def print_chart(exchange, symbol, timeframe):
print("\n" + exchange.name + ' ' + symbol + ' ' + timeframe + ' chart:')
# get a list of ohlcv candles
ohlcv = exchange.fetch_ohlcv(symbol, timeframe)
# get the ohlCv (closing price, index == 4)
series = [x[index] for x in ohlcv]
time = [t[0] for t in ohlcv]
tframe = pd.DataFrame(time)
frame = pd.DataFrame(series)
#print("enter")
print("\n" + frame[-2:].to_string())
print("\n" + tframe[-2:].to_string())
#ts = pd.Series(frame[-150:], tframe[-150:])
#ts = ts.cumsum()
#ts.plot()
#ts = pd.Series(np.random.randn(1000),
#index=pd.date_range('1/1/2000', periods=1000))
#ts = ts.cumsum()
fig = plt.figure()
#plt.plot(1,1)
#plt.show()
# print the chart
print("\n" +
asciichart.plot(series[-150:], {'height': 20})) # print the chart
last = ohlcv[len(ohlcv) - 1][index] # last closing price
return last
def print_chart(exchange, symbol, timeframe):
print("\n" + exchange.name + ' ' + timeframe + 'chart: ')
# get a list of ohlcv candles
ohlcv = exchange.fetch_ohlcv(symbol, timeframe)
# get the attribute we are interested in
index = 2
series = [x[index] for x in ohlcv]
# print chart
print("\n" + asciichart.plot(series[-120:], {'height': 20}))
def print_chart(exchange, symbol, timeframe):
print("\n" + exchange.name + ' ' + symbol + ' ' + timeframe + ' chart:')
# get a list of ohlcv candles
ohlcv = exchange.fetch_ohlcv(symbol, timeframe)
# get the ohlCv (closing price, index == 4)
series = [x[index] for x in ohlcv]
# print the chart
print("\n" + asciichart.plot(series[-length:], {'height': height})) # print the chart
last = ohlcv[len(ohlcv) - 1][index] # last closing price
return last
def print_chart(exchange, symbol, timeframe):
print("\n" + exchange.name + ' ' + symbol + ' ' + timeframe + ' chart:')
# get a list of ohlcv candles
ohlcv = exchange.fetch_ohlcv(symbol, timeframe)
# get the ohlCv (closing price, index == 4)
series = [x[index] for x in ohlcv]
# print the chart
print("\n" + asciichart.plot(series[-length:], {'height': height})) # print the chart
last = ohlcv[len(ohlcv) - 1][index] # last closing price
return last
def tstamp(t):
return datetime.datetime.fromtimestamp(t).strftime('%H:%M:%S.%f')
with open("arbs.json", "r") as f:
arbs = json.load(f)
print(arbs.keys())
for path in arbs["path"]:
print(path)
i = 0
for k, series in arbs["history"].items():
print("Some timeseries")
t = [tstamp(float(a["time"])) for a in series]
z = [t[0], t[-1]]
v = [float(a["value"]) * 1000 - 1000 for a in series]
print(len(v))
if max(v) < 2:
continue
print(plot(v))
input()
plt.ioff()
plt.title(k)
plt.plot(v)
plt.ylabel('Profit per $1,000')
plt.xlabel("{} to {}".format(z[0], z[1]))
plt.savefig('charts/{}.png'.format(k), dpi=plt.gcf().dpi)
plt.close()
