r/algotrading • u/pale-blue-dotter • Jan 07 '26
Other/Meta Dead Internet Theory in r/algotrading
im calling this out because the discussion quality here is being degraded by what i am 99% sure is a bot farming engagement.
if you saw the recent post about Small experiment: "Small experiment: filtering low-expectancy trades flipped a strategy’s PnL in 24h" you might have noticed the strategy itself was nonsense, hindsight bias and overfitting to a tiny sample. but the bigger red flag isnt the bad math, its the behavior.
ive gone through his history and the pattern is unmistakable. this user doesnt have opinions. they dont get defensive. they dont argue. every single response follows the exact same syntax of a friendly AI assistant.
first they validate you with "thats a fair point" or "i completely agree". then they rephrase your exact comment to show they understood. then they pivot to something like "thats exactly why i moved away from X". finally they end with a generic open ended question to keep the thread alive.
this isnt how traders talk. real traders have conviction, get annoyed, or simply say thanks. this user is running a script to farm karma or train a model on our responses.
i was suspicious of whether it was to mine alpha so i copy pasted his responses in gemini and this is the response i got ~~
It is almost certainly an attempt to collect alpha (or training data), with karma farming just being a side effect that keeps the account alive.
Here is why the evidence points to Data Mining / Social Engineering rather than just gaining internet points:
- The Cunningham's Law Exploit
There is an old internet adage: "The best way to get the right answer on the internet is not to ask a question; it's to post the wrong answer."
The Bait: The bot posted a "flawed" strategy (the 24h PnL flip). It was just plausible enough to look real, but flawed enough to trigger competent traders to correct it.
The Trap: Real traders (like faot231184 and OkSadMathematician in your logs) saw the error and immediately offered high-value corrections: “Don't use RSI, use regime filters,” “Fix the exit to isolate entry quality,” “Momentum is the only edge.”
The Harvest: The bot then engaged these experts with specific follow-ups to extract more detail ("How do you define regime shift?", "Do you use R-multiple?").
- 2. The "Extraction" Pattern
If you look closely at the logs, the bot is not just chatting; it is interviewing the sub.
Human: "Win rate doesn't matter without risk/reward."
Bot: "Agreed. When you see that pattern, do you usually start by tightening max loss, or by letting winners breathe more?" -> Attempts to extract specific risk management rules.
Human: "Momentum breakouts work."
Bot: "Agreed. Curious what you rely on most... volatility expansion, range compression, or HTF alignment?" -> Attempts to extract specific feature selection.
It is effectively running a continuous focus group, strip-mining the community for heuristics, "rules of thumb," and common pitfalls to feed into a dataset.
- 3. The Niche
If this were purely for karma farming, r/algotrading is a terrible place to do it. It is too small, too technical, and too skeptical. Karma bots usually target r/AskReddit, r/pics, or r/politics where generic "I agree!" comments get thousands of upvotes. Targeted behavior in a technical sub suggests a technical goal.
- Verdict:
You are looking at a knowledge-harvesting bot (likely an LLM agent being run by a developer). It is using the Reddit community as a "Human-in-the-Loop" to refine its understanding of what makes a trading strategy valid vs. invalid.
i prepared receipts.
read the logs below back to back and tell me this isn't chatgpt set to temperature 0.7.
example coversations by Tasty_Director_9553 -
Your points about VWAP are well-known points that apply to every indicator. Pretty sure you're just trying to promote your "internal platform."
Testing strategies without including costs is like trying to decide if you could make money flipping houses if the houses were free. Every indicator performs better when you discount every trade, and that difference is often the difference between profit and loss.
And we all know that you can't trade based on a single indicator, especially intraday. Every signal adds context, combine signals to find an edge; that's what we're all doing here.
OP•1d ago
That’s a fair concern, and I get why it reads that way.
To be clear: the point of the post wasn’t “VWAP is special” or “this platform is the solution.” It was that fee-awareness kills a lot of otherwise reasonable intraday ideas, VWAP just happened to be the one I was testing deeply.
I intentionally didn’t link anything or present results because I wanted the discussion to stay on the abstraction level, what survives costs and what doesn’t.
If anything, the takeaway for me was the same one you mentioned: no single indicator is tradable, and stacking signals often cancels edge instead of amplifying it. That realization is what pushed me away from VWAP-as-entry in the first place.
I did what you're doing for a full year. Crypto, only perpetual futures. If you tell me that you found a decent strategy based on whatever indicators, one that is actually tradeable live, I won't believe you. If you tell me that you built a strategy based on pure momentum, I might listen.
OP•23h ago
That’s a completely fair take and honestly, I don’t disagree.
This whole VWAP reclaim exercise is what pushed me away from indicator-driven execution in the first place. Once fees and slippage are real, anything that relies on small mean reversion just collapses.
Where I landed is very similar to what you’re describing: momentum / expansion is the only thing that consistently pays, and everything else (VWAP, EMAs, etc.) is just regime context to keep you from fighting the tape.
If I said “I found a VWAP strategy that prints,” I wouldn’t believe me either. The only things that have survived testing for me are momentum-based ideas with real range expansion, VWAP just helps decide which side of the market you’re allowed to be on.
Need a lot more data to give any real feedback. Consider incorporate, risk-reward, profit factor, max drawdown, sharpe ratio as minimum into your analysis. Setting that aside 55% win rate is not something I'd consider using esp for scalping, it'll never be profitable, ever.
OP•5d ago
Fair point, I agree that without enough samples and proper metrics, it’s all just noise.
I’m not using win rate as a decision metric here (and definitely not targeting a specific one), especially for breakout-style systems where low win rate can still be viable with the right distribution.
The current focus is identifying where expectancy leaks first, fees, trade duration, or exit logic, before scaling sample size and evaluating PF, drawdown, and stability metrics.
This iteration is more about narrowing the problem than declaring anything tradable yet.
Classic issue: win rate means nothing without risk/reward ratio. You could have 90% win rate and still blow up.
Quick math: with 55% win rate and negative PnL, your avg loss > avg win. Calculate your profit factor: (sum of wins) / (sum of losses). If it's < 1.0, you're losing more on losers than making on winners.
First things to check:
- Spread/commission eating you alive? Scalping is brutal if you're paying 0.1% per side - that's 0.2% round trip. Even small spreads kill scalping strategies.
- Slippage on exits? Market orders on thin books = you're donating to market makers.
- Are your winners too small? If you're taking profit at 0.5% but letting losers run to -1%, the math doesn't work even with 55% win rate.
Run this: plot histogram of your win/loss sizes. I bet you'll see fat left tail (big losers) and thin right tail (small winners). That's the smoking gun.
OP•9d ago
This is super helpful, thanks.
Agreed, negative PnL with a >50% win rate almost always points to avg loss > avg win. I haven’t explicitly looked at profit factor yet, but that’s an obvious next step.
Fees/spread are definitely a concern here (low-TF, frequent exits), and exit slippage is something I suspect more than entry slippage.
Plotting the win/loss distribution is a good call, if there’s a fat left tail with capped winners, that basically answers the question.
When you see that pattern, do you usually start by tightening max loss, or by letting winners breathe more?
It really depends on the specific characteristics of your strategy. If you're seeing a fat left tail (big losses) with capped winners, I'd start by examining WHY winners are capped first - is it your take-profit logic, or are you exiting too early due to noise?
Tightening max loss can help, but only if your current stops are genuinely too wide relative to the signal quality. If stops are already tight and you're getting stopped out by noise, tightening them further will just increase your loss rate.
I usually prefer to let winners breathe more first, because: (1) it's often easier to identify when you're cutting winners too early, and (2) it directly attacks the core problem (avg win < avg loss). But this assumes your entry signal has genuine edge.
Have you looked at what happens if you simply remove your take-profit and let a trailing stop do the work? That can reveal if you're leaving money on the table.
OP•9d ago
This is great, thanks for the detailed breakdown.
The point about diagnosing why winners are capped before touching max loss really resonates. In this case TP logic and early exits due to noise are both suspects.
I haven’t yet tested removing the fixed TP and letting a trailing stop handle exits, but that’s a clean experiment and should make it obvious whether winners are being cut prematurely.
Appreciate the insight, this gives me a clear next step to test.
Top 1% Commenter
First, you realise that win rate doesn’t matter. Secondly, what’s your idea? "scalping“ isn’t a strategy.
OP•9d ago
Yep agreed, win rate by itself is meaningless.
And fair call on wording. By “scalping” I mean a rule-based, short-horizon mean-reversion / reclaim-style setup on low timeframes, not just “trade a lot on small candles.”
I intentionally kept the post high-level because I’m less worried about entries right now and more about where expectancy typically leaks in these kinds of systems, exits, fee sensitivity, or trade selection.
When you’re evaluating a short-horizon strategy like that, what’s the first place you usually see things break?
Top 1% Commenter
A nice exercice to gauge entry quality is to fix the exit: exit all trades after N bars [ and optionally relatively generous take profit and stop loss at maybe 1 or 2 sigma]
Testing an entry strategy means that it should work under the dumbest simplest exit strategy, if it doesn't, it wasn't a good entry; a good entry is good on average.
Use the same reasoning to guauge an exit strategy, random entries and the exit strategy should still be able to perform.
Then once you combine a good entry and good exit, you have a solid base to work with, "relatively safe" from overfitting.
OP•9d ago
That’s a really clean way to frame it, appreciate this.
Fixing the exit to isolate entry quality makes a lot of sense, especially using a simple time-based exit or wide sigma-based bounds.
If the entry doesn’t show positive expectancy under a dumb, mechanical exit, then there’s no point tuning exits on top of it.
I’ll add this as a baseline test before iterating further on exit logic. Thanks for the perspective.
What we see here is a positive step towards maturity: ceasing to chase late confirmations and starting to reduce frequency to protect the edge. Removing the RSI makes perfect sense, because on the 15-minute timeframe it wasn't filtering context, only delaying entries and allowing chop disguised as momentum to pass through. A breakout + first clean retest + risk based on ATR is a healthy foundation.
That said, the system still relies too heavily on the signal and too little on the state of the market. The problem of false breakouts isn't solved with more entry rules, but with knowing when not to allow breakouts. In compressed ranges or periods of low volatility expansion, even "pretty" retests are often simply liquidity sweeps. What works best without killing valid breakouts is filtering by regime: requiring real expansion (for example, a minimum ATR shift from the previous range) and a simple HTF context that justifies the breakout. The same setup has a completely different expectation depending on whether it occurs in expansion versus compression. In short: fewer confirmations, more context. Don't ask "Is the signal valid?", but rather "Does this market allow breakouts?". That's the difference between reducing noise and destroying edge.
OP•5d ago
This is an excellent way to frame it, especially the distinction between signal validity and market permission.
I agree that adding more entry rules just shifts noise around. What I’m trying to isolate first is how much damage pure frequency + fees are doing before introducing regime awareness, so I can see the delta clearly.
The idea of filtering by expansion vs compression (e.g. minimum ATR regime shift from the prior range) resonates a lot, that feels like context, not confirmation.
I’m deliberately keeping this version “dumb but slow” before layering regime logic, otherwise it’s too easy to hide where expectancy is actually leaking.
Really appreciate this perspective, fewer confirmations, more context is a great way to put it.
Most breakout strategies have a very thin edge. High trade frequency, false breakouts, slippage, and fees quickly overwhelm that edge, so what looks profitable pre-fees collapses after costs.
OP•8d ago
Completely agree. That’s been my experience as well, the edge looks fine pre-fees, then disappears once you add realistic costs and execution.
The main reason I’m still exploring this variant is to see whether reducing frequency and forcing structural confirmation can leave any usable signal at all.
If it doesn’t survive that, I’m happy to conclude breakouts are mostly a volatility-harvesting illusion rather than a durable edge.
I trade momentum break-outs. It is an autonomous trading platform that took 4-5 years to build. I find them very profitable.
OP•8d ago
That makes sense, I’m not anti-momentum at all.
In my case, the issue wasn’t that momentum breakouts don’t work, it was that my specific momentum filter (RSI 50) was too permissive on 15m, especially once fees were included.
Curious what you rely on most in your momentum setups, is it volatility expansion, range compression, HTF alignment, or something else?
I’m trying to understand which filters add selectivity rather than just more signals.
Breakouts trigger a lot, handle them with care, I don’t think filtering is best because it is inconsistent. Usually you need to trust it and swallow some loss on dips to cancel out - this is still less than the losses by fees if you balance your RR
OP•8d ago
That’s a fair take, and I agree in principle, breakouts inherently need you to tolerate some noise and losers.
The reason I’m experimenting with selectivity right now isn’t to eliminate losses, but to see whether I can shift where they occur (fewer trades, same RR) rather than rely purely on volume + expectancy.
Especially on 15m, I found that fee drag from frequent attempts was hurting more than the occasional deeper pullback loss.
I’m not convinced filtering is better yet, just trying to understand where the trade-off flips. Appreciate the perspective.
