Code repository for the Optimal Switching with Jumps (OSJ) algorithm described in A neural network approach to high-dimensional optimal switching problems with jumps in energy markets. This code is written as part of a Ph.D. research project by April Nellis in conjunction with advisors Dr. Erhan Bayraktar and Dr. Asaf Cohen. We investigate applications of deep-learning algorithms to optimal switching problems.
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Aid_LearnJumpSwitchML.py: Multi-power-plant example inspired by the optimal switching problem described in Aid et al.
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CL_LearnJumpSwitchML.py: Adaptation of of Pham's RDBDP algorithm for a d-dimensional jump-diffusion process representing natural gas and electricity prices for a power plant which is trying to predict the optimal production schedule under stochastic price fluctuations. Power plant model is taken from Carmona and Ludkovski's 2008 paper PRICING ASSET SCHEDULING FLEXIBILITY USING OPTIMAL SWITCHING.
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EnergyCLOrig.py: Implementation of Longstaff-Schwartz algorithm involving regression over Monte Carlo paths, described in Carmona, Ludkovski (2008), for comparison purposes.
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visualsML.py: Contains functions for visualizing output of various algorithms (imported to all other files)
Simply type python [filename] in command line.
- Figures will be saved in a Figures/ folder
- Animations will be saved in an Animations/ folder
- Neural network weights will be saved in a Weights/ folder
NOTE: Users may have to manually create empty folders with these names before executing the code.