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recurrent.py
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recurrent.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.preprocessing import MinMaxScaler
from sklearn.metrics import r2_score
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense
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)+"/rnn"
#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 rnn_train(p):
outfile = p+".regressor"
print("building model")
regressor = Sequential()
regressor.add(LSTM(units=50,return_sequences=True,input_shape=(xx.shape[1], xx.shape[2])))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50,return_sequences=True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50,return_sequences=True))
regressor.add(Dropout(0.2))
regressor.add(LSTM(units=50))
regressor.add(Dropout(0.2))
regressor.add(Dense(units=1))
print("training "+p+"------------------->"+d+"/"+outfile)
regressor.compile(optimizer='adam',loss='mean_squared_error')
regressor.fit(xx,yy[p],epochs=6,batch_size=32,verbose=2)
outfile = p+"."+".regressor"
best_score,bm,bt,cnt,pp,rpp,B = simulate(regressor.predict(xt),a[p][ts:],p)
if not os.path.exists(d): os.makedirs(d)
print(outfile+":"+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()
w = open(d+"/"+outfile,'wb')
pickle.dump(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(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 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()
#print("Scaling")
#xx_raw = scaler.fit_transform(xx)
#xt_raw = scaler.fit_transform(xt)
print("yy_raw yt_raw")
for shift in range(16):
for p in pairs99:
if "JPY" in p: continue
yy[p] = y[0][p][:-validation]
yt[p] = y[0][p][-validation:]
xx={}
xt={}
ts = 200
#print("Preparing xx")
#for i in range(ts, len(xx_raw)): xx.append(xx_raw[i-ts:i])
#print("Preparing xt")
#for i in range(ts, len(xt_raw)): xt.append(xt_raw[i-ts:i])
print("Preparing yy,yt")
xx = []
for i in range(ts, len(x[:-validation])):
zz = []
for j in range(i-ts,i):
z = []
for p in pairs99:
if "JPY" in p: continue
z.append(yy[p][j])
zz.append(z)
xx.append(zz)
xx = np.array(xx)
xt = []
for i in range(ts, len(x[-validation:])):
zz = []
for j in range(i-ts,i):
z = []
for p in pairs99:
if "JPY" in p: continue
z.append(yt[p][j])
zz.append(z)
xt.append(zz)
xt = np.array(xt)
for p in pairs99:
if "JPY" in p: continue
yy[p] = np.array(yy[p][ts:])
yt[p] = np.array(yt[p][ts:])
x=[]
y=[]
pool = mp.Pool(7)
for iter in range(iterations):
for p in pairs99:
if "JPY" in p: continue
pl.append(pool.apply_async(rnn_train, (p,)))
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)