Selling Vol Isn't Free Money: The 3 Numbers That Matter
Everyone knows vol mean-reverts. Almost nobody measures when it's actually rich. Research with formulas, real data and code: IV/HV Premium, IV Rank and Vol-of-Vol.
finance// Discipline 01
Quant
Trading, risk and crypto
Strategies, risk and valuation tested against data: from hypothesis to out-of-sample backtest, with real costs and no survivorship bias.
01 · Who it is for
Who it is for
Asset managers, family offices and trading desks
That need to validate a strategy, measure its risk or automate their investment process.
Data and engineering teams
Building research, backtesting or execution platforms and looking for technical support.
Crypto and fintech companies
Working with market, on-chain or risk data and needing auditable models.
02 · How we work
How we work
Consulting
Review of strategies and models: hypotheses, data, biases, costs and risk.
Development
Market-data pipelines, backtest engines and custom research tooling.
Audit
An independent second opinion on a backtest or a model before committing capital.
Training
Workshops for teams on quantitative method and agents applied to finance.
03 · Specialties
Specialties
- Systematic and discretionary strategies
- Risk management and sizing
- Macro and options flow
- Quantitative valuation
- Crypto markets
04 · Method
Method
- 01
Diagnosis
What data exists, which decision depends on it and what has failed so far.
- 02
Design
Hypothesis, method and success criteria in writing, before building anything.
- 03
Build and test
Reproducible code and analysis, tested against adverse cases.
- 04
Handover
Everything is documented and in your team’s hands.
05 · Sectors
Sectors
Finance
- Strategy and signal validation
- Portfolio risk and stress scenarios
- Volatility and options
- Market-data quality and lineage
Defense
- Financial risk of critical supply chains
- Scenario models under high uncertainty
Civil
- Demand and price forecasting in energy and commodities
- Anomaly detection in time series
06 · Use cases
Use cases
Illustrative cases: they describe the kind of work we do, not specific clients.
Validate a strategy before trading it
- Problem
- A promising backtest nobody has tried to break.
- Approach
- Written hypothesis, documented data, out-of-sample validation, real costs and friction.
- Deliverable
- Report with results, reproducible code and limitations.
Measure the real risk of a portfolio
- Problem
- Correlations that change exactly when they matter most.
- Approach
- Market regimes, historical and hypothetical stress, and dependence between assets.
- Deliverable
- Risk map by scenario and concentration alerts.
Volatility and options
- Problem
- Selling volatility looks like easy money until it is not.
- Approach
- Implied versus realized volatility premium, ranks and vol-of-vol with real data.
- Deliverable
- Indicator dashboard and documented decision rules.
Research platform
- Problem
- Every analyst with their own data and their own version of the backtest.
- Approach
- One data pipeline, a shared backtest engine and an experiment log.
- Deliverable
- Code, documentation and handover to the team.
07 · Data and topology
Data and topology
A market is a network of assets that move together and split apart in crises. We measure that structure (correlations, regimes, dependencies) before trusting a signal.
08 · Research
Research
A data problem in this area?
Tell us the context: what data exists, which decision depends on it and what has failed so far.