Flat Fee Consulting, LLC’s Post

🚀 Excited to announce that I've completed the Application of Machine Learning in Finance certification! This journey has equipped me with cutting-edge skills in data analysis, predictive modeling, and algorithmic trading. Ready to leverage these insights to drive innovation in the financial industry! 💡📈 #MachineLearning #Finance #ContinuousLearning #DataScience #FinTech

We all know around here that the mathematics and computational models used in AI (ANNs) and ML (statistical models based mainly on either Bayesian or Markov chains) have the serious limitation of needing to ingest huge amounts of data and undergo long cycles of training to converge with some meaningful output. Hence, their limitation in real systems, which require continuous learning and retraining cycles: AI and ML are useless for prediction in markets with significant volatility, where patterns are probabilistic, not linearly separated clearly in signal and noise because noise and signal are not linearly combined in a probabilistic, most of the time non-Gaussian, manner, given that the noise is indeed mixed with some part of the signal (so you have here indeed an overlapping of probabilistic distributions where normality fails most of the time). We have been working on this problem for some years, and now, together with Roberto Crespo and Alfred Dietrich Steiof, we are nearly ready to launch a new distributed system which introduces a novel learning dynamic model able to relearn on the fly with ongoing market changes, both those continuous distributed (price/volume smooth swings and drifts) and discontinuous (breakouts). The method, contrary to traditional methods in AI and ML, involves filtering the signal contained in the noise with its two types of errors (false positives -risk management- and false negatives -profit management-) that can be further separated by taking a discrete variable a posteriori, which is tested in the next stage for further refinement of those two types of errors. In this way, we can separate very fast the errors from the noise to increase the signal by amplifying the discrete variable when noise is significantly greater than the signal, in a flow we called fast adaptive learning. Of course, we have introduced other mechanisms that can be conceptualized as backward and forward feedback flows, which can be dynamically composed atomically like morphisms without interfering with the global scope (Category Theory stuff) , but this is a story that would need several posts to summarize

TradingView Chart Tutorial for Beginners - https://lnkd.in/dRhdbTny

Both the spot market and the futures market lack a sound and reliable mathematical model to evaluate pricing for given intervals in real-time markets. We do not have a mathematical and scientific method equivalent to the Black-Scholes equation , used to estimate the pricing of options (European options), with which we can assess the pricing in real-time markets of our assets and securities, especially in hyper-volatile novel markets like the cryptocurrency market. This absence of objective pricing in spot and futures markets is why, until now, we have not had a universal, scientifically-based risk management kernel to truly outperform the results of the spot and futures markets themselves by diminishing the risk associated with hyper-volatile real-time pricing fluctuations. Consequently, the best strategy, as we all know here, has been to hedge the volatile, unsecured assets or securities with derivatives and options, trading the risk asset by dealing with puts and calls of the option itself. This lack has hindered and impacted hyper-volatile markets with bubbles and manipulation of volumes in the renowned pump and dump strategy. Without a reliable, or at least approximate, method to estimate pricing—since there's no direct way to manage risk—the manipulation of these prices could be achieved through manipulation with relatively small volumes, creating bubble and burst cycles that hinder the crypto market itself in the middle and long term, leading to a continuous reset and new start with new investors: known among us as the crypto Groundhog Day. Over the last seven years, we have endeavored to develop our innovative risk-management kernel as part of our CryptoFisher platform (in the prelaunching phase with Roberto Crespo and Alfred Dietrich Steiof ) with which we could assess the pricing in the crypto real-time market through continuous pricing exploration with adaptive learning. This employs a new learning paradigm that, in contrast to deep learning and ML, doesn't require terabytes or even petabytes of data and long-term training. Instead, it continuously discovers new pricing conditions of the market through an uninterrupted novel learning process that significantly outperforms the market by enhancing the two types of bayesian success, achieved by first reducing the two types of associated bayesian errors. Our process programmatically uses a Hamiltonian pipeline in adaptive learning stages to increase the signal (successes) by first diminishing the noise (errors). The three sets of four Bayesian random outcomes are fed back (back- and forwards) into the system at every stage of the Hamiltonian pipeline with two discrete variables. The final outcome is very accurate instant pricing on the spot market (we have yet to test it in future markets) after removing most of the noise, quantified by two discrete variables, in every one of the three stages of the Hamiltonian pipeline.

Peering over the horizon to envision the future through to 2035 for CPA Australia ‘s INTHEBLACK, one thing was crystal clear to me - the future of accounting is bright! Why? Because at its heart, accounting is how you understand and run a successful business. While AI and automation will increasingly handle the number-crunching, accountants (ably assisted by AI 'co-intelligence') are the ones who turn data into meaningful action, combining technological expertise with strategic insight. 🤝💡 ✨ What does your crystal ball tell you about the future landscape of accounting? Share what you foresee in the comments! 👇 #Accounting2035 #FutureReady #AIandSustainability #BusinessStrategy CPA Australia UNSW Business School ABDC Communications, Leslie Falkiner-Rose

We are back on the learning train! Just completed LangChain: Chat With Your Data, a dynamic short course exploring Retrieval Augmented Generation (RAG) and chatbot development by DeepLearning.AI. Delved into data loading fundamentals, document splitting, vector stores, advanced retrieval techniques, question answering, and chatbot building using LangChain tools. Ready to apply newfound knowledge to practical data interactions! 🔗 : https://lnkd.in/gm7WxbKE #LangChain #LLMs #DataChatbot 🚀

LLMs as Recommendation Engines: Stock Market / Portfolio Management Case Study https://lnkd.in/gtUuKZxg Not only for stock recommendations (when and what to buy/sell, at what prices) but to automatically manage a well-balanced portfolio depending on your goals: retirement, fast growth, and so on, with a well-balanced mix that matches your intent.  This hands-on workshop is for developers and AI professionals, featuring state-of-the-art technology, case studies, code-share, and live demos. Recording and GitHub material will be available to registrants who cannot attend the free 60-min session. You’ll learn: - The fundamentals of Large Language Models (LLMs) and their application in financial services. - How to build personalized portfolio recommendation engines using LLMs. - Strategies for integrating LLMs into existing financial services infrastructure. - Insights into the benefits and challenges of using LLMs for financial recommendations. - A live demonstration of creating a personalized portfolio recommendation engine. Register at https://lnkd.in/gtUuKZxg #llms #fintech

SW and even HW design and development in recent times can be summarized as tool-fetishism with almost an absence of decent mathematical abstraction behind it. On the other hand, in the realm of mathematics, abstract algebra, and powerful abstractions fetishism, we have the opposite, there is a lack of tooling to be applied to real problem concerns (languages like Haskell have popularized a small subset of that, still a small reflection of the powerful extent of abstract algebra). And because programming tooling lacks decent mathematical abstraction, all are ludicrously plagued with leaky weak abstractions that finally lead to the broken and overengineered systems that are the hallmark of the modern IT industry. Our Risk-Management Kernel design for our Cryptofisher System (nearly ready to be launched together with Roberto Crespo and Alfred Dietrich Steiof )has taken enormous lessons from this. With these lessons in our minds, we have faced the problem not by thinking to solve it with programming tooling, as is the norm in the IT industry, but by thinking with a domain-knowledge perspective and how it can be addressed mathematically both in terms of abstraction and numeric calculation by running controlling experiments over an exhaustive set of real market scenarios and Monte Carlo simulations. With this, we can come up with our first useful mathematical abstractions. Successive experiments led by these abstractions led us to discover a way to design our risk management kernel: with the powerful mathematical abstraction of a #Hamiltonian. A Hamiltonian is used in classical and quantum physics for computing the evolution of energy in a system, typically towards a minimum amount of energy. H = Potential + Kinetic energy. Our results led us to design a Hamiltonian that replaces the Energy with Signal and Noise Bayesian probabilities quantized with discrete variables. So then all that was left for us was to figure out how to program our Hamiltonian system and its evolution according to a simple addition of single Hamiltonians for every component: H = Hsignal + Hnoise + Hinteraction. What have we won with this? The very optimistic expected possibility to decompose the nightmare interaction between noise and signal (the Achilles' heel in most trading systems) into simple additive components Hinteraction = Hsignal + Hnoise In a composite manner, and the goal to reduce the noise-dependent components to a minimum.

🎉 Calling all finance enthusiasts! 🎉 Don't miss our upcoming webinar on "Implementation of Machine Learning in Momentum Trading" - https://bit.ly/4cJ0PXp 📅 Save the Date: April 16, 2024 🕤 Time: 9:30 AM ET | 7:00 PM IST | 9:30 PM SGT 🔍 Dive deep into the Implementation of Machine Learning in Momentum Trading! Unleash the power of machine learning in trading as we explore: ✨ Various momentum trading strategies ✨ Integrating ML seamlessly into trading practices ✨ Practical applications with real-world examples 🔍 Discover how to: 📊 Implement ML-based classifiers and clustering algorithms 📉 Improve ML models for better trading outcomes 🛡️ Manage risks effectively with ML 🔥 Don't miss out on: 🔄 Traditional vs. advanced time series momentum trading 📊 Cross-sectional momentum trading approach 💬 Engage in interactive Q&A sessions with industry experts! Don't let this opportunity slip away. Register now and take your trading strategies to new heights! 💼✨ #MachineLearning #Algotradingwebinar #webinar #MLfortrading #momentumtrading

📌"Celebrating a new milestone achieved! 🎉 Just received my 𝙏𝙝𝙚 𝙁𝙪𝙩𝙪𝙧𝙚 𝙤𝙛 𝙋𝙖𝙮𝙢𝙚𝙣𝙩 𝙏𝙚𝙘𝙝𝙣𝙤𝙡𝙤𝙜𝙞𝙚𝙨 certificate and couldn't be more thrilled. Here's to the journey of continuous learning and growth! 💫 #Certified #payments #cash #fintech #banks #cashmanagement #bankingasaservice #paytech #scrummaster #agilecoaching #productowner