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 that the performance is not a random fluke.
* *High Sharpe Ratio:* Better risk-adjusted returns.
* *Healthy ‘Core Performance’:* The average return after removing the single best outlier trade, for a more conservative view.
* Other key metrics like Profit Factor and Max Drawdown that you can filter by.
I’m looking for my first users and would love your feedback. I’m offering a 30-day free trial of the Pro plan (which includes Telegram alerts) with no credit card required.
You can check it out here: [Your URL]
I’ll be here all day to answer any questions. I’m especially keen to hear feedback on the concept and the metrics.
Thanks, HN!
### Key Changes and Why:
* *Title:* The new title is a bit more punchy and frames the problem more directly. Your original is also very good, so feel free to stick with it.
* *Added Market:* Specifying the asset class `[e.g., US equities, crypto, forex]` is crucial. Users will immediately want to know if it’s relevant to them.
* *Refined Hook:* “I was tired of backtests that looked great on paper but failed in practice” is a very relatable pain point for anyone in this space.
* *Bolding:* Using bold for the key metrics makes them easier to scan.
Comments URL: https://news.ycombinator.com/item?id=45218802
Points: 1
# Comments: 0
Source: www.hikaro.app
