weights = data.isel(time=0, field=0)
weights[:] = 0
def step(data):
latest = data.isel(time=-1)
is_liquid = latest.sel(field="is_liquid")
sroc = latest.sel(field='sroc')
k = latest.sel(field='k')
d = latest.sel(field='d')
need_open = xrf.logical_and(sroc > 0.05, xrf.logical_and(k < 31, d < 31))
need_close = xrf.logical_or(xrf.logical_or(sroc < -0.05, is_liquid == 0),
xrf.logical_and(k > 92, d > 92))
global weights
weights.loc[need_open] = 1
weights.loc[need_close] = 0
return (weights / weights.sum('asset')).fillna(0)
output = test_strategy(data_ext, step=step, init_data_length=200)
stat = qnstats.calc_stat(data, output, max_periods=252 * 3)
print(stat.to_pandas())
qndata.write_output(output)
assets = load_assets()
print(len(assets))
ids = [i['id'] for i in assets]
print(ids)
data = load_data(assets=ids,
min_date='2015-01-01',
dims=(ds.TIME, ds.ASSET, ds.FIELD))
# print(data.sel(asset='AMEX:AIRI').dropna('time').to_pandas().to_csv())
output = test_strategy(data,
init=init,
step=step,
init_data_length=init_data_length)
write_output(output)
# output = output.loc[:'2019-01-05']
# print(len(find_missed_dates(output, data)))
pd.set_option('display.max_colwidth', -1)
stat1 = calc_stat(data, output)
stat2 = calc_stat(data, output[::-1])
print(
xr.concat(
[stat1.sel(field='sharpe_ratio'),
stat2.sel(field='sharpe_ratio')], pd.Index(['f', 'b'],
name='d')).to_pandas())
stat1.to_pandas().to_csv("stat.csv")