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portfolio.py
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portfolio.py
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########################################################################
#
# Classes for optimizing and allocating portfolios of stocks.
#
########################################################################
#
# This file is part of FinanceOps:
#
# https://github.com/Hvass-Labs/FinanceOps
#
# Published under the MIT License. See the file LICENSE for details.
#
# Copyright 2018 by Magnus Erik Hvass Pedersen
#
########################################################################
import numpy as np
from scipy.optimize import differential_evolution as minimize
########################################################################
# Private helper-functions.
def _sigmoid(x):
"""
Sigmoid function that smoothly limits values between 0.0 and 1.0
:param x: Numpy array with float values that are to be limited.
:return: Numpy array with float values between 0.0 and 1.0
"""
return 1.0 / (1.0 + np.exp(-x))
########################################################################
# Public classes.
class Model:
"""
Base-class for a portfolio model providing functions for doing
the optimization and calculating the returns from using the model.
"""
def __init__(self, signals_train, daily_rets_train, min_weights, max_weights):
"""
Create object instance and run optimization of the portfolio model.
:param signals_train: 2-d numpy array with signals.
:param daily_rets_train: 2-d numpy array with daily returns.
:param min_weights: 1-d numpy array with min stock weights.
:param max_weights: 1-d numpy array with max stock weights.
"""
# Copy args.
self.signals_train = signals_train
self.daily_rets_train = daily_rets_train
self.min_weights = min_weights
self.max_weights = max_weights
# Number of stocks.
self.num_stocks = self.signals_train.shape[1]
assert signals_train.shape == daily_rets_train.shape
assert self.num_stocks == len(min_weights) == len(max_weights)
# Optimize the portfolio allocation model.
self._optimize()
def get_weights(self, signals):
"""
Map the signals to stock-weights.
:param signals: 2-d numpy array with signals for the stocks.
:return: (weights: 2-d numpy array, weights_cash: 1-d numpy array)
"""
raise NotImplementedError
@property
def _bounds(self):
"""Parameter bounds for the model that is going to be optimized."""
raise NotImplementedError
def _set_parameters(self, params):
"""
Unpack and set the parameters for the portfolio allocation model.
:param params: 1-d numpy array with the model parameters.
:return: None.
"""
raise NotImplementedError
def value(self, daily_rets, signals=None):
"""
Calculate the portfolio value when rebalancing the portfolio daily.
The stock-weights are calculated from the given signals.
:param daily_rets: 2-d numpy array with daily returns for the stocks.
:param signals: 2-d numpy array with daily signals for the stocks.
:return: 1-d numpy array with the cumulative portfolio value.
"""
# Map the signals to stock-weights.
weights, weights_cash = self.get_weights(signals=signals)
# Calculate the weighted daily returns of the stocks.
weighted_daily_rets = np.sum(daily_rets * weights, axis=1) + weights_cash
# Accumulate the weighted daily returns to get the portfolio value.
value = np.cumprod(weighted_daily_rets)
# Normalize so it starts at 1.0
value /= value[0]
return value
def _optimize(self):
"""
Optimize the portfolio model's parameters using SciPy.
:return: None.
"""
self.optimize_result = minimize(func=self._fitness,
bounds=self._bounds,
maxiter=300,
polish=True)
def _limit_weights(self, weights):
"""
Limit stock-weights between self.min_weights and self.max_weights.
Also ensure the stock-weights sum to 1.0 or less.
:param weights: 2-d numpy array with stock-weights between 0.0 and 1.0
:return: 2-d numpy array with limited stock-weights.
"""
# We could just clip the weights, but if they were created from
# e.g. a sigmoid-mapping then a hard-clip would destroy the softness.
# So we assume weights are between 0.0 and 1.0 so we can rescale them.
# We do not assert this because there could be tiny floating-point
# rounding errors that are unimportant and would then cause a crash.
# Scale weights to be between min and max.
weights = weights * (self.max_weights - self.min_weights) + self.min_weights
# Ensure sum(weights) <= 1
weights_sum = np.sum(weights, axis=1)
mask = (weights_sum > 1.0)
weights[mask, :] /= weights_sum[mask, np.newaxis]
# Recalculate the weight-sum for each day.
weights_sum = np.sum(weights, axis=1)
# If the sum of stock-weights for a day is less than 1.0
# then let the remainder be the cash-weight.
weights_cash = 1.0 - weights_sum
return weights, weights_cash
def _fitness(self, params):
"""
Calculate the fitness-value that is to be minimized.
This should be a good measure of performance for the portfolio model.
:param params: Parameters for the portfolio-model.
:return: Float for the fitness-value to be minimized.
"""
# Set the model parameters received from the optimizer.
self._set_parameters(params=params)
# Calculate the cumulative portfolio value using the training-data.
# This uses the portfolio-model with the parameters we have just set,
# so we can evaluate how well those parameters perform.
value = self.value(daily_rets=self.daily_rets_train,
signals=self.signals_train)
# If you just want to optimize the return over the whole period,
# you can just return the last value divided by the first value.
# Note that the value is negated because we are doing minimization.
# return - value[-1] / value[0]
# Annualized returns for all 1-year investment periods.
rets_1year = value[365:] / value[:-365]
# Annualized returns for all 5-year investment periods.
years = 5
days = int(365.25 * years)
rets_5years = (value[days:] / value[:-days]) ** (1 / years)
# Use the mean-log returns for 5-year returns as the main fitness value.
# This fitness-measure is also known as the Kelly Criterion.
fitness = np.mean(np.log(rets_5years))
# We can penalize the main fitness value to shape the returns in
# different ways. For example, if more than 7% of the 1-year returns
# are losses, then we severely penalize the fitness, so that we
# strongly prefer portfolio-models with few annual losses, but this
# may cause the long-term returns to suffer.
prob = np.sum(rets_1year < 1.0) / len(rets_1year)
if prob > 0.07:
fitness -= 100
# Note the fitness-value is negated because we are doing minimization.
return -fitness
class EqualWeights(Model):
"""
Portfolio model where the stock-weights are always equal.
"""
def __init__(self, num_stocks, use_cash=False):
"""
Create object instance.
This is a special case because the portfolio-model is so simple.
We also don't call Model.__init__() because the model should not
be optimized.
:param num_stocks:
Number of stocks in the portfolio.
:param use_cash:
Boolean whether to use cash as an equal part of the portfolio.
"""
# Copy args.
self.num_stocks = num_stocks
self.use_cash = use_cash
def get_weights(self, signals=None):
"""
Get the stock-weights for the portfolio-model.
:param signals: Ignored.
:return: (weights: 2-d numpy array, weights_cash: 1-d numpy array)
"""
if self.use_cash:
# Stocks and cash get equal weights.
weight = 1.0 / (self.num_stocks + 1)
weights_cash = weight
else:
# Only use stocks and no cash in the portfolio.
weight = 1.0 / self.num_stocks
weights_cash = 0.0
# Create a 2-dim array with the equal stock-weights,
# so it can easily be multiplied and broadcast with daily returns.
weights = np.full(shape=(1, self.num_stocks), fill_value=weight)
return weights, weights_cash
class FixedWeights(Model):
"""
Portfolio model where the stock-weights are always held fixed,
but the best stock-weights are found through optimization.
"""
def __init__(self, *args, **kwargs):
Model.__init__(self, *args, **kwargs)
@property
def _bounds(self):
"""Parameter bounds for the portfolio-model."""
# We want to find the best fixed weights between 0.0 and 1.0
return [(0.0, 1.0)] * self.num_stocks
def _set_parameters(self, params):
"""
Unpack and set the parameters for the portfolio model.
:param params: 1-d numpy array with the model parameters.
:return: None.
"""
# The parameters are actually the raw stock-weights between 0.0 and 1.0
# which are then limited between min_weights and max_weights.
self._weights, self._weights_cash = self._limit_weights(weights=params[np.newaxis, :])
def get_weights(self, signals=None):
"""
Get the stock-weights for the portfolio-model.
:param signals: Ignored.
:return: (weights: 2-d numpy array, weights_cash: 1-d numpy array)
"""
return self._weights, self._weights_cash
class AdaptiveWeights(Model):
"""
Portfolio model where the stock-weights are mapped from predictive signals
using the basic function: weight = sigmoid(a * signal + b) so we want to
find the parameters a and b that result in the best performance according
to the fitness function in Model._fitness().
"""
def __init__(self, *args, **kwargs):
Model.__init__(self, *args, **kwargs)
@property
def _bounds(self):
"""Parameter bounds for the portfolio-model."""
# We want to find the a and b parameters for each stock.
# We allow both a and b to be between e.g. -10.0 and 10.0
k = 10.0
return [(-k, k)] * 2 * self.num_stocks
def _set_parameters(self, params):
"""
Unpack and set the parameters for the portfolio model.
:param params: 1-d numpy array with the model parameters.
:return: None.
"""
self._a = params[0:self.num_stocks]
self._b = params[self.num_stocks:]
def get_weights(self, signals):
"""
Get the stock-weights for the portfolio-model.
:param signals: 2-d numpy array with signals for the stocks.
:return: (weights: 2-d numpy array, weights_cash: 1-d numpy array)
"""
# Linear mapping.
weights = signals * self._a + self._b
# Use sigmoid-function to softly limit between 0.0 and 1.0
weights = _sigmoid(weights)
# Limit the weights between min_weights and max_weights.
weights, weights_cash = self._limit_weights(weights=weights)
return weights, weights_cash
########################################################################