As I climb the stairs and turn the corner my pulse quickens, and the adrenaline kicks up. I can’t help it; the energy and vibe of the trading floor always excites me and gets my pulse racing. The sights, the sounds, the nervous energy, and competitive atmosphere are addictive to an ex-trader.
But as I step onto the floor, I’m taken aback by the dare I say…… relative calm. There is a general level of white noise as traders, schedulers and analysts interact, but in a much more hushed and orderly manner. What happened to the brokers shouting prices down phone terminals? What about dispatchers yelling to cash traders with important information about generating units. Where are the footballs being tossed across the trade floor during market lulls?
Then it hits me. The rise of machine + human collaboration on the trading floor, and more to the point the acceleration of technology. Yes, electronic platforms had become standard protocol when I was still trading, and we had a few third-party tools to provide general market information. But the extent to which technology has rapidly transformed the energy trading business permeates across the trading floor now:
- Electronic trading platforms
- Third party analytics platforms
- Algorithmic trading programs
- ML/AI asset optimization and forecasting models
All quietly running and executing automatically or with simple keyboard strokes and mouse clicks.
A new breed of energy trader
As I meet the desk head and her “traders” it quickly becomes apparent to me I am standing in front of a group that represents something completely different than the past.
I can’t help but notice what is displayed on the monitors over their shoulders. Yes, there is the familiar ICE trading screen and obligatory weather service display, but there are also screens with Python or SQL code, CSV files with rows and rows of data, and visualization screens with what look like statistics on steroids.
Data scientists, quantitative analysts or systematic traders are now occupying trader seats and market leaders are investing in these advanced analytic and trading capabilities supported by technology solutions including:
- Enterprise Analytics Platforms
- Integrated Data Lakes
- Cloud Computing & Supported Applications
Green machine + human
Renewable resources, whether wind, solar or hydro are dominating the landscape both figuratively and literally. Other rising technologies including battery storage resources, DER and VPP assets combined with ever smarter transmission grid applications are being added to this mix. And what do they all have in common? Variability and complexity. Gone are the days of large amounts of “baseload” generation that provided steady, static output that was easily stacked up and managed against power demand. Now we have a highly variable power grid incorporating smart grid technologies and IOT and the need to forecast inputs and outputs to the grid and the impacts of variability between them.
<<< Start >>>
<<< End >>>
What’s interesting is the transition we are seeing to a “greener” energy industry has only helped accelerate the “rise of machines + humans” in energy trading. Why? It comes down to complexity, speed, and the need to be first to capture shrinking energy margins and random arbitrage opportunities that arise.
What is leading to the increased complexity? The inherent variable operating characteristics of renewable resources and the need to solve for these impacts on the power grid. Whereas prior fundamentals analysis focused on the demand side and fuel inputs of the market pricing equation, analyzing, and understanding the following categories has become a required capability:
- Renewable Generation Forecasting (Wind, Solar, Hydro)
- Transmission Modeling (Congestion Pricing, Power Flows)
- Demand Response Forecasting
- Ancillary Service Markets
What’s the final piece of the puzzle? Data. How public, private, and alternative data that is available in structured and unstructured formats as batch downloads, flat files, real-time or streaming is applied in trading applications is a differentiating advantage amongst market participants.
IOT sensors, connected assets and software applications are now providing vast amounts of renewable data that can be leveraged by data scientists to create a competitive advantage for asset owners and operators.
Machine learning forecasting models synced with algorithmic trading - and optimization programs - ingest this operational data to form strategies that maximize market revenue and identify arbitrage opportunities. Data is treated as an asset just as steel on the ground, skilled resources in your organization or cash on the balance sheets.
So, what does this mean for energy traders?
- Invest in a digital workforce: Leading trade desks have a resource mix that is increasingly leaning towards STEM (science, technology, engineering, and mathematics) capabilities. Commercial job postings, especially for junior positions such as trade or business analysts now require coding skills, a quantitative math background, or some tech development capability; and in some instances, at least two of the three skills. These “traders” are now experienced power market data scientists or mathematicians developing algo trading and asset optimization tools to support both manual and automated contract execution. The delineation of duties traditionally divided between a trade desk, fundamental analysis group and I.T. has been blurred with trading staff bringing the requisite skills to front-office operations.
- Adopt advanced technology & systems: Death of the spreadsheet. Yes, I know, customized spreadsheets are still the comfort blanket for a lot of traders and commercial desks. But entities must embrace the digital transformation which includes analytics platforms and integrated data lakes supported by the clouds computing speed, horsepower, and scalability. The mind-boggling amount of data, types of data and speed at which data is available requires entities to develop a comprehensive plan treating data as an asset. Enhanced visualization tools layered on top of the platform provide stakeholders greater business intel and insights to make more confident management and market execution decisions.
<<< Start >>>
<<< End >>>
- Data leveraged as competitive advantage: It’s one thing to capture and store data to use it for reactive post-operations business reporting but the secret sauce is being able to effectively interpret and apply the data into actionable intelligence. Scraping natural gas pipeline flow data can quickly alert traders to important changes in usage patterns, production or constraints that impact market pricing. IOT sensor data showing turbine vibration from a generation plant can help predict outage probabilities so asset managers can adjust bid strategies to limit market exposure in the case of failure. Increasing use of alternate data such as twitter postings, news headlines and satellite photography are being leveraged to get a leg up on market trends, operational updates and supply/demand balances.
AI and advanced analytics are reimagining energy trading, and those that scale and apply it more efficiently and effectively will have a competitive advantage in the market. Please contact me to find out more.