Technical Analysis using trix, ema

Predicting stocks using technical indicators (trix, ema)

You can clone and edit this example there (tab Examples).

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This template shows you the basic steps for taking part to the NASDAQ-100 Stock Long-Short contest.

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from IPython.display import display
import xarray as xr
import qnt.data as qndata
import qnt.output as qnout
import qnt.ta as qnta
import qnt.stats as qns


def multi_trix_v3(data, params):
    s_ = qnta.trix(data.sel(field='high'), params[0])
    w_1 = s_.shift(time=params[1]) > s_.shift(time=params[2])
    w_2 = s_.shift(time=params[3]) > s_.shift(time=params[4])
    weights = (w_1 * w_2) * data.sel(field="is_liquid")
    return weights.fillna(0)


def multi_ema_v3(data, params):
    s_ = qnta.ema(data.sel(field='high'), params[0])
    w_1 = s_.shift(time=params[1]) > s_.shift(time=params[2])
    w_2 = s_.shift(time=params[3]) > s_.shift(time=params[4])
    weights = (w_1 * w_2) * data.sel(field="is_liquid")
    return weights.fillna(0)


def multi_ema_v4(data, params):
    s_ = qnta.trix(data.sel(field='high'), 30)
    w_1 = s_.shift(time=params[0]) > s_.shift(time=params[1])
    s_ = qnta.ema(data.sel(field='high'), params[2])
    w_2 = s_.shift(time=params[3]) > s_.shift(time=params[4])
    weights = (w_1 * w_2) * data.sel(field="is_liquid")
    return weights.fillna(0)


data = qndata.stocks.load_ndx_data(min_date="2005-01-01")

weights_1 = multi_trix_v3(data, [87, 135, 108, 13, 114])
weights_2 = multi_trix_v3(data, [89, 8, 101, 148, 36])
weights_3 = multi_trix_v3(data, [196, 125, 76, 12, 192])
weights_4 = multi_ema_v3(data, [69, 47, 57, 7, 41])

weights_f = (weights_1 + weights_2) * weights_3 * weights_4

weights_5 = multi_trix_v3(data, [89, 139, 22, 8, 112])
weights_6 = multi_trix_v3(data, [92, 139, 20, 10, 110])
weights_7 = multi_ema_v4(data, [13, 134, 42, 66, 133])

weights_t = (weights_5 + weights_6) * weights_7 + weights_3

weights_all = 4 * weights_f + weights_t

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def get_enough_bid_for(weights_):
    time_traded = weights_.time[abs(weights_).fillna(0).sum('asset') > 0]
    is_strategy_traded = len(time_traded)
    if is_strategy_traded:
        return xr.where(weights_.time < time_traded.min(), data.sel(field="is_liquid"), weights_)
    return weights_


weights_new = get_enough_bid_for(weights_all)
weights_new = weights_new.sel(time=slice("2006-01-01",None))

weights = qnout.clean(output=weights_new, data=data, kind="stocks_nasdaq100")

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def print_statistic(data, weights_all):
    import qnt.stats as qnstats

    stats = qnstats.calc_stat(data, weights_all)
    display(stats.to_pandas().tail(5))
    # graph
    performance = stats.to_pandas()["equity"]
    import qnt.graph as qngraph

    qngraph.make_plot_filled(performance.index, performance, name="PnL (Equity)", type="log")

print_statistic(data, weights)

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weights = weights.sel(time=slice("2006-01-01",None))

qnout.check(weights, data, "stocks_nasdaq100")
qnout.write(weights) # to participate in the competition

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weights

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data