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)
fig.append_trace(volumePlot(ticks), 2, 1)
fig['layout'].update(shapes=tendencyShapes(ticks))
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[
train_size:len(dataset), :]
# print(len(train), len(test))
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(dataset, 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)
fig.append_trace(volumePlot(ticks), 2, 1)
fig['layout'].update(shapes = tendencyShapes(ticks))
# -*- 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=0.10)
ticksSlice = ticks
tenShapes = tendencyShapes(ticksSlice)
layout = go.Layout(
xaxis = dict(
autorange=True
),
yaxis = dict(
autorange=True
),
shapes = tenShapes
)
data = [closesPlot(ticksSlice)]
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)
addTendency(dataset, threshold=0.10)
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[
train_size:len(dataset), :]
# print(len(train), len(test))
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)
fig['layout'].update(shapes = tendencyShapes(ticks))