https://github.com/pandede/fdrnn

Deep direct reinforcement learning for financial signal representation and trading
https://github.com/pandede/fdrnn

deep-learning fdrnn finance neural-network reinforcement-learning trading

Last synced: about 1 year ago
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Deep direct reinforcement learning for financial signal representation and trading

• Host: GitHub
• URL: https://github.com/pandede/fdrnn
• Owner: Pandede
• Created: 2020-09-21T09:36:54.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2020-10-07T10:10:08.000Z (over 5 years ago)
• Last Synced: 2025-03-30T09:05:13.918Z (over 1 year ago)
• Topics: deep-learning, fdrnn, finance, neural-network, reinforcement-learning, trading
• Language: Python
• Homepage:
• Size: 27.3 KB
• Stars: 28
• Watchers: 0
• Forks: 14
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# FDRNN

Implementing

```Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2016). Deep direct reinforcement learning for financial signal representation and trading. IEEE transactions on neural networks and learning systems, 28(3), 653-664.```

## Preparation

1. Prepare the index data as **CSV** file. The file must include column *CloseDiff*, which represents the index difference

    `CloseDiff[t] = Index[t] - Index[t-1]`. The CSV files must arrange as following directory structure:

    ```

   +-- Data

   |    +-- futures

   |    |   +-- future_2018-01-01.csv

   |    |   +-- future_2018-01-02.csv

   |    |   +-- ...

   ```

2. To reduce the training time, it is **strongly recommended** that computing the parameters of fuzzy representation before training. The vanilla index file can be transformed into fuzzy version via applying `FuzzyStreamer` in `handler.py`.

    ```python

   from handler import FuzzyStreamer

   #streamer = FuzzyStreamer(, )

   streamer = FuzzyStreamer(lag, fuzzy_degree)

   # streamer.transform(, )

   streamer = streamer.transform('./Data/futures/train', './Data/fuzzy_futures/train')

   ```

3. Adjust the required parameters in `config.ini`

    ```ini

    [default]

    # Number of training epochs

    epochs = 1000

    # Save the model each n epochs

    save_per_epoch = 20

    # Transaction cost

    c = 0.05

    # Window size

    lag = 50

    # Data path

    data_src = ./Data

    # Log path

    log_src = ./Pickle

    

    [fddrl]

    fuzzy_degree = 3

    ```

   

## Run

Running FDRNN - The proposed method in the paper

`python main.py`

Running baseline DDRL - The proposed model without fuzzy representation

`python baseline_ddrl.py`

Running baseline DRL - The proposed method without fuzzy representation and autoencoder

`python baseline_drl.py`