Options Analytics & DigitalAsset Breakout Alert Platform
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.
Customer
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.
Challenge
Data Completeness and Rapid Prototyping
- Data completeness & shape. Pull full option chains (expiries/strikes), Greeks, and impliedvolatility metrics with consistent symbol mapping and timestamps, suitable for storage or immediate UI use—without embedding any clientspecific screening logic.
- Derived analytics. Compute volatility ranks/percentiles and yield metrics in a way that’s explainable and portable into various frontends.
- Digitalasset alerting. Stand up a generic breakout detector on a standard marketdata provider, with clear thresholds and optional volume gates—kept configurable and free of proprietary entry/exit rules.
Solution
Lean Squad Delivery of Data Plane and Alert Prototype
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.
Options analytics framework
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.
Digitalasset breakout alert prototype
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.
Collaboration for productization
Provided API/JSON specs, sample pages, and data contracts for frontend collaborators, enabling rapid UI evolution without changing the backend pipeline. (UI details generalized.)
Technologies & tools
Python, broker API ingestion, pandas/NumPy, JSON/CSV for UI contracts
Standardized digitalasset price/volume feeds
Optional relational storage when persistent history is desired
Process
- Scope & parity targets: Define required option fields (chains, Greeks, IV) and derived analytics (volatility ranks/percentiles, yields).
- Broker integration: Implement ingestion + normalization; validate via spot checks; draft JSON/CSV schemas for UI.
- Derived metrics & watchlists: Compute volatility ranks/percentiles and yield-oriented ranks; produce example watchlists. (Exact formulas omitted.)
- Digital-asset prototype: Implement breakout detector on generic feeds; add ranking/volume gates; deliver alert outputs. (Provider and thresholds removed.)
- Handoff: Docs, data contracts, and example UI stubs for continued product development—no trading rules attached.
Team
Results
- Reusable Options Core: Reusable optionsanalytics core (chains, Greeks, volatility ranks/percentiles, yield metrics) ready for dashboards or further research.
- UI-Friendly Outputs: UIfriendly outputs enabled partners to prototype frontends quickly without exposing trading logic.
- Multi-Asset Prototype: The digitalasset alert provided a fast path to test product ideas without automated execution.
Note: Both components are analytics/information systems; they are not trading advice and do not include client strategies or parameters.
Ship Data Products Faster
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.
Related Case Studies: Algorithmic Trading Development
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