Claude Will Change Trading Forever (Here's How To Prepare) transcript

Claude is going to change trading forever and here's why. Right now, there's a massive shift happening in trading. The exact same systems and strategies used by both hedge funds and quant firms for decades are now accessible to regular people. So, what you're looking at right now, market regime detection models, Monte Carlo simulations for backtesting, portfolio risk dashboards, live sentiment analysis, these are all built completely with Claude code.

Basically, anybody can now build the same exact types of quantitative systems that used to require either or entire team. Now, I'm not saying that you're going to immediately compete with multi-billion dollar funds running thousands of servers with decades of proprietary research, but the tools to think and trade systematically are available to anyone that actually wants to spend the time and learn the concepts. So, in this video, I'm going to break down why Claude is going to change trading forever and why it's more important than ever to adopt AI in your trading.

I'll cover five main sections to show how Claude can make you a better overall trader, and I'll even show you how to build the same exact systems so that you can follow along and use them yourself. So, make sure to stay until the end of the video. Now, as always, this isn't financial advice and I'm not guaranteeing any profits. All I'm doing is showing you the tools and concepts available to you to help you become a more disciplined and systematic trader.

So, to start, the math barrier to use quantitative strategies is basically gone now. One thing to understand is that the general concepts behind the most successful quant strategies have already been published in academic papers for decades. Now, obviously, the specific implementations that made these firms profitable, like their signals and data sources, are proprietary, but the foundational math and the general approach to thinking about the markets have been available for years.

The problem was just that building and testing these systems required programming skills that most traders didn't have. You can't just go into TradingView, run matrix operations, train probabilistic models, or even detect hidden regimes. But now, with AI coding agents like Claude code, you can describe in plain English the type of system you want to build, things you want to test, and it can actually speed up and build out the entire system for you.

So, as my first example, I'm going to build something super quickly that you've probably seen on my channel before, if this isn't your first time. So, what this does is that this model looks at the volatility in the market and then classifies it into different regimes, so that based on these regimes, you can change up your strategies, risk position, and sizing. Now, feel free to copy my prompt exactly here, or you can switch it up a little bit to make it a bit more personalized, but what we want to do here is build a regime detection dashboard as a Streamlit app.

For functionality, we're going to use Yahoo Finance's data, but you can obviously substitute this with a more premium plan if you have other data sources. Then, below this, we have feature engineering, where we want to create features such as logging returns, tracking volatility, and then training the Gaussian model, as well as labeling regimes. Now, there's more nuances that you would want to copy as well if you're going to build the same exact thing as using the forward algorithm only.

Uh when you label the regimes, it would be between lowest to highest volume. There's also a stability filter if you're transitioning in and out of regimes super quickly just to stabilize all of the data and the classifications. Then, below this, we have more UI-centered instructions, like how you want the layout to look. So, we have a top bar, a main chart. Below the chart, we have statistics of regimes, uh confidence timelines, and then finally a sidebar where you can input your ticker, dates, number of regimes, as well as a run analysis to actually start this model.

Now, as a next step, you can either do one of two things. The first is just simply going to Claude AI, having them create the files for you, and then just copying and pasting the files into a blank folder, opening it up yourself. Or, if you want an easier method, you can just simply download something like VS Code, and then inside the IDE, all you would want to do is install Claude code as an extension. What this is is basically Claude's models, but built into an AI coding agent.

So, this way you're able to use Claude as an actual coding agent, building out your files, clicking over your folders, and making sure that everything does work. So, inside VS Code, you would just go to Claude code here and then download the extension. Once it's up and running, you can go ahead and open up Claude code just from the side, and you're able to send over your prompts and instructions directly. So, here, all I'm going to do is paste in that prompt that I just showed you, hit send, and we're going to watch Claude essentially build out this regime dashboard for us, obviously in its base case before we make any additional changes and personalize it to our own trading styles.

And what you're looking at here as a final product is a probabilistic model. So, this basically classifies the market environments by volatility, rather than trying to predict price action. Here, we can see that we're currently in a medium volume regime with around 98% confidence, and this is basically the same exact fundamental approach that quant firms use. So, detect the environment first, then determine strategies and size your positions accordingly.

So, now with Claude, you can build this detection model, test it, break it, refine it, learn from it, all in basically a few hours. Now, one important caveat here, and something that you'll see throughout the rest of the video, is that it's important to know what to ask Claude. So, the right features to add, the number of regimes, uh which algorithms to use, things of that sort. This way, you won't run into issues like look-ahead biases, as well as any other potential contaminants.

So, if you want my exact prompts and full guides on how to exactly structure these builds, I have everything inside our community in the description. If you want to join the most active AI trading group, we're always testing, iterating, and using new concepts, as well as looking for new ways to incorporate AI models like Claude and ChatGPT across any asset class or any trading style. Basically, knowing what to build is probably the most important skill to have, especially combined with your own trading styles and what you're actually trading.

Now, to move on, let's go over building backtesting systems that actually work. So, once you have a strategy in place, you need to prove that the data actually works out for that strategy, at least historically. And now, with tools like Claude code, you can actually build complex backtests beyond what something like TradingView would tell you. So, if you've watched my previous videos, you would know that I talk a lot about proper backtesting.

These are things like walk-forward validations, out-of-sample testing, as well as avoiding look-ahead biases. These are things that you can just build completely from scratch with Claude code. But, what I want to show you today goes even a step further, because even if you were to build out these perfect walk-forward backtests, that's still only one layer of validation. And one of the biggest differences between how retail traders validate strategies versus institutions is the number of layers that they actually test and use before putting real capital on the line.

So, you can just think of it like putting multiple different validations of backtests one on top of each other to make sure that your strategy is actually sound. And again, with Claude, you can basically stack multiple layers of these validations fairly quickly. This way, when you go live, your strategy is actually stress-tested from every single angle that you can think of. So, let me show you what I mean. First, we have what is called a Monte Carlo simulation.

You can think of it like this, right? So, a normal backtest would show one version of what happened, a set number of trades and results. But, what if those trades came in a different order? Um if the timing was slightly different between those trades? This is where Monte Carlo simulations come in. So, Monte Carlos basically run your strategy a thousand times over with different kinds of variations, and this way you can see the full range of what could realistically happen.

So, what we're going to be doing here is basically taking our backtested results and then running a thousand randomized variations. So, we can shuffle the order of trades, we can even randomize entry timing slightly, and this way we can look at the full range of outcomes. So, just like last time, feel free to copy this prompt if you want to build the same exact simulation just like I'm doing. Build a Streamlit app of a Monte Carlo simulation.

This just makes it a little bit easier to visually see what's happening. And then, for the input, we would want to have a CSV upload of backtest results. Now, obviously, you can connect this to a broker if you already have an API-first broker that has your historical trades, but to make things simple, you can also get a CSV of your trade history, and then this way you can create variations and simulations around your historical trades.

So, what we want to calculate is the median final value, 95th percentile probability of loss, probability of drawdowns, max drawdowns. And then, below this, something you can customize yourself, which is entirely focused on design. So, if you want the UI to look differently from mine, you would just go ahead and choose different colors, different styles, different fonts. And then, in terms of the actual output, we'll have a fan chart, which is more so a visual.

Uh it's a Plotly chart with all 1,000 curves, so you can see all 1,000 simulations. And this is just for you to visualize bottom curves, middle curves, and top curves, which are the best outcomes. Again, these are all just tools to help you validate and visualize what's actually happening historically for your trades. So, what you're looking at here is the finished Monte Carlo simulation with my uploaded data. Obviously, there's a lot to get into, but I'll show you how this works.

To start here, we have the probability of loss at 2.7%. This just means that out of a thousand randomized versions of my strategy, only 27 of them ended below the starting capital. This means that 973 basically remained profitable, which in turn means that the strategy did indeed have an edge. Now, next to the right, we have the median return over the 1,000 simulated trades. It's showing 4.5% because we're using demo data that is very conservative.

And then, to the right is one of the more important stats, which is the worst drawdown, and this is called the worst 5% drawdown here. So, this is the average max drawdown of the worst 5% of the simulations. So, out of those 1,000 simulations, we're taking the worst 5% and seeing how bad the drawdown truly was. So, in this case, it was about 28%. And then, you can think of this as basically a tail risk check of what could be the worst possible scenario.

And then, finally, we also want to show overfitting risks as well, just to make sure that we're looking at the data properly. And then, below this, we have a probability cloud that basically ties all of this together with a visualization. So, you can look at this glowing cloud of overlapping lines as basically the thousand simulations plotted on top of each other. So, scrolling down, the red curves here are the worst possible outcomes, and then the green are the best possible outcomes, while the blue here in the middle is more so just a realistic range over all of the different simulations.

Then below this we just have more supporting data like outcome distributions. We have our final portfolio value plotted against max drawdowns and then how to actually interpret this data. So again, this is just a layer to add on to whichever backtest you're running. So generally speaking you would have traditional backtests, a walk forward analysis, and then you can use this Monte Carlo simulation as basically a supplementary part.

This way you're able to stress test your strategy from basically every possible angle. And again, this is only one layer. So the next one that I'll show you is a sensitivity analysis. And this is where you take the key parameters in whichever strategy you're using and then changing them to see what exactly happens. So the idea is that if you have a strategy that works great with like a 20-day lookback, but then if you tweak that parameter to an 18 or 22-day lookback, and then it completely falls apart, you probably just have a fragile strategy.

Ideally, you'd want to see stable performance across an entire range of parameters. So the idea is that if small changes or tweaks to your parameters blow up your whole entire strategy, you're probably just over fitting. And now here's the prompt if you want to follow along and build the same exact sensitivity analysis, which again can be an additional layer for your backtesting. What we're going to do here is once again build it as a Streamlit app.

The core functionality is going to change depending on your strategies. So the idea is that you would switch this built-in demo section with whichever parameters you're running. So below this you can just see all the different parameters and then for each parameter we would obviously want to see the sharp ratios, total returns, uh run a standard backtest, view max drawdowns, uh pretty standard stuff. Then below this we're just going to want to set the data source, whether you want to use Yahoo Finance or a more premium data source if you're subscribed to one, and then the visual design, which again you can change completely depending on how you like to view your visual dashboards.

Um below this we have the main summary heat map, which should show parameters on Y, metrics on X. It's a quick way for you to visualize what's exactly happening. I also added in a part for overall robustness score so you can basically score your strategies and see how well they perform. Below this we then have the per parameter line charts. And then finally some statistic cards at the bottom. So let me go ahead and show you how you would interpret and actually use this sensitivity analysis in your own strategy testing.

So now on our sensitivity analysis dashboard, we can first set the ticker, uh start date and end date, but then more importantly we have our base parameters that we want to gauge our strategies on. So this is where you would change these parameters if you are using a different set on your strategy. But in my case I'll just keep everything default and click run analysis. So this is how the second layer looks like if you want to implement sensitivity analysis.

Now as you saw, I'm basically running an SMA crossover strategy on SPY with 5 years of data. What the system is telling me is that the strategy is basically fragile as expected. This basically means that the strategy's performance is tied way too closely to the very specific parameter values that we set. Now obviously if you were to just look at the base strategy performance, it did fairly well, 31% return. We have a sharp ratio of 0.87 and then a max drawdown of only around 7%.

But obviously this gauge is telling a completely different story. So if you scroll down to the heat map, this is kind of where the real value comes in. And then we can see here red means beyond 40% maximum deviation from the base result and that's if we change the parameters. So the idea is if we change the moving averages, it's going to significantly affect our returns, right? Our win rate is going to go down, our max drawdowns are going to increase, sharp ratios are going to go down, total returns are going to either go down or increase as well.

It's just a very parameter specific. And then if we scroll all the way down, this basically tells us how to interpret the data. Total returns basically swing by 130% if you change some of the parameters with the best case returning 33% and the worst case actually declining 9.5%. Again, same story here. Um we can also see the differences in stop loss and take profits as well. So the moral of the story here is that even though these traditional backtest results aren't bad, the strategy is just extremely fragile, right, to these base parameters and obviously when you go to the live market, that doesn't bode well for how conclusive your strategy really is.

And now as a final layer of backtesting, so what you want to combine with your traditional backtest, Monte Carlo simulations, what we have here, which is sensitivity analysis, we also want to do multi-asset regime comparisons. So the idea is that instead of just backtesting one ticker like SPY, you would run the same exact engines across SPY, Bitcoin, gold, bonds, for example, simultaneously. And then this way you can actually see which asset class the regime works best on.

So maybe your HMMs are better at classifying equities versus crypto because obviously crypto is a lot more volatile. This way you'll just know where to focus and where not to waste time. So here I'll go over the prompt. What we're building is a multi-asset regime backtester. Now you can change these assets around, but I have SPY, Bitcoin, gold, and the 20-year Treasury bond. And again, the main purpose of this is to add a final layer of backtesting where you can see which asset class your strategies and regime detections are working the best on.

So the idea is to benchmark buy and hold, 200-day SMAs per asset, and then we can also add in stress tests so we can see how these assets would have performed in periods where there was significant drawdowns, obviously 2008, 2020, and 2022. And then below this we just have different features that we want to incorporate like equity curves. We want the regime timeline strips. We want a comparison table that shows the sharp improvements.

And then finally we want the stress test sections where we have significant periods of downturns like those three years I mentioned. So now here as a final product, we can see how our strategies performed based on individual asset classes. So for SPY, we can see that our strategy returned 64%, Bitcoin is 53, gold 143, and then for long Treasury bonds it actually decreased in value. So obviously this gives you a lot more insight as to where your strategy and regime classifiers are working and where they aren't.

Uh below this we have the equity curves. You can see how the strategies perform versus either buy and hold or the 200 SMA trend line. And then again, all this validation does is add on more layers to your strategies. So you're not just backtesting from a traditional backtest. You can now see stress tests against certain events like the COVID crash or bear market. You can see your strategy placed against different assets.

You can also see how sensitive your strategy is. And then finally you can just add probabilities to your strategies with Monte Carlo simulations. Each of these layers catch potential issues in your strategy that just one backtest wouldn't originally. And now you can build all these different layers in a matter of hours and days, not weeks and months as it did take before. Now moving on, you don't need to fully automate your trading to benefit from Quant.

You can also just use it to build out research, screening, and analysis tools. So basically you can use Quant and Quant codes to build out tools to help you become a better trader without it ever placing trades for you. Now obviously most institutional firms have things like Bloomberg terminals and extensive data sources coming in daily. Now we won't have the same exact robust processes, but we can actually shorten the gap quite a bit by building our own screening and analysis tools.

So first, let me go ahead and show you how to build a portfolio risk dashboard. So the idea with this is that it'll connect to your broker account, pull your actual positions, and layer regime overlays on top of everything. You can apply your strategies and even look at correlations between the assets you own. So you're not essentially placing the same trade with different assets. This will also make sure that not too much of your portfolio is in the same type of trade, especially when you are running more of these robust strategies in place.

Now on this functionality, you would want to switch out this demo mode with your own broker. Now obviously everyone uses different brokers, so I'm just hardcoding some positions. If you're using Charles Schwab, Alpaca, IBKR, this is the section where you'll want to automatically integrate. Then once we have the trades, we want to see the regime overlays, correlations, flag any pairs that do have correlations so you're not essentially entering the same trade, and then also stress test your current positions with historical drawdowns just to see how bad it can get.

And then below this is pretty standard design elements. We have a top bar, left column, and right columns with the heat maps for correlations, stress tests, and then finally watchlists. And these are simply tickers that you'll want to monitor and see if they fit in your strategy or if you want to enter them at any given point. So here you can see our final portfolio risk analyzer. We have our portfolio value, P&L, market status.

Below this we have our current positions so you'll be able to see how much of your portfolio is allocated. Uh you can see the correlations. This is to make sure things like gold and Bitcoin aren't too correlated and it'll obviously flag if you are in very similar types of trades. And we also just have some useful tidbits if it's a low volume day. If you scroll down, we can see gold is extremely high volume. We can see stress tests and then potential watchlists of different tickers that you want to add, which again get placed into this correlation analysis once again so you can see if, for example, IWM is too correlated with SPY, it's probably not a good trade to take.

This just gives you a lot of different ways to look at your portfolio to make sure that whether you're trading or even long-term investing, you're able to look at different points of view and different layers on the potential of your portfolio. Next what we have is sentiment analysis and the idea here is that it's going to take news articles, social media posts, and earnings transcripts for any tickers on your watchlist.

Then we're going to process all these news and posts through AI and have it basically score your tickers. This way you can see one, whether the public sentiment is positive or negative on assets that you hold, and you can also just see when sentiment shifts or if you want a quick warning scan on how the market is behaving, this is super helpful because obviously it curates the news and scores for only the assets that you hold.

Now to start with the functionality, what we're going to use is Google News, but obviously if you have more premium data sources or if you want to pay for news data, you can go ahead and add your API keys. You can also use News API for a more free source, but this is entirely dependent on the sectors you're covering and what news articles you want. Then we're going to want the AI model to essentially take those news articles, score them, give the key drivers, and then we want our tickers that we want to cover.

Now obviously this is going to depend on what news you actually want this model to read. Then just like before, the visual design, you can change to however you want the styling to look like. We'll have a top header. We'll have ticker sentiment cards based on every single ticker, a detail panel, and then finally an aggregate sentiment bar chart, so you can see how the overall sentiment for the market is, and if the structure is shifting.

And to give you an idea of what this looks like, you can input your coverage, so these are all the tickers you want to cover. We have our sentiment bulletin, and then for each individual ticker, you can see how the sentiment is, whether it's bullish or neutral or even negative. If there's a ton of, you know, negative posts and news articles coming out about that one specific sector or asset. So, this is super helpful if you just want to have a quick morning brief every single morning on how the market is looking at things you hold.

This way, you don't have to read through dozens of articles yourself. Now, what we have last here is a correlation break detector, and this is what I personally use the most. The idea here is that it monitors pairs of assets that normally move together, like spy and QQQ, gold and treasury bonds. Then, it will alert you if the correlation suddenly breaks down. It's basically when two assets move together normally, and then they suddenly don't, it means that something's shifting in the market.

This is a huge indicator that a lot of institutions use to monitor the market, and in our case, we have this tool to essentially track when this happens, so we can apply our own strategies to this event. So, the idea is that if the correlation between spy and an ETF breaks down, you might want to reduce your exposure to one or hedge or even look to a mean reversion trade. So, in our prompt, you'd want to configure your pairs to whichever assets you're trading.

Below this, you can set your own data sources as well. Uh break detection scores, you can keep the same as me. This just depends on how much you want the correlation to break between the two assets. Then, finally, we have historical context, so we can see what has historically happened when these assets broke or weren't correlated with each other. Then, we have the visual design, which you can change to however you like your dashboard to look like.

At the top, we'll have status cards with active break pairs, and then for the main chart, it's going to show the rolling correlation line charts between the two pairs. We also want to see historical means, historical break periods, and just every other bit of data that helps contextualize why and what happens after this correlation breaks. Then, we want different time frames, so we have a 20-day versus a 60-day comparison.

And then finally, we have an alert log at the very bottom if you, for example, want Telegram alerts sent to your phone, emails. So, this is what our correlation monitor looks like. We have all of our assets basically inputted, and it's basically telling us we have one extreme correlation break between XLE and XLF. So, obviously, knowing that context, you'll be able to layer your own strategies on top, whether you want to hedge, enter a mean reversion trade, or just simply exit out of a position.

This is where it would tell you exactly what to do. Below this, we have more context, like the 60-day rolling correlations, short terms versus long terms, and then historical context, so previous instances where this pair broke. And then below this, we have an alert log, which shows all the pairs that have actually broke correlation recently. Now, basically, this whole section shows how you can do research and analysis with Claude Code.

You don't need to fully automate trading or build a bot. All of these supplementary dashboards and models will essentially help you become a better trader, understand market conditions without you needing to do actual work. You're still making every trade yourself, but now with all these tools that you're building, you're able to make more informed trades with more information. And if you are ready to automate, you can basically use Claude or Claude Code to build you an automatic trading bot from start to finish, no matter what you're trading.

Now, I've showed you how to do this in previous videos with Claude Code, but just to go over how you would think about building this bot, you would go through five main stages. So now, in each of these stages, you're going to want Claude or Claude Code to basically craft out all these features for you, and then at the end, they connect them together to have this full trading bot. So, to start, you would always need to have a brain.

This brain is the centerpiece of your trading bot. It's going to classify the market, so everything we talked about before, the beginning of this video, depending on the volume and price action, it can clarify whether we're in a crash, a bear, neutral, or bull run. Then, based on the market structure, we want to determine how much of the portfolio is invested and how to invest the portfolio. So, thinking about it in steps, we first build the brain, and then we build out the strategies and allocations on top of the brain, so that when it's in a bull run, you would use X and Y strategy and allocate this much of your portfolio.

If it's in, you know, a neutral market with low volatility, then you would invest more of your capital or you would use this mean reversion strategy. So, this is probably where you're going to spend the most amount of time, um determining strategies, backtesting back and forth to make sure that your allocation and strategies do actually make sense for what you're trading. Once you've done that, you would want to make sure to implement safety features.

These safety features just make sure that your trading bot doesn't go to zero. So, for example, this could be things like 5% max drawdown in a week or 2% max drawdown in a day. And then after this hits, your bot will just automatically stop. This way, even if you have a good strategy and it's just a bad time period, it's not going to keep compounding losses for you. The idea here is that this works independently of the AI model, so even if the strategies are still valid, if the circuit breaker's hit, the circuit breaker's hit, that means your trading bot will just stop completely.

Once all that is set, we would want to connect to a broker. So, the most popular API first ones are Alpaca, which has a free tier, so a little bit more accessible to the general public, and IBKR. This is helpful if you want to dive deep into high volume trading. Both of these work as brokers, and with Claude Code, you can basically have them send these trades directly to the broker. You would test it out, you would see that they're making trades for you.

Now, to caveat this, I would make sure that you're at least paper trading for a month with your strategies. This way, you can tweak your strategies, allocations, sizing, risk, to make sure that you actually do have an edge in the market once the trading bot is set live. And then finally, you would just create a visual dashboard like what we showed throughout this video, where you can see trades and insights in real time and understand everything that's happening.

So again, I've made a video covering this entire end-to-end process with all the different prompts you need to make this trading bot, so click on the link if you are interested there. Now, the last thing I want to cover is what I think matters most, and that's iteration speed. So, before we had capable models like Claude Code, testing a simple strategy with end-to-end backtesting would take weeks, if not months, between all the coding and debugging.

But now, with Claude and Claude Code currently, you can basically go from idea to results in one afternoon. So, instead of testing like maybe three to four ideas in a month, you can now test hundreds just because of how capable and how quick Claude is able to actually build out these systems. And this obviously matters because most strategies don't work. The edge isn't really in having just one specific consistent strategy, it's actually being able to test enough ideas with the proper methodology, so proper backtesting with all those layers we mentioned.

And this way, you can consistently find strategies that do have edges in, you know, the current market structure that you're in, and this will always change as well. So now, you don't have to build strategies from the ground up as market shifts, cuz obviously, you're not going to run the same strategy from last year to this year. These strategies are constantly going to evolve, and Claude Code just makes sure that you can essentially iterate as fast as possible.

If you see drawdowns increase, you can quickly tighten the entry signal. Maybe you can add like a 48-hour cooldown after every loss. If the market structure has changed considerably, you can retrain the model on the past 6 months of data. And this way, you can just keep adapting your strategies to the market even as the market constantly evolves. Now, Claude is incredibly powerful, but it's not going to do all the thinking for you.

It'll handle the implementation and building out of your ideas, but you still need to understand the concepts. This is why things like regime detection, proper backtesting, and risk mitigation still matter, and that's why these are the portions that we focus on the most as well. I think the gap between retail and institutions is basically shortening as the days go on, and the traders who started using Claude and building out these useful systems and tools are going to have a real advantage over more traditional traders that haven't.

So, if you do want all the prompts from this video, as well as complete guides on how to customize them to your own specific strategies, make sure to check the link in the description for my community. We have hundreds of traders in there just sharing ideas, bouncing tests back and forth, with weekly calls to make sure that everyone does understand the potential of where Claude and AI as a whole is headed for trading.

So, make sure to like, comment, and subscribe as we cover all the latest updates in AI tools specifically for trading.