Usage ¶

vectorbt allows you to easily backtest strategies with a couple of lines of Python code.

Here is how much profit we would have made if we invested $100 into Bitcoin in 2014:

import vectorbt as vbt

price = vbt.YFData.download('BTC-USD').get('Close')

pf = vbt.Portfolio.from_holding(price, init_cash=100)
pf.total_profit()

8961.008555963961

Buy whenever 10-day SMA crosses above 50-day SMA and sell when opposite:

fast_ma = vbt.MA.run(price, 10)
slow_ma = vbt.MA.run(price, 50)
entries = fast_ma.ma_crossed_above(slow_ma)
exits = fast_ma.ma_crossed_below(slow_ma)

pf = vbt.Portfolio.from_signals(price, entries, exits, init_cash=100)
pf.total_profit()

16423.251963801864

Generate 1,000 strategies with random signals and test them on BTC and ETH:

import numpy as np

symbols = ["BTC-USD", "ETH-USD"]
price = vbt.YFData.download(symbols, missing_index='drop').get('Close')

n = np.random.randint(10, 101, size=1000).tolist()
pf = vbt.Portfolio.from_random_signals(price, n=n, init_cash=100, seed=42)

mean_expectancy = pf.trades.expectancy().groupby(['randnx_n', 'symbol']).mean()
fig = mean_expectancy.unstack().vbt.scatterplot(xaxis_title='randnx_n', yaxis_title='mean_expectancy')
fig.show()

For fans of hyperparameter optimization: here is a snippet for testing 10,000 window combinations of a dual SMA crossover strategy on BTC, USD, and LTC:

symbols = ["BTC-USD", "ETH-USD", "LTC-USD"]
price = vbt.YFData.download(symbols, missing_index='drop').get('Close')

windows = np.arange(2, 101)
fast_ma, slow_ma = vbt.MA.run_combs(price, window=windows, r=2, short_names=['fast', 'slow'])
entries = fast_ma.ma_crossed_above(slow_ma)
exits = fast_ma.ma_crossed_below(slow_ma)

pf_kwargs = dict(size=np.inf, fees=0.001, freq='1D')
pf = vbt.Portfolio.from_signals(price, entries, exits, **pf_kwargs)

fig = pf.total_return().vbt.heatmap(
    x_level='fast_window', y_level='slow_window', slider_level='symbol', symmetric=True,
    trace_kwargs=dict(colorbar=dict(title='Total return', tickformat='%')))
fig.show()

Digging into each strategy configuration is as simple as indexing with pandas:

pf[(10, 20, 'ETH-USD')].stats()

Start                          2015-08-07 00:00:00+00:00
End                            2021-08-01 00:00:00+00:00
Period                                2183 days 00:00:00
Start Value                                        100.0
End Value                                  620402.791485
Total Return [%]                           620302.791485
Benchmark Return [%]                        92987.961948
Max Gross Exposure [%]                             100.0
Total Fees Paid                             10991.676981
Max Drawdown [%]                               70.734951
Max Drawdown Duration                  760 days 00:00:00
Total Trades                                          54
Total Closed Trades                                   53
Total Open Trades                                      1
Open Trade PnL                              67287.940601
Win Rate [%]                                   52.830189
Best Trade [%]                               1075.803607
Worst Trade [%]                               -29.593414
Avg Winning Trade [%]                          95.695343
Avg Losing Trade [%]                          -11.890246
Avg Winning Trade Duration    35 days 23:08:34.285714286
Avg Losing Trade Duration                8 days 00:00:00
Profit Factor                                   2.651143
Expectancy                                   10434.24247
Sharpe Ratio                                    2.041211
Calmar Ratio                                      4.6747
Omega Ratio                                     1.547013
Sortino Ratio                                   3.519894
Name: (10, 20, ETH-USD), dtype: object

The same for plotting:

pf[(10, 20, 'ETH-USD')].plot().show()

It's not all about backtesting - vectorbt can be used to facilitate financial data analysis and visualization.

Let's generate a GIF that animates the %B and bandwidth of Bollinger Bands for different symbols:

symbols = ["BTC-USD", "ETH-USD", "ADA-USD"]
price = vbt.YFData.download(symbols, period='6mo', missing_index='drop').get('Close')
bbands = vbt.BBANDS.run(price)

def plot(index, bbands):
    bbands = bbands.loc[index]
    fig = vbt.make_subplots(
        rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.15,
        subplot_titles=('%B', 'Bandwidth'))
    fig.update_layout(template='vbt_dark', showlegend=False, width=750, height=400)
    bbands.percent_b.vbt.ts_heatmap(
        trace_kwargs=dict(zmin=0, zmid=0.5, zmax=1, colorscale='Spectral', colorbar=dict(
            y=(fig.layout.yaxis.domain[0] + fig.layout.yaxis.domain[1]) / 2, len=0.5
        )), add_trace_kwargs=dict(row=1, col=1), fig=fig)
    bbands.bandwidth.vbt.ts_heatmap(
        trace_kwargs=dict(colorbar=dict(
            y=(fig.layout.yaxis2.domain[0] + fig.layout.yaxis2.domain[1]) / 2, len=0.5
        )), add_trace_kwargs=dict(row=2, col=1), fig=fig)
    return fig

vbt.save_animation('bbands.gif', bbands.wrapper.index, plot, bbands, delta=90, step=3, fps=3)

100%|██████████| 31/31 [00:21<00:00,  1.21it/s]

And this is just the tip of the iceberg of what's possible. Check out Resources to learn more.