Built to solve the hardest real-world benchmark for artificial general intelligence: financial markets. Our autonomous scientific discovery engine continuously invents, validates, and adapts trading strategies.
Traditional quant strategies are decaying faster than ever
Quant strategies are now decaying faster than ever. Renaissance, Schonfeld, and Engineers Gate all struggled in 2026.
BlackRock replaces investment analysts with AI agents. Top quants are not safer than software engineers.
Innovation and adaptation speed are survival. The advantage is having a system that continuously discovers strategies.
AGINora operates a fully autonomous scientific discovery engine that continuously invents, validates, and adapts trading strategies. Rather than deploying static models or brittle reasoning-only LLM agents, we use evolutionary program synthesis inspired by—and currently superior to—frontier systems like DeepMind's AlphaEvolve, Sakana.AI's ShinkaEvolve, and ARC-AGI findings on generalization limits.
Reasoning is necessary, but evolution is a must for generalization. Our agents do not just "decide trades," they invent skills needed for the decision-making.
Mines thousands of diverse high-quality alpha strategies
Evolves and adapts strategies during live trading
Allows allocation into maximum uncorrelated bets
def technical_indicator(ohlcv):
# Meta-learned robust policy
# Combines volatility adaptation,
# multi-timeframe analysis,
# and risk-adjusted signals
...
return optimized_signalsJan 9 – Feb 9, 2026 • One Month Live Trading Period
The strategy shows evidence of both active downside volatility control and meaningful upside convexity. A Sortino ratio >3 in real-market conditions is uncommon and typically observed only when downside volatility is actively controlled.
Performance occurred during a choppy and risk-off market environment, providing an early stress test of robustness rather than a benign tailwind. The strategy shows a fatter right tail with more frequent 2-4% upside periods while showing no evidence of outsized left-tail blowups.
A 0.60 correlation to SPY indicates the strategy is meaningfully differentiated from the benchmark—this is not "closet indexing," but an active return stream with substantial idiosyncratic drivers. The outperformance is due to active positioning and selection, not simply riding benchmark moves.
In trading, innovation and adaptation speed are survival. Signals decay, regimes shift, and alpha gets arbitraged away.
Deep Learning is black-box and prone to overfitting. Evolutionary program synthesis creates robust, interpretable policies.
Quantitative Finance is the perfect autonomous scientific discovery domain, as strategies constantly need discovery and adaptation.
Only companies adopting the latest AI technology in their workflows will be able to compete.
Investment advisors will be replaced by AI agents that research companies for portfolio decisions.
AI traders will outperform humans, especially when they can autonomously discover superior strategies.
We're on a multi-billion dollar track. Currently collecting interest for pre-seed funding and extending our team with top talent.
We're proceeding in Stealth mode. Depending on live trading performance, we're on track to establish and grow a hedge fund.