Awesome
Deep-Reinforcement-Stock-Trading
This project intends to leverage deep reinforcement learning in portfolio management. The framework structure is inspired by Q-Trader. The reward for agents is the net unrealized (meaning the stocks are still in portfolio and not cashed out yet) profit evaluated at each action step. For inaction at each step, a negtive penalty is added to the portfolio as the missed opportunity to invest in "risk-free" Treasury bonds. A lot of new features and improvements are made in the training and evaluation pipelines. All evaluation metrics and visualizations are built from scratch.
Key assumptions and limitations of the current framework:
- trading has no impact on the market
- only single stock type is supported
- only 3 basic actions: buy, hold, sell (no short selling or other complex actions)
- the agent performs only 1 action for portfolio reallocation at the end of each trade day
- all reallocations can be finished at the closing prices
- no missing data in price history
- no transaction cost
Key challenges of the current framework:
- implementing algorithms from scratch with a thorough understanding of their pros and cons
- building a reliable reward mechanism (learning tends to be stationary/stuck in local optima quite often)
- ensuring the framework is scalable and extensible
Currently, the state is defined as the normalized adjacent daily stock price differences for n
days plus [stock_price, balance, num_holding]
.
In the future, we plan to add other state-of-the-art deep reinforcement learning algorithms, such as Proximal Policy Optimization (PPO), to the framework and increase the complexity to the state in each algorithm by constructing more complex price tensors etc. with a wider range of deep learning approaches, such as convolutional neural networks or attention mechanism. In addition, we plan to integrate better pipelines for high quality data source, e.g. from vendors like Quandl; and backtesting, e.g. zipline.
Getting Started
To install all libraries/dependencies used in this project, run
pip3 install -r requirement.txt
To train a DDPG agent or a DQN agent, e.g. over S&P 500 from 2010 to 2015, run
python3 train.py --model_name=model_name --stock_name=stock_name
model_name
is the model to use: eitherDQN
orDDPG
; default isDQN
stock_name
is the stock used to train the model; default is^GSPC_2010-2015
, which is S&P 500 from 1/1/2010 to 12/31/2015window_size
is the span (days) of observation; default is10
num_episode
is the number of episodes used for training; default is10
initial_balance
is the initial balance of the portfolio; default is50000
To evaluate a DDPG or DQN agent, run
python3 evaluate.py --model_to_load=model_to_load --stock_name=stock_name
model_to_load
is the model to load; default isDQN_ep10
; alternative isDDPG_ep10
etc.stock_name
is the stock used to evaluate the model; default is^GSPC_2018
, which is S&P 500 from 1/1/2018 to 12/31/2018initial_balance
is the initial balance of the portfolio; default is50000
where stock_name
can be referred in data
directory and model_to_laod
can be referred in saved_models
directory.
To visualize training loss and portfolio value fluctuations history, run:
tensorboard --logdir=logs/model_events
where model_events
can be found in logs
directory.
Example Results
Note that the following results were obtained with 10 epochs of training only.
Frequently Asked Questions (FAQ)
- How is this project different from other price prediction approaches, such as logistic regression or LSTM?
- Price prediction approaches like logistic regression have numerical outputs, which have to be mapped (through some interpretation of the predicted price) to action space (e.g. buy, sell, hold) separately. On the other hand, reinforcement learning approaches directly output the agent's action.
References:
- Deep Q-Learning with Keras and Gym
- Double Deep Q Networks
- Using Keras and Deep Deterministic Policy Gradient to play TORCS
- Practical Deep Reinforcement Learning Approach for Stock Trading
- Introduction to Learning to Trade with Reinforcement Learning
- Adversarial Deep Reinforcement Learning in Portfolio Management
- A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem