Benchmarks
Greatest papers with code
Qlib: An AI-oriented Quantitative Investment Platform
• microsoft/qlib
• 
Quantitative investment aims to maximize the return and minimize the risk in a sequential trading period over a set of financial instruments.
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market.
Stock Price Correlation Coefficient Prediction with ARIMA-LSTM Hybrid Model
Predicting the price correlation of two assets for future time periods is important in portfolio optimization.
Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization
Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profile.
Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
• qcappart/hybrid-cp-rl-solver
•
In this work, we propose a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems.
COMBINATORIAL OPTIMIZATION PORTFOLIO OPTIMIZATION TRAVELING SALESMAN PROBLEM
Online Mixed-Integer Optimization in Milliseconds
• bstellato/mlopt
•
Compared to state-of-the-art MIO routines, the online running time of our method is very predictable and can be lower than a single matrix factorization time.
Deep Deterministic Portfolio Optimization
• CFMTech/Deep-RL-for-Portfolio-Optimization
•
Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies?
Bayesian Optimization of Risk Measures
NeurIPS 2020
• saitcakmak/BoRisk
•
We consider Bayesian optimization of objective functions of the form $\rho[ F(x, W) ]$, where $F$ is a black-box expensive-to-evaluate function and $\rho$ denotes either the VaR or CVaR risk measure, computed with respect to the randomness induced by the environmental random variable $W$.
DECISION MAKING DECISION MAKING UNDER UNCERTAINTY PORTFOLIO OPTIMIZATION
Computation of optimal transport and related hedging problems via penalization and neural networks
• stephaneckstein/transport-and-related
• 
This paper presents a widely applicable approach to solving (multi-marginal, martingale) optimal transport and related problems via neural networks.
Solving Portfolio Optimization Problems Using MOEA/D and Levy Flight
Portfolio optimization is a financial task which requires the allocation of capital on a set of financial assets to achieve a better trade-off between return and risk.