Machine Learning Lorentzian Classification - by jdehorty & team

Summary

In this fascinating discussion, jdehorty and team dive into the Machine Learning Lorentzian Classification algorithm, which attempts to predict market price movements inspired by Einstein's theory of general relativity. This educational deep dive explores how traditional linear measurement systems fall short in capturing the chaotic and unpredictable nature of financial markets. By using the concept of Lorentzian space, the algorithm allows for a revolutionary way of visualizing market behavior, adapting for the real-time warping caused by significant events. This approach provides traders with an innovative tool that combines historical data with the unfolding market context, offering a nuanced understanding of financial dynamics.

Highlights

• The algorithm draws inspiration from Einstein's concept of relativity, altering how we perceive market dynamics. 🌟
• Traditional linear measurements can't capture the chaotic nature of financial markets, but Lorentzian space can. 📊
• Key concept: warping of price-time to accommodate significant market events like interest hikes or earnings reports. 📉
• Approximate nearest neighbors method helps the algorithm match not only prices but entire price journeys for deeper insights. 🔎
• The challenges remain: it's crucial to balance historical data with unpredictable future market behaviors. ⚖️

Key Takeaways

• The algorithm is inspired by Einstein's general relativity, offering a new way to predict market trends using a concept of warping space-time. 🌌
• Traditional market measurements fall short in chaotic financial contexts, but Lorentzian Classification offers a flexible alternative. 📈
• Backtesting allows for historical data analysis, letting traders experiment with different settings to find what works best. 🔍
• Lorentzian Classification emphasizes the importance of context, not just patterns, in understanding market behaviors. 🎢
• While promising, this algorithm is a tool for deeper analysis—not a foolproof financial prediction method. ⚠️

Overview

Imagine using Einstein’s theory of relativity to predict stock prices! That’s exactly what the Machine Learning Lorentzian Classification algorithm sets out to do. At its core, it addresses the chaotic and unpredictable nature of financial markets by reimagining traditional distance measurements. Instead of a straight line, it suggests an adaptable model that bends and flexes according to market events—much like space-time in general relativity.

Now, what really makes this algorithm stand out is its ability to account for the real-time warping of market conditions. Think about it: a sudden interest rate spike or a bombshell earnings report can drastically alter market trajectories. Traditional indicators could never keep up, but by leveraging Lorentzian space, this method sees beyond surface-level chaos, highlighting patterns hidden from conventional view.

Of course, the journey doesn’t end with discovery. The team emphasizes that, while this tool offers groundbreaking insights, it remains just that—a tool. Ideal for deepening market understanding rather than guaranteeing success, the algorithm invites traders to blend historical context, personal judgment, and a pinch of critical analysis for those looking to decode the symphony of market rhythms.

Chapters

• 00:00 - 00:30: Introduction to Machine Learning Lorentzian Classification This chapter introduces a unique machine learning algorithm known as 'Machine Learning Lorentzian Classification.' The transcript reveals that the algorithm is used to predict market price movements and is intriguingly inspired by Einstein's theory of general relativity. The discussion highlights the innovative use of such a scientific theory in financial contexts. Additionally, the algorithm is associated with a trading view open-source script, credited to a user on the platform. Overall, the chapter sets the stage for an exploration of this groundbreaking approach in machine learning.
• 00:30 - 01:00: Purpose and Disclaimer The chapter titled 'Purpose and Disclaimer' is a preface to a deeper discussion. The hosts, Jordi and his companion, emphasize that the deep dive is intended solely for educational purposes, explicitly stating that they are not providing financial advice or stock tips. The focus of their discussion is on unraveling the ideas and concepts behind a specific algorithm. They briefly touch on the innovative approach of measuring distance, mentioning something akin to 'warping space-time' to gain understanding.
• 01:00 - 02:00: Market Distance Measurement This chapter explores how traditional methods of measuring distance don't accurately capture the complexities of financial markets. Unlike straightforward measurements, financial prices change erratically due to unexpected events, making it difficult for traditional linear methods to reflect real market behaviors, particularly during unpredictable 'Black Swan' events that disrupt standard paradigms.
• 02:00 - 03:00: Lorentzian Space and Market Context This chapter introduces the concept of Lorentzian Space in the context of markets. It explains how simple metrics like price and time may not accurately represent the market's dynamics, as they fail to consider the broader market context and surrounding events. The chapter discusses how major occurrences, such as surprise interest rate hikes or disappointing company earnings, can warp the standard measurements of price and time. The concept of Lorentzian Space is presented as a solution that accounts for these distortions, providing a more accurate means of measuring market conditions.
• 03:00 - 04:00: Algorithm Visualization and Interpretation In the chapter titled 'Algorithm Visualization and Interpretation,' the discussion focuses on how flexible algorithms can adapt to market fluctuations. The narrative describes a scenario where traditional methods fail to interpret chaotic market events, whereas advanced algorithms can navigate through distortions to identify underlying patterns. This approach aids in visualizing complex data and providing clearer insights into market behaviors.
• 04:00 - 05:00: Pattern Recognition in Warped SpaceTime The chapter titled 'Pattern Recognition in Warped SpaceTime' explores the limitations of traditional financial indicators. It illustrates this with an example: when a company announces stellar earnings, prices might jump, presenting a buy signal based solely on the price increase. However, if a scandal surfaces days later, that initial price spike may have been misleading. Traditional indicators might still view it as a buy opportunity, merely due to the price movement. In contrast, the Lorenzian algorithm would take into account the changes over time, avoiding the trap of hasty decisions based on incomplete information.
• 05:00 - 07:00: Algorithm Customization and Backtesting The chapter titled 'Algorithm Customization and Backtesting' discusses the importance of understanding the sequence of events in trading, pointing out that a positive event happening before a negative one can prevent false signals. It emphasizes that analyzing how the price reaches a certain point is crucial, not just the price itself. The discussion includes visuals from JD Hordy that demonstrate these concepts, likening them to comparisons of neighborhoods in Ukian and Lenan space to illustrate complex ideas.
• 07:00 - 10:00: Trading Signal Limitations and Risks This chapter discusses two contrasting concepts: Ukian space and Lorenzian space. It describes Ukian space as a neat sphere where all directions are treated equally. In contrast, Lorenzian space is depicted as a stretched out hyperboloid warped by the time axis. Events closer in time have a greater influence, almost as if the time axis pulls everything towards it, giving the impression that events leave behind a significant impact because of their temporal proximity.
• 10:00 - 13:00: Insights Beyond Buy and Sell Signals This chapter discusses how algorithms can be adapted to factor in recent events more heavily than past events when predicting current financial prices. The conversation explores the concept of a 'gravitational pole' applied to algorithmic thinking, suggesting a nuanced approach where recent data has a greater significance than older data. It highlights the importance of considering temporal proximity in financial predictions, akin to the principles in Lorentzian space, thereby adding a new dimension to understanding and forecasting financial markets.
• 13:00 - 17:00: Concluding Thoughts and Future Potential The concluding chapter explores the algorithm's method in pinpointing patterns using the approximate nearest neighbors (AMN) approach. This method functions like a detective navigating through historical price data to locate similarities with the current scenario. The focus is on matching the entire price trajectory, not merely the price levels, within a distorted SpaceTime framework, highlighting its sophistication in detecting price patterns.

Machine Learning Lorentzian Classification - by jdehorty & team Transcription

• 00:00 - 00:30 okay so get this we're diving into a machine learning algorithm oh yeah yeah and it gets better this one's being used to try to predict price movements in the market all right and it's inspired by Einstein's theory of general relativity wow using uh general relativity to make money I know right it's true we're going to unpack this trading view open source script it's called machine learning lenan classification gety title I know right and it was created by this trading view user
• 00:30 - 01:00 Jordi all right but uh before we go any further I think we need to make something yeah crystal clear okay this deep dive is purely for educational purposes absolutely we are not offering Financial advice no hot stock tips here no no yeah we just want we're here to unravel the ideas behind yeah the uh the concepts behind this algorithm right um and I've skimmed the material okay and it really seems to hinge on a totally different way of measuring distance yeah I mean warping space time to understand
• 01:00 - 01:30 the market that's pretty Next Level it is so think of it like this traditional methods measure distance in a straight line okay but financial markets they're anything but straight right they're all over the place exactly prices Zig and zag unexpected events throw everything off yeah it's chaos it's a mess yeah yeah so a straight line really wouldn't capture the wild swings would it no especially those Black Swan events that come out of nowhere and Shake everything up exactly so traditional measures they
• 01:30 - 02:00 might tell you two points are close together just based on simple price and time but but they could be World apart in terms of actual Market context right like the surrounding uh events yeah yeah okay and that's where this lenine space comes in okay it's uh it's a way of measuring these distances but it takes into account the warping of price time caused by major events okay like uh I don't know maybe a surprise interest rate hike or a company's earnings report totally bombing so instead of a rigid
• 02:00 - 02:30 ruler we're using something that bends and flexes with the Market's mood swings that's a great way to visualize it okay so now imagine you're looking at a chart right okay a big event happens and suddenly prices go Haywire right traditional methods they would struggle to make sense of that yeah corre but lorenci and space the algorithm can kind of see through the chaos oh wow it it adjusts for those distortions yeah and that helps to spot patterns that you wouldn't be able to see otherwise wait
• 02:30 - 03:00 so you're saying it can find opportunities that uh traditional indicators Miss yeah that's huge yeah think about it this way okay a company announces Stellar earnings right okay yeah prices jump of course but what if like a few days later there's news of an internal Scandal oh suddenly that price spike it was a mirage okay but traditional indicators might still see it as a Buy Signal just based on price alone just because the price went up right but the lorenzian algorithm yeah well it would consider that that time
• 03:00 - 03:30 context the fact that that positive event happened before the negative one oh interesting and it can potentially avoid a false signal so it's not just about where the price is s but about how it got T there yeah that's really cool precisely okay so let's take a look at some visuals that JD hordy included okay I think they really drive this home perfect a picture is worth a thousand words especially when we're talking about warped SpaceTime right so take a look at this comparison of neighborhoods in ukian and lenan space right you see
• 03:30 - 04:00 on one side you have a very neat sphere yeah that's ukian space okay all directions are treated equally but lorenzian space well it's a stretched out hyperboloid oh wow it's uh it's warped by the time axis okay and events closer in time they actually have a greater influence than those further in the past so it's like the time axis is pulling everything towards it yeah almost as if uh events leave a kind of
• 04:00 - 04:30 gravitational pole behind them that's a great way to think about it okay you're catching on quickly so this warping allows the algorithm to really factor in how recent events yeah impact current prices right it's like saying yesterday's news yeah is more relevant than something that happened a year ago makes sense especially when things change so quickly in finance yeah exactly okay I'm I'm getting the picture I think this lorenzian space is adding uh like a whole new dimension to the
• 04:30 - 05:00 analysis yeah but but how does the algorithm pinpoint the patterns okay so within this worked reality that's where the approximate nearest neighbors method comes in okay approximate nearest neighbors orn amn got it's like a detective scouring through historical price data and it's hunting for matches to the current situation okay but it's not just matching price levels it's matching the entire price journey within this warped SpaceTime continuum so it's not just finding similar prices it's
• 05:00 - 05:30 finding yeah events that led to those prices yeah exactly incredible and it uses this thing called nearest neighbors okay meaning you can set it to find I don't know say the five most similar historical patterns so it's like saying show me five times in history yeah when the market behaved like it's behaving now exactly so it's like having a crystal ball but instead of the future yeah it shows you Echoes of the past that might hold clues about what's coming next precisely now remember yeah
• 05:30 - 06:00 this isn't giving you some guaranteed prediction right of course this is about providing you with a new lens a new way to see the market a lens that takes into account the very fabric of space and time yeah okay my mind is officially blown but I'm hooked all tell me more about how this script actually works well like most indicators it gives you buy and sell signals okay but these signals are based on that you know lorenzian classification we were talking about so each signal is uh the
• 06:00 - 06:30 algorithm's best guess yeah about whether the price is going to go up or down based on what happened in the past right and here's the kicker okay the script allows you to customize the analysis oh cool you can adjust how many neighbors it considers what features it focuses on even how it detects Trends so it's like you can fine-tune your Market radar yeah zero in on the signals that matter most to you that's really cool right but let's be honest okay how
• 06:30 - 07:00 accurate is this thing so you know yeah there are no guarantees in trading of course this is a tool not a crystal ball right right however the script does have a back testing feature that allows you to see how it would have performed in the past so you can kind of run these simulations yeah see how different settings play out get a feel for its strength and weaknesses exactly so it's kind of like a time machine you can test drive different approaches see what works best you got it but uh of course past per performance isn't a perfect
• 07:00 - 07:30 predictor of course yeah of future success the Market's always changing right what worked yesterday might not work tomorrow but it can give you some valuable insight into the algorithm's strengths and weaknesses right I mean we have to emphasize this again yeah this is purely for educational purposes right we're here to explore the possibilities yeah not give Financial advice not telling you what to buy and sell I know all right so let's dive into some of those settings and features okay because there's a lot to unpack there it is I'm
• 07:30 - 08:00 ready to go deeper down this rabbit hole all right let's do it let's see what this lorenzian classification algorithm has to offer sounds good welcome back to our uh Deep dive into lorenzian classification yeah last time we really dug into the theoretical side yeah and I have to say it's really intriguing to actually see how it visualizes the market I know it's like a it's like suddenly seeing it in 3D or something yeah yeah exactly but uh let's get practical okay how are Traders actually using this to make decisions yeah that's
• 08:00 - 08:30 what I want to know so it's uh it's really important to remember that this is not some magic formula for instant riches okay yeah that's good to uh remember it's it's a tool that can give you additional insights help you spot those patterns you might otherwise Miss so it's like having an extra set of eyes scanning the market for those hidden opportunities yeah that's a great analogy okay so would a Trader use this as their main signal or more as a a way to conf confirm what other indicators
• 08:30 - 09:00 are telling them it really depends on the individual Trader okay their style their risk tolerance yeah yeah some might be comfortable using it as the primary trigger yeah but I wouldn't recommend that for everyone yeah it's a uh it's a complex algorithm working in an even more complex market right false signals are bound to happen exactly so it's it's often wiser to use it as a confirmation tool okay say you see a Buy Signal you might then look for other evidence like
• 09:00 - 09:30 uh a bullish Candlestick pattern or maybe a breakout above a resistance level so you're looking for a couple factors to line up yeah increase the odds of success right it's like a checks and balances system for your trades yeah okay and uh and that's where all the customization options come in you know you can fine-tune the settings the filters to match your strategy you your risk tolerance exactly you know you can adjust the number of neighbors it considers what features it focuses on so many options it's all about making the algorithm work for you you talked about
• 09:30 - 10:00 back testing earlier can you just uh remind me how Traders actually use that to evaluate the script so back testing is uh it's crucial it lets you run simulations on historical data yeah you can see how the script would have performed under different market conditions okay experiment with different settings and identify which configurations generated the most profitable trades so it's like a time machine letting you test drive different approaches and see what works you got it
• 10:00 - 10:30 but of course past performance isn't a foolproof predictor of future success of course yeah but it gives you valuable insights into the algorithm's strengths and weaknesses okay right the market is always evolving yeah so what worked yesterday might not work tomorrow exactly okay so let's uh let's flip the script for a second right what are some of the pitfalls or limitations okay that Traders need to watch out for with this approach well like I said before it's
• 10:30 - 11:00 not a crystal ball right it relies on historical data okay to make predictions and uh the market isn't always predictable yeah it can be pretty random exactly so there's a risk that it could give a false signal right and even if the signal is accurate the market can just decide to do something totally unexpected always keeps you guessing that's why it's essential to use this with other forms of analysis yeah your own judgment not just rely on the signals okay it's all about balance yeah
• 11:00 - 11:30 not putting all your eggs in one basket even if that basket is woven from SpaceTime exactly another potential Pitfall is over optimization okay so what it's that so uh the script offers so many settings and filters yeah it can be tempting to keep tweaking them okay chasing those perfect back test results so you end up with a setup that works great in the past yeah but then falls apart when you actually start trading that's the danger all right so you have to find that sweet spot between optimiz in for the past and uh generalizing for
• 11:30 - 12:00 the future right you need a setup that's adaptable yeah not just a perfect fit for what's already happened speaking of past performance I can't help but think about that disclaimer included in the script and that disclaimer is there for a reason yeah it states clearly that this script isn't financial advice okay and shouldn't be taken as any guarantee it's for education and exploration right not a get-rich quick scheme so Traders should never just blindly follow those signals yeah do their own research
• 12:00 - 12:30 understand the risks ex maybe even talk to a financial adviser absolutely it's about responsible trading yeah we're here to you know explore machine learning and finance but it's important to do so with a clear understanding of the downsides I think we've covered a lot of ground here yeah we've seen how Traders can use it explored back testing even uh talked about the pitfalls we're getting a deeper understanding of this script's potential and how it can potentially help Traders but um there's one more piece of the puzzle I
• 12:30 - 13:00 think we've explored the how of the algorithm yeah but I want to dig into the why ah you're talking about the bigger implications yeah the insights this algorithm offers Beyond just the buy and sell signals exactly what can this approach teach us about the nature of the market itself that's a uh that's a fascinating question yeah and it leads us to some really thought-provoking territory okay lead the way all right so join us for the final part of our Deep dive okay we explore what this lorenzian
• 13:00 - 13:30 classification reveals about the hidden rhythms of the market ooh the hidden rhythms and what it might mean for the future of trading welcome back to the final part of our Deep dive into uh lorenzian classification right we've explored how it works talked about how Traders are using it even uncovered some of those uh pitfalls right but now I'm curious what does it all mean okay what can this approach teach us about the market yeah that other methods might miss that's the
• 13:30 - 14:00 that's the million-dollar question isn't it yeah I think the answer lies in how it challenges our very understanding of how the market works okay I'm intrigued yeah break it down so think about traditional technical analysis for a second right it relies on the assumption that patterns repeat right history Rhymes but uh what if those patterns aren't as reliable as we think okay what if the market isn't just a cyclical machine so you're saying there's more to it than just looking for the same shapes on a chart exactly so lorenzian
• 14:00 - 14:30 classification suggests that the Market's memory you know it isn't linear it's not just about finding those same price levels or formations it's it's about how the timing the events surrounding those patterns can totally change their meaning it's like uh saying the same word can have different meanings depending on the sentence it's in the tone of voice the context Perfect Analogy yeah so like take um a head and shoulders pattern okay a classic bearish
• 14:30 - 15:00 signal right and traditional analysis would say sell it's going down yeah but what if that pattern formed after a major positive Catalyst like maybe a a breakthrough product launch yeah well the context suggests that that downward move might be temporary oh interesting not the start of a of a big downtrend so lening classification is helping us see beyond just the surface level to understand those nuances that uh traditional analysis might miss
• 15:00 - 15:30 precisely okay and I think that's the big takeaway here right this algorithm isn't just a new Tool uh it's a new way of thinking about the market right it's telling us that uh the past isn't just a blueprint for the future yeah it's a collection of stories okay each with its own unique uh twists and turns yeah exactly yeah I can see why this is so fascinating yeah it's it's acknowledging that the market is this complex Le system right not uh not some simple
• 15:30 - 16:00 equation to solve exactly and I think that's a really crucial insight for Traders yeah it it pushes us to be more adaptable or critical Yeah in our analysis not just blindly follow signals right but to understand the story behind those signals it's like being a detective not just a chart reader I like that yeah looking for Clues weighing evidence considering different interpretations before making a move this has been a mind extending Deep dive
• 16:00 - 16:30 I have to say yeah I feel like I've learned about a new algorithm yeah but also gained A New Perspective I'm glad to hear that yeah for sure and remember this is just the beginning okay as machine learning keeps evolving uhhuh we can expect even more Innovative approaches to market analysis approaches that challenge how we think and push us to think differently yeah it's an exciting time to be a Trader it really is yeah and we'll be here to break it all down of course help you navigate this crazy world absolutely so if you're
• 16:30 - 17:00 interested in this kind of stuff exploring finance and Cy Edge technology yeah make sure to subscribe yeah subscribe to the Deep dive we'll keep bringing you those insights that matter helping you stay ahead of the curve and uh as always we encourage you to do your own research absolutely explore this open source script we've been talking about yeah and keep those Minds open yeah to new possibilities until next time happy trading see you later