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ts-simple-adaptive3.py
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ts-simple-adaptive3.py
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from __future__ import print_function
import time
import datetime
import os, fnmatch
import psutil
import csv
import sys
import numpy as pynum
import pickle
#import sqlite3
import mysql.connector
from secrets import randbelow
pFile = ""
table = ""
PriceCol = 0
SignalCol = 1
FdaCol = 2
FdaAll = 3
period = 0 # period for calculating FDA in our adaptive thing
fda_select = 0.5
arrReports=[]
fda_low = 0.0
fda_high = 0.0
def write2mysql(ss):
mydb = mysql.connector.connect(
host="localhost",
user="root",
passwd="xsjhk%123SD==-Ghk11",
database="altreva"
)
mycursor = mydb.cursor()
for s in ss:
#print (s)
a = s.split(",")
#print(table)
'''
0 05k_7btc_3-optimized.pkl,
1 period
2 0.55,
3 0.55,
4 1,
5 0.5,
6 -2855.340000000004,
7 -18.049999999997453
'''
sql = "INSERT INTO " + table + " (file, fda_period, fda_min, fda_max, maj_threshold, fda_all_min, dd, PnL) VALUES ('" + a[0] + "'," + str(period) + "," + a[1] + "," + a[2]+ "," +a[3] + "," + a[4] + "," + a[5] + "," + a[6] + ")"
#print(sql)
mycursor.execute(sql)
mydb.commit()
mydb.close()
#print('wrote to mysql')
def system_test():
mmin = 1
mmax = 1 # len(arrReports)/3 # TODO: actually delete that value by 4! /// this gets us the amount of models in total
mstep = 1
sql =""
#print(len(arrReports),arrReports[0])
#exit()
rows = len(arrReports[0])
s = []
for fda_all_t in pynum.arange(0.50, 0.52, 0.005): # pynum.arange(0.50, 0.52, 0.005):
# majority voting threshold value
#print("mmax: ", mmax)
for maj_threshold in [1]: # pynum.arange(mmin, mmax, mstep):
maj_threshold = 1
try:
pips = 0 # p&l in pips, start calculating from row 2
dd = 0 # TODO: add drawdown calculations
for r in range(rows-1): # because we can't calculate PnL of the last row (we don't know values of the next one!)
# that's header
if (r<2+period): # we know the future price only on the next row of a signal, so row 1 has no future price / also we need quite aa number of rows for the period calculation
continue
else:
row_signal = 0 # signal across a row with all models
m = 1
for model in arrReports:
#if ((model[r][FdaCol])==""): model[r][FdaCol]=0 # some of the initial values might have spaces
# if ( float(model[r][FdaCol]) >= fda_low and float(model[r][FdaCol]) <= fda_high and float(model[r][FdaAll]) >= fda_all_t): # make sure FDA is within needed range, then we count that signal
fda = 0.0
# TODO optimization: calculate historical fda using numpy array from {row-period to row}
for row in pynum.arange(r-period, r, 1):
if (model[r][FdaCol] != ""): fda += float(model[r][FdaCol])
fda = fda / period # average
if ( fda >= fda_low and fda <= fda_high and float(model[r][FdaAll]) >= fda_all_t): # make sure FDA is within needed range, then we count that signal
row_signal += model[r][SignalCol]
m+=1
model = arrReports[0] # just take first model for calculations
if ( abs(row_signal) >= maj_threshold): # it was a valid signal and we execute it
if (row_signal > 0):
pips += float(model[r+1][PriceCol]) - float(model[r][PriceCol])
elif (row_signal < 0):
pips += float(model[r][PriceCol]) - float(model[r+1][PriceCol])
if (pips < 0 and pips < dd):
dd = pips
#print(pFile + "," + str(fda_low) + "," + str(fda_high) + "," + str(maj_threshold) + "," + str(fda_all_t) + "," + str(dd) + "," + str(pips) )
s.append ( pFile + "," + str(fda_low) + "," + str(fda_high) + "," + str(maj_threshold) + "," + str(fda_all_t) + "," + str(dd) + "," + str(pips) )
except:
print("Unexpected error:", sys.exc_info()[0])
print("file: ", pFile)
print("row: ", r)
print("model: ", m)
print("model[r][FdaCol])=", model[r][FdaCol], "/")
raise
write2mysql(s)
# this will be called from the main function
def adaptive_system_test():
mmin = 1
mmax = 1 # len(arrReports)/5 # TODO: actually delete that value by 4! /// this gets us the amount of models in total
mstep = 1
sql =""
#print(len(arrReports),arrReports[0])
#exit()
rows = len(arrReports[0])
s = []
for fda_all_t in pynum.arange(0.50, 0.52, 0.005): # pynum.arange(0.50, 0.52, 0.005):
# majority voting threshold value
for fda_check_bar in pynum.arange(100, 3000, 100):
maj_threshold = 1
try:
pips = 0 # p&l in pips, start calculating from row 2
dd = 0 # TODO: add drawdown calculations
for r in range(rows-1): # because we can't calculate PnL of the last row (we don't know values of the next one!)
# that's header
if (r<2+period): # we know the future price only on the next row of a signal, so row 1 has no future price / also we need quite aa number of rows for the period calculation
continue
else:
row_signal = 0 # signal across a row with all models
m = 1
for model in arrReports:
#if ((model[r][FdaCol])==""): model[r][FdaCol]=0 # some of the initial values might have spaces
# if ( float(model[r][FdaCol]) >= fda_low and float(model[r][FdaCol]) <= fda_high and float(model[r][FdaAll]) >= fda_all_t): # make sure FDA is within needed range, then we count that signal
fda = 0.0
# TODO optimization: calculate historical fda using numpy array from {row-period to row}
for row in pynum.arange(r-period, r, 1):
if (model[r][FdaCol] != ""): fda += float(model[r][FdaCol])
fda = fda / 44 # average
if ( fda >= fda_low and fda <= fda_high and float(model[r][FdaAll]) >= fda_all_t): # make sure FDA is within needed range, then we count that signal
row_signal += model[r][SignalCol]
m+=1
model = arrReports[0] # just take first model for calculations
if ( abs(row_signal) >= maj_threshold): # it was a valid signal and we execute it
if (row_signal > 0):
pips += float(model[r+1][PriceCol]) - float(model[r][PriceCol])
elif (row_signal < 0):
pips += float(model[r][PriceCol]) - float(model[r+1][PriceCol])
if (pips < 0 and pips < dd):
dd = pips
print(pFile + "," + str(fda_low) + "," + str(fda_high) + "," + str(maj_threshold) + "," + str(fda_all_t) + "," + str(dd) + "," + str(pips) )
s.append ( pFile + "," + str(fda_low) + "," + str(fda_high) + "," + str(maj_threshold) + "," + str(fda_all_t) + "," + str(dd) + "," + str(pips) )
except:
print("Unexpected error:", sys.exc_info()[0])
print("file: ", pFile)
print("row: ", r)
print("model: ", m)
print("model[r][FdaCol])=", model[r][FdaCol], "/")
raise
write2mysql(s)
# print something with timestamp
def ts(s):
now = datetime.datetime.now()
print(now.strftime("%Y-%m-%d %H:%M:%S") + " // " + s)
if __name__ == "__main__":
#
pFile = sys.argv[1]
#ts("::>> started " + pFile)
pkl_file = open(pFile, 'rb')
arrReports = pickle.load(pkl_file)
pkl_file.close()
table = sys.argv[2]
period = int(sys.argv[3])
fda_low = float(sys.argv[4])
fda_high = float(sys.argv[5])
#print("array file: " + pFile)
#print("table: " + table)
#print("period:" + str(period))
system_test()
#ts("<<:: finished " + pFile)