So here's the big question. Can Google's new
genius AI, Gemini 3, actually write pine script
code that makes you serious money in the crypto
market? That is the high stakes question, isn't
it? And we're talking about real rigorous testing
here. Right. We found one successful code conversion
that led to just a staggering 2 ,497 % total
profit on Bitcoin. Wow. But, and this is the
part we have to talk about, that massive return
came with a terrifying catch, a 46 % maximum
drawdown. OK. And that risk is the first thing
we absolutely have to unpack. Welcome back to
the deep dive. Today, we are really trying to
shortcut your path to being well -informed about
AI and algorithmic trading. Our source material
is this meticulous multi -test analysis, and
it's focused solely on Gemini 3's capability
with Pinescript. That's the core language for
TradingView. Yeah, and our mission today is just
clarity. We want to show you exactly where the
AI's new deep -thinking abilities really shine,
like converting a visual indicator into an automated
backdustful strategy. It's amazing at that. And
critically, we're going to show you where it
fails. I mean where it feels... spectacularly
when it tries to invent a new strategy from scratch
or you know fix a fundamentally flawed financial
idea. Okay let's start under the hood then. I
mean the chatter is that Gemini 3 is a huge step
up from older models. For someone who might just
be using say standard chat GPT -4 what's the
core shift here? What makes this new model so
relevant? It's all about what they call reasoning
abilities. The older AIs they essentially looked
at a prompt and tried to jump straight to the
answer. They were just guessing. They were guessing.
And sometimes very accurately. Like a high school
student skipping the work to just circle the
multiple choice answer. Exactly. The new AI,
Gemini 3, it actually pauses first. It writes
an internal plan before it generates the final
output. A plan? They call it deep thinking. It
makes the AI agentic, which just means it constructs
this precise step -by -step list of what it needs
to do. And for something like Pinescript, where
a single missed bracket can cost you thousands.
That planning skill is not just critical. It's
essential. So the plan is the difference. And
you mentioned file handling, this ability to
upload images, CSVs, and video all at the same
time. How does that change the game when you're
analyzing trading history, which is usually just
a long, boring spreadsheet? Oh, it's a massive
efficiency boost. I mean, imagine being able
to upload years of raw price data, a screenshot
of a chart setup you like, and the old code of
an indicator. All at once. All at once. The AI
saves you hours of trying to explain all these
visual concepts just through text. Which brings
us to the famous context window, the memory.
Can you define that simply for us? The context
window is basically the AI's short -term memory.
It's how much information you can hold at one
time in a conversation without forgetting what
you said at the beginning. So if a competitor
like Claude can hold maybe a medium -sized book's
worth of info. Gemini 3 is carrying around a
massive library. We're talking up to two million
tokens. Two million. It is a truly staggering
amount of information. Two million tokens, though.
I mean, that sounds like massive overkill for
trading data. Was that huge memory capacity actually
necessary in these PineScript tests, or is it
more of a flex? It's a great question. For short
scripts, no, it's not strictly necessary. But
what it fundamentally unlocks for traders is
huge. You can feed the AI 10 years of minute
-by -minute price data without it forgetting
the start. And that's vital for spotting those
long -term macro trends that really impact your
profit. So if we connect this to the bigger picture,
it just means the AI can absorb way more granular
history to inform its coding decisions. Precisely.
It gives the AI more data points to work with
for that internal plan we talked about. Let's
move to the success story then, test one, converting
an indicator into a full strategy. First, what's
the actual difference between an indicator and
a strategy? It's pretty simple. An indicator
is just informational. It shows you arrows or
lines on the chart based on some math. It's visual
guidance. OK. But a strategy lets you automate
those decisions. And this is the crucial part.
It lets you do back testing. running the system
against historical data to see how it would have
performed. The testing process here, it really
highlights the rigor you need. It wasn't just
dumping a file in. The prep was intense. You
had to get clean CSV price data, use that mandatory
ISO time format, and critically turn off every
single other indicator on the chart before exporting.
Oh, that level of rigor is just non -negotiable.
If you have messy data going in, you get unusable
code coming out. But what's fascinating is the
prompt they used, they gave Gemini a specific
role expert Pinescript coder, a really clear
task, and extremely strict rules. And these rules
in ensured a dose of realism, right? Setting
initial equity at $1 ,000, mandating 100 % equity
usage, and forcing in a realistic 0 .1 % commission
fee on every trade. And the AI handled it all
perfectly. It wrote a usable, compliant script.
The numbers on Bitcoin were just immense. The
BTC results showed a total profit of 2 ,497%.
Yeah. You see that number, and you immediately
think you've cracked the code. This is where
we have to pause. The massive asterisk here is
the max drawdown. It hit 46%. And we need to
linger on that, because drawdown is that scary
number. It's what separates academic success
from real -world trading. Explain drawdown again,
just really clearly. Drawdown is the largest
peak to trough drop you've experienced. So if
your $10 ,000 account hit a high point of, say,
$20 ,000, a 46 % drawdown means that $20 ,000
balance later dropped all the way down to $10
,800 before it recovered. That kind of volatility
is just devastating for a person. And the risk
profile got even more terrifying with something
like Solana. The profit there soared to 50 ,000%,
but the drawdown ballooned to 66%. Yeah. Wait,
66 %? That's not a dip. That's blowing up most
trading accounts. Is the AI just endorsing high
-spakes gambling? Well, the AI is simply following
the math of the indicator perfectly. It doesn't
care about the emotional or practical consequences
of that volatility. I still wrestle with prompt
drift myself, and seeing that 66 % drawdown is
a real -world wake -up call, even when the code
itself is technically perfect. So beyond the
success of the code itself, what lesson did that
extreme drawdown teach us about relying on the
AI? That the AI writes the code perfectly. But
the human must still understand and manage that
extreme account -crushing risk. That's the takeaway.
Okay, let's pivot to the failure test, the flawed
logic trap. The goal here was to take a working
long -only strategy and try to improve it by
adding shorting logic. You know, profiting when
the price drops. Right. We specifically modified
the Don Chin channel strategy. We asked Gemini3
to short when the price closed below the lower
band and then close that short when the price
got back to the middle line. It sounds logical.
It sounds totally logical. But the code executed
and the results were disastrous. The original
strategy was already delivering 2 ,700 % profit.
And Gemini's improved version. It dropped down
to a dismal 150 % profit. Just massive losses
were introduced by this improvement. So we have
to analyze the why here. This is where that agentic
reasoning just failed. It completely failed to
override a flawed financial premise. The AI wrote
the code for those short signals perfectly. Flawlessly.
But it failed to consider the overwhelming upward
bias of the crypto market. It missed the domain
expertise. It did. It prioritized coding execution
over a sanity check of how the market actually
works. Exactly. Crypto historically is long biased
because of adoption, scarcity, inflation dynamics.
Shorting is inherently a low probability bet
over the long term. Gemini 3, despite all this
deep thinking, did not act like a seasoned trader.
It was just a brilliant code monkey. And it's
important to note this wasn't just a Gemini failure.
Grok and ChatGPT also failed this exact test.
Even Claude couldn't beat the simple long -only
version. Right, and it raises this really important
question. Since the AI is supposed to be agentic,
why didn't its internal plan include a step to
challenge instructions that were mathematically
guaranteed to lower profit? And the short answer
is it prioritizes accurate code execution over
challenging the user's potentially flawed financial
premise. We're going to pause right there. When
we come back, we'll look at why the ultimate
dream prompt, the one everyone wants to run,
failed spectacularly, and which AI tool is actually
the undisputed champion for heavy -duty reliable
coding. Welcome back to the deep dive. We're
now diving into where Gemini 3 hits its computational
and I guess ethical limitations. The first two
tests were practical, but test 3 was aiming for
the moon. It really was. The dream prompt Test
3 was simple. Find a pattern. Make me a strategy
that wins with drawdown under 30%. With no indicators
mentioned. No indicators. Just the raw price
data. This is what every trader wants. New alpha
discovery. So detail the result. What happened
when you asked it for a new profitable pattern?
Gemini 3's deep thinking icon, it's bunned for
a while. It started the analysis, trying to write
an internal plan to process all this data. And
then it just crashed. Time down. And you tried
again. I tried again thinking it was a fluke.
It crashed again. Why? I mean if it has all this
memory and this capacity to run a step -by -step
plan, why did it give up on the most fundamental
task of a data analyst? Because the task was
just too vast. It was too vague. For the AI to
truly find a new profitable pattern, it has to
check billions of permutations. Billions. It
would need to test every combination of moving
averages, every oscillator setting, every volume
threshold. against the entire data set. Whoa!
Imagine scaling that to a billion queries. That
level of computation just exceeds the AI's time
limit for a single response. It's usually capped
at around 60 seconds. It's not that it can't
do it in theory, but it can't finish it within
the constraints of the chat model interface.
It needs direction. So the lesson here is you
can't just ask it to find a strategy. No, you
have to provide boundaries. You have to guide
it. Tell it, use the RSI indicator combined with
a 50 -day moving average or look for specific
breakout price action. Specificity prevents the
algorithmic overload. And the timeout. Exactly.
That's a crucial operational lesson. Let's look
at the final test, the ethical barrier. Asking
the AI to reverse engineer a closed source indicator.
This is a really common desire in the trading
community. People pay hundreds for indicators
with locked code. Right. We asked Gemini3 to
look at screenshots of the indicators lines and
raw data values and then try to recreate the
underlying PineScript math. And the immediate
response was? An immediate refusal. Gemini3 said
very politely, I am having a hard time fulfilling
your request and then it just stopped trying.
What were the reasons for that besides just the
sheer complexity of it? It's primarily the safety
filters that Google put in place. There's a real
fear of intellectual property theft or, you know,
similar nefarious uses. And recreating proprietary
code would trigger that. It's exactly the kind
of task that triggers those strong safety locks.
What's so fascinating here is the contrast with
the competition. Absolutely. Yeah. Claude has
succeeded in performing similar reverse engineering
tasks before. So this tells us Gemini 3 either
has much stricter filters, or it just has less
patience for that complexity once the IP filter
gets triggered. So we have a pretty clear dividing
line now. What is the core difference between
a successful prompt, like the conversion, and
a timeout or crash prompt, like finding a new
strategy? Success requires specific variables
and strict boundaries. Vagueness leads to the
AI timing out because the computation just becomes
too massive. Okay, let's summarize the final
rankings. After all this testing conversion,
flawed logic, vague tasks... Where do the three
major AI players stand for Pinescript crypto
coding? Yeah, we have a clear hierarchy for utility
here. Start with Gemini 3, our focus today. Gemini
3 is best for absolute beginners and for simple
indicator conversions. The pros? It's fast, the
interface is clean, and that multi -file upload
is superb. And the cons? The cons. It gives up
too easily on hard tasks and just have poor endurance
for that long computational deep thinking. And
Grok? Grok is the tool for analyzing real -time
news. And that's simply because it has that great
access to ex -Twitter data for sentiment analysis.
But for coding? For pure coding rigor, it's average.
The code can be pretty sloppy. Which leaves the
undisputed winner for the heavy lifting, the
one that acts like a true senior developer. That
is Claude 3 .5 Sonnet or Opus. It is the winner
for complex coding. When you give Claude a hard
task, it just keeps iterating. It keeps working.
It writes an incredibly long, clean code. And
if you paste in an error message, it acts like
a patient developer and just fixes it without
you needing to start all over. The only real
downside there is the lower message limit, which
forces you to pay if you're doing a massive workload.
Correct. But for high quality, complex code generation
that requires that kind of perseverance, it's
still superior. So the final practical advice
is this. Use Gemini 3 to brainstorm ideas, maybe
clean up your data, but then rely on Claude when
you need that complex, final, robust code written
for your TradingView bot. Exactly. And let's
recap the core lesson for you, the listener.
AI is an exceptional conversion tool for code.
It can save you hours of manual work. But it
is not capable of creating new profitable logic
from scratch or fixing a fundamentally flawed
financial idea that just contradicts basic market
behavior. The core lesson is clear. AI is a tool,
not a boss. You are the traitor. That's the mandate.
Remember, always check that max drawdown number.
Never trust the code blindly, even if the profit
percentage looks amazing. And if the AI prioritizes
perfect code execution over self -preservation,
as we saw in that terrifying 66 % drawdown, what
non -financial risks are we blindly automating
in other complex fields just because the generated
code works? What happens when the AI optimizes
for a goal that we haven't properly checked for
catastrophic failure? Something for you to mull
over. Until next time. Take care.