The trap

Trading desks are still hiring, and they pay well. The first gate is the same as it always was: a CV that shows relevant experience. It is hard to get relevant experience without the seat. People stay in that trap for years.

A hiring manager does not hire on promise. They open something you built and ask one question. Does this person already understand how the money is made?

So you build the thing they open. Here, with someone who built the desks, and whose name goes next to it.

What you leave with

Not a certificate. Five things a hiring manager can open.

A model on GitHub. Public market data in, real results out, thirty days of them, the error stated honestly.
A record of what it did on real days. Views, dispatches or orders, kept. Including the wrong ones.
One day explained. What the market did, what the weather did, and what your model made of it.
A data map. Every source you use, what arrives late or wrong, and what you do then.
A mock interview behind you. A desk's real questions, with Jordan's notes on your answers.

Then the introductions. Jordan sends your name to the recruiters he works with in power trading. Your CV arrives with a name on it, not in the pile.

Three tracks. Pick the seat you are aiming at.

Power market analyst

You sit with the traders and work out what electricity will cost tomorrow. Weather, outages, gas, cross-border flows, in. A forecast of 96 prices, out. Then the question that matters: where is the market wrong, and why?

You build: A merit-order model for one market on ENTSO-E data. Ninety-six prices for tomorrow. A thirty-day backtest. A battery in the stack.
You keep: Four weekly views: what the market said, where you disagreed, why, and what happened.
The roadmap: Senior Power Market Analyst, free. The bootcamp is this roadmap, done with Jordan.

Quant developer

You sit between the people who forecast prices and the exchange that trades them. You build the code that turns a model into orders, every quarter-hour, and the code that watches it. You are judged on what it did the night the data arrived late.

You build: A battery dispatch optimiser on ENTSO-E prices. A thirty-day backtest. The clock-change days handled. Orders into a toy exchange. A bot that runs for a week on paper.
You keep: The bot's control log. Every order and cancel, the forecast each acted on, the limit that stopped it, the kill switch test.
The roadmap: Quantitative Developer, short-term power, free. The bootcamp is this roadmap, done with Jordan.

Power trader

You take the analyst's forecast and the desk's position and decide what to buy and sell before the auction closes. You are judged on the book: what it made, what it lost, and whether you knew why.

You build: A paper position book on the day-ahead auction. A view and a position each day, marked at the clearing price. Thirty trading days. A risk limit sheet, and the trade it stopped.
You keep: The book, with its P&L stated and the worst day explained. Every daily view, including the wrong ones.
The roadmap: Power Trader, short-term power. Published this month.

The nine weeks

The spine is the same on every track. Weeks one and two: how a power price is made, hour by hour, and what a trader does with it. Weeks three and four: your model on live market data, repository public by the end of week four. Weeks five to seven: harden it the way a desk would, backtest it, state its errors. Week eight: the mock interview. Week nine: the five things finished, and the introductions.

About twelve hours a week, in your own time. Evenings and weekends work. One group call a week, in the evening, London time, fixed with the cohort in week one. Jordan reviews your code whenever you push.

The guides named below are in the community. A seat includes them for the nine weeks.

Power market analyst

Week	Do	Done means
1	Forming a View. Physical Foundations. Project 1, phases 1 to 3.	You can name the type of plant that set yesterday's German price, any hour.
2	The Global Gas Market. The Spark Spread. Interconnectors and Carbon. Project 1, phases 4 to 7: the merit order.	Your simulator runs a merit order. You can work out a gas plant's margin by hand.
3	Day-Ahead and Intraday. Forward Curves and the Practice briefing. Market Data Operations.	One page on a day when intraday moved away from day-ahead, and the forecast change that caused it.
4	Writing Professional Python. The merit-order model on ENTSO-E data for one country. First backtest.	Repository public. Ninety-six prices for tomorrow. The error over thirty days, charted. First view written.
5	Production Python for Trading Systems. Harden the model: late data, reruns that agree, the clock-change days.	The model reruns and agrees with itself. Second view.
6	Dispatch Optimisation: a battery in the stack.	One day where the battery changed the merit order, shown. Third view.
7	Power Trading Strategies. Risk Management Fundamentals. The thirty-day backtest, written up.	The backtest page, with the days it was most wrong named and explained.
8	Energy Market Regulation: the REMIT inside-information chapter. Mock interview.	The interview, with Jordan's notes. Fourth view.
9	One day explained. The data map. The five things, finished.	Five things a hiring manager can open. Introductions sent.

Quant developer

Week	Do	Done means
1	Physical Foundations. Day-Ahead and Intraday. Project 1, phases 1 to 3.	You can say why intraday left day-ahead yesterday, for one hour.
2	Market Microstructure. The Spark Spread. BESS Fundamentals. Revenue Markets. Project 1, phases 4 to 7.	You can explain continuous matching and write the battery's constraints by hand.
3	Dispatch Optimisation. Revenue Stacking. Writing Professional Python. A linear programme on one day of ENTSO-E prices.	A dispatch a trader would agree with, and the state-of-charge chart.
4	Production Python for Trading Systems. Harden it: clock-change days, late data, reruns that agree. Thirty-day backtest.	Repository public. The backtest says what it leaves out.
5	The integer constraint: no charging and discharging at once. The formulation, written out.	The constraint you added, and what it cost in runtime.
6	Project 3: A Toy Power Exchange. Wire the optimiser to it.	Orders submitted, acknowledged and filled on the exchange.
7	Market Data Operations. Algorithmic Trading in Power Markets, with the cadence bot. Power Trading Strategies.	The bot runs on paper with limits. The kill switch is tested.
8	Risk Management Fundamentals. The REMIT II algorithmic trading chapter. The paper week begins. Mock interview.	The interview, with Jordan's notes. The control log filling.
9	The incident write-up. The data map. The bot's log for the week. The five things, finished.	Five things a hiring manager can open. Introductions sent.

Power trader

Week	Do	Done means
1	Trading Fundamentals. Forming a View. Physical Foundations.	You can name the plant that set yesterday's price, and say what a trader did about it.
2	The Global Gas Market. The Spark Spread. Interconnectors and Carbon.	A gas plant's margin by hand. One sentence on where tomorrow's price is likely wrong.
3	Day-Ahead and Intraday Power Trading. Market Microstructure and Order Books.	One page on a day when intraday left day-ahead. How an order is matched at gate closure.
4	Writing Professional Python. The position book: a daily view, a position, the clearing price, the mark. First paper trade.	Repository public. The book marks a position against the auction.
5	Forward Curves and Market Data. Energy Derivatives: Swaps and Options.	A hedge priced. The book carries a forward position.
6	Power Trading Strategies. Risk Management Fundamentals. The risk limit sheet: size, loss, stop.	A trade stopped by a limit, logged.
7	Trade Lifecycle and Settlement. The paper record so far, written up.	P&L stated. The worst day explained.
8	Energy Market Regulation: inside information and market abuse. Mock interview.	The interview, with Jordan's notes.
9	The thirty-day record closed, and its post-mortem. The data map. The five things, finished.	Five things a hiring manager can open. Introductions sent.

Who it is for

You can write a Python function and run a script. Everything else is taught, including the Python. Past cohorts ran from complete beginners to senior engineers.

You work somewhere near energy, or you want to. An analyst at a supplier. A data engineer at a consultancy. A process engineer who reads the power price every morning. A graduate with a model and no desk.

If you already run a dispatch model for a living, you skip the reading you could teach. You do not skip the model, the record or the interview. Desks hire the record, not the CV.

The models are fundamentals-based: merit order, dispatch, position. There is no machine-learning module. If you already use ML, it goes on top, and the backtest will tell you whether it earned its place.

Who teaches it

Jordan Dimov built the trading core behind six commodity desks at Shell. Trade processing at hundreds of thousands of data points a minute at Centrica. Day-ahead automation on EPEX and Nord Pool for a trading floor at Limejump. He teaches Python for a living.

One of his February 2026 cohort joined as a junior analyst at an energy supplier. By May he had a much better paying role on a gas trading desk.

"Commodity trading software is one of those niches of the market where domain knowledge is actually king. The ability to interpret and model a domain and become knowledgeable on it is heavily underrated."

Victor Sandoval, software engineer, bootcamp graduate

October 2026 cohort

£4,850

Half now to reserve your seat. Half at week two. Six seats.

Monday 12 October to Friday 11 December 2026.
One track: analyst, developer or trader.
Weekly group call. Code and model reviews whenever you push.
Mock interview in week eight. Recruiter introductions in week nine.
Full community access for the nine weeks. Pay the full price and it becomes lifetime.
Anything you have paid A115 before, for a bootcamp or the community, comes off the price.
Invoice to an employer on request.
Full refund within ten days of reserving. After that, no refunds. You keep the materials and the community.

Reserve a seat: £2,425 now

Not sure which track? Message Jordan on LinkedIn with ANALYST, DEVELOPER or TRADER, or email jdimov@a115.co.uk.

Not included, on purpose: the simulator, which is being rebuilt, and any promise of a job.

Questions

How much Python? A function and a script. Jordan teaches the rest, and reviews what you write until it is production quality.

How many hours? About twelve a week. Two thirds reading, one third building on the analyst track. Half and half on the developer track. Less if you can skip the reading.

I am not in London. Everything is online. The weekly call is in the London evening. If that cannot work for you, say so before you reserve.

I already work in the industry. Then you are here for the record and the introduction. Skip the reading you could teach. The model and the interview stay.

What if I fall behind? Say so on the call. The weeks are a spine, not a cage. The thing that cannot slip is the repository being public by week four.

Is there a job at the end? No promise. There is a record a hiring manager can open, and a mock interview behind you. Your name goes to the recruiters Jordan works with.

Six seats from 12 October.

Nine weeks. One model. Five things a hiring manager can open. A name next to your CV.

Reserve a seat