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ridgecv_op_rbcu.py
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ridgecv_op_rbcu.py
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#!/usr/bin/env python2
#coding: utf-8
from __future__ import division
import os
import shutil
import csv
import copy
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
plt.style.use('ggplot')
import seaborn as sns
from pandas.tseries.offsets import Milli
import datetime
from sklearn.linear_model import Ridge, RidgeCV, ElasticNet, ElasticNetCV
from sklearn.svm import SVR, LinearSVR
from sklearn.neighbors import KNeighborsRegressor
import optunity
import optunity.metrics
from optunity.constraints import wrap_constraints
from optunity.solvers.GridSearch import GridSearch
from optunity.solvers.ParticleSwarm import ParticleSwarm
import multiprocessing
import gc
try:
import cPickle as pickle
except ImportError:
import pickle
##########################################################################################
class predict():
def __init__(self, file_path='IF.csv'):
self.file_path = file_path
self.skip_rows = 30000
self.nrows = 30000
assert self.skip_rows >= self.nrows
self.data = None
self.features = None
self.timeshift = 1
self.resample = 1
assert self.timeshift >= self.resample
self.maxlag = 17
self.indices = None
self.cross_predict_days = None
self.cross_predict_periods = None
self.cross_predict_num = None
self.cross_predict_reindex = 0
self.windowsize = 5000
self.threshold = 0
self.Close_test = None
self.X_test = None
self.Close_train = None
self.X_train = None
self.y_train = None
self.diff_train = None
self.step = 8*60 # update model by step
self.real_time_report = True
#### you can change optunity target here!!!
self.target = 'adjusted_sigsum' # 'accuracy', 'sigsum', 'sig_per_trade', 'adjusted_sigsum'
self.y_pred = np.array([])
self.y_targets = np.array([])
self.diff_targets = np.array([])
self.results_pred = []
self.logs = True
self.algorithm = 'ridgecv'
def dataProcess(self, load_data=True):
if load_data is True:
self.data = self.loadTimeSeriesDataPro() # Read in data
## ---------------------------------------------------------------------------------------
# day_night_list = []
# data_time = self.data.Time
# for idx in xrange(self.data.shape[0]):
# if '09:00:00'<=data_time.iloc[idx]<='15:00:00':
# day_night_list.append('day')
# elif '21:00:00'<=data_time.iloc[idx]<='23:59:59' or '00:00:00'<=data_time.iloc[idx]<='01:00:00':
# day_night_list.append('night')
# else:
# day_night_list.append('')
# self.data['day_night'] = day_night_list
# data_day = self.data.ix[self.data['day_night']=='day']
# data_night = self.data.ix[self.data['day_night']=='night']
## ---------------------------------------------------------------------------------------
# self.removeDuplicate()
# self.removeOutOfTradingtime()
self.features = self.generateLagMatrix().astype(np.float16)
#### save the data and features
# with open('./data_feature', 'wb') as fp:
# pickle.dump((self.data, self.features), fp)
else:
if os.path.exists('./data_feature'):
with open('./data_feature', 'rb') as fp:
self.data, self.features = pickle.load(fp)
else:
self.data, self.features = None, None
def loadTimeSeriesData(self):
assert self.skip_rows >= self.nrows
def count_lines(filename):
count = 0
buffer_size = 1024*1024
with open(filename,'rb') as f:
while 1:
temp = f.read(buffer_size)
if not temp:
break
count += temp.count('\n')
return count
lines = count_lines(self.file_path)
# lines = np.sum(1 for _ in csv.reader(open(self.file_path)))
# print lines
if lines < self.skip_rows:
self.skip_rows = lines
if lines < self.nrows:
self.nrows = lines
# data = pd.read_csv(self.file_path, header=None, engine='c')
data = pd.read_csv(self.file_path,
engine='c',
header=None,
usecols=[0,1,2,4,6],
names=['Date','Time','Low','High','Close'],
skiprows=lines-self.skip_rows,
nrows=self.nrows,
) # read the latest nrows
# print data.groupby(data.Date).size() # describe data size order by Date
data.index = pd.to_datetime(data.Date+' '+data.Time+'.0',format='%Y-%m-%d %H:%M:%S.%f')
# data.sort_index(axis=0, ascending=True, inplace=True) # Sort the data by time index
data.drop_duplicates(keep='first', inplace=True) # Remove duplicate column
# del data['Date'], data['Time']
return data
def loadTimeSeriesDataPro(self):
assert self.skip_rows >= self.nrows
def count_lines(filename):
count = 0
buffer_size = 1024*1024
with open(filename,'rb') as f:
while 1:
temp = f.read(buffer_size)
if not temp:
break
count += temp.count('\n')
return count
lines = count_lines(self.file_path)
# lines = np.sum(1 for _ in csv.reader(open(self.file_path)))
# print lines
if lines < self.skip_rows:
self.skip_rows = lines
if lines < self.nrows:
self.nrows = lines
# data = pd.read_csv(self.file_path, header=None, engine='c')
data = pd.read_csv(self.file_path,
engine='c',
header=None,
usecols=[0,1,2,3,4,5,6],
names=['Date','Time','Open','High','Low','Close','Volume'],
skiprows=lines-self.skip_rows,
nrows=self.nrows,
) # read the latest nrows
# print data.groupby(data.Date).size() # describe data size order by Date
data.index = pd.to_datetime(data.Date+' '+data.Time+'.0',format='%Y-%m-%d %H:%M:%S.%f')
# data.sort_index(axis=0, ascending=True, inplace=True) # Sort the data by time index
data.drop_duplicates(keep='first', inplace=True) # Remove duplicate column
# del data['Date'], data['Time']
return data
def removeDuplicate(self):
uniques = np.unique(self.data.index, return_index=True)[1]
uniques.sort()
# print uniques
#### calculate index count
# new_uniques = np.append(uniques, len(self.data.index))
# diff_new_uniques = np.diff(new_uniques)
# # print diff_new_uniques
# count1, count2, count_other = 0, 0, 0
# for i, x in enumerate(diff_new_uniques):
# if x == 1:
# # print self.data.index[new_uniques[i]]
# count1 += 1
# elif x == 2:
# # print self.data.index[new_uniques[i]]
# count2 += 1
# else:
# count_other += 1
# print count1, count2, count_other
self.data['new_index'] = self.data.index+Milli(500)
self.data['new_index'].ix[uniques] -= Milli(500)
self.data.set_index('new_index', drop=True, append=False, inplace=True)
uniques_ = np.unique(self.data.index, return_index=True)[1]
uniques_.sort()
self.data = self.data.ix[uniques_]
'''
self.data = self.data.ix[np.unique(self.data.index, return_index=True)[1]] # Remove duplicate data
'''
'''
position_dict = {}
k, last_index = 0, None
for index in self.data.index:
if index != last_index:
position_dict[index] = k
# else:
# print 'Same timestamp %s' % index
k += 1
last_index = index
self.data = self.data.ix[np.sort(position_dict.values())]
'''
def removeOutOfTradingtime(self):
def isInTradingTime(timestamp):
trading_time = timestamp.time()
starttime1 = datetime.time(9, 15)
stoptime1 = datetime.time(11, 30)
starttime2 = datetime.time(13, 0)
stoptime2 = datetime.time(15, 15)
if starttime1<=trading_time<=stoptime1 or starttime2<=trading_time<=stoptime2:
return True
else:
return False
index = [item for item in self.data.index if isInTradingTime(item)]
self.data = self.data.ix[index]
def generateLagMatrix(self):
def Price_change_ratio(price1, price2):
# delta = price1 / price2-1
delta = price1-price2
return delta
self.data['Middle'] = (self.data['High']+self.data['Low'])/2.0
features = pd.DataFrame(index=self.data.index)
MAX = 100
# for i_shift in np.arange(1, MAX+1, 1):
# features['Close_lag_' +str(i_shift)] = Price_change_ratio(self.data.Close.shift(i_shift), self.data.Close)
# features['Middle_lag_'+str(i_shift)] = Price_change_ratio(self.data.Middle.shift(i_shift), self.data.Middle)
for i_shift in np.arange(1, MAX+1, 1):
features['Close_lag_' +str(i_shift)] = Price_change_ratio(self.data.Close.shift(i_shift), self.data.Close.shift(i_shift-1))
features['Middle_lag_'+str(i_shift)] = Price_change_ratio(self.data.Middle.shift(i_shift), self.data.Middle.shift(i_shift-1))
# features = features.fillna(0)
features = features.drop(features.index[:MAX+1])
self.data = self.data.drop(self.data.index[:MAX+1])
return features
def selectFeatures(self):
self.features = self.features.ix[:, :self.maxlag*2]
def targetDefine(self, threshold=0.0):
self.data['Diff'] = self.data.Close.diff(self.timeshift).shift(-self.timeshift).fillna(0)
pos = pd.Series(self.data['Diff']>threshold).astype(int)
neg = -pd.Series(self.data['Diff']<-threshold).astype(int)
self.data['Label'] = pos+neg
self.data['Diff_'] = self.data.Close.diff(self.resample).shift(-self.resample).fillna(0)
pos_ = pd.Series(self.data['Diff_']>threshold).astype(int)
neg_ = -pd.Series(self.data['Diff_']<-threshold).astype(int)
self.data['Label_'] = pos_+neg_
def run(self, mode='test'):
if mode == 'forward':
i_shift = 0
self.indices = []
if self.cross_predict_days is not None:
self.daysToIndices(i_shift)
elif self.cross_predict_periods is not None:
self.periodsToIndices(i_shift)
elif self.cross_predict_num is not None:
self.numToIndices(i_shift)
else:
pass
self.cross_predict_num = len(self.indices)
print 'cross_predict_num:', self.cross_predict_num
y_pred_array = np.zeros(self.cross_predict_num)
y_targets_array = np.zeros(self.cross_predict_num)
diff_targets_array = np.zeros(self.cross_predict_num)
Regression = None
local_threshold = 0
for i_cross_predict in np.arange(self.cross_predict_num):
#### the test sets
test_index = self.indices[i_cross_predict]
# print test_index
self.Close_test = self.data.Close.ix[test_index]
self.X_test = self.features.ix[test_index]
y_target = self.data.Label_.ix[test_index]
diff_target = self.data.Diff_.ix[test_index]
#### the train sets
if i_cross_predict%self.step == 0:
start_index = test_index-self.windowsize-self.timeshift+1
end_index = test_index-self.timeshift+1
# print start_index
# print end_index
if start_index < 0:
start_index = 0
self.Close_train = self.data.Close.ix[start_index:end_index]
self.X_train = self.features.ix[start_index:end_index]
self.y_train = self.data.Label.ix[start_index:end_index]
self.diff_train = self.data.Diff.ix[start_index:end_index]
if self.algorithm == 'ridgecv':
Regression = RidgeCV().fit(self.X_train, self.diff_train)
elif self.algorithm == 'elasticnetcv':
Regression = ElasticNetCV().fit(self.X_train, self.diff_train)
elif self.algorithm == 'knnreg':
k = 200
Regression = KNeighborsRegressor(algorithm='auto', n_neighbors=k).fit(self.X_train, self.diff_train)
elif self.algorithm == 'linearsvr':
Regression = LinearSVR().fit(self.X_train, self.diff_train)
# Regression = SVR(kernel='linear').fit(self.X_train, self.diff_train)
else:
Regression = None
# abs_diffs = np.sort(np.abs(self.diff_train))
X_diff_pred = Regression.predict(self.X_train)
abs_diffs = np.sort(np.abs(X_diff_pred))
if self.threshold == 0:
local_threshold = 0
elif self.threshold == 100:
local_threshold = abs_diffs[-1]
else:
threshold_index = int(len(abs_diffs)*self.threshold*0.01)
local_threshold = abs_diffs[threshold_index]
#### prediction stage
y_diff_pred = Regression.predict(self.X_test.reshape(1, -1))[-1] # Only one predict in the result
if np.abs(y_diff_pred) < local_threshold:
y_pred = 0
elif y_diff_pred > 0.0:
y_pred = 1
elif y_diff_pred < 0.0:
y_pred = -1
else:
y_pred = 0
# print 'Last point:%s close: %6.1f Prediction for next point: %2d' % (self.X_test.name, self.Close_test, y_pred)
if self.logs:
self.results_pred.append('%s\t%2d' % (self.X_test.name, y_pred))
y_pred_array[i_cross_predict] = y_pred
y_targets_array[i_cross_predict] = y_target
diff_targets_array[i_cross_predict] = diff_target
#### describe
accuracy_list, sigsum, real_sigsum, adjusted_sigsum, trade_count, sig_per_trade = self.resultsDescribe(y_pred_array, y_targets_array, diff_targets_array)
self.y_pred = np.append(self.y_pred, y_pred_array)
self.y_targets = np.append(self.y_targets, y_targets_array)
self.diff_targets = np.append(self.diff_targets, diff_targets_array)
last_accuracy = accuracy_list[-1]
last_sigsum = sigsum[-1]
last_adjusted_sigsum = adjusted_sigsum[-1]
last_trade_count = trade_count[-1]
last_sig_per_trade = sig_per_trade[-1]
return last_accuracy, last_sigsum, last_adjusted_sigsum, last_trade_count, last_sig_per_trade
elif mode == 'test':
last_accuracy_list = np.array([])
last_sigsum_list = np.array([])
last_sig_per_trade_list = np.array([])
last_adjusted_sigsum_list = np.array([])
last_trade_count_list = np.array([])
for i_shift in np.arange(self.resample):
self.indices = []
if self.cross_predict_days is not None:
self.daysToIndices(i_shift)
elif self.cross_predict_periods is not None:
self.periodsToIndices(i_shift)
elif self.cross_predict_num is not None:
self.numToIndices(i_shift)
else:
pass
self.cross_predict_num = len(self.indices)
print 'cross_predict_num:', self.cross_predict_num
y_pred_array = np.zeros(self.cross_predict_num)
y_targets_array = np.zeros(self.cross_predict_num)
diff_targets_array = np.zeros(self.cross_predict_num)
Regression = None
local_threshold = 0
for i_cross_predict in np.arange(self.cross_predict_num):
#### the test sets
test_index = self.indices[i_cross_predict]
# print test_index
self.Close_test = self.data.Close.ix[test_index]
self.X_test = self.features.ix[test_index]
y_target = self.data.Label_.ix[test_index]
diff_target = self.data.Diff_.ix[test_index]
#### the train sets
if i_cross_predict%self.step == 0:
start_index = test_index-self.windowsize-self.timeshift+1
end_index = test_index-self.timeshift+1
# print start_index
# print end_index
if start_index < 0:
start_index = 0
self.Close_train = self.data.Close.ix[start_index:end_index]
self.X_train = self.features.ix[start_index:end_index]
self.y_train = self.data.Label.ix[start_index:end_index]
self.diff_train = self.data.Diff.ix[start_index:end_index]
if self.algorithm == 'ridgecv':
Regression = RidgeCV().fit(self.X_train, self.diff_train)
elif self.algorithm == 'elasticnetcv':
Regression = ElasticNetCV().fit(self.X_train, self.diff_train)
elif self.algorithm == 'knnreg':
k = 200
Regression = KNeighborsRegressor(algorithm='auto', n_neighbors=k).fit(self.X_train, self.diff_train)
elif self.algorithm == 'linearsvr':
Regression = LinearSVR().fit(self.X_train, self.diff_train)
# Regression = SVR(kernel='linear').fit(self.X_train, self.diff_train)
else:
Regression = None
if self.threshold == 0:
local_threshold = 0
else:
X_diff_pred = Regression.predict(self.X_train)
abs_diffs = np.sort(np.abs(X_diff_pred))
threshold_index = int(len(abs_diffs)*self.threshold*0.01)
local_threshold = abs_diffs[threshold_index]
# abs_diffs = np.sort(np.abs(self.diff_train))
# threshold_index = int(len(abs_diffs)*self.threshold*0.01)
# local_threshold = abs_diffs[threshold_index]
#### prediction stage
y_diff_pred = Regression.predict(self.X_test.reshape(1, -1))[-1] # Only one predict in the result
if np.abs(y_diff_pred) < local_threshold:
y_pred = 0
elif y_diff_pred > 0.0:
y_pred = 1
elif y_diff_pred < 0.0:
y_pred = -1
else:
y_pred = 0
# print 'Last point:%s close: %6.1f Prediction for next point: %2d' % (self.X_test.name, self.Close_test, y_pred)
y_pred_array[i_cross_predict] = y_pred
y_targets_array[i_cross_predict] = y_target
diff_targets_array[i_cross_predict] = diff_target
#### describe
accuracy_list, sigsum, real_sigsum, adjusted_sigsum, trade_count, sig_per_trade = self.resultsDescribe(y_pred_array, y_targets_array, diff_targets_array)
last_accuracy_list = np.append(last_accuracy_list, accuracy_list[-1])
last_sigsum_list = np.append(last_sigsum_list, sigsum[-1])
last_adjusted_sigsum_list = np.append(last_adjusted_sigsum_list, adjusted_sigsum[-1])
last_trade_count_list = np.append(last_trade_count_list, trade_count[-1])
last_sig_per_trade_list = np.append(last_sig_per_trade_list, sig_per_trade[-1])
#### calculate average
last_accuracy = np.mean(last_accuracy_list)
last_sigsum = np.mean(last_sigsum_list)
last_adjusted_sigsum = np.mean(last_adjusted_sigsum_list)
last_trade_count = np.mean(last_trade_count_list)
last_sig_per_trade = np.mean(last_sig_per_trade_list)
return last_accuracy, last_sigsum, last_adjusted_sigsum, last_trade_count, last_sig_per_trade
else:
return None
def daysToIndices(self, i_shift):
assert self.timeshift >= self.resample
date_list = np.sort(list(set(self.data.Date)))
test_dates = date_list[-self.cross_predict_days:]
# print test_dates
# print len(test_dates)
start = self.data[test_dates[0]].ix[0].name
start_index = len(self.data.ix[:start])-1
self.indices = np.arange(start_index-self.cross_predict_reindex+i_shift, len(self.data.index)-self.cross_predict_reindex, self.resample)
def periodsToIndices(self, i_shift):
assert self.timeshift >= self.resample
start_index = len(self.data.index)-self.cross_predict_periods
self.indices = np.arange(start_index-self.cross_predict_reindex+i_shift, len(self.data.index)-self.cross_predict_reindex, self.resample)
def numToIndices(self, i_shift):
assert self.timeshift >= self.resample
start_index = len(self.data.index)-self.cross_predict_num*self.resample
self.indices = np.arange(start_index-self.cross_predict_reindex+i_shift, len(self.data.index)-self.cross_predict_reindex, self.resample)
def resultsDescribe(self, y_pred, y_targets, diff_targets):
#### describe
siglist = np.array(y_pred*diff_targets)
sigsum = np.cumsum(siglist)
# accuracy_list = self.cal_accuracy(siglist)
# max_drawdown, mdd_duration = self.cal_maxDrawDown(sigsum)
# str1 = 'accuracy: %.4f, max_drawdown: %.1f, mdd_duration: %d' % (accuracy_list[-1], max_drawdown, mdd_duration)
# if self.real_time_report:
# print str1
real_siglist, real_sigsum, trade_count = self.real_calculate(y_pred, diff_targets)
accuracy_list = self.cal_accuracy(real_siglist)
max_drawdown, mdd_duration = self.cal_maxDrawDown(real_sigsum)
slide_penalty = 1
adjusted_sigsum = real_sigsum-trade_count*slide_penalty
max_drawdown, mdd_duration = self.cal_maxDrawDown(adjusted_sigsum)
sig_per_trade = adjusted_sigsum*1.0/trade_count
str2 = 'accuracy: %.4f, max_drawdown: %.1f, mdd_duration: %d' % (accuracy_list[-1], max_drawdown, mdd_duration)
str3 = 'trade_count: %d, real_sigsum: %6.1f, adjusted_sigsum: %6.1f' % (trade_count[-1], real_sigsum[-1], adjusted_sigsum[-1])
if self.real_time_report:
print str2
print str3
return accuracy_list, sigsum, real_sigsum, adjusted_sigsum, trade_count, sig_per_trade
@staticmethod
def cal_accuracy(siglist):
predict_bool = np.array(siglist!=0).astype(int)
# correct_list = np.array(predict_bool&(siglist>=0)).astype(int)
correct_list = np.array(siglist>0).astype(int)
accuracy_list = np.cumsum(correct_list).astype(float)/np.cumsum(predict_bool)
# last_accuracy = accuracy_list[-1]
# last_accuracy = float(np.sum(correct_list))/np.sum(predict_bool)
return accuracy_list
@staticmethod
def cal_maxDrawDown(sigsum):
'''
Calculate max drawn down within sigsum.
Use numpy.maximum.accumulate to generate running maximum, then identifies the max drop
Returns max drawdown in float
'''
bottom_index = np.argmax(np.maximum.accumulate(sigsum)-sigsum) # end of the period, the bottom
# peak_index = np.argmax(sigsum[:bottom_index]) # start of period, the peak
# max_drawdown = sigsum[peak_index]-sigsum[bottom_index]
# mdd_duration = np.abs(bottom_index-peak_index)
# return max_drawdown, mdd_duration
if bottom_index == 0:
return 0, 0
else:
peak_index = np.argmax(sigsum[:bottom_index]) # start of period, the peak
max_drawdown = sigsum[peak_index]-sigsum[bottom_index]
mdd_duration = np.abs(bottom_index-peak_index)
return max_drawdown, mdd_duration
# @staticmethod
# def cal_maxDrawDown(sigsum):
# '''
# Return the absolute value of the maximum drawdown of sequence X.
#
# Note
# ----
# If the sequence is strictly increasing, 0 is returned.
# '''
# peak = bottom = sigsum[0]
# peak_index = bottom_index = 0
# max_drawdown = 0
# for i, x in enumerate(sigsum):
# if x > peak:
# peak = x
# peak_index = i
# # drawdown = (peak - x) / peak
# drawdown = peak - x
# if drawdown > max_drawdown:
# max_drawdown = drawdown
# bottom = x
# bottom_index = i
# mdd_duration = bottom_index-peak_index
# return max_drawdown, mdd_duration
@staticmethod
def real_calculate(y_pred, diff_targets):
df = pd.DataFrame()
df['preds'] = y_pred
df['diffs'] = diff_targets
## -------------------------------------------------------------------------------
'''replace 0 with values before'''
df['preds'] = df['preds'].replace(0, np.nan).fillna(method='ffill').fillna(0)
## -------------------------------------------------------------------------------
df['preds_turning'] = df['preds'].diff(1).replace(0, np.nan) # the first point is nan, and replace the unchange points with nan
real_siglist = np.array(df['preds']*df['diffs'])
real_sigsum = np.cumsum(real_siglist) # calculate real_sigsum
trade_count = np.zeros(len(df['preds_turning']))
for i in range(len(df['preds_turning'])):
trade_count[i] = len(df['preds_turning'][:i+1].dropna())+1
return real_siglist, real_sigsum, trade_count
@staticmethod
def pp(pic_path, accuracy_list, sigsum, real_sigsum, adjusted_sigsum):
plt.figure()
plt.subplot(211)
plt.plot(accuracy_list, label='$accuracy$')
plt.legend()
plt.ylabel('accuracy')
# plt.figtext(0.39, 0.95, 'accuracy:{:.4f}'.format(accuracy_list[-1]), color='green')
# plt.figtext(0.13, 0.91, 'sigsum:{:6.1f}'.format(sigsum[-1]), color='green')
# plt.figtext(0.39, 0.91, 'real_sigsum:{:6.1f}'.format(real_sigsum[-1]), color='green')
# plt.figtext(0.65, 0.91, 'adjusted_sigsum:{:6.1f}'.format(adjusted_sigsum[-1]), color='green')
plt.title('accuracy:{:.4f}, sigsum:{:6.1f}, real_sigsum:{:6.1f}, adjusted_sigsum:{:6.1f}'.format(
accuracy_list[-1], sigsum[-1], real_sigsum[-1], adjusted_sigsum[-1]))
plt.subplot(212)
plt.plot(sigsum, 'r-', label='$sigsum$')
plt.plot(real_sigsum, 'g-', label='$realsigsum$')
plt.plot(adjusted_sigsum, 'b-', label='$adjustedsigsum$')
plt.legend()
plt.ylabel('sigsum')
plt.savefig(pic_path)
plt.close()
plt.figure()
plt.plot(adjusted_sigsum, 'b-', label='$adjustedsigsum$')
plt.legend()
plt.ylabel('adjusted_sigsum')
# plt.figtext(0.39, 0.95, 'adjusted_sigsum:{:6.1f}'.format(adjusted_sigsum[-1]), color='green')
plt.title('adjusted_sigsum:{:6.1f}'.format(adjusted_sigsum[-1]))
plt.savefig(os.path.splitext(pic_path)[0]+'_'+os.path.splitext(pic_path)[1])
plt.close()
##########################################################################################
def para_optunity(aa):
def my_object(maxlag, windowsize, threshold):
aa.maxlag = int(maxlag)
aa.windowsize = int(windowsize)
aa.threshold = threshold
aa.selectFeatures()
last_accuracy, last_sigsum, last_adjusted_sigsum, last_trade_count, last_sig_per_trade = aa.run(mode='test')
'''you can change target here'''
if aa.target == 'accuracy':
target = last_accuracy
elif aa.target == 'sigsum':
target = last_sigsum
elif aa.target == 'sig_per_trade':
target = last_sig_per_trade
elif aa.target == 'adjusted_sigsum':
target = last_adjusted_sigsum
else:
target = last_sigsum # default
return target
def my_object_algo(algorithm, maxlag, windowsize, threshold):
aa.algorithm = algorithm
aa.maxlag = int(maxlag)
aa.windowsize = int(windowsize)
aa.threshold = threshold
aa.selectFeatures()
last_accuracy, last_sigsum, last_adjusted_sigsum, last_trade_count, last_sig_per_trade = aa.run(mode='test')
'''you can change target here'''
if aa.target == 'accuracy':
target = last_accuracy
elif aa.target == 'sigsum':
target = last_sigsum
elif aa.target == 'sig_per_trade':
target = last_sig_per_trade
elif aa.target == 'adjusted_sigsum':
target = last_adjusted_sigsum
else:
target = last_sigsum # default
return target
##########################################################################################
'''
PSO
http://optunity.readthedocs.io/en/latest/_modules/optunity/solvers/ParticleSwarm.html#ParticleSwarm
d = dict(kwargs)
if num_evals > 1000:
d['num_particles'] = 100
elif num_evals >= 200:
d['num_particles'] = 20
elif num_evals >= 10:
d['num_particles'] = 10
else:
d['num_particles'] = num_evals
d['num_generations'] = int(math.ceil(float(num_evals) / d['num_particles']))
return d
'''
maxlag = [10, 50]
windowsize = [1000, 10000]
threshold = [70, 90]
search = {
'algorithm':{'ridgecv':None},
# 'algorithm':{'ridgecv':None,'elasticnetcv':None,'knnreg':None,'linearsvr':None},
'maxlag':maxlag,
'windowsize':windowsize,
'threshold':threshold,
}
num_evals = 100
##################################################################################
#### number_of_processes must equal to num_particles!!!
if num_evals > 1000:
number_of_processes = 100
elif num_evals >= 500:
number_of_processes = 50
elif num_evals >= 300:
number_of_processes = 30
elif num_evals >= 100:
number_of_processes = 20
elif num_evals >= 30:
number_of_processes = 10
elif num_evals >= 10:
number_of_processes = 5
else:
number_of_processes = num_evals
## -------------------------------------------------------------------------------
#### ParticleSwarm_New
from optunity.solvers.ParticleSwarm_New import ParticleSwarm_New
best_params, info, _ = optunity.maximize(
# best_params, info, _ = optunity.minimize(
my_object,
solver_name = 'particle swarm new', # default:'particle swarm'
# solver_name = 'grid search', # default:'particle swarm'
num_evals = num_evals,
maxlag = maxlag,
windowsize = windowsize,
threshold = threshold,
# pmap = optunity.pmap, # Parallel map using multiprocessing
# pmap = pmap,
pmap = create_pmap(number_of_processes),
)
# print info.optimum
## -------------------------------------------------------------------------------
# #### ParticleSwarm
# best_params, info, _ = optunity.maximize(
# # best_params, info, _ = optunity.minimize(
# my_object,
# solver_name = 'particle swarm', # default:'particle swarm'
# # solver_name = 'grid search', # default:'particle swarm'
# num_evals = num_evals,
# maxlag = maxlag,
# windowsize = windowsize,
# threshold = threshold,
# # pmap = optunity.pmap, # Parallel map using multiprocessing
# # pmap = pmap,
# pmap = create_pmap(number_of_processes),
# )
# # print info.optimum
## -------------------------------------------------------------------------------
# #### ParticleSwarm
# best_params, info, _ = optunity.maximize_structured( # default:'particle swarm'
# # best_params, info, _ = optunity.minimize_structured( # default:'particle swarm'
# my_object_algo,
# search_space=search,
# num_evals = num_evals,
# # pmap = optunity.pmap, # Parallel map using multiprocessing
# # pmap = pmap,
# pmap = create_pmap(number_of_processes),
# )
# # print info.optimum
##################################################################################
df = optunity.call_log2dataframe(info.call_log)
df.sort_values('value', ascending=False, inplace=True)
return best_params, info.optimum, df
def _fun(f, q_in, q_out):
while True:
i, x = q_in.get()
if i is None:
break
value = f(*x)
if hasattr(f, 'call_log'):
k = list(f.call_log.keys())[-1]
q_out.put((i, value, k))
else:
q_out.put((i, value))
# http://stackoverflow.com/a/16071616
def pmap(f, *args, **kwargs):
"""Parallel map using multiprocessing.
:param f: the callable
:param args: arguments to f, as iterables
:returns: a list containing the results
.. warning::
This function will not work in IPython: https://github.com/claesenm/optunity/issues/8.
.. warning::
Python's multiprocessing library is incompatible with Jython.
"""
nprocs = kwargs.get('number_of_processes', multiprocessing.cpu_count())
# nprocs = multiprocessing.cpu_count()
q_in = multiprocessing.Queue(1) # q_in = multiprocessing.Queue()
q_out = multiprocessing.Queue()
proc = [multiprocessing.Process(target=_fun, args=(f, q_in, q_out))
for _ in range(nprocs)]
for p in proc:
p.daemon = True
'''
Some threads do background tasks,
like sending keepalive packets,
or performing periodic garbage collection,
or whatever.
These are only useful when the main program is running,
and it's okay to kill them off once the other, non-daemon, threads have exited.
Without daemon threads, you'd have to keep track of them,
and tell them to exit, before your program can completely quit.
By setting them as daemon threads, you can let them run and forget about them,
and when your program quits, any daemon threads are killed automatically.
'''
p.start()
sent = [q_in.put((i, x)) for i, x in enumerate(zip(*args))]
##########################################################################################
## best way
# [q_in.put((None, None)) for _ in range(nprocs)]
# res = [q_out.get() for _ in range(len(sent))]
## ---------------------------------------------------------------------------------------
## best way
res = [q_out.get() for _ in range(len(sent))]
[q_in.put((None, None)) for _ in range(nprocs)]
##########################################################################################
# for p in proc:
# p.terminate()
##########################################################################################
for p in proc:
p.join()
# FIXME: strong coupling between pmap and functions.logged
if hasattr(f, 'call_log'):
for _, value, k in sorted(res):
f.call_log[k] = value
return [x for i, x, _ in sorted(res)]
else:
return [x for i, x in sorted(res)]
def create_pmap(number_of_processes):
def pmap_bound(f, *args):
return pmap(f, *args, number_of_processes=number_of_processes)
return pmap_bound
##########################################################################################
if __name__ == '__main__':
start_time = datetime.datetime.now()
##########################################################################################
# file_path = './data/rb-cu/rb09_16.csv'
# a = predict(file_path=file_path)
# # a = predict(file_path='rb1610_tick.csv')
# # a = predict(file_path='/opt/share/rb1610_tick.csv')
# # a = predict(file_path='./data/rb1610_tick.csv')
# a.real_time_report = True
#
# ## ---------------------------------------------------------------------------------------
# a.skip_rows = 30000
# a.dataProcess(load_data=True)
#
# a.timeshift = 1
# # a.resample = a.timeshift
# a.resample = 1
# a.targetDefine()
#
# # a.cross_predict_days = 1
# a.cross_predict_periods = 8*60
# # a.cross_predict_num = 8*60
#
# a.algorithm = 'ridgecv'
# a.maxlag = 17 ## <=100
# a.windowsize = 2000
# a.threshold = 30
#
# a.selectFeatures()
# last_accuracy, last_sigsum, last_adjusted_sigsum, last_trade_count, last_sig_per_trade = a.run(mode='forward')
# print 'accuracy: %.4f, trade_count: %d, sigsum: %6.1f, adjusted_sigsum: %6.1f' % \
# (last_accuracy, last_trade_count, last_sigsum, last_adjusted_sigsum)
#
# accuracy_list, sigsum, real_sigsum, adjusted_sigsum, trade_count, sig_per_trade = a.resultsDescribe(a.y_pred, a.y_targets, a.diff_targets)
# results_path = 'results'
# if os.path.exists(results_path):
# shutil.rmtree(results_path)
# if not os.path.exists(results_path):
# os.makedirs(results_path)
# with open(os.path.join(results_path, 'results.pkl'), 'wb') as fp:
# pickle.dump((accuracy_list, sigsum, real_sigsum, adjusted_sigsum), fp)
# print 'accuracy: %.4f, trade_count: %d, sigsum: %6.1f, real_sigsum: %6.1f, adjusted_sigsum: %6.1f' % \
# (accuracy_list[-1], trade_count[-1], sigsum[-1], real_sigsum[-1], adjusted_sigsum[-1])
# a.pp(os.path.join(results_path, 'accuracy_sigsum_tradecount%d.png' % trade_count[-1]), accuracy_list, sigsum, real_sigsum, adjusted_sigsum)
##########################################################################################
file_path = './data/rb-cu/rb09_16.csv'
## ---------------------------------------------------------------------------------------
#### calculate data size according to date
temp = predict(file_path=file_path)
# temp = predict(file_path='rb1610_tick.csv')
# temp = predict(file_path='/opt/share/rb1610_tick.csv')
# temp = predict(file_path='./data/rb1610_tick.csv')
temp.real_time_report = False
temp.skip_rows = 2000000
temp.nrows = 2000000
temp.dataProcess(load_data=True)
print temp.data.groupby(temp.data.Date).size() # describe data size order by Date
print temp.data.groupby(temp.data.Date).size()['2016-04-01':'2016-04-15']
size1 = np.sum(temp.data.groupby(temp.data.Date).size()['2015-01-01':])
size2 = np.sum(temp.data.groupby(temp.data.Date).size()['2016-01-01':])
# size2 = 0
print size1, size2
del temp
gc.collect()
####
# size1 = 8*60
# size2 = 0
## ---------------------------------------------------------------------------------------
resample_num_list = [1]
# resample_num_list = [1, 2, 4, 8]
# resample_num_list = [1, 2, 3, 4, 5, 6, 7, 8]
timeshift_num_list = [1]
# timeshift_num_list = [1, 2, 4, 8, 16]
# timeshift_num_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 18, 20]
# results_dir = './'
results_dir = './results_rb_dir/'
default_nrows = 30000
for resample_num in resample_num_list:
'''you can change the condition here: whether timeshift equals resample or not'''
# timeshift_num_list = [resample_num] ## timeshift == resample
timeshift_num_list = filter(lambda x: x>=resample_num, timeshift_num_list) ## timeshift >= resample
for timeshift_num in timeshift_num_list:
## ---------------------------------------------------------------------------------------
#### calculate the first best parameter sets
temp = predict(file_path=file_path)
# temp = predict(file_path='rb1610_tick.csv')
# temp = predict(file_path='/opt/share/rb1610_tick.csv')
# temp = predict(file_path='./data/rb1610_tick.csv')
temp.real_time_report = False
num = 8*60
temp.skip_rows = default_nrows+size1
temp.dataProcess(load_data=True)
temp.timeshift = timeshift_num
# temp.resample = temp.timeshift
temp.resample = resample_num
temp.targetDefine()
# temp.cross_predict_days = 1
temp.cross_predict_periods = num
# temp.cross_predict_num = num
best_params, optimum, df_sort = para_optunity(temp)
print 'best parameters:', best_params
print 'best score:', optimum
print 'sorted best parameters:'
print df_sort
del temp
gc.collect()
# save_path = './best_parameters'
# with open(save_path, 'wb') as fp:
# pickle.dump((best_params, optimum, df_sort), fp)
####
# save_path = './best_parameters'
# with open(save_path, 'rb') as fp:
# best_params, optimum, df_sort = pickle.load(fp)
# best_params = {
# 'algorithm':'ridgecv',
# 'maxlag':40, ## <=100
# 'windowsize':300000,
# 'threshold':30
# }
## ---------------------------------------------------------------------------------------
#### Dynamic parameter optimization
dynamic = False
a = predict(file_path=file_path)
# a = predict(file_path='rb1610_tick.csv')
# a = predict(file_path='/opt/share/rb1610_tick.csv')
# a = predict(file_path='./data/rb1610_tick.csv')
a.real_time_report = True
num = 8*60
data_file = './Data.h5'
features_file = './Feature.h5'