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exotics.py
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exotics.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
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
import xgboost as xgboost
import indicators as ind
from symbols import *
from math import sqrt
import matplotlib.pyplot as plt
tf = 120
iterations = 1
boundary = 0.38
#starting = int(60/tf*24*23*39)
validation = int(60/tf*24*23*2)
testing = int(60/tf*24*23*2)
start = 20080101
#test = "_test"
test = ""
#d = "combi_"+str(tf)+"/neural_network"
#d = "combi_"+str(tf)+"/svr"
d = "combi_"+str(tf)+"/gradient_boosting"
#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................................")
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(12):
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
xx.append(o-w[11])
if (cnt==0): xfc.append(0)
xx.append(o-w[10])
if (cnt==0): xfc.append(0)
xx.append(o-w[9])
if (cnt==0): xfc.append(0)
xx.append(o-w[8])
if (cnt==0): xfc.append(0)
xx.append(o-w[7])
if (cnt==0): xfc.append(0)
xx.append(o-w[6])
if (cnt==0): xfc.append(0)
xx.append(o-w[5])
if (cnt==0): xfc.append(0)
xx.append(o-w[4])
if (cnt==0): xfc.append(0)
xx.append(o-w[3])
if (cnt==0): xfc.append(0)
xx.append(o-w[2])
if (cnt==0): xfc.append(0)
xx.append(o-w[1])
if (cnt==0): xfc.append(0)
xx.append(o-w[0])
if (cnt==0): xfc.append(0)
w = w[1:]
w.append(a)
#for period in [6,12,18,24,30,40,60,80,100,150,200,400,600,800,1000,1500,2000]:
for period in [12,18,24,30,40,60,80,100,150,200,400,600,800,1000,1500,2000]:
ema = ind.indicator(p,"ema",tf,period,d,t,1)
if (ema==-999):
print(p+' skipping '+str(d)+' '+t+"............................................")
break
xx.append(ema-o)
ema2 = ind.indicator(p,"ema",tf,period,d,t,2)
if (ema2==-999):
print(p+' skipping '+str(d)+' '+t+"............................................")
break
xx.append(ema-ema2)
if (cnt==0): xfc.append(0)
if (cnt==0): xfc.append(0)
xx.append(ind.indicator(p,"wpr",tf,period,d,t,1))
if (cnt==0): xfc.append(-1)
xx.append(ind.indicator(p,"sd",tf,period,d,t,1))
if (cnt==0): xfc.append(1)
xx.append(ind.indicator(p,"bb",tf,period,d,t,1))
if (cnt==0): xfc.append(0)
xx.append(ind.indicator(p,"breakout",tf,period,d,t,1))
if (cnt==0): xfc.append(0)
xx.append(ind.indicator(p,"cci",tf,period,d,t,1))
if (cnt==0): xfc.append(0)
xx.append(ind.indicator(p,"rsi",tf,period,d,t,1))
if (cnt==0): xfc.append(1)
xx.append(ind.indicator(p,"atr",tf,period,d,t,1))
if (cnt==0): xfc.append(1)
#xx.append(ind.indicator(p,"macd",tf,period,d,t,1))
#if (cnt==0): xfc.append(0)
#xx.append(ind.indicator(p,"macd_signal",tf,period,d,t,1))
#if (cnt==0): xfc.append(0)
xx.append(ind.indicator(p,"demarker",tf,period,d,t,1))
if (cnt==0): xfc.append(1)
#xx.append(ind.indicator(p,"trix",tf,period,d,t,1))
#if (cnt==0): xfc.append(0)
#xx.append(ind.indicator(p,"adx",tf,period,d,t,1)[0])
#if (cnt==0): xfc.append(1)
#xx.append(ind.indicator(p,"adx",tf,period,d,t,1)[1])
#if (cnt==0): xfc.append(0)
xx.append(ind.indicator(p,"momentum",tf,period,d,t,1))
if (cnt==0): xfc.append(0)
#xx.append(ind.indicator(p,"rvi",tf,period,d,t,1)[0])
#if (cnt==0): xfc.append(0)
#xx.append(ind.indicator(p,"rvi",tf,period,d,t,1)[1])
#if (cnt==0): xfc.append(0)
if ema!=-999 and ema2!=-999:
#xx.append(ind.indicator(p,"psar",tf,-1,d,t,1))
#if (cnt==0): xfc.append(0)
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)/o
if isTrender:
if xx[4]*la > 0: y[shift].append(la)
else : y[shift].append(0)
else : 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):
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 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 gb_train(s,c):
lr = 0.2
best_score=-9999
best_regressor=None
LV = randint(50,500)
SP = randint(2,13)
SS = uniform(0.3,0.9)
r=GradientBoostingRegressor(verbose=0, warm_start=True, loss='huber', learning_rate=lr, n_estimators=1,max_depth=13,min_samples_leaf=LV,min_samples_split=SP,subsample=SS)
outfile = c+"."+str(s)+"_"+str(LV)+"_"+str(SP)+"_"+str(SS)+".regressor"
print("training "+c+str(s)+"------------------->"+d+"/"+outfile)
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(n_estimators=j)
r.fit(xx[s][c],yy[s][c])
#score = r.score(xt,yt[s][c])
#score = r2_score(yt[s][c],r.predict(xt))
score = my_score(yt[s][c],r.predict(xt[s][c]))
if score>best_score:
bj=j
best_score = score
best_regressor = deepcopy(r)
print(outfile+":"+str(j)+": "+str(score)+" <<<")
else:
print(outfile+":"+str(j)+": "+str(score)+"......"+str(r.get_params()["learning_rate"]))
if j-bj>6: 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))
w.close()
print("written to "+d+"/"+outfile)
def resume_gb_train(s,c,outfile,r,j_start,bj,best_score,best_regressor):
print("(RESUME) training "+c+str(s)+"------------------->"+d+"/"+outfile)
for j in range(j_start,3000):
w = open(d+"/"+outfile+".tmp",'wb')
pickle.dump([r,j,bj,best_score,best_regressor],w)
w.close()
r.set_params(n_estimators=j)
r.fit(xx,yy[s][c])
#score = r.score(xt,yt[s][c])
score = r2_score(yt[s][c],r.predict(xt))
if score>best_score:
bj=j
best_score = score
best_regressor = deepcopy(r)
print(outfile+":"+str(j)+": "+str(score)+" <<<")
else:
print(outfile+":"+str(j)+": "+str(score)+"......"+str(r.get_params()["learning_rate"]))
if j-bj>6: 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))
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=[]
#print(lot[:10])
for i in range(0,len(lot)):
lt = lot[i]
if ol>=0:
if lt>ol: l=lt
if lt<0: l=lt
if ol<=0:
if lt<ol: l=lt
if lt>0: l=lt
#l = lt
if abs(l)>0.00000000001: 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
#profit = (l * r) * unit[pair] / value[pair[-3:]]
if profit > 0: pcnt+=1
if profit > 1200: profit=1200
if profit <-1200: profit=-1200
balance += profit
if balance > maxB: maxB = balance
if maxB - balance > maxDD: maxDD = maxB - balance
B.append(balance)
ol=l
cnt = len(lot)
#print(pair+":"+str(maxDD)+" "+str(cnt)+" "+str(tcnt))
if maxDD==0 or cnt==0 or tcnt<5:
return balance/1000,0.001,0,tcnt,0,0,B
#if uniform(0,1)<0.001:
#print(pair+":"+str(balance)+":"+str(tcnt))
return balance, -100/-maxDD, float(pcnt)/cnt,tcnt,fdp(float(positive)/cnt),fdp(float(rpositive)/cnt),B
def reset_sum():
w = open("combi_"+str(tf)+"/sum.txt",'w')
w.write("0")
w.close()
def test(p,m,ts,x,index):
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)))
a = []
lot = m.predict(x)
for i in range(len(ts)):
lot[i] = lot[i]
dt=ts[i].split(" ")
a.append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
#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(lot,a,p,d[0][0],d[0][1],d[0][2],d[0][3])
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*m))+" "+str(round(m,3))+ " lots")
#add_sum(balance*m)
return balance * m, B, m, cnt
def get_models():
models = {}
for p in exotics:
acc = []
files=glob(d+"/"+p+"*regressor.accuracy")
mx = -999
mf = ""
ms = -1
for f in files:
r = open(f,'r')
acc = float(r.readline())
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
if mx>-99:
print(mf+" chosen: "+str(ms)+" : "+str(mx))
r = open(mf[:-9],'rb')
models[p]=pickle.load(r)
r.close()
return models
def get_very_best_models():
models = {}
for c in exotics:
mx = -999
mf = ""
ms = -1
me = -999999
mt = -1
for t in [120,240,360,480]:
xf,yf,ts,x,y = pload("data/"+c+str(t)+".s"+str(0))
ts=ts[-testing:]
x = x[-testing:]
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)
print(f+": "+str(ms)+" : "+str(acc) + " : "+str(earning)+"("+str(cnt)+")")
if earning*acc>me and earning<5000:
if t<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
me=earning*acc
mx=acc
mf=f
mt=t
if me>-99:
r = open(mf[:-9],'rb')
models[c]=pickle.load(r)
r.close()
t = int(mf[6:9])
xf,yf,ts,x,y = pload("data/"+c+str(t)+".s"+str(0))
ts=ts[-testing:]
x = x[-testing:]
earning,B,m,cnt = test(c,models[c],ts,x,0)
fig = plt.figure()
plt.plot(np.array(range(len(B))), np.array(B), label= '3x')
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/mx))+"("+str(cnt)+")\n\n\n\n")
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 = -999
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>-99:
print(mf+" chosen: "+str(ms)+" : "+str(mx))
r = open(mf[:-9],'rb')
models.append(pickle.load(r))
r.close()
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 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")
w.close()
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)
if __name__ == '__main__':
pl=[]
#initialize_last_close(pairs99)
if argv[1]=="degap":
for p in exotics:
os.rename("Data/"+p+str(tf)+".csv","Data/"+p+str(tf)+".degap.csv")
print("Data/"+p+str(tf)+".csv --> Data/"+p+str(tf)+".degap.csv")
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 exotics:
pl.append(pool.apply_async(prepare,(p,tf)))
for pl2 in pl: pl2.get()
elif argv[1]=="train":
xx = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
xt = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
yy = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
yt = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
for shift in range(16):
for p in exotics:
xf,yf,ts,x,y = pload("data/"+p+str(tf)+".s"+str(shift))
xx[shift][p], xt[shift][p], yy[shift][p], yt[shift][p] = train_test_split(x[:-validation],y[:-validation],test_size=0.15, random_state=1688, shuffle=False)
#while (len(xx[0])!=5348):
# xx = xx[1:]
# yy[shift][p] = yy[shift][p][1:]
print(p+" "+str(shift)+": Train Test Split X_33333 Veritication: " + str(xx[shift][p][333][0])+" Y:"+str(yy[shift][p][333]))
pool = mp.Pool(28)
for iter in range(iterations):
for shift in range(16):
for p in exotics:
pl.append(pool.apply_async(gb_train, (shift,p)))
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:-testing]
x = x[-validation:-testing]
y = []
pool = mp.Pool(7)
for c in curr2:
pl.append(pool.apply_async(obtain_threshold2,(c,models[c],ts,x)))
for pl2 in pl: pl2.get()
elif argv[1]=="test":
models = get_models()
pool = mp.Pool(7)
for p in exotics:
xf,yf,ts,x,y = pload("data/"+p+str(tf)+".s"+str(0))
ts = ts[-testing:]
x = x[-testing:]
pl.append(pool.apply_async(test,(p,models[p],ts,x,1)))
for pl2 in pl: pl2.get()
elif argv[1]=="prepare_realtime":
models=get_very_best_models()
xf = {}
for tff in [120,240,360,480]:
xf[tff] = {}
for p in exotics:
xf[tff][p],yf,ts,x,y = pload("data/"+p+str(tff)+".s"+str(0))
lots = {}
tfm = {}
for p in exotics:
r = open("final_selection/"+p+".lots",'r')
d = list(csv.reader(r, quoting=csv.QUOTE_NONNUMERIC))
r.close()
lots[p] = float(d[0][0])
tfm[p] = int(d[0][1])
print("models and factors loaded")
with gzip.open("final_selection/exotics.realtime", 'wb') as f:
pickle.dump([models,xf,lots,tfm], f)
elif argv[1]=="realtime":
with gzip.open("final_selection/exotics.realtime", 'rb') as f:
data=pickle.load(f)
models=data[0]
xf=data[1]
lots=data[2]
tfm=data[3]
def st(num):
return str(round(num*100000))
def predict(p):
csvfile = open('porting/'+p+'.export', 'r',newline='')
lt = list(csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC))
csvfile.close()
x1 = lt[-1]
for i in range(len(x1)):
try:
x1[i] = max(-1,min(1,x1[i]/xf[tfm[p]][p][i]))
except:
x1[i] = 0
pa = models[p].predict([x1])[0] * lots[p]
#print(p+" "+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%15>0 or time.localtime().tm_sec>5): continue
firstTime = False
print("-----------------------------------------------------------------------------------------------------------------------------------------------------------------")
for p in exotics:
w = open('/mnt/c/Users/Intel/AppData/Roaming/MetaQuotes/Terminal/Common/Files/porting/'+p+'.import', 'w',newline='')
pr = predict(p)
w.write(str(pr)+"\n")
print(p+" "+str(time.localtime().tm_hour).zfill(2)+":"+str(time.localtime().tm_min).zfill(2)+":"+str(time.localtime().tm_sec).zfill(2)+" >> "+str(round(pr,3)))
w.close()
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()