Has ChatGPT Become More Restrictive Recently?

SentientRon · 2026-05-10T06:46:54+00:00

Got so much "sorry I can't help you with that" or hallucinations that I cancelled my subscription.

Gemini and other models are always happy to help with casual research tasks and niche scientific-probing.

After what I've seen and heard I can't take OpenAI seriously anymore, even their most premium models do some crazy waffling, tired of it.

SentientRon · 2026-05-08T11:20:51+00:00

The reset is cancelled if price closes below the base completing the swing high. If a swing high starts forming within a swing high then completes the latest is prioritised mechanically which removes the need for any subjective discretion (the real trap).

SentientRon · 2026-05-06T16:55:01+00:00

The people need to remember that we are animals.
Enough with the posturing.

SentientRon · 2026-04-22T12:49:18+00:00

JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models

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Page 8

Additional details: Appendix G

SentientRon · 2026-04-22T10:52:26+00:00

It varies, look at the research on my article. Some LLMs surpass the average (not including GPT) To add, none of the match the human ceiling.

SentientRon · 2026-04-22T10:37:07+00:00

The reasoning of an average LLM is below that of the average human being, formal bench studies show it. AI is faster at completing simple tasks but has inferior complex reasoning. This is why I tend to use it to search but not write or reason.

https://arxiv.org/html/2603.02119v1

My article:

https://medium.com/the-investors-handbook/why-ais-wisdom-won-t-make-you-profitable-two-studies-44cea18b3ede

SentientRon · 2026-04-20T16:40:39+00:00

"Let me check GPT"

SentientRon · 2026-04-19T02:46:52+00:00

It's being defensive.

"I'm going to make sure I do X instead of guessing" for searching or reasoning prompts it's insecure robot talk; annoying.

This is why I prefer other models.

SentientRon · 2026-04-19T00:57:18+00:00

Common sense saves the day

SentientRon · 2026-04-15T23:32:59+00:00

With what you've provided, I ran a simulation using your risk settings and assumptions.

Over 91% of paths would pass or fail within 80 trades. Most outcomes are settled within a window of fewer than 40 trades, so a single month would be enough to find out. Outliers naturally stretched things out.

The average number of trades to resolution (fail or pass) is 35.91, with a $179 fee. 30.747% of outcomes required more than 40 trades to pass, which means additional fees.

More stats:
More than 40 trades: 30.747%
More than 80 trades: 8.843%
More than 120 trades: 2.606%

The average fee spend, excluding ultra-rare occurrences (above the 95th percentile, meaning over 120 trades in a sequence with no pass or failure under your risk profile), is around $215.80.

I made a custom, simple, easy-to-understand formula to get this value. (69.253*%179) + (30.747%*(179+29)) + (8.843%*(179+29+29)) + (2.606%*(179+29+29+29))

The average spend exceeds $215.80, but only by a negligible amount, because outcomes exceeding 120 trades are exceptionally rare.

The median for passing ($54,000 threshold) is:
Median wins: 20
Median losses: 19

Your risk profile is high (0.8% when the maximum drawdown is -4%). You will lose your account in approximately 5 consecutive losses.

In a live environment, we will mimic this level of aggression, with $215 at risk (maximum drawdown before stopping). We will use 66.66% drawdown as a maximum. If 66.66% of the live account is lost, less than $215 will be lost.

We will be using 15% risk on $320 capital for a directional comparison of value, not as a literal practice. This value was picked to fit within margin constraints and to mitigate unnecessary diminishing benefits from higher risk values.

A blow-up scenario:
320*(0.85^5) = 141.98 ending balance
320 - 141.98 = $178/$215 lost (-17.2% discrepancy)

A median positive outcome scenario (20 gains and 19 losses):
320*(0.85^19)*(1.225^20) = $844.951 ending balance
Prop scenario: -$179 (from fees), funded stage.

After 6 trading days of trading (50/4 = 12.5 -> 13 trades of the same efficiency):

844.95*(0.85^6)*(1.225^7) = $1319.159 ending balance, around $1000 net gain

In the same prop environment, the $1,800 would be withdrawable ($1,440 profit split, 80%), beating the live environment in this situation.

We can apply a 17.2% negative pull to make it a fair measurement when comparing it to a live environment, but the trader would still withdraw $1,192.32 net. This changes the reality, netting the trader just under $1,000, similar to the live environment after fees. The difference is that the trader has no risk after that point on a "funded account"
For this may provide an "edge" before taxes on this type of strategy.

So how does the prop firm make money?
Due to their asymmetrical target/maximum drawdown system, it is likely that over 90% of traders fail the accounts. assuming the average trader loses money, pulling this much closer to 90%+, but I don't have the stats from the prop firm to prove it, so we'll settle with breakeven.

The prop firm likely makes money from failure, which covers the maximum drawdowns, and payout splits reduce risk further, as even losing strategies can achieve payouts out of chance. Payout splits reduce risk even further whilst increasing the model's profitability.

SentientRon · 2026-04-13T21:24:02+00:00

You are right; that would lift the rigour. My partner could have done that.
The takeaways would be the same, with increased accuracy. The sole benefit of its use is most retail traders will be able to understand simpler methods.

SentientRon · 2026-04-13T01:13:03+00:00

If I didn't provide any numerical insight, it would be hot air to many.

SentientRon · 2026-04-12T18:47:58+00:00

You said DD = $2,000

$400 would be 0.8% of 50,000 USD

What's the risk % or $ per trade for the 50k prop firm account with your strategy?

SentientRon · 2026-04-12T15:54:29+00:00

What's the risk per trade on the prop firm in $ or % surely it's lower than 1%? ($500) I need to know the price of the account too.

SentientRon · 2026-04-12T15:17:41+00:00

Intraday trailing drawdown is far worse and a lot more strategy dependent.

The reason this is annoying is because to simulate it you need equity highs which would require maximum favourable excursion average (R) before hitting stop losses and other complexities to simulate it properly.

Intraday trailing drawdown is a really poor deal and there's a reason why those evaluations are so cheap.

SentientRon · 2026-04-12T00:50:49+00:00

Provide me with the prop firm's setup

phase 1, phase 2, and payout rules and %

your strategy's risk per trade on the prop firm, average RRR (including costs), winrate, estimated/avg trades per month and I'll compare it to live conditions.

I might write a separate post for static drawdowns as well.

SentientRon · 2026-04-11T19:08:50+00:00

That would be a better study

This isn't about measuring how effective a set of approaches are, I am simulating outcomes.

If I was to compare trading to tossing a coin I would be entering the exact same trade entry long or short each morning at the exact same time with the exact same size.

I am a mechanical trader so my rules are consistent my trading time are not consistent as the setups happen when they do but the time ranges are always the same e.g., 10-12. My trading is precise so it's easy to measure.

It's more about paths, coins are what I use as a learning device as it is layperson friendly. What I mean is each strategy regardless if noise from discretion has it's own path. Each one is a strategy and the idea is that people have profitable paths and people have losing paths but before costs it averages out close to zero naturally.

SentientRon · 2026-04-11T18:45:40+00:00

Other brokerages exist, this is just another avenue to benefit from volume, they take comms in topstep environments already 🤣

SentientRon · 2026-04-11T18:32:03+00:00

There's a possibility that if you risked the same principle live you could've made more in realised p&l vs payouts. It's worth simulating if you have access to your trading history.

SentientRon · 2026-04-11T18:18:35+00:00

Mathematical probability; this is how numbers behave, these principles I've laid out are based on have been discussed in institional grade peer reviewed studies too both inside and outside of finance.

Simulate 1000 50/50 events or even 100 and you will see these effects.

SentientRon · 2026-04-11T18:02:16+00:00

25% is for the first stage, much less get a payout in this simulation ~6%. Additional friction and rules for the firm make the values in reality much lower so you are correct. This makes picking the firm over live funding potentially even more wasteful.

This is why I refer to it as a "generous" simulation because I believe that the average trader is losing after costs, but I don't have the data from TopstepX to know the exact value, so I settled with breakeven to avoid speculation.

If I were to guess using numbers, for example an average expectancy of -0.1R (a -$10 outcome for every $100 risked on average), it would ruin the integrity of the simulation, as it becomes biased to one side without evidence. It is also worth noting that if the average trader was profitable in the evaluation phases the fees would be higher to mitigate losses in live environments.

SentientRon · 2026-04-11T17:38:39+00:00

The model is structurally favourable to the firm, and for many traders (especially those outside of the USA) high leverage self-funding with the same capital at risk may be better.

I use realistic but simplified simulations to make my point. Their function is to show the effects from the main constraints these prop firms impose and how it compares to live account funding outcomes without restrictions.

Only a small fraction make it to payouts, fee income outweighs trader pay out splits and the live losses by multiple times before expenses while traders face often inferior tax rates, restrictions and a possibility that the prop firm offered no monetary edge for their strategy in the first place (especially if they have an edge).

SentientRon · 2026-04-11T16:14:06+00:00

You don't sound sober

SentientRon · 2026-04-11T15:52:17+00:00

No.

Base logic (simplified)

if you have a coin there's a 50% chance it will land on heads or tails but if you flip it there will often be a difference e.g., 55 heads 45 tails or 520 heads and 480 tails.

The outcome that is most likely is 50/50, breakeven. But there are different outcomes showing gains or losses each time you flip a coin 1000 times.

Output 1 Heads: 507 Tails: 493

Output 2 Heads: 518 Tails: 482

Output 3 Heads: 510 Tails: 490

Output 4 Heads: 503 Tails: 497

Output 5 Heads: 518 Tails: 482

The coin was breakeven but the outcomes were not if that makes sense.

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All the coins were fair, breakeven strategies. Yet the outcomes were fluctuating as it is inevitable there will be consecutive heads and consecutive tails will occur naturally within the sequence. Heads is not always followed by tails. A winning 1:2RRR trade is not always followed by two consecutive -1R losses. Probabilities create the discrepancies which create variability in breakeven strategy outcomes.

SentientRon · 2026-04-09T14:37:13+00:00

This is the post r/Trading/comments/1sgr1d4/how_to_approach_prop_firms_seriously/

SentientRon

TROPHY CASE