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1m.py
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1m.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
from sklearn.neural_network import MLPRegressor
#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 indicators as ind
from symbols import *
from math import sqrt
import matplotlib.pyplot as plt
tf = 1
month = int(60/tf*24*23) # months to test
iterations = 88
boundary = 0.38
starting = 60
train_period = 12
validation = 1
testing = 1
#d = "combi_"+str(tf)+"/svr"
#d = "combi_"+str(tf)+"/gradient_boosting"
d = "combi_"+str(tf)+"/random_forest"
#d = "combi_"+str(tf)+"/neural____network"
#d = "combi_"+str(tf)+"/xg_boosting"
def getSortKey(item):
global sortKey
return item[sortKey]
def normalizeX(x,f,boundaryF=-1):
global sortKey
sortKey = 0
d = []
factors=[]
for dd in x: d.append(dd[:])
last = len(x[0])
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)
return factors,x
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):
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)
w=[]
x=[]
yo=[]
y=[[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]]
ts=[]
xfc=[]
cnt=0
for i in range(30):
d,t,o,h,l,c,a,v = ind.parseLine(r)
oo = o
w.append(a)
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)
if d==-1: break
for i in range(1,30):
xx.append(o-w[30-i])
if (cnt==0): xfc.append(0)
w = w[1:]
w.append(a)
xx.append(float(t.split(":")[0]))
if (cnt==0): xfc.append(24)
x.append(xx)
for shift in range(16):
if o==0: o=oo
la = ind.indicator(p,"lookahead",tf,0,d,t,shift)
if la==-999:
print(p+" : invalid value at "+str(d)+" "+t+"..."+str(y[shift][-1]))
y[shift].append(0)
else:
#la = la / o
y[shift].append(la)
ts.append(str(d)+' '+t)
cnt+=1
oo=o
r.close()
ind.data={}
print(p+" closed with "+str(cnt)+ " records...")
xf,x = normalizeX(x,xfc)
for i in range(16):
finalize_training_data("data/"+p+str(tf)+".s"+str(i),xf,ts,x,y[i])
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):
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 accuracy(actual,predictions):
hits = 0
zeroes = 0
sum = 0
l = 0
for i in range(len(actual)):
if np.isnan(predictions[i]): continue
l=l+1
if (actual[i]>0 and predictions[i]>0) or (actual[i]<0 and predictions[i]<0): hits+=1
if actual[i]*predictions[i]==0: zeroes+=1
#sum = sum + (actual[i]*(predictions[i]/abs(predictions[i])))
return round(hits/l,4),sum/l,round(zeroes/l,4)
def my_score(a,y): # actuals, predictions
r = 0
v = 0
for i in range(len(a)):
r = r + a[i] * y[i]
v = v + abs(a[i]) * abs(y[i])
return round(r / v * 100,1)
def rf_train(start,p):
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=2,n_jobs=34)
outfile = p+"."+str(start)+"_"+str(LV)+"_"+str(SP)+"_"+str(SS)+".regressor"
print("training "+p+str(s)+"------------------->"+d+"/"+outfile)
bj=0
bm=0
for j in range(100,101,30):
r.set_params(n_estimators=j)
r.fit(xx[(-starting+start)*month:(-starting+start+train_period)*month],yy[0][p][(-starting+start)*month:(-starting+start+train_period)*month])
score,m,bt,cnt,pp,rpp,B = obtain_threshold2(p,r,r.predict(xt[(-starting+start+training_period)*month:(-starting+start+train_period+validation)*month]),a[p][(-starting+start+training_period)*month:(-starting+start+train_period+validation)*month],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>5: break
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()
print("written to "+d+"/"+outfile)
def simulate(lot,a,pair):
balance = 0
ol = 0
l = 0
maxB = -999999
maxDD = -999999
cnt=0
tcnt=0
pcnt=0
positive=0
rpositive=0
B=[]
order=[0,0,0,0,0,0,0,0]
factor=8
#print(lot[:10])
for i in range(len(lot)):
lt = lot[i]
#l = lt
if ol==0:
if lt<sc : l=-1
elif lt>bc : l=1
else : l=ol
elif ol>0:
if lt<sc : l=-1
elif lt<bcc : l=0
else : l=ol
elif ol<0:
if lt>bc : l=1
elif lt>scc : l=0
else : l=ol
if abs(l)>0.000000001: tcnt+=1
r=a[i]
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
if profit > 0: pcnt+=1
if profit > 500: profit=500
if profit <-500: profit=-500
balance += profit
if balance > maxB: maxB = balance
if maxB - balance > maxDD: maxDD = maxB - balance
B.append(balance)
ol = l
cnt = len(lot)
if maxDD==0 or cnt==0 or tcnt<20:
return balance/1000,0.001,0,tcnt,0,0,B
n = len(B)
tg = 0 # total gains count
for i in range(n-1):
if B[i+1]>B[i]: tg += 1
elif B[i+1]<B[i]: tg -= 1
return tg/n, (-100/-maxDD), float(pcnt)/cnt,tcnt,fdp(float(positive)/cnt),fdp(float(rpositive)/cnt),B
def obtain_threshold2(p,m,x,a,outfile):
a = []
for i in range(len(ts)):
dt=ts[i].split(" ")
a.append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
y = m[p].predict(x)
mx = -999999999
bc = 0
bcc = 0
sc = 0
scc = 0
bB = None
bm = 0
debug = False
factor = 1000000
for j in range(1,10000):
t = j / factor
balance,m,wRate,cnt,pp,rpp,B = simulate(y,a,p,t,0,-t,0)
if (debug): print(p+":"+str(t)+": "+str(round(balance*m,2))+":"+str(cnt)+":"+str(round(m,3)))
if cnt<0:break
if balance*m>mx:
mx = balance*m
bc = t
sc = -t
bB = B
bm = m
for j in range(1,3000):
t = 0 - j / factor
balance,m,wRate,cnt,pp,rpp,B = simulate(y,a,p,bc,t,sc,-t)
if (debug): print(p+":"+str(t)+": "+str(round(balance*m,2))+":"+str(cnt)+":"+str(round(m,3)))
if balance*m>mx:
mx = balance*m
bcc= t
scc=-t
bB = B
bm = m
mx = -999999999
for j in range(1,10000):
t = 0 - j / factor
balance,m,wRate,cnt,pp,rpp,B = simulate(y,a,p,9,0,t,0)
if (debug): print(p+":"+str(t)+": "+str(round(balance*m,2))+":"+str(cnt)+":"+str(round(m,3)))
if cnt<20:break
if balance*m>mx:
mx = balance*m
sc = t
bB = B
bm = m
for j in range(1,3000):
t = j / factor
balance,m,wRate,cnt,pp,rpp,B = simulate(y,a,p,bc,bcc,sc,t)
if (debug): print(p+":"+str(t)+": "+str(round(balance*m,2))+":"+str(cnt)+":"+str(round(m,3)))
if balance*m>mx:
mx = balance*m
scc= t
bB = B
bm = m
balance,bm,wRate,cnt,pp,rpp,B = simulate(y,a,p,bc,bcc,sc,scc)
plt.plot(np.array(range(len(bB))), np.array(bB), label= '3x')
plt.savefig("combi_"+str(tf)+"/"+outfile+"_testing_balance.png")
plt.close()
#print(p+" best tresholds: "+str(round(mx))+" : \t"+str(bc)+"\t"+str(bcc)+"\t"+str(sc)+"\t"+str(scc))
print(p+" best tresholds: "+str(round(mx,5))+" : \t"+str(bt))
w = open("combi_"+str(tf)+"/"+outfile+".index",'w')
w.write(str(bt)+","+str(round(bm,5))+","+str(round(mx,3)))
w.close()
def reset_sum():
w = open("combi_"+str(tf)+"/sum.txt",'w')
w.write("0")
w.close()
def test(p,m,ts,x,index,t):
def add_sum(num):
r = open("combi_"+str(tf)+"/sum.txt",'r')
d = list(csv.reader(r, quoting=csv.QUOTE_NONNUMERIC))
r.close()
w = open("combi_"+str(tf)+"/sum.txt",'w')
w.write(str(float(d[0][0])+num))
w.close()
print("SUM added: "+str(round(d[0][0]+num))+"("+str(round(num))+")")
a = []
for i in range(len(ts)):
dt=ts[i].split(" ")
a.append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
c1 = p[:3]
c2 = p[-3:]
if c1=="USD":
lots = m[c2].predict(x)
for i in range(len(lots)): lots[i] = -lots[i]
elif c2=="USD":
lots = m[c1].predict(x)
else:
cl1 = m[c1].predict(x)
cl2 = m[c2].predict(x)
lots=[]
for i in range(len(cl1)): lots.append(cl1[i]-cl2[i])
r = open("combi_"+str(tf)+"/"+p+'.index','r')
d = list(csv.reader(r, quoting=csv.QUOTE_NONNUMERIC))
r.close()
#print(p+":"+str(bc))
#balance,m,wRate,cnt,pp,rpp,B = simulate(lot,a,p)
#balance,m,wRate,cnt,pp,rpp,B = simulate3(lots,a,p,d[0][0] ,d[0][1],d[0][2] ,d[0][3])
balance,m,wRate,cnt,pp,rpp,B = simulate3(lots,a,p,max(d[0][0],-d[0][2]),d[0][1],min(-d[0][0],d[0][2]),d[0][3])
#balance,m,wRate,cnt,pp,rpp,B = simulate3(lots,a,p,max(d[0][0],-d[0][2]), max(d[0][1],-d[0][3]), min(-d[0][0],d[0][2]), min(-d[0][1],d[0][3]))
#balance,m,wRate,cnt,pp,rpp,B = simulate3(lots,a,p,(d[0][0]-d[0][2])/2 , (d[0][1]-d[0][3])/2 , (-d[0][0]+d[0][2])/2 , (-d[0][1]+d[0][3])/2)
#balance,m,wRate,cnt,pp,rpp,B = simulate3(lots,a,p,d[0][0]*1.5 ,d[0][1],d[0][2]*1.5 ,d[0][3])
#balance,m,wRate,cnt,pp,rpp,B = simulate3(lots,a,p,0,0,0,0)
fig = plt.figure()
plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
plt.savefig("combi_"+str(tf)+"/"+p+"_final_testing_balance.png")
plt.close()
#m=d[0][1]
#print(p+": TEST : "+str(round(balance))+" "+str(round(m,3))+ " lots")
w = open("combi_"+str(tf)+"/"+p+".lots",'w')
w.write(str(m))
w.close()
add_sum(balance*m)
return balance , B, m, cnt
def get_models():
models = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
bms = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
bc = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
bcc = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
sc = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
scc = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
score = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
for p in pairs99:
#if "JPY" in p: continue
for s in range(16):
acc = []
files=glob(d+"/"+p+"."+str(s)+"_*regressor.accuracy")
mx = -999999999
mf = ""
ms = -1
for f in files:
r = open(f,'r')
ln = r.readline()
acc = float(ln.split(",")[0])
m = float(ln.split(",")[1])
r.close()
if acc>mx:
if tf<100: ms=f[34:36]
else: ms=f[35:37]
if ms[-1]=="_": ms = int(ms[:-1])
else : ms = int(ms)
if ms>18: continue
mx=acc
mf=f
bm=m
if mx>-999999:
print(mf+" chosen: "+str(ms)+" : "+str(mx)+" : "+str(round(bm,3)))
r = open(mf[:-9],'rb')
models[s][p]=pickle.load(r)
r.close()
bms[s][p] = bm
score[s][p] = mx
#csvfile = open(mf[:-9]+".index", 'r',newline='')
#ind = list(csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC))[0]
#csvfile.close()
#bc[s][p] = ind[0]
#bcc[s][p] = ind[1]
#sc[s][p] = ind[2]
#scc[s][p] = ind[3]
#score[s][p] = ind[5]
return models,bms,score
def get_all_models():
models = {}
for p in pairs99:
#if "JPY" in p: continue
files=glob(d+"/"+p+"*regressor.accuracy")
models[p] = []
for mf in files:
print(mf)
r = open(mf[:-9],'rb')
models[p].append(pickle.load(r))
r.close()
return models
def get_l1_models():
models = []
for c in ["JPY","EUR","GBP","AUD","CAD","CHF","NZD"]:
acc = []
for s in range(12):
files=glob(d+"/"+c+"."+str(s)+"_*accuracy")
mx = -999999
mf = ""
ms = -1
for f in files:
r = open(f,'r')
acc = float(r.readline())
r.close()
if acc>mx:
mx=acc
mf=f
if tf<100: ms=f[31:33]
else: ms=f[32:34]
if ms[-1]=="_": ms = int(ms[:-1])
else : ms = int(ms)
if mx>-99999:
print(mf+" chosen: "+str(ms)+" : "+str(mx))
r = open(mf[:-9],'rb')
models.append(pickle.load(r))
r.close()
return models
def get_l2_models():
models = {}
for c in ["JPY","EUR","GBP","AUD","CAD","CHF","NZD"]:
files=glob(d+"/"+c+".*2.accuracy")
mx = -999
mf = ""
for f in files:
r = open(f,'r')
acc = float(r.readline())
r.close()
if acc>mx:
mx=acc
mf=f
if mx>-99:
print(mf+" chosen: "+str(mx))
r = open(mf[:-9],'rb')
models[c]=pickle.load(r)
r.close()
return models
def get_very_best_models():
models = {}
for c in curr2:
mx = -999
mf = ""
ms = -1
me = -999999
mt = -1
mm = -1
for t in [120,240,360,480]:
xf,ts,x,y = load_combi("",t)
ts=ts[-int(60/t*24*23*mtt):]
x = x[-int(60/t*24*23*mtt):]
dd = "combi_"+str(t)+"/gradient_boosting"
files=glob(dd+"/"+c+"*regressor.accuracy")
for f in files:
r = open(f,'r')
acc = float(r.readline())
r.close()
r = open(f[:-9],'rb')
mo = pickle.load(r)
r.close()
earning,B,m,cnt = test(c,mo,ts,x,0,t)
print(f+": "+str(ms)+" : "+str(acc) + " : "+str(round(earning,1))+"("+str(cnt)+")")
if earning>me and earning<10000000:
if tf<100: ms=f[31:33]
else: ms=f[32:34]
if ms[-1]=="_": ms = int(ms[:-1])
else : ms = int(ms)
if ms>18: continue
me=earning
mx=acc
mf=f
mt=t
mm=m
if me>-99:
r = open(mf[:-9],'rb')
models[c]=pickle.load(r)
r.close()
t = int(mf[6:9])
xf,ts,x,y = load_combi("",t)
ts=ts[-int(60/mt*24*23*mtt):]
x = x[-int(60/mt*24*23*mtt):]
earning,B,m,cnt = test(c,models[c],ts,x,0,mt)
fig = plt.figure()
plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
plt.ticklabel_format(style='plain')
plt.ticklabel_format(useOffset=False)
plt.savefig("final_selection/"+c+".png")
plt.close()
w = open("final_selection/"+c+".lots",'w')
w.write(str(m)+","+str(mt))
w.close()
print(mf+" chosen: "+str(ms)+" : "+str(mx) + " : "+str(round(me))+"("+str(cnt)+")\n\n\n\n")
return models
def load_l1_data(pairs,s):
data = [{},{},{},{},{},{},{},{},{},{},{},{},{}]
for i in range(13):
p = pairs[i]
if test!="": xfo,yf,ts,x,ya = pload("data/"+p+str(tf)+".s"+str(s))
xf,yf,ts,x,yo = pload("data/"+p+str(tf)+test+".s"+str(s))
for j in range(len(ts)):
if test!="":
for k in range(len(x[0])): x[j][k] = max(-1,min(1,x[j][k]*xf[k]/xfo[k]))
data[i][ts[j]]=[x[j],yo[j]]
print(pairs[0]+": "+p+"("+str(i)+")...loaded")
return data
def get_correlated_pairs(p):
results = []
pre1 = p[:3]
suf1 = p[3:]
for pair in pairs99:
if p==pair:
results = [pair] + results
else:
pre2 = pair[:3]
suf2 = pair[3:]
if (pre1==pre2 or pre1==suf2 or suf1==pre2 or suf1==suf2): results.append(pair)
return results
def get_best_l2_model(p):
csvfile = open("combi_"+str(tf)+"/"+p+str(tf)+'.results','r', newline='')
le = list(csv.reader(csvfile))
csvfile.close()
maxE=-999999999
for i in range(len(le)-1,-1,-1):
if len(le[i][0].split(':'))<11: continue
e = float(le[i][0].split(':')[6])
if e>maxE:
maxE=e
mi = int(le[i][0].split(':')[3])
ml = float(le[i][0].split(':')[5])
en = float(le[i][0].split(':')[7])
ex = float(le[i][0].split(':')[8])
sh = int(le[i][0].split(':')[10])
print(p+": model "+str(mi)+" chosen")
r = open("combi_"+str(tf)+"/gradient_boosting_l2/"+p+"_"+str(mi)+"_"+str(sh)+".o.regressor",'rb')
regressor = pickle.load(r)
r.close()
print("level 2: "+str(mi)+"_"+str(sh)+" chosen")
return regressor,ml,maxE,mi,en,ex,sh
def record(p,x1,pa):
y,m,d,h,n,s = today_datetime()
dt = y*10000 + m*100 + d
w = open(p+str(tf)+'.records', 'a')
w.write(str(dt)+',"'+str(time.localtime().tm_hour)+":"+str(time.localtime().tm_min)+":"+str(time.localtime().tm_sec)+" >> "+'"'+","+str(round(pa,5))+"\n")
w.write(','.join(map(str,x1))+"\n")
#print(x1[:5])
w.close()
def return_metatrader_data(p,d,h):
csvfile = open(p+str(tf)+'.records', 'r',newline='')
lines = list(csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC))
csvfile.close()
h = h+8
if h>=24:
h=h%24
d=d+1
for i in range(0,len(lines),3):
if int(lines[i][0])+100==d and int(lines[i][1].split(':')[0])==h and int(lines[i][1].split(':')[1])==0:
return lines[i+1],lines[i+2],float(lines[i][2]),lines[i+4],lines[i+5],float(lines[i+3][2]),float(lines[i][3]),float(lines[i][4])
def return_backtest_data(pair,d,h):
pairs = get_correlated_pairs(pair)
x1 = []
x11 = []
for p in pairs:
xfo,yfo,tso,x,y = pload("data/"+p+str(tf)+".s0")
xft,yft,tst,x,y = pload("data/"+p+str(tf)+test+".s0")
x1l = len(xft)
for j in range(len(x)):
if tst[j]==str(d)+" "+str(h).zfill(2)+":00:00":
for i in range(x1l): x1.append(max(-1,min(1,x[j][i]*xft[i]/xfo[i])))
if tst[j]==str(d)+" "+str(h).zfill(2)+":30:00":
for i in range(x1l): x11.append(max(-1,min(1,x[j][i]*xft[i]/xfo[i])))
x2 = []
x22 = []
models = get_l1_models(pairs) # models:[13][2*5*3]
print("l1 models loaded")
for i in range(13):
for m in models[i]:
x2.append(m.predict([x1[i*x1l:(i+1)*x1l]])[0])
x22.append(m.predict([x11[i*x1l:(i+1)*x1l]])[0])
xfo,yf,ts,x,y = pload("combi_"+str(tf)+"/l1_merger/"+pair+"_0.merger.o2")
for i in range(len(x2)): x2[i] = max(-1,min(1,x2[i]/xfo[i]))
m2a,lots,earning,mi,en,ex,sh = get_best_l2_model(pair)
print("l2 model loaded")
pa = m2a.predict([x2])[0]
paa= m2a.predict([x22])[0]
return x1,x2,pa,x11,x22,paa
def empty(f):
return (not Path(f).is_file()) or Path(f).stat().st_size==0
def save_validation():
with gzip.open("data/combi"+str(tf)+".x2", 'wb') as f:
ubjson.dump([xf,ts,x2,y[0]], f)
def save_combi():
xf,ts,x,y = combi(tf)
with gzip.open("data/combi"+str(tf)+".x", 'wb') as f:
ubjson.dump([xf,ts,x,y], f)
def load_combi(i="",tm=tf):
with gzip.open("data/combi"+str(tm)+".x"+i, 'rb') as f:
data=ubjson.load(f)
return data[0],data[1],data[2],data[3]
if __name__ == '__main__':
pl=[]
#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 pl2 in pl: pl2.get()
elif argv[1]=="prepare":
pool = mp.Pool(14)
#pairs99 = ["EURCAD","EURCHF","EURAUD","EURNZD","GBPCAD","GBPCHF","GBPAUD","GBPNZD","CADCHF","AUDCAD","NZDCAD","AUDCHF","NZDCHF","AUDNZD"]
for p in pairs99 + chosen:
pl.append(pool.apply_async(prepare,(p,tf)))
for pl2 in pl: pl2.get()
elif argv[1]=="combi":
save_combi()
elif argv[1]=="train":
p="EURUSD"
xf,yf,ts,x,y = pload("data/"+p+str(tf)+".s"+str(0))
print(str(len(y[0]["EURUSD"]))+" records")
yy = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
yt = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
a = {}
for p in [p]:#pairs99:
if "JPY" in p: continue
a[p] = []
for i in range(len(ts[-validation:])):
dt=ts[-validation:][i].split(" ")
a[p].append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
xx = x[:-validation]
xt = x[-validation:]
scaler = MinMaxScaler()
xx = scaler.fit_transform(xx)
xt = scaler.fit_transform(xt)
for shift in range(16):
#for p in pairs99:
if "JPY" in p: continue
yy[shift][p] = y[shift][p][:-validation]
yt[shift][p] = y[shift][p][-validation:]
print(p+" "+str(shift)+": Train Test Split X_33333 Veritication: " + str(xx[3333][0])+" Y:"+str(yy[shift][p][3333]))
pool = mp.Pool(1)
for iter in range(iterations):
for start in range(starting-train_period-validation-testing):
if "JPY" in p: continue
#pl.append(pool.apply_async(gb_train, (shift,p)))
pl.append(pool.apply_async(rf_train, (start,p)))
#nn_train(shift,p)
#xg_train(shift,p)
for pl2 in pl: pl2.get()
elif argv[1]=="simulate":
simulate()
elif argv[1]=="prepare_l2":
xf = {}
models = get_l1_models()
print(str(len(models))+" models loaded")
xf,ts,x,y = load_combi()
#while (len(x[0])!=5348):
# ts = ts[1:]
# x = x[1:]
# y = y[1:]
# print("skipping...")
print("combi loaded. predicting...")
x2=[]
for xx in x:
xx2=[]
for m in models:
xx2.append(m.predict([xx])[0])
x2.append(xx2)
if len(x2)%100==0: print(len(x2))
save_validation()
print("validation saved")
x=[]
elif argv[1]=="train_l2":
xf,ts,x,yy= load_combi()
xf,ts,x,y = load_combi("2")
a = {}
for c in curr2:
a[c] = []
if (c+"USD") in pairs99: p = c+"USD"
else: p = "USD"+c
for i in range(len(ts[-validation:])):
dt=ts[-validation:][i].split(" ")
a[c].append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
yy = {}
yt = {}
for c in curr2:
xx = x[:-validation]
xt = x[-validation:-testing]
yy[c] = y[c][:-validation]
yt[c] = y[c][-validation:-testing]
pool = mp.Pool(20)
for c in curr2: pl.append(pool.apply_async(gb_train_l2, (c,)))
for pl2 in pl: pl2.get()
elif argv[1]=="obtain_threshold":
if True:
#if False:
models = get_models()
xf,ts,x,y = load_combi()
else:
models = get_l2_models()
xf,ts,x,y = load_combi("2")
ts=ts[-validation:]
x = x[-validation:]
y = []
#pool = mp.Pool(28)
#for p in ["GBPAUD","GBPUSD","AUDNZD"]:#pairs99:
for p in pairs99:
if "JPY" in p: continue
#pl.append(pool.apply_async(obtain_threshold3,(p,models,ts,x)))
obtain_threshold3(p,models,ts,x)
for pl2 in pl: pl2.get()
elif argv[1]=="test":
if True:
#if False:
models = get_models()
xf,ts,x,y = load_combi()
else:
models = get_l2_models()
xf,ts,x,y = load_combi("2")
ts=ts[-testing:]
x = x[-testing:]
y = []
pool = mp.Pool(28)
for p in pairs99:
if "JPY" in p : continue
pl.append(pool.apply_async(test,(p,models,ts,x,1,tf)))
for pl2 in pl: pl2.get()
elif argv[1]=="prepare_realtime":
models,bms,score = get_models()
print("models and factors loaded")
with gzip.open("combi.realtime", 'wb') as f:
pickle.dump([models,bms,score], f)
elif argv[1]=="realtime":
with gzip.open("combi.realtime", 'rb') as f:
data=pickle.load(f)
models= data[0]
bms = data[1]
score = data[2]
def st(num):
return str(round(num*100000))
def predict(s,p):
pa = models[s][p].predict([x1])[0]
#print(cc+" "+str(time.localtime().tm_hour).zfill(2)+":"+str(time.localtime().tm_min).zfill(2)+":"+str(time.localtime().tm_sec).zfill(2)+" >> "+":" + str(pa))
#record(p,x1,pa)
return pa
firstTime = True
while True:
time.sleep(3)
if (not firstTime) and (time.localtime().tm_min>0 or time.localtime().tm_sec>2): continue
firstTime = False
print("-----------------------------------------------------------------------------------------------------------------------------------------------------------------")
csvfile = open('porting/combi'+str(tf)+'.export', 'r',newline='')
lt = list(csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC))
csvfile.close()
x1 = lt[-1]
#for p in ["GBPAUD","GBPUSD","AUDNZD"]:
acc = {"USD":0, "EUR":0, "GBP":0, "AUD":0, "NZD":0, "CAD":0, "CHF":0}
for s in range(16):
for p in pairs99:
if "JPY" in p: continue
if not (p in models[s]): continue
pa = predict(s,p)
acc[p[:3]] += (pa/abs(pa)) * (bms[s][p]/abs(bms[s][p])) * score[s][p]
acc[p[3:]] -= (pa/abs(pa)) * (bms[s][p]/abs(bms[s][p])) * score[s][p]
print(acc)
maxK = max(acc, key=acc.get)
minK = min(acc, key=acc.get)
if (maxK+minK) in pairs99:
bestP=maxK+minK
decision=1
else:
bestP=minK+maxK
decision=-1
w = open('/mnt/c/Users/Intel/AppData/Roaming/MetaQuotes/Terminal/Common/Files/porting/combi120.import', 'w',newline='')
w.write(bestP+","+str(decision))
w.close()
print(str(time.localtime().tm_hour).zfill(2)+":"+str(time.localtime().tm_min).zfill(2)+":"+str(time.localtime().tm_sec).zfill(2)+" >> \t"+str(decision)+" >> \t"+" ***\t"+bestP)
elif argv[1]=="generateTF": generateTF(argv[2])
elif argv[1]=="verify": # degap -> prepare -> verify -> compare
pr = argv[2]
dt = 20191008
hr = 5
labels1 = []
labels2 = []
base = ['low','high','dif1','dif2','dif3','dif4','dif5','dif6','dif7','dif8','dif9','dif10','dif11','dif12']
for i in range(13):
labels1 = labels1 + base
for j in [12,18,24,30,40,60,80,100,150,200,400,600,800,1000,1500,2000]:
labels1.append(str(i)+"_"+str(j)+"_ema")
labels1.append(str(i)+"_"+str(j)+"_ema_dif")
labels1.append(str(i)+"_"+str(j)+"_wpr")
labels1.append(str(i)+"_"+str(j)+"_sd")
labels1.append(str(i)+"_"+str(j)+"_bb")
labels1.append(str(i)+"_"+str(j)+"_breakout")
labels1.append(str(i)+"_"+str(j)+"_cci")
labels1.append(str(i)+"_"+str(j)+"_rsi")
labels1.append(str(i)+"_"+str(j)+"_atr")
labels1.append(str(i)+"_"+str(j)+"_demarker")
labels1.append(str(i)+"_"+str(j)+"_momentum")
labels1.append("hour")
for p in get_correlated_pairs(pr):
for i in range(12): labels2.append(p+"_"+str(i))
x1a,x2a,paa,q,q,q,q,q = return_metatrader_data(pr,dt,hr)
x1b,x2b,pab,q,q,q, = return_backtest_data(pr,dt,hr)
w = open('test_results.csv', 'w')
for i in range(len(x1a)):
if i%100==0: print("writing..."+str(i))
if i==0 : w.write(str(paa)+",,"+str(pab)+",\n")
if i<len(x2b): w.write(labels1[i]+","+str(x1a[i])+","+str(x2a[i])+","+str(x1b[i])+","+str(x2b[i])+","+labels2[i]+",,"+str(abs(x1b[i]-x1a[i]))+","+str(abs(x2b[i]-x2a[i]))+"\n")
else : w.write(labels1[i]+","+str(x1a[i])+",0,"+str(x1b[i])+",0,,,"+str(abs(x1b[i]-x1a[i]))+"\n")
gsum = 0
isum = {}
psum = {}
for i in range(190):
sum = 0
for j in range(i,2483,191):
err = abs(x1b[j]-x1a[j])
sum = sum + err
gsum = gsum + err
inx = str(int(j/191))
if inx in psum: psum[inx] = psum[inx] + err
else: psum[inx] = err
for ind in ['ema','dif','wpr','sd','bb','breakout','cci','rsi','atr','demarker','momentum']:
if ind in labels1[j]:
if ind in isum: isum[ind] = isum[ind]+err
else: isum[ind]=err
print(f'{labels1[i]:16}'[2:]+":"+str(round(sum/13*100,2))+"%")
print("")
p = get_correlated_pairs(pr)
for i in range(13): print(p[i]+" error: "+str(round(psum[str(i)]/190*100,2))+"%")
print("")
for ind in ['ema','dif','wpr','sd','bb','breakout','cci','rsi','atr','demarker','momentum']: print(f'{ind:10}' +' error:' +str(round(isum[ind]/16/13*100,2))+'%')
print("")
print("Level 1 error: "+str(round(gsum/13/190*100,2))+"%")
print("")
gsum = 0
for i in range(len(x2b)): gsum = gsum + abs(x2b[i]-x2a[i])
print("Level 2 error: "+str(round(gsum/len(x2b)*100,2))+"%")
w.close()
elif argv[1]=="compare":
pr = argv[2]
paa=[0,0]
pab=[0,0]
tradeA = 0
tradeB = 0
w = open('compare_results.csv', 'w')
for dt in range(20191007,20191011):
for hr in range(24):
x1a,x2a,paa[0],x1a,x2a,paa[1],en,ex = return_metatrader_data(pr,dt,hr)
x1b,x2b,pab[0],x1b,x2b,pab[1] = return_backtest_data(pr,dt,hr)
if tradeA==1 and paa[0]<-ex: tradeA=0
elif tradeA==-1 and paa[0]>ex: tradeA=0
if paa[0]>en: tradeA = 1
elif paa[0]<-en: tradeA = -1
if tradeB==1 and pab[0]<-ex: tradeB=0
elif tradeB==-1 and pab[0]>ex: tradeB=0
if pab[0]>en: tradeB = 1
elif pab[0]<-en: tradeB = -1
w.write(str(dt)+","+str(hr)+":00,"+str(paa[0])+","+str(pab[0])+","+str(tradeA)+","+str(tradeB)+"\n")
if tradeA==1 and paa[1]<-ex: tradeA=0
elif tradeA==-1 and paa[1]>ex: tradeA=0
if paa[1]>en: tradeA = 1
elif paa[1]<-en: tradeA = -1
if tradeB==1 and pab[1]<-ex: tradeB=0
elif tradeB==-1 and pab[1]>ex: tradeB=0
if pab[1]>en: tradeB = 1
elif pab[1]<-en: tradeB = -1
w.write(str(dt)+","+str(hr)+":30,"+str(paa[1])+","+str(pab[1])+","+str(tradeA)+","+str(tradeB)+"\n")
w.close()
elif argv[1]=="compare_training_lengths":
d="test"
def compare_tl(i):
sum = 0
for j in range(5):
sum = sum + gb_train("data/AUDNZD30.s0", d, "EURUSD.regressor", i)
print("-------------------------------------------------------------------------------"+str(i)+ ": AVERAGE ACCURACY: " + str(sum/5))
pool = mp.Pool(20)
for i in range(0,100,5):
pl.append(pool.apply_async(compare_tl,[i]))
for pl2 in pl: pl2.get()