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combi.py
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combi.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)+"/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(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)
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 xg_train(s,p):
best_score=-9999999
best_regressor=None
train=xgboost.DMatrix(xx,yy[s][p])
ev=xgboost.DMatrix(xt,a[p])
outfile = p+"."+str(s)+"_"+str(uniform(0.001,0.999))+".regressor"
print("training "+p+str(s)+"------------------->"+d+"/"+outfile)
r = regressor = xgboost.train({'max_depth':13, 'eta':0.1, 'verbosity':2 ,'subsample':0.5,'grow_policy':'lossguide','max_leaves':0}, train,1000, evals=[(ev,'eval')],early_stopping_rounds=5)
pf,m,bt,cnt,pp,rpp,B = simulate5(r.predict(xgboost.DMatrix(xt)),a[p],p)
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)
def nn_train(s,p):
best_score=-9999999
best_regressor=None
r=regressor = MLPRegressor(warm_start=True,verbose=2,tol=1e-7,learning_rate='adaptive',hidden_layer_sizes=(len(x[0])),solver='sgd')
outfile = p+"."+str(s)+"_"+str(uniform(0.001,0.999))+".regressor"
print("training "+p+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(max_iter=1)
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)+"......"+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()
w = open(d+"/"+outfile+".accuracy",'w')
w.write(str(best_score)+","+str(bm))
w.close()
print("written to "+d+"/"+outfile)
def gb_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=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 = 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(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,yy[s][p])
#score = r.score(xt,yt[s][p])
#score = r2_score(yt[s][p],r.predict(xt))
#score = my_score(yt[s][p],r.predict(xt))
#score,m,bt,cnt,pp,rpp,B = simulate9(r.predict(xt),a[p],p,outfile,best_score)
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)+"......"+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()
w = open(d+"/"+outfile+".accuracy",'w')
w.write(str(best_score)+","+str(bm))
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 gb_train_l2(c):
lr = 0.2
best_score=-9999999
best_regressor=None
LV = randint(50,500)
SP = randint(2,13)
SS = uniform(0.25,0.9)
r=GradientBoostingRegressor(verbose=0, warm_start=True, loss='huber', learning_rate=lr, n_estimators=1,max_depth=9,min_samples_leaf=LV,min_samples_split=SP,subsample=SS)
outfile = c+"."+"_"+str(LV)+"_"+str(SP)+"_"+str(SS)+".regressor2"
print("training "+c+"------------------->"+d+"/"+outfile)
bj=0
if (c+"USD") in pairs99: p = c+"USD"
else: p = "USD"+c
for j in range(1,3000):
r.set_params(n_estimators=j)
r.fit(xx,yy[c])
#score = r.score(xt,yt[s][c])
#score = r2_score(yt[c],r.predict(xt))
pf,m,wRate,cnt,pp,rpp,B = simulate(r.predict(xt),a[c],p)
score = pf
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
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):
sign = 1
cnt = len(lot)
for repeat in range(2):
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: 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 simulate5(lot,a,pair):
mx = -999999999
bB = None
bm = 0
debug = False
factor = 1000000
for j in range(45):
t = 0.1 + float(j)*.2
pf,m,wRate,cnt,pp,rpp,B = simulate4(lot,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
return mx,bm,bt,cnt,pp,rpp,bB
def simulate7(lot,a,pair):
mx = -999999999
bB = None
bm = 0
debug = False
factor = 1000000
for j in range(45):
t = 0.1 + float(j)*.1
pf,m,wRate,cnt,pp,rpp,B = simulate6(lot,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
return mx,bm,bt,cnt,pp,rpp,B
def simulate6(lot,a,pair,power):
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(len(lot)):
lt = lot[i]
l = pow(float(abs(lt)), float(power))
if lt<0: l=-l
if (ol>0 and l>0 and l<ol) or (ol<0 and l<0 and l>ol): l = ol
if abs(l)>0.000000001: 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
if profit > 0: pcnt+=1
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 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]
if tl<0.000001:tl=0.000001
pf = tg/tl # profit factor
return pf, (-100/-maxDD), float(pcnt)/cnt,tcnt,fdp(float(positive)/cnt),fdp(float(rpositive)/cnt),B
def simulate4(lot,a,pair,power=1):
balance = 0
ol = 0
l = 0
maxB = -999999
maxDD = -999999
cnt=0
tcnt=0
pcnt=0
positive=0
rpositive=0
switch=0
B=[]
#print(lot[:10])
for i in range(len(lot)):
lt = lot[i]
l = pow(float(abs(lt)), float(power))
if lt<0: l=-l
#if lt <0: l=-1
#if lt >0: l= 1
#if lt==0: l= 0
#if lt<0: l=-1
#elif lt>0: l=1
#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<-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
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)
if (ol<0 and l>0) or (ol>0 and l<0): switch+=1
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)
for i in range(len(B)): B[i] = B[i]/B[-1]*100
mg = (B[-1] - B[0]) / (len(B)-1)
s = 0 # straightness
for i in range(n): s += abs(mg*i+B[0]-B[i])
#gain = 0
#loss = 0
#for i in range(1,len(B)):
# dif = B[i] - B[i-1]
# if dif > 0: gain += dif**0.5
# else: loss += abs(dif)**0.5
#if loss==0:
# loss=0.0000001
# print("echo division by zero")
return n/s*(abs(balance)/balance), (-100/-maxDD), float(pcnt)/cnt,tcnt,fdp(float(positive)/cnt),fdp(float(rpositive)/cnt),B
#return gain/loss, (-100/-maxDD), float(pcnt)/cnt,tcnt,fdp(float(positive)/cnt),fdp(float(rpositive)/cnt),B
def simulate3(lot,a,pair,bc=0,bcc=0,sc=0,scc=0):
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 simulate9(lots,a,p,f,omx):
print("simulate 9 begins..."+f)
mx = -999999999
bc = 0
bcc = 0
sc = 0
scc = 0
bB = None
bm = 0
debug = False
factor = (max(lots)-min(lots))/2/1000
for j in range(1,1000):
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>mx:
mx = balance
bc = t
sc = -t
bB = B
bm = m
for j in range(1,300):
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>mx:
mx = balance
bcc= t
scc=-t
bB = B
bm = m
mx = -999999999
for j in range(1,1000):
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>mx:
mx = balance
sc = t
bB = B
bm = m
for j in range(1,300):
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>mx:
mx = balance
scc= t
bB = B
bm = m
if mx > omx:
#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(d+"/"+f+"_testing_balance.png")
plt.close()
print(p+" best tresholds: "+str(round((bc-sc)/2/factor/10,1))+"% : \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(d+"/"+f+".index",'w')
w.write(str(bc)+","+str(bcc)+","+str(sc)+","+str(scc)+","+str(round(bm,5))+","+str(round(mx,3)))
w.close()
return mx,bm,(bc-sc)/2,0,0,0,B
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