Alternatives
Products that do what DDUBUCK Universe Financial Data does
Long-form fundamentals with rule-based market signals
- 1IQ
Quick background: I used to code. Studied it in school, wrote some projects, but eventually convinced myself I wasn't cut out for it. Too slow, too many bugs, imposter syndrome — the usual story. So I pivoted, ended up as an investment associate at an early-stage angel fund, and haven't written real code in years. Fast forward to now. I'm a Buffett nerd — big believer in compound interest as a mental model for life. I run compound interest calculations constantly. Not because I need to, but because watching numbers grow over 30-40 years keeps me patient when markets get wild. It's basically…
Jan 2026 · calquio.com
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- 4IB
Hi HN, I'm a solo dev and for the last few months I've been building Hikaro, a tool to find statistically significant trading signals for [e.g., US equities, crypto, forex]. I built this to solve my own problem: I was tired of backtests that looked great on paper but failed in practice. Simple metrics like "win rate" can be misleading, so I wanted a way to quickly tell if a signal's performance was genuine or just noise. Hikaro ingests daily market data and runs statistical analysis on various trading signals. The goal is to surface signals with strong properties, like: Low p-value: Evidence…
Sep 2025 · hikaro.app
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- 6TI
Hey HN, For the past few years, I've been obsessed with trying to get AI to produce accurate trading signals. The main challenges I found with AI trading models are lack of consistency, context window bottlenecks, hard to backtest, and high cost. Asking ChatGPT "Should I buy Bitcoin today?" doesn't work well because the LLM doesnt have a set trading strategy to opperate from. In addition, the small context window makes it challenging to fit enough historical data into. Not to mention it gets very expensive as well. My solution is a hybrid approach. Instead of having the LLM make direct…
2025 · trend.fi
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Hey HN, we built an Econ+Finance database to let AI agents do investment research. We spend a lot of tokens to organize macro releases and SEC filings into a clean format, so that your agents have more context to do actual analysis. The problem AI agents are great at data analysis. But they become ineffective if most of their context window is spent on gathering and cleaning data, instead of validating hypotheses. Data in the wild is messy and rarely standardized. Definitions and measurements change over time. This problem is compounded by a fragmented data universe. Point solutions exist…
Jul 2026 · github.com
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I’ve been experimenting with a graph-based approach to a classic trading problem: why most dip-buying strategies can’t tell the difference between a temporary overreaction and a genuine structural collapse. Most systems treat a −5% move the same regardless of context. My hypothesis was that where a company sits in the market’s structure matters more than the price move itself. The engineering idea I built a knowledge graph of the U.S. public markets with ~207k edges across ~21 relationship types, organized into four layers: Operational: supply-chain relationships (SUPPLIES_TO, PRODUCES)…
Dec 2025
- 11FF
Hey HN, I'm Steve, co-founder of Factor.fyi, a new data platform for querying and visualizing financial datasets using SQL. During the pandemic, I took much more of an active role in managing my portfolio. I wanted to be able to make informed decisions about the investments I was making, and explore financial data in new ways. Market and Econ data are some of the most talked-about and widely-available datasets out there, but I was frustrated by the lack of options to answer questions I had, like, "What would have happened if I had started dollar-cost averaging VTI back in 2013?"[1] Or, "How…
2022 · factor.fyi
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- 13AF
Hey HN! I've always been fascinated by financial markets. This month, I decided to build a tool to help with the research of stocks and cryptocurrencies. I'm using YFinance as a data source because it's free and provides a wide range of reliable market data. For sentiment analysis, I'm leveraging Google Trends to gauge public interest and sentiment over time. The tool, named Zenith, is a command-line utility with four main features: Market Analysis: Provides insights like moving averages, RSI, and volatility for selected stocks or cryptocurrencies. Sentiment Analysis: Uses Google Trends to…
2024 · github.com
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Rules-based futures signals, drawn right on your TradingView
Jul 2026 · bunnyprinter.tech
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