Future Research Topics

Related Topics

• Optimal Execution

  • Optimal liquidation or acqurization within 15-30 minutes with agent trained on the simulated environment.

• Optimal Scheduling with Predicted Trading Volume

  • In vwap strategies, split the task into smaller sizes according to the predicted trading volume.

• Order-flow Generating

  • Mathematical Perspective: Order flow as a general spatial point process

  • Time-series Forecasting for Order Flow

  • Order Flow Generating by Large Language Models

• Representation Learning

  • Representation Learning for the States/Observations

  • In the Optimal Execution, e.g. the all snapshots of limit order book in the past 30 seconds.

  • VAE, GAN and other encoder models.

• Price Impact Research through Market Clearing

  • Mathematical Perspective: Market clearing as a deterministic operator acting on the distributions of buy and sell orders.

  • Calculate the price impact without the assumption of impact function

• Indirect Market Impact

  • Agent’s Impact on Triggering the Modification of other Agents’ Actions

  • Different from the price impact, which is the direct maret impact.

• Agent Based Modelling/Simulation

  • Generative adversarial network approach simulation

  • Market Simulation

• Recover Trader’s Reward Function

  • Recover Trader’s Reward Function by Inverse RL

• Unsupervised Environment Design

  • Adversarial Learning by the differentiable environment

Related Papers

• Related Sections

  • Simulated Markets

  • Learning Trading Strategies

  • Forecasting Financial Data

• ICAIF2022

  • High Related

    • Dynamic Calibration of Order Flow Models with Generative Adversarial Networks

    • Learning to simulate realistic limit order book markets from data as a World Agent

    • Cost-Efficient Reinforcement Learning for Optimal Trade Execution on Dynamic Market Environment

  • Mid Related

    • Market Making under Order Stacking Framework: A Deep Reinforcement Learning Approach

    • Graph and tensor-train recurrent neural networks for high-dimensional models of limit order books

    • Computationally Efficient Feature Significance and Importance for Predictive Models

    • LaundroGraph: Self-Supervised Graph Representation Learning for Anti-Money Laundering

    • Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions

    • Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization

• ICAIF2021

  • High Related

    • Towards a fully RL-based Market Simulator

    • Towards Realistic Market Simulations: a Generative Adversarial Networks Approach

    • Learning to simulate realistic limit order book markets from data as a World Agent.

    • Bit by Bit: How to realistically simulate a Crypto-Exchange

  • Mid Related

    • Deep Q-learning market makers in a multi-agent simulated stock market

    • FinRL: deep reinforcement learning framework to automate trading in quantitative finance

    • Sig-wasserstein GANs for time series generation

    • Agent-based markets: equilibrium strategies and robustness

    • Intelligent trading systems: a sentiment-aware reinforcement learning approach

    • High frequency automated market making algorithms with adverse selection risk control via reinforcement learning

  • Low Realted

    • An automated portfolio trading system with feature preprocessing and recurrent reinforcement learning

    • Monte carlo tree search for trading and hedging

    • Visual time series forecasting: an image-driven approach

    • Trading via selective classification

    • Timing is money: the impact of arrival order in beta-bernoulli prediction markets

    • An agent-based model of strategic adoption of real-time payments

    • FinRL-podracer: high performance and scalable deep reinforcement learning for quantitative finance

    • Stability effects of arbitrage in exchange traded funds: an agent-based model

• ICAIF2020

  • High Related

    • Get Real: Realism Metrics for Robust Limit Order

    • Multi-Agent Reinforcement Learning in a Realistic Limit Order Book Market Simulation

    • Deep Reinforcement Learning for Automated Stock Trading: An Ensemble Strategy

  • Mid Related

    • A tabular sarsa-based stock market agent

    • Dynamic prediction length for time series with sequence to sequence network

• OMI Research Newsletter

  • fa

• Other related papers

  • StockGAN: Robust Stock Price Prediction Using GAN Algorithm.

  • Generating Realistic Stock Market Order Streams

  • Deep Reinforcement Learning in Agent Based Financial Market Simulation

  • Many learning agents interacting with an agent-based market model

  from OMI Research Newsletter – April 2023

Related Techniques

• Transformers

  • Time Series Forecasting with Transformers

    • Transformers in Time Series: A Survey

  • Transformer in Low Signal-noise Ratio System

    • Sparse Transfomer: Generating Long Sequences with Sparse Transformers

• Long Sequence Modelling

  • Efficiently Modeling Long Sequences with Structured State Spaces(S4)

  • Simplified state space layers for sequence modeling(S5)

• Unsupervised Environment Design

  • Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design

• Behavior Cloning

  • BC-IRL: Learning Generalizable Reward Functions from Demonstrations

Related Issues

• Hard to generalize. There might be several reasons jointly contribute to this situation:

  1. The signal-to-noise ratio of financial market data is much lower than that of other artificial intelligence fields.

  2. The financial market is not a closed system and will evolve on its own.

  3. The financial market is a derivative of the economy and therefore can be impacted by external factors.