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_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)
pf.mc_simulation()
pf.plot_mc_simulation()
pf.print_mc_results()
def test_portfolio_moex_pass_0():
d = d_pass_moex[0]
data = download(**d)
data = clean_data(data)
pf = Portfolio(data=data)
pf.mc_simulation()
pf.plot_mc_simulation()
pf.print_mc_results()
def test_download_moex_pass_4():
d = d_pass_moex[4]
data = download(**d)
print(data.head())
isinstance(data, pd.DataFrame)
data = clean_data(data)
tickers = data.columns.tolist()
print(tickers)
assert sorted(tickers) == sorted(tickers_pass_moex[4])
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_1():
d = d_pass_yfinance[1]
data = download(**d)
print(data.head())
isinstance(data, pd.DataFrame)
def test_download_csv_pass_0():
d = d_pass_csv[0]
data = download(**d)
print(data.head())
isinstance(data, pd.DataFrame)
import pandas as pd
from pfo.stocks.cluster import cluster_stocks
from pfo.stocks.stock import Stock
from pathlib import Path, WindowsPath
from pfo.utils.market_data import download, Source
from pfo.utils.data_utils import clean_data
path = (Path.cwd() / ".." / "cache" / "moex_all.csv").resolve()
data = download(
Source.MOEX,
tickers=[],
boards=[
{
"board": "TQBR",
"shares": "shares"
},
{
"board": "TQTF",
"shares": "shares"
},
{
"board": "FQBR",
"shares": "foreignshares"
},
],
)
data = clean_data(data)
data.to_csv(path)
import datetime
import matplotlib.pyplot as plt
from pfo.pf.portfolio import Portfolio
import numpy as np
from pfo.utils.market_data import download, Source
from pfo.utils.data_utils import clean_data
start_date = datetime.datetime(2019, 1, 1)
end_date = "2020-11-20"
tickers = [
"GM",
"WMT",
"NVDA",
"TSLA",
"GE",
"AAL",
"AMD",
"ADBE",
"AMZN",
]
data = download(
source=Source.YFINANCE, tickers=tickers, start_date=start_date, end_date=end_date
)
data = clean_data(data)
pf = Portfolio(data=data, risk_free_rate=0.001, freq=252)
pf.discrete_allocation()
from pfo.stocks.stock import Stock
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.width", None)
pd.set_option("display.max_colwidth", None)
pd.set_option("display.width", 150)
start_date = datetime.datetime(2020, 8, 27)
end_date = datetime.datetime(2020, 11, 27)
data = download(
source=Source.MOEX,
tickers=["AFKS", "APTK", "LNZL", "MAGEP", "MRKS", "PLZL", "ROLO", "SELG"],
board={
"board": "TQBR",
"shares": "shares"
},
start_date=start_date,
end_date=end_date,
)
data = clean_data(data)
pf_stocks = []
for ticker in data.columns:
stk = Stock(ticker=ticker, data=data)
stk.plot_prices()
stk.plot_daily_returns()
pf_stocks.append(ticker)
pf1 = Portfolio(data=data, risk_free_rate=0.01, freq=252)
pf1.mc_simulation(10000)
import datetime
import matplotlib.pyplot as plt
import pandas as pd
from pfo.stocks.cluster import cluster_stocks
from pfo.stocks.stock import Stock
from pfo.utils.market_data import download, Source
from pfo.utils.data_utils import clean_data
start_date = datetime.datetime(2018, 11, 20)
end_date = datetime.datetime(2020, 11, 20)
data = download(source=Source.MOEX,
tickers=[],
boards=[{
"board": "TQBR",
"shares": "shares"
}])
data = clean_data(data)
data = data[start_date:end_date]
isnull = data.isnull().sum()
for ticker in data.columns:
try:
if isnull[ticker] > 50:
data.drop(ticker, axis=1, inplace=True)
except:
pass
pf_stocks = []
for ticker in data.columns:
stk = Stock(ticker=ticker, data=data)
if stk.sharp >= 1.0 or stk.sortino >= 1.0:
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)
