Quark Research publishes daily and intraday signals — regime probabilities, factor-structure reads, alpha attribution, and per-asset weights — delivered via versioned REST, WebSocket, and CSV. Suited to systematic desks, multi-asset overlays, and risk teams that need a second-opinion architecture on top of conventional tools.
Every dataset is point-in-time-reproducible, versioned under /api/v1/,
and carries explicit deprecation headers when surfaces change. Webhooks deliver push events
whenever a regime, signal, or weight crosses a threshold.
Posterior probabilities for risk-on / neutral / risk-off / crisis, plus the dominant classifier and a confidence score. Daily refresh, intraday on stress events.
GET /api/v1/regime-probabilityRegime label, tail-risk read, recommended allocation summary, and risk parameters for use in overlays. Compact JSON suited to webhook delivery.
GET /api/v1/signals/currentFull per-signal contributions across the 18-factor stack: regime score, factor stability, mispricing intensity, correlation persistence, and ten more orthogonal reads.
GET /api/v1/signals/rawReplay any signal back through the available history window. JSON for systems, CSV for spreadsheet ingestion, both with consistent schema.
GET /api/v1/signals/history.csvRebuild what the model would have produced at a specific historical timestamp. Designed for backtest reproducibility and walk-forward validation.
GET /api/v1/signals/at?as_of=...Per-signal alpha contribution, normalized weights, and rolling attribution windows. Lets risk teams see exactly which signals drove the period's return.
GET /api/v1/alpha/attributionThe quark-research Python SDK ships on PyPI under MIT license.
Public endpoints (regime, current signal, benchmark) require no key. The institutional
surfaces and the unfiltered signal stack require a qk_ token from your tier.
# pip install quark-research from quark_research import QuarkClient client = QuarkClient(api_key="qk_...") # public endpoints work without a key print(client.signals_current()) print(client.regime())
# Public — no auth required curl https://api.quarkresearch.cc/api/v1/regime-probability # Authenticated — institutional tier curl -H "Authorization: Bearer qk_..." \ https://api.quarkresearch.cc/api/v1/signals/raw
A sample report, a sample CSV from the public history endpoint, and the methodology whitepaper — enough to satisfy a portfolio-engineering review without a sales call.
One-page weekly publication covering regime classification, factor reads, and the recommended overlay tilt. Mirrors what Professional+ subscribers receive.
90-day historical signal feed, CSV format. Includes regime, tail-risk read, and allocation summary per session. Free public tier — no key required.
Plain-English notes on how the signal stack composes the regime classifier, factor stability monitor, and tail-risk module. Linked references for desk diligence.
All tiers include the regime classifier, weekly publication, and dashboard access where applicable. API access opens at the Automated tier; auto-rebalance at Automated Plus. Cancel any time from the customer portal.
The signal stack composes a Markov-switching regime classifier with a factor-stability monitor, an order-flow imbalance read, and a tail-risk module that watches the tails of the return distribution rather than its centre. Each component runs independently and combines into a single composite read, with full attribution preserved on every cycle.
Models target one specific failure mode of vanilla quantitative tools: their tendency to flag regime changes after the fact. By tracking the structure of factor returns (not just their level) the architecture sees rotation events earlier in the process, while still anchoring to public benchmarks.
Every published signal is reproducible point-in-time. Methodology paper, full attribution backtests, and a sample data feed are linked below.
Read insights →Designed for desks that already have a primary quantitative process and want a structurally orthogonal second read.
The crash-barrier and topological-stress reads update before the conventional VIX / credit-spread proxies catch up. Risk teams use them as a leading indicator on the drawdown shield.
Live regime probabilities feed into dynamic asset-class tilts. The recommended allocation surface is a starting point for an in-house overlay or a direct lift.
Point-in-time signals make it trivial to replay any internal strategy against the live signal stack as a counter-factual — an honest answer to "what would Quark have said two years ago?"
Public endpoints are open for evaluation; institutional-tier feeds and the unfiltered signal stack require a license. Live performance, sample reports, and the methodology paper are linked below.