黄金矿工

1
# Define hyper-parameter space
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# 49 fast x 49 slow x 19 signal
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fast_windows, slow_windows, signal_windows = vbt.utils.params.create_param_combs(
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    (product, (combinations, np.arange(2, 51, 1), 2), np.arange(2, 21, 1)))
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pd.DataFrame({"fast_windows":fast_windows, "slow_windows":slow_windows, "signal_windows":signal_windows})
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Out[6]:
8
       fast_windows  slow_windows  signal_windows
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0                 2             3               2
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1                 2             3               3
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2                 2             3               4
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3                 2             3               5
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4                 2             3               6
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...             ...           ...             ...
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22339            49            50              16
16
22340            49            50              17
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22341            49            50              18
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22342            49            50              19
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22343            49            50              20

1
# Run MACD indicator
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macd_ind = vbt.MACD.run(
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    price,
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    fast_window=fast_windows,
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    slow_window=slow_windows,
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    signal_window=signal_windows
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)
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# Long when MACD is above zero AND signal
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entries = macd_ind.macd_above(0) & macd_ind.macd_above(macd_ind.signal)
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# Short when MACD is below zero OR signal
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exits = macd_ind.macd_below(0) | macd_ind.macd_below(macd_ind.signal)
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exits
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Out[16]:
17
macd_fast_window       2                        ...     49
18
macd_slow_window       3                        ...     50
19
macd_signal_window     2                    3   ...     19     20
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split_idx               0      1      2      0  ...      2      0      1      2
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0                   False  False  False  False  ...  False  False  False  False
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1                   False  False  False  False  ...  False  False  False  False
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2                   False  False  False  False  ...  False  False  False  False
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3                    True   True   True   True  ...  False  False  False  False
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4                    True  False   True   True  ...  False  False  False  False
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5                    True  False   True   True  ...  False  False  False  False
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6                    True   True  False   True  ...  False  False  False  False
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7                   False  False  False  False  ...  False  False  False  False
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8                   False   True   True  False  ...  False  False  False  False
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9                    True  False   True   True  ...  False  False  False  False

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# Build portfolio
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pf = vbt.Portfolio.from_signals(
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    price.vbt.tile(len(fast_windows)), entries, exits, fees=0.001, freq='1D')

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price.shape
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Out[26]: (16, 3)
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len(fast_windows)
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Out[27]: 22344
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entries.shape
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Out[28]: (16, 67032)

1
Out[21]:price.head()
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split_idx         0         1         2
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0          0.374540  0.304242  0.065052
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1          0.950714  0.524756  0.948886
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2          0.731994  0.431945  0.965632
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3          0.598658  0.291229  0.808397
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4          0.156019  0.611853  0.304614
8

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price.vbt.tile(2)
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Out[22]:
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split_idx         0         1         2         0         1         2
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0          0.374540  0.304242  0.065052  0.374540  0.304242  0.065052
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1          0.950714  0.524756  0.948886  0.950714  0.524756  0.948886
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2          0.731994  0.431945  0.965632  0.731994  0.431945  0.965632
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3          0.598658  0.291229  0.808397  0.598658  0.291229  0.808397
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4          0.156019  0.611853  0.304614  0.156019  0.611853  0.304614
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5          0.155995  0.139494  0.097672  0.155995  0.139494  0.097672

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# Draw all window combinations as a 3D volume
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fig = pf.total_return().vbt.volume(
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    x_level='macd_fast_window',
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    y_level='macd_slow_window',
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    z_level='macd_signal_window',
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    slider_level='split_idx',
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    trace_kwargs=dict(
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        colorbar=dict(
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            title='Total return',
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            tickformat='%'
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        )
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    )
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)
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fig.show()