
HRMV
Trading framework built from real losses, not theory
What it does
Most traders don't lose because of bad setups. They lose because they have no process. HRMV is a framework built after blowing two accounts. The problem was never the market. It was the absence of structure. Includes: Core System with risk rules and checklist, Setups Playbook with 4 trade models, Case Studies with real decisions and trader traps. Built for crypto and forex. No signals. No promises. Just structure.
Does the same job
all alternatives →- IBI built a website to create financial models for any stock online2024 · useequityval.com · ▲287
- VTVibeTrade – Trading Harness for ClaudeMar 2026 · github.com · ▲7
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…
- IBI built a trading simulation game – free, no sign up2025 · dare2trade.com · ▲5
Practice trading with real historical market data by setting entry, stop-loss, and take-profit levels. The goal is to help traders practice without taking any financial risk. It's completely free, and no sign up is required to jump right in. I'd love for you to try it out: https://dare2trade.com/ It's best experienced on desktop devices for now.
- IBI built a tool to find trading signals that aren't just random luckSep 2025 · hikaro.app · ▲9
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…


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Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
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