We design autonomous trading systems that learn from markets — combining machine intelligence, rigorous research and low-latency execution across global venues.
Every decision in our pipeline — from signal discovery to execution — is systematic, measurable and continuously improved by machines.
Large-scale models mine structure from market data, alternative datasets and order flow — turning noise into testable hypotheses at machine speed.
Statistical arbitrage, market making and momentum systems, validated with disciplined backtesting and deployed fully autonomously.
Purpose-built infrastructure co-located with major venues. Smart order routing and adaptive execution minimize slippage around the clock.
Real-time exposure limits, drawdown controls and kill-switches are embedded in every layer — risk management is code, not policy.
# signal → position, fully autonomous from zenquant import Alpha, Risk, Execution class ZenAlpha(Alpha): def on_features(self, f): p = self.model.predict_proba(f) return Signal( side=side_of(p), size=self.kelly(p, cap=0.02), horizon="4h", ) engine = Execution( risk=Risk(max_dd=0.08, var_99=0.03), venues=["nyse", "nasdaq", "cme"], ) engine.run(ZenAlpha()) # 24/7, no human in the loop
For partnerships, talent and investor inquiries:
hello@zenquant.cn