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(t2 - t1, t3 - t2, t4 - t3, "relative delta =",
(abs(obv1 - obv2) * 2 / (obv1 + obv2)).mean().values,
"t(talib)/t(this) =", (t2 - t1) / (t3 - t2))
import qnt.data as qndata
import qnt.stats as qnstats
import qnt.xr_talib as qnxrtalib
import xarray as xr
import pandas as pd
from qnt.stepper import test_strategy
import xarray.ufuncs as xrf
data = qndata.load_data(min_date="2010-01-01",
max_date=None,
dims=("time", "field", "asset"),
forward_order=True)
wma = qnxrtalib.WMA(data.sel(field='close'), 120)
sroc = qnxrtalib.ROCP(wma, 60)
stoch = qnxrtalib.STOCH(data, 8, 3, 3)
k = stoch.sel(field='slowk')
d = stoch.sel(field='slowd')
data_ext = xr.concat([wma, sroc, k, d],
pd.Index(['wma', 'sroc', 'k', 'd'], name='field'))
data_ext = xr.concat([data, data_ext], 'field')
weights = data.isel(time=0, field=0)
weights[:] = 0
def step(data):
latest = data.isel(time=-1)
macd_line = fast - slow
signal_line = signal_ma(macd_line)
hist = macd_line - signal_line
return macd_line, signal_line, hist
if __name__ == '__main__':
from qnt.data import load_data, load_assets, ds
from qnt.xr_talib import MACD
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).sel(field="close")
t1 = time.time()
macd1 = MACD(data, 12, 26, 9)
t2 = time.time()
macd2_line, macd2_signal, macd2_hist = macd(data, 12, 26, 9)
t3 = time.time()
macd3 = macd(data.to_pandas(), 12, 26, 9)
t4 = time.time()
print(t2 - t1, t3 - t2, t4 - t3, "relative delta =", (abs(
(macd1.sel(macd='hist') - macd2_hist) * 2 / macd2_hist)).mean().values,
"t(talib)/t(this) =", (t2 - t1) / (t3 - t2))
import matplotlib.pyplot as plt
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,
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})
init_data_length = 14 # data length for init
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)
import qnt.data as qndata # data loading and manipulation
import qnt.forward_looking as qnfl # forward looking checking
qndata.MAX_DATE_LIMIT = None
import datetime
data1 = qndata.load_data(
min_date="2015-01-01",
# max_date="2019-03-04", # You should not limit max_date for final calculations!
dims=("time", "field", "asset"),
forward_order=True)
last_date = datetime.datetime.now().date()
last_date = last_date - datetime.timedelta(days=182)
qndata.MAX_DATE_LIMIT = last_date
data2 = qndata.load_data(
min_date="2015-01-01",
# max_date="2019-03-04", # You should not limit max_date for final calculations!
dims=("time", "field", "asset"),
forward_order=True)
qndata.MAX_DATE_LIMIT = None
print(data1)
print(data2)
qnfl.check_forward_looking(
data2.sel(field='close').where(data2.sel(field='is_liquid') > 0),
data1.sel(field='close').where(data1.sel(field='is_liquid') > 0))
