I’m Kayla. I trade energy with code. Not cute crypto bots. Real power, gas, and grid stuff. It’s nerdy, loud, and weirdly fun. Some days it stings. Some days it sings.
You know what? It felt like juggling weather, wires, and money—while the weather keeps changing the rules.
So… what is algorithmic energy trading?
It’s when a bot helps place bids and offers for power and gas. The bot watches data. It makes calls based on rules and forecasts. It can trade:
- Day-Ahead (DA): you plan what you’ll buy or sell for tomorrow.
- Real-Time (RT): you handle surprises that pop up today.
- Futures and options: you hedge prices for later.
I ran bots in places like ERCOT and PJM. These are power markets in the U.S. I also tested on EPEX Spot in Europe. Each one has its own rules. Think of them like different sports. Same ball. New playbook. If you want the full blow-by-blow with screenshots, grab the longer recap here.
For an industry-level look at how algo trading is evolving—complete with innovation wins, regulatory headaches, and cautionary tales—check out this deep-dive from Energy Trading Week.
My setup (simple, then not so simple)
I started small. A laptop. Python scripts. A few APIs.
- Data: grid prices, load forecasts, wind and solar forecasts, and public weather (NOAA feeds).
- Tools: Python with Pandas. I tried XGBoost for price and load guesses. I logged trades in PostgreSQL.
Pro tip: if you ever need to squeeze gigabytes of historical price curves or weather rasters so they load in milliseconds, the guides over at DataCompression.info are pure gold. For deeper nerdery, their experiment with asymmetric-gained deep image compression and continuous rate adaptation sparked a few tricks of my own. And if you’re archiving screen-capture walkthroughs of your trades, their head-to-head review of the best video compression tools will keep your storage bill honest.
- Links: ISO feeds (that’s the grid operator), and a broker for futures on Nodal Exchange. For real-time pushes, I used Kafka to stream data. Not fancy, just steady.
I kept a big red “kill switch.” It was a one-line script that set position size to zero. It saved me more than once.
Real trades I ran (the good, the bad, and the sweaty)
1) Battery ping-pong in Texas heat
We managed a 1 MW battery tied to ERCOT. My bot watched 15-minute prices and state of charge. It had two simple rules:
- Charge when price was under $40/MWh.
- Discharge when price was over $150/MWh.
On a hot summer day, prices jumped fast in late afternoon. The bot charged at around $28/MWh before noon. It sold at $182/MWh at 5:00 pm. Then it cycled again and sold at $210/MWh at 6:15 pm.
Sounds clean, right? Not quite. Round-trip losses took a bite. So did telemetry lag. Net for the day was about $350 after fees. That’s not moon money, but it was steady. The win came from discipline, not magic.
2) Day-Ahead vs Real-Time spread in PJM
This one is called virtual bidding. You guess where DA price will miss RT price. My bot liked a certain zone that was often high in DA on storm days.
One morning, radar looked rough, so the bot sold DA (it assumed RT would be lower). Then the storm drifted. RT spiked. I ate a loss.
Damage control: I had a cap—no more than $2,500 risk per day on that strategy. I hit the cap, shut it down, and went for a walk. The next week, the same rule caught a small win. Lesson: limits first, pride second.
3) Wind forecast hedge for a PPA
We had a 50 MW wind PPA with imbalance risk. The bot used a quantile forecast (a “low, mid, high” wind guess). It sold 70% of the mid guess in DA. Then it shaped the rest with a small RT rule.
On a breezy weekend, actual wind came in 10% higher than mid. We were short DA, but we had room. RT prices were low, so the fill was cheap. Net: lower balancing cost than our old manual method.
When it went wrong? A cold front hit faster than our feed said. Output dropped. RT prices were high. We paid up. Since then, I run two weather feeds, not one.
4) Spark spread watch: gas vs power
A small plant I help hedge needs to watch gas and power together. The bot checked the heat rate (how much gas you need to make 1 MWh). When power price didn’t cover gas plus costs, it pinged me.
One icy morning, power looked rich, but gas jumped at the hub just before bids closed. The bot flagged it. We hedged with a small power short and a tiny gas long. Margin saved. Nothing flashy, but it kept the plant safe.
The good stuff
- Speed with calm: The bot places orders on time. No panic clicks.
- Less guessy: Backtests showed where the edge was thin. That kept me honest.
- Guards: Hard limits, circuit breakers, and daily stop-loss saved the day often.
The hard parts
- Data lies: Weather feeds lag. Grid prices can get revised. Bad in, bad out.
- Rules change: ISO rules shift. Fees shift. You must read the notices. Boring, but vital.
- Latency: Even tiny delays hurt in RT. I moved some code closer to the data to cut lag.
- Bot drift: Models get stale. I retrain at least weekly. Sometimes daily in summer.
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A recent episode of the SP Global Commodities Focus podcast digs into many of these headaches—especially what volatility and the renewable build-out mean for algo desks—if you prefer learning by ear, you can listen here.
What I track every single day
- PnL by strategy, not just total. It shows who’s the real hero.
- Hit rate and average win vs loss. If win size shrinks, I pause and check.
- Slippage and fees. These eat small edges for breakfast.
- Data health: if any feed is late or blank, the bot goes to “safe mode.”
Safety rails I won’t trade without
- Position caps by node and by hour.
- A kill switch I can hit from my phone.
- Shadow trading first: two weeks of paper trades before real money.
- A morning checklist (prices, weather, outages, data) and a 3 pm check-in.
- Logs that tell a story in plain words. If I can’t explain a trade, I stop it.
Who should try this?
- Small shops with some tech skill and patience.
- Battery owners who want better cycling rules.
- Load managers and DR folks who want smart bids.
- Prop teams that like edges built on weather and grid quirks.
If you hate logs, rules, or math, you won’t enjoy it. If you like puzzles, you might smile.
A few tiny lessons I learned the hard way
- Don’t chase every spike. You’ll catch the wrong one.
- Two data sources beat one. Always.
- Start with small size. Then add size slowly. Then stop. Breathe.
- Simple rules live longer than fancy ones.
- Treat high heat days like finals week. Stay close to the screen.
My verdict
Algorithmic energy trading works—but only with care. The bot is a tool. It’s fast, fair, and blunt. It does what you tell it, not what you meant. When I gave it good rules and good guard rails, it helped me a lot. When I got cute, it slapped me.
Would I use it again? Yes. I use it every day. Just with limits, logs, and coffee.
And a big red button