Napoleon Hernandez’s Post

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Napoleon Hernandez

Machine Learning Quant | eTrading | Entrepreneur

Generative AI will lead to a disruption of the likes we haven't seen since the internet became mainstream. Following the money is important for investors and disruptors alike. There are massive opportunities awaiting those who want to participate in it.

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Kelvin Mu

Early Stage Investor @ Translink Capital

This one took a while to put together but here it is: A list of top investors in generative AI today. A few things to call out: -- Obviously, this is just a select list. There are many other great investors worth mentioning but there's simply not enough room. Note that I have also left out accelerators/incubators. -- This is not meant to be 100% accurate or comprehensive. My apologies if I have inadvertently left out any companies or made a mistake. -- The sources for this list include Pitchbook, Crunchbase, and investor websites. Lastly, let me share a little bit about my firm Translink Capital and our investment focus: -- While we aren’t featured on this list today, we are actively exploring investment opportunities in this space. We prefer to invest at the Series A stage, although we will occasionally participate in seed or later-stage rounds. Our firm is backed by 30+ Asian multinationals, and we have a proven track record of connecting our portfolio companies to Asia, among other things. If you think there might be a mutual fit, feel free to get in touch. [UPDATE] If you would like the PDF, I have attached it to a new post. Please feel free to download directly.   #artificialintelligence #generativeai #startups #venturecapital #VC #AI #ML #machinelearning

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  • View profile for Napoleon Hernandez, graphic
    Napoleon Hernandez

    Machine Learning Quant | eTrading | Entrepreneur

    I cannot express how much I agree with this post. For years there has been talk within the Finance community to be "REST first" without thought about alternatives and trade offs. REST APIs are great within the right context but just like any other technology they are not "silver bullets". I can remember plenty of times when I wondered why data vendors were so keen in forcing their customers towards awkward process flows to collect their data for no good reason.

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    Lou Lindley

    Head of Quantitative Analytics at Databento

    Most people should stop writing RESTful APIs for financial data. I think this trend is driven by three reasons: 1. Many newer fintech firms were inspired by the crypto exchange wave and just copied these crypto exchanges' REST APIs blindly, without having seen best practices in mature asset classes. 2. There's a lot of tooling, standards, and support around RESTful APIs, with OpenAPI/Swagger, Redoc, etc. so it's an easy default. 3. There are many high quality, openly-documented web APIs based on REST, like Stripe and Twilio SMS, so it's easy to copy them blindly too. Folks on Databento's API team, myself included, have worked with many internal APIs at top trading firms and proprietary vendor APIs, and we've rarely seen RESTful APIs in use aside from powering simple dashboards or click-trading interfaces. REST at its core is about manipulating entities. For example, a use case that's mostly CRUD on elementary resources, like a dashboard backend API for adding users, deleting them etc., is a very good fit for REST. Idiomatic REST APIs avoid verbs in their endpoint paths as HTTP verbs like GET are sufficiently expressive for basic CRUD operations. But more abstract mutations like subsampling, space, merging—which are essential in electronic trading—are hard to express in this manner. In the words of my colleague Renan Gemignani: once you start doing things involving multiple resources (e.g. datasets, venues, symbology type, schemas, formats, symbols) at once and across multiple domains (e.g. resolve symbols, fetch this range and merge these CUSIPs and then submit this batch job with output symbols remapped to native exchange IDs), your REST endpoints become either unnecessarily complicated, too rigid, or unexpressive. It's awkward to force entities like schemas (top-of-book, MBO, OHLCV, etc.), symbols, dates, etc. to become resource-oriented. Neither do they really behave as simple sorts and filters that typically get treated as query parameters under RESTful convention. What are some indirect symptoms of your vendor fighting REST conventions? I've seen several, e.g. - Their API forces you to query 1 symbol or 1 date at a time. - Their API has a much larger API surface than a raw feed's wire protocol. - Their API introduces nearly duplicate interfaces to deal with different asset classes or multi-symbol and full-venue scenarios. - Their API has a disorderly, deeply-nested structure of URL parameters. - It's unpredictable how more abstract mutations like subsampling, space, merging are expressed. In our case, we opted for a more flexible, RPC-style HTTP API. A good HTTP API doesn't have to be RESTful. Slack's API is a great example of a HTTP API that isn't—it's based on a JSON RPC style instead. gRPC has a fallback protocol based on protobufs over HTTP. The end result is an API with a small surface of orthogonal methods and very little overlap between them. #restapi #http #apis #marketdata #algotrading #crypto

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  • View profile for Napoleon Hernandez, graphic
    Napoleon Hernandez

    Machine Learning Quant | eTrading | Entrepreneur

    Today I attended Bloomberg's sell side leaders forum where industry practitioners and BBG leadership spoke about the future of trading using AI and other emergent technologies in the front office. It was exciting to hear from others how the industry is moving towards efficiencies where humans and machines play unsure unique rules while creating synergies. Interestingly most examples were crafted around the experience of Fixed Income and Credit traders, indirectly signaling the shift of these market towards a digital model. #trading #future #ai #bloomberg #tds

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  • View profile for Napoleon Hernandez, graphic
    Napoleon Hernandez

    Machine Learning Quant | eTrading | Entrepreneur

    What is one of the key drivers to reach success at work, relationships and even LLMs? Always strive to ask the right questions! Figuring them out is more than half the battle. More often than not we look at limited choices and as a result we get limited outcomes. Re-framing with a curious mind allow us to find new answers in places that we thought were already closed to us.

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    Alexander Dagres

    ⚠️ Here before #Web3 becomes mainstream || The Community Manager that you didn't know you need 👉👈

    easy peasy

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  • View profile for Napoleon Hernandez, graphic
    Napoleon Hernandez

    Machine Learning Quant | eTrading | Entrepreneur

    As Peter Drucker famously said "If you can't measure it, you can't manage it". And if on top of that turns out that "no plan survives first contact with the customer" (borrowing from a famous military saying) it is easy to see why it is so important to define SMART KPIs and track them. Picking the right one is an art and not a science. To a degree it doesn't matter much which one is picked, as long as it is relevant, measurable and is related to business success. As business mature the KPIs of importance change and that is OK.

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    Napoleon Hernandez

    Machine Learning Quant | eTrading | Entrepreneur

    Most, if not all, of these practices are not critical only in academia, but for practitioners in the industry as well. Overfitting or biasing a model in a critical field such as Finance or Healthcare has to be avoided proactively and on every situation.

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    Bruno Neri

    Technical Leader - Artificial Intelligence and Deep Learning Enthusiast - Senior Software Engineer at ALTEN Italia

    An updated must-read version of Michael Lones' amazing paper "How to Avoid the Pitfalls of Machine Learning: A Guide for Academic Researchers" "This document is a concise outline of some of the common mistakes that occur when using machine learning, and what can be done to avoid them. Whilst it should be accessible to anyone with a basic understanding of machine learning techniques, it was originally written for research students, and focuses on issues that are of particular concern within academic research, such as the need to do rigorous comparisons and reach valid conclusions. It covers five stages of the machine learning process: what to do before model building, how to reliably build models, how to robustly evaluate models, how to compare models fairly, and how to report results." Paper: https://lnkd.in/dyp7iEZJ #machinelearning

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  • Napoleon Hernandez reposted this

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    Ken Shih Ken Shih is an Influencer

    Head of Wealth Management GC at Saxo Markets | Reimagining the Wealth Management experience |

    💰US$4 BILLION💰is the 'pay check' Citadel hedge fund manger Ken Griffin took home in 2022 🤯 His hedge fund BEAT US stocks by +58% 📈 in 2022. 3️⃣ INSIGHTS on the hedge fund winners and losers. Even though global stock markets had it rough last year with US stocks down -19% in 2022, there were a number of hedge funds that continued to OUTPERFORM. According to Bloomberg the top 15 highest earning hedge fund managers in 2022 on average returned +35% (an outperformance of 54% against US stocks). Payday wise, the top 15 hedge fund managers collectively earned US$14 BILLION 🤯 3 insights: 1️⃣ The best performing hedge funds in 2022 had multi-strategy, macro or quantitive investment strategies These strategies benefitted in 2022 due to the Federal Reserve hiking interest rates at its most aggressive pace since the 1970s. "Multi-strategy" as the name suggests, is one which employs more than one type of investment strategy, typically across many different asset classes. The investment objective of multi-strategy is to deliver consistently positive returns regardless of the directional movement in stocks, interest rate or currency markets. Citadel would be a good example as the world's largest hedge fund (US$62 billion assets) and multi strategy fund was up +38% in 2022. 2️⃣ BUT some funds also utilised HIGH amounts of leverage "Leverage" is an investment strategy of using borrowed money to increase your returns. They borrow money to invest with the intention that their return will EXCEED their borrowing cost. Haidar Capital a global macro fund did just that to return +193% 📈 in 2022. "Global macro" is a strategy that make investment decisions and aims to profit from massive economic and political changes. They analyse macroeconomic trends then take focused 'bets' on interest rates, sovereign bonds, and currencies. Haidar 'only' manages US$1.2 billion but reported assets of US$62 billion which means they were utilising A LOT of borrowed funds. Haidar bet big that interest rates would rise rapidly and correctly profited from the surge in inflation that led to the most aggressive central bank tightening pace in the last 40 years. 3️⃣ There were losers as well with Tiger Global's manager Chase Coleman LOSING US$1.7 billion 📉 in 2022 🤯 According to Bloomberg a big reason Tiger Global’s fund did poorly in 2022 were due its 'bets' on China, tech stocks and private startups that DID NOT pan out. Tiger Global was DOWN -56% in 2022. To be fair Tiger Global had massive gains especially in 2020 up +47% and its manager Chase Coleman taking home US$3 BILLION in pay that year 🤯 What's your view of these hedge fund returns and manager paydays? - #markets #wealth #hedgefund - Hit the "follow" button in my profile to receive helpful daily market and wealth related insights like this -> Ken Shih - Click the 🔔 in my profile to get notified of my posts - DM me if you have investing or wealth questions. Source: Bloomberg

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  • Napoleon Hernandez reposted this

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    Avi Chawla

    Founder @ Daily Dose of Data Science (82k readers) | Follow to learn about Data Science, Machine Learning Engineering, and best practices in the field.

    I reviewed 1,000+ Python libraries and discovered these hidden gems I never knew even existed. Here are some of them that will make you fall in love with Python and its versatility (even more). Please read the full list here: https://bit.ly/py-gems 1) PyGWalker: Analyze Pandas dataframe in a tableau-like interface in Jupyter. Link: https://bit.ly/pyg-walker 2) Science plots: Make professional matplotlib plots for presentations, research papers, etc. Link: https://bit.ly/sciplt 3) CleverCSV: Resolve parsing errors while reading CSV files with Pandas. Link: https://bit.ly/clv-csv 4) fastparquet: Speed-up parquet I/O of pandas by 5x. Link: https://bit.ly/fparquet 5) Dovpanda: Generate helpful hints as you write your Pandas code. Link: https://bit.ly/dv-pnda 6) Drawdata: Draw a 2D dataset of any shape in a notebook by dragging the mouse. Link: https://bit.ly/data-dr 7) nbcommands: Search code in Jupyter notebooks easily rather than manually doing it. Link: https://bit.ly/nb-cmnds 8) Bottleneck: Speedup NumPy methods 25x. Especially better if array has NaN values.  Link: https://bit.ly/btlneck 9) multipledispatch: Enable function overloading in python. Link: https://bit.ly/func-ove 10) Aquarel: Style matplotlib plots.  Link: https://bit.ly/py-aql 11) Uniplot: Lightweight plotting in the terminal with Unicode. Link: https://bit.ly/py-uni 12) pydbgen: Random pandas dataframe generator. Link: https://bit.ly/pydbgen 13) modelstore: Version machine learning models for better tracking. LinkedIn: https://bit.ly/mdl-str 14) Pigeon: Annotate data with button clicks in Jupyter notebook. Link: https://bit.ly/py-pgn 15) Optuna: A framework for faster/better hyperparameter optimization. Link: https://bit.ly/py-optuna 16) Pampy: Simple, intuitive and faster pattern matching. Works on numerous data structures.  Link: https://bit.ly/py-pmpy 17) Typeguard: Enforce type annotations in python. Link: https://bit.ly/typeguard 18) KnockKnock: Decorator that notifies upon model training completion. Link: https://bit.ly/knc-knc 19) Gradio: Create an elegant UI for ML model. LinkedIn: https://bit.ly/py-grd 20) Parse: Reverse f-strings by specifying patterns. Link: https://bit.ly/py-prs 21) handcalcs - Write and display mathematical equations in Jupyter Link: https://bit.ly/py-hcals 22) Osquery: Write SQL-based queries to explore operating system data. Link: https://bit.ly/py-osqry 23) D3Blocks: Create and export interactive plots as HTML. (Matplolib/Plotly lose interactivity when exported). Link: https://bit.ly/py-d3 24) itables: Show Pandas dataframes as interactive tables. Link: https://bit.ly/py-itbls 25) jellyfish: Perform approximate and phonetic string matching. Link: https://bit.ly/jly-fsh That’s a wrap!! What cool Python libraries would you add to this list? 👇 Drop your suggestions in the replies below 👇 👉 Check out my daily newsletter to learn something new about Python and Data Science every day: https://bit.ly/DailyDS.

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  • View profile for Napoleon Hernandez, graphic
    Napoleon Hernandez

    Machine Learning Quant | eTrading | Entrepreneur

    One of the most concise and complete guides I have seen on #machinelearning

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    Nicola D.

    A Brief Introduction to Machine Learning for Engineers Are you looking for a LinkedIn post based on this text? If so, here's a suggestion: Are you looking to learn more about the key concepts and algorithms in machine learning? Check out this monograph, which provides a concise yet comprehensive introduction to the field. With a focus on probabilistic models for supervised and unsupervised learning, this resource offers a unified notation and mathematical framework for building on first principles and exploring more advanced topics. Whether you're an engineer with a probability and linear algebra background or simply interested in learning more about machine learning, this monograph is a great starting point. #MachineLearning #DataScience #Engineering #AI

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