Project Manager, Developer
(Energy, water and environment)
Keywords
Risk Analysis
Oil and Gas
Risk Management
Innovation
Data Analysis
Computer Programming
Energy Market
Forecasting
Python (Programming Language)
Liquidity Risk
Skills
An Energy Risk Management Professional with 20 years of experience overseeing a wide variety of risk functions (covered market risk, credit risk, FX risk and liquidity risk), along with in-depth expertise in creating documentation for trading strategies and proposing risk management measures, volume limits, regulatory risk, stop loss, mean-risk analysis, value-at risk, and profit-at-risk.
Possess a thorough understanding of all parts of the risk management spectrum related to energy (power, natural gas, coal, oil, and certificates) along with developing, implementing, and presenting strategic independent energy
market risk analysis to senior management. Conceptualised, developed, and deployed machine learning algorithms and Python coding focused on forecasting German day-ahead and intra-day power market prices and wind power
generation.
Actively manage processes to optimize the efficient analysis of risk across portfolios, manage change, and prioritize accordingly. Identify potential risks, develop, and analyse models, portfolio assumptions, market inputs, pricing issues, valuation parameters, and instrument types. Profound knowledge in modelling, programming, and data
analytics along with an excellent statistical understanding and the ability to successfully navigating large amounts of data by utilizing innovative methodologies for analysis. A collaborative communicator, developer of policies and a strong ability to translate complex analysis into concrete and easily understandable recommendations and actions.
Possess a thorough understanding of all parts of the risk management spectrum related to energy (power, natural gas, coal, oil, and certificates) along with developing, implementing, and presenting strategic independent energy
market risk analysis to senior management. Conceptualised, developed, and deployed machine learning algorithms and Python coding focused on forecasting German day-ahead and intra-day power market prices and wind power
generation.
Actively manage processes to optimize the efficient analysis of risk across portfolios, manage change, and prioritize accordingly. Identify potential risks, develop, and analyse models, portfolio assumptions, market inputs, pricing issues, valuation parameters, and instrument types. Profound knowledge in modelling, programming, and data
analytics along with an excellent statistical understanding and the ability to successfully navigating large amounts of data by utilizing innovative methodologies for analysis. A collaborative communicator, developer of policies and a strong ability to translate complex analysis into concrete and easily understandable recommendations and actions.
Project history
02/2020
-
02/2021
Aim:
Output:
- The client wanted to develop the process of complex industrial sales of power and natural gas.
- At the current status of the risk management was done using Excel/VBA. Due to the number of contracts and complexity the process was quite time consuming.
- The company wanted to have a portfolio/sub portfolio view of the contracts
- My role was acting as:
- Project manager:
- Made sure that the aim of the project was clear for all involved parties (from trading /sales manager, risk management, IT)
- I made sure that all requirements were understood
- I made sure that the tasks were set in with proper priority
- If new tasks came up change the priorities if needed
- Developer: developed the new risk management framework in Python
- Project manager:
- I updated the project team on a frequent basis
- Document each step of the process and present the progress to the project owners
- My first task was to understand the nature of each individual contracts based on e.g.:
- Energy type – Natural gas and power
- Market area / country
- The risk components of each contract e.g.:
- Type of flexibility:
- Demand driven flexibility – The flexibility is not driven by energy prices
- Market driven flexibility - The flexibility is driven by the market price (e.g. aluminium and cement companies)
- The pricing mechanism – how the prices are being calculated
- The frequency of the delivery of their schedule
- Based on the different categories:
- I defined the different sub portfolios were the contracts should be allocated in
- I proposed to enter the deals for the individual contracts in the ETRM system as ‘skeleton’ deals (deals that belonged to specific contracts could be linked to a ‘master’ deal)
- Made sure that the existing deals were entered correctly in the ETRM system
- Prototyped code for the end of day (EoD) simulations the ETRM system
- The risk management could not be done directly in the ETRM system.
- The risk manager were using Python (to small extent) earlier
- Based on the EoD simulation the output (Blob) was then read in to Python
- Based on the requirement from the risk manager the risk measures I used were e.g. profit- at-risk, value-at risk, and volume limits (delta). The choice of risk measure depends on the nature of the contract and the sub portfolios)
- I supported the credit risk team how to use the new setting in their work
- I performed GAP analyses to identify the need for tools design to validate and settle contracts (back office)
Output:
- EoD simulation of the complete portfolio
- Python code
- Full code as well as documentation of the codes
- Documentation of the calculation of marked-to-market, the risk measure I implemented
- Supporting document for credit risk and back-office
03/2018
-
02/2019
Project Manager, Developer
(Energy, water and environment)
Aim:
The company wanted to set up an in-house ETRM system for the PPA trading
The company was using Python to large extent already, which lead me to use Python too
I made sure that the relevant data was in place e.g.:
Wind data (from SCADA) for the locations each wind parks.
German power futures (from EEX)
Guarantees of Origins (GoO)
Feed-in-tariff
The time frame of PPA’s are often extends the time frame of EEX’s German power futures, which means that OTC prices are also needed
The company wanted to have the opportunity to trade different PPA structures e.g. corporate PPA’s with fixed base load, with variable base load, pay as produced.
I started with setting up templates for the different PPA contract structures
Based on the templates and data I developed Python codes for the valuation and risk management. The output from Python was then exported to Excel using VBA (in future in Power BI).
Regulatory reporting:
REMIT: Made sure that the PPA’s are incorporated into existing REMIT reporting structure.
EMIR: Exchange traded and OTC traded contracts/deals used for hedging the PPA’s needs to be reported (there are exceptions that legal needed to clarify). I made sure that the correct format, content was set up for the contracts/deals that have to be reported.
Documented the IT process and GAP analyses
Outputs
- The company wanted to set up risk management (from scratch)
- Valuation and marked-to-market
- Risk measures: Value-at-risk, liquidity risk (when trading outside the liquid periods of the market exchange)
- The risk management was not only related to market risk, it also included credit risk, and regulatory reporting
- My role was acting as:
- Project manager:
- Made sure that the aim of the project was clear for all involved parties (e.g. trading /sales manager, risk management, legal, IT)
- I made sure that the requirements were understood within the project group
- I made sure that the tasks were set in with proper priority
- If new tasks came up change the priorities if needed
- Developer: developed the new risk management framework in Python
- Project manager:
- I updated the project team on a frequent basis
- Document each step of the process and present the progress to the project owners
The company wanted to set up an in-house ETRM system for the PPA trading
The company was using Python to large extent already, which lead me to use Python too
I made sure that the relevant data was in place e.g.:
Wind data (from SCADA) for the locations each wind parks.
German power futures (from EEX)
Guarantees of Origins (GoO)
Feed-in-tariff
The time frame of PPA’s are often extends the time frame of EEX’s German power futures, which means that OTC prices are also needed
The company wanted to have the opportunity to trade different PPA structures e.g. corporate PPA’s with fixed base load, with variable base load, pay as produced.
I started with setting up templates for the different PPA contract structures
Based on the templates and data I developed Python codes for the valuation and risk management. The output from Python was then exported to Excel using VBA (in future in Power BI).
Regulatory reporting:
REMIT: Made sure that the PPA’s are incorporated into existing REMIT reporting structure.
EMIR: Exchange traded and OTC traded contracts/deals used for hedging the PPA’s needs to be reported (there are exceptions that legal needed to clarify). I made sure that the correct format, content was set up for the contracts/deals that have to be reported.
Documented the IT process and GAP analyses
Outputs
- Full documentation of:
- The different templates (containing the contract structure the company was looking for)’
- The Python code I developed for the valuation and risk management
- The IT-process and performed GAP analysis e.g.:
- How to incorporate the PPA’s into the short-term trading desk
- The need to enhance the back-office process to handle PPA’s
- The need to enhance the regulatory IT-landscape (to include e.g. EMIR)
- The need to enhance the credit risk management in case deals are not cleared in a centralised clearing house
02/2017
-
12/2017
Project Manager, Developer
(Energy, water and environment, 10-50 employees)
Aim:
My role
Worked closely with trades and IT (the project team)
I also developed and implemented Python codes for back-test and market risk management
Tasks:
Documented the inputs to the algorithmic trading
For example: German day-ahead and intra-day power prices (EEX), fundamental factors that drives the power demand and supply and the order book.
Investigated other possible data sources for the fundamental factors in case the API malfunctioned
Prototyped processes to clean the input data and imputing missing data
Made sure that the input to the algorithmic trading could not be manipulated by the traders during the trading
Set up a back-testing framework for different trading strategies. Example of strategies:
Moment driving trading strategy
Day-ahead / intra-day
Technical trading (based on one or several different technical indices)
Proposed and implemented market risk management concepts (e.g. stop loss, position limits, mean-risk analysis, and extreme value-at -risk) based on Python
Outputs
- The company was in the start-up of setting up an algorithmic power trading desk and wanted to establish risk management processes
My role
- Project manager
Worked closely with trades and IT (the project team)
- Developer
I also developed and implemented Python codes for back-test and market risk management
Tasks:
Documented the inputs to the algorithmic trading
For example: German day-ahead and intra-day power prices (EEX), fundamental factors that drives the power demand and supply and the order book.
Investigated other possible data sources for the fundamental factors in case the API malfunctioned
Prototyped processes to clean the input data and imputing missing data
Made sure that the input to the algorithmic trading could not be manipulated by the traders during the trading
Set up a back-testing framework for different trading strategies. Example of strategies:
Moment driving trading strategy
Day-ahead / intra-day
Technical trading (based on one or several different technical indices)
Proposed and implemented market risk management concepts (e.g. stop loss, position limits, mean-risk analysis, and extreme value-at -risk) based on Python
Outputs
- Document of:
- The data and data sources needed for the company’s algorithmic trading
- Code (Prototyped) to clean and/or impute data that are missing
- Full Python code for the back-testing the current trading strategies. Documented how to enhance the back-testing to cover new trading strategies
- Full Python code for the market risk management + documentation of the codes
09/2013
-
01/2014
Business Analyst
(Energy, water and environment, 1000-5000 employees)
- Aim:
- The client wanted to stream line / improve the processes in their enterprise risk management (ERM) framework
- My part was to review the processes related to market risk management / analysis
- My role
- Working as business analyst between business, risk departments and IT
- I was a part of a larger project group
Tasks:
- Documented the current market risk management processes (the IT framework)
- Investigated the possibilities of synergy effects, were it is possible to use the same processes to save time and to decrease the problems related to using different processes for the same tasks
- Areas of potential improvements:
- Finding processes that could be automated
- To replace some of the tasks, were the users currently were using Excel/VBA, to be replaced by e.g. Matlab. This was related to the operational risk of using large Excel work books and to make the transfer of the processes to the new enterprise risk management platform easier.
- Outputs
- Documentation of the IT-processes
- Related to energy market risk
- That were redundant
- That could be automated (going from Excel to e.g. Matlab) or were redundant
Certifications
Machine Learning
Coursera
2018
Machine Learning Engineer
Udacity
2018
Oxford Algorithmic Trading Programme
Said Business School, University of Oxford
2018
Agile Project Management
APMG International
2016
EMIR (Basic)
Entrima
2016
REMIT – Integrity Energy Markets
Entrima
2016
Local Availability
Open to travel worldwide
