def download_data(self, pair, start, end):
"""Download trade data and store as .csv file.
Args:
pair (str): Currency pair.
start (int): Start UNIX of trade data to download.
end (int): End UNIX of trade data to download.
"""
dataio = DataIO(savedir=self._savedir, fieldnames=self.FIELDNAMES)
if dataio.csv_check(pair):
last_row = dataio.csv_get_last(pair)
newest_id = int(last_row['trade_id']) + 1
newest_t = int(last_row['time'])
else:
newest_id = self.__find_start_trade_id(pair, start)
newest_t = 0
while newest_t < end:
# new -> old
r = self.__get_slice(pair, newest_id)
# old -> new, add unix timestamp
new_r = []
for row in r:
row['time'] = row['T'] // 1000
row['date'] = timeutil.unix_to_iso(row['time'])
row['price'] = row['p']
row['size'] = row['q']
row['side'] = 'sell' if row['m'] == True else 'buy'
row['best_price_match'] = row['M']
row['trade_id'] = row['a']
row.pop('a', None)
row.pop('p', None)
row.pop('q', None)
row.pop('f', None)
row.pop('l', None)
row.pop('T', None)
row.pop('m', None)
row.pop('M', None)
new_r.append(row)
# save to file
dataio.csv_append(pair, new_r)
# break condition
if len(r) < self.__MAX_LIMIT:
break
# prepare next iteration
newest_id = new_r[-1]['trade_id'] + 1
newest_t = new_r[-1]['time']
print('Binance\t| {} : {}'.format(timeutil.unix_to_iso(newest_t),
pair))
print('Binance\t| Download complete : {}'.format(pair))
def download_data(self, pair, start, end):
"""Download trade data and store as .csv file.
Args:
pair (str): Currency pair.
start (int): Start UNIX of trade data to download.
end (int): End UNIX of trade data to download.
"""
dataio = DataIO(savedir=self._savedir, fieldnames=self.FIELDNAMES)
if dataio.csv_check(pair):
newest_t = float(dataio.csv_get_last(pair)['time'])
else:
newest_t = self.__find_start_trade_time(pair, start)
while newest_t < end:
# old -> new
r = self.__get_slice(pair, newest_t + 1e-4)
# list to dict
r = self.__to_dict(r)
# save to file
dataio.csv_append(pair, r)
# break condition
if len(r) < self.__MAX_LIMIT:
break
# prepare next iteration
newest_t = float(r[-1]['time'])
print('Kraken\t| {} : {}'.format(timeutil.unix_to_iso(newest_t),
pair))
print('Kraken\t| Download complete : {}'.format(pair))
def download_data(self, pair, start, end):
"""Download trade data and store as .csv file.
Args:
pair (str): Currency pair.
start (int): Start UNIX of trade data to download.
end (int): End UNIX of trade data to download.
"""
dataio = DataIO(savedir=self._savedir, fieldnames=self.FIELDNAMES)
if dataio.csv_check(pair):
last_row = dataio.csv_get_last(pair)
newest_id = int(last_row['trade_id']) + 1
newest_t = int(last_row['time'])
else:
newest_id = self.__find_start_trade_id(pair, start)
newest_t = 0
last_trade_id = self.__find_last_trade_id(pair)
while newest_t < end:
# new -> old
r = self.__get_slice(pair, newest_id + self.__MAX_LIMIT)
# break condition
to_break = False
# old -> new, add unix timestamp
new_r = []
for row in reversed(r):
if row['trade_id'] > newest_id:
row['date'] = row['time']
row['time'] = timeutil.iso_to_unix(row['time'])
new_r.append(row)
if row['trade_id'] == last_trade_id:
to_break = True
# save to file
dataio.csv_append(pair, new_r)
# break condition
if to_break:
break
# prepare next iteration
newest_id = new_r[-1]['trade_id']
newest_t = new_r[-1]['time']
print('GDAX\t| {} : {}'.format(timeutil.unix_to_iso(newest_t),
pair))
print('GDAX\t| Download complete : {}'.format(pair))
def download_data(self, pair, start, end):
"""Download trade data and store as .csv file.
Args:
pair (str): Currency pair.
start (int): Start UNIX of trade data to download.
end (int): End UNIX of trade data to download.
"""
dataio = DataIO(savedir=self._savedir, fieldnames=self.FIELDNAMES)
if dataio.csv_check(pair):
last_row = dataio.csv_get_last(pair)
newest_id = int(last_row['trade_id']) + 1
newest_t = int(last_row['time'])
else:
newest_id = self.__find_start_trade_id(pair, start)
newest_t = 0
while newest_t < end:
# new -> old
r = self.__get_slice(pair, newest_id)
# old -> new, add unix timestamp
new_r = []
for i, row in enumerate(r):
row['time'] = timeutil.iso_to_unix(row['timestamp'])
row['date'] = row['timestamp']
row['trade_id'] = newest_id + i
row['side'] = row['side'].lower()
row.pop('timestamp', None)
row.pop('symbol', None)
new_r.append(row)
# save to file
dataio.csv_append(pair, new_r)
# break condition
if len(r) < self.__MAX_LIMIT:
break
# prepare next iteration
newest_id = new_r[-1]['trade_id'] + 1
newest_t = new_r[-1]['time']
print('Bitmex\t| {} : {}'.format(
timeutil.unix_to_iso(newest_t), pair))
print('Bitmex\t| Download complete : {}'.format(pair))
def download_data(self, pair, start, end):
"""Download trade data and store as .csv file.
Args:
pair (str): Currency pair.
start (int): Start UNIX of trade data to download.
end (int): End UNIX of trade data to download.
"""
dataio = DataIO(savedir=self._savedir, fieldnames=self.FIELDNAMES)
last_row = None
if dataio.csv_check(pair):
last_row = dataio.csv_get_last(pair)
newest_t = int(last_row['time'])
else:
newest_t = self.__find_start_trade_time(pair, start) - 1
# break condition
last_trade_time = self.__find_last_trade_time(pair)
while newest_t < end:
# new -> old
r = self.__get_slice(pair, newest_t)
# old -> new; remove duplicate data by trade ID
new_r = []
for row in reversed(r):
if last_row is not None:
if int(last_row['tradeID']) >= row['tradeID']:
continue # remove duplicates
last_row = row
row['time'] = timeutil.iso_to_unix(row['date'])
new_r.append(row)
if newest_t > last_trade_time:
break
# save to file
dataio.csv_append(pair, new_r)
# prepare next iteration
newest_t += self.__MAX_RANGE
print('Poloniex| {} : {}'.format(
timeutil.unix_to_iso(newest_t), pair))
print('Poloniex| Download complete : {}'.format(pair))
def __to_dict(self, data):
"""Convert API trade data response into a list of dict.
Args:
data (list of list): API trade data response.
Returns:
List of python dict.
"""
new_data = []
for row in data:
row_dict = {
'date': timeutil.unix_to_iso(row[2]),
'time': row[2],
'size': row[1],
'price': row[0],
'side': row[3],
'order_type': row[4]
}
new_data.append(row_dict)
return new_data
def plot(ax, r, plot_title, show_legend=False):
# format OHLC data
dates = [timeutil.unix_to_iso(unix) for unix in r['time']]
opens = r['open']
highs = r['high']
lows = r['low']
closes = r['close']
volumes = r['volume']
sell_volumes = r['sell_volume']
sell_weighted_avgs = r['sell_weighted_average']
buy_weighted_avgs = r['buy_weighted_average']
n_clip = 40
ax.grid()
ax.set_title(plot_title)
ax.set_xticks([x for x in range(len(dates) - n_clip)[::10]])
ax.set_xticklabels(dates[n_clip::10])
for tick in ax.get_xticklabels():
tick.set_rotation(45)
# plot chart OHLC
candlestick2_ohlc(ax=ax,
opens=opens[n_clip:],
highs=highs[n_clip:],
lows=lows[n_clip:],
closes=closes[n_clip:],
width=0.6,
colorup='green',
colordown='red')
# legend
lines, labels = [], []
# close
l, = ax.plot(closes[n_clip:], color='black', linewidth=0.5)
lines.append(l)
labels.append('Close')
# weighted averages
l, = ax.plot(sell_weighted_avgs[n_clip:], color='pink', linewidth=1.5)
lines.append(l)
labels.append('Weighted Sell Average')
l, = ax.plot(buy_weighted_avgs[n_clip:], color='cyan', linewidth=1.5)
lines.append(l)
labels.append('Weighted Buy Average')
# volume
ax_twin = ax.twinx()
ax_twin.yaxis.set_visible(False)
ind = [x for x in range(len(volumes) - n_clip)]
ax_twin.bar(ind, volumes[n_clip:], alpha=0.2)
ax_twin.bar(ind, sell_volumes[n_clip:], color='red', alpha=0.2)
# SMA
n = 20
ma = datautil.ma(closes, n)
l, = ax.plot(ma[n_clip:], color='blue', linewidth=1.5)
lines.append(l)
labels.append('SMA(n={})'.format(n))
# BB
n = 20
k = 2
vol_std = datautil.vol(closes, n)
std_upper = ma + vol_std * k
std_lower = ma - vol_std * k
ax.plot(std_upper[n_clip:], color='blue', linewidth=0.5)
l, = ax.plot(std_lower[n_clip:], color='blue', linewidth=0.5)
lines.append(l)
labels.append('BB(k={},n={})'.format(k, n))
# EMA
n = 20
ema = datautil.ema(closes, n)
l, = ax.plot(ema[n_clip:], color='red')
lines.append(l)
labels.append('EMA(n={})'.format(n))
# EMA
n = 30
ema = datautil.ema(closes, n)
l, = ax.plot(ema[n_clip:], color='red', dashes=[3, 2])
lines.append(l)
labels.append('EMA(n={})'.format(n))
# DEMA
n = 20
dema = datautil.dema(closes, n)
l, = ax.plot(dema[n_clip:], color='green')
lines.append(l)
labels.append('DEMA(n={})'.format(n))
# DEMA
n = 30
dema = datautil.dema(closes, n)
l, = ax.plot(dema[n_clip:], color='green', dashes=[3, 2])
lines.append(l)
labels.append('DEMA(n={})'.format(n))
# TEMA
n = 20
tema = datautil.tema(closes, n)
l, = ax.plot(tema[n_clip:], color='orange')
lines.append(l)
labels.append('TEMA(n={})'.format(n))
# TEMA
n = 30
tema = datautil.tema(closes, n)
l, = ax.plot(tema[n_clip:], color='orange', dashes=[3, 2])
lines.append(l)
labels.append('TEMA(n={})'.format(n))
# RSI
n = 30
rsi = datautil.rsi(closes, n)
ax_twin = ax.twinx()
ax_twin.yaxis.set_visible(False)
l, = ax_twin.plot(rsi[n_clip:], color='orange', alpha=0.5)
lines.append(l)
labels.append('RSI(n={})'.format(n))
# draw legend
if show_legend:
ax.legend(lines, labels, loc=2)
import timeutil
if __name__ == '__main__':
unix = 1500000000
# unix to ...
iso = timeutil.unix_to_iso(unix)
utc_date = timeutil.unix_to_utc_date(unix)
local_date = timeutil.unix_to_local_date(unix)
print('unix conversion:', iso)
print('unix conversion:', utc_date)
print('unix conversion:', local_date)
# iso to ...
unix_1 = timeutil.iso_to_unix(iso)
utc_date_1 = timeutil.iso_to_utc_date(iso)
local_date_1 = timeutil.iso_to_local_date(iso)
print('iso conversion:', unix_1)
print('iso conversion:', utc_date_1)
print('iso conversion:', local_date_1)
# utc_date to ...
unix_2 = timeutil.utc_date_to_unix(utc_date)
iso_2 = timeutil.utc_date_to_iso(utc_date)
local_date_2 = timeutil.utc_date_to_local_date(utc_date)
print('utc_date conversion:', unix_2)
print('utc_date conversion:', iso_2)
print('utc_date conversion:', local_date_2)
# local_date to ...
unix_3 = timeutil.local_date_to_unix(local_date)