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rf.py
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rf.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
mtt = 7 # months to test
tf = 120
iterations = 88
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 = 0#int(60/tf*24*23*mtt)
start = 20100101
#test = "_test"
test = ""
#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(800):
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 [1,2,3,4,5,6,7,8,9,10,25,50,100,200,400,800]:
xx.append(o-w[800-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):
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 combi(tf):
global cache
if not empty("data/combi"+str(tf)+".s"):
r = open("data/combi"+str(tf)+".s","rb")
cache["data/combi"+str(tf)+".s"] = pickle.load(r)
r.close()
return
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 get_change(yy):
pc = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
data = {}
for p in pairs99:
#if "JPY" in p: continue
print("processing "+p)
da = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
for j in range(16):
for i in range(len(ts[p])):
da[j][ts[p][i]] = yy[p][j][i]
data[p] = deepcopy(da)
print("PROCESSED")
for t in ts["EURUSD"]:
for i in range(16):
cs = {} # currency strength
all_in = True
for p in pairs99:
#if "JPY" in p: continue
if not (t in data[p][i]):
all_in = False
break
if (all_in):
pc[i][t] = {}
for p in pairs99:
#if "JPY" in p: continue
pc[i][t][p] = deepcopy(data[p][i][t])
return pc
xf = {}
yf = {}
ts = {}
x = {}
y = {}
fp = {}
data = {}
for p in pairs99 + chosen:
y[p] = [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]
for i in range(16):xf[p],yf[p],ts[p],x[p],y[p][i] = pload2("data/"+p+str(tf)+".s"+str(i))
#xf[p],yf[p],ts[p],x[p],y = pload2("data/"+p+str(tf)+".s0")
print(str(len(ts[p]))+" records")
data[p] = {}
for i in range(len(ts[p])): data[p][ts[p][i]] = x[p][i]
fp[p] = open('data/'+str(p)+str(tf)+'.degap.csv','r')
print("saving...")
w = open("data/combi"+str(tf)+".y","wb")
pickle.dump(y,w,protocol=pickle.HIGHEST_PROTOCOL)
w.close()
#r = open("data/combi"+str(tf)+".y","rb")
#y=pickle.load(r)
#r.close()
c = get_change(y)
print(str(len(c[0]))+" y records")
y = None
print("RESTRUCTURED")
rxf = []
rts = []
rx = []
ry = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
for i in range(16):
for p in pairs99:
#if "JPY" in p: continue
ry[i][p] = []
for p in pairs99 + chosen: rxf = rxf + xf[p]
for t in c[0]:
if not (t in c[0]):continue
all_present = True
for p in pairs99 + chosen:
#if "JPY" in p: continue
if not (t in data[p]):
all_present = False
print(p+": "+t+" not present")
break
if not all_present: continue
if len(rx)%1000==0:print(t)
xx = []
for p in pairs99 + chosen:xx = list(xx) + list(data[p][t])
#for cc in curr2:
#if cc=="JPY":continue
#v = ind.indicator("combi","strength",tf,0,int(t.split()[0]),t.split()[1],1)
#print(v)
#print(int(t.split()[0]))
#print(t.split()[1])
#if v==-999: xx.append(0)
#else: xx.append(v)
#if (len(rx)==0):rxf.append(1)
rts.append(t)
rx.append(xx)
for p in pairs99:
#if "JPY" in p: continue
for i in range(16): ry[i][p].append(c[i][t][p])
print(str(len(rxf))+" features")
print(str(len(rx[0]))+" features")
print(str(len(rx))+" x records")
print("ended at..."+rts[-1])
print("Combi finalized")
return rxf,rts,rx,ry
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(s,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(s)+"_"+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,yy[s][p])
score,m,bt,cnt,pp,rpp,B = simulate(r.predict(xt),a[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>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()
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):
sign = 1
cnt = len(lot)
for repeat in range(1):
balance = 0
ol = 0
l = 0
maxB = -999999
maxDD = -999999
tcnt=0
pcnt=0
positive=0
rpositive=0
B=[]
#print(lot[:10])
for i in range(len(lot)):
lt = lot[i]
if lt>0 : l=sign
elif lt<0: l=-sign
else : l=0
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
balance += profit
if balance > maxB: maxB = balance
if maxB - balance > maxDD: maxDD = maxB - balance
B.append(balance)
ol=l
if balance>0 or sign<0: break
psave = [balance, (-100/-maxDD)*sign, float(pcnt)/cnt,tcnt,fdp(float(positive)/cnt),fdp(float(rpositive)/cnt),B]
sign = -1
#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))
n = len(B)
#tg = 0 # total gain
#tl = 0 # total loss
#for i in range(n-1):
# if B[i+1]>B[i]: tg += B[i+1]-B[i]
# if B[i+1]<B[i]: tl += B[i]-B[i+1]
#pf = tg/tl # profit factor
#mg = (B[-1] - B[0]) / (len(B)-1)
#s = 0 # straightness
#for i in range(n): s += abs(mg*i+B[0]-B[i])
#for i in range(n-1): s += abs(B[i+1]-B[i]-mg)
#for i in range(len((B))): B[i] = B[i] * (-100/-maxDD)
#return balance*(-100/-maxDD), (-100/-maxDD), float(pcnt)/cnt,tcnt,fdp(float(positive)/cnt),fdp(float(rpositive)/cnt),B
if sign>0 or True: return balance, (-100/-maxDD)*sign, float(pcnt)/cnt,tcnt,fdp(float(positive)/cnt),fdp(float(rpositive)/cnt),B
else:
if psave[0]*psave[1] > balance * (-100/-maxDD): return psave[0],psave[1],psave[2],psave[3],psave[4],psave[5],psave[6]
else : return balance, (-100/-maxDD)*sign, float(pcnt)/cnt,tcnt,fdp(float(positive)/cnt),fdp(float(rpositive)/cnt),B
def obtain_threshold(c,m,ts,x):
a = []
lots = m.predict(x)
if (c+"USD") in pairs99: p = c+"USD"
else: p = "USD"+c
for i in range(len(ts)):
dt=ts[i].split(" ")
a.append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
mx = -999999999
bf = -1
bi = -1
bm = -1
bB = None
print("ACCURACY: "+str(accuracy(a,lots)))
for k in range(100,500,10):
for j in range(2000,10000,10):
lot = deepcopy(lots)
index = j / 100
for i in range(len(ts)):
l = lot[i]*k
if (c+"USD") in pairs99: lot[i] = (abs(l)/l)*(abs(l)**index)
else: lot[i] = -(abs(l)/l)*(abs(l)**index)
balance,m,wRate,cnt,pp,rpp,B = simulate(lot,a,p)
#print(p+":"+str(k)+":"+str(index)+":"+str(round(balance*m,2))+":"+str(cnt)+":"+str(round(m,3)))
if balance*m>mx:
mx = balance*m
bf = k
bi = index
bm = m
bB = B
print(p+" best index: "+str(bf)+" : "+str(bi)+" : "+str(round(mx)))
w = open("combi_"+str(tf)+"/"+p+".index",'w')
w.write(str(bf)+","+str(bi)+","+str(bm)+","+str(round(mx)))
w.close()
plt.plot(np.array(range(len(bB))), np.array(bB), label= '3x')
plt.savefig("combi_"+str(tf)+"/"+p+"_testing_balance.png")
plt.close()
def obtain_threshold3(p,m,ts,x):
a = []
for i in range(len(ts)):
dt=ts[i].split(" ")
a.append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
lots = m[p].predict(x)
mx = -999999999
bB = None
bm = 0
debug = False
factor = 1000000
for j in range(1000):
t = 0.1 + float(j)/100.0
pf,m,wRate,cnt,pp,rpp,B = simulate4(lots,a,p,t)
if (debug): print(p+":"+str(t)+": "+str(round(pf,4))+":"+str(cnt)+":"+str(round(m,3)))
if cnt<0:break
if pf>mx:
mx = pf
bt = t
bB = B
bm = m
plt.plot(np.array(range(len(bB))), np.array(bB), label= '3x')
plt.savefig("combi_"+str(tf)+"/"+p+"_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 thresholds: "+str(round(mx,5))+" : \t"+str(bt))
w = open("combi_"+str(tf)+"/"+p+".index",'w')
w.write(str(bt)+","+str(round(bm,5))+","+str(round(mx,3)))
w.close()
def obtain_threshold2(p,m,ts,x):
a = []
for i in range(len(ts)):
dt=ts[i].split(" ")
a.append(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],0))
lots = m[p].predict(x)
#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=[]
# if (c1+"USD" in pairs99) and (c2+"USD" in pairs99):
# for i in range(len(cl1)): lots.append(cl1[i]-cl2[i])
# if (not (c1+"USD" in pairs99)) and (c2+"USD" in pairs99):
# for i in range(len(cl1)): lots.append(-cl1[i]-cl2[i])
# if (c1+"USD" in pairs99) and (not (c2+"USD" in pairs99)):
# for i in range(len(cl1)): lots.append(cl1[i]+cl2[i])
# if (not (c1+"USD" in pairs99)) and (not (c2+"USD" in pairs99)):
# for i in range(len(cl1)): lots.append(-cl1[i]+cl2[i])
#dt=ts[0].split(" ")
#print(p+":"+str(ind.indicator(p,"lookahead",tf,0,dt[0],dt[1],7))+":"+str(sum(a[:8])))
mx = -999999999
bc = 0
bcc = 0
sc = 0
scc = 0
bB = None
bm = 0
debug = False
factor = 1000000
for j in range(500):
t = 0.1 + float(j)/100.0
pf,m,wRate,cnt,pp,rpp,B = simulate4(lots,a,p,t)
if (debug): print(p+":"+str(t)+": "+str(round(pf,4))+":"+str(cnt)+":"+str(round(m,3)))
if cnt<0:break
if pf>mx:
mx = pf
bt = t
bB = B
bm = m
#for j in range(1,10000):
# t = j / factor
# balance,m,wRate,cnt,pp,rpp,B = simulate3(lots,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 = simulate3(lots,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 = simulate3(lots,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 = simulate3(lots,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
#tc = max(bc,-sc)
#sc = min(-bc,sc)
#bc = tc
#balance,bm,wRate,cnt,pp,rpp,B = simulate3(lots,a,p,bc,bcc,sc,scc)
plt.plot(np.array(range(len(bB))), np.array(bB), label= '3x')
plt.savefig("combi_"+str(tf)+"/"+p+"_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)+"/"+p+".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":
#save_combi()
xf,ts,x,y = load_combi()
print(str(len(y[0]["EURUSD"]))+" records")
yy = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
yt = [{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}]
a = {}
for p in 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 f in glob(d+"/*tmp"):
# r = open(f,'rb')
# d = pickle.load(r)
# r.close()
# if f[32]=="_": pl.append(pool.apply_async(resume_gb_train, (int(f[31:32]),f[27:30],f[27:-4],d[0],d[1],d[2],d[3],d[4])))
# else : pl.append(pool.apply_async(resume_gb_train, (int(f[31:33]),f[27:30],f[27:-4],d[0],d[1],d[2],d[3],d[4])))
for iter in range(iterations):
for shift in range(16):
#for p in ["GBPUSD","AUDUSD","GBPCAD","GBPAUD","NZDCAD","AUDNZD"]:#pairs99:
#for p in ["GBPAUD","GBPUSD","AUDNZD"]:#pairs99:
for p in pairs99:
if "JPY" in p: continue
#pl.append(pool.apply_async(gb_train, (shift,p)))
pl.append(pool.apply_async(rf_train, (shift,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=[]