Something worth checking on any multi year stock test, and I got this wrong in plenty of my own early ones.

You pick a universe, usually the current S&P 500 or the current Nasdaq 100, and you run a rule across ten years of history on those names. The list you used is todays list. Every company that dropped out of the index over those ten years, whether it got bought, went under, or just shrank out of it, is not in the test at all, because it was never in the list you started from.

What you are left with is ten years of history belonging to companies that were healthy enough to still be in the index this morning. The index swaps names out every year and over a decade that is a long list of companies that a current member list simply does not contain.

This hits mean reversion rules hardest. A rule that buys a drawdown and waits gets tested only on the drawdowns that recovered, because the names where the drawdown kept going are precisely the ones that left the index. The test never sees them, so the recovery rate it reports is not the recovery rate you would have lived through.

Its a bit like judging a high school program by asking the seniors how it went. The kids who quit in tenth grade arent in the room to ask.

____

Two things that actually help.

The first is a universe dated to the day the rule fired, so a name enters the test when it entered the index and leaves when it left. Point in time membership data is sold rather than given away, which is why so few retail tests have it.

The second is cheaper. Run the same rule on a fixed basket you chose for a reason that has nothing to do with current membership, a handful of names you picked by sector at the start date, and compare. If the current member version is much stronger, the gap between them is a rough measure of how much of the result was survivorship.

Worth saying that the length of the history is not what fixes this. The equity candles I test on in Agenticks go back to 2000, and a test built on a current name list carries exactly the same bias at 26 years as it does at 10. The bias comes out of the list of names, and it can only be removed there.

I put this on TradingView a few months ago and its sitting at 22,435 views and 1.5k boosts. Its fully open source, so the Pine is right there on the listing and anyone can read it, copy it or pull it apart.

Opening Range + Session Windows + Volume Profile

What it draws:

• opening range high, low and the range box
• configurable session windows
• the current session volume profile and the previous session one
• POC, HVN and LVN levels, labelled LIVE or PRIOR so developing structure doesnt get mixed up with levels carried over from the last session
• a HUD for session state, trend, range size and the active nodes
• buy and sell volume impulse labels that only print when participation is well above normal, otherwise the chart turns into a wall of text

The reason it exists is that the opening range on its own tells you very little. A break into a high volume shelf and a break through a thin pocket look identical if all youre looking at is the level, so I wanted the volume location sitting next to it on the same chart instead of on a second one.

Its not a system and it doesnt place trades or tell you what to do. Its a framework for reading intraday structure, and what you do with it is on you.

If you want one built to your own rules, thats basically why I built Agenticks, you describe the indicator in a prompt and it writes the Pine v6 and gives you a script that compiles.

Most people find out if a trading idea works by paying for it first. A course, a signal Discord, an indicator sub, an eval fee. Then they trade it live and find out the expensive way.

This shouldn't be the case when you can just test it.

I founded Agenticks in September (2025), which lets you describe a strategy in a prompt the way you'd say it to another trader...

eg. "5 minute opening range breakout on NQ, entries after 9:45 only, 30 point stop, 3 to 1 target, one trade a day."

or... even a research request.

eg. "find academic research on intraday price movement in futures and build a strategy around the strongest findings.

An agent writes and runs actual Python code, handing back every trade log, a live equity curve, and 100+ other metrics seen in the table below. No manual-code, no block builder, no scripting language to learn. Just an AI with a sandbox to run any custom request fed into a bar-by-bar runtime, which has no access to future look ahead (it feeds the backtest engine one candle at a time). We also let users take it a step further than the backtest and live automate to either a paper trade or live brokerage account, should they feel confident in their strategy.

We offer a free trial, but also took it a step further to celebrate our milestone of over 1,000 users this August by adding credits for all free accounts too. For reference on what the rest of this space costs: TrendSpider's AI add-on is $49 a month for 100 messages, QuantPad is around $170 USD (reg: $300 USD) and is an IDE where you still write PineScript or EasyLanguage, Composer is $40 and is block-based ETF rotation. A course is $500 to $2,000 once, with no backtest attached.

To anyone calling this a "wrapper" without seeing whats under the hood, we additionally include over two decades of market history, down to the one-minute time frame, of over 1,600 instruments.

I think this is the least technical way to get from an idea to a real backtest that exists right now. If you know something easier, I genuinely want to see it.

I'd love any feedback or opinions from you all. All I ask is that before you try to criticize the platform, just give it a try and see if you like what it has to offer. Thanks, and happy software Sunday! 🎉

As always: backtests are history, not indicative of the future, and none of this is financial advice.

As mentioned above, here is everything it computes on each run...

Category	Metrics
OVERVIEW	--------------------------------------------
Verdict	Letter grade, Score /100, Profitability, Drawdown Control, Risk-Adjusted Returns, Trade Quality, Consistency
Headline	Net P&L, Return, Max drawdown, Win rate, Profit factor, Trades
Return metrics	Total Return ($), Annualized Return (%), Average Winning Trade ($), Average Losing Trade ($), Largest Win ($), Largest Loss ($), Best Day (%), Worst Day (%), Expectancy ($)
Trade statistics	Total Trades, Win Rate (%), Loss Rate (%), Avg R:R, Payoff Ratio, Average Holding Period, Max Consecutive Wins, Max Consecutive Losses, Avg Bars in Winners, Avg Bars in Losers, Recovery Factor
Efficiency metrics	Avg Max Favorable Excursion (%), Avg Max Adverse Excursion (%), Edge Ratio (MFE/MAE), System Quality Number (SQN)
Benchmark comparison	Buy & Hold Return (%), Alpha (%), Beta, Correlation to Benchmark, Information Ratio, Omega Ratio
Run facts	Starting capital, Days scanned, Bars tested, Date range
Charts	Equity vs buy and hold, Monthly returns heatmap
Notes	Risk warnings, Strengths, Context notes, Major issues
SUMMARY	--------------------------------------------
Setup	Market, Symbol, Timeframe, Direction
Indicators & Inputs	Every indicator and parameter the strategy uses
Entry Rules	Each entry condition in plain English
Exit Rules	Each exit, target and stop in plain English
Invalidation	The conditions that cancel a setup
Risk & Protection	Stop placement and protective rules
Execution & Assumptions	Sizing, session, data source, causality
TRADE ANALYSIS	--------------------------------------------
Trade quality	Best trade, Worst trade, Avg win, Avg loss, Win streak, Loss streak, Avg hold, Long/short split
Trade log columns	Side, Entry time, Exit time, Entry price, Exit price, P&L ($), P&L (%), Bars held, Exit reason, MAE %, MFE %
Filters	All, Winners, Losers, Long, Short
RISK ANALYSIS	--------------------------------------------
Drawdown	Max drawdown, Drawdown duration, Avg drawdown, Recovery factor
Risk-adjusted returns	Sharpe, Sortino, Calmar, Ulcer index
Tail risk	VaR (95%), CVaR (expected shortfall), Best day, Worst day
Charts	Underwater curve, Rolling Sharpe (30 day), Rolling win rate (30 trade), Daily return distribution
REGIME ANALYSIS	--------------------------------------------
Market states	Trending Up, Trending Down, Volatile, Choppy / Ranging
Computed inside each state	Trades, Win rate, Total P&L, Avg P&L, Profit factor, Avg hold, Sharpe, Max DD
Comparisons	Trade distribution, Win rate by regime, Profit factor by regime
Performance by period	1M, 3M, 6M, 1Y
MONTE CARLO	--------------------------------------------
Outcomes	Median return, 5th percentile, 95th percentile, Median max DD, Worst-case DD
Percentile table	Final equity, Return, Max drawdown at each percentile
Controls	Resample trades, Shuffle order, Starting balance
Chart	1,000 simulated equity paths
PROP FIRM SIM	--------------------------------------------
Presets	Topstep, FTMO, Apex, Custom
Rule set	Account size, Profit target, Daily loss limit, Max drawdown, EOD trailing / Intraday trailing / Static, Minimum days, Time limit (days)
Results	Pass rate, Simulations passed, Simulations failed, Target progress, Buffer to pass, Drawdown used
Failure modes	Median steps to pass, Median steps to fail, Hit floor first, Hit neither
Activity	Trading days, Trades, Consistency rule

Go into any trading subreddit right now and count how many "profitable bot" posts include something you can actually open without paying, not just a cropped equity curve or a win rate with no avg loss beside it, but something you can read the details of and verify.

I have never found one, and not because everyone is lying, but because the entire space is built on unverifiable claims and there has been no format for showing your work. A screenshot is not evidence, it is a marketing asset, and everyone in this industry knows it. I don't care how good your backtest results are if I can't see the actual code that you use in it.

Because of this, Agenticks' August Update introduced "Agenticks Community " a public feed where people share...

1. Backtests,
   2) Strategies
   3) Research studies
   4) Indicators

Every one of them opens, showing the actual rules, the settings, the equity curve, and hundreds of other metrics with no required signup. You can send someone a link to a specific build and they can check your work for themselves.

Think of the TradingView community where people share their projects, except with the ceiling taken off as you are not limited to sharing indicator code. You can share full strategies, complete result sets, and research studies, and anyone reading can see the results and run them for themselves instead of taking your word for it.

The part that actually separates it is that the platform builds the thing you are posting. TradingView hands you a script editor and wishes you luck, while Agenticks writes the strategy, runs it across real historical data, and produces the results you can publish, so you arent expected to do all the heavy lifting yourself and then go find somewhere to show it. Building it and publishing it are the same workflow, and any backtest can be automated live to countless brokers using the Agenticks servers + Snaptrade.

What we want out of this is simple: OPINIONS.

Should you be able to upload your own code that was not built on the site? or is this better kept to things that can actually be opened and run?

Out of backtests, strategies, research studies, and indicators, which one do you actually care about seeing from other people?

or...

Would you ever want a marketplace on top of this for bigger projects you have spent real time on, or does money ruin the whole thing the second it shows up?

Anything else, say it. Initial opinions are what we are after.

A large percentage of retail trading gurus preach their strategies that use 50 different confluences, and a lot of gullible people think that this is a positive when it's actually the exact opposite. Hear me out...

When somebody's strategy relies on 40 different things to be considered and the ones that actually matter are a judgment call subjective to each user, there becomes a major flaw that is often exploited by influencers.

Any time you lose a trade, it is NOT the fault of the strategy. It is the fault of YOU for choosing the wrong parts of it to use. This dynamic creates a handful of issues...

1. The people selling the strategy have an excuse for why it didn't work.

2. it becomes essentially impossible to test historically

3. it introduces extreme complexity, which can harm decision-making rather than make it clearer.

One of the most common reasons and benefits behind having a strategy is that it is a repeatable set of rules. Many gurus often boast their ability to use discretion to make a profit, which is counterintuitive because in intra-day trading, being right because of a gut feeling often boils down to luck being on their side.

Imagine if somebody gave you a huge list of ingredients for a really good dinner but didn't tell you which exact recipe to use. If it tastes too salty, that's your fault. If it came out burnt, that's your fault. If it wasn't sweet enough, that's your fault, on the off chance that it ended up tasting good, you "followed the recipe correctly", but here's the problem...

The next time you want to make that dish again, you're screwed because you have no idea what exactly you did to make it work last time, and neither is the person who sold you the recipe.

But there is one issue, I think, that stands out the most because it is the one that is most commonly exploited by course sellers... THE FAKE WIN RATES.

We've already established that you can't repeat rules if subjectivity is a so-called "aspect of the strategy", so I picked one to call out…

RP Profits has a video called THIS TRADING STRATEGY NEVER LOSES (92% Win Rate Strategy Revealed), and I took the rules as closely as I can to how he teaches them. Sweep the liquidity into an unmitigated higher timeframe FVG, wait for displacement away from it, drop to the 1 minute for a fresh FVG, enter on the retracement, stop beyond the HTF FVG, target opposing liquidity. That's his own shorthand, Sweep into Displacement into FVG into Target. I pasted the whole thing into an Agenticks prompt which you can see exactly sent in the video.

Over a period of 5 years trading NQ futures, this strategy yielded +42.5% with a 36.6% win rate, while the QQQ buy-and-hold yielded over double.

It made money, to be fair. +42.5%, profit factor 1.18, 17% max drawdown, $2,038 average winner against a $999 average loser. That's a real strategy and I'm not going to call it garbage.  However, the fact that this yielded less than half of what the buy-and-hold would have isn't even my issue.

The real problem is that this is my interpretation of the strategy he's described, which he would likely claim to be “Different than what he trades.”

This is the whole problem though. I was able to conclude statistical evidence of the historic performance of this strategy, which doesn't predict the future but allows me to gauge how something is performed before I risk my own money with it.  When discretion is involved, you are completely blind to historic performance and are solely relying on gut feelings and spontaneous judgment to yield a profit.

-----

But there's a reason why this matters more than you’d think…

If a discretionary strategy can't be tested historically (we've established it can't), then a 92% win rate can only be coming from his own live trading results (theoretically). Fine. But live discretionary results are one person's judgment on one path through the market. You can't inherit somebody's discretion by buying their course. So even if he really did hit 92% (which I HIGHLY doubt), that number is telling you something about him and nothing about what you're going to get, because the part that produced it is the part he can't hand over.  

Look at the Monte Carlo in the clip. Median (average) outcome +41.8%, 5th percentile -5.7%, and the worst case max drawdown across those paths is 75%. A profitable strategy still blows most evaluations, and that's before anybody's discretion enters the picture. Now add a rule set where the entry is a judgment call that varies by the day and by the trader, and tell me what the pass rate is supposed to mean. 

The answer: it doesn’t.

If you can't look at the same exact chart on two different occasions and come to the exact same conclusion, you're not trading a system (strategy), you’re gambling.

-----

A lot of these guys also like to argue that even if they could describe the exact discretion that they use it's still “not testable” (...cough cough Powell Trades), and even if it was, it's too complicated for people to do as retail traders, which is completely false.  The video above literally shows Agenticks coding, executing, and explaining a backtest while giving me the option to automate it to a live broker in less than 1 minute. It would take you longer to load up TradingView replay mode than to do this, and it…

1. Requires zero coding experience
2. Includes 25+ years of 1m-1D data (1600+ stocks/ETFs/futures)
3. Includes you a server to run on and handles all of the setup automatically
4. Shows every entry, exit, duration, TP/SL, etc.,
5. Lets you compare performance in each market regime to see when a strategy works best.
6. Can automate to paper account (walk-forward test) or deploy to a live broker in seconds
7. Simulate thousands of outcomes using Monte Carlo, shuffling the order of your trades
8. Use those same simulations to estimate prop firm evaluation pass odds
9. A 0-100 grade broken into profitability, drawdown control, risk-adjusted returns, trade quality, consistency and prop firm survivability
10. Gives full risk page: Sharpe, Sortino, Calmar, Ulcer index, recovery factor, VaR 95, expected shortfall, drawdown duration in days, and the underwater curve
11. Shows rolling 30-day Sharpe and rolling 30-trade win rate, so you can see whether the edge decayed or held up
12. Compares benchmarked against buy and hold, with alpha, beta, correlation and information ratio
13. Shows monthly returns heatmap by year, and P&L broken out by period
14. Gives insights as for payoff ratio, average R, expectancy per trade, max consecutive wins and losses, average holding time, bars in winners vs losers, long/short split
15. Lets you click any individual trade and replay it on its own timeframe with the entry, stop, target and exit marked
16. Provides the option to create a TradingView indicator instead of an automation
17. Guarantees no lookahead bias by design: the engine runs your strategy through one bar at a time instead of handing it the whole dataset, so it can never see future candles.
18. Writes a full summary of all the rules and explanations of the code it wrote, as well as any details it filled in that weren't specified in the prompt

*and much more*

Most importantly, if any of the above is too complicated to understand, it has full access to all the results and can answer any and all questions.

Call it a shameless plug, I genuinely don't care. These courses run into the thousands, and you're expected to pay first and find out afterwards whether the method holds up. For a fraction of one of these pyramid schemes, Agenticks lets anyone do it in the other order. Take whatever the guru is teaching, write their rules out as literally as given, and run them over 2 decades of history before you pay a dollar. 

Same goes for your own setup, the one you've been demoing for three months and still aren't sure about. Find out whether it ever made money historically before you find out with an eval fee attached to the answer.

Agenticks doesn't hand anybody a guaranteed strategy, and if it did you should ignore it for exactly the same reason you should ignore most course sellers. What you get is the evidence, including the evidence that your idea is potentially bad, which is the cheapest thing you will ever pay for.

____

QUESTION: I'm genuinely curious what anybody else thinks of my opinion on all of this. Do you use discretion in your strategy, and if so, how do you validate your concepts before using them? Or if you use somebody else's strategy, do you find that the rules that they give you are often ambiguous and make you feel like the reason why a model loses trades?

The “AI quant” space is getting buried in generic, low-effort slop. The same looking sites with the same Lovable watermark, same landing page, and same promise that you can “Become a quant overnight and backtest anything with AI.” Then you look do some digging and realize how many of them are far less substantial than the UI makes them look as AI has made it incredibly easy to produce a polished trading interface. Where that progress hasn’t carried over nearly as much is the backend which that is where the difference becomes obvious: weaker infrastructure, fewer real systems, and often no comprehensive path from backtest to execution.

And before someone says “AI-generated website,” that is not my criticism. I own Agenticks (est. 09/2026) and we use AI in our frontend development.

Generating frontend code is not the hard part anymore. Last yr Anthropic found that AI is hitting frontend work far faster than backend engineering, stating that “simple applications and user interfaces may face disruption from AI systems sooner than those focused purely on backend work” (Anthropic, 2025).

What’s even more important is that AI-written code is becoming standard practice in professional software development. Sundar Pichai reported this year that “75% of all new code at Google is now AI-generated and approved by engineers” (Swartz, 2026) and Klarna has used generative AI to produce more than 1,000 campaign images while significantly cutting production costs (Klarna, 2024). Pull up the home page for Robinhood, Coinbase or Stripe today and plenty of people would tell you those look AI-generated despite them being huge industry names with more than enough money to spend on devs. Everything looks the same now, professional or not, which is exactly why the way a site looks tells you nothing.

It's because of this that I think it's silly to criticize someone for not manually writing out lines of JavaScript UI for every page in their platform, its not the 90s anymore. The question is whether there is real engineering underneath it, which is why the word “approved” is bold in that Google quote. Whether somebody used AI to write their React components tells you almost nothing about whether the product is serious. The hard part is the architecture, data, testing, execution and production infra/systems underneath it.

The UI being the easy part now is exactly why there are suddenly so many “AI quant backtesters” with Lovable watermarks popping up every day which is not what bothers me. The issue is what happens after you press Run. Market data, security, deterministic backtesting, job infrastructure, strategy versioning, broker connections, risk controls, live position state, execution and failure handling are the actual product. Architecture and implementation for a system like this is extensive and took my team thousands of both dollars and hours to build.

(For reference, the table below shows what we actually compute in a backtest and what the live automation layer does after it.)

The entire reason you backtest something in code is to remove discretion. The strategy enters when the rule says enter and doesn't hesitate, move a stop early, skip Monday bc Friday sucked or enter two candles late. But... if the backtest ends and there is no way to paper trade or execute that exact logic... what was the point? You removed yourself from the strategy to prove whether it worked, then put yourself right back in charge of executing it.

QuantPad is a great counterexample here because it has the same kind of AI-era SaaS look that suddenly turns everyone into a professional UI critic, but underneath it is genuinely serious research software, which is exactly why I think judging these products by the frontend is useless. As the founder of a somewhat comparable platform I have no problem giving them credit for that, but I would like to acknowledge a separate limitation. With it being an IDE/research environment, you can validate a strategy extremely well but with no native path to execute that same strategy directly into paper or live markets.

So that is really how I see the market right now:

1. the low-effort end gives you generated code and a backtest
2. 2) the serious research end gives you much better answers but still usually stops before execution.

Because of this outlook, Agenticks is designed specifically to take the same strategy through the full process; from research, rules, backtesting and validation to walk-forward analysis and live automation.

That is where the market splits in a way I think is useful. A lot of the low-effort “AI backtesters” are basically strategy-to-script wrappers: you describe an idea, an LLM writes Pine v6 (or similar) code, somebody else’s engine runs the test, and you leave with a script or report but no execution layer behind it. QuantPad sits at the opposite end: genuinely sophisticated research infrastructure, but still primarily research.

Agenticks is trying to connect those two halves. The strategy is turned into explicit rules and Python, shown back to you before it runs, backtested and stress-tested, and then that same strategy can continue into paper or live execution through your connected brokerage under limits you control.

(The attached video shows that flow pretty quickly.)

Just to summarize my opinion: looking AI-generated tells you almost nothing about whether a piece of software is actually good anymore. Frontend has become dramatically easier to produce; the real distinction is what has actually been engineered underneath it and how far the product carries the user once they press Run.

And on the trading side specifically, I do not think a backtest should be the product. It should be a step:

idea → rules → code → backtest → stress test → paper → live

Some products stop at generating the script. Some build genuinely serious research infrastructure but stop before execution. Either way, if the software stops halfway through that chain, the user is still responsible for building or manually replacing the part that comes next.

For people who actually build or use these tools: what separates a real platform from a wrapper to you, and what do you actually do with the strategy after the backtest?

___

References

Anthropic. (2025, April 28). Anthropic Economic Index: AI's impact on software development. https://www.anthropic.com/news/impact-software-development

Swartz, J. (2026, April 29). Google CEO says 75% of new code is AI-generated. DevOps.com. https://devops.com/google-ceo-says-75-of-new-code-is-ai-generated/

Klarna. (2024). AI helps Klarna cut marketing agency spend by 25% and run more campaigns. https://www.klarna.com/international/press/ai-helps-klarna-cut-marketing-agency-spend-by-25-and-run-more-campaigns/
_____

FOR REFERENCE: WHAT THE AGENTICKS BACKTEST COMPUTES + WHAT HAPPENS AFTER DEPLOYMENT

Category	What you get
Grade	A+ to F scored 0–100 · subscores for profitability · drawdown control · risk-adjusted returns · trade quality · consistency · prop firm survivability
Headline	Net return · net P&L · max drawdown · win rate · profit factor · closed trades
Performance	Gross profit · gross loss · average win · average loss · largest win · largest loss · expectancy per trade · average trade return
Risk	Sharpe · Sortino · Calmar · recovery factor · payoff ratio · Ulcer index · max drawdown in % and $ · average drawdown · drawdown duration · VaR 95% · conditional VaR
Trade stats	Winning and losing trades · longest win streak · longest loss streak · average hold in bars and wall clock · average R multiple · best and worst trade · long vs short split
Every trade listed	Side · entry and exit time · entry and exit price · P&L in $ and % · bars held · exit reason · MFE% and MAE% · sortable · filterable by winners, losers, long or short
Market regime	Every trade tagged trending up, trending down, volatile or choppy · win rate, profit factor, P&L, average hold, Sharpe and max drawdown broken out per regime · performance at 1M, 3M, 6M and 1Y
Monte Carlo	1,000 simulations · resample trades or shuffle their order · median return · 5th and 95th percentile · median and worst-case drawdown · p5 to p95 table · every equity path drawn
Prop firm sim	10,000 bootstrapped paths · FTMO, Topstep and Apex presets or your own · static, EOD trailing or intraday trailing drawdown · pass rate · failure rate · median steps to pass or fail · buffer to pass
Charts	Equity curve · monthly returns heatmap by year · P&L by period · benchmark comparison
Run context	Symbol · timeframe · tested range · starting and ending balance · bars tested · days scanned · execution costs charged · a flag if data coverage was partial
Export	The whole report as a PDF
Live routes	Signal only, with entry, stop and target to your Telegram the moment the bar closes · broker execution, where signed orders go to one trade-enabled account · paper trade, recording every trade with no orders and no alerts
Where it runs	On our servers · not your machine · nothing to leave open · nothing to install
Locked at deploy	Strategy · version hash · symbol · bar size · account · order size with a ceiling it can never exceed · long only or long and short · market or limit · warm-up bars before the first decision
Live risk limits	A cap on trades per day · a dollar loss limit · either or both · pause · resume · stop · emergency stop that kills everything at once
You arm (activate/approve) it	Nothing trades until you activate it on the last step · editing the strategy creates a new version that needs approving again
Live monitor	Running, paused or needs attention · recent trades as they fill · realized P/L · P/L over time and by period · win rate · profit factor · max drawdown · wins and losses · total closed · average win and loss by price move · CSV export
Across deployments	Total deployments · total realized · a leaderboard ranking your strategies against each other
Your existing account	Reads the brokerage you already have · breaks down holdings, concentration and risk · prepares an order you confirm

A gura named Powell has been all over my feed for weeks and everybody has been backtesting his 10am strategy lately so i figured i'd try it for myself

I told Agenticks to backtest his 10am strategy on NQ over the last 3 years, then it went and read what he actually teaches, turned it into a real strategy, wrote the code, and replayed it almost a million 1m NQ bars. The video attached shows it start to finish.

Heres what it came back with...

NQ 1m, july 2023 to feb 2026, 100k account, 1 contract:

• starting capital: 100k
• 535 trades
• 60.2% win rate
• profit factor 0.98
• net -$3,675
• max drawdown 25.55%
• average win $522, average loss $806

It wins 6 out of every 10 trades and still loses money. thats the whole story. the losers are 1.5x the size of the winners so the win rate is doing nothing for you, expected value comes out around negative 7 bucks a trade.

It even gets worse when you split it up. 280 of the 535 trades happened in volatile conditions and those lost $6,764 at a 0.93 profit factor. The only regime that it made money during was uptrends (152 trades, 1.08 profit factor, +$3,307) so the "edge" is mostly just the NASDAQ going up. Buy and hold over the same window took 100k to about 170k while this did -3.7%. With all of the all-time highs we're at right now, it makes sense why this has picked up so much traction in recent months.

I also ran it through a topstep 50k sim and out of 10,000 simulations; 39.1% of them hit the 3k target before the loss limit. This doesn't mean it has an edge, but it does mean that, technically speaking, it can beat the constraints of a prop firm (with a hint of luck).

Now the fair part, and this is the part id want somebody to say if it was my strategy getting torn apart

What he teaches publicly is the shape of it, not a rulebook. Mark the 10am candle, wait for one side to get swept, wait for the move back through, take the continuation into first liquidity. That's real but its not testable as written. "first liquidity" is something you read off a chart, not a number, and there is no published stop, no cutoff time, no trade limit. So the strategy that got built is the mechanical skeleton with sane defaults filled in, and two of those choices genuinely change what youre testing. it only took longs, and he trades both ways. and it entered on a plain break and retest instead of requiring the displacement leg, which is the part that supposedly separates a real setup from a level being tapped

So im not saying his strategy is bad, i'm saying the mechanical version of it (the version you could actually hand to a beginner) is a coinflip that gives back more on the losers than it makes on the winners. if the money is in the discretionary read, which side got swept and where liquidity actually sits, then somebody should say that out loud, bc that's a totally different product than a 3 step setup.

If you know the real/exact rules or think Agenticks got them wrong tell me exactly what to change and ill rerun it and post whatever comes out.

video of the full run is attached, sped up through the loading parts because nobody wants to watch a progress bar for 3 minutes.

not advice, backtests dont predict anything, im just a guy who got tired of seeing it on his feed

let me know if you guys want me to automate this strategy to see how it would perform on a paper account in real conditions, or if you want your own strategy tested.

For those of you who didn't already hear, **Truth Social (**Donald Trumps' media platform) is releasing Truth API on the first of August exclusively for Institutional & Wall Street customers (banks, hedge funds, and especially algorithmic / HFT firms).

As stated in the article below, Truth API "will effectively allow Trump's company to sell early access to the president's market-moving posts." (Quartz staff, 2026).

It's no secret at this point how much volatility follows any statement by Trump, but this makes it even more unpredictable. Retail investors/traders will now be at a delay in acting on information that is already priced in by the time they finish reading whatever he posts.

I think that the insider trading seen almost daily will be worsened by this, but also that the corruption isn't going anywhere, or at least not anytime soon. I definitely don't think this is legal, but then again, when has that ever stopped this administration.

❓ Curious what everyone else thinks. Does this actually change anything, or was it already happening? (or both)

In 2024, Topstep reported that only 12.4% of Trading Combines were successfully completed.

Why is that?

There obviously is not one single answer, but I believe one major reason is that many traders are actively using strategies they found in YouTube videos, Discord servers, or online communities without having any idea how those strategies have historically performed.

They see a few winning examples, understand the general logic, develop a gut feeling that the strategy makes sense, and immediately begin risking evaluations on it.

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“Historical results are not indicative of future performance.”

That is a completely true statement.

My response would be:

Would you ever bet on a sports team without knowing its record?

Would you try to predict whether it will rain tomorrow without knowing whether the last 50 days were sunny, cloudy, rainy, or stormy?

The past does not dictate the future.

You will never be able to say with certainty that a strategy will continue making money. No backtest can guarantee what happens tomorrow, next month, or five years from now.

What you can do is determine whether the strategy has ever demonstrated measurable value in the first place.

You might not be able to prove that a strategy will remain profitable for the next 10 years.

You can at least make sure you are not paying for an evaluation to trade a strategy that has been net negative for the previous 10.

Historical performance is not proof of future performance, but having no historical evidence whatsoever is not somehow better.

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The second major problem is subjectivity.

For anybody who golfs, I always use the same analogy.

Imagine you are a scratch golfer with an extremely consistent swing.

During your last two rounds, you felt slightly steep in your backswing. Based entirely on those two recent experiences, you decide to make an adjustment.

That one adjustment completely ruins your game.

Nothing was necessarily wrong with the swing that made you a scratch golfer. You changed it because of a gut feeling and recency bias.

Trading strategies are no different.

A trader can spend months developing a profitable set of rules, take two losses in a row, decide that one condition no longer feels right, add another confirmation indicator, widen a stop-loss, skip the next valid trade, or enter early because the setup looks too good to miss.

At that point, you are no longer trading the strategy you tested.

More importantly, you might no longer be trading something that can even be tested.

The second you introduce undefined subjectivity into a strategy is the second it stops being repeatable.

When it is not repeatable, it is not testable.

Plain and simple.

The problem is that everybody says “test your strategy” as though it has always been as simple as clicking a button.

Until recently, it really was not.

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You would generally need to know Python or another programming language, create a runtime capable of processing historical market data, manually obtain or purchase that data, translate every part of the strategy into code, determine what information should appear in the results, and understand how to avoid issues such as look-ahead bias, overfitting, unrealistic fills, missing costs, and misleading sample sizes.

Even after doing all of that, you still had to understand what the results were actually telling you.

A high return means very little without context.

A high win rate means very little without context.

You need to understand the number of trades, drawdown, expectancy, market regimes, sensitivity to small rule changes, and whether the strategy depends almost entirely on one unusually profitable period.

That frustration is exactly why I built Agenticks.

The idea was simple:

What if somebody could explain a trading strategy exactly as they understand it, have the rules made explicit, test those rules programmatically, and then have an actual conversation about the results without needing to know how to code?

With Agenticks, you can describe a strategy in normal language or have AI research and test different theories.

The platform converts the idea into explicit rules, writes the required code behind the scenes, runs the historical test, visualizes the results, and allows you to ask questions directly about what happened and why.

Instead of only receiving a return percentage, you can inspect the actual trades, drawdowns, risk metrics, market regimes, assumptions, and weaknesses behind the result.

It can also run Monte Carlo simulations that repeatedly reshuffle the order of trades to show how dramatically the path of returns can change even when the underlying trades remain exactly the same.

For prop firm traders, that matters.

A strategy can be profitable overall and still repeatedly violate a trailing drawdown or daily loss rule depending on the order in which its wins and losses occur.

By applying the rules of an evaluation to those simulations, you can estimate how frequently the same historical strategy might have passed or failed under different trade sequences before paying for the evaluation itself.

That does not mean a backtest predicts the future.

It does not mean a Monte Carlo simulation guarantees that you will pass.

It means you can identify whether your strategy has historically demonstrated an edge, understand the conditions in which it struggles, and determine whether its drawdown characteristics are even compatible with the account rules you intend to trade.

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Agenticks is genuinely something that would have completely changed the way I approached prop firms when I first started.

I was always frustrated by how advanced AI models had become without there being a simple way for somebody to speak, research, test, understand, and eventually automate a strategy without needing to write the code themselves.

That is why Agenticks exists.

Not to remove uncertainty from trading.

Nothing removes uncertainty from trading.

The purpose is to make sure traders are not repeatedly paying for evaluations using strategies that have never been clearly defined, have never been historically tested, or only work when the trader changes the rules after every losing streak.

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QUESTION:

Before buying an evaluation, how much historical evidence do you personally need before you are willing to trust a strategy?

I look forward to responding to as many of you as possible

In my experience as a trader, almost every AI tool, site, and piece of software in this space only does one thing in my workflow. I kept running into the same issues, asking the exact same question as thousands of other traders…

"Why am I paying for a dozen different tools for a dozen different things?"

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The answer I wanted was one platform that does all of it, for a fraction of the cost;

i) An AI-powered hub that researches, backtests, and automates strategies live (or to TopStep) across 10,000+ instruments and with 10+ years of multi-timeframe history included, no coding experience or data required.

ii) An AI model that builds any custom TradingView indicator in Pine Script v6.

iii) Professionally built indicators for popular concepts: OTE/STDV, Bookmap-style order flow proxies for TradingView, and Volume Profile (AMT).

iv) Real-time news, intraday GEX levels, sector overviews, and order flow feeds.

v) Advanced stock screening: earnings, key metrics, fundamentals, analyst opinions, TradingView charting, and live news.

vi) Daily, weekly, and monthly pre-market AI stock picks, performance-tracked, built from thousands of factors in a custom database.

vii) A 100% free, start-to-finish, beginner-to-expert course packed with structured learning paths, interactive lessons + exercises, and a full glossary, that I wish I had when I first started.

viii) Dedicated local-execution software for TopStep that abides by their API rules and includes custom charting, economic calendars, volume standard-deviation gauges, and more.

ix) a hub to connect to a live brokerage, analyze positions, model long-term portfolios, and monitor my algorithms.

And most importantly, ONE AI agent that can actually do all of it.

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Because nothing like this existed, I spent eight months relentlessly researching and developing my platform (with the help of the best professional developers I know) to make my dream a reality.

WealthLearn.ca brings every feature above into a single platform for a fraction of the cost, so you can stop tangling together scattered tools and paying for a dozen separate subscriptions.

It connects directly with TradingView, live brokerages (Wealthsimple, Moomoo, Kraken, Coinbase, Webull, and more), and TopStep... having two clear goals;

1) Bring the benefits of quant and algo trading to retail traders, WITHOUT the coding, complexity, or the institutional price tag.

2. Unify every tool needed, from day trading to macro investing, into one ecosystem, instead of a dozen expensive, single-purpose tools.

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I really don't want this to feel like a pitch, but there's so much low-effort vibe-coded stuff going around right now that I thought something I've poured this much real work into could actually be useful to people. have a look at the video above for a glimpse of what we've built, and if you don't want to spend anything, there's a free trial so you can see exactly what I'm talking about.

Happy to answer anything and everything in the comments. Whether it be about the platform, its tech stack, my team's development experience, or trading in general, feel free to* ask away.

>DISCLAIMER: As always, Educational software and tools designed for non-personalized analysis and deterministic execution , not financial advice. Trading is extremely high risk and is not suitable for every investor.

💬🚨 UPDATE SINCE POSTING:

I want to thank all of you for your support, questions, feedback, and comments. I've done my best to respond to each and every one of them in as much detail as possible.

I'll be flying out to NYC tomorrow to attend the Vercel's Ship 26 event as well as a few scheduled meetings for seed funding, but I hope to continue to respond to all new replies in the coming days when available.

Best,
The WealthLearn Team

Bit of an experiment; I got tired of "AI stock pick" accounts that only screenshot their winners, so I built the opposite.

Every morning AI Stock Hub publishes a pre-market direction call (long or short) list, containing its highest confidence stocks, then it logs the performance by the end of day. Wins and losses both stay on the board, everything stay as visible and transparent. It also does the same on a weekly and monthly basis.

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The model is powered by Claude Opus 4.8 which analyses WealthLearn's custom live database across these core market factors:

• Global macro: rates, inflation prints, dollar strength, risk-on/risk-off tone
• Sector and industry trend (relative strength vs the broad market)
• Earnings: recent results, surprises, forward estimate revisions
• Price action and momentum across multiple timeframes
• Relative volume and participation
• Volatility regime (expanding vs contracting)
• Valuation context (where it sits vs its own range and peers)
• Recent news headlines and catalyst flow
• Market breadth and correlation to the index

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Here is where it actually stands, not some make-believe stats;

It's running a 47% win rate...

... but a hit only counts as the stock closing in the direction it was called, regardless of how far it moved. 

I've attached a video example so you could see what I mean by this

Summary: June 22nd - Market Hour Change

• LULU, Bearish, $111.38 → $105.43 (-5.34%)
• MU, Bullish, $1196.21 → $1211.38 (+1.27%)
• INSM, Bearish, $95.82 → $98.61 (+2.91%)
• NKE, Bearish, → $43.19 (-3.20%)
• SNDK, Bullish, $229.. → $2273.73 (-0.85%)
• MSTR, Bearish, $116.21 → $109.46 (-5.81%)
• COIN, Bearish, $164.94 → $164.84 (-0.06%)

TLDR: The model could still be net profitable even at a 20% win rate, as long as the losers are constrained in percentage and winners continue to be large gains.

The point of the project was never "trust the robot." It was to have a public, gradeable track record instead of cherry-picked wins. The board and the screener are fully free to look at to try out.

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Curious what this sub thinks: when you judge a model like this, do you weight the win rate or the average size of the moves it catches? I keep going back and forth on the best way to sum it up.

Educational software and analytical tools, not financial advice.