Imagine a world where you could automate some
of the most complex, high -stakes tasks in your
life. What if you could build a system, almost
entirely without code, that delivers truly remarkable
results? We're talking about a strategy that,
in backtesting, that's simulating its performance
on historical data to see how it would have performed,
could yield up to, say, 22x returns. This isn't
just some hypothetical dream. It's actually a
very real blueprint. Welcome to the Deep Dive.
Today, we're plunging into the fascinating world
of building AI -powered trading strategies, and
we're going to walk you through how to do it,
step by step. That's right. We're talking about
tools you might already know, actually. TradingView
for charting, Pinescript, which is really just
a simple scripting language for those charts,
and of course, ChatGPT. The cool part, you absolutely
don't need a PhD in computer science or a Wall
Street background. Just curiosity. Our mission
in this Deep Dive is to demystify the whole process.
How you conceptualize, build, debug, and then
really optimize an automated trading strategy.
We'll follow a real -world example that, in backtesting,
first generated an impressive 11x return on Bitcoin.
Right. And then, after some thoughtful AI optimization,
soared. to an astonishing 22X. Yeah, we're going
to cover the whole journey from that first spark
of an idea to finding the right indicators, using
AI to actually write the code, then debugging
the glitches, because there will be glitches,
rigorously testing, improving it piece by piece,
and finally stress testing it all before you
even think about putting real money on the line.
It's basically a full roadmap for something that
sounds complex, but well, it doesn't have to
be. Okay, so let's unpack this. The first step,
and I think it's often overlooked, is really
visualizing success. before you write any code
it's almost a philosophical starting point isn't
it it absolutely is we call it dream first code
later you know most people get excited about
some cool indicator and just jump straight into
coding that's like starting a road trip without
knowing where you're going it's just inefficient
frustrating right so instead the idea is to open
TradingView, the charting platform, pull up a
chart, maybe Bitcoin, maybe something else, on
a higher time frame, like daily or weekly. Then
you literally just draw circles on the chart
where you wish you had bought and sold in the
past. Exactly. And this isn't just daydreaming.
It's serious planning. You're basically creating
a job description for your AI. You're showing
it, look, this is what success looks like. Build
me something that does this. We want to buy after
a clear uptrend starts, sell when it looks like
it's reversing, stay out during those nasty bear
markets, and only get back in when a new bull
run looks confirmed. It's about riding confirmed
waves, not predicting exact tops or bottoms.
Let the market show its hand first. That dreaming
first idea really changes things, doesn't it?
It sounds simple, but why do you think it gets
skipped so often? And what's its real power?
It creates that visual roadmap, that north star
for your AI. It defines success clearly up front.
Okay. With that visual map in mind, the next
step is finding the engine for the strategy,
the base indicator. This is where we start making
the dream concrete. Precisely. With that vision
locked in, now we hunt for the right engine.
Think of it like indicator shopping on TradingView.
You're looking for something reliable, understandable,
and crucially, something that lines up with those
ideal buy -sell points you marked. For this example
strategy, the Williams alligator really stood
out, developed by Bill Williams, a trading legend.
It's surprisingly elegant. Ah, the alligator.
Call that because it uses three smooth -moving
averages that look kind of like its jaw, teeth,
and lips on the chart, right? It's a vivid picture.
Exactly. You've got the jaw, the blue line, slowest,
showing the main trend, the teeth, red line,
medium speed for intermediate changes, and the
lips, green line, fastest, giving those initial
signals. The logic is super simple. Mouth open
lines spread. Moving up means it's feeding on
an uptrend. When the lines cross and bunch up,
the alligator's sleeping. Time to stay out. And
often its movements just line up beautifully
with those points we marked in the dreaming stage.
It visually confirms the market state. That sounds
really intuitive. The visual of the alligator
opening and closing is clear. But how do we get
an AI to actually, you know... read that and
turn it into buy and sell orders. That's the
next step. We use ChatGPT to translate that visual
logic into specific coded buy -sell rules. Right.
This is where the AI magic really kicks in. Using
ChatGPT to turn a passive indicator into an active
back -testable strategy, outsourcing the heavy
lifting. Exactly. This is where the AI becomes
your co -pilot. In TradingView, you find the
Williams alligator, click the little source code
icon, it looks like, and that opens the Pine
Editor. You'll see all its code. Now, don't panic.
We're not coding this ourselves. We let the AI
handle the translation. And for this, a good
prompt is key. You copy that Pinescript code,
head over to ChatGPT, the paid plus version,
GPT -3 model, tends to work best for code. It's
more reliable. Yep. Then you give it a detailed
prompt. Start with role playing. You are a skilled
Pinescript version 6 developer. Then specific
rules. Go long when the green line crosses the
blue line going up. Close long when the green
line crosses any other line going down. Now,
that exit rule, we'll improve that later. It's
a bit too eager to sell initially. And you also
give it critical instructions, right? Convert
indicator code to strategy code. Keep the time
frame logic. Fill any gaps. Plot the signals
clearly. Tell it only long and flat, no shorting
to this one. Specify backtesting settings. Initial
capital. Commission. Slippage. Make it realistic.
And importantly, tell it to avoid functions that
might cheat by looking ahead, like look ahead.
That gives fake results. Add star 10 dates for
the back test, maybe Jan 1, 2018 to way out,
descent to 31, 2069. And finally, maybe prepend
AI to the name so you know which one it is. Yeah.
And honestly, I still wrestle with prompt drift
myself sometimes, getting the wording just right
so the AI understands exactly what you mean.
It's a skill. But OK, once the AI spicks out
the script, copy it, paste it into a new training
view strategy in the Pine Editor and hit save.
So simple copy and paste and boom, perfect first
time. Flawless strategy. Light chuckle. I wish.
That's the dream, right? But here's the reality
check, and where people often get discouraged.
AI code rarely works perfectly first try. It's
not a failure. It's just, well, it's part of
the process, the iterative dance. And honestly,
debugging is where the real learning happens.
That's right, the reality check. AI code needs
debugging. It's normal. It's expected. It's a
collaboration. Absolutely. Expect error messages
in TradingView's Pine Editor. Don't freak out.
This is where the AI becomes your debugging buddy.
Just screenshot the error, upload it to ChatGPT,
and say something simple like, this code gave
an error. Can you please fix this? Nine times
out of ten, the AI goes, oops, my bad, and gives
you corrected code. Oh, and a pro tip. Always
use paste and match style or paste without formatting.
Sometimes weird characters sneak in and cause
new errors. Okay. Once it's error -free, you
click add to chart. And you can see those little
buy and sell arrows pop up on your chart. That
must be a cool moment. It is. Seeing it visually
come alive is neat. But the real moment of truth
is the strategy tester tab. TradingView runs
the backtest automatically, showing you how it
would have performed. Our initial Bitcoin run,
for example, showed about 11x return, 1100 %
profit since 2018. Not bad, but it also had a
50 % maximum drawdown, meaning at its worst point,
the value dropped by half before recovering.
So good start, but room to improve. OK, 11x return
sounds pretty great right out of the gate. Yeah.
But after seeing that initial result. What's
the very first thing you look at? Where do you
start refining? We immediately zero in on the
biggest losing trades. Got to understand why
they failed and fix them. Ah, the detective work.
Refining, systematically improving by tackling
losses and optimizing wins. Sounds like surgery.
It kind of is. In the strategy tester, you go
to list of trades, sort by profit loss, find
the worst offenders. We found one trade with
a nasty 23 % loss. We clicked show on chart to
see exactly what the market was doing then. It's
forensics. Why did this happen? And what was
the solution you came up with for that one? a
dynamic stop loss based on ATR, average true
range. It measures volatility. So we prompted
ChatGPT again. This strategy is OK, but has big
losses. Please add an ATR -based stop loss. After
adding that, performance jumped to 14x returns
and the max drawdown fell to about 44%. A definite
improvement. And this is where the human creativity
really comes in, isn't it? Identifying the next
problem trade, figuring out why choppy market.
sudden reversal than finding other rules or indicators
like maybe avoiding trades when volatility is
super high and testing those ideas. It's that
ongoing conversation. Absolutely. And the key
is you have to test carefully to make sure your
fix doesn't accidentally filter out good trades,
too. The goal isn't zero losses. That's impossible.
It's about incremental improvement, shaving off
the bad, enhancing the good. And it wasn't just
about losses. We also looked at winning trades.
We realized our first prompt made the strategy
exit too soon. Remember? Exit when the green
line crossed any other line. Too sensitive. Ah,
so you were leaving money on the table, getting
out too early. Exactly, and that's often harder
to spot than the losses, the missed upside. The
fix was simple but powerful. Clarify the prompt.
Exit only when the green line crosses below the
blue line, the slowest one, the jaw matching
the entry logic. This let the strategy ride the
trend much longer, only getting out when the
fundamental trend seemed to be breaking down,
not just on minor dips. That one change took
it from 14x all the way up to 22x returns. Huge
difference. 22x? That's incredible. After all
that work tweaking, refining, how do you know
it's genuinely robust? How do you know it wasn't
just luck or fitting it perfectly to that specific
Bitcoin history? Right. That's the million dollar
question. And that's where the stress tests are
absolutely crucial. Checking for overfitting
and repainting. Ignore these and you could be
in for a nasty surprise. These stress tests sound
vital. Ensuring the strategy isn't fragile or
deceptive before you even think about real money.
First up is the overfitting test. Overfitting
is when your strategy is too perfectly tuned
to the past data it was built on. It's like,
it learned that specific history perfectly, but
it can't adapt. It's a one trick pony. Fails
on new data or other assets. The test. Simple.
apply the exact same strategy no changes to multiple
different diverse assets see how it does so we
ran our 22x strategy on other cryptos bitcoin
baseline 2212 profit 43 drawdown okay ethereum
88 70 profit 49 drawdown good works on another
major crypto but check this out solana 28 ,000,
46 % profit with a 53 % drawdown. Whoa. Seeing
Solana's 28 ,000 % profit from these simple rules,
it just really heads home, you know? The power
of clear logic amplified by AI. Now, Cardano
was profitable, but the drawdown was huge, like
93%. That's a warning sign. Maybe too aggressive
for assets that have really deep corrections,
but seeing a consistent positive edge across
different markets, that suggests it's probably
not overfitted. Okay, that makes sense. The second
critical test you mentioned, the repainting check,
this sounds ominous. Silent killer, you called
it. Yeah, repainting is incredibly dangerous,
especially for newcomers. It's when an indicator
uses future price data to calculate its past
values. It basically cheats on the historical
chart, making it look like a perfect prediction
machine. Shows you this amazing, holy grail backtest.
But it completely falls apart in live trading
because, well, it can't see the future in real
time. It's a massive trap. Very common. How do
you spot that then? What are the red flags for
a repainting indicator? Well, a rule of thumb.
Be extra suspicious of custom indicators that
look too good to be true. Standard, well -known
indicators like the Williams Alligator, moving
averages, RSI, MassD, they're generally safe.
For a visual check, put the indicator on a really
fast chart, like a one -minute chart, and just
watch it. If you see past signals changing or
moving around after new price bars form, it's
repainting. A non -repainting indicator's past
signals are fixed. They never change once the
bar closes. You have to be aware of repainting
before going live. It's probably the single biggest
reason why fantastic backtests turn into terrible
real -world results. Those traps sound serious.
A lot of diligence needed. So after all these
checks, the overfitting test, the repainting
check, is it finally ready? Ready for real money?
Almost. You still need to paper trade it first.
Always. Right. Which brings us to actually taking
it live and really thinking about this human
-AI partnership. It's not just about the code,
is it? Not at all. And by the way, we've made
the complete optimized strategy code available
for listeners to copy and mess around with. Taking
it live, that's a more advanced step. It involves
connecting trading view alerts to exchanges like
Binance or Coinbase using automation platforms
that lets a trade 24 -7 automatically freeze
you up. But the crucial advice for going live,
you mentioned paper trading first, simulated
money. Absolutely crucial. Paper trade first
to make sure it behaves live as you expect from
the back test. Real markets have nuances. And
then when you do use real capital, start very
small, tiny position sizes. Monitor it closely.
Build confidence slowly. Don't rush. Okay. So
this whole journey we've walked through, it's
shown how to conceptualize, build, debug, optimize
a pretty sophisticated strategy using AI as a
partner. identifying good trades visually, evaluating
indicators, using AI for the coding, the debugging,
the improvements, and then those critical stress
tests. Yeah, and strategy development is always
iterative. It takes patience, creativity, lots
of experimentation. There's no holy grail you
just find overnight. Every little improvement
builds on the last. The AI is an amazing co -pilot,
truly powerful, but it's not a magic crystal
ball. It absolutely needs human insight, doesn't
it? For the initial vision, the creativity for
improvements, the judgment calls all along the
way. Exactly. The real magic isn't just the AI.
It's that combination. Your strategic direction,
your insights, and the AI's flawless, super fast
execution. And of course, the usual disclaimer.
This is all educational, not financial advice.
Trading involves risk. Always. Thinking beyond
just trading. What's the biggest takeaway here
for listeners? If they want to use AI in their
own work or learning, what's the metal lesson?
AI isn't here to replace human thinking. It's
here to amplify it, incredibly so. We really
have journeyed from just a basic idea, a human
intuition, all the way to a refined 22X returning
strategy and backtesting, all powered by this
collaborative dance with AI with surprisingly
little actual coding needed from the human. It's
pretty remarkable. Yeah, the core message really
is about that synergy. Your human intuition for
strategy, your creative spark string debugging,
combined with the AI speed and coding and iterating,
it's about empowering your ideas, making them
real, not replacing your role. And this applies
way beyond. trading bots. It's a new way to build,
create, solve problems in almost any field. We
really hope you'll experiment with these ideas,
not just in trading, maybe, but anywhere. Data
analysis automation could give you leverage.
Try giving AI simple prompts related to your
own data or work. Explore how this human -machine
partnership can open up new possibilities for
you. And maybe think about this. How could this
kind of human -AI collaboration change what expertise
even means? Is the future less about knowing
every single fact and more about being really
skilled at guiding these powerful tools to find
insights and build new things? Something to ponder.
Thank you for joining us on this deep dive. Out
of your own music.