Need help with T-1000?
Click the β€œchat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

Draichi
142 Stars 36 Forks MIT License 484 Commits 0 Opened issues

Description

:zap: :zap: π˜‹π˜¦π˜¦π˜± π˜™π˜“ 𝘈𝘭𝘨𝘰𝘡𝘳𝘒π˜₯π˜ͺ𝘯𝘨 𝘸π˜ͺ𝘡𝘩 π˜™π˜’π˜Ί π˜ˆπ˜—π˜

Services available

!
?

Need anything else?

Contributors list

# 192,303
TypeScr...
Python
Shell
rl
472 commits
# 43,076
xslt
laravel...
api-fra...
scrapy-...
1 commit

T-1000 Advanced Prototype

ubuntu

ubuntu

OS

windows

Codacy Badge

gif

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

This repository is no longer maintained


Prerequisites

An API Key on CryptoCompare


Setup

Ubuntu

# 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

# 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

# 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

# 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
  • hyperparam grid search
  • multi agent parallelization
  • learning rate schedule
  • result analysis

"It just needs to touch something to mimic it." - Sarah Connor, about the T-1000


Monitoring

Some nice tools to keep an eye while your agent train are (of course)

tensorboard
,
gpustat
and
htop
# 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


To do

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

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.