def test_portfolio_moex_pass_0():
d = d_pass_moex[0]
data = download(**d)
data = clean_data(data)
pf = portfolio(data = data)
pf.plot_portfolios()
pf.print_results()
def test_portfolio_yfinance_pass_1():
d = d_pass_yfinance[0]
data = download(**d)
data = clean_data(data)
pf = portfolio(data=data, risk_free_rate=0.001, freq=252, num_portfolios=10000)
pf.plot_portfolios()
pf.print_results()
def test_download_csv_warn_1():
d = d_warn_csv[1]
with pytest.warns(UserWarning):
data = download(**d)
print(data.head())
isinstance(data, pd.DataFrame)
def test_portfolio_csv_pass_0():
d = d_pass_csv[0]
data = download(**d)
data = clean_data(data)
pf = portfolio(data = data, num_portfolios=10000)
pf.plot_portfolios()
pf.print_results()
start_date17_20 = datetime.datetime(2017, 11, 20)
end_date17_20 = datetime.datetime(2020, 11, 19)
tickers = ['CSCO', 'V', 'ABBV', 'SBUX', 'MCD', 'INTC', \
'GM', 'HPQ', 'EA', 'FDX', 'NKE', 'BERY', \
'GOOGL', 'GOOG', 'WMT', 'NVDA', 'TSLA', 'GE', \
'AAL', 'AMD', 'ADBE', 'AMZN', 'PYPL', 'MSFT', \
'ATVI', 'FB', 'NEM', 'NFLX', 'AVGO', \
'QCOM', 'BABA', 'MA', 'AAPL', 'BA', 'TWTR', \
'MU', 'T', 'F', 'BIDU', 'BIIB', 'XOM', \
'DIS', 'PFE', 'BMY']
data = download(source=Source.YFINANCE,
tickers=tickers,
start_date=start_date14_17,
end_date=end_date14_17)
data = clean_data(data)
df_ratios = ratios(data=data)
df_sharp = df_ratios[df_ratios['Sharp Ratio'] > 1.0]
df_sortino = df_ratios[df_ratios['Sortino Ratio'] > 1.0]
pf_stocks_sortino = df_sortino.index.to_list()
pf_stocks_sharp = df_sortino.index.to_list()
print('=' * 80)
print('Sortino portfolio 14-17')
sortino_data14_17 = download(source=Source.YFINANCE,
tickers=pf_stocks_sortino,
start_date=start_date14_17,
from pfo.market_data import download, Source, clean_data
start_date = datetime.datetime(2017, 11, 20)
end_date = '2020-11-20'
tickers = ['CSCO', 'V', 'ABBV', 'SBUX', 'MCD', 'INTC', \
'GM', 'HPQ', 'EA', 'FDX', 'NKE', 'BERY', \
'GOOGL', 'GOOG', 'WMT', 'NVDA', 'TSLA', 'GE', \
'AAL', 'AMD', 'ADBE', 'AMZN', 'PYPL', 'MSFT', \
'ATVI', 'FB', 'NEM', 'NFLX', 'AVGO', \
'QCOM', 'BABA', 'MA', 'AAPL', 'BA', 'TWTR', \
'MU', 'T', 'F', 'BIDU', 'BIIB', 'XOM', \
'DIS', 'PFE', 'BMY']
data = download(source=Source.YFINANCE,
tickers=tickers,
start_date=start_date,
end_date=end_date)
data = clean_data(data)
df_ratios = ratios(data=data)
df_sharp = df_ratios[df_ratios['Sharp Ratio'] > 1.0]
df_sortino = df_ratios[df_ratios['Sortino Ratio'] > 1.0]
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
print(df_sharp)
print(df_sortino)
pf_stocks_sortino = df_sortino.index.to_list()
pf_stocks_sharp = df_sortino.index.to_list()
sortino_data = download(source=Source.YFINANCE,
tickers=pf_stocks_sortino,
def test_download_csv_pass_1():
d = d_pass_csv[1]
data = download(**d)
print(data.head())
isinstance(data, pd.DataFrame)
def test_download_moex_pass_3():
d = d_pass_moex[3]
data = download(**d)
print(data.head())
isinstance(data, pd.DataFrame)
def test_download_yfinance_pass_0():
d = d_pass_yfinance[0]
data = download(**d)
print(data.head())
isinstance(data, pd.DataFrame)
'GM', 'HPQ', 'EA', 'FDX', 'NKE', 'BERY', \
'GOOGL', 'GOOG', 'WMT', 'NVDA', 'TSLA', 'GE', \
'AAL', 'AMD', 'ADBE', 'AMZN', 'PYPL', 'MSFT', \
'ATVI', 'FB', 'NEM', 'NFLX', 'AVGO', \
'QCOM', 'BABA', 'MA', 'AAPL', 'BA', 'TWTR', \
'MU', 'T', 'F', 'BIDU', 'BIIB', 'XOM', \
'DIS', 'PFE', 'BMY']
#
# tickers = ['BABA', 'MA', 'AAPL', 'BA', 'TWTR', \
# 'MU', 'T', 'F', 'BIDU', 'BIIB', 'XOM', \
# 'DIS', 'PFE', 'BMY']
#tickers = ['AKRN', 'PIKK', 'PLZL', 'SELG']
data = download(source=Source.YFINANCE,
tickers=tickers,
start_date=start_date,
end_date=end_date)
data = clean_data(data)
#data.to_csv(path)
#
#
pf = portfolio(data=data, risk_free_rate=0.01, freq=252)
pf.mc()
pf.plot_portfolios()
pf.print_results()
#
ef = sharp_efficient_frontier(data=data)
msr = ef.max_sharpe_ratio()
mv = ef.min_volatility()
#
res = pf_valuation(msr['x'], data=data)
def test_cluster_stocks_pass_0():
d = d_pass_csv[0]
data = download(**d)
data = clean_data(data)
data.dropna(how="all").replace([np.inf, -np.inf], np.nan)
cluster_stocks(data, n_clusters=4, verbose=True)