If you've ever actually tried to read a 10K,
you know, that huge 100 -page annual report from
a big company, you know the feeling. Your brain
just gets completely bogged down. All that complexity,
it just turns into this dense, overwhelming fog
of detail. Yeah, it's instant information overload,
isn't it? But, okay, now imagine you have this
tireless, completely emotion -free research assistant,
one who can read, like... thousands of pages
and seconds and instantly find the exact financial
ratios or the specific risk factors you're looking
for. That's really the power of AI we're going
to try and unlock today. Welcome to the deep
dive. We're doing a specialized deep dive today
looking at source material focus on using some
pretty advanced AI tools, things like specialized
versions of chat GPT and notebook LM to basically
revolutionize how you can do smarter investing.
Exactly. And our mission here is to get past
the simple summaries you see everywhere and give
you a real actionable roadmap. We're breaking
down five core steps today. Things like sophisticated
charting analysis, coding your own custom indicators,
even if you can't code, automating that dense
data extraction from PDFs, cross -referencing
legal documents, which is super important, and
finally, building an auto -updating tracking
dashboard. The real key takeaway here, I think,
is just a radical boost in your research. speed
and depth. Okay, so let's maybe unpack the foundational
mindset first, because this is important, right?
A lot of newer investors, they kind of mistakenly
believe they can treat AI like some kind of oracle.
They just ask, should I buy Tesla next week?
or something. Right. And that expectation is,
well, it's fundamentally flawed. AI is really
at its most powerful when you combine it with
human knowledge and, critically, market judgment.
You really need to view this tech as a, hmm,
let's say a dedicated, smart, and just relentlessly
hardworking research assistant, not some kind
of guru. So it's not the decision maker itself.
It's more like the processor. Exactly. And AI
solves, I think, three huge problems for any
serious investor. First is just information overload.
It can rapidly summarize the essential bits from
massive reports and news feeds. Second, the time
constraint issue. It handles those repetitive
jobs like gathering data points completely automatically,
saves so much time. And third, maybe the most
interesting, emotional decisions. It strictly
looks at data logically. That helps you avoid
making choices driven purely by like, short -term
fear or greed. Mm -hmm. That emotional distance,
that's crucial in markets, isn't it? So okay,
if AI is handling all the heavy lifting of the
research side, what's left for the investor?
What's their remaining most critical job? Applying
their own unique knowledge and ultimately their
final judgment to all that analyzed data. That's
the human element. Okay, let's jump straight
into the technical side then. Starting with step
one, using AI for... quantitative technical analysis.
And this really needs specificity. You can't
just ask, what's this chart doing? We need to
prompt the AI like a domain expert would. Yeah,
we looked at a really good example involving
a chart for Tesla, ticker TSLA. You upload an
image, one that shows common indicators like
RSI. which measures the speed and change of price
moves, and Mazze for momentum. And then you give
it this highly structured prompt. That's the
secret weapon, really. We basically forced the
AI to produce four distinct components in its
analysis. One, trend and structure. So identify
the main trend and find key support or resistance
zones. Two, volatility analysis. Are the Bollinger
Bands the ones that track price deviations? Are
they contracting or expanding? Tells you about
volatility. Right. And three is the momentum
diagnosis. specifically checking the RSI for
like overbought or oversold signals and looking
for those MedZ crossovers. And the fourth component,
this is maybe the most vital, synthesis. This
forces the AI to actually put all three factors
together for a probabilistic short -term outlook.
This is where we integrate that golden rule of
prompting we talked about. Always break the problem
down. I noticed that. That four -part structure,
it makes sure the AI can't just... waffle or
give you some vague prediction right has a check
multiple specific boxes and the output might
highlight something really specific, like a bullish
divergence. Precisely. And a bullish divergence,
that's a highly specific technical signal. It's
where the stock price makes a lower low, but
the RSI indicator, it doesn't follow along. It
makes a higher low instead. And that signals
that selling pressure might be weakening, even
if the price itself is still dropping. It's a
human insight, really, but delivered by machine
analysis. So how does forcing the AI to combine
these specific technical indicators, how does
that elevate the analysis beyond just a general
summary. It compels a highly structured you could
say expert level breakdown of those confluence
factors. It forces rigor. Okay now here's where
for me anyway gets really interesting especially
for the quantitatively curious folks. turning
a complex trading idea into actual working PineScript
code for platforms like TradingView. That's step
two. And it sounds like it's accessible even
if you, like me, can't really code. Oh, it's
a total game changer for scaling up your analysis.
It really is. Let's detail that bullish pullback
setup strategy from the source. It's actually
quite complex. It requires four conditions to
be met simultaneously. First, the uptrend condition.
The price has to be trading above both the 50
-day and the 200 -day simple moving averages,
the SMAs. Clear uptrend. OK, makes sense. Second,
the pullback condition. Yeah. The price needs
to fall back, sort of pause, near the 20 -day
exponential moving average, the EMAs, the market
takes a breather. Exactly. And then come the
crucial confirmation layers. Third, the healthy
momentum condition. During that pullback, the
RSI indicator must hold above the 40 level. This
suggests sellers haven't really taken control
despite the dip. Right. And fourth, volume exhaustion.
The volume on that specific signal candle, the
one that might trigger the entry, it must be
lower than the average volume of the previous
10 candles. So you feed those four detailed rules
to the AI, and it just generates executable code
that scans the market for you. Yeah. It spits
out PineScript you can plug right into TradingView.
The feeling, honestly, of turning this abstract
multi -part strategy into code that just runs.
It's powerful. Beat. Whoa. I mean, imagine scaling
this kind of system across thousands of stocks
instantly, running these complex screens constantly.
That sounds incredible, yeah. But, okay, if I'm
not a coder myself, how much risk am I taking?
I mean, couldn't the AI -generated pine script
have some subtle bug I wouldn't catch? Isn't
this just trading one kind of complexity for
another? That's a fair point. It is a trade -off.
To be honest, I still wrestle with prompt drift
myself sometimes where the AI doesn't quite get
it right. And this is where we learn the value
of using different models for their strengths.
You might use a model like Claude, which seems
to be better at writing and fixing complex code,
and then, this is non -negotiable, you must back
-test it rigorously and visually verify the results
on charts. You can't just trust it blindly. Okay,
that makes sense. Trust, but verify. Heavily.
Alright, moving to step three. Automating the
fundamental data extraction. Using AI to analyze
those official PDF annual reports. The 10Ks.
This used to take analysts... Days, right? Manual
data entry for really rigorous work. Oh, easily.
Days. Our source material used a detailed Microsoft
MSFT example here. And the key was that the prompt
demanded several things way beyond just raw data.
It wasn't just asking what's the revenue. That's
too simple. Right. It required extracting four
specific core metrics. Total revenue, gross profit,
operating income, and R &D spending for three
consecutive years. Yes. And then, crucially,
it had to calculate three specific ratios based
on that data, like gross margin and operating
margin, all within the same prompt request. But
here's the really critical part. The prompt demanded
the AI provide the exact page number from the
source document for every single number it extracted.
Ah, that verification step again. Absolutely
key. That page number requirement, it turns the
AI from just a summarizer into a precise data
locator, doesn't it? Exactly. So what's the biggest
vulnerability then when you're trusting an AI
to generate code or pull financial ratios directly
from a PDF like that the risk of hallucination
basically the AI making things up or just outright
errors which means those manual checks using
those page numbers are absolutely necessary if
double -check okay moving to step four deploying
the AI almost like an investigative journalist
using tools like notebook LM can you define notebook
LM for us quickly why use that specifically over
maybe a general chat model when you're handling
documents. Sure. Notebook LM is specifically
designed by Google to ground its responses only
in the documents you provide it. It's built for
source citation and research tasks. So that inherently
gives it a much lower hallucination rate when
you're dealing with uploaded PDFs or transcripts
compared to more general models. It tends to
stick to the facts presented. Got it. So for
this step, it involves some serious cross -referencing.
The task in the example was comparing Nvidia's,
NVDA's earnings call transcript, which is often
full of, you know, spoken optimism and forward
-looking statements. Comparing that against the
static official 10k report, the legal document
that details all the documented risks. A very
different tone, usually. Exactly. And the investigative
prompt for Notebook LM broke down into three
really powerful parts. First, optimism versus
reported risks. So compare the CEO's talk of,
say, unprecedented demand directly against specific
enumerated risks listed in the 10K, things like
customer concentration or known demand fluctuation
risks. OK. Second, supply chain nuance. Cross
-check those general mentions of supply chain
in the upbeat call against the really detailed
dependencies, specific suppliers, or maybe geopolitical
risks that are listed way down in the 10K's disclosures.
And third, the competitive moat. Compare the
CEO perhaps claiming a stronger than ever competitive
moat against the explicit competitors and the
stated strategy you find in the 10K's official
competition section. They have to list that stuff.
Wow. So this method really turns the AI into
an investigator, doesn't it? Uncovering potentially
critical differences between the public relations
narrative for management and the legally required
filings. It does. So why is that cross -referencing,
specifically comparing the official 10K against
the often more upbeat earnings call, why is that
so essential for proper investor due diligence?
Well, it helps you identify those potentially
unaddressed risks. Things that could seriously
challenge the rosy narrative management might
be painting. It's about finding the gaps. Okay,
so we've covered analysis and investigation.
Now let's talk about automating the tracking
itself. That's step five, building a live dashboard.
We can actually use AI to write Google Apps script
code and build an auto -updating dashboard right
there in Google Sheets. Yeah, exactly. We asked
the AI in the example to write a specific function
called update financials. And this function,
it basically iterates through a list of stock
tickers that you provide in your sheet. And it
automatically populates four key data points
using the built -in Google Finance function,
things like current price, P -E ratio, the 52
-week high, and market cap. That's fantastic.
Just pure automation, saving you that tedious
manual data check every single morning. And this
brings us back nicely to step six, these advanced
techniques, which involves using different AI
models for their respective strengths. Yeah,
it's really essential to choose the right tool
for the right job. You don't use a hammer for
a screw, right? Use Notebook LM, as we said,
for summarizing and pulling exact quotes from
your uploaded documents because of that grounding
strength, that low hallucination rate. Then maybe
use a model like ChatGPT for more creative brainstorming,
exploring ideas, asking what if scenarios where
it's broader knowledge base is actually helpful.
Right. And as we mentioned earlier, you might
lean towards something like Claude for writing
and especially debugging complex code, like that
Pinescript example, where it's stra - than handling
longer context windows really pays off. And all
of this just reinforces that fundamental truth,
our golden rule, which is step seven. Always
be specific and break down the problem. Break
it into sequential specific tasks for the AI.
Avoid asking one huge general question and just
hoping for the best. That's how you end up with
generic unhelpful answers. Specificity is key.
Okay, so beyond just the raw speed increase,
how does automating all this repetitive data
collection, how does that really impact an investor's
allocation of their valuable time and focus?
It frees up so much mental energy, allowing you
to focus almost solely on the high level deep
analysis and crucially the application of your
own judgment. Less drudgery, more thinking. OK,
let's try and wrap up with some really practical
guidance here. We absolutely must emphasize two
key mistakes that even experienced investors
can make when they start adopting these powerful
tools. First, and this is huge, do not ask AI
for direct investment advice. It simply lacks
your personal financial context, your goals,
your risk tolerance. It doesn't know you. Right.
And second, as we've kept stressing throughout
this, don't trust the numbers 100%, especially
initially. Always, always double -check critical
financial data points like revenue or profit
or margins against that verified page number
in the original report. You have to treat the
AI output as a really powerful, synthesized first
draft, not the final word. Exactly. What you
should be doing, though, is using AI to radically
expand your knowledge base. That's where the
power lies. Use it to speed up your research
dramatically, to quickly find correlations you
might have missed, and to explore dozens, maybe
hundreds, of companies that you could never humanly
manage to analyze on your own timeline. It's
about breadth and speed. Ultimately, AI just
can't replace your deep, nuanced knowledge of
a business, its leadership team, its culture,
or its core competitive advantage in the market.
Your informed judgment, based on all your experience
in that AI -assisted research, that remains the
single most important part of making a truly
smart investment decision. The big idea here
is pretty clear, I think. The future of AI -assisted
investing, it's not about replacing human judgment
at all. It's about dramatically improving human
capability. Success is going to belong to those
investors who learn how to ask the right, really
structured questions and then rigorously verify
the results the AI gives back. So here's maybe
a provocative thought to leave you with. If AI
can eventually read every single 10k and write
every conceivable custom indicator basically
for free, What unique, perhaps non -technical
edge will the really successful investor need
to rely on next year or the year after? Something
to think about. We really encourage you to start
experimenting with these tools, asking those
specific questions, and always applying that
critical human judgment on top. Thank you for
sharing your sources with us for this deep dive
today.