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VWAP_handler.py
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VWAP_handler.py
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import numpy as np
import pandas as pd
import time
from datetime import datetime
from datetime import timedelta
from datetime import date as date
from datetime import time as dt_time
import scipy.interpolate
from sklearn.linear_model import Lasso
from statsmodels.tsa.arima_model import ARMA
from math import ceil
import warnings
from os import listdir
def cov(a,b):
a_mean = np.mean(a)
b_mean = np.mean(b)
sum = 0
for i in range(0,len(a)):
sum += (a[i] - a_mean)*(b[i] - b_mean)
return sum / (len(a) - 1)
def getL(y):
"""By linear regression predict the next value"""
x = np.array(range(0,len(y)))
b = cov(x,y) / cov(x,x)
a = np.mean(y) - b * np.mean(x)
return b * len(y) + a
def rolling_mean(a,n = 5):
x = [0.] * (len(a)-n + 1)
for i in range(n,len(a) + 1):
x[i - n] = a[i-n:i].mean()
return x
def rolling_linear(a, n = 5):
x = [0.] * (len(a)-n + 1)
for i in range(n , len(a)+ 1):
x[i - n] = getL(a[i-n:i])
return x
def get_log(r_vol,p_vol,p_per):
return {'r_vol':r_vol, 'p_vol':p_vol, 'p_per':p_per}
def datetime_range(T_START_TIME, T_END_TIME ,delta):
current = T_START_TIME
end = T_END_TIME
while current < end:
yield current
current += delta
class VWAP_handler(object):
"""
a single VWAP object will track and predict one ticker
"""
def __init__(self, interval, tickers, data_path ,lasso_lambda = 812314, n_tick_threshold = 1000, market_close_time = '15:00:00'):
self.tickers = {}
self._params = {
# 'TODAY': date.today(),
'TODAY': datetime.strptime(today_for_test, "%Y-%m-%d")
'T_START_TIME': dt_time(hour = 9, minute = 30, second = 0, microsecond = 0),
'T_END_TIME': datetime.strptime(market_close_time, '%H:%M:%S').time(),
'LASSO_LAMBDA': lasso_lambda,
'N_TICK_THRESHOLD': n_tick_threshold, # Tracey to notice
'DATA_PATH': data_path
}
for ticker in tickers:
self.tickers[ticker] = VWAP(interval, ticker, self._params)
class VWAP(object):
"""
a single VWAP object will track and predict one ticker
"""
def __init__(self, interval, ticker, kwargs):
if (interval % 5 != 0) or (7200 % interval != 0):
raise ValueError('interval must be a multiple of 5 secs and can divide 2 hours')
# ugly!
tickercsv = ticker + '.csv'
if not tickercsv in listdir(kwargs['DATA_PATH']):
raise Exception('no data for %s' % ticker)
dates = set(listdir(kwargs['DATA_PATH']))
self.TODAY = kwargs['TODAY']
# self.TODAY = datetime.strptime(today_for_test, "%Y-%m-%d") # Tracey to notice
self.T_START_TIME = kwargs['T_START_TIME']
self.T_START_NANO_SEC = time.mktime(datetime.combine(self.TODAY, dt_time(hour = 9, minute = 30, second = 0, microsecond = 0) ).timetuple())
# self.T_START_TIME = self.TODAY.replace(hour = 9, minute = 30, second = 0, microsecond = 0)
self.T_END_TIME = kwargs['T_END_TIME']
self.T_END_SECS = total.seconds(self.T_END_TIME - self.T_START_TIME)
# self.T_END_TIME = self.TODAY.replace(hour = 15, minute = 00, second = 0, microsecond = 0)
self.LASSO_LAMBDA = kwargs['LASSO_LAMBDA']
self.N_TICK_THRESHOLD = kwargs['N_TICK_THRESHOLD'] # Tracey to notice
self.DATA_PATH = kwargs['DATA_PATH'] + self.TODAY.strftime("%Y%m%d") + '/'
# self.DATA_PATH = './data_path/' # Tracey to notice
self._interval = interval
self._interval_timedelta = timedelta(seconds = self._interval)
self._am_n_interval = int(self.HALFTIME / self._interval_timedelta)
self._n_interval = self._am_n_interval + ceil((self.T_END_TIME - dt_time(hour = 13,
minute = 0, second = 0, microsecond = 0)) / interval)
self._features_to_train = np.ones((11,3),dtype=float) # CA, M, L, A
self._histo_volume = np.full((10, self._n_interval),0, dtype=float) # historical trading volume
self._intraday_percentage = [1 / self._n_interval] * self._n_interval # notice .sum() =self._n_interval
# self._AR_pars = np.array([1,0],dtype =float) # (u and phi)
self._AR_pars = [0., 1.]
self._CA_today = 0
self._predicted_V = 0.
self._is_V_predicted = 0
self._last_update = 0
self._iter = 0
self._datetime_index = ( [str(dt) for dt in datetime_range(self.T_START_TIME,
dt_time(hour = 11, minute = 30, second = 0,
microsecond = 0),timedelta(seconds = self._interval))] +
[str(dt) for dt in datetime_range(dt_time(hour = 13,
minute = 0, second = 0, microsecond = 0),
self.T_END_TIME,timedelta(seconds = self._interval))])
self._today_vol = [0.] * self._n_interval
self._p_per = [0.] * self._n_interval
self._p_vol = [0] * self._n_interval
self._VWAP_log = {}
histo_date = self.TODAY
past_days = 0
x_output = np.concatenate(np.arange(0 + self._interval , 7200 + self._interval, self._interval),
np.arange(12600 + self._interval, self.T_END_SECS, self._interval), np.array([self.T_END_SECS]))
iter = 1
while iter < 11:
if not bool(dates):
raise Exception('Insufficient historical data')
histo_date = histo_date - timedelta(days = 1)
past_days += 1
if history_date.weekday() in set([5,6]):
continue
if str(histo_date) not in dates:
continue
dates.remove(str(histo_date))
try:
dat = pd.read_csv(self.DATA_PATH + tickercsv, header = 0)
except Exception:
print(print('Error in reading %s for %s, go to the previous day.' % (tickerfile, str(histo_date))))
continue
if dat.shape[0] < self.N_TICK_THRESHOLD:
print('%s in %s has few data for prediction' % (tickerfile, str(histo_date)))
continue
if past_days > 20:
warnings.warn('Lack historical data. Time span of data for predicting intraday_volume of today has exceeded 20 days.'
'We are using data %d days from today' % past_days)
try:
dat.Nano = dat.Nano / 1e9 - time.mktime(datetime.combine(histo_date, dt_time(hour = 9, minute = 30, second = 0, microsecond = 0)).timetuple())
dat = dat.as_matrix(columns = ['Nano', 'Volume']) # there will be Microsecond
datCA = dat[dat[:,0] < 0][-1,1] # Tracey to notice
if datCA < 1: # no data or no trade ?
continue
self._features_to_train[10 - iter,0] = datCA
dat = dat[dat[:,0] > 0]
# Tracey by reviewing the data from ctp finds it impossible
if any( 7200 < t < 7230 for t in dat[:,0]): # tracey_to_notice
dat = np.vstack( (dat[dat[:, 0] < 7200], [7200, dat[(dat[:,0] >= 7200)*(dat[:,0] < 7230),1][-1] ], dat[dat[:, 0] > 7230]))
if any(t >= 198000 for t in dat[:,0]):
dat = np.vstack((dat[dat[:, 0] < 19800], [19800,dat[dat[:,0] >= 19800,1][-1]])) # tracey to notice
dat[-1,0] = 198000
x_input = np.append(0, dat[:,0])
# volume_cumsum = np.append(0,dat[:,1].cumsum())
volume_cumsum = np.append(0, dat[:, 1]) # tracey to notice
y_interp = scipy.interpolate.interp1d(x_input,volume_cumsum) # ,interval)
intraday_volume = y_interp(x_output)
intraday_volume = np.append(intraday_volume[0],(intraday_volume[1:] - intraday_volume[:-1]))
self._histo_volume[10 - iter] = intraday_volume
except Exception:
print('Error when read file %s, you may check its format' % filename)
continue
iter += 1
volume_sums = np.zeros(5,dtype=float)
while iter < 16:
if not bool(dates):
raise Exception('Insufficient historical data')
history_date = history_date - timedelta(days = 1)
past_days += 1
if history_date.weekday() in set([5,6]):
continue
if str(histo_date) not in dates:
continue
dates.remove(str(histo_date))
try:
dat = pd.read_csv(self.DATA_PATH + tickercsv, header = 0)
except Exception:
print('Error in reading %s for %s, go to the previous day.' % (tickercsv, str(histo_date)))
continue
if dat.shape[0] < self.N_TICK_THRESHOLD:
print('%s in %s has few data for prediction' % (tickercsv, str(histo_date)))
continue
if past_days > 30:
warnings.warn('Lack efficacious historical data. Time span of data for predicting total trading volume of today has exceeded 30 days.')
try:
dat.Nano = dat.Nano / 1e9 - time.mktime(datetime.combine(histo_date, dt_time(hour = 9, minute = 30, second = 0, microsecond = 0)).timetuple())
dat = dat.as_matrix(columns = ['Nano', 'Volume']) # there will be Microsecond
volume_sums[15 - iter] = dat[ (dat[:,0] > 0) * (dat[:,0] < self.T_END_SECS),1][-1] # Tracey to notice
except Exception:
print('Error to read file %s, you may check its format' % tickercsv)
continue
iter += 1
# preparing sample for predicting today's total volume
self.volume_to_train = self._histo_volume.sum(axis = 1)
volume_sums = np.append(volume_sums, self.volume_to_train)
self._features_to_train[:,1] = rolling_mean(volume_sums)
self._features_to_train[:,2] = rolling_linear(volume_sums)
# get intraday pattern and intialize intraday prediction
intraday_mean = self._histo_volume.mean(axis = 0)
self._p_vol[0] = float(intraday_mean[0])
self._intraday_percentage = list(np.divide(intraday_mean, intraday_mean.sum()) * self._n_interval)
if any( i < 1 / (self._n_interval * 10 ) for i in self._intraday_percentage):
warnings.warn('adjust intraday trading volume pattern for irregular data')
tmp = np.divide(intraday_mean, intraday_mean.sum()) * self._n_interval
if np.any(tmp < 0.1):
warnings.warn('adjust intraday trading volume pattern for irregular data')
tmp[tmp >= 0.1] = tmp[tmp > 0.1] * sum(self._n_interval - 0.1 * len(tmp[tmp < 0.1]) / sum(tmp[tmp >= 0.1]))
tmp[tmp < 0.1] = 0.1
self._intraday_percentage = list(tmp)
self._p_per[0] = self._intraday_percentage[0] / self._n_interval
self._VWAP_log[self._datetime_index[0]] = get_log(None, self._p_vol[0], self._p_per[0])
# compute AR
arma = ARMA( self._histo_volume[-1] / self._intraday_percentage, order = (1,0))
self._AR_pars = arma.fit().params.tolist()
def pred_V(self):
if self._CA_today == 0:
self._features_to_train[10,0] = self._features_to_train[:,0].sum()
else:
self._features_to_train[10,0] = self._CA_today
lm = Lasso(alpha = self.LASSO_LAMBDA)
lm.fit(self._features_to_train[0:-1,:],self.volume_to_train)
self._predicted_V = int(lm.predict(self._features_to_train[-1].reshape(1,-1))[0])
if self._predicted_V < 0:
warnings.warn('We some how get a exceeding low volume prediction for today. We strongly urge you check your tick data.')
self._predicted_V = 1 # Tracey to notice
self._is_V_predicted = 1
print('finish: pred_V')
def get_predict(self):
return(self._VWAP_log)
def push_tick(self, nano, volume):
sec_time = nano / 1e9 - self.T_START_NANO_SEC
if sec_time < 0:
self.CAtoday += volume
elif sec_time < self.T_END_SECS:
if not self.is_V_predicted:
self.pred_V()
iter = int(sec_time / self._interval)
if iter > self._semi_n_interval: # in the afternoon
iter -= int(self._n_interval * 3 / 8)
self._today_vol[iter] += volume
self._iter = iter
if self._iter == self._last_update:
pass
elif self._iter - self._last_update == 1:
self._VWAP_log[self._datetime_index[self._last_update]] = get_log(self._today_vol[self._last_update], self._p_vol[self._last_update], self._p_per[self._last_update])
self._p_vol[self._iter] = int ((self._AR_pars[1] * (self._today_vol[self._last_update] / self._intraday_percentage[self._last_update] - self._AR_pars[0] ) + self._AR_pars[0] ) * self._intraday_percentage[self._iter])
if self._iter < (self._n_interval - 1):
self._p_per[self._iter] = self._p_vol[self._iter] * (1 - sum(self._p_per[0:self._iter])) / (self._predicted_V * (1 - sum(self._intraday_percentage[0:self._iter])/ self._n_interval ))
else:
self._p_per[self._n_interval - 1] = 1 - sum(self._p_per[0:(self._n_interval - 1)])
self._VWAP_log[self._datetime_index[self._iter]] = get_log(None, self._p_vol[self._iter], self._p_per[self._iter])
self._last_update = self._iter
elif self._iter - self._last_update > 1:
warnings.warn('Over %d secs without receiving data' % self.interval)
self._today_vol[iter] =+ volume
self._today_vol[self._last_update:self._iter] = [a + b for a, b in zip(self._today_vol[self._last_update:self._iter], [volume * s / sum(self._intraday_percentage[self._last_update:self._iter]) for s in self._intraday_percentage[self._last_update:self._iter]])]
for i in range(self._last_update, self._iter):
self._VWAP_log[self._datetime_index[i]] = get_log(self._today_vol[i], self._p_vol[i], self._p_per[i])
self._p_vol[i + 1] = int ((self._AR_pars[1] * (self._today_vol[i] / self._intraday_percentage[i] - self._AR_pars[0] ) + self._AR_pars[0] ) * self._intraday_percentage[i + 1])
if i + 1 < (self._n_interval - 1):
self._p_per[i + 1] = self._p_vol[i + 1] * (1 - sum(self._p_per[0:(i + 1)])) / (self._predicted_V * (1 - sum(self._intraday_percentage[0:(i + 1)])/ self._n_interval ))
else:
self._p_per[self._n_interval - 1] = 1 - sum(self._p_per[0:(self._n_interval - 1)])
self._VWAP_log[self._datetime_index[i + 1]] = get_log(None, self._p_vol[i + 1], self._p_per[i + 1])
self._last_update = self._iter
else: # when self._iter < self._last_update, we only update real volume
pass
else: # datetime > T_END_TIME
if self._iter - self._last_update == 1:
pass
else:
warnings.warn('Over %d secs without receiving data' % self.interval)
self._today_vol[iter] =+ volume
self._today_vol[self._last_update:self._iter] = [a + b for a, b in zip(self._today_vol[self._last_update:self._iter], [volume * s / sum(self._intraday_percentage[self._last_update:self._iter]) for s in self._intraday_percentage[self._last_update:self._iter]])]
for i in range(self._last_update, self._n_interval - 1):
self._VWAP_log[self._datetime_index[i]] = get_log(self._today_vol[i], self._p_vol[i], self._p_per[i])
self._p_vol[i + 1] = int ((self._AR_pars[1] * (self._today_vol[i] / self._intraday_percentage[i] - self._AR_pars[0] ) + self._AR_pars[0] ) * self._intraday_percentage[i + 1])
if i + 1 < (self._n_interval - 1):
self._p_per[i + 1] = self._p_vol[i + 1] * (1 - sum(self._p_per[0:(i + 1)])) / (self._predicted_V * (1 - sum(self._intraday_percentage[0:(i + 1)])/ self._n_interval ))
else:
self._p_per[self._n_interval - 1] = 1 - sum(self._p_per[0:(self._n_interval - 1)])
self._VWAP_log[self._datetime_index[i + 1]] = get_log(None, self._p_vol[i + 1], self._p_per[i + 1])
self._last_update = self._n_interval - 1
self._today_vol[self._n_interval - 1] += volume
self._VWAP_log[self._datetime_index[self._n_interval - 1]] = get_log(self._today_vol[self._n_interval - 1], self._p_vol[self._n_interval - 1], self._p_per[self._n_interval - 1])
if __name__ == "__main__":
interval = 60
today_for_test = '20170714'
Tracey = VWAPs(interval, ['SH600884'], lasso_lambda = 812314)
a = Tracey['SH600884']
print('DoneVWAP')
df = pd.read_csv(a.DATA_PATH + "SH6008842017-07-13.csv")
df.columns = ['DateTime','Volume']
def toInput(t):
x = t[0]
y = t[1]
x = "2017-07-14 " + x
x = datetime.strptime(x, "%Y-%m-%d %H:%M:%S")
return x,y
for i, row in enumerate(df.values.tolist()):
a.push_tick(*toInput(row))
today_vol = np.array(a.today_vol)
vwap = np.array(a.predp)
twap = np.array([1 / len(a.predp)] * len(a.predp))
s = np.array(a.intraday_percentage)
print(interval)
print('abs')
print(sum( np.absolute(today_vol / sum(today_vol) - twap)) )
print(sum( np.absolute(today_vol / sum(today_vol) - vwap)))
print(sum( np.absolute(today_vol / sum(today_vol) - s / len(a.predp))))
print(interval)