def test_greedy_portfolio_allocation():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S)
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(w, latest_prices, short_ratio=0.3)
allocation, leftover = da.greedy_portfolio()
assert allocation == {
"MA": 20,
"FB": 12,
"PFE": 54,
"BABA": 4,
"AAPL": 4,
"BBY": 2,
"SBUX": 1,
"GOOG": 1,
}
total = 0
for ticker, num in allocation.items():
total += num * latest_prices[ticker]
np.testing.assert_almost_equal(total + leftover, 10000, decimal=4)
# Cover the verbose parameter,
allocation_verbose, leftover_verbose = da.greedy_portfolio(verbose=True)
assert allocation_verbose == allocation
assert leftover_verbose == leftover
def test_lp_portfolio_allocation_different_params():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S)
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(w,
latest_prices,
total_portfolio_value=80000,
short_ratio=0.4)
allocation, leftover = da.lp_portfolio()
# assert allocation == {
# "GOOG": 3,
# "AAPL": 32,
# "FB": 99,
# "BABA": 34,
# "AMZN": 2,
# "BBY": 15,
# "MA": 164,
# "PFE": 438,
# "SBUX": 15,
# }
total = 0
for ticker, num in allocation.items():
total += num * latest_prices[ticker]
np.testing.assert_almost_equal(total + leftover, 80000, decimal=4)
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
def opt_data(ticker, start, end, amt):
weight = np.array([1 / len(ticker)] * len(ticker))
hist_data = get_data(ticker, start, end)
daily_returns = hist_data.pct_change()
cov_annual_mat = daily_returns.cov() * 255
port_variance = np.dot(weight.T, np.dot(cov_annual_mat, weight))
port_volatility = np.sqrt(port_variance).round(4)
port_simple_annual_return = np.sum(daily_returns.mean() * weight) * 255
mu = expected_returns.mean_historical_return(hist_data)
S = risk_models.sample_cov(hist_data)
ef = EfficientFrontier(mu, S)
w = ef.max_sharpe()
cw = ef.clean_weights()
e = ef.portfolio_performance(verbose=False)
latest_prices = get_latest_prices(hist_data)
da = DiscreteAllocation(cw, latest_prices, total_portfolio_value=amt)
allocation, leftover = da.lp_portfolio()
return {
"orignal_port_volatility": str(round(port_volatility, 3) * 100) + "%",
"orignal_annual_return":
str(round(port_simple_annual_return, 3) * 100) + "%",
"new_port_volatility": str(round(e[1], 3) * 100) + "%",
"new_annual_return": str(round(e[0], 3) * 100) + "%",
"Allocation": cw,
"AllocationNo": allocation,
"Left_Amount": str(round(leftover, 2))
}
def getPortfolio(self, endDate, value):
#Ensure dates are in correct format
if (not dateHelper.isDateFormatCorrect(endDate)):
util.printErrorAndDie(
'DataProvider.getData: start date is incorrect format')
startDate = dateHelper.subtractDaysFromDate(endDate, self.lookbackDays)
threads = [
Thread(target=self.calcWeights, args=([x, startDate, endDate]))
for x in self.allStocks
]
[x.start() for x in threads]
[x.join() for x in threads]
data = self.dataProvider.getData(self.sectorPortfolios, startDate,
endDate)
weights = self.getWeights(self.markowitz(data))
latest_prices = data.loc[endDate]
da = DiscreteAllocation(weights,
latest_prices,
total_portfolio_value=value)
allocation, leftover = da.lp_portfolio()
return pd.Series(allocation)
def test_greedy_portfolio_allocation():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S)
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(w, latest_prices)
allocation, leftover = da.greedy_portfolio()
assert da.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)
def test_lp_portfolio_allocation_different_params():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S)
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(
w,
latest_prices,
min_allocation=0.002,
total_portfolio_value=80000,
short_ratio=0.4,
)
allocation, leftover = da.lp_portfolio()
assert da.allocation == {
"GOOG": 1,
"AAPL": 43,
"FB": 95,
"BABA": 44,
"AMZN": 4,
"BBY": 69,
"MA": 114,
"PFE": 412,
"SBUX": 51,
}
total = 0
for ticker, num in allocation.items():
total += num * latest_prices[ticker]
np.testing.assert_almost_equal(total + leftover, 80000)
def optimize(tickers, cash=1000, longshort=False):
print(f'Cash: ${cash}')
date_start = 20 * 6
df = pd.DataFrame()
for t in tickers:
path = os.path.join(os.path.dirname(__file__),
f'../data/price/{t}.csv')
price = pd.read_csv(path, parse_dates=True,
index_col='Date')['Adj Close'].rename(t)
df[t] = price[-date_start:]
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
# Optimise for maximal Sharpe ratio
ef = EfficientFrontier(mu,
S,
weight_bounds=((-1, 1) if longshort else (0, 1)))
raw_weights = ef.max_sharpe()
clean_weights = ef.clean_weights()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(raw_weights,
latest_prices,
total_portfolio_value=cash)
allocation, leftover = da.lp_portfolio()
print('\nWeights:', clean_weights)
print('\nShares:', allocation)
print(f'\n${leftover:.2f} leftover')
ef.portfolio_performance(verbose=True)
def get_mean_variance_share_allocation():
dates, ticker_to_closing_prices = get_ticker_to_closing_prices(
START_DATE, TEST_START_DATE - timedelta(days=1))
tickers = ticker_to_closing_prices.keys()
prices = list(
zip(*[ticker_to_closing_prices[ticker] for ticker in tickers]))
df = pd.DataFrame(
prices,
index=dates,
columns=tickers,
)
mu = mean_historical_return(df)
S = CovarianceShrinkage(df).ledoit_wolf()
ef = EfficientFrontier(mu, S)
weights = ef.max_sharpe()
_, ticker_to_closing_prices_at_test_start = get_ticker_to_closing_prices(
TEST_START_DATE, TEST_END_DATE)
prices_at_test_start = pd.Series([
float(ticker_to_closing_prices_at_test_start[ticker][0])
for ticker in tickers
],
index=tickers)
da = DiscreteAllocation(weights,
prices_at_test_start,
total_portfolio_value=INITIAL_PORTFOLIO_VAL)
allocation, leftover = da.lp_portfolio()
for ticker in tickers:
if ticker not in allocation:
allocation[ticker] = 0
return allocation
def get_momentum_stocks(df, date, portfolio_size, cash):
# Convert dates and filter all momentum scores to include only top `portfolio_size` movers
df_top_movers = df.loc[df['date'] == pd.to_datetime(date)]
df_top_movers = df_top_movers.sort_values(by='momentum', ascending=False).head(portfolio_size)
# Create a universe of top momentum stocks
universe = df_top_movers['symbol'].tolist()
# Create universe as DF for these stocks
df_universe_top_movers = df.loc[df['symbol'].isin(universe)]
# Create pre-optimzed portfolio
df_universe_top_movers = df_universe_top_movers.pivot_table(
index='date',
columns='symbol',
values='close',
aggfunc='sum')
# Calculate expected returns and sample covariance
mu = expected_returns.mean_historical_return(df_universe_top_movers)
S = risk_models.sample_cov(df_universe_top_movers)
# Optimize by 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_universe_top_movers)
allocated = DiscreteAllocation(
cleaned_weights,
latest_prices,
total_portfolio_value=cash)
allocation = allocated.lp_portfolio()[0]
# Put the stocks and the number of shares from the portfolio into a df
symbols = []
num_shares = []
for sym, shares in allocation.items():
symbols.append(sym)
num_shares.append(shares)
# Create the to-buy dataframe
df_buy = df.loc[df['symbol'].isin(symbols)]
# Filter out irrelevant dates
df_buy = df_buy.loc[df_buy['date'] == date].sort_values(by='symbol')
# Add quantity allocations into dataframe
df_buy['qty'] = num_shares # has thrown -> ValueError
# Calculate the new/desired equity for each stock
df_buy['equity'] = df_buy['close'] * df_buy['qty']
df_buy = df_buy.loc[df_buy['qty'] != 0]
return df_buy
def getoptimprices(df, weights, portvalue):
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(weights,
latest_prices,
total_portfolio_value=portvalue)
allocation, leftover = da.lp_portfolio()
listshares = []
for k in allocation.values():
listshares.append(k)
return allocation, leftover, listshares
def test_remove_zero_positions():
raw = {"MA": 14, "FB": 12, "XOM": 0, "PFE": 51, "BABA": 5, "GOOG": 0}
da = DiscreteAllocation({}, pd.Series())
assert da._remove_zero_positions(raw) == {
"MA": 14,
"FB": 12,
"PFE": 51,
"BABA": 5
}
def getDiscreteAllocations(df, weights):
latest_prices = get_latest_prices(df)
#plotting.plot_weights(weights)
total_portfolio_value = 15000
da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=total_portfolio_value)
allocation, leftover = da.lp_portfolio()
print("Best portfolio possible today for the given shares and given contraints for an investment of ", total_portfolio_value, ": ")
print("Shares allocation:", allocation)
print("Funds remaining: ${:.2f}".format(leftover))
def create_portfolio():
today = str(date.today())
#Create the DataFrame and fill with historical data
df = pd.read_csv(csv, header = 0)
df = df.clip(lower=0.1)
print("NEGATIVE: " + str(df.agg(lambda x: sum(x < 0)).sum()))
df = df.iloc[:, 0:200]
assets = df.columns
#Calculate the expected annualized returns and the annualized ...
mu = expected_returns.mean_historical_return(df)
S = risk_models.sample_cov(df)
#Optimize fot the maximal Sharpe ratio
ef = EfficientFrontier(mu,S)
weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
ef.portfolio_performance(verbose=True)
#Get the discrete allocation of each share per stock
latest_prices = get_latest_prices(df)
weights = cleaned_weights
da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=portfolio_val)
allocation, leftover = da.lp_portfolio()
#print('Discrete allocation: ', allocation)
#print('Funds Rreaming $', leftover)
symbols = []
discrete_allocation_list = []
company_name = []
for symbol in allocation:
company_name.append(get_company_name(symbol))
discrete_allocation_list.append(allocation.get(symbol))
#symbols.append(allocation.get(symbol))
portfolio_df = pd.DataFrame(columns=['Cég', 'Szimbólum', 'Részvények_száma'])
portfolio_df['Cég'] = company_name
portfolio_df['Szimbólum'] = allocation
portfolio_df['Részvények_száma'] = discrete_allocation_list
above_df = "Várható éves hozam: " + str(round(ef.portfolio_performance(verbose=True)[0] * 100, 2)) + "% | " + "Volatilitás: " + str(round(ef.portfolio_performance(verbose=True)[1] * 100, 2)) + "% | " + "Sharpe-ráta: " + str(round(ef.portfolio_performance(verbose=True)[2], 3))
st.write("Az első két oszlop a cég nevét és tőzsdei szimbólumát mutatja, a harmadik pedig azt, hogy hány darab részvényt kell belőle venned a kalkuláció szerint")
st.success(str(above_df))
st.write(portfolio_df, 'A végén marad: ' + str(round(leftover, 2)) + "$")
st.write("Elemzéshez felhasznált részvények listája:")
st.write(assets)
def test_lp_allocation_rmse_error_short():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(w, latest_prices, short_ratio=0.3)
da.lp_portfolio()
assert da._allocation_rmse_error(verbose=False) < 0.1
def test_greedy_allocation_rmse_error():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S)
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(w, latest_prices)
da.greedy_portfolio()
np.testing.assert_almost_equal(da._allocation_rmse_error(), 0.0383845)
def test_lp_allocation_rmse_error_short():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(w, latest_prices)
da.lp_portfolio()
np.testing.assert_almost_equal(da._allocation_rmse_error(), 0.02901217)
def calc(stockStartTime, stockEndTime):
#calculating 2 years prior of the start date
tempdate = dt.datetime.fromisoformat(stockStartTime)
tempdate = tempdate - dt.timedelta(weeks=104)
prices = ffn.get('pfe,ibm,wmt,msft,cat',
start=tempdate.strftime('%Y-%m-%d'),
end=stockStartTime)
# Expected returns and sample covariance
mu = expected_returns.mean_historical_return(prices)
S = risk_models.sample_cov(prices)
#Minimum volatility. May be useful if you're trying to get an idea of how low the volatility could be,
#but in practice it makes a lot more sense to me to use the portfolio that maximises the Sharpe ratio.
# Optimise portfolio for maximum Sharpe Ratio to serve as benchmark
ef = EfficientFrontier(mu, S)
raw_weights = ef.min_volatility() #ef.max_sharpe()
cleaned_weights = ef.clean_weights()
print("Cleaned weights:\n", cleaned_weights)
print("Portfolio Performance:\n", ef.portfolio_performance(verbose=True))
#To achieve beta neutrality
ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
print(" Weights: ",
ef.efficient_return(target_return=0.15, market_neutral=True))
weights = ef.efficient_return(target_return=0.2, market_neutral=True)
weight_sum = sum(w for w in weights.values() if w > 0)
#normalised_weights = {k:v/weight_sum for k,v in weights.items()}
#print("Normalized weights: ",normalised_weights)
#We then need to convert these weights into an actual allocation, telling you how many shares of each asset you should purchase.
latest_prices = get_latest_prices(prices)
da = DiscreteAllocation(weights,
latest_prices,
total_portfolio_value=1000000)
allocation, leftover = da.lp_portfolio()
#print(allocation)
print("")
for key, val in allocation.items():
print("Number of positions in ", key, " stock: ", val)
print("")
print("Funds remaining: ${:.2f}".format(leftover))
print("")
prices2 = ffn.get('pfe,ibm,wmt,msft,cat',
start=stockStartTime,
end=stockEndTime)
latest_prices2 = get_latest_prices(prices2)
sum1 = 0
for key, val in allocation.items():
sum1 = sum1 - (latest_prices[key] * val)
print("Value of Portfolio after short sales :\t", abs(sum1))
new = 0
for key, val in allocation.items():
new = new + (latest_prices2[key] * val)
sum1 = sum1 + (latest_prices2[key] * val)
print("Value at end of period :\t\t", new)
print("Profit at end of time period :\t\t", sum1)
return sum1
def get_portfolio(universe, df_tr, port_value, cutoff, df_m):
'''create a portfolio using the stocks from the universe and the closing
prices from df_tr with a given portfolio value and a weight cutoff value
using the value of a momemntum indicator to limit the quantity of the stocks'''
df_t = select_columns(df_tr, universe)
mu = capm_returns(df_t)
S = CovarianceShrinkage(df_t).ledoit_wolf()
# Optimize the portfolio for min volatility
ef = EfficientFrontier(mu, S) # Use regularization (gamma=1)
weights = ef.min_volatility()
#weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights(cutoff=cutoff)
# Allocate
latest_prices = get_latest_prices(df_t)
da = DiscreteAllocation(cleaned_weights,
latest_prices,
total_portfolio_value=port_value)
allocation = da.greedy_portfolio()[0]
non_trading_cash = da.greedy_portfolio()[1]
# Put the stocks and the number of shares from the portfolio into a df
symbol_list = []
mom = []
w = []
num_shares_list = []
l_price = []
tot_cash = []
for symbol, num_shares in allocation.items():
symbol_list.append(symbol)
mom.append(df_m[df_m['stock'] == symbol].values[0])
w.append(round(cleaned_weights[symbol], 4))
num_shares_list.append(num_shares)
l_price.append(latest_prices[symbol])
tot_cash.append(num_shares * latest_prices[symbol])
df_buy = pd.DataFrame()
df_buy['stock'] = symbol_list
df_buy['momentum'] = mom
df_buy['weights'] = w
df_buy['shares'] = num_shares_list
df_buy['price'] = l_price
df_buy['value'] = tot_cash
df_buy = df_buy.append(
{
'stock': 'CASH',
'momentum': 0,
'weights': round(1 - df_buy['weights'].sum(), 4),
'shares': 1,
'price': round(non_trading_cash, 2),
'value': round(non_trading_cash, 2)
},
ignore_index=True)
df_buy = df_buy.set_index('stock')
return df_buy, non_trading_cash
def test_lp_allocation_rmse_error():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S)
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(w, latest_prices)
da.lp_portfolio()
np.testing.assert_almost_equal(da._allocation_rmse_error(verbose=False),
0.017070218149194846)
def get_discrete_allocation(self):
latest_prices = get_latest_prices(self.price_data_df)
da = DiscreteAllocation(self.weights,
latest_prices,
total_portfolio_value=self.portfolio_value)
self.allocation, self.leftover = da.lp_portfolio()
self.allocation = {k: int(v) for k, v in self.allocation.items()}
print('Discrete allocation: {}'.format(self.allocation))
print('Funds remaining: ${:.2f}'.format(self.leftover))
self.exp_return, self.volatility, self.sharpe_ratio = self.ef.portfolio_performance(
verbose=True)
def test_greedy_allocation_rmse_error_short():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(w, latest_prices, short_ratio=0.3)
da.greedy_portfolio()
np.testing.assert_almost_equal(da._allocation_rmse_error(verbose=False),
0.06063511265243106)
def calculate_allocation(stock_price, cash, Portfolio):
mu = expected_returns.mean_historical_return(stock_price)
S = risk_models.sample_cov(stock_price)
ef = EfficientFrontier(mu, S)
cleaned_weights = ef.clean_weights()
latest_prices = get_latest_prices(stock_price)
da = DiscreteAllocation(cleaned_weights,
latest_prices,
total_portfolio_value=cash)
allocation, leftover = da.lp_portfolio()
print("Discrete allocation:", allocation)
print("Funds remaining: ${:.2f}".format(leftover))
def build_portfolio(self, price_pivot, portfolio_total=10000):
''' build a portfolio from price data'''
mu = expected_returns.mean_historical_return(price_pivot)
shrink = risk_models.CovarianceShrinkage(price_pivot)
S = shrink.ledoit_wolf()
ef = EfficientFrontier(mu, S, weight_bounds=(0, 0.2), gamma=0.8)
weights = ef.max_sharpe()
weights = ef.clean_weights()
latest_prices = get_latest_prices(price_pivot)
weights = {k: v for k, v in weights.items() if weights[k] > 0.0}
da = DiscreteAllocation(weights, latest_prices, total_portfolio_value=portfolio_total)
allocation, leftover = da.lp_portfolio()
# print("Discrete allocation:", allocation)
return allocation
def calculate_allocation(self, cash):
# if you have cash to allocate to a set of stocks, this function will return how to allocate that
# see rebalance_weight to identify most suitable portfolio
self.cash = cash
ef = EfficientFrontier(self.mu, self.S)
weights = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
latest_prices = get_latest_prices(self.stock_prices)
da = DiscreteAllocation(cleaned_weights,
latest_prices,
total_portfolio_value=cash)
allocation, leftover = da.lp_portfolio()
print("Discrete allocation:", allocation)
print("Funds remaining: ${:.2f}".format(leftover))
def GetPort(
): #tickers = ['BSX','AES','BRK-B','SEE','QQQ','SPY'], first = 0, funds = 10000):
ticks = request.args.get('tickers')
tickers = list(ticks.split(" "))
first = request.args.get('first')
funds = request.args.get('Funds')
thelen = len(tickers)
price_data = []
for ticker in range(thelen):
prices = web.DataReader(tickers[ticker],
start='2015-01-01',
end='2020-10-17',
data_source='yahoo')
price_data.append(prices.assign(ticker=ticker)[['Adj Close']])
df_stocks = pd.concat(price_data, axis=1)
df_stocks.columns = tickers
#print(df_stocks.head())
#nullin_df = pd.DataFrame(df_stocks,columns=tickers)
#print(nullin_df.isnull().sum())
mu = expected_returns.mean_historical_return(df_stocks)
Sigma = risk_models.sample_cov(df_stocks)
if int(first) == 1:
ef = EfficientFrontier(mu, Sigma, weight_bounds=(0, 1))
else:
ef = EfficientFrontier(mu, Sigma, weight_bounds=(-1, 1))
sharpe_pfolio = ef.max_sharpe()
cleaned_weights = ef.clean_weights()
#print(cleaned_weights)
#ef.portfolio_performance(verbose=True)
latest_prices = get_latest_prices(df_stocks)
da = DiscreteAllocation(cleaned_weights,
latest_prices,
total_portfolio_value=int(funds))
allocation, leftover = da.lp_portfolio()
#print("Discrete allocation:", allocation)
#print("Funds remaining: ${:.2f}".format(leftover))
#new_alloc = {str(key): str(value) for key, value in allocation}
new_alloc = {}
for key in allocation:
new_alloc[key] = str(allocation[key])
allocjson = json.dumps(new_alloc)
#print(allocjson)
return jsonify(allocation=new_alloc, leftover=leftover)
def test_rmse_decreases_with_value():
# As total_portfolio_value increases, rmse should decrease.
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S)
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da1 = DiscreteAllocation(w, latest_prices, total_portfolio_value=10000)
da1.greedy_portfolio()
rmse1 = da1._allocation_rmse_error(verbose=False)
da2 = DiscreteAllocation(w, latest_prices, total_portfolio_value=100000)
da2.greedy_portfolio()
rmse2 = da2._allocation_rmse_error(verbose=False)
assert rmse2 < rmse1
def test_allocation_errors():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S)
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
with pytest.raises(TypeError):
DiscreteAllocation(ef.weights, latest_prices)
with pytest.raises(TypeError):
DiscreteAllocation(w, latest_prices.values.tolist())
with pytest.raises(ValueError):
DiscreteAllocation(w, latest_prices, total_portfolio_value=0)
with pytest.raises(ValueError):
DiscreteAllocation(w, latest_prices, short_ratio=-0.4)
def run_iteration(
ld: LazyDictionary,
strategy,
first_prices,
latest_prices,
filtered_stocks,
**kwargs,
):
assert ld is not None
strategy(ld, **kwargs)
ld["optimizer_performance"] = lambda ld: portfolio_performance(ld)
ld["allocator"] = lambda ld: DiscreteAllocation(
ld["raw_weights"],
first_prices,
total_portfolio_value=ld["total_portfolio_value"],
)
ld["portfolio"] = lambda ld: ld["allocator"].greedy_portfolio(
) # greedy_portfolio returns (dict, float) tuple
ld["cleaned_weights"] = lambda ld: clean_weights(ld["raw_weights"], ld[
"portfolio"][0], first_prices, latest_prices)
ld["latest_prices"] = latest_prices
ld["len_latest_prices"] = lambda ld: len(ld["latest_prices"])
# only plot covmatrix/corr for significant holdings to ensure readability
ld["m"] = lambda ld: CovarianceShrinkage(filtered_stocks[list(ld[
"cleaned_weights"].keys())[:30]]).ledoit_wolf()
def test_greedy_portfolio_allocation_short():
df = get_data()
mu = mean_historical_return(df)
S = sample_cov(df)
ef = EfficientFrontier(mu, S, weight_bounds=(-1, 1))
w = ef.max_sharpe()
latest_prices = get_latest_prices(df)
da = DiscreteAllocation(w, latest_prices, short_ratio=0.3)
allocation, leftover = da.greedy_portfolio()
assert allocation == {
"MA": 19,
"PFE": 42,
"FB": 7,
"GOOG": 1,
"BABA": 5,
"AAPL": 4,
"SBUX": 8,
"BBY": 6,
"XOM": 4,
"WMT": 3,
"BAC": -32,
"AMD": -48,
"SHLD": -132,
"GM": -9,
"RRC": -19,
"GE": -14,
"T": -5,
"UAA": -8,
}
long_total = 0
short_total = 0
for ticker, num in allocation.items():
if num > 0:
long_total += num * latest_prices[ticker]
else:
short_total -= num * latest_prices[ticker]
np.testing.assert_almost_equal(long_total + short_total + leftover,
13000,
decimal=4)
# Cover the verbose parameter,
allocation_verbose, leftover_verbose = da.greedy_portfolio(verbose=True)
assert allocation_verbose == allocation
assert leftover_verbose == leftover
