An innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets is presented.
Authors
Thibaut Théate
1 papers
D. Ernst
1 papers
References48 items
1
Inside the black box
2
A deep reinforcement learning framework for continuous intraday market bidding
3
Time-driven feature-aware jointly deep reinforcement learning for financial signal representation and algorithmic trading
4
A Survey of Deep Reinforcement Learning in Video Games
5
An intelligent financial portfolio trading strategy using deep Q-learning
6
Improving financial trading decisions using deep Q-learning: Predicting the number of shares, action strategies, and transfer learning
7
Decision-making for financial trading: A fusion approach of machine learning and portfolio selection
8
Reinforcement learning applied to Forex trading
9
A Study on Overfitting in Deep Reinforcement Learning
10
Rainbow: Combining Improvements in Deep Reinforcement Learning
11
Deep Reinforcement Learning: A Brief Survey
12
Proximal Policy Optimization Algorithms
13
A Distributional Perspective on Reinforcement Learning
14
A deep learning framework for financial time series using stacked autoencoders and long-short term memory
15
Noisy Networks for Exploration
16
Deep Direct Reinforcement Learning for Financial Signal Representation and Trading
17
Deep Reinforcement Learning: An Overview
18
High-Frequency Trading Strategy Based on Deep Neural Networks
19
Mastering the game of Go with deep neural networks and tree search
20
Dueling Network Architectures for Deep Reinforcement Learning
21
Prioritized Experience Replay
22
Deep Reinforcement Learning with Double Q-Learning
23
Deep Recurrent Q-Learning for Partially Observable MDPs
24
Human-level control through deep reinforcement learning
25
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
26
Adam: A Method for Stochastic Optimization
27
An Automated Framework for Incorporating News into Stock Trading Strategies
28
Pseudo-Mathematics and Financial Charlatanism: The Effects of Backtest Overfitting on Out-of-Sample Performance
29
Playing Atari with Deep Reinforcement Learning
30
Algorithmic trading review
31
Algorithmic Trading: Winning Strategies and Their Rationale
32
Algorithmic Trading
33
Event-Driven Trading and the “New News”
34
Twitter mood predicts the stock market
35
Does Algorithmic Trading Improve Liquidity?
36
Algorithms for Reinforcement Learning
37
Reinforcement Learning and Dynamic Programming Using Function Approximators
38
Quantitative Trading: How to Build Your Own Algorithmic Trading Business
39
An automated FX trading system using adaptive reinforcement learning