Don't Sell Alpha.
Sell Intelligence.
Empower your Quants and Traders to validate hypotheses in seconds, not days. The first Context-Aware Memory engine for financial markets.
The Analyst's Dilemma
The Rule-Based Trap
Traditional indicators (RSI, MACD) are rigid. They miss context. A "breakout" in 2024 looks different than in 1990.
The Black Box Trap
Neural networks give a probability, but no explanation. A Portfolio Manager cannot bet $10M on "Model says 0.75".
The Solution: Context-Aware Memory
"Imagine if every time you looked at a chart, you instantly remembered every similar setup from the last 20 years across all assets."
> Searching 2.5M+ candles...
> Found 100 nearest neighbors (0.2s)
> Regime: STABLE_TRENDING (85% conf)
Infrastructure for Decision Making
We don't sell signals. We provide the "Search Engine for Price Action" that makes your team smarter.
Idea Validation
Eliminate "Survivorship Bias". Validate hypotheses in seconds. If a pattern failed in 2008 and 2020, your analysts will see it immediately.
Risk Management
The "Graveyard of Analogues". Before betting $10M, see the worst-case historical scenarios for this exact market structure.
Quant Infrastructure
Retrieval-Augmented RL. Feed "Nearest Neighbor Embeddings" into your LSTMs/Transformers to reduce hallucinations and improve robustness.
Regime Switching Architecture
Strategies that print money in a Bull Market often fail in a Bear Market. Our Regime Detection API solves this.
- ✓Auto-Classification: Instantly identify if the market is in
STABLE_TRENDING,HIGH_VOLATILITY, orMEAN_REVERSION. - ✓Dynamic Leverage: Automatically adjust risk parameters based on the current regime's historical win rate.
- ✓Meta-Learning: Switch between specialized agents (e.g., "Bull Bot" vs "Crab Bot") in real-time.
# 1. Detect Regime
regime = api.detect_regime(price_vector)
if regime.id == 'STABLE_TRENDING':
agent = TrendFollower(leverage=3.0)
elif regime.id == 'HIGH_VOLATILITY':
agent = MeanReversion(leverage=1.0)
# 2. Execute
agent.act()