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.