strategy

Technical Analysis using atr, lwma

Predicting stocks using technical indicators (atr, lwma)

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]:
import xarray as xr

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

import qnt.ta as qnta
import qnt.backtester as qnbt
import qnt.data as qndata


def strategy2(data, wma, limit):
    vol = data.sel(field="vol")
    liq = data.sel(field="is_liquid")
    close = data.sel(field="close")
    high = data.sel(field="high")
    low = data.sel(field="low")

    atr = qnta.atr(high=high, low=low, close=close, ma=14)
    ratio = atr / close
    weights = xr.where(ratio > limit, 0, 1)

    money_vol = vol * liq * close
    total_money_vol = money_vol.sum(dim='asset')
    money_vol_share = money_vol / total_money_vol

    return qnta.lwma(money_vol_share, wma) * weights


data = qndata.stocks.load_ndx_data(min_date="2005-01-01")
weights_1 = strategy2(data, wma=135, limit=0.0205)
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Data loaded 9s
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_1)
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...
WARNING! Strategy trades non-liquid assets.
Fix 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
2024-06-25 2.576978 0.001575 0.049049 0.000000 -0.102797 1.073198 0.052639 1.0 183.0 0.026691 22.302122
2024-06-26 2.579232 0.000875 0.049044 0.000000 -0.102797 1.074087 0.052677 1.0 183.0 0.026687 22.302324
2024-06-27 2.581096 0.000723 0.049039 0.000000 -0.102797 1.074803 0.052707 1.0 184.0 0.026693 22.294776
2024-06-28 2.576744 -0.001686 0.049036 -0.001686 -0.102797 1.072676 0.052599 1.0 184.0 0.026691 22.297563
2024-07-01 2.577802 0.000410 0.049030 -0.001276 -0.102797 1.073032 0.052611 1.0 184.0 0.026687 22.396060
In [4]:
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 - 2024-07-01
Sharpe Ratio = 1.0730315678352975
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: 4654, asset: 249)>
array([[0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,
        0.        ],
       ...,
       [0.        , 0.        , 0.00387869, ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.00385684, ..., 0.        , 0.        ,
        0.        ],
       [0.        , 0.        , 0.00383693, ..., 0.        , 0.        ,
        0.        ]])
Coordinates:
  * time     (time) datetime64[ns] 2006-01-03 2006-01-04 ... 2024-07-01
  * asset    (asset) <U10 'NAS:AAL' 'NAS:AAPL' ... 'NYS:RHT' 'NYS:TEVA'
In [6]:
data
Out[6]:
<xarray.DataArray 'stocks_nasdaq100' (field: 9, time: 4906, asset: 249)>
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  ],
        ...,
        [ 11.12  , 214.69  , 150.46  , ...,  41.02  ,      nan,
          16.5   ],
        [ 11.24  , 215.77  , 151.54  , ...,  41.13  ,      nan,
          16.38  ],
        [ 11.33  , 212.09  , 152.2   , ...,  40.79  ,      nan,
          16.37  ]],

       [[     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 ... 2024-07-01
  * asset    (asset) <U10 'NAS:AAL' 'NAS:AAPL' ... 'NYS:RHT' 'NYS:TEVA'
  * field    (field) object 'open' 'low' 'high' ... 'split_cumprod' 'is_liquid'