Core project of the Crypto Fund.

What is it for?

• Price arbitrage between markets
• Market making
• Time Series forecasting

Installation

virtualenv -p python3 venv
source venv/bin/activate
git clone https://github.com/crypto-fund/core.git
cd core
pip3 install -r requirements.txt

Start to trade on the simulator

Run the simulator

cd simulator
export PYTHONPATH=../:$PYTHONPATH; python3 main.py
# * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

Run the trader

In another terminal tab, run:

cp credentials.json.example credentials.json # for simulation you don't need valid credentials but you need credentials.json.
curl -X POST http://127.0.0.1:5000/v2/balance/ # should display a nice JSON with all the variables.
# in constant.py, ADMIN_PROD_FLAG = False (by default)
python3 start_trading.py

# wait a few minutes
# Ctrl+C will quit the run script and will finish by a mass cancel of all open orders.

curl -X POST http://127.0.0.1:5000/v2/balance/

# expect the eur_available, eur_balance to be slightly different.

The model deployed here is a dummy model whose weights are in MODEL_CHECKPOINTS_DIR = dummy_checkpoints.

The next section is about training.

Training

In this section, we're going to see how:

• to train a model from scratch
• the correct format and conventions to make a nice interoperability with the inference part.

Starting a training is actually very simple. Here's how to start it for the dummy model (model_0):

rm -rf dummy_checkpoints/* # let's clean the dummy weights.
python3 model_training/model_0_training.py
ls -l dummy_checkpoints # should contain some files!

Training is totally independent of inference. The only interface are the weights themselves.

The inference task expects two main things.

• a specific naming to retrieve the nodes easily:

MODEL_INPUT_TENSOR_NAME = 'Input/ClosePrices'
MODEL_OUTPUT_TENSOR_NAME = 'Output/Prediction'

• 3 outputs for the output node (should be buy, hold and sell confidences and they should add up to 1).

A simple model graph filling all those requirements are:

import tensorflow as tf
import tensorflow.contrib.slim as slim
from easy_model_saving import model_saver

input_close_prices = tf.placeholder(tf.float32, shape=[None, 1], name=MODEL_INPUT_TENSOR_NAME) # here the name is important.
m = slim.fully_connected(inputs=input_close_prices, num_outputs=20, activation_fn=tf.nn.relu)
softmax_output = slim.fully_connected(inputs=m, num_outputs=3, activation_fn=tf.nn.softmax) # here the num_outputs=3 is important.
tf.identity(softmax_output, name=MODEL_OUTPUT_TENSOR_NAME) # here the name is important.

[None, 1] will be updated to [None, sequence_length] where sequence_length is the lookback window of the LSTM model.

A very simple training procedure should look like this:

import tensorflow as tf
import tensorflow.contrib.slim as slim
from easy_model_saving import model_saver

def run_dummy_training():
    with tf.Session() as sess:
        graph()
        sess.run(tf.global_variables_initializer())
        saver = model_saver.Saver(MODEL_CHECKPOINTS_DIR)
        saver.save(global_step=10)

        last_step = model_saver.restore_graph_variables(MODEL_CHECKPOINTS_DIR)
        assert last_step != 0

All those snippets in this section are from model_training/model_0_training.py.

That's it!