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https://github.com/Draichi/T-1000

:zap: :zap: 𝘋𝘦𝘦𝘱 𝘙𝘓 𝘈𝘭𝘨𝘰𝘵𝘳𝘢𝘥𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘙𝘢𝘺 𝘈𝘗𝘐
https://github.com/Draichi/T-1000

algotrading bot ray reinforcement-learning-bot rl rllib trading trading-bot

Last synced: 3 months ago
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:zap: :zap: 𝘋𝘦𝘦𝘱 𝘙𝘓 𝘈𝘭𝘨𝘰𝘵𝘳𝘢𝘥𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘙𝘢𝘺 𝘈𝘗𝘐

Host: GitHub
URL: https://github.com/Draichi/T-1000
Owner: Draichi
License: mit
Created: 2018-07-10T22:09:37.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2023-11-09T16:42:43.000Z (about 1 year ago)
Last Synced: 2024-08-11T08:01:41.071Z (5 months ago)
Topics: algotrading, bot, ray, reinforcement-learning-bot, rl, rllib, trading, trading-bot
Language: Python
Homepage: https://ray.readthedocs.io/en/latest/index.html
Size: 21.7 MB
Stars: 172
Watchers: 21
Forks: 43
Open Issues: 1
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml

Awesome Lists containing this project

README

        # T-1000 Advanced Prototype

![ubuntu](https://img.shields.io/badge/ubuntu-supported-000.svg?colorA=00cc25&longCache=true&style=for-the-badge "ubuntu")

![ubuntu](https://img.shields.io/badge/tested_on-ubuntu-000.svg?colorA=00cc25&longCache=true&style=for-the-badge "ubuntu")

![OS](https://img.shields.io/badge/OS-unkown-000.svg?colorA=&longCache=true&style=for-the-badge "OS")

![windows](https://img.shields.io/badge/windows-not_supported-000.svg?colorA=d11431&longCache=true&style=for-the-badge "windows")

[![Codacy Badge](https://api.codacy.com/project/badge/Grade/ebdf89dcba744a3c8aafdda210d3aeb6)](https://app.codacy.com/app/Draichi/cryptocurrency_prediction?utm_source=github.com&utm_medium=referral&utm_content=Draichi/cryptocurrency_prediction&utm_campaign=Badge_Grade_Dashboard)

![gif](assets/t-1000.gif)

Deep reinforcement learning multi-agent algorithmic trading framework that learns to trade from experience and then evaluate with brand new data

* * *

## Prerequisites

-   [Miniconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html) or Anaconda

An API Key on [CryptoCompare](https://min-api.cryptocompare.com/)

* * *

## Setup

### Ubuntu

```sh

# paste your API Key on .env

cp .env.example .env

# make sure you have these installed

sudo apt-get install gcc g++ build-essential python-dev python3-dev -y

# create env

conda env create -f t-1000.yml

# activate it

conda activate t-1000

```

* * *

## Usage

### On command line

```sh

# to see all arguments available

# $ python main.py --help

# to train

python main.py -a btc eth bnb -c usd

# to test

python main.py /

    --checkpoint_path results/t-1000/model-hash/checkpoint_750/checkpoint-750

```

### On your own file

```py

# instatiate the environment

T_1000 = CreateEnv(assets=['OMG','BTC','ETH'],

                  currency='USDT',

                  granularity='day',

                  datapoints=600)

# define the hyperparams to train

T_1000.train(timesteps=5e4,

              checkpoint_freq=10,

              lr_schedule=[

                  [

                      [0, 7e-5],  # [timestep, lr]

                      [100, 7e-6],

                  ],

                  [

                      [0, 6e-5],

                      [100, 6e-6],

                  ]

              ],

              algo='PPO')

```

Once you have a sattisfatory reward_mean benchmark you can see how it performs with never seen data

```py

# same environment

T_1000 = CreateEnv(assets=['OMG','BTC','ETH'],

                  currency='USDT',

                  granularity='day',

                  datapoints=600)

# checkpoint are saved in /results

# it will automatically use a different time period from trainnig to backtest

T_1000.backtest(checkpoint_path='path/to/checkpoint_file/checkpoint-400')

```

* * *

## Features



-   state of the art [agents](https://ray.readthedocs.io/en/latest/rllib-algorithms.html)

-   hyperparam grid search

-   multi agent parallelization

-   learning rate schedule

-   result [analysis](https://ray.readthedocs.io/en/latest/tune-package-ref.html#ray.tune.Analysis)

> "It just needs to touch something to mimic it." - [Sarah Connor, about the T-1000](https://terminator.fandom.com/wiki/T-1000)

* * *

## Monitoring

Some nice tools to keep an eye while your agent train are (of course) `tensorboard`, `gpustat` and `htop`

```sh

# from the project home folder

$ tensorboard --logdir=models

# show how your gpu is going

$ gpustat -i

# show how your cpu and ram are going

$ htop

```

* * *

## Credits

-   [Papers](https://github.com/Draichi/Portfolio-Management-list/blob/master/README.md)

-   [Analyzing cryptocurrency markets using python](https://blog.patricktriest.com/analyzing-cryptocurrencies-python/)

-   [Q-trader](https://github.com/edwardhdlu/q-trader)

-   [Trading-Gym](https://github.com/thedimlebowski/Trading-Gym)

-   [Tensor Trade](https://github.com/notadamking/tensortrade)

* * *

## To do

-   [ ] Bind the agent's output with an exchange place order API