def __init__(self,
cryptoCoin='ETH',
buyPercentage=0.5,
initialUsd=1000.0,
initialCrypto=1.0,
isRealTime=True):
self.initialCrypto = initialCrypto
self.initialUsd = initialUsd
self.isRealTime = isRealTime
self.startTime = datetime.datetime.now().isoformat()
self.usd = initialUsd
self.crypto = initialCrypto
self.cryptoCoin = cryptoCoin
self.coinPair = '{}-USD'.format(self.cryptoCoin)
self.buyPercentage = buyPercentage
self.predictor = Predict_model(modelName)
self.pastCurrentPrice = None
self.pastPastCurrentPrice = None
self.predictedDelta = None
self.predictPrice = None
self.valueHistory = []
startingData = data_loader.getCandles('ETH-USD', 60,
save=False)[['open']]
for currentPrice in startingData.values:
if self.pastCurrentPrice:
self.predictor.get_model(self.pastCurrentPrice, currentPrice)
self.pastPastCurrentPrice = self.pastCurrentPrice
self.pastCurrentPrice = currentPrice
import data_loader
import pandas as pd
import logging
from statsmodels.graphics import tsaplots
logging.basicConfig(level=logging.INFO)
def stats_graph(data):
def label(ax, string):
ax.annotate(string, (1, 1), xytext=(-8, -8), ha='right', va='top', xycoords='axes fraction', textcoords='offset points')
fig, axes = plt.subplots(nrows=2)
fig.tight_layout()
axes[0].plot(data)
label(axes[0], 'Raw Data')
pd.plotting.autocorrelation_plot(data, ax=axes[1])
label(axes[1], 'Pandas Autocorrelation')
# Remove some of the titles and labels that were automatically added
for ax in axes.flat:
ax.set(title='', xlabel='')
plt.show()
candles = data_loader.getCandles('ETH-USD', 60, start='2018-02-14T00:00:25+01:00', end='2018-03-14T00:00:25+01:00', save=True)
data = candles['open']
ewma = pd.ewma(data, span=30)
stats_graph(data)
# -*- coding: utf-8 -*-
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "../"))
import pandas as pd
import plotly.offline as py
import plotly.graph_objs as go
from plotly import tools
import data_loader
from utils.indicators import addTendency
from utils.plots import candlesPlot, closesPlot, movingAveragePlot, bollingerBandsPlots, volumePlot, tendencyShapes
ticks = data_loader.getCandles('ETH-USD',
60,
start='2016-10-14T00:00:25+01:00',
end='2018-03-22T00:00:25+01:00',
save=True)
ticks = data_loader.getCandles('ETH-USD', 3600, save=False)
addTendency(ticks, threshold=3)
fig = tools.make_subplots(rows=2, cols=1)
fig.append_trace(candlesPlot(ticks), 1, 1)
fig.append_trace(closesPlot(ticks), 1, 1)
fig.append_trace(movingAveragePlot(ticks, 10), 1, 1)
(bbPlot1, bbPlot2) = bollingerBandsPlots(ticks, 10)
fig.append_trace(bbPlot1, 1, 1)
fig.append_trace(bbPlot2, 1, 1)
action='store_const',
const=True,
default=False,
help='Only visualize results of previously saved model')
parser.add_argument('-r',
'--resume',
action='store_const',
const=True,
default=False,
help='resume from previous checkpoint')
args = parser.parse_args()
"""Get Data"""
dataset = data_loader.getCandles('ETH-USD',
60,
start='2018-02-01T00:00:25+01:00',
end='2018-05-01T00:00:25+01:00',
save=True)[['open']]
"""###Normalize data"""
# normalize the dataset
dataset1 = dataset.values[:-1]
dataset2 = dataset.values[1:]
dataset_value = dataset
dataset = dataset2 / dataset1
"""###Split data into training and test. Training is the past, test is the future."""
# split into train and test sets
train_size = int(len(dataset) * 0.7)
test_size = len(dataset) - train_size
logging.basicConfig(level=logging.INFO)
def stats_graph(data):
def label(ax, string):
ax.annotate(string, (1, 1), xytext=(-8, -8), ha='right', va='top',
size=14, xycoords='axes fraction', textcoords='offset points')
fig, axes = plt.subplots(nrows=4, figsize=(8, 12))
fig.tight_layout()
axes[0].plot(data)
label(axes[0], 'Raw Data')
axes[1].acorr(data, maxlags=data.size-1)
label(axes[1], 'Matplotlib Autocorrelation')
tsaplots.plot_acf(data, axes[2])
label(axes[2], 'Statsmodels Autocorrelation')
pd.plotting.autocorrelation_plot(data, ax=axes[3])
label(axes[3], 'Pandas Autocorrelation')
# Remove some of the titles and labels that were automatically added
for ax in axes.flat:
ax.set(title='', xlabel='')
plt.show()
candles = data_loader.getCandles('ETH-USD', 60, '2018-02-01T00:00:25+01:00', '2018-02-28T23:58:25+01:00')
data = candles['open']
stats_graph(data)
parser.add_argument(
'--visualize_results',
'-v',
action='store_true',
default=False,
help='use this to visualize the results of the currently trained model')
args = parser.parse_args()
use_cuda = torch.cuda.is_available()
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
best_loss = 99999
###Load data
dataset = data_loader.getCandles('ETH-USD',
60,
start='2018-03-12T13:19:54.527842',
end='2018-03-15T13:19:54.527861',
save=True)
addTendency(dataset, threshold=0.05)
scaler = StandardScaler()
scaler.fit(dataset[['open', 'volume']])
t = scaler.transform(dataset[['open', 'volume']])
tdf = pd.DataFrame(t, columns=['open_norm', 'vol_norm'], index=dataset.index)
dataset = pd.concat([dataset, tdf], axis=1)
"""###Split data into training and test. Training is the past, test is the future."""
# split into train and test sets
train_size = int(len(dataset) * 0.7)
test_size = len(dataset) - train_size
train, test = dataset.iloc[0:train_size, :], dataset.iloc[
if __name__ == '__main__':
if args.realtime:
logging.basicConfig(level=logging.INFO)
sim = Simulator()
tickerFeed = TickerFeed(sim.coinPair, 60)
tickerFeed.onTickerReceived(sim.process)
tickerFeed.run()
elif args.perfect:
logging.basicConfig(level=logging.INFO)
logging.info('USING A PERFECT MODEL')
sim = Simulator(isRealTime=False)
simTestData = data_loader.getCandles('ETH-USD',
60,
start='2018-02-14T00:00:25+01:00',
end='2018-03-14T00:00:25+01:00',
save=True)[['open']]
for i in range(0, len(simTestData) - 1):
item = simTestData.iloc[i]
price = item[0]
time = item.name.isoformat()
nextItem = simTestData.iloc[i + 1]
nextPrice = nextItem[0]
sim.process({
'price': price,
'time': time
}, {'delta': nextPrice / price})
parser.add_argument('-v',
'--visualize',
action='store_const',
const=True,
default=False,
help='Only visualize results of previously saved model')
parser.add_argument('-r',
'--resume',
action='store_const',
const=True,
default=False,
help='resume from previous checkpoint')
args = parser.parse_args()
"""Get Data"""
dataset = data_loader.getCandles('ETH-USD', 60, '2018-02-01T00:00:25+01:00',
'2018-05-01T00:00:25+01:00')[['open']]
"""###Normalize data"""
# normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
scaler.fit(dataset)
dataset = scaler.transform(dataset)
scaler_path = os.path.join(os.path.dirname(__file__),
'checkpoint/{}/scaler.pkl'.format(checkpoint_name))
joblib.dump(scaler, scaler_path)
"""###Split data into training and test. Training is the past, test is the future."""
# split into train and test sets
train_size = int(len(dataset) * 0.7)
test_size = len(dataset) - train_size
train, test = dataset[0:train_size, :], dataset[train_size:len(dataset), :]
# -*- coding: utf-8 -*-
import os
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "../"))
import pandas as pd
import plotly.offline as py
import plotly.graph_objs as go
from plotly import tools
import data_loader
from utils.indicators import addTendency
from utils.plots import candlesPlot, closesPlot, movingAveragePlot, bollingerBandsPlots, volumePlot, tendencyShapes
#ticks = data_loader.getCandles('ETH-USD', 60, start='2016-10-14T00:00:25+01:00', end='2018-03-22T00:00:25+01:00', save=True)
ticks = data_loader.getCandles('ETH-USD', 3600, save=False)
addTendency(ticks, threshold=0.05)
fig = tools.make_subplots(rows=2, cols=1)
fig.append_trace(candlesPlot(ticks), 1, 1)
fig.append_trace(closesPlot(ticks), 1, 1)
fig.append_trace(movingAveragePlot(ticks, 10), 1, 1)
(bbPlot1, bbPlot2) = bollingerBandsPlots(ticks, 10)
fig.append_trace(bbPlot1, 1, 1)
fig.append_trace(bbPlot2, 1, 1)
fig.append_trace(volumePlot(ticks), 2, 1)
