Chakra Quant unifies data, features, models, validation, risk and delivery into a single governed workflow — so researchers spend their time finding edge, not fixing plumbing.
Chakra Quant is in development. Features described on this page are planned and may change before launch.
Streaming and batch connectors land exchange, on-chain, news, macro and alternative data in a versioned lakehouse. Point-in-time snapshots mean your research only ever sees what was knowable at the time.
Engineer technical, microstructure, on-chain and language-model sentiment features in a governed feature store. Every feature carries its definition, owner and history.
Time-series transformers, gradient-boosted trees, recurrent networks and regime-switching models are blended into calibrated ensembles that output full distributions — P10, P50 and P90 — across horizons from one hour to one year.
Every strategy is trained on the past and tested on data it has never seen, fold after fold. Then we replay historical crashes to see how it behaves when it matters most.
Metrics are illustrative research measures and are not a guide to future performance.
Volatility, drawdown, VaR and stress-test analytics for research ideas. Chakra AI provides research tools and data; it does not execute trades or manage money.
Anomaly-detection models trained on order-flow and on-chain behaviour flag pump-and-dump patterns, wash trading and spoofing-like activity — the engine behind CryptoChakra’s Manipulation Detector.
Retrieval-augmented agents reason over prices, on-chain metrics, news and filings. Every claim is cited; every note passes a governance check for sources, risk language and data freshness.
Our open-access AI cloud, re-tuned for quant workloads: model training, hyper-parameter sweeps and Monte Carlo simulation at scale. Access is granted on a rolling basis.
Research in notebooks, ship with the same code. The Chakra SDK wraps data access, features, backtests and signal delivery in a few lines.
from chakra import Research
r = Research(asset="ETH-USD", horizon="7d")
r.features(["funding_rate", "exchange_netflow",
"sentiment_24h"])
# walk-forward validation
report = r.backtest(walk_forward=True, folds=8)
report.summary() # Sharpe, drawdown, hit rate| Capability | Typical in-house build | Chakra AI |
|---|---|---|
| Point-in-time data across markets and chains | Build it yourself | Planned |
| Walk-forward backtesting | Custom code | Planned |
| Probabilistic forecasts | Research project | P10 / P50 / P90 |
| Manipulation detection | No | Available on CryptoChakra |
| Agentic research copilot | No | In development |
| Model governance and audit | Ad hoc | Model cards + logs |
Join the waitlist, register API interest, or talk to us about a research collaboration.