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pandas_gp_resample_2.py
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# -*- coding: utf-8 -*-
"""
GP Fitness using resampling
Created on Sun Aug 21 10:40:14 2016
@author: jm
"""
import operator
import math
import random
import cPickle
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import networkx as nx
from deap import algorithms
from deap import base
from deap import creator
from deap import tools
from deap import gp
#from scoop import futures, IS_ORIGIN
primes = [2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,
97,101,103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,
181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,271,
277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,373,379,
383,389,397,401,409,419,421,431,433,439,443,449,457,461,463,467,479,
487,491,499] #,503,509,521,523,541,547,557,563,569,571,577,587,593,599,
# 601,607,613,617,619,631,641,643,647,653,659,661,673,677,683,691,701,
# 709,719,727,733,739,743,751,757,761,769,773,787,797,809,811,821,823,
# 827,829,839,853,857,859,863,877,881,883,887,907,911,919,929,937,941,
# 947,953,967,971,977,983,991,997,1009,1013,1019,1021,1031,1033,1039,
# 1049,1051,1061,1063,1069,1087,1091,1093,1097,1103,1109,1117,1123,
# 1129,1151,1153,1163,1171,1181,1187,1193,1201,1213,1217,1223,1229,
# 1231,1237,1249,1259,1277,1279,1283,1289,1291,1297,1301,1303,1307,
# 1319,1321,1327,1361,1367,1373,1381,1399,1409,1423,1427,1429,1433,
# 1439,1447,1451,1453,1459,1471,1481,1483,1487,1489,1493,1499,1511,
# 1523,1531,1543,1549,1553,1559,1567,1571,1579,1583,1597,1601,1607,
# 1609,1613,1619,1621,1627,1637,1657,1663,1667,1669,1693,1697,1699,
# 1709,1721,1723,1733,1741,1747,1753,1759,1777,1783,1787,1789,1801,
# 1811,1823,1831,1847,1861,1867,1871,1873,1877,1879,1889,1901,1907,
# 1913,1931,1933,1949,1951,1973,1979,1987,1993,1997,1999,2003,2011,
# 2017,2027,2029,2039,2053,2063,2069,2081,2083,2087,2089,2099,2111,
# 2113,2129,2131,2137,2141,2143,2153,2161,2179,2203,2207,2213,2221,
# 2237,2239,2243,2251,2267,2269,2273,2281,2287,2293,2297,2309,2311,
# 2333,2339,2341,2347,2351,2357,2371,2377,2381,2383,2389,2393,2399,
# 2411,2417,2423,2437,2441,2447,2459,2467,2473,2477,2503,2521,2531,
# 2539,2543,2549,2551,2557,2579,2591,2593,2609,2617,2621,2633,2647,
# 2657,2659,2663,2671,2677,2683,2687,2689,2693,2699,2707,2711,2713,
# 2719,2729,2731,2741,2749,2753,2767,2777,2789,2791,2797,2801,2803,
# 2819,2833,2837,2843,2851,2857,2861,2879,2887,2897,2903,2909,2917,
# 2927,2939,2953,2957,2963,2969,2971,2999,3001,3011,3019,3023,3037,
# 3041,3049,3061,3067,3079,3083,3089,3109,3119,3121,3137,3163,3167,
# 3169,3181,3187,3191,3203,3209,3217,3221,3229,3251,3253,3257,3259,
# 3271,3299,3301,3307,3313,3319,3323,3329,3331,3343,3347,3359,3361,
# 3371,3373,3389,3391,3407,3413,3433,3449,3457,3461,3463,3467,3469,
# 3491,3499,3511,3517,3527,3529,3533,3539,3541,3547,3557,3559,3571,
# 3581,3583,3593,3607,3613,3617,3623,3631,3637,3643,3659,3671,3673,
# 3677,3691,3697,3701,3709,3719,3727,3733,3739,3761,3767,3769,3779,
# 3793,3797,3803,3821,3823,3833,3847,3851,3853,3863,3877,3881,3889,
# 3907,3911,3917,3919,3923,3929,3931,3943,3947,3967,3989,4001,4003,
# 4007,4013,4019,4021,4027,4049,4051,4057,4073,4079,4091,4093,4099,
# 4111,4127,4129,4133,4139,4153,4157,4159,4177,4201,4211,4217,4219,
# 4229,4231]
# load the data
dataset = cPickle.load(open("ibex.pickle", "rb"))
#we need to have pd_df_bool terminals for gp to work
#better to have them as a column in the dataset
#then to use a terminal calling np.ones or np.zeros
#as we are not constrained by the size of the vector
dataset["Ones"] = True
dataset["Zeros"] = False
# Transaction costs - 10 Eur per contract
lot_size = 10
cost = 10
# Split into train and test
train = dataset["2000":"2015"].copy()
test = dataset["2016":].copy()
# date indices for resampling fitness
indices = np.unique(train.index.date)
# Functions and terminal for GP
class pd_df_float(object):
pass
class pd_df_bool(object):
pass
def f_gt(df_in, f_value):
return df_in > f_value
def f_lt(df_in, f_value):
return df_in < f_value
def protectedDiv(left, right):
try: return left / right
except ZeroDivisionError: return 0.0
def pd_add(left, right):
return left + right
def pd_subtract(left, right):
return left - right
def pd_multiply(left, right):
return left * right
def pd_divide(left, right):
return left / right
def pd_diff(df_in, _periods):
return df_in.diff(periods=abs(_periods))
def sma(df_in, periods):
return pd.rolling_mean(df_in, abs(periods))
def ewma(df_in, periods):
if abs(periods) < 2:
return df_in
else:
return pd.ewma(df_in, abs(periods), min_periods=abs(periods))
def hh(df_in, periods):
if abs(periods) < 2:
return df_in
else:
return pd.rolling_max(df_in, abs(periods), min_periods=abs(periods))
def ll(df_in, periods):
if abs(periods) < 2:
return df_in
else:
return pd.rolling_min(df_in, abs(periods), min_periods=abs(periods))
def pd_std(df_in, periods):
if abs(periods) < 2:
return df_in
else:
return pd.rolling_std(df_in, abs(periods), min_periods=abs(periods))
pset = gp.PrimitiveSetTyped('MAIN', [pd_df_float,
pd_df_float,
pd_df_float,
pd_df_float,
pd_df_float,
pd_df_bool,
pd_df_bool],
pd_df_bool)
pset.renameArguments(ARG0='Open')
pset.renameArguments(ARG1='High')
pset.renameArguments(ARG2='Low')
pset.renameArguments(ARG3='Close')
pset.renameArguments(ARG4='Volume')
# need to have pd_df_bool terminals for GP to work
pset.renameArguments(ARG5='Ones')
pset.renameArguments(ARG6='Zeros')
pset.addPrimitive(sma, [pd_df_float, int], pd_df_float, name="sma")
pset.addPrimitive(ewma, [pd_df_float, int], pd_df_float, name="ewma")
pset.addPrimitive(hh, [pd_df_float, int], pd_df_float, name="hh")
pset.addPrimitive(ll, [pd_df_float, int], pd_df_float, name="ll")
pset.addPrimitive(pd_std, [pd_df_float, int], pd_df_float, name="pd_std")
pset.addPrimitive(np.log, [pd_df_float], pd_df_float)
pset.addPrimitive(pd_diff, [pd_df_float, int], pd_df_float)
pset.addPrimitive(pd_add, [pd_df_float, pd_df_float], pd_df_float, name="pd_add")
pset.addPrimitive(pd_subtract, [pd_df_float, pd_df_float], pd_df_float, name="pd_sub")
pset.addPrimitive(pd_multiply, [pd_df_float, pd_df_float], pd_df_float, name="pd_mul")
pset.addPrimitive(pd_divide, [pd_df_float, pd_df_float], pd_df_float, name="pd_div")
pset.addPrimitive(operator.add, [int, int], int, name="add")
pset.addPrimitive(operator.sub, [int, int], int, name="sub")
#pset.addPrimitive(operator.mul, [int, int], int, name="mul")
pset.addPrimitive(protectedDiv, [int, int], int, name="div")
pset.addPrimitive(f_gt, [pd_df_float, float], pd_df_bool )
pset.addPrimitive(f_lt, [pd_df_float, float], pd_df_bool )
pset.addEphemeralConstant("short", lambda: random.randint(2,60), int)
pset.addEphemeralConstant("medium", lambda: random.randint(60,100), int)
pset.addEphemeralConstant("long", lambda: random.randint(100,200), int)
pset.addEphemeralConstant("xtralong", lambda: random.randint(200,20000), int)
pset.addEphemeralConstant("rand100", lambda: random.randint(0,100), int)
pset.addEphemeralConstant("randfloat", lambda: np.random.normal() / 100. , float)
pset.addPrimitive(operator.lt, [pd_df_float, pd_df_float], pd_df_bool)
pset.addPrimitive(operator.gt, [pd_df_float, pd_df_float], pd_df_bool)
pset.addPrimitive(np.bitwise_and, [pd_df_bool, pd_df_bool], pd_df_bool)
pset.addPrimitive(np.bitwise_or, [pd_df_bool, pd_df_bool], pd_df_bool)
pset.addPrimitive(np.bitwise_xor, [pd_df_bool, pd_df_bool], pd_df_bool)
pset.addPrimitive(np.bitwise_not, [pd_df_bool], pd_df_bool)
pset.addPrimitive(operator.add, [float, float], float, name="f_add")
pset.addPrimitive(operator.sub, [float, float], float, name="f_sub")
pset.addPrimitive(protectedDiv, [float, float], float, name="f_div")
pset.addPrimitive(operator.mul, [float, float], float, name="f_mul")
#Better to pass this terminals as arguments (ARG5 and ARG6)
#pset.addTerminal(pd.TimeSeries(data=[1] * len(train), index=train.index, dtype=bool), pd_df_bool, name="ones")
#pset.addTerminal(pd.TimeSeries(data=[0] * len(train), index=train.index, dtype=bool), pd_df_bool, name="zeros")
pset.addTerminal(1.618, float)
pset.addTerminal(0.1618, float)
pset.addTerminal(0.01618, float)
pset.addTerminal(0.001618, float)
pset.addTerminal(-0.001618, float)
pset.addTerminal(-0.01618, float)
pset.addTerminal(-0.1618, float)
pset.addTerminal(-1.618, float)
pset.addTerminal(1, int)
for p in primes:
pset.addTerminal(p, int)
for f in np.arange(0,0.2,0.002):
pset.addTerminal(f, float)
pset.addTerminal(-f, float)
creator.create('FitnessMax', base.Fitness, weights=(1.0,))
creator.create('Individual', gp.PrimitiveTree, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register('expr', gp.genHalfAndHalf, pset=pset, min_=1, max_=6)
toolbox.register('individual', tools.initIterate, creator.Individual, toolbox.expr)
toolbox.register('population', tools.initRepeat, list, toolbox.individual)
toolbox.register('compile', gp.compile, pset=pset)
#toolbox.register('map', futures.map)
def evalFitness(individual, points):
#print(individual)
func = toolbox.compile(expr=individual)
s = func(points.Open,
points.High,
points.Low,
points.Close,
points.Volume,
points.Ones,
points.Zeros)
s = s.shift(1)*1
w = (s * points.Open.diff().shift(-1) * lot_size) - np.abs(s.diff())*cost
w.fillna(0, inplace=True)
w = w.resample("1D", how="sum")
sharpe = w.mean() / w.std() * math.sqrt(255)
if np.isnan(sharpe) or np.isinf(sharpe):
sharpe = -99999
return sharpe
toolbox.register('evaluate', evalFitness, points=train)
toolbox.register('select', tools.selTournament, tournsize=3)
toolbox.register('mate', gp.cxOnePoint)
toolbox.register('expr_mut', gp.genFull, min_=0, max_=3)
toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr_mut, pset=pset)
def plot(individual):
nodes, edges, labels = gp.graph(individual)
g = nx.Graph()
g.add_nodes_from(nodes)
g.add_edges_from(edges)
pos = nx.drawing.nx_agraph.graphviz_layout(g, prog="dot")
nx.draw_networkx_nodes(g, pos)
nx.draw_networkx_edges(g, pos)
nx.draw_networkx_labels(g, pos, labels)
plt.show()
if __name__ == '__main__':
#random.seed(10)
pop = toolbox.population(n=200)
hof = tools.HallOfFame(1)
stats = tools.Statistics(lambda ind: ind.fitness.values)
stats.register('avg', np.mean)
stats.register('min', np.min)
stats.register('max', np.max)
pop, log = algorithms.eaMuPlusLambda(pop, toolbox, 200, 200, 0.6, 0.1, 50, stats=stats, halloffame=hof)
# get the info of best solution
print("Best solution found...")
print(hof[0])
plot(hof[0])
f=toolbox.compile(hof[0])
# Check training results
s=f(train.Open, train.High, train.Low, train.Close, train.Volume, train.Ones, train.Zeros)
s=s*1
w = (s * train.Close.diff().shift(-1) * lot_size) - np.abs(s.diff())*cost
r = w / (float(train.Close[0]) * 10)
Wt = w.cumsum()
Rt = r.cumsum()
df_plot = pd.DataFrame(index=train.index)
df_plot['GP Strategy'] = Rt
df_plot['IBEX'] = train.Close
#Normalize to 1 the start so we can compare plots.
df_plot['IBEX'] = (df_plot['IBEX'] / df_plot['IBEX'][0]) - 1
df_plot.plot()
# Check testing results
s=f(test.Open, test.High, test.Low, test.Close, test.Volume, test.Ones, test.Zeros)
s=s*1
w = (s * test.Close.diff() *10) - np.abs(s.diff())*cost
r = w / (float(test.Close[0]) * 10)
Wt = w.cumsum()
Rt = r.cumsum()
df_plot = pd.DataFrame(index=test.index)
df_plot['GP Strategy'] = Rt
df_plot['IBEX'] = test.Close
#Normalize to 1 the start so we can compare plots.
df_plot['IBEX'] = (df_plot['IBEX'] / df_plot['IBEX'][0]) - 1
df_plot.plot()