import qnt.data as qndata
import qnt.stats as qnstats
import qnt.forward_looking as qnfl
import time
from qnt.neutralization import neutralize
import datetime as dt
data = qndata.load_data(tail=dt.timedelta(days=365 * 3),
forward_order=True,
dims=("time", "field", "asset"))
assets = qndata.load_assets()
print(qnstats.calc_avg_points_per_year(data))
output = data.sel(field=qndata.f.IS_LIQUID)
output = qndata.sort_and_crop_output(output)
output = neutralize(output, assets, 'industry')
output *= 1
print(output.to_pandas())
print(output[0, 0].item())
print(qnstats.calc_slippage(data).to_pandas()[13:])
stat2 = qnstats.calc_stat(data, output, slippage_factor=0.05, per_asset=True)
# ss = qnstats.calc_stat(data, output, max_periods=252 * 3, slippage_factor=0.05, per_asset=True)
print(
stat2.sel(field=[ # qnstats.stf.RELATIVE_RETURN,
v = v * volume
s = np.nancumsum(v)
s[np.isnan(v)] = np.nan
return s
def obv(close: nda.NdType, volume: nda.NdType) -> nda.NdType:
return nda.nd_universal_adapter(obv_np_1d, (close, volume), ())
if __name__ == '__main__':
from qnt.data import load_data, load_assets, ds
from qnt.xr_talib import OBV
import time
assets = load_assets()
ids = [i['id'] for i in assets[0:2000]]
data = load_data(assets=ids,
dims=(ds.TIME, ds.ASSET, ds.FIELD),
forward_order=True)
t1 = time.time()
obv1 = OBV(data)
t2 = time.time()
obv2 = obv(data.sel(field='close'), data.sel(field='vol'))
t3 = time.time()
obv3 = obv(
data.sel(field='close').to_pandas(),
data.sel(field='vol').to_pandas())
t4 = time.time()
print("init")
def step(self, data):
assets = data.sel(**{ds.FIELD: f.OPEN})
assets = assets.where(
data.sel(**{ds.FIELD: f.IS_LIQUID}) > 0) # liquidity check
assets = assets.isel(**{ds.TIME: 0})
assets = assets.dropna(ds.ASSET)
assets = assets.coords[ds.ASSET]
pct = 1. / max(len(assets), 1)
return xr.DataArray(np.full([len(assets)], pct, dtype=np.float64),
dims=[ds.ASSET],
coords={ds.ASSET: assets})
assets = load_assets(min_date='2018-01-01')
print(len(assets))
ids = ["NASDAQ:AAPL", "NASDAQ:AMZN", "NASDAQ:FB", "NASDAQ:GOOG"]
data = load_data(assets=ids,
min_date='2015-01-01',
dims=(ds.TIME, ds.ASSET, ds.FIELD))
output = test_strategy(data, SimpleStrategy())
write_output(output)
RRo = calc_relative_return(data, output)
print(RRo.mean())
RRn = calc_relative_return(data, output)
