Quantitative financeProject record · Sep 2026

Quantitative Finance & Markets

Options & variance-risk-premium research

Options research and execution tooling spanning implied-versus-realized volatility, short-strangle protocols, and an earnings-event variance decomposition study.

PythonIBKR APIOptions pricingVariance decompositionWalk-forward research
Why it sits here. Placed by the scope of implementation, the available contribution evidence, and the distinct technical capability it demonstrates.
06 / Two sides. One uncertain future.STUDY IN SPACE

01 / IMPLEMENTATION & CONTRIBUTION

What the work involves

Donald implemented pricing, option-chain selection, strategy/backtest modules, and an IBKR API layer. A related earnings-event study separates jump variance from diffusion variance.

Technical depth

Black-Scholes pricing and delta solvers, chain/expiry selection, walk-forward protocols, overlap-aware variance evaluation, tail-risk filters and event-versus-diffusion variance with natural bid/ask fill comparisons.

The project family

IV_trading

02 / RESULTS

What came out of it

Reusable options research and execution components support walk-forward protocols and offline tests. The event study specifies prior-only jump estimates, shrinkage, and quote-quality gates.

03 / SUPPORTING EVIDENCE

Follow the source

Implementation notes, project records, and supporting artifacts.

Source context & project scope

Not a claim of returns or live deployment. Simplified daily Black-Scholes marks differ from executable option quotes; natural-fill and mid-fill analyses must remain distinct. No broker/account state accessed.

Source layout for pricing, chains, strategies, backtesting and execution.

SOURCE · 2026-09-17

Variance-risk-premium hypotheses and target/input timing distinction.

SOURCE · 2026-09-17

Earnings event-variance math, prior-only shrinkage and natural-quote structure evaluation.

SOURCE · 2026-09-17

LiveEngine source implements an order decision flow; not execution evidence.

SOURCE · 2026-09-17
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