def simulate(start, now):
p = generateHistoricalData(start, now.strftime('%Y-%m-%d'))
# commandCenter = LongtermStrategy(p)
commandCenter = ShortTermStrategy(p)
while now < datetime.datetime(2019, 4, 1):
next = now + datetime.timedelta(days=1)
data = ts.get_hist_data('cyb',
start=now.strftime('%Y-%m-%d'),
end=next.strftime('%Y-%m-%d'),
ktype="5")[::-1]
for row in data.iterrows():
open = float(row[1]['open'])
close = float(row[1]['close'])
high = float(row[1]['high'])
low = float(row[1]['low'])
date = row[0].split(' ')[0]
currentPrice = Price(high, low, open, close, date)
decision = commandCenter.do(currentPrice)
if decision.Type == 'Quit with benefit' or decision.Type == 'First Position' \
or decision.Type == "Stop with loss" or decision.Type == 'Follow Buying':
logging.info(decision)
logging.info(row)
now = next
return commandCenter.account
def simulate(start, now):
p = generateHistoricalData(start, now)
# commandCenter = LongtermStrategy(p)
commandCenter = ShortTermStrategy(p)
with open('resource/BTCUSD_1h.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
cursor = 0
for row in reader:
date = row['Date'].split(' ')[0]
if date < now:
continue
h = float(row['High'])
l = float(row['Low'])
o = float(row['Open'])
c = float(row['Close'])
currentPrice = Price(h, l, o, c)
decision = commandCenter.do(currentPrice, cursor % 24)
if decision.Type == 'Quit with benefit' or decision.Type == 'First Position' \
or decision.Type == "Stop with loss" or decision.Type == 'Follow Buying':
logging.info(decision)
logging.info(row)
cursor += 1
return commandCenter.account
def generateHistoricalData(startDate, endDate):
data = ts.get_hist_data('cyb', start=startDate, end=endDate, ktype="D")
dailyPrices = []
for row in data.iterrows():
open = float(row[1]['open'])
close = float(row[1]['close'])
high = float(row[1]['high'])
low = float(row[1]['low'])
date = row[0]
dailyPrices.insert(0, Price(high, low, open, close, date))
return dailyPrices
def dayPriceFromHourPrice(self, hours):
h = 0
l = sys.maxsize
o = 0
c = hours[-1].close
for hour in hours:
if o == 0:
o = hour.open
if h < hour.high:
h = hour.high
if l > hour.low:
l = hour.low
return Price(h, l, o, c, hours[0].date)
def retrieveLastHourData(self):
URL = 'https://www.bitstamp.net/api/ticker_hour/'
try:
r = requests.get(URL)
raw_body = json.loads(r.text)
open = float(raw_body['open'])
close = float(raw_body['last'])
high = float(raw_body['high'])
low = float(raw_body['low'])
date = datetime.datetime.utcfromtimestamp(
int(raw_body['timestamp'])).strftime('%Y-%m-%d')
return Price(high, low, open, close, date)
except requests.ConnectionError:
print("Error querying Bitstamp API")
return None
def retrieveHistoricalData(self, from_date, to_date=None, coin='bitcoin'):
"""Retrieve basic historical information for a specific cryptocurrency from coinmarketcap.com
Parameters
----------
from_date : the starting date (as string) for the returned data;
required format is %Y-%m-%d (e.g. "2017-06-21")
to_date : the end date (as string) for the returned data;
required format is %Y-%m-%d (e.g. "2017-06-21")
Optional. If unspecified, it will default to the current day
coin : the name of the cryptocurrency (e.g. 'bitcoin', 'ethereum', 'dentacoin')
Returns
-------
pandas Dataframe
"""
if to_date is None:
to_date = datetime.date.today().strftime("%Y-%m-%d")
try:
output = pd.read_html(
"https://coinmarketcap.com/currencies/{}/historical-data/?start={}&end={}"
.format(coin, from_date.replace("-", ""),
to_date.replace("-", "")))[0]
except:
# future versions may split out the different exceptions (e.g. timeout)
raise
output = output.assign(Date=pd.to_datetime(output['Date']))
for col in output.columns:
if output[col].dtype == np.dtype('O'):
output.loc[output[col] == "-", col] = 0
output[col] = output[col].astype('int64')
output.columns = [
re.sub(r"[^a-z]", "", col.lower()) for col in output.columns
]
result = []
for row in output.iterrows():
open = float(row[1]['open'])
close = float(row[1]['close'])
high = float(row[1]['high'])
low = float(row[1]['low'])
date = row[1]['date'].strftime('%Y-%m-%d')
result.insert(0, Price(high, low, open, close, date))
return result
def generateHistoricalData(startDate, endDate):
dailyPrices = []
with open('resource/BTC-USD.csv', newline='') as csvfile:
reader = csv.DictReader(csvfile)
for row in reader:
if row['Date'] < startDate:
continue
if row['Date'] >= endDate:
break
h = float(row['High'])
l = float(row['Low'])
o = float(row['Open'])
c = float(row['Close'])
p = Price(h, l, o, c)
dailyPrices.append(p)
return dailyPrices