Green Voltis
Quant Trading Intern
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Responsibilities
This is a three-month research internship with a concrete deliverable: a document and a working Python model that becomes the starting point for our production regime-switching forecasting capability. Successful completion of the Internship may lead to full-time opportunity at the desk.
The work has three phases:
Phase 1 — Data Gathering (Weeks 1–4)
- Collect historical price and volume data from Nord Pool (SE price zones), EPEX (PL), and OPCOM (RO) across FCR, mFRR, DA, and intraday markets.
- Identify and document the structural features — price spikes, negative prices, seasonal patterns, capacity scarcity events, hydro correlation in the Nordic, and RES penetration effects in Romania and Poland.
- GreenVoltis will support with credentials.
Phase 2 — Literature Review (Weeks 3–6)
- Survey the academic and practitioner literature on regime-switching models applied to energy markets.
- The document must cover at minimum:
- Markov-switching models (Hamilton 1989 and extensions)
- Hidden Markov models for price regimes
- Threshold autoregressive models
- Recent ML-augmented approaches
- The literature review is not decorative — it must end with a justified model selection for each market.
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Phase 3 — Model Build (Weeks 5–12)
- Implement the selected regime-switching model in Python on the gathered data.
- The model must produce:
- Regime state probabilities at each point in time
- Transition probability matrices
- Per-regime price distributions — mean, variance, and tail behaviour
- A forward-looking regime forecast that can be consumed by a scenario generator
The output should be a peer-review paper level work which we will endeavour to get published. Ultimately the artifacts created will become the working Python codebase for further development and a specification document that a quant developer can extend into production for trading a BESS with.
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Qualifications
- Masters or PhD student in mathematics, statistics, econometrics, or a quantitative discipline with a strong forecasting specialisation. We will consider exceptional final-year undergraduates in pure mathematics.
Technical
- Proficiency in Python.
- Familiarity with time series modelling — ARIMA, state-space models, or equivalent.
- Exposure to probabilistic forecasting is a strong plus.
Domain
- No energy markets experience required.
- Intellectual curiosity about why electricity prices behave as they do is required.
Disposition
- Supervision and guidance is provided when needed but the project is independent.
- You are expected to read primary literature, make modelling decisions, and defend them.
- Hypothesis testing is the guiding light here.
- Direction of project will be steered but ultimate decision making regarding model is with you.
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