def strategy(data):
# calc weights:
close = data.sel(field="close")
is_liquid = data.sel(field='is_liquid')
ma_slow = qnta.lwma(close, 50)
ma_fast = qnta.lwma(close, 10)
return xr.where(ma_fast > ma_slow, 1, 0) * is_liquid
def strategy(data):
close = data['futures'].sel(field="close")
currency = data['currency']
ma1 = qnta.lwma(currency,10)
ma2 = qnta.lwma(currency,50)
ma3 = qnta.lwma(currency,250)
if ma1.isel(time=-1) > ma2.isel(time=-1) and ma2.isel(time=-1) < ma3.isel(time=-1):
return xr.ones_like(close.isel(time=-1))
else:
return xr.zeros_like(close.isel(time=-1))
def get_features(data):
trend = qnta.roc(qnta.lwma(data.sel(field="close"), 90), 1)
k, d = qnta.stochastic(data.sel(field="high"), data.sel(field="low"),
data.sel(field="close"), 21)
volatility = qnta.tr(data.sel(field="high"), data.sel(field="low"),
data.sel(field="close"))
volatility = volatility / data.sel(field="close")
volatility = qnta.lwma(volatility, 21)
volume = data.sel(field="vol")
volume = qnta.sma(volume, 7) / qnta.sma(volume, 80)
volume = volume.where(np.isfinite(volume), 0)
# combine features to one array
result = xr.concat([trend, d, volatility, volume],
pd.Index(
['trend', 'stochastic_d', 'volatility', 'volume'],
name='field'))
return result.transpose('time', 'field', 'asset')
def strategy_long(data, asset=None, ma_period=150):
# filter by asset, we will need it for further optimization
if asset is not None:
data = data.sel(asset=[asset])
close = data.sel(field='close')
ma = qnta.lwma(close, ma_period)
ma_roc = qnta.roc(ma, 1)
# define signal
buy_signal = ma_roc > 0
buy_stop_signal = ma_roc < 0
# transform signals to positions
position = xr.where(buy_signal, 1, np.nan)
position = xr.where(buy_stop_signal, 0, position)
position = position.ffill('time').fillna(0)
# clean the output (not necessary)
# with qnlog.Settings(info=False,err=False): # suppress logging
# position = qnout.clean(position, data)
return position
import qnt.ta as qnta
import qnt.data as qndata
data_all = qndata.futures.load_data(tail=120)
close_all = data_all.sel(field='close')
data = qndata.futures.load_data(assets=['F_GC', 'F_DX'], tail=120)
high = data.sel(field='close')
low = data.sel(field='low')
close = data.sel(field='close')
volume = data.sel(field='vol')
print("Moving averages:")
sma = qnta.sma(close, 20)
ema = qnta.ema(close, 20)
lwma = qnta.lwma(close, 20)
wma = qnta.wma(close, [3, 2, 1])
wi_ma = qnta.wilder_ma(close, 20)
vwma = qnta.vwma(close, volume, 20)
print("SMA(20)")
print(sma.to_pandas().tail().T)
print("---")
print("Oscillators:")
stoch_k = qnta.stochastic_k(high, low, close, 14)
stoch_fast_k, stoch_fast_d = qnta.stochastic(high, low, close, 14)
stoch_slow_k, stoch_slow_d = qnta.stochastic(high, low, close, 14)
rsi = qnta.rsi(close, 14)
roc = qnta.roc(close, 7)
sroc = qnta.sroc(close, 13, 21)