3d (edited) • 💻 Technical Skills
New Online Course: Energy Storage Trading & Arbitrage in Python
The course (available in Classroom) teaches how to develop a profit-maximizing arbitrage strategy for energy storage using mathematical optimization (Linear and Mixed-Integer programming) in Python.
Full Python code available to download and fully explained in the video (1 hour and 15 minutes). No prerequisites (beginner-friendly). Energy storage can make money through various ways, one of which is energy storage trading (arbitrage). See the attached figure summarising the energy storage arbitrage strategy.
The algorithms and strategies taught in this course are the industry standard for the following roles:
- Quantitative Analysts (Quants) in energy firms
- Energy Traders
- Data Scientists (Energy)
- Asset Managers
Where is this code used?
This specific type of optimization (Arbitrage & Dispatch) is used in firms across the energy and financial sectors:
- Hedge Funds & Prop Trading
- Investment Banks
- Commodity Trading Houses
- Energy Majors & Tech
Energy Storage :
- Utilities (e.g., Duke Energy, NextEra, Enel) own batteries (energy storage) to help stabilise the electricity grid.
- Independent Power Producers (IPPs) (e.g., Vistra, AES, Neoen) are companies that build and own power plants (solar, wind, batteries) specifically to sell electricity for profit. They are very active users of arbitrage strategies.
- Investment Funds (e.g., Gresham House, Gore Street Capital) are specialized funds that buy batteries as financial assets, similar to how a real estate fund buys apartment buildings to collect rent.
- Hedge funds (like Citadel, D.E. Shaw, or Millennium) thrive on volatility. In energy markets, prices can jump from $20 to $2,000 in minutes.
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New Online Course: Energy Storage Trading & Arbitrage in Python
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A beginner-friendly training program for a career in the energy sector. Regardless of your location, age, sex, education or experience level.
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