def test_yahoo():
y = YahooData(['DIS', 'GOOG'])
date = datetime(2016, 5, 23)
date = np.datetime64(date)
out = y.filter_dates(end=date)
# out = y.filter_before(date)
# y.get_symbols_before(date)
df3 = out.dataframes['GOOG']
assert df3.index[-1] <= date
return
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from backtester.stockdata import YahooData
from backtester.definitions import DF_ADJ_CLOSE
from backtester.indicators import TrailingStats
y = YahooData()
start = np.datetime64('2003-01-01')
end = np.datetime64('2005-01-01')
y = y.filter_dates(start=start, end=end)
s = y.dataframes['SPY'][DF_ADJ_CLOSE]
window = 100
windows = [
100,
]
for window in windows:
ts = TrailingStats(s, window)
loss = ts.max_loss
# loss99 = np.percentile(loss, 99)
plt.subplot(2, 1, 1)
plt.plot(ts.times, ts.max_loss, label='max loss')
import matplotlib.pyplot as plt
import seaborn as sns
from globalcache import Cache
sns.set_theme()
cache = Cache(globals())
# %% Get stock data
y_data = YahooData()
start_date = np.datetime64('2012-01-01')
symbols = y_data.retrieve_symbol_names()
y_data = y_data.filter_dates(start=start_date)
# symbols = symbols[0:5]
# symbols = ['GME']
symbols.append('SPY')
# %% Define calculations
def sortino_ratio(r: np.ndarray, target: float, bins: int = 25) -> float:
"""https://en.wikipedia.org/wiki/Sortino_ratio
Parameters
----------
r : np.ndarray