strategy

Technical Analysis using trix, ema¶

Predicting stocks using technical indicators (trix, ema)

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

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

In [1]:

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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In [2]:

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")

Output cleaning...
fix uniq
ffill if the current price is None...
Check liquidity...
Ok.
Check missed dates...
Ok.
Normalization...
Output cleaning is complete.

In [3]:

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)

field	equity	relative_return	volatility	underwater	max_drawdown	sharpe_ratio	mean_return	bias	instruments	avg_turnover	avg_holding_time
time
2024-09-04	59.314591	0.000717	0.241497	-0.150600	-0.378042	1.013586	0.244778	1.0	218.0	0.168097	11.680150
2024-09-05	59.090863	-0.003772	0.241474	-0.153804	-0.378042	1.012399	0.244468	1.0	218.0	0.168062	11.680150
2024-09-06	58.766421	-0.005491	0.241453	-0.158450	-0.378042	1.010727	0.244043	1.0	218.0	0.168028	11.680150
2024-09-09	59.129089	0.006171	0.241430	-0.153257	-0.378042	1.012282	0.244395	1.0	218.0	0.167995	11.680150
2024-09-10	59.084880	-0.000748	0.241405	-0.153890	-0.378042	1.011942	0.244287	1.0	218.0	0.167980	11.694732

In [4]:

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

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

Check liquidity...
Ok.
Check missed dates...
Ok.
Check the sharpe ratio...
Period: 2006-01-01 - 2024-09-10
Sharpe Ratio = 1.0119415494402122
Ok.
Check correlation.

WARNING! Can't calculate correlation.
Correlation check failed.

Write output: /root/fractions.nc.gz

In [5]:

weights

Out[5]:

<xarray.DataArray (time: 4703, asset: 256)>
array([[0.        , 0.01123596, 0.        , ..., 0.        , 0.01123596,
        0.01123596],
       [0.        , 0.01190476, 0.        , ..., 0.        , 0.01190476,
        0.01190476],
       [0.        , 0.01190476, 0.        , ..., 0.        , 0.01190476,
        0.01190476],
       ...,
       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.34615385, 0.        , ..., 0.        , 0.        ,
        0.        ]])
Coordinates:
  * time     (time) datetime64[ns] 2006-01-03 2006-01-04 ... 2024-09-10
  * asset    (asset) <U10 'NAS:AAL' 'NAS:AAPL' ... 'NYS:RHT' 'NYS:TEVA'

In [6]:

data

Out[6]:

<xarray.DataArray 'stocks_nasdaq100' (field: 9, time: 4955, asset: 256)>
array([[[     nan,   1.1568,      nan, ...,  12.08  ,  13.41  ,
          29.81  ],
        [     nan,   1.1393,      nan, ...,  12.49  ,  13.05  ,
          29.2   ],
        [     nan,   1.1511,      nan, ...,  12.11  ,  12.6   ,
          27.97  ],
        ...,
        [ 10.78  , 223.95  , 116.37  , ...,  44.76  ,      nan,
          18.51  ],
        [ 10.85  , 220.82  , 115.11  , ...,  44.51  ,      nan,
          18.09  ],
        [ 11.21  , 218.92  , 117.01  , ...,  45.5   ,      nan,
          17.88  ]],

       [[     nan,   1.1179,      nan, ...,  11.9   ,  13.03  ,
          29.06  ],
        [     nan,   1.1245,      nan, ...,  11.97  ,  12.54  ,
          28.02  ],
        [     nan,   1.1438,      nan, ...,  11.97  ,  12.25  ,
          27.31  ],
...
        [  1.    ,   1.    ,   1.    , ...,   1.    ,      nan,
           1.    ],
        [  1.    ,   1.    ,   1.    , ...,   1.    ,      nan,
           1.    ],
        [  1.    ,   1.    ,   1.    , ...,   1.    ,      nan,
           1.    ]],

       [[     nan,   1.    ,      nan, ...,   0.    ,   0.    ,
           1.    ],
        [     nan,   1.    ,      nan, ...,   0.    ,   0.    ,
           1.    ],
        [     nan,   1.    ,      nan, ...,   0.    ,   0.    ,
           1.    ],
        ...,
        [  0.    ,   1.    ,   1.    , ...,   0.    ,      nan,
           0.    ],
        [  0.    ,   1.    ,   1.    , ...,   0.    ,      nan,
           0.    ],
        [  0.    ,   1.    ,   1.    , ...,   0.    ,      nan,
           0.    ]]])
Coordinates:
  * time     (time) datetime64[ns] 2005-01-03 2005-01-04 ... 2024-09-10
  * asset    (asset) <U10 'NAS:AAL' 'NAS:AAPL' ... 'NYS:RHT' 'NYS:TEVA'
  * field    (field) object 'open' 'low' 'high' ... 'split_cumprod' 'is_liquid'

xarray.DataArray

'stocks_nasdaq100'

field: 9
time: 4955
asset: 256

nan 1.157 nan 31.5 37.02 38.86 38.4 ... nan 0.0 0.0 0.0 0.0 nan 0.0

array([[[     nan,   1.1568,      nan, ...,  12.08  ,  13.41  ,
          29.81  ],
        [     nan,   1.1393,      nan, ...,  12.49  ,  13.05  ,
          29.2   ],
        [     nan,   1.1511,      nan, ...,  12.11  ,  12.6   ,
          27.97  ],
        ...,
        [ 10.78  , 223.95  , 116.37  , ...,  44.76  ,      nan,
          18.51  ],
        [ 10.85  , 220.82  , 115.11  , ...,  44.51  ,      nan,
          18.09  ],
        [ 11.21  , 218.92  , 117.01  , ...,  45.5   ,      nan,
          17.88  ]],

       [[     nan,   1.1179,      nan, ...,  11.9   ,  13.03  ,
          29.06  ],
        [     nan,   1.1245,      nan, ...,  11.97  ,  12.54  ,
          28.02  ],
        [     nan,   1.1438,      nan, ...,  11.97  ,  12.25  ,
          27.31  ],
...
        [  1.    ,   1.    ,   1.    , ...,   1.    ,      nan,
           1.    ],
        [  1.    ,   1.    ,   1.    , ...,   1.    ,      nan,
           1.    ],
        [  1.    ,   1.    ,   1.    , ...,   1.    ,      nan,
           1.    ]],

       [[     nan,   1.    ,      nan, ...,   0.    ,   0.    ,
           1.    ],
        [     nan,   1.    ,      nan, ...,   0.    ,   0.    ,
           1.    ],
        [     nan,   1.    ,      nan, ...,   0.    ,   0.    ,
           1.    ],
        ...,
        [  0.    ,   1.    ,   1.    , ...,   0.    ,      nan,
           0.    ],
        [  0.    ,   1.    ,   1.    , ...,   0.    ,      nan,
           0.    ],
        [  0.    ,   1.    ,   1.    , ...,   0.    ,      nan,
           0.    ]]])

Coordinates: (3)

time

(time)

datetime64[ns]

2005-01-03 ... 2024-09-10

array(['2005-01-03T00:00:00.000000000', '2005-01-04T00:00:00.000000000',
       '2005-01-05T00:00:00.000000000', ..., '2024-09-06T00:00:00.000000000',
       '2024-09-09T00:00:00.000000000', '2024-09-10T00:00:00.000000000'],
      dtype='datetime64[ns]')

asset

(asset)

<U10

'NAS:AAL' 'NAS:AAPL' ... 'NYS:TEVA'

array(['NAS:AAL', 'NAS:AAPL', 'NAS:ABNB', ..., 'NYS:QGEN', 'NYS:RHT',
       'NYS:TEVA'], dtype='<U10')

field

(field)

object

'open' 'low' ... 'is_liquid'

array(['open', 'low', 'high', 'close', 'vol', 'divs', 'split', 'split_cumprod',
       'is_liquid'], dtype=object)

Attributes: (0)