Show HN: Pumbaa – A portfolio simulation tool for passive, index investments Hi HN, About a year ago I wrote a few scripts and spreadsheets to calculate the historical performance of various passive investment portfolios. There is portfoliovisualizer.com but if you are not from the US it’s less relevant because everything is calculated with respect to the US dollar, and you cannot combine funds from other countries (say from the EU). In the past month I managed to turn it into a web app with the goal of giving individuals better tools for financial education. It allows you to build a portfolio composed of a few index funds (ETFs or mutual funds) and simulate its historical performance. It currently works with 7 currencies and includes funds from the US, EU, UK, Canada and Israel. Intentionally, only broad-market, passive index funds are included. If all of this doesn’t mean much to you, you can visit the examples page to get a taste of some interesting portfolios. Here is an interesting example: For the past 22 years, US long-term treasuries had very similar returns to the S&P 500 (cherry-picked dates, but still interesting) [1]. On the technical side: The backend is a simple Flask app. A task runs once a day in the background to fetch the latest prices of ETFs and mutual funds from 20+ sources (central banks, stock exchanges). Frontend is Tailwind for CSS and a bit of native Javascript with d3. Everything runs on a $10/mo VPS. This is my first web app and there is still a lot of room for improvement so any feedback is welcome. Thanks in advance! [1] https://ift.tt/21y0ikz… https://pumbaa.app/ February 11, 2022 at 08:15PM

from Blogger https://ift.tt/06T5jnH
via IFTTT

Show HN: Msgspec – a fast JSON/MessagePack library for Python https://ift.tt/KNexwWE February 10, 2022 at 11:57PM

from Blogger https://ift.tt/rOKzDJP
via IFTTT

Show HN: Zingg – open-source entity resolution for single source of truth Hello HN, I am Sonal, a data consultant from India. For the past few months(and years!), I have been working on an entity resolution tool to build a single source of truth for customers, suppliers, products and parts. Here is a short demo of Zingg in action https://www.youtube.com/watch?v=zOabyZxN9b0 As a data consultant, I often struggled to build unified views of core entities on the datalake and the warehouse. Data spread across different systems has variations and consistencies making Customer 360, KYC, AML, segmentation, personalization and other analytics difficult. As I talked with different clients facing this issue, I searched for existing solutions which I could use or recommend. Unfortunately, most of them were very expensive MDM solutions like Tamr, or CDP solutions like Amperity. There were many open source libraries, but they did not tie well into the datalake/warehouse scenarios we were working with, did not scale and/or needed a decent bit of programming or did not generalize. I even tried to build something internally and failed miserably, and that got me hooked 🙂 As I dug deeper into the problem, I realized that there were multiple challenges. Data matching, at its very core, becomes a cartesian join, as you need to compare every pair of records to figure out the matches. With millions of records, this becomes extremely tough to scale. I referred to various research papers and then implemented a blocking algorithm to overcome this. More details at https://ift.tt/6o5PFhz The second challenge was to say which pairs are a match. I wanted to have a machine learning-based approach to handle the different types of entities and the variety of differences in real world data. But I also felt that non ML experts should be able to use Zingg easily, hence took the approach of abstracting the feature generation and hyper-parameter tuning for the classifier. Once I settled on the ML approach, the problem of training data quickly arose, which led me to pick up active learning and build an interactive labeler through which sample records can be marked as matches and non matches to build training sets quickly. I still feel that we should have an unsupervised approach as well, but I have not yet figured out the right way to do so. The Zingg repository is hosted at https://ift.tt/YKIHOz9 and we have close to 60 members on our Slack(https://ift.tt/96rscu8). We are now two developers working full time on Zingg!!! I am super happy that early users have been able to use Zingg and push us to build more stuff – model documentation, using pre-existing training data, native Snowflake integration etc. I have been an open source consumer all my dev life, and this is the first time I have made a decent contribution. It is my first time trying to build a community as well. Not sure how the future will unfold, but wanted to reach out to the community here and hear what you think about the problem, the approach, any ideas or suggestions. Thanks for reading along, and please do post your thoughts in the comments below. February 9, 2022 at 10:40PM

from Blogger https://ift.tt/L6Gtn5j
via IFTTT

Show HN: Open Policy Registry: a Docker-inspired workflow for OPA policies https://ift.tt/G3OiTnV February 9, 2022 at 10:31PM

from Blogger https://ift.tt/tgjKNbQ
via IFTTT

Show HN: Never have an SSL certificate expire again https://ift.tt/rzK5hix February 9, 2022 at 12:16AM

from Blogger https://ift.tt/NyZE8cd
via IFTTT