Imagine an artificial intelligence with, well,
a flawless resume. It claims grandmaster level
skills in coding, even complex reasoning for
financial markets. Sounds like a golden ticket,
right? The kind of thing that could just change
everything overnight. Exactly. But what if that
AI, you know, despite all that brilliance, was
fundamentally blind to the very patterns that
actually make a trading strategy profitable?
Welcome to the Deep Dive. Today, we're taking
a really close look at the much hyped GPT -5,
specifically its real world performance when
it was put to the test building trading view
strategies. Yeah, when GPT -5 first came out,
the market was just buzzing. Everyone asking,
is this it? Is this the AI that lets us mint
money? We've got the, let's say, brutal, honest
answers today. We'll unpack that initial hype,
reveal the results from three pretty rigorous
hands -on tests, and ultimately we'll show you
why the future of trading probably isn't AI replacing
humans, but more like a powerful human -machine
partnership. Right. Our mission today is really
to cut through all that noise. We're going to
show you where GPT -5 shines as this high -performance
engine, which it is. It's remarkable. But maybe
more importantly, where it still critically falls
short. where it needs your human intelligence,
your intuition to actually succeed. We're getting
to the unfiltered truth here. Okay, let's unpack
that initial phase then. When GPT -5 dropped,
the buzz, like you said, was huge. It really
was presented like the perfect job candidate
for, say, a quant trading firm, boasting grandmaster
reasoning, coding skills, especially, it seemed,
for Pinescript on TradingView. People were excited.
Oh, absolutely. It was an incredibly impressive
interview, you could say. Yeah. The AI seemed
to grasp. The specific world of Pinescript and
TradingView almost instantly, it felt like it
remembered past chats, you know, adapting in
a way that suggested it was way more than just
some generic chatbot. So that created this really
powerful first impression, made a lot of people
think, wow, this could revolutionize strategy
development. But underneath all that, the big
question was still there. Could it actually create
profitable strategies from scratch? Or just follow
orders well. So just based on that first impression
before the real test began, what was the biggest
takeaway? It felt like a true specialist, but,
you know, talk is cheap. Right. So past the interview
phase, time for the skills tests. The first challenge
was something pretty common for algo traders.
Turn an indicator into a strategy. They picked
a complex sounding one, the Luxago AI Super Trend
Clustering Oscillator for Bitcoin. Visually,
it was clear, blue dots for buy, red dots for
sell. Right on the chart. Yeah. Easy for a human
to see the pattern. So in round one, they just
gave GPT -5 total control, fed it the Pinescript
code, gave it a really open -ended prompt, transform
this indicator into a strategy as you see fit.
And the result, an absolute unmitigated disaster.
Seriously. Oh, bad. Lost 32 % since 2018 on Bitcoin.
Just, wow. A money shredding machine, literally.
Ouch. It just hammered home that processing power
isn't market wisdom. Not at all. But here's where
it gets really interesting. Round two, they tried
this human -AI partnership. Instead of being
vague, they gave it clear instructions based
on that visual pattern a human could see explicitly.
Buy when a blue dot appears, sell when a red
dot appears. Simple. And that, well, that was
a total transformation. Just night and day, that
human -guided strategy. It produced a plus 200
% profit. And the drawdown was reasonable, too,
around 37%. The critical lesson here is really
profound. It shows a fundamental limit. GPT -5.
cannot see the market like a human. It's blind
to visual patterns on a chart. It processes text,
not shapes or where dots are relative to price.
It doesn't get candlestick patterns. But once
you, the human strategist, see that pattern and
tell it exactly what to do. Then it works. Then
it becomes an incredibly fast, efficient code
monkey. Yeah. Translates your idea into Pinescript
almost instantly. Amazing speed. So boil it down
for us. What's the core difference when a human
steps in to guide it? Your human intuition provides
the strategic direction it lacks. Okay. Next
up, they tested its enhanced reasoning on something
trickier, fixing an underperforming strategy.
They used a Gaussian channel trend following
strategy. Like a lot of trend followers, it tended
to lose money in choppy sideways markets. Classic
problem. Right. How do you keep the good trend
trades but ditch the sideways chop losses? The
prompt was clear. Enhance this strategy. Avoid
sideways channels. More percent profit with lower
percent max drawdown. Specific goals. What followed
was, honestly, a multi -hour coding nightmare.
The AI kept trying to use a function called ADX
in Pinescript that doesn't actually exist in
standard Pinescript that way. Wait, it hallucinated
a function? Yeah. ADX, the average directional
index, is a real indicator. Super common for
trend strength. But GPT -5 kept getting the implementation
wrong, trying a non -existent built -in function.
It took multiple rounds of debugging, feeding
it error messages, even screenshots. I mean,
I still wrestle with prom drift myself sometimes,
so I get the frustration. So after all that back
and forth, that debugging headache, what was
the final result? Was it worth it? Well, it was
a big bag, but ultimately a failure on the main
goal. How so? It did manage to lower the max
drawdown a bit, and the win rate went up slightly.
But the total profit was actually lower than
the original strategy. Lower profit. So it didn't
really fix it. No. It hadn't really solved the
core issue of sideways markets. What it did instead
was just remove some of the best, most profitable
trades from the good trending periods. It did
that just to bring the drawdown number down.
It couldn't tell the difference between a good
loss, you know, an acceptable part of the strategy,
and a bad loss from the chops. So it lacked nuance.
Completely. It just didn't have that market intuition,
honestly, for this task. Yeah. Not much better
than GPT -4. Why do you think it struggled so
much with actually improving the strategy, not
just, you know, cutting trades? It couldn't grasp
the market context of good versus bad trades.
OK, that leads us to the biggest test, the ultimate
challenge. Could GPT -5 create a profitable strategy
totally from scratch? The hope was maybe its
massive training data held some hidden alpha,
some pattern it could synthesize on its own.
Well, it was in another coding marathon, let
me tell you. The usual suspects popped up those
ADEX errors again, incomplete codes, stuff needing
human fixes. But after all that debugging, something
genuinely interesting did emerge. Quite surprising,
actually. The final strategy it built wasn't
the absolute highest profit winner in terms of
sheer percentage gain. It was one of the best
strategies they tested in terms of stability.
Risk management was top notch. Stability. How
so? It had an incredibly low maximum drawdown,
just 15 .46%, plus a really solid 61 % win rate
and a profit factor of 7 .1. That's strong. 7
.1 profit factor. Yeah. That's impressive. But
low drawdown is the key here. Explain why that's
such a big deal. Yeah, this is super important,
especially if you're trading serious capital.
A strategy with really low drawdown isn't just
nice. It's a foundational advantage. It means
you can safely amplify it. You can increase your
position size, maybe use a bit of leverage. much
more comfortably like even with just 2x amplification
which is pretty modest, that GPT -5 strategy
could suddenly produce competitive profits, maybe
even better than some higher raw profit strategies,
and its drawdown would still be well within reasonable
risk limits. Think about applying that to significant
capital. Whoa. Yeah, imagine scaling that kind
of stability. It changes the whole psychological
experience of trading, doesn't it? Absolutely.
A high win rate plus low drawdown that's much
easier to stick with psychologically in the real
world. No huge swings, fewer long losing streaks.
It built a really solid foundation. So bottom
line for us, what makes that low drawdown result
so valuable to a trader? It offers psychological
ease and safer amplification of capital. Midroll
sponsor read. OK, so after running GPT -5 through
these three different pretty demanding tests,
what's the brutal unvarnished truth? What are
its real capabilities for trading? Well, I think
the best analogy is this. Think of GPT -5 as
a world class, high performance race car engine.
incredibly powerful, an amazing feat of engineering
capable of immense output. But here's the catch.
It's delivered to you with no steering wheel,
no brakes, and no GPS. Ah, powerful but uncontrollable.
Exactly. It's an engine of immense power, but
without a skilled human driver providing the
crucial controls, giving it direction, navigating
the markets, twists and turns, it's going to
crash, guaranteed. It's a tool with huge potential,
but it's definitely not a complete solution on
its own. That's a really clear analogy. So let's
drill down then. Where does this powerful engine
actually shine? What are its core strengths we
should be using? Okay, despite those limits,
it has three really clear strengths. First, code
implementation. It is genuinely world -class
at translating human logic into code. You define
the rules, tell it what you want, and boom, it
writes clean, efficient Pinescript. The speed
is just incredible. Okay, translation expert.
What else? Second, low -risk architecture. This
was interesting. It seems to default towards
building strategies that have inherently reasonable
drawdowns and pretty solid risk management baked
in, almost like a safety engineer designed it.
So it leans towards being cautious. Seems like
it, yeah. And third, it's a tireless debugging
partner. Feed it clear error messages, show it
screenshots of problems, and it's surprisingly
good at finding and fixing coding errors its
own, or even yours, like a super patient assistant.
All right, so great coder, lean safe, good debugger.
Now the flip side. Where are those missing controls,
the parts that lead to the crashes? Right. It
fails because it lacks some critical things.
Number one, true market intelligence. Like we
saw, it's blind. It cannot look at a chart and
identify a profitable visual pattern by itself.
It doesn't see charts. It sees text tokens. No
eyes for the market. Exactly. No intuition. Second,
strategic optimization. It has zero market feel.
It cannot understand why a trade is good or bad
in the bigger picture. It'll chop out good trades
just to lower drawdown, remember. It doesn't
get the why. And finally, coding consistency.
Despite being brilliant, it makes these simple
repeated mistakes like hallucinating functions.
It needs that human iteration, that correction
loop. Okay, so this brings up the big question
for anyone wanting to use this. If it has these,
frankly, pretty big flaws, how do we actually
make it profitable? How do we use it effectively?
The conclusion is actually really clear. and
maybe even a bit liberating. GPT -5 is not a
replacement for human traders. It's not after
your job. Instead, think of it as a powerful
implementation tool that absolutely requires
human market intelligence to guide it. The winning
formula of the future here is what we're calling
the trading center, a seamless human machine
partnership. Trading center. I like that. Human
brain, AI body. Pretty much. Your job as the
human brain is pattern recognition. You study
the charts, use your experience to spot potential
patterns, you form hypotheses. GPT -5's job,
the powerful body, is implementation. You give
it clear, detailed instructions based on your
insights, and it handles the coding grunt work.
Then it's a cycle, human -driven iteration. You
test what it builds, find the flaws, refine your
ideas, and guide the AI to make specific tweaks.
And critically, you amplify AI strengths. Lean
on it for the debugging, the automation, quickly
creating variations of your ideas. Let it do
what it does best. So basically an operator's
manual for the listener. Clear do's and don'ts.
Exactly. Simple breakdown. DO use GPT -5 for
converting indicators you already understand
well, implementing very specific training rules
you've defined, debugging Pinescript code, and
creating lots of variations fast. Don't use GPT
-5 for discovering profitable patterns from scratch.
You can't see them. Optimizing strategies without
your explicit guidance, it'll optimize poorly.
And don't expect it to understand why a strategy
works or fails or notice subtle market shifts.
That's still your job. So this Centaur approach,
this human AI team. How does that really change
the game for traders going forward? It combines
human insight with AI's raw coding power. Okay,
wrapping this up then. What does this all mean
for you listening? The big idea here seems to
be that GPT -5 is not the holy grail. It's not
push -button riches. Not at all. What's really
fascinating is seeing it clearly as a sophisticated
coding assistant. It's an amplifier for human
intelligence, not a replacement. The winning
combo is simple. You bring the market insight,
the pattern recognition, the strategic thinking.
GPT -5 brings the code implementation, the debugging
speed, the rapid iteration power. And together,
that human machine team can create and test potentially
profitable strategies way faster than ever before.
It's about combining your hard -won experience
with the AI speed. So for you, the listener,
this points to a clear path. If you're a beginner
trader, your absolute first priority is still
learning market fundamentals, technical analysis
basics. GPT -5 won't help you if you don't understand
why a trade should work in the first place. Absolutely
critical. For experienced traders, GPT -5 could
be a massive game changer for implementation
speed. Testing your nuanced ideas may be 10 times
faster than coding manually. Imagine that speed.
And for strategy developers, you can use GPT
-5 to churn out variations of your core ideas,
test different parameters, different filters
at a pace that was impossible before. The traders
really winning with AI right now. They're not
expecting magic. They're wisely combining their
own intelligence, their edge with the AI speed.
It's about working smarter. So the real advantage
isn't found in trying to fully automate your
thinking, but in wisely augmenting your own intelligence,
making yourself faster, more efficient. Thanks
for joining us for this deep dive.