CryptoFun

set up virtual environment and/or install pip requirements pip install -r requirements.txt should work.

To test Orderbook:

import GDAXWrapper as gd
ob = gd.Orderbook()
ob.plot(live=True, fraction=.01)

Note, the orderbook live plot can only be canceled currently through a KeyboardInterupt error (Ctrl+C)

To test the Genetic Algorithm

Note, this currently doesn't actually give better results than doing nothing, but it does work (albeit poorly).

import GDAXWrapper as gd
import pandas as pd
import matplotlib.pyplot as plt

## read data set
data_set = pd.read_csv('two_years.tsv', sep='\t')

## split data to training and testing set
cutoff = int((2/3)*len(data_set))
train_set = data_set.iloc[:cutoff]
test_set = data_set.iloc[cutoff:];

## initialize first model
dataframe, params = gd.simulateModel(train_set)

## Run for multiple generations, it will run for max of 100 if no children in any generation improve
previous_best_value = float(dataframe.value.iloc[-1])
best_dataframe, best_params, p = gd.simulateGeneration(train_set, params, previous_best=previous_best_value)

## when done, you can view the results in a plot
best_dataframe.plot(x='time', y=['currency1', 'currency2', 'value'], logy=True)
plt.show()

Important note, I occasionally get an overflow error (and you end up with extreme numbers for the value). I don't currently have a solution (mostly just haven't looked into), but for now, just rerun the gd.simulateGeneration() line until it doesn't happen. It is fairly rare, in general.