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The best way to use simulation data as to create your own data in an abitrage formation that way you account for all possible outcomes. The problem with using real or large data you might be moving in cycles if you are not awere they may look different but in reality they are the same. What iam trying to say using data as a sample of simulation you may end up getting not all possible outcomes of which is a walk forward method .

Amazing book. 🎉

is there a code?

Hi! Thanks for the great video- wondering is there a link to the Presentation for the ML approach?

Try to understand Prado's concepts, your videos help a lot, thanks. Where can I get the source code and data to play myself?

Your course and its possible influence are impressive! I would be delighted to work with you to spread the word about it. Let's talk about how I can effectively assist in getting the word out. I'm interested in hearing your opinions! Warm regards.

Fake

Whats the difference between using the spread a - b or the ratio a/b when looking for stationarity and cointegration?

Too much mouse-clicking noise...

what is the point of making a state space with a big dimension ?

amazing video

Hoodsohne and tomes need to get better speekers

explication is too vague

Your mic is so bad

акцент выдал с первой секунды(

The tick imbalance bars concept is still unclear. The book seems to jump right into the algorithm rather than explaining the concept behind it. What sources do you recommend referring to aside from this video and your book?

Hello, I hope this message finds you well. I would greatly appreciate a few minutes of your time for a constructive conversation regarding your Udemy course. If possible, would you be available for a quick call? Thank you in advance for considering my request.

This video is missing several key aspects of explaining TBM and its practical considerations.

please next time only put up a video with 10,000 trades mininum and 20+ assets

Thanks for the clear and easy to understand lecture. ❤❤

Random comment, I hear your South African accent lol. Much love from SA!

So, is this approach correct? First, we train the primary model using historical data. Then, we train a secondary model to identify false positives. For instance, if the primary model predicts a positive outcome, we rely on the secondary model to assess whether to trust that prediction or not. We don't apply this process to negatives since it doesn't impact our pnl. However, considering market stationarity and other factors, I'm unsure how to effectively evaluate these models. Traditional train-test methods are flawed due to the non-stationary nature of the market, and it seems we lack reliable tools to prevent overfitting.

Quality stuff, thank you

could you give an idea about the architecture used for DQN?

Somebody's been quite busy in last few hrs :D I am not complaining, good to see quality content after a big break :) Excited for more.

You know, it's a shame that clickbait financial videos have millions of views, when videos like this are where the real money is

These eastern Europeans barely can string an Articulate sentence

Understand the rapid development timeline of large language models and its implication for open-source initiatives. 0:25 Adopt low-rank adaptation (Lora) and quantization techniques to make the training of large language models more accessible. 5:59 Explore FinGPT as a potential framework for generating financial large language models, despite its current limitations. 10:00 Recognize the significance of data quality over data scale in fine-tuning large language models. 13:14 Consider creating open, goal-specific, fine-tuned financial large language models to advance the field beyond the capabilities of closed models like Bloomberg GPT. 18:18 Publish and share data sets and trained model weights to democratize the development of financial large language models. 21:32 Challenge proprietary control over large language models and contribute to the open-source development to ensure equitable access and innovation. 21:53

they just wanted to get published in NeuralIPS conf. and get some media attention. FinGPT is a crap. I looked at the code. I can develop that in a week. But what is the point of developing such a useless open source which solves nothing. Bloomberg GPT is also crap if any one evaluated it in detail.

Problem. Can it replicate radial asymmetry and association strength being an increasing function of volatility and financial distress

You have mentioned machine learning many times in the video. Isnt Pairs trading based on Statistics (White Box Models) not on Machine Learning (Black Box Models)?

If the beta is calculated by the MLL then why overfit the ou model with another calculate😂

Truly enjoy watching your research and analysis, keep it going .

I think the project has advanced in the last couple of months.

🎯 Key Takeaways for quick navigation: 00:00 🤖 演讲者介绍与金融数据结构概览 - Valeriia Pervushyna介绍金融数据类型和构造方法 02:36 📈 金融数据类型的分类与特性 - 基础、市场、分析和替代数据的优缺点分析 04:10 🔄 市场数据的研究潜力 - 市场数据如何揭示交易策略的机会 07:40 📊 数据条形的应用与分类 - 数据条形对机器学习研究的贡献和不同类型的介绍 11:05 ✨ 不平衡条形与运行条形的应用 - 不平衡条形和运行条形如何帮助预测市场变化 13:12 📝 金融数据处理的实用技巧 - 实践技巧和使用美元条形的优势 19:00 🏆 研究结论与工具推荐 - 强调原始数据的价值和数据结构优化的方法 21:36 📚 学习资源与订阅渠道 - 提供学习资源和进一步研究的指引- 00:00 🤖 金融数据结构讲座和演讲者介绍 - 讲座将探讨金融数据类型和微观结构 01:10 📚 数据结构对金融机器学习的影响 - 数据结构在金融定量分析中扮演关键角色 02:36 📈 主要金融数据类型的分类与特点 - 基础数据、市场数据和替代数据等的分类讨论 04:10 🔄 市场数据在交易策略中的应用 - 通过市场数据揭示交易算法的特点 07:40 📊 数据条形的分类及其在金融数据中的作用 - 标准条形与信息条形在数据处理中的应用 13:12 📝 数据处理的实用技巧与结论 - 使用美元条形进行研究的优势和技巧 19:00 🏆 研究工具推荐与演讲总结 - 介绍研究工具并强调原始数据的价值 21:36 📚 推荐的学习资源和资料 - 提供学习资源并鼓励观众深入研究 Made with HARPA AI

great knowledge! thank you!