Jim Simon's RRS Strategy Replicated with OpenAI — Here’s How (STEP BY STEP)

Summary

In a step-by-step adventure, Moon Dev meticulously replicates Jim Simons' famed RRS (Real Relative Strength) strategy using OpenAI, with a focus on transforming trading perceptions by adhering to the system without human interference. Packed with insights from Renaissance insiders, the video crafts a comprehensive roadmap to automate trading systems by leveraging code, emphasizing patience and precision over emotional trading. With a dedicated journey into Python scripting, data fetching, and backtesting processes, the tutorial highlights the immense potential in algorithmic trading for creating a personal financial edge. Moon Dev showcases how relentless persistence, iterative learning, and core programming skills can mimic Jim Simons’ billion-dollar success blueprint—making it accessible to everyday traders.

Highlights

• Jim Simons emphasizes the importance of not overriding the trading system. 🚫
• Algorithmic trading can be very powerful when utilized correctly and unemotionally. 📊
• RRS strategy revolves around analyzing real relative stock strengths. 📈
• Data fetching, cleaning, and processing are foundational steps. 🧹
• Strategically automated trading minimizes emotional errors. 🛠️
• Using Python and backtesting techniques can refine strategies. 🐍
• Importance of community and learning from others to advance skills. 🌐
• Raising awareness on the necessity of coding in trading. 🚦
• Step-by-step guidance reinforces one's trading craft. 📚

Key Takeaways

• Never override your trading system! 🚫
• Jim Simons’ secret: trusting the algorithm above emotions. 🤖
• Automating trading enforces discipline and accuracy. 🎯
• Real Relative Strength (RRS) measures a stock’s performance against a benchmark. 📈
• Data dogs: fetching crucial market data is key. 🐾
• Iterate and backtest till you succeed! 🚀
• Understand your strategy deeply before execution. 🔍
• Coding is the cornerstone—learn to build anything! 💻
• Embrace the journey: steady improvement and learning. 🌱

Overview

Jim Simons, heralded as one of the most successful algorithmic traders ever, operates on one unyielding principle: never override your system. Moon Dev explores this central tenet, replicating Simons' RRS strategy through a hands-on tutorial, demonstrating how unwavering discipline in algorithmic trading can lead to monumental financial success.

In a world where emotional trading is the norm, Moon Dev advocates for data-driven decisions, where automation becomes the bedrock of a trader's success story. By building from the ground up, from fetching data to rigorous backtesting, Moon inspires aspiring traders to rise above the noise and rely on historical data for their strategic moves.

The video is not just a guide but a movement towards embracing technology in trading. With clarity and passion, Moon Dev articulates the importance of learning to code, iterating continuously, and engaging with a community to master the art of trading. This journey into algorithmic trading underscores how a disciplined, educated approach equips traders to build their financial futures.

Chapters

• 00:00 - 02:00: Introduction to Jim Simons and RRS Strategy The chapter introduces Jim Simons, acknowledged as one of the greatest algorithmic traders with a net worth of $31 billion derived from automated trading. The intention is to delve into some valuable clips related to his strategies and then guide on developing one of his strategies, specifically the RS strategy inspired by Jim Simons.
• 02:00 - 07:00: RRS and The Importance of Not Overriding Systems This chapter discusses the trading philosophy of Jim Simons, specifically emphasizing the importance of adhering to predefined systems. It highlights Jim Simons' rule of never overriding the computer or the trading system. The approach is to follow the computer's indications without human interference, underlining trust in systematic trading over subjective decisions.
• 07:00 - 13:00: Real Relative Strength Strategy Introduction The 'Real Relative Strength Strategy Introduction' chapter talks about a successful strategy that has yielded an annual return of 60% for 30 years, resulting in a net worth of 31 billion the protagonist achieved by sticking to a system. The key takeaway is to never go against this established system, as it cannot be accurately replicated or predicted, implying a combination of resilience and adherence to a proven method is critical for success.
• 13:00 - 18:00: Building The RRS Algorithm The chapter titled 'Building The RRS Algorithm' discusses the limitations of backtesting strategies. It emphasizes that while you can use historical data to simulate and check if strategies would have worked in the past, it doesn't guarantee future success. Modifying or overriding the system based on personal judgment can lead to inaccurate results and doesn't reflect the system's true potential. Respondents are cautioned against altering established algorithms based on subjective decisions.
• 18:00 - 28:00: Data Fetching with Coinbase The chapter delves into the topic of data fetching, specifically focusing on the Coinbase platform. It begins by discussing a tested methodology for algorithmic trading, echoing the processes used in a system referred to as the RBI (Research, Backtest, Implement) system. This approach is outlined as a free resource available on moddev.com. The chapter emphasizes the importance of researching trading strategies and conducting backtests to verify their effectiveness based on historical data. While past success does not guarantee future performance, it suggests a higher likelihood compared to non-tested strategies. A key takeaway from the chapter is a principle advocated by Jim Syman: 'never override your trading system.' This underscores the importance of discipline in following backtested systems.
• 28:00 - 36:00: Processing Data and Introducing RRS Calculation The chapter discusses the role of computation in decision-making, emphasizing the importance of sticking to a planned approach rather than questioning changes that can't be simulated or studied from past data. There is an allusion to a specific example, '31B, dude,' and a hypothetical choice between manual and automated trading. Overall, the observed strategy has been effective.
• 36:00 - 53:00: Analyzing Initial Testing of RRS Strategy The chapter discusses the importance of distinguishing between trading based on a boss's system or emotions and trading based on historical data analysis. It emphasizes the value of backtesting, which involves looking at past data to determine if trading strategies were effective. If a strategy worked well in the past, it is more likely to be successful in the future. The chapter suggests that conducting long-term backtesting is essential for developing effective trading strategies.
• 53:00 - 81:00: Challenges and Adjustments in Backtesting The chapter discusses the importance of backtesting in trading systems. It emphasizes that by backtesting over various time frames such as a month, 6 months, a year, 5 years, and 10 years, traders can ensure that their systems would have worked in the past, thereby eliminating ineffective strategies. Additionally, it mentions building algorithms inspired by successful firms like Jim Simons' that rely extensively on model-based advice for traders.
• 81:00 - 91:00: Encouragement and Learning Mindset In this chapter, the importance of seeking advice and being open to it is discussed. It highlights that accepting or rejecting advice based solely on personal preference isn't a scientific approach. The chapter emphasizes the need for a structured model rather than making decisions based on how one feels emotionally at a given moment. It reflects on the subjective nature of feelings and decisions, noting that such approaches can't be reliably simulated or utilized in predictive models, particularly in fields like investing. This underscores the importance of a learning mindset that values structure and long-term strategies over short-term emotional responses.
• 91:00 - 100:00: Review and Troubleshooting in Code Execution The chapter emphasizes the importance of adhering to systematic approaches in code execution, warning against overriding such systems based on personal feelings or past experiences. It highlights a message from an expert who advises against deviating from established models, as doing so can lead to significant failures. The chapter encourages readers to follow reliable sources and begins introducing an 'RS strategy' for execution.
• 100:00 - 105:00: Conclusion and Final Thoughts on RRS Strategy The chapter titled 'Conclusion and Final Thoughts on RRS Strategy' focuses on exploring the concept of 'RS'. The narrator notes the input of individuals with experience in various financial funds, highlighting the value of their shared insights in their discussions. The chapter aims to delve into the thoughts of a particular individual who previously worked for a major fund, as a point of interest and further exploration into the subject matter.

Jim Simon's RRS Strategy Replicated with OpenAI — Here’s How (STEP BY STEP) Transcription

• 00:00 - 00:30 Jim Simons is one of the best or if not the best algorithmic trader ever in humankind existence. He ran up a net worth of $31 billion all off of automated trading. And Gosh posted a couple good clips here that I want to watch to start off today and then show you how to build out one of his strategies. R it's an RS strategy from Jim Simons. Let's go ahead and first start with this because this is
• 00:30 - 01:00 very insightful and a lot of people are trading entirely different than this. So the only this is his only rule that he follows. He always follows no matter what. This is Jim Simons. The only rule is we never override the computer. No one ever comes in any day and says the computer wants to do this. That's crazy. We shouldn't do it. It you don't don't
• 01:00 - 01:30 even think about it, bro. Just keep going. Let them find out who it was. They going to be on you because you can't you can't simulate that. You can't study the past and wonder whether the boss was going to come in and change change his mind about something. So, you just stick with it and it's and it's uh and it's worked. So, so he had like an annual return of 60% for like 30 years. Anyways, he ran up a net worth of 31 billion. And he says essentially the number one rule is to never go against the system because
• 01:30 - 02:00 you can't simulate the past. Meaning he used to back test strategies to see if they worked in the past. And you can simulate that. You can see if they worked in the past, yes or no. If they did work in the past, okay, they're much more likely to work in the future, but they're definitely not going to work if you change up things. If you go ahead and say, "Hey, you know, the system's taking a long here and I don't think it should take a long." or if it's taking a short here, I don't think it should take it short. If you override the system, it's not actually the system that you
• 02:00 - 02:30 tested in the past. Very similar to my RBI system I follow. This is free on moddev.com. It's the whole road map for algo trading. The process of automating your trading starts with the research of trading strategies, then back test those strategies. See if they actually worked in the past. If they do, they're not guaranteed to work in the future, but they're much more likely than something that didn't work in the past. Right? That makes sense. So that's the RBI system research back test. And that's why Jim Syman says you must never override your system. The only rule is we never override the
• 02:30 - 03:00 computer. No one ever comes in any day and says the computer wants to do this. That's crazy. We shouldn't do it. It you don't do it because you can't you can't simulate that. You can't study the past and wonder whether the boss was going to come in and change change his mind about something. So you just stick with it. And it's and it's uh and it's worked. It's worked very well. It worked very well for him. Again, 31B, dude. So, if you had a choice of trading by hand and
• 03:00 - 03:30 not really being able to control if you're trading based off the boss's system or the boss's emotions, you know, you come in and you decide to go long or short. That's a whole different way of trading than if you look at data from the past and you run strategies across that data to see if they worked in the past, which is actually called back testing. If you want to Google it a little bit more, you can see if strategies worked in the past. If they worked in the past, they're much more likely to work in the future. So, um, if you do that for a long period of time, a
• 03:30 - 04:00 month, 6 months, a year, 5 years, 10 years, and you never override the system, you will only trade systems that you know that at least work in the past. So, it's going to get a lot of trash out of there. Let's watch this other video as well. And then I'm going to start building out a um an algorithm that I heard he likes to use. Jim Simons uh you know investing firms say oh they have models and what they typically mean is you know we have a model and it it advises the trader
• 04:00 - 04:30 what to do and if he likes the advice he'll take it and if he doesn't like the advice he won't take it. Well that's you can't that's not science. uh you can't simulate how you would do how how was how were you feeling when you got out of bed uh you know 13 years ago when you're looking at at uh historical simulations uh you know investing firms say oh they have you hear that how are you feeling how are you feeling you can't trade like that you can't you can't have a model that tells you to go long or short and
• 04:30 - 05:00 then decide if you're actually going to follow the model that's not scientific how are you going to trade like that how are you going to how are you going to know how you felt 10 years ago go on that day that you blew up because you decided to override the system. Never override the system is essentially what he's trying to get past to us. So, um these are some great posts. Definitely go follow him. Uh and let's go ahead and dive in here because I've got a lot I want to build here today. And we're going to start with this RS strategy.
• 05:00 - 05:30 So, there's a something called RS. And let's dive deeper into it. Before I dive into it, I have a note. And we have some people that used to work at different funds and stuff that tell us things here in the chat a lot, which is pretty cool. I want to dive into this one guy's thoughts here, and he said he used to work for a big fund, and I want to go
• 05:30 - 06:00 ahead and dissect what he said and then rebuild that. He said he used to work with Jim. If you used to work with Jim, build it. So again, this is a Jim Simon system. So I just want to build whatever he was building and then I can build my own stuff as well. And that's how we just make our
• 06:00 - 06:30 system. It's better and better and better. just like he said. So, I'm gonna give it all of these screenshots I have from this conversation inside of the chat. So, if you have fire in the chat, I will be happy to go over it. I think me doing
• 06:30 - 07:00 this alone is one thing, but me showing you guys everything is way better. Please go ahead and analyze the chat messages I sent above. This is from an unnamed Renaissance employee who said, "I should look into RS, which is real relative strength. Please break that down and combine it with the knowledge that you have in order to explain clearly the strategy that he was talking
• 07:00 - 07:30 about and how I can go ahead and build that into a algorithm that I can trade off of. and if there's a way to back test it, that's even better. So, talk about that a bit as well. Okay, so let's go ahead and see what they have to say.
• 07:30 - 08:00 Okay, so let's unpack it. This Renaissance trader GK is laying out the real relative strength RS and let's also make sure that we have it in here in our GitHub. So, let me go to the correct GitHub. This is my AI agents GitHub. So,
• 08:00 - 08:30 I'm going to go to my trading bot GitHub. Okay, here it is.
• 08:30 - 09:00 Okay, wonderful. So, let's go over to the Hyperlquid. I'm going to make a folder in here called RRS. And inside of
• 09:00 - 09:30 RS, here's all my code. Um, inside of RS, I want to make a folder here called um Let's start with config. py here. Okay,
• 09:30 - 10:00 I'm going to show you all the code, too. So, this is the RS, real relative strength, and we're going to say config. py config. py. And I'm going to paste in this code here. So, I've worked on this a bit and I want to just be able to show you everything. So, I'm importing OS time frames. I'm looking at look back dates. You see everything? Okay, great. So, data directory here. I'm going to need to change this to be let's go here and say RS
• 10:00 - 10:30 data. Let's copy path here. And I'm going to put it in the data folder here. And then relative strength results here. Let's go ahead and put this results folder here as well. results. Okay, sick.
• 10:30 - 11:00 And it's just going to be the same link, but results instead. Results. Okay, sick. So, that's the first part of the code here.
• 11:00 - 11:30 and let's go ahead and read through it here. I'm going to go ahead and make a read me here as well for make a choice. Right? You just decide what it's going to be, who you're going to be, how you're going to do it. Just decide and then from that point, the universe is going to get out your way. It's like it's water. It wants to it wants to move and go around stuff, you know, and I'll call it the RS trading strategy. Now, of course, there's a chance that this is not something Jim
• 11:30 - 12:00 Simons used, but regardless, all I do each day is I research, back test, and implement. So, um whether or not this guy that gave us this idea is credible or not, he seemed very credible uh the way he was speaking and the things he was talking about. So, it got me interested enough in order to dive into this today. And I dove into it about 4 months ago and then got a new lease on the idea here when Homeboy came back in and told us a little more. So, let's go
• 12:00 - 12:30 ahead and read what he said and then come up with our own algorithm for it. Now, nothing is going to be exactly like theirs. Of course not. Of course not. But it's an idea that leads us to our own stuff. And that's what it's all about. Building your own edge. Nobody's going to give you a bajillion dollars, dude. I'm sorry. I wish they would, but they're not going to. And I'm sorry about that. But I will show you every
• 12:30 - 13:00 thought I have and give you everything I can every single day. Much love to you, 777. So, what is real relative strength? Not RSI. This isn't the usual 14 period momentum oscillator. Core idea. Measure how a stock's performance stacks up against its benchmark like the spy. Then normalized that stock's own volatility and trading volume. Breadcrumbs. Tiny steady upticks in relative
• 13:00 - 13:30 performance plus volume that big institutions leave when they quietly build a position. Key points from the chat. Differential performance coefficient. Compute return of stock I minus return of spy over a look back period. Okay, I think that's important. Normalize for volatility and
• 13:30 - 14:00 volume. Divide that differential by the stock's realized volatility and maybe its average volume low high volume build. A steadily rising RS with smooth lowvall price action and creeping volume equals institutional footing. So the whole idea behind this is that
• 14:00 - 14:30 institutions are buying a certain asset slowly and when things rebound or the main thing that you're following for my case it would probably be BTC maybe SPY then we'll be able to see which ones are going to come in after that going to move based off of that based on how a how institutions are acquiring that
• 14:30 - 15:00 asset and then we just pick up the breadcrumbs. Low V high volume build a steadily rising RS with smooth lowvall price action and creeping volume equals institutional footing. A spike in price plus RSI without volume volatility confirmation. Likely retail fueled fake breakout.
• 15:00 - 15:30 Interesting. Best in weakness. When the market spy is under pressure, a stock with positive RS really stands out. Turning RS into an algorithm. Here's a step-by-step blueprint step data what to do. Grab data on intraday price and volume for your universe plus spy. So our stock stock.close PCT change rbench spy.close. Okay. Volume
• 15:30 - 16:00 normalizer signal rules entry RS crosses above a threshold plus.5 market under zero spy is down strength and weakness confirm with multi-time frame eg weekly RS over the daily RS exit RS rolls over below threshold big stop-loss take profit seven position sizing and risk cap App total exposure back
• 16:00 - 16:30 testing. Plug the above into backtesting. py or vector bt. Back test tips. Walk forward. Optimize your end threshold on a rolling basis. Permutation test. Shuffle returns versus spy to ensure RS isn't just luck. Volume filters require volume over 1.2 average to avoid low liquidity noise. Market regimes test separately in bull
• 16:30 - 17:00 versus bare markets. TLDDR real relative strength equals stock return minus benchmark return divided by stock volatility times re relative volume. Look for steady low vol outperformance especially when the market's weak and you've got a quiet signal of institutional buying. Plug it into any Python back tester. Back testing py vectorbt zipline and you're off to the races. Feel free to ping me if you wanted deeper code samples. So, I do
• 17:00 - 17:30 want to try to back test this. I do want to try that. And I think that's going to be interesting. So, let's go ahead and start with getting our data. We have to get the data because we're you're right.
• 17:30 - 18:00 We're data dogs. You know it. You know it by now. So, we're data dogs. So, we have to get our data. So, how do we get our data dog? Well, we go to data fetcher. We need to make a data fetcher
• 18:00 - 18:30 here. So, I want to build out here's a data fetcher. Here I have this code. I've got the above code that is a bit of a data fetcher, but I want to update it. So, it is working based off of Coinbase data instead. Now, I have this code other code above that is Coinbase data. So, you can go ahead and update the
• 18:30 - 19:00 above code to include both in you can update the above code in order to get the open, high, low, close volume data from Coinbase. Instead, I'll also put my config.py so you can see what my config looks like. Okay. So, let's go ahead and put my code here for the Coinbase data. Where is that? That's in the data section of the
• 19:00 - 19:30 GitHub. Coinbase data here. Okay. Copy that. Please make it just one file that you send back the code for. Thank you. And then let's also give it my config. My nick config here. Here's my config. Okay. So, let's have OpenAI do
• 19:30 - 20:00 this for us. And then we're going to call this file here data fetcher. But I'm going to use Coinbase data instead because it's easier. It's better. Not easier. It's better. There's more. There's more. Easy. Good. Fast. Whatever. Suck on the
• 20:00 - 20:30 Okay, so here we go. Here's a single
• 20:30 - 21:00 self-contained script that pulls open, high, low, close, volume data and saves the under the data Coinbase. Okay, perfect. path data Coinbase. I'm gonna change the path, but that's okay. Okay. Sick, dude. So, let's copy this in and hopefully it uses my EMV like a good kid. Mad
• 21:00 - 21:30 city. Okay, so data the path the save data path RS data dude. Copy path. I'm only going to use Coinbase data for this one cuz Coinbase has the most. Okay. The weeks here. Okay. 90 weeks, 1 hour. This is actually 6 hours. Imagine you had everything you wanted. Everything. Imagine you were everything you ever wanted to
• 21:30 - 22:00 be and capture that feeling. That's the easiest way to manifest what you want. That's the easiest way, dude. That's the easiest way. You can see I'm bringing in my Coinbase API key and my secret. Just make sure you see all this code. I want to make sure you always see
• 22:00 - 22:30 all the code. And if I don't, then please let me know. Okay. So, here we import OS. We import time. We've got our symbols here and these are Are these going to be correct? I think those should be fine. You can see we're loading from my ENV here. We're signing the requests.
• 22:30 - 23:00 Time to granularity. Get historical data here. Pulls in up to 300 candles per request. slices the full week's range into chunks while cursor is under end time. Okay. PD data frame and def main. Okay. Let's try to run this. Fetching data for BTC 1 minute. Okay. So, I don't need to do one
• 23:00 - 23:30 minute data. So, let's It's good to see that it's working. I'm just going to do the 1 hour, 6 hour, and one day, 90 weeks. That's about two years. Let's actually make it two years. Two years of data. Okay. So, fetching 400 or 104 weeks of 1 hour data for BTCUSD. Okay. Thank you, Coinbase. I
• 23:30 - 24:00 love you. So, I suppose this might take a while if
• 24:00 - 24:30 I use Okay, so you can now see it's fetching six-hour data. Damn, this is going to take a minute. Okay. So, um we got to fetch data. Let's do six hour and then let's change this to be let's go to Coinbase. Damn, dude. This is actually very data intensive. Interesting. Interesting stuff
• 24:30 - 25:00 here. Let's go to exchanges here and say crypto exchanges. I just want to get some good Coinbase ones. Wow, Coinbase, good job, buddy. They're number two exchange now. Very good, son. It's good to see you stepping on the gas. What is Arigo? Hold up. How you got all these coins? We don't even know about Max. Oh god.
• 25:00 - 25:30 Sooie, let's put in some fun ones in here. Okay, BTC and ETH. Uh, okay. So, let's go here. BTC ETH soul. Um, let's put Pepe in there instead of ADA because what is ADA? Let's get Dot the out of here. Let's get Tao in there instead of
• 25:30 - 26:00 DOT. What else? I just want some fun ones. What about What about fat instead of link? Stay off the fat B. Okay, I'm just going to do these for now and then when you're not here, maybe I'll do longer. So, obviously long uh add more add all Coinbase symbols here above. But for
• 26:00 - 26:30 example, be shorter. Okay. So, we're just doing 6 hour one day. Why? Because it's going to be faster to get all the data, dog. Okay. So, we're starting with we've loaded cash. You got C. You cashed out already on the data for the the BTC. Ain't no way. There is no Oh, you already got it. You cashed out on that BTC data. I see
• 26:30 - 27:00 you, dog. Look at that. I didn't even ask for a cash. I didn't even ask for cash and it gave it to me. Look at that. That is fire. Okay, so the reason we need all the data is because remember this is looking at the accumulation of BTC and comparing the symb other symbols. Okay, so what's the next thing we need to do here? We probably need to make a data processor.
• 27:00 - 27:30 So, I'm going to say this. [Music] Um, so I use I want to use BTC as the one that we as our stable, the one we compare against. So, like in the example, we were going to use Spy, but we're going to use BTC and then a bunch of altcoins as the others.
• 27:30 - 28:00 data processor, RS calculator,
• 28:00 - 28:30 um top RS here. And let's see what else. RS calculator. Did I put that in there already? I think I
• 28:30 - 29:00 did. I think I already did that. data fetcher, data processor. Here's my um I put a bunch of code above that can actually be another file and we'll keep the data fetcher the same. You don't need to change anything in the below. I made some changes to the symbols. I just wanted to update you on it. Please send me another file that does all the above
• 29:00 - 29:30 functions all in one file. So we don't have to use multiple. Okay. So pretty much what I asked it to do was to put together some files because I don't want to have it on so many different things. So many different um whatchamacallits pages I guess. So we got all the data that we need cuz we're data dogs. We got TA, we got Pepe, we got
• 29:30 - 30:00 that. All right. Sick. So, I'm gonna put that away now. RS pipeline. I suppose I can try this. I mean, I I kind of asked for it not to do this, but that's fine. I'll try it.
• 30:00 - 30:30 Keep it going, buddy. You got this. There you go. Thought you were wimping out on me for a second there, pal. I believe in you.
• 30:30 - 31:00 I believe in you too, dude. You lock in every day with me. I lock in every day with you. And piece by piece, step by step, we're going to build everything. And I'm going to show you everything. Let's change the fetcher. Okay, let's close this and say, "Hey, fetch it." Now you should have all of it. Perfect. Top RS scores. Process
• 31:00 - 31:30 this. Okay. Sick. Loading the cache. Perfect. Top RS is sent to results. Top RS. Top RS. Okay, this is great. So, this is
• 31:30 - 32:00 awesome. Great job with this. Now, it outputs a top RS.csv, which looks great. But can you please explain to me how to generate the signal from this? Just explain the CSV. Learn baby, learn so that we can earn. Baby, earn. Yo, MLK Jr., be a little quieter, okay? I just turned you down. No offense. Much love to you. Thank you. Thank you for Thank you for letting me be here. Bri, thank you. Thank you.
• 32:00 - 32:30 Thank you. Thank you. I'm so grateful for for you. I am so grateful for you, Martin Luther King Jr., thank you. Thank you. Thank you. Thank you. Thank you for my freedom. Thank you for my freedom, fam. Thank you. Thank you. Thank you. I love you. I love you. I love you. Sorry to turn you down. I just I know that was loud for you guys. Here's what the top RS is telling you and how you can turn into an actionable signals understanding each column. So the columns, let's look at them. We got time frame
• 32:30 - 33:00 symbol step time step RS score. Yo, by the way, by the way, it looks like the time frame column isn't working, huh? Can you um send back fix code that has it actually worked? Thanks. I'm such a D. Okay, so time stamp the close of the bar signal time. You'd submit a buy at
• 33:00 - 33:30 the open of the next bar after time stamp. Define your exit rule. Drift down exit when RS falls back below a lower threshold. EG10. Now let's back this up. Understanding each column symbol time step RS the real relative strength value at the time stamp. Higher means more institutional breadcrumbs. Okay. So higher score means
• 33:30 - 34:00 more institutional breadcrumb. How to analyze RS? Higher score means more institutional bread crumbs. Okay.
• 34:00 - 34:30 Breadcrumbs source is which per symbol RS file that came from from the fetch. Okay, that came from that the s the the CSV is simply the top N. You chose three RS peaks across all symbols and time frames sorted by RS. So if we look at the data fetcher here, top three here. So this gets the top three for
• 34:30 - 35:00 each time frame. E From RS peaks to trade signal, define your entry rule. Event RS crosses above your entry threshold. For example, a fixed value like 5.0 or top 1% of historic RS time stamp, the close of
• 35:00 - 35:30 that bar, your CSV time stamp signal time. You'd submit a buy at the open of the next bar after the time stamp. Define your exit rule. Drift down. Exit when RS falls back below a lower number. Eg 1.0 time stop. Exit after X or Y bars. Okay. Position sizing. Simply allocate a fixed percent of your equity per trade. ATR sized. Risk one to two ATR from entry to
• 35:30 - 36:00 stop. You'd feed signals DF into your execution engine. Automate it end to end. Recomputee the RS time series for each pair. TLDDR CSV is your heat map. Okay, here's the updated RS pipeline and symbol correctly pulled from each file name. Okay, so let's try this again. And then I'm gonna delete
• 36:00 - 36:30 all of this. So, let's just delete all of them. Delete. Delete. Revert. Revert. Data fetcher. Okay, let's run it back, cousin. There we go. Top RS. Okay, now it's Gucci. Beautiful. Beautiful. Beautiful. Now, I want to say this. This is going to be tricky, but we'll see.
• 36:30 - 37:00 Can you please go ahead and use backtesting py in order to back test this RS strategy? Below is some working back testing. py code that you can use as a template. Okay, so here on moon.com which is the
• 37:00 - 37:30 free roadmap and resources where I explain my RBI system, answer questions, give you the road map, all that good stuff, the book list, etc. I also have a template here for me and you because if anything I have I try to give to you because I believe code is a great equalizer and I believe if you learn how to code you can build anything for the rest of your life, dude. for the rest of your life. For the rest for the re for the re for the rest of your dude, you
• 37:30 - 38:00 don't get how serious this is, dude. Where the a everybody's talking about AI and only devs are getting benefits from AI. If you don't learn to code, dude, I can't help you. You need to learn how to code if you don't know how to code. I'm just saying that, bro. It's just facts. Send back the full back testing. Code. This will be in a different folder, but you know where all my data is at. for the rest of your life. Do you care about the rest of your
• 38:00 - 38:30 life? Do you care about your family? If nobody in your family knows how to code and you sit on the computer all day, dude, and you don't know how to code yet, come on. Please, dude. Do it for me. Do it for you. Do it for your family, dude. This is B. Tim Simons, this is bigger than Open AI. This is about you and your future,
• 38:30 - 39:00 bro. Learn to freaking code. Learn to code and you will never have to beg me for anything or anybody ever again. You see my DMs, dude. Please, please, please send me this. Please, please, please send me that. Learn to code. Please, please, please, please. If you learn to code, you can build anything for the rest of your life. And you know that, dude. You know, everything is built in these clothing trenches, but you too scared. I was scared, too. I was scared. I was scared. I was scared, dude. It's okay to be scared. It's okay to be scared of
• 39:00 - 39:30 things, dude. Just attack your fears. Just attack viciously. Attack, attack, attack, attack, attack, attack. Come on, let's go. Let's just keep pushing step by step, piece by piece, day by day, minute by minute. One more minute. Let's get one more minute in. How about that? How about that? So let's get one more minute in. Okay. So you can see the long entry be RS crosses above the threshold. This is very simple. Let's see how it do.
• 39:30 - 40:00 Barry. This is very simple. But I don't know. I don't know if we're going to get this done with back testing. py. We might have to use a different one. We might have to use a different one. That's okay. I'mma keep pushing. Dude, I can't fly. So, I run. My knees is hurting. I can't run. I
• 40:00 - 40:30 walk. When I'm tired, man, I can't walk. I crawl. I'm just going to keep going. Rest in peace, MLK Junior. Thank you for our freedom. Thank you for your insights. Look at that stuff, dude. Crazy. Okay, let's just send a bug in there because I'm not about to do anything. Please go ahead and fix this bug. It looks like there's an error. Send back and forth code. Thank
• 40:30 - 41:00 you. Go ahead and add a bunch of debugging if you need to. Let me know if you need any data or anything. I have everything here and let's just work this through and get this bug fixed. We want to go ahead and be able to back test this big liquidation here on Solana. Solana, no drama.
• 41:00 - 41:30 Don't run with the pack. If everyone is
• 41:30 - 42:00 trying to solve the same problem or a whole group of people, if that's the latest and greatest thing to do, don't
• 42:00 - 42:30 do that. Don't do that. Do something original. Don't follow. What is this, bro? What the heck? I've never seen that before. Cool. Cool. Cool. Cool. Cool beans, bro. Cool beans. Let's run it. Okay, so optimization error RS is missing parameter return
• 42:30 - 43:00 statistics. Looks like we're still running into a bit of an error. If you could go ahead and fix that, that'd be awesome. Thank you. Dope boy magic, dude. Dope boy
• 43:00 - 43:30 magic. It's all it is is dope boy magic. Just keep pushing, dude. Come on. Come on, dude. I believe in you. You are your thoughts. You are your
• 43:30 - 44:00 thoughts. Please, please, please know that you can do anything in this world. If you know you can do anything in this world, you can do anything in this world. Don't listen to anybody negative, bro. Don't listen to anybody negative. This is your life. You get to create it. You're the creator of your future. Your thoughts starts with your thoughts, dude. It starts with your thoughts. You can see here that I actually didn't get any trades here. So, um please um fix that. Thank
• 44:00 - 44:30 you. 777 blood. Much love to you. Thinking think about it. 04 mini. Hi. Hi. Hi. H high. How you doing? I'm good. 777. Much love to you. So, I just want to back test this because I think it would be cool to back test. I know I can just test it in the live market, but that's a little crazy. That's a little crazy. I follow this system. I try to back test
• 44:30 - 45:00 everything that I can. Research, back test, and then implement. So, I I've had this on the shelf for a minute now, and I wanted to get it to you. I wanted you to see it all. I want to see see how this back test does. If I can't figure out how to back test it, okay, that's fine. data path here bet back tests the real relative strength uses proper crossover detection um class RS strategy here
• 45:00 - 45:30 threshold I'm curious entry threshold I noticed that I noticed that you only brought in the FET data. Is there a reason behind that? Uh we're still not getting any trades. So, let's please go ahead and
• 45:30 - 46:00 iterate until we do. Just iterate constantly. Iterate, bro. Iterate to success. Iterate until you're successful because your ideas are good. I don't care if other people say your ideas are bad. F them, bro. They're
• 46:00 - 46:30 not in your mind like you are. You've got the credibility, bro. You've got you've got the sauce, dude. It's you. It's in your mind. Don't listen to the other people. They don't know you, dog. They don't know your experiences. is they don't know what you know. I know you're a killer, dog. Let's get after it every day relentlessly. Every single day. Just keep stepping on the gas. They tell you to slow down, speed up. They tell you to take a break. Step
• 46:30 - 47:00 on the gas. That's it. That's life. That's how you win at life, dude. I don't care. I don't care. I don't care what happened yesterday. Okay. Yesterday's yesterday. Today's day. Let's go. Let's go. Let's go. It's another day, another dollar. Learn, baby. Learn so we can earn. Baby. So MLK says, "Not even me, dude. Thank you, MLK. Thank you. Much love to
• 47:00 - 47:30 you." All right, so we're not getting any good trades here. Thanks for the update. Um, please walk me through why you think that we're not getting hella trades. I feel like this is a we've got all the data in here and everything, but there's no trades. Um, please explain that to me so maybe I can help and then make your updates that you think are needed
• 47:30 - 48:00 here. Learn, baby. Learn so we can earn, baby. So we can earn, baby. So we can earn, baby. Earn, learn, baby. Learn so we can earn, baby. Earn. That's it. That's it. Every day. Can't stop the learning train, bro. Bro. Not mine. At least. Not mine. Not mine. I'mma keep my eyes open. Keep my ears open. Of course.
• 48:00 - 48:30 I'll consume. I'll consume it all, dude. But I'm not going to take it all. No way. I'm going to go with my mind. I've refined my mind. I put the work in every single day. My body and my mind. So, I'm just going to follow that mind every single day. And I'm going to be wrong so often that I become right. You only got to be right once, bro. Bro, you only have to be right once.
• 48:30 - 49:00 But a lot of people out here, they're going to try to drag you down. Don't let them drag you down. Keep it high. Keep it high. Keep it going, dude. This is the time. This is the time. The time.
• 49:00 - 49:30 That's the only time. It's the only time to get it in. It's our time, baby. Let's go. All right. So, here are likely reasons you're still getting zero trades and some concrete tweaks to get you firing signals. Your threshold is still too strict given how you're using smooth RS.
• 49:30 - 50:00 You're smoothing with an EWM span equal 14 on a sixh hour or one day series that turns every little RS blip into a slowmoving line that may never actually cross the zero line in a clean way. Even if raw R as the unsmood differential times volume ratio oscillates around zero a lot, your
• 50:00 - 50:30 smooth curve could stay clamped mostly above or below it. So you never hit that crossover condition. You're using a fixed zero entry on a series whose bulk sits just above or just below zero. Your quartiles show that 75% of your smoothed RS lives between about negative and plus.2, crossing exactly zero is a narrow
• 50:30 - 51:00 target. If your series starts out above zero for your very first non-nan number bar, then trends mostly around zero without painting a signal sign flip. You'll never see the condition. You'll never see the condition if you don't try. Please try. Please at least try. Please at least try. Just do that for me, big dog. Just at least try. Every single day. Just
• 51:00 - 51:30 give us your Give us your all. I'll give you my all every single day. Throw the fire in the chat. Please use a dynamic threshold. After you load DF, then in your strategy, do that. Lower the smoothing span if you still want some noise suppression. Verify your back test is actually seeing the column. It is. Print the first few. Thank you. Do that, dude. That's a good idea. Print those first few.
• 51:30 - 52:00 Um, send me back the code with printing to verify and then any of the changes you think that are going to make in order to get this signal to fire a bit more. And I got to take a whistle. Remove all the motions and actually automate your trading. I can show you from a point of not knowing how to code at all. If you don't know how to code at all, I can show you exactly how
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• 53:00 - 53:30 for your open AI claude. We have channels in our discord that give you 100% free AI. So you can cancel all your subscriptions. You have a team of us that are all working towards the same goal. We're all chasing this guy. This is Jim Simons. He ran up a net worth of $ 31.4 billion. And he always says, "You just have to make your systems better and better and better because that's what everybody else is trying to do." So, I can show you step by step how to get
• 53:30 - 54:00 your ideas out of your brain into an automated trading system and you'll be able to code all of it. I spent 10 years in technology scared to code because I thought it was just for super smart people who went to Stanford or knew a bunch of math and I was neither of those. Then I locked in because I had to automate my trading. I learned Python and I learned how to actually automate my trading. It's the most most secretive
• 54:00 - 54:30 industry in the entire world. Nobody else is sharing this information. So, you can see a closer look of what's in the boot camp here. There's 15 full days. And on the first day, in case you don't know how to code, I show you how to code. So, don't worry. I give you an intro to algo trading. I get all of your your orders set up, all your risk management set up. I get you to a place where you can back test any strategy that you have for the
• 54:30 - 55:00 rest of your life. So, while I don't just say, "Hey, here's a bot that you can run make a million dollars, I think that's a scam. If you ever see that on the internet, which I know you will, just run away because nobody is going to give you a trading bot that just makes you a bunch of money. You have to come up with your own strategy and your own edge. But for a crazy low price, I can show you step by step how to get to that point where you can just
• 55:00 - 55:30 sit here like I do. You see me on live every single day. I show everything on YouTube. And I just test test the RBI method. We must keep moving. We must keep going. If you can't fly, run. If you can't run, walk. If you can't walk, crawl. But by all means, keep moving. And dude, I
• 55:30 - 56:00 understand. I understand how emotional this game is and sometimes you feel defeated because you're just tired of looking at these charts go up and down. You have FOMO and then you have fear and you're scared. You're in a position, you're in a long position, then all of a sudden you're in a short position and all of a sudden you're using 40x leverage. Dude, I totally understand that. But that was Martin Luther King Jr. and he says, "If you can't fly, then run. If you can't run, then walk. If you can't walk, then crawl. But by all means, keep going."
• 56:00 - 56:30 And I know trading can rip your heart out. The sad thing about trading, which is like not good for entrepreneurs or people who like to work really really hard and just like just bang down doors and get anything done like you and me, dude. You know, we're ambitious. We're data dogs. We're curious. We're fearless. You can't just work harder at trading and get better. You can't just put more hours in. Like everything else in the world. Most things you can just
• 56:30 - 57:00 put more hours in, work harder, and be better at trading. With trading, you have to have patience, you cannot be greedy, and you can't be fearful. Those are three things that humans just can't do. So, in this boot camp, I show you step by step how to remove all of your emotions, automate your trading. I give you free AI. I give you a bunch of examples of trading algorithms that will enable you to then build any strategy
• 57:00 - 57:30 that you want. And then I connect you with hundreds of other algorithmic traders so you can make connections for life and work on this with other people opposed to just on your own. Remember, this is the prize. This is what I'm chasing. But I don't need $31 billion. I would rather have a few hundred of us get $31 billion, you know? So, I'm not saying that this is this is guaranteed by any
• 57:30 - 58:00 means, but for me, it's something that I can invest my time into and just like he says, just keep making my systems better and better and better. Now, I've been at this for 4 years. So, if you want to fastass and get everything I know about algorithmic trading and how to automate your trading, you can see the prices right here. $69 per month. It's nothing crazy. And you can go to our Trust Pilot. Just Google Moon Dev reviews or Algo Trade Camp reviews. Here's some Q&A
• 58:00 - 58:30 down here. I want to make sure you know that we have a no questions asked 90-day money back guarantee. So, for whatever reason you want your money back, just get your money back. You can keep all the code and just all the videos just you get your money back 100%. I follow the Costco guarantee here. This is the 90-day money back guarantee. Go ahead and check out the link in the description and just scroll down and see what people like you have to say. I know you're smart because I didn't run ads
• 58:30 - 59:00 and I didn't beg you to come here. You found me somehow. You're a good researcher already. And the process of automating your trading starts with research. So obviously you're looking for a better way to automate your trading. So you're intelligent enough to know that everything can be done in this world as long as you're able to research. This is my RBI system. I'll talk about in the algo trade camp, but it comes down to researching, back testing, and implementing. So, go do your research on the internet. Go look at all of these testimonials we have on
• 59:00 - 59:30 our page. And if it seems like it's going to be a good fit, if you want to automate your trading, remove all emotions, connect with hundreds of other ALGO traders, connect with me, get free AI, have the 90-day money back guarantee, then click the link below and join right now. remove all emo. All right. So, let's see how she did here. What she changed up for
• 59:30 - 60:00 us. So, I've provided a pure Python script raw RS back test. Nice, dude. Let's go ahead and run it. This is weird. Always show details. Copy. It's very odd that it does that, but it's okay. I want to get you all of this code here so you can play around with it yourself. So, I'm going to paste this in now. And then here's all of the code. Let's go through it because I think this
• 60:00 - 60:30 is a very interesting idea. Now, I truly believe in this system. That's why I follow it, my RBI system. And you heard what Jim said at the start here. You have to you have to see if things work in the past essentially. So let's go ahead and keep playing with this back testing over time. If you want to see a part two to this, please let me know below. Say part two. But here's all of the code that we have completed so far
• 60:30 - 61:00 today. This is all the back testing code. But I also want to get you the data code. the data fetcher and the calculation code is here. So now you have all of the code that I have access to when it comes to the RRS and Jim Simon's real real relative strength. Is this actually his? I don't know, dude. I just somebody told me about it. They told me about it in the
• 61:00 - 61:30 chat and I just build it. I just research back tests and implement every single day. Now, obviously, there's going to be some tweaking that needs to be done, but I truly believe this is powerful to give you access to my thoughts, my code, and then you can step on the gas in which way you want to go because we can both look at this exercise today and come up with different ideas. Entirely different
• 61:30 - 62:00 ideas. But that's the beauty of Quant, dude. And I'm trying to flip it on his head. Everybody else wants to be so secretive about this industry. I'm going to show you everything every single day.