Alternatives
Products that do what CraftersWealth does
The SIP Experience for Direct Equity
- 1WP
Thank you for your comments, just some context: - The app is a simple desktop application that works on macOS, Windows, and Ubuntu. - I developed this app for my own needs. Getting tired of SaaS app subscriptions and privacy concerns. - For now, the activities are logged manually or imported from a CSV file. No integration with Plaid or other platforms. - No monetization is planned for now (only a "buy me a coffee" if you use and appreciate the app).
2024 · wealthfolio.app
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Hey everyone, we’re JJ and Mark from Double (https://double.finance). Over the past few months we’ve been working on an investing app that lets anyone design and invest in their own stock index. Start by picking one or more strategies. You can find 20+ starting points in Double that vary from direct index versions of classic ETFs (like SPY) to strategies focused on specific industries, market trends, or themes (like YC public companies). You can also easily build your own grouping of stocks, and tilt your strategy towards or away from certain stocks or sectors. Once you’ve chosen…
2024
- 6IJ
Backstory: I wanted to play with intraday stock data but couldn't find a free dataset anywhere. IEXCloud [1] offers API access to 1-minute granularity intraday historical price data, but I was worried that it could get expensive or unwieldy to build up a substantial dataset via API calls. Plus, IEX gives out their raw data for free. I probably should have just used the IEXTools python library [2] to parse IEX's raw data dumps, but I was working on a Julia project, so it felt more thematically appropriate to build a new tool from scratch. I haven't been actively using InvestorsExchange.jl a…
2022 · github.com
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2019 · eqzen.com
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My main motivation was that I wanted to be able to drill down and filter across all the available stocks, look at the data for myself, and narrow down on the stocks I am interested based on my own sets of criteria, and make data-driven analysis for my personal investment strategies. I used PostgreSQL as the backend database for ELT data pipelines, and used Citus Data cstore_fdw for columnar compression for the final dataset. All financial data is coming from SEC Edgar, https://www.sec.gov/developer. I used Python for downloading most of the data. I also run the data load…
2023 · tesseractanalytics.ai
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Hey HN, As someone who geeks out on both investment data and privacy tech, I built Capitally to scratch my own itch. It's encrypted on-device so that once I can afford to hire a second engineer, he cannot peek into my own data! I wanted a way to monitor ALL my investments (stocks, crypto, real estate, angel investments, etc.) in one place and really dig into the data - but without compromising my privacy. Here's how Capitally makes that possible: On the data side: Import data from CSVs, spreadsheets, or any other source via a flexible no-code editor Map any data format into a standardized…
2024 · mycapitally.com
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Hey HN, I’m Dan. I’ve been working on forecasting for the last six years at Google, then Metaculus, and now at FutureSearch. For a long time, I thought prediction markets, “superforecasting”, and AI forecasting techniques had nothing to say about the stock market. Stock prices already reflect the collective wisdom of investors. The stock market is basically a prediction market already. Recently, though, AI forecasting has gotten competitive with human forecasters. And we’ve found a way of modeling long-term company outcomes that is amenable to our forecasting approach. Iteration by…
Nov 2025
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Hi HN, We’re a small team working on 13Radar.com, which we launched about two weeks ago after 4 months of development. I’m the founder, and together with the team we’re building a platform that tracks hedge fund portfolios in real-time based on SEC Form 13F filings. AI has been a major helper in our workflow. For a single webpage, we often consult multiple AI systems in parallel, generating different versions and comparing them side by side before deciding on the final design or implementation. More than 60% of the research, design, and coding involved AI assistance. For UI design we used…
Nov 2025 · 13radar.com
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2019 · eqzen.com
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Post-Opus 4.6, LLMs feel much better at using bash, code, local files, and tools. So I kept coming back to a simple question: if a model can use a computer reasonably well, why can’t I just give it my broker account, a strategy, and let it trade? My conclusion is that the blocker is not model capability in the abstract. It is the system around the model. A raw LLM breaks on a few practical things almost immediately: • no persistent operating memory across sessions • no trustworthy record of what it did and why • no hard approval boundary before money moves • no cheap always-on monitoring if…
Mar 2026 · github.com
- 22PW
Problem: If you have less than $100k to invest, you get a robo-advisor that asks you 5 questions and dumps you into one of three cookie-cutter portfolios. If you have more than $100k, you get a human advisor who charges 1-1.5% annually to... basically do the same thing with a smile and calming voice attached. Meanwhile, institutional investors get custom strategies built around specific durations, target dates, tax situations and actual investment goals. Not because the math is harder—but because the economics only work at scale. Here's the thing: Both traditional advisors and robo-advisors…
2025 · fulfilledwealth.co
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