def calculateInvestment(limit=10,
count=10,
write_to_file=True,
show_cla=False,
tpv=20000):
symbols = getSymbolsFromDatabase()
prices = createDataFrame(symbols[:limit], count)
mu = expected_returns.mean_historical_return(prices)
S = risk_models.CovarianceShrinkage(prices).ledoit_wolf()
ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
ef.add_objective(objective_functions.L2_reg)
ef.min_volatility()
c_weights = ef.clean_weights()
if write_to_file == True:
ef.save_weights_to_file("weights.txt")
if show_cla == True:
cla = CLA(mu, S)
ef_plot(cla)
ef.portfolio_performance(verbose=True)
latest_prices = disc_alloc.get_latest_prices(prices)
allocation_minv, leftover = disc_alloc.DiscreteAllocation(
c_weights, latest_prices, total_portfolio_value=tpv).lp_portfolio()
return allocation_minv, leftover
cov_matrix_annual
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
# Portfolio Optimization
# Calculate the expected returns and annualized sample covariance matrix of asset returns
mu = expected_returns.mean_historical_return(df, compounding=True)
S = risk_models.sample_cov(df)
# Optimize for maximum Sharpe Ratio
ef = EfficientFrontier(mu, S, weight_bounds=(0.05, 0.2))
# weights = ef.max_sharpe(risk_free_rate=0.01)
weights = ef.min_volatility()
ef.save_weights_to_file('weights.csv')
cleaned_weights = ef.clean_weights()
print(cleaned_weights)
ef.portfolio_performance(verbose=True)
# Get the discrete allocation of each share per stock
from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
latest_prices = get_latest_prices(df)
weights = cleaned_weights
da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=15000)
allocation, leftover = da.lp_portfolio()
print('Discrete allocation:', allocation)
print('Funds remaining: ${:.2f}'.format(leftover))
cs = CovarianceShrinkage(assets).ledoit_wolf()
# Compute the sample covariance matrix of returns
sample_covariance = assets.pct_change().cov() * 252
# Create the EfficientFrontier instance variable
ef = EfficientFrontier(mu, cs)
# Compute the Weights portfolio that maximises the Sharpe ratio
weights = ef.max_sharpe()
# clean_weights() method truncates tiny weights to zero and rounds others
cw = ef.clean_weights()
with changepath(path):
ef.save_weights_to_file("weights.txt") # saves to file
print2(cw)
# Evaluate performance of optimal portfolio
ef.portfolio_performance(verbose=True)
# Create a dictionary of time periods (or 'epochs')
epochs = {
'before': {
'start': starttime,
'end': '31-12-2006'
},
'during': {
'start': '1-1-2007',
'end': '31-12-2008'
from pypfopt.risk_models import fix_nonpositive_semidefinite
dataTable = None # The raw input from file, contains the whole data table.
exp_return = None
cov_matrix = None # The covariance matrix
corr_matrix = None # The correlation coefficient matrix
assetsList = None # in the Same order in summary file
asset_to_num = None # a dictionary for the reverse index of assetsList
if __name__ == "__main__":
# Step1: get expected return and cov matrix from data
# This step will takes about 10-20s
dataTable, summaryTable = init.read_CSV()
assetsList, asset_to_num = init.getAssetsList()
init.preprocess()
exp_return = init.calcExpectedReturn_1()
cov_matrix, corr_matrix = init.calcCovMatrix()
# Step2: ...
# Step3:
cov_matrix = fix_nonpositive_semidefinite(cov_matrix)
ef = EfficientFrontier(exp_return, cov_matrix)
raw_weights = ef.max_sharpe(risk_free_rate=0.008674) #shibor O/N 2020-05-15
cleaned_weights = ef.clean_weights()
ef.save_weights_to_file("weights.csv")
print(cleaned_weights)
ef.portfolio_performance(verbose=True)
