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combi2.py
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combi2.py
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from sys import argv
import queue, threading
import multiprocessing as mp
import concurrent.futures
import csv
import copy
from copy import deepcopy
import sched, time, datetime
import pickle
import gzip
import ubjson
import math
from pathlib import Path
from glob import glob
import numpy as np
import random
import scipy.stats as stats
from random import randint, uniform
from sklearn.metrics import r2_score
#from sklearn.model_selection import cross_val_score
#from sklearn.neural_network import MLPRegressor
from sklearn.ensemble import AdaBoostRegressor,RandomForestRegressor,BaggingRegressor,GradientBoostingRegressor,ExtraTreesRegressor
from sklearn.svm import LinearSVR, SVR, NuSVR
from sklearn.neural_network import MLPRegressor
from sklearn.linear_model import SGDRegressor
from sklearn.neighbors import KNeighborsRegressor
#from sklearn.neighbors import RadiusNeighborsRegressor
from sklearn.model_selection import train_test_split
from sklearn.model_selection import RandomizedSearchCV
from sklearn.preprocessing import MinMaxScaler
import xgboost as xgboost
#import lightgbm as lgb
import indicators as ind
from symbols import *
from math import sqrt
import matplotlib.pyplot as plt
import pydnn
mtt = 3 # months to test
tf = 15
iterations = 10
boundary = 0.38
#starting = int(60/tf*24*23*39)
#validation = int(60/tf*24*23*mtt)
validation = int(60/tf*24*23*mtt*2)
testing = int(60/tf*24*23*mtt)
start = 20130101
#test = "_test"
test = ""
#d = "combi_"+str(tf)+"/svr"
d = "combi_"+str(tf)+"/gradient_boosting"
#d = "combi_"+str(tf)+"/neural____network"
#d = "combi_"+str(tf)+"/xg_boosting"
def getSortKey(item):
global sortKey
return item[sortKey]
def normalizeX(p,x,f,ts,y,boundaryF=-1):
global sortKey
sortKey = 0
d = []
factors=[]
for dd in x: d.append(dd[:])
try:
last = len(x[0])
except:
print(p+": no record")
l = len(d)
if boundaryF<0: ff = boundary
else : ff = boundaryF
for i in range(last):
print("normalizing key "+str(i))
sortKey=i
d.sort(reverse=True,key=getSortKey)
if f[i]==1 : fac = abs(d[round(ff/100*l)][i])
elif f[i]==0 : fac = (abs(d[round(ff/200*l)][i])+abs(d[round((1-ff/200)*l)-1][i]))/2
elif f[i]==-1: fac = -100
elif f[i]==24: fac = 24
#if fac!=0:
# for j in range(l): x[j][i] = max(-1,min(1,x[j][i]/fac))
#elif (fac>450): print("index "+str(i)+" exceeded 450................................")
fac = 1
factors.append(fac)
xx=[]
tss=[]
yy=[[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]]
print(p+': sorting done')
for i in range(len(x)):
#if (x[i][19]<20):
xx.append(x[i])
tss.append(ts[i])
print(p+': xx tss done')
for j in range(1):
for i in range(len(x)):
#if (x[i][19]<20):
yy[j].append(y[j][i])
return factors,xx,yy,tss
def finalize_training_data(outfile,xf,ts,x,y,bf=-1):
ff = bf
if bf==-1: ff = boundary
#l = len(y)
#yy = y[:]
#yy.sort(reverse=True)
#yf = (abs(yy[round(ff/200*l)])+abs(yy[round((1-ff/200)*l)-1]))/2
#for i in range(l): y[i] = max(-1,min(1,y[i]/yf))
w = open(outfile,'wb')
pickle.dump([xf,1,ts,x,y],w)
w.close()
print("written to "+outfile)
def prepare(p,tf,isTrender=False):
if p=="USATECHIDXUSD": divisor = 1000
else: divisor = 1
ema=0
r = open('data/'+p+str(tf)+'.degap.csv','r')
r.readline()
d=0
while d<start:
d,t,o,h,l,c,a,v = ind.parseLine(r,divisor)
w=[]
x=[]
yo=[]
y=[[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]]
ts=[]
xfc=[]
cnt=0
#if (p[:3]+"USD") in pairs99: p1 = p[:3]+"USD"
#else: p1 = "USD"+p[:3]
#if (p[3:]+"USD") in pairs99: p2 = p[3:]+"USD"
#else: p2 = "USD"+p[3:]
for i in range(50):
d,t,o,h,l,c,a,v = ind.parseLine(r,divisor)
oo = o
w.append(o)
while True:
xx=[]
xx.append(c-l)
if (cnt==0): xfc.append(1)
xx.append(h-c)
if (cnt==0): xfc.append(1)
d,t,o,h,l,c,a,v = ind.parseLine(r,divisor)
if d==-1: break
for i in [1,2,3,4,5,6,7,8,10,12,14,16,20,25,30,40,50]:
#for i in range(1,13):
xx.append(o-w[50-i])
if (cnt==0): xfc.append(0)
#period = i*10
#ema = ind.indicator(p,"ema",tf,period,d,t,1)
#if (ema==-999):
# print(p+' skipping '+str(d)+' '+t+"............................................")
# break
#xx.append(ema-o)
#if (cnt==0): xfc.append(0)
#xx.append(ind.indicator(p,"wpr",tf,period,d,t,1))
#if (cnt==0): xfc.append(-1)
w = w[1:]
w.append(o)
if ema!=-999:
hr = float(t.split(":")[0])
if (hr<22 and hr>3): continue
#print(str(d)+" "+t)
xx.append(hr)
if (cnt==0): xfc.append(24)
x.append(xx)
for i in range(1):
la = ind.indicator(p,"lookahead",tf,0,d,t,i)
th = commission[p]/unit[p]+spread[p]
if la > th: la=la
elif la < -th: la=la
else: la=la
y[i].append(la)
ts.append(str(d)+' '+t)
cnt+=1
r.close()
ind.data={}
print(p+" closed with "+str(cnt)+ " records...")
with gzip.open(p+".x", 'wb') as f: pickle.dump([x], f)
print(len(x[0]))
print(len(x))
xf,x,y,ts = normalizeX(p,x,xfc,ts,y)
print(p+": normalization done")
scaler = MinMaxScaler()
x = scaler.fit_transform(x)
print(p+": MinMaxScaler done")
for i in range(1):
finalize_training_data("data/"+p+str(tf)+".s"+str(i),xf,ts,x,y[i])
print(p+": finalized training data")
print(len(xf))
print(p+" done!")
cache = {}
def cload(currency,file="data/combi15.s",s=0):
global cache
print(file in cache)
if file in cache:
d = cache[file]
print(file+" loaded from cache")
else:
r = open(file,'rb')
d = pickle.load(r)
r.close()
#if test=="_test": cache[file] = d
cache[file] = d
print(file+" loaded")
return d[0],d[1],np.array(d[2]),np.array(d[3][s][currency])
def pload(file):
global cache
if file in cache:
d = cache[file]
print(file+" loaded from cache")
else:
r = open(file,'rb')
d = pickle.load(r)
r.close()
#if test=="_test": cache[file] = d
#cache[file] = d
print(file+" loaded")
return d[0],d[1],d[2],np.array(d[3]),np.array(d[4])
def pload2(file):
r = open(file,'rb')
d = pickle.load(r)
r.close()
#print(file+" loaded")
return d[0],d[1],d[2],np.array(d[3]),np.array(d[4])
def xg_train(p, trial):
best_score=-9999999
best_regressor=None
train=xgboost.DMatrix(xx[p],yy[p])
ev=xgboost.DMatrix(xt[p],at[p])
outfile = p+"."+str(trial)+"_"+str(uniform(0.001,0.999))+".regressor"
print("training "+p+str(trial)+"------------------->"+d+"/"+outfile)
r = regressor = xgboost.train({'max_depth':1, 'eta':0.1, 'verbosity':0 ,'subsample':0.9,'grow_policy':'lossguide','max_leaves':0}, train,1000, evals=[(ev,'eval')],early_stopping_rounds=10)
#if not os.path.exists(d): os.makedirs(d)
#w = open(d+"/"+outfile,'wb')
#pickle.dump(best_regressor,w)
#w.close()
#print(outfile+": "+str(pf)+": "+str(m)+": "+str(bt)+" <<<")
#w = open(d+"/"+outfile+".accuracy",'w')
#w.write(str(pf))
#w.close()
#print("written to "+d+"/"+outfile)
score,m,bt,cnt,pp,rpp,B = simulate(r.predict(xgboost.DMatrix(xtt[p])),att[p],p)
print("================================================================================================================================"+p+": trial "+str(trial)+": "+str(score*abs(m)))
return score*abs(m), r
def lgb_train(p, trial, lot=-1):
best_score=-9999999
best_regressor=None
train=lgb.Dataset(xx[p],yy[p])
ev=lgb.Dataset(xtt[p],att[p])
r = lgb.train({'num_leaves': 127, 'objective': 'l1', 'boosting':'gbdt', 'num_threads':30, 'verbose':-1,'max_depth':6, 'min_data_in_bin':3000, 'data_seed':randint(0,999999)}, train,1000, valid_sets=[ev], early_stopping_rounds=15,verbose_eval=False)
best_score,bm,bt,bcnt,pp,rpp,B,bth = simulate(r.predict(xtt[p]),att[p],p,ddd[p])
if lot>0:print(p+": trial "+str(trial)+": "+str(best_score*lot)+"("+str(round(lot,2))+"): "+str(bcnt)+" / " +str(len(xtt[p])))
else :print(p+": trial "+str(trial)+": "+str(best_score*bm)+"("+str(round(bm,2))+"): "+str(bcnt)+" / " +str(len(xtt[p])))
plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
plt.savefig(d+"/"+p+"."+str(trial)+"_testing_balance.png")
plt.close()
if lot>0: return best_score*lot, best_regressor,lot, bth, bcnt
else: return best_score*bm , best_regressor, bm, bth, bcnt
def nn_train(p,trial):
best_score=-9999999
best_regressor=None
l = len(xx[p][0])
r=regressor = MLPRegressor(warm_start=True,verbose=0,learning_rate_init=0.001,learning_rate='adaptive',hidden_layer_sizes=(l,l),solver='sgd',activation='relu')
outfile = p+"."+str(trial)+"_"+str(uniform(0.001,0.999))+".regressor"
print("training "+p+str(trial)+"---------------"+str(l)+" features")
bj=0
lr=0.1
for j in range(1,3000):
#w = open(d+"/"+outfile+".tmp",'wb')
#pickle.dump([r,j,bj,best_score,best_regressor],w)
#w.close()
r.set_params(max_iter=1,learning_rate_init=lr)
r.fit(xx[p],yy[p])
score,m,bt,cnt,pp,rpp,B,th = simulate(r.predict(xt[p]),at[p],p,dd[p])
if score*abs(m)>best_score:
lr=lr/2
bj=j
bm=m
best_score = score*abs(m)
best_regressor = deepcopy(r)
print(outfile+":"+str(j)+": "+str(best_score)+": "+str(bt)+": "+str(bm)+" <<<")
plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
plt.savefig("testing_balance.png")
plt.close()
else:
print(outfile+":"+str(j)+": "+str(score*abs(m))+": "+str(bt)+": "+str(m)+"......"+str(r.get_params()["learning_rate"]))
if j-bj>15: break
#if j>6:
#bj=j
#bm=m
#best_score = score*abs(m)
#best_regressor = deepcopy(r)
#break
#Path(d+"/"+outfile+".tmp").unlink()
#if best_score>0:
if not os.path.exists(d): os.makedirs(d)
#w = open(d+"/"+outfile,'wb')
#pickle.dump(best_regressor,w)
#w.close()
#w = open(d+"/"+outfile+".accuracy",'w')
#w.write(str(best_score)+","+str(bm))
#w.close()
score,m,bt,bcnt,pp,rpp,B,bth = simulate(best_regressor.predict(xtt[p]),att[p],p,ddd[p])
print("================================================================================================================================"+p+": trial "+str(trial)+": "+str(score*abs(m))+": "+str(bcnt)+" / " +str(len(xtt[p])))
return score*abs(m), best_regressor, m, bth,bcnt
def nn_gpu_train(p,trial):
best_score=-9999999
best_regressor=None
l = len(xx[p][0])
r=regressor = MLPRegressor(warm_start=True,verbose=0,learning_rate_init=0.1,learning_rate='adaptive',hidden_layer_sizes=(l,l,l,l,l),solver='sgd')
outfile = p+"."+str(trial)+"_"+str(uniform(0.001,0.999))+".regressor"
print("training "+p+str(trial)+"---------------"+str(l)+" features")
bj=0
for j in range(1,3000):
#w = open(d+"/"+outfile+".tmp",'wb')
#pickle.dump([r,j,bj,best_score,best_regressor],w)
#w.close()
r.set_params(max_iter=1)
r.fit(xx[p],yy[p])
score,m,bt,cnt,pp,rpp,B,th = simulate(r.predict(xt[p]),at[p],p,dd[p])
if score*abs(m)>best_score:
bj=j
bm=m
best_score = score*abs(m)
best_regressor = deepcopy(r)
print(outfile+":"+str(j)+": "+str(best_score)+": "+str(bt)+": "+str(bm)+" <<<")
#plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
#plt.savefig(d+"/"+outfile+"_testing_balance.png")
#plt.close()
#else:
#print(outfile+":"+str(j)+": "+str(score*abs(m))+": "+str(bt)+": "+str(m)+"......"+str(r.get_params()["learning_rate"]))
if j-bj>10: break
# build the neural network
net = pydnn.nn.NN(pre, 'images', 121, 64, rng, pydnn.nn.relu)
net.add_convolution(72, (7, 7), (2, 2))
net.add_dropout()
net.add_convolution(128, (5, 5), (2, 2))
net.add_dropout()
net.add_convolution(128, (3, 3), (2, 2))
net.add_dropout()
net.add_hidden(3072)
net.add_dropout()
net.add_hidden(3072)
net.add_dropout()
net.add_logistic()
# train the network
lr = pydnn.nn.Adam(learning_rate=pydnn.nn.LearningRateDecay(
learning_rate=0.006,
decay=.1))
net.train(lr)
#Path(d+"/"+outfile+".tmp").unlink()
#if best_score>0:
if not os.path.exists(d): os.makedirs(d)
#w = open(d+"/"+outfile,'wb')
#pickle.dump(best_regressor,w)
#w.close()
#w = open(d+"/"+outfile+".accuracy",'w')
#w.write(str(best_score)+","+str(bm))
#w.close()
score,m,bt,bcnt,pp,rpp,B,bth = simulate(best_regressor.predict(xtt[p]),att[p],p,ddd[p])
print("================================================================================================================================"+p+": trial "+str(trial)+": "+str(score*abs(m))+": "+str(cnt)+" / " +str(len(xtt[p])))
return score*abs(m), best_regressor, m, bth,bcnt
def gb_train(p,trial,lot=-1):
lr = 0.1
best_score=-9999999
best_regressor=None
LV = uniform(0.002,0.01)
SP = uniform(0.01,0.05)
SS = 1
r=GradientBoostingRegressor(verbose=0, warm_start=True, loss='huber', learning_rate=lr, n_estimators=1,max_depth=3,min_samples_leaf=LV,min_samples_split=SP,subsample=SS, alpha=0.9)
outfile = p+"."+str(trial)+"_"+str(LV)+"_"+str(SP)+"_"+str(SS)+".regressor"
#print("training "+p+"------------------->"+d+"/"+outfile)
bj=0
bm=0
bcnt=0
bth=0
for j in range(1,3000):
r.set_params(n_estimators=j)
try:
r.fit(xx[p],yy[p])
except:
print(p+" error")
score,m,bt,cnt,pp,rpp,B,th = simulate(r.predict(xt[p]),at[p],p,dd[p])
if score*abs(m)>best_score:
bj=j
bm=m
bth=th
bcnt=cnt
best_score = score*abs(m)
best_regressor = deepcopy(r)
#print(outfile+":"+str(j)+": "+str(best_score)+": "+str(bt)+": "+str(bm)+" <<<")
#plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
#plt.savefig(d+"/"+outfile+"_testing_balance.png")
#plt.close()
#else: print(outfile+":"+str(j)+": "+str(score*abs(m))+": "+str(bt)+": "+str(m)+"......"+str(r.get_params()["learning_rate"]))
if j-bj>5: break
#Path(d+"/"+outfile+".tmp").unlink()
#if best_score>0:
if not os.path.exists(d): os.makedirs(d)
#w = open(d+"/"+outfile,'wb')
#pickle.dump(best_regressor,w)
#w.close()
best_score,bm,bt,bcnt,pp,rpp,B,bth = simulate(best_regressor.predict(xtt[p]),att[p],p,ddd[p])
#w = open(d+"/"+outfile+".accuracy",'w')
#w.write(str(trial)+","+str(score*abs(m))+","+str(bm))
#w.close()
#print("written to "+d+"/"+outfile)
if lot>0:print(p+":"+str(len(xx[p]))+":"+str(len(xtt[p]))+": trial "+str(trial)+": "+str(round(best_score*lot))+"("+str(round(lot,2))+"): "+str(bcnt)+" / " +str(len(xtt[p])))
else :print(p+":"+str(len(xx[p]))+":"+str(len(xtt[p]))+": trial "+str(trial)+": "+str(round(best_score*bm))+"("+str(round(bm,2))+"): "+str(bcnt)+" / " +str(len(xtt[p])))
if lot>0: return best_score*lot, best_regressor,lot, bth, bcnt
else: return best_score*bm , best_regressor, bm, bth, bcnt
def nearest_train(p,trial,lot=-1):
lr = 0.1
best_score=-9999999
best_regressor=None
#print("training "+p+"------------------->"+d+"/"+outfile)
bj=0
bm=0
bcnt=0
bth=0
for j in range(100,3000,100):
r=KNeighborsRegressor(n_neighbors=j, weights='uniform', algorithm='auto', leaf_size=30, p=3, metric='minkowski',n_jobs=34)
try:
r.fit(xx[p],yy[p])
except:
print(p+" error")
score,m,bt,cnt,pp,rpp,B,th = simulate(r.predict(xt[p]),at[p],p,dd[p])
if score*abs(m)>best_score:
bj=j
bm=m
bth=th
bcnt=cnt
best_score = score*abs(m)
best_regressor = deepcopy(r)
print(p+":"+str(j)+": "+str(best_score)+": "+str(bt)+": "+str(bm)+" <<<")
#plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
#plt.savefig(d+"/"+outfile+"_testing_balance.png")
#plt.close()
else: print(p+":"+str(j)+": "+str(score*abs(m))+": "+str(bt)+": "+str(m)+"......")
if j-bj>500: break
#Path(d+"/"+outfile+".tmp").unlink()
#if best_score>0:
if not os.path.exists(d): os.makedirs(d)
#w = open(d+"/"+outfile,'wb')
#pickle.dump(best_regressor,w)
#w.close()
best_score,bm,bt,bcnt,pp,rpp,B,bth = simulate(best_regressor.predict(xtt[p]),att[p],p,ddd[p])
#w = open(d+"/"+outfile+".accuracy",'w')
#w.write(str(trial)+","+str(score*abs(m))+","+str(bm))
#w.close()
#print("written to "+d+"/"+outfile)
if lot>0:print(p+":"+str(len(xx[p]))+":"+str(len(xtt[p]))+": trial "+str(trial)+": "+str(round(best_score*lot))+"("+str(round(lot,2))+"): "+str(bcnt)+" / " +str(len(xtt[p])))
else :print(p+":"+str(len(xx[p]))+":"+str(len(xtt[p]))+": trial "+str(trial)+": "+str(round(best_score*bm))+"("+str(round(bm,2))+"): "+str(bcnt)+" / " +str(len(xtt[p])))
if lot>0: return best_score*lot, best_regressor,lot, bth, bcnt
else: return best_score*bm , best_regressor, bm, bth, bcnt
def svm_train(p,trial,lot=-1):
best_score=-9999999
best_regressor=None
r=LinearSVR(verbose=0,max_iter=3000,tol=0.1)
#print("training "+p+"------------------->"+d+"/"+outfile)
bj=0
bm=0
bcnt=0
bth=0
for j in range(1,2):
try:
r.fit(xx[p],yy[p])
except:
print(p)
score,m,bt,cnt,pp,rpp,B,th = simulate(r.predict(xt[p]),at[p],p,dd[p])
if score*abs(m)>best_score:
bj=j
bm=m
bth=th
bcnt=cnt
best_score = score*abs(m)
best_regressor = deepcopy(r)
#print(outfile+":"+str(j)+": "+str(best_score)+": "+str(bt)+": "+str(bm)+" <<<")
#plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
#plt.savefig(d+"/"+outfile+"_testing_balance.png")
#plt.close()
#else: print(outfile+":"+str(j)+": "+str(score*abs(m))+": "+str(bt)+": "+str(m)+"......"+str(r.get_params()["learning_rate"]))
if j-bj>10: break
#Path(d+"/"+outfile+".tmp").unlink()
#if best_score>0:
if not os.path.exists(d): os.makedirs(d)
#w = open(d+"/"+outfile,'wb')
#pickle.dump(best_regressor,w)
#w.close()
best_score,bm,bt,bcnt,pp,rpp,B,bth = simulate(best_regressor.predict(xtt[p]),att[p],p,ddd[p])
#w = open(d+"/"+outfile+".accuracy",'w')
#w.write(str(trial)+","+str(score*abs(m))+","+str(bm))
#w.close()
#print("written to "+d+"/"+outfile)
if lot>0:print(p+":"+str(len(xx[p]))+":"+str(len(xtt[p]))+": trial "+str(trial)+": "+str(best_score*lot)+"("+str(round(lot,2))+"): "+str(bcnt)+" / " +str(len(xtt[p])))
else :print(p+":"+str(len(xx[p]))+":"+str(len(xtt[p]))+": trial "+str(trial)+": "+str(best_score*bm)+"("+str(round(bm,2))+"): "+str(bcnt)+" / " +str(len(xtt[p])))
if lot>0: return best_score*lot, best_regressor,lot, bth, bcnt
else: return best_score*bm , best_regressor, bm, bth, bcnt
def rf_train(p,trial):
lr = 0.03
best_score=-9999999
best_regressor=None
LV = randint(50,500)
SP = randint(2,13)
SS = uniform(0.3,0.9)
r = RandomForestRegressor(warm_start=True,verbose=0,n_jobs=1)
outfile = p+"."+str(trial)+"_"+str(LV)+"_"+str(SP)+"_"+str(SS)+".regressor"
print("training "+p+str(trial)+"------------------->"+d+"/"+outfile)
bj=0
bm=0
for j in range(1,322,1):
r.set_params(n_estimators=j)
r.fit(xx[p],yy[p])
score,m,bt,cnt,pp,rpp,B = simulate(r.predict(xt[p]),at[p],p)
if score*abs(m)>best_score:
bj=j
bm=m
best_score = score*abs(m)
best_regressor = deepcopy(r)
print(outfile+":"+str(j)+": "+str(best_score)+": "+str(bt)+": "+str(bm)+" <<<")
plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
plt.savefig(d+"/"+outfile+"_testing_balance.png")
plt.close()
else:
print(outfile+":"+str(j)+": "+str(score*abs(m))+": "+str(bt)+": "+str(m)+"......")
if j-bj>10: break
if not os.path.exists(d): os.makedirs(d)
score,m,bt,cnt,pp,rpp,B = simulate(best_regressor.predict(xtt[p]),att[p],p)
print("================================================================================================================================"+p+": trial "+str(trial)+": "+str(score*abs(m)))
return score*abs(m), best_regressor
def simulate(lot,a,pair,ddd):
sign = 1
cnt = len(lot)
balance = 0
ol = 0
l = 0
maxB = -999999
maxDD = -999999
tcnt=0
pcnt=0
positive=0
rpositive=0
B=[]
tp=0
tb=0
#print(lot[:10])
th = (max(lot)-min(lot))/2*0.3
#if "IDX" in pair: th = (max(lot)-min(lot))/2*0.00333
cth = (max(lot)-min(lot))/2*0.03
switches = 0
for i in range(4,len(lot)):
lt = lot[i]
#print(p+str(lt)+":"+str(a[i]))
hr = int(ddd[i].split(' ')[1].split(':')[0])
mn = int(ddd[i].split(' ')[1].split(':')[1])
if hr == 3: l=0
elif hr==0 or (hr==1 and mn<30): l=ol
elif lt>th and hr<=22 : l=1
elif lt<-th and hr<=22: l=-1
elif (abs(ol)<0.0000000000000000001 and lt<th and lt>-th) or (ol>0.5 and lt<-cth) or (ol<-0.5 and lt>cth) : l=0
else: l = ol
if abs(l)>0.00000000001: tcnt+=1
r=a[i]
if (r<-990): continue # rejects -999
if (r>0 and l>0) or (r<0 and l<0):
positive+=1
if abs(l)>0.000000005:
rpositive+=1
profit = (l * r - abs(l - ol) * spread[pair] / 2) * unit[pair] / value[pair[-3:]] - abs(l - ol) * commission[pair] / 2
#profit = (l * r) * unit[pair] / value[pair[-3:]]
if profit > 0: pcnt+=1
if profit > 1600: profit=1600
if profit <-1600: profit=-1600
#print(str(ddd[i])+": "+str(l)+" "+str(profit)+" "+str(lt)+" "+str(r)+" "+str(th))
#print(xtt[p][i])
balance += profit
tp += profit
if balance > maxB: maxB = balance
if maxB - balance > maxDD: maxDD = maxB - balance
B.append(balance)
if (l<0.5 and ol>0.5) or (l>-0.5 and ol<-0.5):
switches = switches +1
#print(str(ddd[i])+": "+str(l)+":"+str(ol)+":"+str(tp))
tp=0
ol=l
plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
plt.savefig(pair+"_testing_balance.png")
plt.close()
#print('threshold:'+str(th))
#print("switches: "+str(switches))
#print("balance: "+str(balance))
if maxDD==0 or cnt<5 or tcnt<5 or switches<int(len(lot)*0.003):
print(pair+" perfect?" + str(maxDD)+":"+str(cnt)+":"+str(tcnt)+":"+str(switches))
return balance/1000000000,0.001,0,tcnt,0,0,B,th
n = len(B)
return balance, (-100/-maxDD)*sign, float(pcnt)/cnt,switches,fdp(float(positive)/cnt),fdp(float(rpositive)/cnt),B,th
if __name__ == '__main__':
pairs99 = ["EURUSD","GBPUSD","USDCHF","AUDUSD","NZDUSD"]
pl=[]
xf = {}
yf = {}
ts = {}
x = {}
y = {}
xx = {}
xt = {}
yy = {}
yt = {}
at = {}
xtt= {}
att= {}
dd={}
ddd={}
#initialize_last_close(pairs99)
if argv[1]=="test_i":
ind.test("CADCHF",60,10,20190903,"07:00:00")
elif argv[1]=="degap":
pool = mp.Pool(28)
for p in pairs99: pl.append(pool.apply_async(degap, (p,tf)))
#for p in pairs99 + chosen: pl.append(pool.apply_async(degap, (p,tf)))
for pl2 in pl: pl2.get()
elif argv[1]=="prepare":
pool = mp.Pool(28)
#pairs99 = ["EURCAD","EURCHF","EURAUD","EURNZD","GBPCAD","GBPCHF","GBPAUD","GBPNZD","CADCHF","AUDCAD","NZDCAD","AUDCHF","NZDCHF","AUDNZD"]
#for p in pairs99: prepare(p,tf)
for p in pairs99 :
#for p in ["EURNZD"]:
#for p in ["XAUUSD"]:
if "JPY" in p: continue
pl.append(pool.apply_async(prepare,(p,tf)))
for pl2 in pl: pl2.get()
elif argv[1]=="combi":
save_combi()
elif argv[1]=="train":
tscore = 0
def evaluate_accuracy(p):
xf[p],yf[p],ts[p],x[p],y[p] = pload2("data/"+p+str(tf)+".s0")
#if "JPY" in p: continue
mm=[]
ww=[]
score=0
for q in range(13):
#for q in range(12,13):
mon = int(60/tf*24/4*23)
last = mon*((14-q)*3)+1
xx[p] = x[p][q*3*mon:-last]
xt[p] = x[p][-last:-last+mon*3]
xtt[p]= x[p][-last+mon*3:-last+mon*6]
yy[p] = y[p][q*3*mon:-last]
yt[p] = y[p][-last:-last+mon*3]
at[p] = []
att[p] = []
dd[p] = ts[p][-last:-last+mon*3]
ddd[p] = ts[p][-last+mon*3:-last+mon*6]
for i in range(len(ts[p][-last:-last+mon*3])):
dt=ts[p][-last:-last+mon*3][i].split(" ")
at[p].append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
for i in range(len(ts[p][-last+mon*3:-last+mon*6])):
dt=ts[p][-last+mon*3:-last+mon*6][i].split(" ")
att[p].append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
s, r, m, t, w = gb_train(p,q)
#s, r, m, t, w = lgb_train(p,q)
#s, r, m, t, w = nn_train(p,q)
#s, r, m, t, w = nn_gpu_train(p,q)
#s, r = rf_train(p,q)
#s, r = xg_train(p,q)
#s, r, m, t, w = svm_train(p,q)
#s, r, m, t, w = nearest_train(p,q)
score = score + s
mm.append(m)
ww.append(w)
print("..........................................................................................................."+p+":"+str(score/13)+"...lot determiner")
lot = (sum(mm)-max(mm)-min(mm))/11
switches = (sum(ww)-max(ww)-min(ww))/11
with gzip.open(p+".lot_switches", 'wb') as f:
pickle.dump([lot,switches], f)
print("written to "+p+".lot_switches")
score=0
sw=0
imp = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
iter = 5
for ii in range(iter):
for q in range(13):
#for q in range(12,13):
mon = int(60/tf*24/4*23)
last = mon*((14-q)*3)+1
xx[p] = x[p][q*3*mon:-last]
xt[p] = x[p][-last:-last+mon*3]
xtt[p]= x[p][-last+mon*3:-last+mon*6]
yy[p] = y[p][q*3*mon:-last]
yt[p] = y[p][-last:-last+mon*3]
at[p] = []
att[p] = []
dd[p] = ts[p][-last:-last+mon*3]
ddd[p] = ts[p][-last+mon*3:-last+mon*6]
for i in range(len(ts[p][-last:-last+mon*3])):
dt=ts[p][-last:-last+mon*3][i].split(" ")
at[p].append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
#for i in range(len(ts[p][-last+mon*3:-last+mon*6])):
# dt=ts[p][-last+mon*3:-last+mon*6][i].split(" ")
# att[p].append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
for i in range(len(ts[p][-last+mon*3:-last+mon*6])):
dt=ts[p][-last+mon*3:-last+mon*6][i].split(" ")
att[p].append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
s, r, m, t, w = gb_train(p,q,lot)
#s, r, m, t, w = lgb_train(p,q,lot)
#s, r, m, t, w = nn_train(p,q)
#s, r, m, t, w = nn_gpu_train(p,q)
#s, r = rf_train(p,q)
#s, r = xg_train(p,q)
#s, r, m, t, w = svm_train(p,q,lot)
#s, r, m, t, w = nearest_train(p,q,lot)
score = score + s
sw = sw + w
#for i in range(25): imp[i] = imp[i] + r.feature_importances_[i]
print(">>>........................................................................................................"+p+":"+str(score/13/(ii+1))+"..."+str(ii)+"...lot:"+str(lot)+"...switches:"+str(switches))
qq.put([score/13/iter,sw/13/iter,imp])
#for i in range(25): imp[i] = imp[i]/12
#print(imp)
pr=[]
qq = mp.Queue()
sww = 0
imp2 = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
#for p in ["EURCAD","EURNZD","GBPCAD","GBPAUD","GBPNZD","NZDCAD","AUDNZD","XAUUSD", "CHFSGD", "EURNOK", "EURPLN", "EURSEK", "EURSGD", "USDNOK", "USDPLN", "USDSGD", "EURCHF"]:
#for p in pairs99 + chosen:
#for p in ["EURGBP"]:
for p in pairs99:
#for p in ["USDCHF","EURGBP","EURCAD","EURCHF","EURAUD","EURNZD","GBPCAD","GBPCHF","GBPAUD","GBPNZD","CADCHF","NZDCAD","AUDCHF","NZDCHF","AUDNZD","CHFSGD","EURNOK","EURPLN","EURSEK","EURSGD","USDNOK","USDPLN","USDSEK","USDSGD"]:
if "JPY" in p : continue
pr.append(mp.Process(target=evaluate_accuracy, args=(p,)))
pr[-1].start()
#evaluate_accuracy(p)
#data = qq.get()
#score=tscore+data[0]
#ww = sww+data[1]
#for i in range(25): imp2[i] = imp2[i]+data[2][i]
#pr[-1].join()
for j in range(len(pr)):
data = qq.get()
tscore=tscore+data[0]
sww = sww+data[1]
for i in range(25): imp2[i] = imp2[i]+data[2][i]
print(str(j)+":"+str(tscore)+"------"+str(data[0])+"--------"+str(round(sww)))
print(str(tscore)+"------"+str(round(sww)))
#for i in range(25): imp2[i] = imp2[i]/12/21
#print(imp2)
for j in range(len(pr)): pr[j].join()
#pool = mp.Pool(1)
#for iter in range(iterations):
# for p in pairs99:
# if "JPY" in p: continue
# pl.append(pool.apply_async(nn_train, (shift,p)))
#for pl2 in pl: pl2.get()
elif argv[1]=="generate":
regressors = {}
lots = {}
th = {}
xo = {}
pr=[]
qq = mp.Queue()
sw={}
def obtain_best(p):
with gzip.open(p+".lot_switches", 'rb') as f:
data=pickle.load(f)
lot = data[0]
switches = data[1]
with gzip.open(p+".x", 'rb') as f: data=pickle.load(f)
q = 12
xo2 = data[0]
mon = int(60/tf*24/3*23)
last = mon*((14-q)*3)+1
xx[p] = x[p][q*3*mon:-last]
xt[p] = x[p][-last:-last+mon*3]
xtt[p]= x[p][-last+mon*3:-last+mon*6]
yy[p] = y[p][q*3*mon:-last]
yt[p] = y[p][-last:-last+mon*3]
at[p] = []
att[p] = []
dd[p] = ts[p][-last:-last+mon*3]
ddd[p] = ts[p][-last+mon*3:-last+mon*6]
#xx[p] = x[p][mon:-mon*3]
#xt[p] = x[p][-mon*3:]
#xtt[p] = x[p][-mon*3:]
#yy[p] = y[p][mon:-mon*3]
#at[p] = []
#att[p] = []
#dd[p] = ts[p][-mon*3:]
#ddd[p] = ts[p][-mon*3:]
for i in range(len(ts[p][-mon*3:])):
dt=ts[p][-mon*3:][i].split(" ")
at[p].append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
for i in range(len(ts[p][-mon*3:])):
dt=ts[p][-mon*3:][i].split(" ")
att[p].append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
regressors2=[None, None, None]
lots2=[None, None, None]
th2=[0,0,0]
for i in range(1):
best_score = -99999
for j in range(1):
s, r, m, t, w = gb_train(p,12)
if s>best_score:
best_score = s
regressors2[i] = r
lots2[i] = m
th2[i] = t
print(p+":"+str(i)+"/"+str(j)+" :"+str(s)+":"+str(m)+":"+str(w))
qq.put([p,regressors2,[lot,lot,lot],th2,xo2,switches])
#for p in pairs99+chosen:
#for p in ["EURCAD","EURNZD","GBPCAD","GBPAUD","GBPNZD","NZDCAD","AUDNZD","XAUUSD", "CHFSGD", "EURNOK", "EURPLN", "EURSEK", "EURSGD", "USDNOK", "USDPLN", "USDSGD", "EURCHF"]:
#for p in ["USDCHF","EURGBP","EURCAD","EURCHF","EURAUD","EURNZD","GBPCAD","GBPCHF","GBPAUD","GBPNZD","CADCHF","NZDCAD","AUDCHF","NZDCHF","AUDNZD","CHFSGD","EURNOK","EURPLN","EURSEK","EURSGD","USDNOK","USDPLN","USDSEK","USDSGD"]:
#for p in ["USATECHIDXUSD"]:
for p in pairs99:
if "JPY" in p: continue
xf[p],yf[p],ts[p],x[p],y[p] = pload2("data/"+p+str(tf)+".s0")
pr.append(mp.Process(target=obtain_best, args=(p,)))
pr[-1].start()
for i in range(len(pr)):
d=qq.get()
p=d[0]
regressors[p] = d[1]
lots[p] = d[2]
th[p] = d[3]
xo[p] = d[4]
sw[p] = d[5]
for i in range(len(pr)): pr[i].join()
mx = {}
mn = {}
#for p in pairs99+chosen:
#for p in ["EURCAD","EURNZD","GBPCAD","GBPAUD","GBPNZD","NZDCAD","AUDNZD","XAUUSD", "CHFSGD", "EURNOK", "EURPLN", "EURSEK", "EURSGD", "USDNOK", "USDPLN", "USDSGD", "EURCHF"]:
#for p in ["USDCHF","EURGBP","EURCAD","EURCHF","EURAUD","EURNZD","GBPCAD","GBPCHF","GBPAUD","GBPNZD","CADCHF","NZDCAD","AUDCHF","NZDCHF","AUDNZD","CHFSGD","EURNOK","EURPLN","EURSEK","EURSGD","USDNOK","USDPLN","USDSEK","USDSGD"]:
#for p in ["USATECHIDXUSD"]:
for p in pairs99:
if "JPY" in p: continue
mx[p] = [0] * 207
mn[p] = [0] * 207
for j in range(len(xo[p][0])):
mxx = -999999
mnn = 999999
for i in range(len(xo[p])):
if xo[p][i][j]>mxx: mxx=xo[p][i][j]
if xo[p][i][j]<mnn: mnn=xo[p][i][j]
mx[p][j] = mxx
mn[p][j] = mnn
print(p+ " fitted")
with gzip.open("regressors.all", 'wb') as f:
pickle.dump([regressors,lots,th,sw,mx,mn], f)
elif argv[1]=="realtime":
def try_predictor(model, x):
trees = model.estimators_
y_pred = model.init_.predict([x])[0]
#print("*** "+str(y_pred) + " *** "+ str(model.init_.predict([10]*25)[0]))
for tree in trees:
pred = tree[0].predict([x])[0]
y_pred = y_pred + model.learning_rate*pred # Summing with LR
return y_pred
with gzip.open("regressors.all", 'rb') as f:
data=pickle.load(f)
print("loaded")
regressors = data[0]
lots = data[1]
th = data[2]
sw = data[3]
mx = data[4]
mn = data[5]
def realize(p):
#print(regressors[p][0].feature_importances_)
#print(regressors[p][1].feature_importances_)
#print(regressors[p][2].feature_importances_)
#continue
try:
csvfile = open('porting/'+p+'.input', 'r',newline='')
lt = list(csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC))
csvfile.close()
x = lt[-1]
if p == "USA30IDXUSD" :
for ii in range(len(x)-1): x[ii] = ((x[ii]*1000) - mn[p][ii]) / (mx[p][ii] - mn[p][ii])
ii=len(x)-1
x[ii] = (x[ii] - mn[p][ii]) / (mx[p][ii] - mn[p][ii])
c = "US30: "
elif p == "USA500IDXUSD":
for ii in range(len(x)-1): x[ii] = ((x[ii]*1000) - mn[p][ii]) / (mx[p][ii] - mn[p][ii])
ii=len(x)-1
x[ii] = (x[ii] - mn[p][ii]) / (mx[p][ii] - mn[p][ii])
c = "US500: "
elif p == "XAUUSD":
for ii in range(len(x)-1): x[ii] = ((x[ii]*100) - mn[p][ii]) / (mx[p][ii] - mn[p][ii])
ii=len(x)-1
x[ii] = (x[ii] - mn[p][ii]) / (mx[p][ii] - mn[p][ii])
c = "XAUUSD: "
else :
for ii in range(len(x)): x[ii] = (x[ii] - mn[p][ii]) / (mx[p][ii] - mn[p][ii])
c = p+": "
for i in range(3):
y = regressors[p][i].predict([x])[0]
print(str(y)+" "+str(th[p][i]))
#print(try_predictor(regressors[p][i],x))
if y> th[p][i]: yy = lots[p][i]
if y<-th[p][i]: yy =-lots[p][i]
if y< th[p][i] and y>0: yy = 999
if y>-th[p][i] and y<0: yy =-999
c=c+str(round(yy,3))+"\t"
if p == "USA30IDXUSD" : w = open('/mnt/c/Users/Intel/AppData/Roaming/MetaQuotes/Terminal/Common/Files/US30.'+str(i)+'.output', 'w',newline='')
elif p == "USA500IDXUSD": w = open('/mnt/c/Users/Intel/AppData/Roaming/MetaQuotes/Terminal/Common/Files/US500.'+str(i)+'.output', 'w',newline='')
else : w = open('/mnt/c/Users/Intel/AppData/Roaming/MetaQuotes/Terminal/Common/Files/'+p+'.'+str(i)+'.output', 'w',newline='')
w.write(str(yy))
w.close()
c=c+"........................"+str(round(lots[p][0],3))+"\t"+str(round(sw[p]))
print(c)
except:
print(p+": error")
firstTime = True
while True:
time.sleep(3)
#if (not firstTime) and ((time.localtime().tm_min % 30>0 and time.localtime().tm_min % 30<16) or time.localtime().tm_sec>2): continue
#if (not firstTime) and time.localtime().tm_sec>2: continue
firstTime = False
print("------------------------------------------------------------------------------------------------------------------------------------ "+str(time.localtime().tm_hour)+":"+str(time.localtime().tm_min))
pr=[]
#for p in pairs99+chosen:
for p in ["GBPAUD","GBPNZD", "CHFSGD", "EURNOK", "EURPLN","EURSGD", "USDNOK","USDSGD"]:
pr.append(mp.Process(target=realize, args=(p,)))
pr[-1].start()
pr[-1].join()
for i in range(len(pr)): pr[i].join()
elif argv[1]=="export":
def export_regressor(p, i, model, l, t, mxx, mnn):
def wr(w,d): w.write(str(d)+"\r\n")
def wrtree(w,t):
wr(w,len(t.feature))
for i in range(len(t.feature)):
wr(w,t.children_left[i])
wr(w,t.children_right[i])
wr(w,t.feature[i])
wr(w,t.threshold[i])
wr(w,t.value[i][0][0])
#w = open('/mnt/c/Users/Intel/AppData/Roaming/MetaQuotes/Terminal/Common/Files/'+p+'.'+str(i)+'.regressor', 'w',newline='')
w = open('/mnt/c/Users/Lip Phang/AppData/Roaming/MetaQuotes/Terminal/Common/Files/'+p+'.regressor', 'w',newline='')
wr(w,l)
wr(w,t)
wr(w,model.learning_rate)
wr(w,model.init_.predict([0])[0])
wr(w,len(model.estimators_))
for tree in model.estimators_: wrtree(w,tree[0].tree_)
for j in range(20): wr(w,mnn[j])
for j in range(20): wr(w,mxx[j])
print(p+": mn-"+str(len(mnn))+" : mx-"+str(len(mxx)))
w.close()
with gzip.open("regressors.all", 'rb') as f:
data=pickle.load(f)
print("loaded")