Marsbridge delivered a Pythonbased options analytics framework to ingest option chains, Greeks, and impliedvolatility measures, compute volatility ranks/percentiles and annualized yield metrics, and prepare clean, UIready data structures for dashboards. We also prototyped a digitalasset breakout alert on a generic marketdata feed with optional volume gating—enabling multiasset research and rapid product experiments on a shared data backbone, without exposing internal trading logic.
The brief: (1) create a repeatable options-data pipeline comparable to typical industry dashboards, and (2) test a lightweight breakout-alert concept for digital assets with low operational overhead and clean outputs suitable for a future web/desktop UI—while keeping all trade/strategy logic under the client’s control.
Approach: A lean squad—Tech/Quant Lead, Data Engineer, Frontend–facing Analyst, and QA—built the options data plane first, then iterated on the digitalasset alert prototype using the same packaging conventions.
API ingestion: Retrieve chains, Greeks, and impliedvolatility fields via a broker API; normalize to tidy data frames with symbol metadata. (No clientspecific screens or symbols disclosed.) Derived fields: Compute volatility rank/percentile and annualized yield metrics, plus listfriendly sort keys for UI watchlists. (Exact formulas and thresholds omitted.) UIready outputs: Emit JSON/CSV with stable keys so a web/desktop frontend can render watchlists, detail panes, and filters without backend rewrites.
Data source: Use a generic marketdata provider for price/volume time series. (Provider names removed.) Signals: Configurable breakout rules on rolling windows with optional volume gating and simple ranking (magnitude/persistence). (Thresholds and ranking coefficients removed.) Artifacts: Lightweight CSV/JSON outputs and alert hooks designed to feed future UI components—no automated trading included.
Provided API/JSON specs, sample pages, and data contracts for frontend collaborators, enabling rapid UI evolution without changing the backend pipeline. (UI details generalized.)
Python, broker API ingestion, pandas/NumPy, JSON/CSV for UI contracts
Standardized digitalasset price/volume feeds
Optional relational storage when persistent history is desired
Note: Both components are analytics/information systems; they are not trading advice and do not include client strategies or parameters.
Need options analytics and a path to UI without months of plumbing? We deliver clean data contracts and researchfirst code—so your team can ship dashboards and alerts quickly across options and digital assets, without exposing proprietary trading details.
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