Alternatives
Products that do what Built a 200k-edge market knowledge graph to filter false dip-buy signals does
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)…
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OP here: I created this visualization tool as the byproduct of a supply chain class I taught at Columbia. The pedagogical exercise grew into a full blown visualization and paper about global oil trade. The model: The mechanics are the same as the financial network Eisenberg-Noe: Instead of banks, every country consumes oil interconnected via bilateral trading. Shocks propagate throughout the network, depleting oil reserves when bottleneck nodes (such as the Strait of Hormuz) are blocked. Insights: The interesting part is the mechanics of how the crisis unfolds: for example, France receives 0…
Jul 2026 · globaloilnetwork.staffinganalytics.io
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Hey HN, I aggregated a bunch of GTM advice, including their rankings. Thought it could be useful for some founders here. Let me know if you have any feedback. Thanks!
2025 · fellowry.com
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2018 · scatterstocks.com
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Hello, I wanted to share with you all a interactive map of the economics and physics constraints of the AI buildout. It has macro drivers, industrial chokepoints, and where that shows up in markets. I've added 393 nodes and 562 edges to capture other supply / physics constraints as well. There's no sign up, and no pay wall, it's all free. Please let me know what you think!
Jun 2026 · atomprophet.io
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2015 · quantnews.com
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Read the Tape gives players the same 5 S&P500 stock charts per day to predict. You select low, medium or high confidence and then call the chart UP or DOWN. It's a 1d chart which then resolves over 5 days. Alpha is scored against the Monkey Index, a basket of 11 random coin flips at low confidence which provides a tangible win/lose condition. We're two weeks in and some interesting data is being kicked up. Players like to call tops even though stonks go up- 60% of the 70 charts so far resolve higher, players' down calls have only been right 31% of the time. There's a full stats dive at…
Jul 2026 · readthetape.cc
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Share your LinkedIn. Receive a decision + reasoning in 24h.
Dec 2025
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2020 · tradytics.com
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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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I built a web page that aggregates data about data center buildup, sovereign fund investments into AI and bottlenecks. The objective is to predict AI race cooldown by looking at a potential decrease of activity involving these elements. The website looks at the quarterly forms from the 5 biggest hyperscalers and adds their CapEx into the mix, calculating a composite index in the end showing how likely it is for the AI race to slow down. Enjoy!
Jul 2026 · laurentiugabriel.github.io
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2023 · stockstack.ai
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Stop journaling trades. Start building your edge.
Jul 2026 · youredgeflow.com
- 16HF
Hey everyone, we’re an ex YC team building retail-intelligence.ai where we suggest 5 trades to you every 6 hours. Our product maps out a strategy with you into logic trees, transforming it into custom signals, live dashboards, and automated alerts. We’re excited to bring a new experience to retail markets and am keen to see what strategies you have! You can get your recommended trades here: https://www.retail-intelligence.ai/
Jun 2026 · retail-intelligence.ai
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When OpenAI released Learning Transferable Visual Models From Natural Language Supervision in 2021, it sparked a multimodal semantic search revolution. While the concept wasn’t new, the paper catalyzed a shift in the industry, inspiring a wave of semantic search tools. These advances delivered a leap forward in search quality compared to traditional lexical search, with adopters reporting significant improvements in conversion rates and revenue. But semantic search alone isn’t the endgame. Productionizing search remains a challenge, and hybrid search—combining keyword precision with semantic…
2024 · play.shaped.ai
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This paper formally defines where current AGI hits a structural wall — not a technical one. It shows that no amount of scaling, reinforcement learning, or recursive optimization will break through three deep epistemological and formal constraints: 1. Semantic Closure — An AI system cannot generate outputs that require meaning beyond its internal frame. 2. Non-Computability of Frame Innovation — New cognitive structures cannot be computed from within an existing one. 3. Statistical Breakdown in Open Worlds — Probabilistic inference collapses in environments with heavy-tailed uncertainty.…
2025
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2016 · github.com
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How it works (tech stack): -Built entirely with Lovabl.dev (no-code front-end + logic) -ChatGPT / Claude for research and inspiration -Powered by GPT-4 Vision to interpret charts visually -Hosted on Supabase for performance & caching It’s not meant to replace analysts — just to speed up how traders interpret data. I’m a designer exploring AI tools, and this is my first attempt to turn an idea into a functional product. Would love to know what you think.
Oct 2025 · quantify-ai.co
- 21IB
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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