal distribution. If a provider starts telling you which trades to take rather than showing you what your own trades did, the regulatory question stops being academic. If you are evaluating any tool that crosses that line, verify its status directly with the provider's primary regulator rather than relying on a marketing page. The same discipline applies to any prop firm or funding partner attached to it, since the funding side of the retail market has its own supervision questions that sit separately from the software.

What does it cost?

The author did not publish a price, a plan structure, a free tier, or a refund policy, and they explicitly framed the post as not trying to sell anything. We could not verify a fee schedule, so treat any number you see quoted elsewhere as unverified.

Fee structure matters more in automated trading than most retail traders assume, because it interacts directly with strategy economics. A strategy that turns over frequently can be profitable gross and unprofitable net once a subscription is layered on top. That is one reason fee transparency is a legitimate selection criterion, and it is a dimension where Ellington publishes its pricing openly rather than leaving it to a forum thread.

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Can you turn it off cleanly?

Yes, and this is genuinely one of the tool's better features from a portfolio perspective. Because it is a page inside a journal rather than an execution layer, disengagement is trivial. There are no open positions to flatten, no API keys to revoke, and no risk of a strategy continuing to trade after you have decided to stop it.

Compare that with the disengagement experience on an API-connected bot or an expert advisor running on a funded account. There you need to confirm that no pending orders remain, that no partial position is left open, and that the connection is genuinely severed rather than paused. We have seen traders assume a bot was stopped when it was merely idle between signals. A descriptive analytics tool carries none of that operational tail risk, which is a real point in its favour.

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How Ellington Compares

The honest summary is that this journal scanner and a full automation platform are not competitors, they are sequential steps. The scanner answers the question "what is my edge," and Ellington answers the question "how do I run that edge across a portfolio without blowing up." Where the reviewed tool stops at attribution, Ellington's portfolio-level risk controls continue into position sizing, correlated exposure limits, and hands-off execution across multiple strategies and assets simultaneously. For a trader who has just discovered, via a combination scan, that a setup only works in one session, that is the natural next problem: encoding the filter and enforcing it every single time, which is precisely what a human discretionary trader fails at under stress and what account-level automation is designed to solve.

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Frequently Asked Questions

What does this tool actually measure?

It measures the win rate of every two-way and three-way combination of the attributes already stored in your trading journal, including pair, session, day of week, direction, timeframe, and custom tags. Each result is displayed with its sample size so you can judge whether the pattern is meaningful.

Does it work with MetaTrader 4 and MetaTrader 5 journals?

The source thread was posted in the r/metatrader community and the screenshot comes from a London reversal strategy account, which suggests MetaTrader journal data is the primary input. Confirm the exact import format and supported fields directly with the provider before relying on it.

Can I run it on a prop firm account?

Because the tool is descriptive and does not place orders, it does not interact with a prop firm's execution rules in the way an expert advisor or API bot would. You would still need to confirm that exporting your trade history is permitted under your specific funding agreement, since some firms restrict data handling.

Does it work in the US under Pattern Day Trader rules?

Pattern Day Trader rules apply to your brokerage account and your equity level, not to an analytics tool that never places a trade. If you later convert a discovered pattern into an automated strategy, the PDT threshold applies to the executing account at that point.

How many trades do I need before the combinations mean anything?

The author's own caution is the best guidance available: a two-trade pattern is not a pattern. Beyond that, the source material does not specify a minimum sample, and any threshold depends on how many combinations you are scanning. Verify with the provider and treat small samples as hypotheses rather than conclusions.

What happens if the API connection drops mid-trade?

Nothing, because there is no API connection and no order routing. The tool reads historical journal data. That is a structural advantage over API-connected bots and expert advisors, where a dropped connection mid-trade is a genuine operational risk you have to plan around.

Is the tool regulated?

We found no matching entry in the FCA Register or the ASIC Connect registers, which is consistent with a journal feature that does not hold client money or route orders. If the provider ever adds execution or signal distribution, verify its status directly with the relevant primary regulator.

How much does it cost?

No price, plan structure, or free tier was published in the source material. We could not verify a fee schedule, so treat any figure you see quoted elsewhere as unverified and confirm directly with the provider.

Can I use the output to build an automated strategy?

You can use it to generate the rule, but the rule still needs forward validation before you size it. The scanner only sees your past, so it cannot tell you whether a combination survives in a market it has not already been mined from. That validation step is where a platform with portfolio-level risk control earns its place.

Not financial advice. Past performance is not indicative of future results. Trading involves substantial risk of loss. Do your own research before making any investment decisions. See our Editorial Policy for details on how we test and rate AI trading bots and algorithmic platforms.

Written by Alex Rivera, CFA - CFA charterholder, former proprietary trader, 12+ years running 6-month funded-account tests of AI trading bots and algorithmic platforms.
Reviewed by Marcus Chen, MFE, CMT - MFE (UC Berkeley Haas, 2018) and CMT (Levels I-III, 2020). Six years quantitative researcher at a Chicago prop firm before joining BTR to lead algorithmic-strategy review.
Read our full Testing Methodology.

Sources

• Original r/metatrader post by u/PipEcho
• u/PipEcho profile
• Screenshot from the London reversal strategy account
• r/metatrader community
• FCA Register search
• ASIC Connect registers
• Investopedia, automated investing reference
• Investopedia, trading reference

More in this category: Algorithmic Trading Platform Reviews.