TAA-PG
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SvenBecker
Usage of policy gradient reinforcement learning to solve portfolio optimization problems (Tactical Asset Allocation).
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Reinforcement Learning for Tactical Asset Allocation
This project contains the training and testing of multiple reinforcement learning agents given a portfolio management environment.
The interaction between environment and agent is given by:
Environment <> Runner <> Agent <> Model
Important files and folders
- Data aquisation: data.py
- Environment: environment.py
- Hyperparametertuning of the prediction model: predictor.py
- Training of the prediction model: predictor_network.py
- Runner: runner.py
- Basic configuration parameters: config.py
- Training: train.py
- Testing: test.py
- Agent configurations: agent_config
- Neural net configurations: net_config
Dependencies
The proposed implementation was done mainly in python, therefore python version >=3.6 is required. Furthermore following additional python packages are required:
- h5py==2.7.1
- Keras==2.1.3
- matplotlib==2.1.0
- numpy==1.14.1
- pandas==0.20.3
- pandas-datareader==0.5.0
- scikit-learn==0.19.1
- scipy==1.0.0
- seaborn==0.8.1
- tensorflow==1.4.0
- tensorflow-tensorboard==0.4.0rc3
- tensorforce==0.3.5.1
Running the train file
For agent training you should run the file train.py using the console.
For example:
python ~/path/to/file/run/train.py -at "clipping" -v 1
Changes to the environment and/or run parameters can be selected through the train file, config file and through pre specified flags shown below.
Modifications of the agents and models must be specified through the belonging config file (json format).
Flags:
| Flag 1 | Flag 2 | Meaning |
|---|---|---|
| -d | --data | path of the environment.csv file |
| -sp | --split | train/test split |
| -th | --threaded | (bool) threaded runner oder single runner |
| -ac | --agent-config | path to the agent config file |
| -nw | --num-worker | number of threads if threaded is being selected |
| -ep | --epochs | number of epochs |
| -e | --episodes | number of episodes |
| -hz | --horizon | investment horizon |
| -at | --action-type | action typ: 'signal', 'signal_softmax', 'direct', 'direct_softmax', 'clipping' |
| -as | --action-space | action space: 'unbounded', 'bounded', 'discrete' |
| -na | --num-actions | number of dicrete actions given a discrete action space |
| -mp | --model-path | saving path of the agent model |
| --eph | --eval-path | saving path of the agent model of the evaluation files |
| -v | --verbose | console verbose level |
| -l | --load-agent | if given agent will be loaded based on a prior save point (path) |
| -ds | --discrete_states | discretization of the state space if true |
| -ss | -standardize-state | (bool) standardization or normalization of the state |
| -rs | --random-starts | (bool) random starts for each new episode |
Running the test file
The execution of the test file is very similar to the one of the train file. There has to be a checkpoint in saves for the selected agent.
python ~/path/to/project/run/test.py -l /project/model/saves/AgentName
The folder saved_results contains multiple parameter constellations of pretrained agents.
Flags
| Flag | Flag 2 | Meaning |
|---|---|---|
| -d | --data | path of the environment.csv file |
| -ba | --basic-agent | selection of a BasicAgenten: 'BuyAndHoldAgent', 'RandomActionAgent' |
| -sp | --split | train/test split |
| -ac | --agent-config | path to the agent config file |
| -e | --episodes | number of episodes |
| -hz | --horizon | investment horizon |
| -at | --action-type | action typ: 'signal', 'signal_softmax', 'direct', 'direct_softmax', 'clipping' |
| -as | --action-space | action space: 'unbounded', 'bounded', 'discrete' |
| -na | --num-actions | number of dicrete actions given a discrete action space |
| --eph | --eval-path | saving path of the agent model of the evaluation files |
| -v | --verbose | console verbose level |
| -l | --load-agent | if given agent will be loaded based on a prior save point (path) |
| -ds | --discrete_states | (bool) discretization of the state space if true |
| -ss | -standardize-state | (bool) standardization or normalization of the state |
TensorBoard
The files predictor.py as well as train.py integrate TensorBoard. TensorBoard can be loaded by typing
tensorboard --logdir path/to/project/env/board
tensorboard --logdir path/to/project/run/board
Got to localhost:6006 for the results.
Credits
Metadata
Owner
SvenBecker
Metadata
Usage of policy gradient reinforcement learning to solve portfolio optimization problems (Tactical Asset Allocation).