def test_sample_cov_type_warning():
df = get_data()
cov_from_df = risk_models.sample_cov(df)
returns_as_array = np.array(df)
with warnings.catch_warnings(record=True) as w:
cov_from_array = risk_models.sample_cov(returns_as_array)
assert len(w) == 1
assert issubclass(w[0].category, RuntimeWarning)
assert str(w[0].message) == "prices are not in a dataframe"
np.testing.assert_array_almost_equal(
cov_from_df.values, cov_from_array.values, decimal=6
)
def setup_cla(data_only=False):
df = get_data()
mean_return = expected_returns.mean_historical_return(df)
sample_cov_matrix = risk_models.sample_cov(df)
if data_only:
return mean_return, sample_cov_matrix
return CLA(mean_return, sample_cov_matrix)
def test_sample_cov_real_data():
df = get_data()
S = risk_models.sample_cov(df)
assert S.shape == (20, 20)
assert S.index.equals(df.columns)
assert S.index.equals(S.columns)
assert S.notnull().all().all()
def setup_efficient_frontier(data_only=False):
df = get_data()
mean_return = expected_returns.mean_historical_return(df)
sample_cov_matrix = risk_models.sample_cov(df)
if data_only:
return mean_return, sample_cov_matrix
return EfficientFrontier(mean_return, sample_cov_matrix)
def test_shrunk_covariance_frequency():
df = get_data()
cs = risk_models.CovarianceShrinkage(df, frequency=52)
# if delta = 0, no shrinkage occurs
shrunk_cov = cs.shrunk_covariance(0)
S = risk_models.sample_cov(df, frequency=52)
np.testing.assert_array_almost_equal(shrunk_cov.values, S)
def test_exp_cov_limits():
df = get_data()
sample_cov = risk_models.sample_cov(df)
S = risk_models.exp_cov(df)
assert not np.allclose(sample_cov, S)
# As span gets larger, it should tend towards sample covariance
S2 = risk_models.exp_cov(df, span=1e20)
assert np.abs(S2 - sample_cov).max().max() < 1e-3
def test_shrunk_covariance_extreme_delta():
df = get_data()
cs = risk_models.CovarianceShrinkage(df)
# if delta = 0, no shrinkage occurs
shrunk_cov = cs.shrunk_covariance(0)
np.testing.assert_array_almost_equal(
shrunk_cov.values, risk_models.sample_cov(df))
# if delta = 1, sample cov does not contribute to shrunk cov
shrunk_cov = cs.shrunk_covariance(1)
N = df.shape[1]
F = np.identity(N) * np.trace(cs.S) / N
np.testing.assert_array_almost_equal(shrunk_cov.values, F * 252)
def test_negative_sharpe():
df = get_data()
e_rets = mean_historical_return(df)
S = sample_cov(df)
w = np.array([1 / len(e_rets)] * len(e_rets))
sharpe = objective_functions.negative_sharpe(w, e_rets, S)
assert isinstance(sharpe, float)
assert sharpe < 0
sigma = np.sqrt(np.dot(w, np.dot(S, w.T)))
negative_mu = objective_functions.negative_mean_return(w, e_rets)
np.testing.assert_almost_equal(sharpe * sigma - 0.02, negative_mu)
# Risk free rate increasing should lead to negative Sharpe increasing.
assert sharpe < objective_functions.negative_sharpe(
w, e_rets, S, risk_free_rate=0.1
)
def test_sample_cov_dummy():
data = pd.DataFrame(
[
[4.0, 2.0, 0.6],
[4.2, 2.1, 0.59],
[3.9, 2.0, 0.58],
[4.3, 2.1, 0.62],
[4.1, 2.2, 0.63],
]
)
test_answer = pd.DataFrame(
[
[0.006661687937656102, 0.00264970955585574, 0.0020849735375206195],
[0.00264970955585574, 0.0023450491307634215, 0.00096770864287974],
[0.0020849735375206195, 0.00096770864287974, 0.0016396416271856837],
]
)
S = risk_models.sample_cov(data) / 252
pd.testing.assert_frame_equal(S, test_answer)
def test_portfolio_allocation_errors():
df = get_data()
e_ret = mean_historical_return(df)
cov = sample_cov(df)
ef = EfficientFrontier(e_ret, cov)
w = ef.max_sharpe()
latest_prices = discrete_allocation.get_latest_prices(df)
with pytest.raises(TypeError):
discrete_allocation.portfolio(ef.weights, latest_prices)
with pytest.raises(TypeError):
discrete_allocation.portfolio(w, latest_prices.values.tolist())
with pytest.raises(ValueError):
discrete_allocation.portfolio(w, latest_prices, min_allocation=0.5)
with pytest.raises(ValueError):
discrete_allocation.portfolio(w, latest_prices, total_portfolio_value=0)
def test_portfolio_allocation():
df = get_data()
e_ret = mean_historical_return(df)
cov = sample_cov(df)
ef = EfficientFrontier(e_ret, cov)
w = ef.max_sharpe()
latest_prices = discrete_allocation.get_latest_prices(df)
allocation, leftover = discrete_allocation.portfolio(w, latest_prices)
assert allocation == {
"MA": 14,
"FB": 12,
"PFE": 51,
"BABA": 5,
"AAPL": 5,
"AMZN": 0,
"BBY": 9,
"SBUX": 6,
"GOOG": 1,
}
total = 0
for ticker, num in allocation.items():
total += num * latest_prices[ticker]
np.testing.assert_almost_equal(total + leftover, 10000)
# import pandas as pd
# import numpy as np
# import cvxpy as cp
from pypfopt import risk_models
# from pypfopt import expected_returns
# from pypfopt import EfficientFrontier
# from pypfopt import objective_functions
# from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
# from pypfopt import HRPOpt
# from pypfopt import CLA
# from pypfopt import black_litterman
# from pypfopt import BlackLittermanModel
# from pypfopt import plotting
import pandas_datareader.data as web
from datetime import datetime
import matplotlib.pylab as plt
import pandas as pd
dfs = {}
for ticker in ['AAPL', 'MSFT', 'KO']:
df = web.DataReader(ticker, 'stooq', '2020-01-01', '2020-09-30', api_key='6JUN1FV7A3MTWJ1Y')
dfs[ticker] = df["Close"]
stock_data = pd.DataFrame(dfs)
stock_data.sort_index(inplace=True)
risk_models.sample_cov(stock_data)
print(stock_data)
print("Before Optimization")
print("Expected annual return: "+ percent_ret)
print("Annual volatility / risk: "+ percent_vols)
print("Annual variance: "+ percent_var)
# There is a python package that can analyze the portfolio and give a higher return with a lower risk.
pip install PyPortfolioOpt
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
# Optimization of the portfolio
# Calculate the expected returns and the annualized sample covariance matrix of asset returns
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Optimize for max sharpe ratio
ef = EfficientFrontier(mu, S)
weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
print("After Optimization")
print(cleaned_weights)
ef.portfolio_performance(verbose = True)
0.04904 + 0.17874 + 0.06929 + 0.25513 + 0.0 + 0.19851 + 0.24929
# Get the discrete allocation of each share per stock
from pypfopt.discrete_allocation import DiscreteAllocation, get_latest_prices
latest_prices = get_latest_prices(df)
def load_portfolio_dash(RANDOM_NUMBER_SELECTED_IN, PORTFOLIO_VALUE_IN):
START_DATE_FOR_STOCK_PRICES = '2015-1-1' # When you want your Stocks to go back to
STOCK_TICKERS_CSV = 'stock_ticker_list.csv'
PERCENTAGE_TO_USE = 10 # The % in which the amount a column must have otherwise it will be deleted
# MAX 400 Tickers at this moment in time
RANDOM_NUMBER_SELECTED = RANDOM_NUMBER_SELECTED_IN # The number used to select the number of element from a larger list
PORTFOLIO_VALUE = PORTFOLIO_VALUE_IN # The amount in $ you wish to invest
# Load the tickers list from the csv file of tickers names
with open(STOCK_TICKERS_CSV, newline='') as f:
reader = csv.reader(f)
data = list(reader)
tickers_list_imported = []
for d in data:
ticker = str(d).replace("'",
"").replace("[",
"").replace("]",
"").replace(" ", "")
tickers_list_imported.append(ticker)
# 2 tickers just to start the dataframe in the correct format
tickers_list_start = ['AAPL', 'TSLA']
# Fetching the data from the list of tickers
data = yf.download(tickers_list_start,
START_DATE_FOR_STOCK_PRICES)['Adj Close']
df = pd.DataFrame(data)
ticker_count = 0
LIST_TO_USE = random.sample(tickers_list_imported, RANDOM_NUMBER_SELECTED)
#LIST_TO_USE = tickers_list_imported
progress_count = 0
my_progress_bar = st.progress(progress_count)
val_to_progress_up = (100 / len(LIST_TO_USE)) / 100
for i in range(len(LIST_TO_USE)):
if ticker_count > 0 and ticker_count % 300 == 0:
#df.to_csv ('stock_dataframe.csv', index=True, header=True)
st.write('\nSleeping for 2 Minutes\n')
time.sleep(120)
ticker_count = ticker_count + 1
data_new_col_name = str(LIST_TO_USE[i])
#st.write('\n' + str(ticker_count) + '/' + str(len(LIST_TO_USE)) + ' - Downloading: ' + data_new_col_name)
try:
data_new = yf.download(LIST_TO_USE[i],
START_DATE_FOR_STOCK_PRICES)['Adj Close']
df[data_new_col_name] = data_new
except:
continue
progress_count = progress_count + val_to_progress_up
if progress_count <= 1:
my_progress_bar.progress(progress_count)
else:
my_progress_bar.progress(100)
#Drooping stock values
no_data_for_ticker_list = []
row_count_based_on_percentage = int(len(df) * (PERCENTAGE_TO_USE / 100))
for (columnName, columnData) in df.iteritems():
if df[columnName].isnull().all():
#st.write('Dropping : ' + str(columnName) + ' - it is all NAN')
no_data_for_ticker_list.append(df.columns.get_loc(columnName))
#elif df[columnName].count() < row_count_based_on_percentage:
# st.write('Dropping : ' + str(columnName) + ' - < than ' + str(PERCENTAGE_TO_USE) + '% of then number of rows')
# no_data_for_ticker_list.append(df.columns.get_loc(columnName))
df = df.drop(df.columns[no_data_for_ticker_list], axis=1)
# Creating a csv file
#df.to_csv ('stock_dataframe.csv', index=True, header=True)
# Calculate the expected annualised returns and the annualised sample covariance matrix of the daily asset returns
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Optimise for the maximal Sharpe ratio - describes how much excess return you recieve from the extra volitility you endure for holding a riskier asset
ef = EfficientFrontier(mu, S) # Create the Efficient Frontier object
raw_weights = ef.max_sharpe(
) # maximise the Sharpe Ratio and get the raw weights
# Helper method to clean the raw weights setting any weights whos absolute values are below the cut off point to zero and rounding the rest
# can cause some rounding errors - should not be off by a lot just good to know
cleaned_weights = ef.clean_weights()
# Get the discret allocations of each share per stock and the leftover money from investment
latest_price = get_latest_prices(df)
weights = cleaned_weights
da = DiscreteAllocation(weights,
latest_price,
total_portfolio_value=PORTFOLIO_VALUE)
allocation, leftover = da.lp_portfolio()
# Store the company name into a list & Get descrete allocation values
company_name = []
discrete_allocation_list = []
for symbol in allocation:
company_name.append(get_company_name(symbol))
discrete_allocation_list.append(allocation.get(symbol))
# Create the portfolio
# Create DF for portfolio
portfolio_df = pd.DataFrame(columns=[
'Company_Name', 'Company_Ticker', 'Discrete_val_' +
str(PORTFOLIO_VALUE)
])
# Add data to portfolio df
portfolio_df['Company_Name'] = company_name
portfolio_df['Company_Ticker'] = allocation
portfolio_df['Discrete_val_' +
str(PORTFOLIO_VALUE)] = discrete_allocation_list
# How the portfolio would expect to return
perf = ef.portfolio_performance(verbose=True)
st.write()
st.write(
'Any Sharpe ratio over 1.0 is considered acceptable to good buy investment:'
)
st.write('Expected annual return:', round(perf[0], 2), '%')
st.write('Annual volatility:', round(perf[1], 2), '%')
st.write('Sharpe Ratio:', round(perf[0], 2))
st.write()
st.write('Funds Remaining: $', round(leftover, 2))
# Show the portfolio
st.write(portfolio_df)
def performance(request):
current_user = request.user
user = Profile.objects.get(user = current_user)
all_portfolios_of_current_users = Portfolio.objects.filter(user = user)
stock_and_prices = {}
stocks_held = {}
all_stocks = []
total_assets_worth = 0
for indv in all_portfolios_of_current_users:
all_stocks.append(indv.stock)
for stock in all_stocks:
price = si.get_live_price(stock)
stock_and_prices[stock] = {'price': price}
print(stock_and_prices)
for indv in all_portfolios_of_current_users:
total_assets_worth += stock_and_prices[indv.stock]['price'] * indv.number
for indv in all_portfolios_of_current_users:
stock = indv.stock
number = indv.number
worth = stock_and_prices[indv.stock]['price'] * indv.number
# total_assets_worth += worth
percentage = stock_and_prices[indv.stock]['price'] * indv.number/ total_assets_worth
stocks_held[stock] = percentage
assets = []
weights = []
for stock, percentage in stocks_held.items():
assets.append(stock)
weights.append(percentage)
if request.method == "POST":
assets = assets
weights = np.array(weights)
#Get the stock starting date
stockStartDate = '2017-01-01'
# Get the stocks ending date aka todays date and format it in the form YYYY-MM-DD
today = datetime.today().strftime('%Y-%m-%d')
#Create a dataframe to store the adjusted close price of the stocks
df = pd.DataFrame()
#Store the adjusted close price of stock into the data frame
for stock in assets:
df[stock] = web.DataReader(stock,data_source='yahoo',start=stockStartDate , end=today)['Adj Close']
# Create the title 'Portfolio Adj Close Price History
title = 'Portfolio Adj. Close Price History '
#Get the stocks
my_stocks = df
#Create and plot the graph
plt.figure(figsize=(12.2,4.5)) #width = 12.2in, height = 4.5
# Loop through each stock and plot the Adj Close for each day
for c in my_stocks.columns.values:
plt.plot( my_stocks[c], label=c)#plt.plot( X-Axis , Y-Axis, line_width, alpha_for_blending, label)
plt.title(title)
plt.xlabel('Date',fontsize=18)
plt.ylabel('Adj. Price USD ($)',fontsize=18)
plt.legend(my_stocks.columns.values, loc='upper left')
returns = df.pct_change()
cov_matrix_annual = returns.cov() * 252
port_variance = np.dot(weights.T, np.dot(cov_matrix_annual, weights))
port_volatility = np.sqrt(port_variance)
portfolioSimpleAnnualReturn = np.sum(returns.mean()*weights) * 252
percent_var = str(round(port_variance, 2) * 100) + '%'
percent_vols = str(round(port_volatility, 2) * 100) + '%'
percent_ret = str(round(portfolioSimpleAnnualReturn, 2)*100)+'%'
mu = expected_returns.mean_historical_return(df)#returns.mean() * 252
S = risk_models.sample_cov(df) #Get the sample covariance matrix
ef = EfficientFrontier(mu, S)
weights = ef.max_sharpe() #Maximize the Sharpe ratio, and get the raw weights
cleaned_weights = ef.clean_weights()
weight_list = list(cleaned_weights.values())
#return the dictionary
#print(cleaned_weights) #Note the weights may have some rounding error, meaning they may not add up exactly to 1 but should be close
#print out the porfolio performance
#ef.portfolio_performance(verbose=True)
tuple_elements = ef.portfolio_performance(verbose=False)
ef_return = tuple_elements[0]
ef_volatility = tuple_elements[1]
ef_ratio =tuple_elements[2]
stock_dict = {}
for i in range(len(weight_list)):
percentage = "{:.2%}".format(weight_list[i])
stock_dict[assets[i]] = percentage
name_list = ['return','volatility','ratio']
optimized_dict = {}
for i in range(len(name_list)):
percentage = "{:.2%}".format(tuple_elements[i])
optimized_dict[name_list[i]] = percentage
return render(request, 'performance.html',{'weight_list':weight_list,'assets':assets, 'ef_return':ef_return,'ef_volatility':ef_volatility,'ef_ratio':ef_ratio,'percent_var':percent_var, 'percent_vols':percent_vols, 'percent_ret':percent_ret,'stock_dict':stock_dict,'optimized_dict':optimized_dict})
else:
assets = assets
weights = np.array(weights)
#Get the stock starting date
stockStartDate = '2017-01-01'
# Get the stocks ending date aka todays date and format it in the form YYYY-MM-DD
today = datetime.today().strftime('%Y-%m-%d')
#Create a dataframe to store the adjusted close price of the stocks
df = pd.DataFrame()
#Store the adjusted close price of stock into the data frame
for stock in assets:
df[stock] = web.DataReader(stock,data_source='yahoo',start=stockStartDate , end=today)['Adj Close']
# Create the title 'Portfolio Adj Close Price History
title = 'Portfolio Adj. Close Price History '
#Get the stocks
my_stocks = df
#Create and plot the graph
plt.figure(figsize=(12.2,4.5)) #width = 12.2in, height = 4.5
# Loop through each stock and plot the Adj Close for each day
for c in my_stocks.columns.values:
plt.plot( my_stocks[c], label=c)#plt.plot( X-Axis , Y-Axis, line_width, alpha_for_blending, label)
plt.title(title)
plt.xlabel('Date',fontsize=18)
plt.ylabel('Adj. Price USD ($)',fontsize=18)
plt.legend(my_stocks.columns.values, loc='upper left')
returns = df.pct_change()
cov_matrix_annual = returns.cov() * 252
port_variance = np.dot(weights.T, np.dot(cov_matrix_annual, weights))
port_volatility = np.sqrt(port_variance)
portfolioSimpleAnnualReturn = np.sum(returns.mean()*weights) * 252
percent_var = str(round(port_variance, 2) * 100) + '%'
percent_vols = str(round(port_volatility, 2) * 100) + '%'
percent_ret = str(round(portfolioSimpleAnnualReturn, 2)*100)+'%'
return render(request, 'performance.html',{'percent_var':percent_var, 'percent_vols':percent_vols, 'percent_ret':percent_ret,'assets':assets})
for ticker in range(thelen):
prices = web.DataReader(tickers[ticker],
start='2019-01-01',
end='2020-06-06',
data_source='yahoo')
price_data.append(prices.assign(ticker=ticker)[['Adj Close']])
df_stocks = pd.concat(price_data, axis=1)
df_stocks.columns = tickers
# computing risk and returns
mu = expected_returns.mean_historical_return(
df_stocks) #Sample Variance of Portfolio
Sigma = risk_models.sample_cov(df_stocks)
# computing portfolio
# optimisation functions
def MinimiseRisk(mu, Sigma):
n = len(mu)
x = cp.Variable(n)
A = np.random.randn(n)
B = np.ones(n)
prob = cp.Problem(cp.Minimize(cp.quad_form(x, Sigma)),
[x >= np.zeros(n), B @ x == 1])
prob.solve()
def calculateSampleCovarianceMatrix(self):
S = risk_models.sample_cov(self.df) #Get the sample covariance matrix
return S
def BLmain():
#Excell Call
sht = xw.Book.caller().sheets['Optim']
shtdata = xw.Book.caller().sheets['Data']
sht.range('J17').value = 'Optimizing...'
#Clear Values
sht.range('L23').expand().clear_contents()
shtdata.range('A1').expand().clear_contents()
shtdata.range('J1').expand().clear_contents()
#Set variables from excel
rf = sht.range('J10').value
MinWeight = sht.range('J11').value
MaxWeight = sht.range('J12').value
Delta = sht.range('J13').value
Tau = sht.range('J14').value
Output = sht.range('J15').value
ModelOptim = sht.range('J8').value
RiskModel = sht.range('J9').value
listticker = xw.Range('B3').expand().value
indexname = sht.range('J7').value
startdate = sht.range('J3').value
enddate = sht.range('J6').value
EFBool = sht.range('J16').value
traintestdate = sht.range(
'J4'
).value #Dataset is divided in two sub: train (optimization) and test for backtest
#Initializing
train, test = initialize(startdate, enddate, traintestdate, listticker)
trainindex, testindex = initializeIndex(startdate, enddate, traintestdate,
indexname) #for risk aversion
#Black Litterman
if RiskModel == 'historicalcov':
S = risk_models.sample_cov(train)
elif RiskModel == 'exphistoricalcov':
S = risk_models.exp_cov(train)
if Delta != None:
delta = Delta
else:
delta = black_litterman.market_implied_risk_aversion(trainindex,
risk_free_rate=rf)
s = data.get_quote_yahoo(listticker)['marketCap']
mcaps = {tick: mcap
for tick, mcap in zip(listticker, s)
} #Dictionnary of Market Cap for each stock
#Expected returns implied from the market
prior = black_litterman.market_implied_prior_returns(mcaps,
delta,
S,
risk_free_rate=rf)
views, picking = createviews(listticker)
bl = BlackLittermanModel(S, Q=views, P=picking, pi=prior, tau=Tau)
rets = bl.bl_returns()
cov = bl.bl_cov()
#Two ways of displaying outputs: either using Optimizer, either returning implied weights
if Output == 'Optimization':
ef = EfficientFrontier(rets, S, weight_bounds=(MinWeight, MaxWeight))
#RiskModel
if ModelOptim == 'min_volatility':
raw_weights = ef.min_volatility()
elif ModelOptim == 'max_sharpe':
raw_weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
finalw = [cleaned_weights.get(i, 1) for i in listticker]
perf = ef.portfolio_performance(verbose=True, risk_free_rate=rf)
sht.range('H21').value = perf
elif Output == 'Return-Implied-Weight':
bl.bl_weights(delta)
weights = bl.clean_weights()
finalw = [weights.get(i, 1) for i in listticker]
finalr = [rets.get(i, 1) for i in listticker] #E(R) from BL
#Display results
sht.range('L23').options(transpose=True).value = listticker
sht.range('M23').options(transpose=True).value = finalw
sht.range('N23').options(transpose=True).value = finalr
#Copy Data in Data Range
shtdata.range((1, 1)).value = train
shtdata.range((1, len(listticker) + 3)).value = test
#numshares, left = getoptimprices(test, cleanW, InitialAmountInPortfolio)
#Visualisation
sht.charts['BLweights'].set_source_data(
sht.range((23, 12), (22 + len(listticker), 13)))
CorrMap(sht, 'CorrMatPrior', S, 'coolwarm')
CorrMap(sht, 'CorrMatBL', cov, 'YlGn')
if EFBool == "YES":
effrontier(rets, S, sht, 'EFBL')
#Done
sht.range('J17').value = 'Optimization Done'
def test_volatility():
df = get_data()
S = sample_cov(df)
w = np.array([1 / df.shape[1]] * df.shape[1])
vol = objective_functions.volatility(w, S)
np.testing.assert_almost_equal(vol, 0.21209018103844543)
##
def dict_print(df_dict):
for key, value in df_dict.items():
print('{key}:{value}'.format(key=key, value=value))
return
##
"""
portdf.to_csv("files/date.csv")
"""
##
mu = expected_returns.mean_historical_return(portdf)
S = risk_models.sample_cov(portdf)
print(mu)
print(S)
##
"""strategy-1"""
"""max sharpe method optimal portfolio"""
ef_maxsp = EfficientFrontier(mu, S)
raw_weights = ef_maxsp.max_sharpe()
cleaned_weights = ef_maxsp.clean_weights()
"""ef.save_weights_to_file("/files/weights.csv") # saves to file"""
print("the weights of max sharpe portfolio")
dict_print(cleaned_weights)
ef_maxsp.portfolio_performance(verbose=True)
SPY_returns = SPY.pct_change().dropna()
Factors = ETF_Prices.iloc[:,1:]
Factor_returns = Factors.pct_change().dropna()
# Loop to solve for portfolio weights each period
backtest_wgts = []
return_lb = 6*21 #(eight months)
covar_lb = 2*252 # 252 trading days
rows = len(ETF_Prices.index) - covar_lb + 1 # length of loop
dates = ETF_Prices.index[covar_lb-1:] # datetime index
for i in range(rows):
mu1 = expected_returns.mean_historical_return(Factors.iloc[covar_lb-return_lb+i:covar_lb+i])
mu = np.clip(mu1, 0.01, 0.20)
S = risk_models.sample_cov(Factors.iloc[i:covar_lb+i,:])
ef = EfficientFrontier(mu, S, weight_bounds=(0, 0.30))
try:
raw_weights = ef.max_sharpe()
wgts = list(ef.clean_weights().values())
except:
wgts = [0.2] * 5
backtest_wgts.append(wgts)
wgts_df = pd.DataFrame(backtest_wgts, index=dates, columns=factor_names)
wgts_arr = np.array(backtest_wgts[:-1])
returns_arr = (Factor_returns.iloc[-len(wgts_df)+1:]).to_numpy()
model_returns = (wgts_arr * returns_arr).sum(axis=1)
SPY_returns = SPY_returns[-len(model_returns):]
dts = SPY_returns.index[-len(model_returns):]
import time
import pypfopt
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt import expected_returns
from pypfopt import risk_models
import settings
start_time = time.time()
start_date = datetime.datetime(settings.start_date[0], settings.start_date[1],
settings.start_date[2])
end_date = datetime.datetime(settings.end_date[0], settings.end_date[1],
settings.end_date[2])
price_data = web.DataReader(settings.portfolio, 'yahoo', start_date,
end_date)['Adj Close']
price_data.index.rename('date', True)
returns = expected_returns.mean_historical_return(price_data)
cov = risk_models.sample_cov(price_data)
ef = EfficientFrontier(returns, cov, weight_bounds=(.1, .4))
sharpe = ef.max_sharpe()
min_vol = ef.min_volatility()
performance = ef.portfolio_performance()
# returns expected returns, volatility, sharpe ratio
elapsed = round(time.time() - start_time, 2)
print("Portfolio optimized..")
print("Completed in {} seconds..".format(elapsed))
def test_market_implied_prior():
df = get_data()
S = risk_models.sample_cov(df)
prices = pd.read_csv(
"tests/spy_prices.csv", parse_dates=True, index_col=0, squeeze=True
)
delta = black_litterman.market_implied_risk_aversion(prices)
mcaps = {
"GOOG": 927e9,
"AAPL": 1.19e12,
"FB": 574e9,
"BABA": 533e9,
"AMZN": 867e9,
"GE": 96e9,
"AMD": 43e9,
"WMT": 339e9,
"BAC": 301e9,
"GM": 51e9,
"T": 61e9,
"UAA": 78e9,
"SHLD": 0,
"XOM": 295e9,
"RRC": 1e9,
"BBY": 22e9,
"MA": 288e9,
"PFE": 212e9,
"JPM": 422e9,
"SBUX": 102e9,
}
pi = black_litterman.market_implied_prior_returns(mcaps, delta, S)
assert isinstance(pi, pd.Series)
assert list(pi.index) == list(df.columns)
assert pi.notnull().all()
assert pi.dtype == "float64"
np.testing.assert_array_almost_equal(
pi.values,
np.array(
[
0.14933293,
0.2168623,
0.11219185,
0.10362374,
0.28416295,
0.12196098,
0.19036819,
0.08860159,
0.17724273,
0.08779627,
0.0791797,
0.16460474,
0.12854665,
0.08657863,
0.11230036,
0.13875465,
0.15017163,
0.09066484,
0.1696369,
0.13270213,
]
),
)
mcaps = pd.Series(mcaps)
pi2 = black_litterman.market_implied_prior_returns(mcaps, delta, S)
pd.testing.assert_series_equal(pi, pi2, check_exact=False)
def _getEfficientFrontier(self, portfolio_data) -> EfficientFrontier:
mu: Series = expected_returns.mean_historical_return(
portfolio_data) # returns.mean() * 252
S: DataFrame = risk_models.sample_cov(
portfolio_data) # Get the sample covariance matrix
return EfficientFrontier(mu, S)
def update_graph1(n_clicks, stock_ticker, lookforward_period, risk):
exp_ret = pd.Series()
content1 = list(stock_ticker)
data3 = pd.DataFrame()
for contents in content1:
data3 = pd.concat([
data3,
yf.download(f"{contents}", start="2015-01-01",
end="2020-01-01").iloc[:, 4]
],
axis=1,
sort=False)
data3.columns = content1
data4 = data3.dropna(how="all")
data4 = data4.dropna(axis='columns', how="any")
cumulative_ret_data = pd.DataFrame()
for contents in content1:
cumulative_ret_data[f"{contents}"] = (
1 + (data4[f"{contents}"]).pct_change()).cumprod()
cumulative_ret_data = cumulative_ret_data.fillna(1)
S = risk_models.sample_cov(data4)
if lookforward_period == 2:
exp_ret = pypfopt.expected_returns.mean_historical_return(
data4, frequency=500)
if (risk == 2):
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.max_sharpe(risk_free_rate=0.02)
elif (risk == 3):
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.max_quadratic_utility(risk_aversion=0.00001,
market_neutral=False)
else:
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.min_volatility()
elif (lookforward_period == 3):
exp_ret = pypfopt.expected_returns.mean_historical_return(
data4, frequency=750)
if (risk == 2):
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.max_sharpe(risk_free_rate=0.02)
elif (risk == 3):
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.max_quadratic_utility(risk_aversion=0.00001,
market_neutral=False)
else:
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.min_volatility()
else:
exp_ret = pypfopt.expected_returns.mean_historical_return(
data4, frequency=250)
if (risk == 2):
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.max_sharpe(risk_free_rate=0.02)
elif (risk == 3):
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.max_quadratic_utility(risk_aversion=0.00001,
market_neutral=False)
else:
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.min_volatility()
#exp_ret=pypfopt.expected_returns.mean_historical_return(data4, frequency=250)
#exp_ret=pypfopt.expected_returns.mean_historical_return(data4, frequency=252)
if (risk == 2):
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.max_sharpe(risk_free_rate=0.02)
elif (risk == 3):
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.max_quadratic_utility(risk_aversion=0.00001,
market_neutral=False)
else:
ef = EfficientFrontier(exp_ret, S, weight_bounds=(-1, 1), gamma=1)
weights_ef = ef.min_volatility()
dictlist = []
for key, value in weights_ef.items():
temp = [key, value]
dictlist.append(temp)
weights = pd.DataFrame(dictlist, columns=['ticker',
'weight']).set_index('ticker')
weights = weights.iloc[:, 0]
weights = weights.sort_index()
weight_list = weights.tolist()
HRP_df = weights.multiply(weights, weight_list)
HRP_cumu = cumulative_ret_data.reindex(
sorted(cumulative_ret_data.columns),
axis=1) #sort column by company names
HRP_df = HRP_cumu.mul(weight_list, axis=1)
HRP_df['final_returns'] = HRP_df.sum(axis=1)
fig2 = {
# set data equal to traces
'data': [{
'x': HRP_df.index,
'y': HRP_df["final_returns"],
'name': tic
}],
# use string formatting to include all symbols in the chart title
'layout': {
'title': ', '.join(stock_ticker) + ' portfolio'
}
}
return fig2
def mo_portfolio(budget, yesterday, end_period):
# debug
print(f'Hello from mo_portfolio')
print(f'- budget:\t\t {budget}')
print(f'- yesterday:\t {yesterday}')
print(f'- end_period:\t {end_period}')
if end_period is None:
return 'False'
global g_budget
g_budget = budget
global g_date
g_date = [yesterday, end_period]
# yesterday: day before the creation of the portfolio
# end_period: end of investment
chart = pd.DataFrame()
# chart: adj closes fino a yesterday
for s in stocks:
chart = pd.concat(
[chart, dataframes[s]['Adj Close'].loc[:yesterday, ]], axis=1)
chart.columns = stocks
# compute montly (default value = 'Y') cc return
chart_rt = {}
for s in chart:
tmp = chart[s].groupby(pd.Grouper(freq="Y"))
tmp2 = tmp.mean()
chart_rt[s] = np.log(tmp2 / tmp2.shift(1))
chart_rt = pd.DataFrame.from_dict(chart_rt)
chart_rt = chart_rt.dropna()
chart_rt.columns = [
"AAPL CC returns", "NVDA CC returns", "KO CC returns", "UL CC returns",
"BAC CC returns", "AXP CC returns"
]
# adding transition costs (1,5% fee per share)
chart = chart.apply(lambda x: x + (x * 0.015))
# Optimal portfolio
# computes CC return on year granularity
avg_returns = expected_returns.mean_historical_return(chart)
# sample covariance matrix
S = risk_models.sample_cov(chart)
ef = EfficientFrontier(avg_returns, S)
# Minimize the volatily of the portfolio (Markowitz)
weights = ef.min_volatility()
# rounding weights values, meaning they may not add up exactly to 1 but should be close
weights = ef.clean_weights()
Mop_pw = weights
opt_return, opt_risk, _ = ef.portfolio_performance(verbose=False)
global g_expected_return_volat
g_expected_return_volat = [opt_return, opt_risk]
recap = {}
for s in weights:
# print(f'{s} budget {budget}, {type(budget)}') # debug
# print(f'{s} weights[s]/chart[s].iloc[-1] {weights[s]/chart[s].iloc[-1]}, {type(weights[s]/chart[s].iloc[-1])}') # debug
recap[s] = [int(np.floor(budget * weights[s] / chart[s].iloc[-1]))
] # number of shares
price_no_fee = np.round(chart[s].iloc[-1] -
(chart[s].iloc[-1] * 1.5 / 101.5),
decimals=2)
recap[s].append(price_no_fee) # price for each shares
recap[s].append(np.round(price_no_fee * 0.015,
2)) # transaction costs 1,5%
tot_cost = np.around(recap[s][0] * (recap[s][1] + recap[s][2]),
decimals=2)
recap[s].append(
tot_cost
) # total cost of the investment in s (shares * (price for each s + transaction costs))
recap = pd.DataFrame.from_dict(recap, orient='index')
recap.columns = [
'Num of shares', 'Price for each share $', 'Transaction costs $',
'Purchase cost $'
]
global g_recap
g_recap = recap
total = 0
for _, row in recap.iterrows():
total += row['Purchase cost $']
total = np.around(total, decimals=2)
global g_spent
g_spent = total
global g_left
g_left = str(np.around(budget - total, decimals=2))
price_end = {}
tot_port = 0
for s in dataframes:
price_end[s] = dataframes[s]['Adj Close'].loc[end_period]
act_return = 0
for index, row in recap.iterrows():
tot_port += np.around(row['Num of shares'] *
(price_end[index] + row['Transaction costs $']),
decimals=2)
rtn = (price_end[index] + row['Transaction costs $']
) / recap.loc[index, 'Price for each share $'] - 1
act_return += weights[index] * rtn
global g_returns
g_returns = str(np.around(tot_port, decimals=2)) + ' [' + str(
np.round(100 * act_return, decimals=2)) + '%]'
print(g_returns)
return "True"
def test_bl_tau():
df = get_data()
S = risk_models.sample_cov(df)
prices = pd.read_csv("tests/spy_prices.csv",
parse_dates=True,
index_col=0,
squeeze=True)
delta = black_litterman.market_implied_risk_aversion(prices)
mcaps = {
"GOOG": 927e9,
"AAPL": 1.19e12,
"FB": 574e9,
"BABA": 533e9,
"AMZN": 867e9,
"GE": 96e9,
"AMD": 43e9,
"WMT": 339e9,
"BAC": 301e9,
"GM": 51e9,
"T": 61e9,
"UAA": 78e9,
"SHLD": 0,
"XOM": 295e9,
"RRC": 1e9,
"BBY": 22e9,
"MA": 288e9,
"PFE": 212e9,
"JPM": 422e9,
"SBUX": 102e9,
}
prior = black_litterman.market_implied_prior_returns(mcaps, delta, S)
viewdict = {"GOOG": 0.40, "AAPL": -0.30, "FB": 0.30, "BABA": 0}
# Need to change omega for this test to work
omega = np.diag([0.01, 0.01, 0.01, 0.01])
bl0 = BlackLittermanModel(S,
pi=prior,
absolute_views=viewdict,
tau=1e-10,
omega=omega)
bl1 = BlackLittermanModel(S,
pi=prior,
absolute_views=viewdict,
tau=0.01,
omega=omega)
bl2 = BlackLittermanModel(S,
pi=prior,
absolute_views=viewdict,
tau=0.1,
omega=omega)
# For tiny tau, posterior should roughly equal prior
np.testing.assert_allclose(bl0.bl_returns(), bl0.pi.flatten(), rtol=1e-5)
# For bigger tau, GOOG should be given more weight
assert bl1.bl_returns()["GOOG"] > bl0.bl_returns()["GOOG"]
assert bl2.bl_returns()["GOOG"] > bl1.bl_returns()["GOOG"]
def _get_sample_cov(self):
return risk_models.sample_cov(self.price_data_df)
display_tear_sheet() # # Modern Portfolio Theory # # Import the packages from pypfopt import risk_models from pypfopt import expected_returns from pypfopt.efficient_frontier import EfficientFrontier # Calculate expected returns mu mu = expected_returns.mean_historical_return(stock_prices) # Calculate the covariance matrix S Sigma = risk_models.sample_cov(stock_prices) # Obtain the efficient frontier ef = EfficientFrontier(mu, Sigma) print(mu, Sigma) # Get the returns from the stock price data returns = stock_prices.pct_change() # Calculate the annualized covariance matrix covMatrix = returns.cov() * 252 # Calculate the covariance matrix Sigma from a`PyPortfolioOpt` function Sigma = risk_models.sample_cov(stock_prices) # Print both covariance matrices
def show_ef(stocks: List[str], other_args: List[str]):
"""Display efficient frontier
Parameters
----------
stocks : List[str]
List of the stocks to be included in the weights
other_args : List[str]
argparse other args
"""
parser = argparse.ArgumentParser(
add_help=False,
prog="ef",
description="""This function plots random portfolios based
on their risk and returns and shows the efficient frontier.""",
)
parser.add_argument(
"-p",
"--period",
default="3mo",
dest="period",
help="period to get yfinance data from",
choices=period_choices,
)
parser.add_argument(
"-n",
"--number-portfolios",
default=300,
type=check_non_negative,
dest="n_port",
help="number of portfolios to simulate",
)
try:
if other_args:
if "-" not in other_args[0]:
other_args.insert(0, "-n")
ns_parser = parse_known_args_and_warn(parser, other_args)
if not ns_parser:
return
if len(stocks) < 2:
print("Please have at least 2 loaded tickers to calculate weights.\n")
return
stock_prices = process_stocks(stocks, ns_parser.period)
mu = expected_returns.mean_historical_return(stock_prices)
S = risk_models.sample_cov(stock_prices)
ef = EfficientFrontier(mu, S)
_, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
# Generate random portfolios
n_samples = ns_parser.n_port
w = np.random.dirichlet(np.ones(len(mu)), n_samples)
rets = w.dot(mu)
stds = np.sqrt(np.diag(w @ S @ w.T))
sharpes = rets / stds
ax.scatter(stds, rets, marker=".", c=sharpes, cmap="viridis_r")
plotting.plot_efficient_frontier(ef, ax=ax, show_assets=True)
# Find the tangency portfolio
ef.max_sharpe()
ret_sharpe, std_sharpe, _ = ef.portfolio_performance()
ax.scatter(std_sharpe, ret_sharpe, marker="*", s=100, c="r", label="Max Sharpe")
ax.set_title(f"Efficient Frontier simulating {ns_parser.n_port} portfolios")
ax.legend()
plt.tight_layout()
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
if gtff.USE_ION:
plt.ion()
plt.show()
print("")
except Exception as e:
print(e)
print("")
print('no file!')
df = pd.DataFrame()
for tick in tickers:
try:
df[tick[0]] = web.DataReader(tick[0],
data_source='yahoo',
start="2009-01-01",
end=today)['Adj Close']
print(tick[0], "added")
except:
print(tick[0], "not added")
pass
mu = expected_returns.mean_historical_return(df) #returns.mean() * 252
S = risk_models.sample_cov(df) #Get the sample covariance matrix
ef = EfficientFrontier(mu, S)
weights = ef.max_sharpe() #Maximize the Sharpe ratio, and get the raw weights
cleaned_weights = ef.clean_weights()
print(
cleaned_weights
) #Note the weights may have some rounding error, meaning they may not add up exactly to 1 but should be close
ef.portfolio_performance(verbose=True)
latest_prices = get_latest_prices(df)
weights = cleaned_weights
da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=2000)
allocation, leftover = da.lp_portfolio()
print("Discrete allocation:", allocation)
print("Funds remaining: ${:.2f}".format(leftover))
def test_fix_npd_different_method():
df = get_data()
S = risk_models.sample_cov(df)
assert risk_models._is_positive_semidefinite(S)
S = risk_models.sample_cov(df, fix_method="diag")
assert risk_models._is_positive_semidefinite(S)
from pypfopt.expected_returns import mean_historical_return
from pypfopt.risk_models import sample_cov
from mcos.covariance_transformer import DeNoiserCovarianceTransformer
from mcos.error_estimator import ExpectedOutcomeErrorEstimator, SharpeRatioErrorEstimator, VarianceErrorEstimator
from mcos.mcos import simulate_optimizations, simulate_optimizations_from_price_history
from mcos.observation_simulator import MuCovObservationSimulator, MuCovLedoitWolfObservationSimulator,\
MuCovJackknifeObservationSimulator
from mcos.optimizer import HRPOptimizer, MarkowitzOptimizer, NCOOptimizer, RiskParityOptimizer
prices_df = pd.read_csv('tests/stock_prices.csv',
parse_dates=True,
index_col='date')
mu = mean_historical_return(prices_df).values
cov = sample_cov(prices_df).values
@pytest.mark.parametrize(
'simulator, estimator, transformers, expected_mean, expected_stdev',
[(MuCovObservationSimulator(mu, cov, n_observations=5),
ExpectedOutcomeErrorEstimator(), [DeNoiserCovarianceTransformer()],
np.array([0.07580845, 0.05966212, -0.02893896, 0.0085226]),
np.array([0.03445259, 0.03214469, 0.01724587, 0.01244282])),
(MuCovObservationSimulator(
mu, cov, n_observations=5), ExpectedOutcomeErrorEstimator(), [],
np.array([0.05043029, -0.0761952, -0.03200537, -0.00413669
]), np.array([0.05422127, 0.25850676, 0.0196157, 0.01376204])),
(MuCovObservationSimulator(mu, cov, n_observations=5),
SharpeRatioErrorEstimator(), [DeNoiserCovarianceTransformer()],
np.array([0.44088768, 0.32030003, -0.26876011, 0.15122857
def test_sample_cov_frequency():
df = get_data()
S = risk_models.sample_cov(df)
S2 = risk_models.sample_cov(df, frequency=2)
pd.testing.assert_frame_equal(S / 126, S2)
def test_bl_weights():
df = get_data()
S = risk_models.sample_cov(df)
viewdict = {
"AAPL": 0.20,
"BBY": -0.30,
"BAC": 0,
"SBUX": -0.2,
"T": 0.131321
}
bl = BlackLittermanModel(S, absolute_views=viewdict)
prices = pd.read_csv(resource("spy_prices.csv"),
parse_dates=True,
index_col=0,
squeeze=True)
delta = black_litterman.market_implied_risk_aversion(prices)
bl.bl_weights(delta)
w = bl.clean_weights()
assert abs(sum(w.values()) - 1) < 1e-5
# check weights are allocated in same direction as views
# (in absence of priors)
assert all(viewdict[t] * w[t] >= 0 for t in viewdict)
# numerical check
test_weights = {
"GOOG": 0.0,
"AAPL": 1.40675,
"FB": 0.0,
"BABA": 0.0,
"AMZN": 0.0,
"GE": 0.0,
"AMD": 0.0,
"WMT": 0.0,
"BAC": 0.02651,
"GM": 0.0,
"T": 2.81117,
"UAA": 0.0,
"SHLD": 0.0,
"XOM": 0.0,
"RRC": 0.0,
"BBY": -1.44667,
"MA": 0.0,
"PFE": 0.0,
"JPM": 0.0,
"SBUX": -1.79776,
}
assert w == test_weights
bl = BlackLittermanModel(S, absolute_views=viewdict)
bl.optimize(delta)
w2 = bl.clean_weights()
assert w2 == w
bl = BlackLittermanModel(S, absolute_views=pd.Series(viewdict))
bl.optimize(delta)
w2 = bl.clean_weights()
assert w2 == w
def test_volatility():
df = get_data()
S = sample_cov(df)
w = np.array([1 / df.shape[1]] * df.shape[1])
var = objective_functions.volatility(w, S)
np.testing.assert_almost_equal(var, 0.04498224489292057)
def test_sample_cov_frequency():
df = get_data()
S = risk_models.sample_cov(df)
S2 = risk_models.sample_cov(df, frequency=2)
pd.testing.assert_frame_equal(S / 126, S2)
def test_volatility():
df = get_data()
S = sample_cov(df)
w = np.array([1 / df.shape[1]] * df.shape[1])
var = objective_functions.portfolio_variance(w, S)
np.testing.assert_almost_equal(var, 0.04498224489292057)
local_df = local_df.replace(0, np.nan).dropna(axis=1)
columns = list(local_df.columns)
drop = list(set(cryptos) - set(columns))
local_df = returns[start_date_i:start_portfolio_i]
local_df = local_df.iloc[1:] # remove first row
local_df = local_df.drop(drop, axis=1)
columns = list(local_df.columns)
mu = expected_returns.mean_historical_return(local_df,
returns_data=True,
frequency=365)
rf = 0.00 #risk free rate - 0.02 (in this case = 0)
mu_excess = mu - rf
Sigma = risk_models.sample_cov(local_df, returns_data=True, frequency=365)
Sigma = rmt.clipped(Sigma, return_covariance=True)
Sigma = pd.DataFrame(data=Sigma, index=columns, columns=columns)
ones = [1] * (len(columns))
arr_ones = np.array(ones)
arr_ones = pd.DataFrame([arr_ones], columns=columns)
Sigma_inv = pd.DataFrame(np.linalg.pinv(Sigma.values), Sigma.columns,
Sigma.index)
x = arr_ones.dot(Sigma_inv)
y = x.dot(mu_excess)
y = y**-1
weights = Sigma_inv.multiply(float(y))
weights = weights.dot(mu_excess)
stock_weights = [] sharpe_ratio = [] num_assets = len(codes) num_portfolios = NUM_PORTFOLIOS returns_daily_all = table.pct_change().dropna(how="all") returns_daily = returns_daily_all.mean() # returns_annual = returns_daily.mean() print(returns_daily_all.std()) cov_daily = returns_daily_all.cov() # cov_annual = cov_daily # print(returns_annual) avg_returns = expected_returns.mean_historical_return(table) cov_mat = risk_models.sample_cov(table) ef = EfficientFrontier(avg_returns, cov_mat) # CLA # cla = CriticalLineAlgorithm() # cla.allocate(expected_asset_returns=returns_daily,covariance_matrix=cov_daily, solution='efficient_frontier', asset_names=codes) # cla_weights = cla.weights # means, sigma = cla.efficient_frontier_means, cla.efficient_frontier_sigma # plt.plot(sigma, means) # plt.show() cla = CriticalLineAlgorithm() cla.allocate(asset_prices=table, asset_names=codes, solution='min_volatility') cla_weights = cla.weights.sort_values(by=0, ascending=False, axis=1) weights = cla_weights.loc[0] avg_returns = expected_returns.mean_historical_return(table)
import pandas as pd
import numpy as np
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt import risk_models
from pypfopt import expected_returns
from pypfopt.hierarchical_risk_parity import hrp_portfolio
from pypfopt.value_at_risk import CVAROpt
from pypfopt import discrete_allocation
# Reading in the data; preparing expected returns and a risk model
df = pd.read_csv("tests/stock_prices.csv", parse_dates=True, index_col="date")
returns = df.pct_change().dropna(how="all")
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Long-only Maximum Sharpe portfolio, with discretised weights
ef = EfficientFrontier(mu, S)
weights = ef.max_sharpe()
ef.portfolio_performance(verbose=True)
latest_prices = discrete_allocation.get_latest_prices(df)
allocation, leftover = discrete_allocation.portfolio(weights, latest_prices)
print("Discrete allocation:", allocation)
print("Funds remaining: ${:.2f}".format(leftover))
"""
Expected annual return: 33.0%
Annual volatility: 21.7%
Sharpe Ratio: 1.43
Discrete allocation: {'MA': 14, 'FB': 12, 'PFE': 51, 'BABA': 5, 'AAPL': 5,
def get_momentum_stocks(df, date, portfolio_size, cash):
# Filter the df to get the top 10 momentum stocks for the latest day
df_top_m = df.loc[df['date'] == pd.to_datetime(date)]
df_top_m = df_top_m.sort_values(by='momentum', ascending=False).head(portfolio_size)
# Set the universe to the top momentum stocks for the period
universe = df_top_m['symbol'].tolist()
# Create a df with just the stocks from the universe
df_u = df.loc[df['symbol'].isin(universe)]
# Create the portfolio
# Pivot to format for the optimization library
df_u = df_u.pivot_table(
index='date',
columns='symbol',
values='close',
aggfunc='sum'
)
# Calculate expected returns and sample covariance
mu = expected_returns.mean_historical_return(df_u)
S = risk_models.sample_cov(df_u)
# Optimise the portfolio for maximal Sharpe ratio
ef = EfficientFrontier(mu, S, gamma=1) # Use regularization (gamma=1)
weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
# Allocate
latest_prices = get_latest_prices(df_u)
da = DiscreteAllocation(
cleaned_weights,
latest_prices,
total_portfolio_value=cash
)
allocation = da.lp_portfolio()[0]
# Put the stocks and the number of shares from the portfolio into a df
symbol_list = []
num_shares_list = []
for symbol, num_shares in allocation.items():
symbol_list.append(symbol)
num_shares_list.append(num_shares)
# Now that we have the stocks we want to buy we filter the df for those ones
df_buy = df.loc[df['symbol'].isin(symbol_list)]
# Filter for the period to get the closing price
df_buy = df_buy.loc[df_buy['date'] == date].sort_values(by='symbol')
# Add in the qty that was allocated to each stock
df_buy['qty'] = num_shares_list
# Calculate the amount we own for each stock
df_buy['amount_held'] = df_buy['close'] * df_buy['qty']
df_buy = df_buy.loc[df_buy['qty'] != 0]
return df_buy
value=True)
allocmo = st.sidebar.checkbox(
'Επιλεγμένο επιλέγει τον υπολογισμό του μοντέλου του greedy_portfolio αλλιώς επιλέγει το lp_portfolio.',
value=True)
cutoff = st.sidebar.slider(
'Ελάχιστο Ποσοστό Συμμετοχής μιας Μετοχής στο Χαρτοφυλάκιο.', 0.01, 0.30,
0.10)
c1, c2, c3, c4 = st.beta_columns((1, 1, 1, 1))
#-----Χαρτοφυλάκιο Νο1 γενικό
#Calculate portofolio mu and S
mu = expected_returns.mean_historical_return(df_t)
if riskmo:
S = CovarianceShrinkage(df_t).ledoit_wolf()
else:
S = risk_models.sample_cov(df_t)
# Optimise the portfolio
ef = EfficientFrontier(mu, S, gamma=2) # Use regularization (gamma=1)
if weightsmo:
weights = ef.max_sharpe()
else:
weights = ef.min_volatility()
cleaned_weights = ef.clean_weights(cutoff=cutoff, rounding=3)
ef.portfolio_performance()
c1.subheader('Χαρτοφυλάκιο Νο1')
c1.write(
'Το προτινόμενο χαρτοφυλάκιο από τις ιστορικές τιμές των επιλεγμένων μετοχών έχει τα παρακάτω χαρακτηριστικά'
)
c1.write('Αρχική Αξία Χαρτοφυλακίου : ' + str(port_value) + '€')
c1.write('Sharpe Ratio: ' + str(round(ef.portfolio_performance()[2], 2)))
import pandas as pd
import matplotlib.pyplot as plt
from pypfopt import risk_models
from pypfopt import expected_returns
from pypfopt.efficient_frontier import EfficientFrontier
from pypfopt.black_litterman import BlackLittermanModel
prbr = pd.read_excel(r'C:\Users\Jhona\OneDrive\Área de Trabalho\PRBR11.xlsx',
index_col='Data',
parse_dates=['Data'])
###Expectativa de retornos de mu
mu = expected_returns.mean_historical_return(prbr)
###Matrix de covariancia
sigma = risk_models.sample_cov(prbr)
###Fronteira eficiente Max sharpe
ef = EfficientFrontier(mu, sigma)
weights = ef.max_sharpe()
ef.efficient_risk(2.0)
ef.efficient_return(1.5)
cleaned_weights = ef.clean_weights()
print(weights, cleaned_weights)
ef.portfolio_performance(verbose=True, risk_free_rate=0.0225)
###Fronteira eficiente Min Vol
ef = EfficientFrontier(mu, sigma)
raw_weights = ef.min_volatility()
cleaned_weights = ef.clean_weights()
print(cleaned_weights)
