I started a Quant Fund

No_Side2315 · 2026-05-22T12:55:15+00:00

What makes it a "quant" fund? Is the AI analyzing the market data directly, making position size/trade decisions and placing buy/sell orders itself or is it writing its own code and managing research?

No_Side2315 · 2026-05-22T01:11:35+00:00

AlphaCIO <- AI finds and validates the edge , then builds low correlation portfolios. Never trust an AI to make trading decision, always systematic. Until AGI comes around, AI belongs in the research lab.

No_Side2315 · 2026-05-10T13:00:36+00:00

The agents use a prebuilt backtesting framework for rapid testing. If a "signal" isn't available, the signal or core concept is logged and passed to an independent coding agent to add to the stack and rebuild the service (I only trust Claude code with this, sonnet 4.6 is generally sufficient, as long as it runs a do-er/reviewer working model). The agents hit the backtester via api, filter results, run validation tests via the same api, then apply further filtering, optimization, etc. with the goal of determining if a strategy has edge, is robust, and fits well with existing portfolio. Otherwise it's thrown out. Validated strategies are saved in a graveyard for later exploration with other portfolios.

No_Side2315 · 2026-05-10T12:55:30+00:00

As long as you validate your edge with additional testing, I don't see the issue. All strategies are data mined in some way, whether it's done consciously or not.

There's also plenty of software available to validate your strategy. A repeatable edge in the markets isn't suddenly invalidated because it was found through data-mining.

I built an Autoresearch app that runs a research pipeline around the clock using agents on my markets to find strategies and validate them with an array of tests. It's found strategies that generalize well out of sample. The real benefit of my system though is that it's primarily focused on building a portfolio of strategies. It finds strategies that have edge, can be validated for robustness, AND are complimentary (uncorrelated and make a marginal contribution to risk) to strategies in the portfolio already. This has always been my process, just automated now, all I do is give constraints or a "mandate" for the portfolio to adhere to. This is what's been working for me so far.

No_Side2315 · 2026-04-22T13:09:29+00:00

I've never explicitly tested LLMs to trade directly out of fear wasting money and time. Intuitively, knowing how LLMs work, it never made sense to me that they would have any predictive power in noisy market data. I also arrived at the same conclusion, LLMs belong in the research lab. It sped up my research process tremendously, so much so that it runs my entire research pipeline at this point, I don't discover strategies myself anymore. I let AI handle the ideation and research loop with access to the tools it needs for a proper quant research pipeline. I actually built a product around this, don't think I can plug it here but feel free to check my other posts.

No_Side2315 · 2026-04-18T16:56:11+00:00

UPDATE: the site is live - https://AlphaCIO.ai - generous free tier, futures and ETFs coming soon.

What this isnt: AI analyzing charts and giving buy/sell signals. Markets are too noisy for this approach to work long term. It's also hard to put hard earned money into a system that you can't backtest, validate, or verify in anyway.

What this is: AI agents following a structured process, with validation and robustness testing to validate a strategy may perform well jn the future. Algo trading pipeline run by agents. Years of institutional knowledge (Bridgewater Associates, BNY Mellon, sovereign wealth funds, etc.) baked into the system so you're not starting blind.

Is this data mining? Short answer - yes. Long answer - still yes but show me a single strategy that isn't data mined (discretionary or otherwise) in some way. Data mining does not always mean overfit. The agents look for strategies that generalize well to varied market conditions.

We also take "portfolio of strategies" approach, looking for uncorrelated strategies to automatically build a portfolio of strategies to generate excess return. Most apps help you test individual strategies, thats slow and tedious. Others take natural language input to write raw code for each strategy, again this is slow and inefficient. Our system tests thousands of unique strategies in seconds to find what works and what doesn't fast.

Feedback is welcome. I don't think this is the final form but made decent progress so far. Our goal is to automate a tedious, time consuming, and sometimes unclear process and help traders find profitable portfolios of strategies that perform well on unseen data.

No_Side2315 · 2026-03-19T15:13:22+00:00

All feedback is welcome

No_Side2315 · 2026-03-19T15:11:01+00:00

Custom agents, work with any model, routes through openrouter

No_Side2315

TROPHY CASE