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:sparkles: Usage

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

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
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
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()

rand_scatter.svg

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()
<img width="650" src="https://raw.githubusercontent.com/polakowo/vectorbt/master/docs/docs/assets/images/usage_dmac_heatmap.gif">

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()

dmac_portfolio.svg

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

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]
<img width="750" src="https://raw.githubusercontent.com/polakowo/vectorbt/master/docs/docs/assets/images/usage_bbands.gif">

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

Installation

pip install -U vectorbt

To also install optional dependencies:

pip install -U "vectorbt[full]"

Colab Notebook

Google Colaboratory

License

This work is fair-code distributed under Apache 2.0 with Commons Clause license. The source code is open and everyone (individuals and organizations) can use it for free. However, it is not allowed to sell products and services that are mostly just this software.

If you have any questions about this or want to apply for a license exception, please contact the author.

Installing optional dependencies may be subject to a more restrictive license.

Star History

Star History Chart

Disclaimer

This software is for educational purposes only. Do not risk money which you are afraid to lose. USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.