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https://github.com/polakowo/vectorbt

Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.
https://github.com/polakowo/vectorbt

algorithmic-trading algorithmic-traiding backtesting cryptocurrency data-science data-visualization finance machine-learning portfolio-optimization quantitative-analysis quantitative-finance time-series trading trading-strategies

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Find your trading edge, using the fastest engine for backtesting, algorithmic trading, and research.

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## :sparkles: 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:



```python

import vectorbt as vbt



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



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

pf.total_profit()

```



```plaintext

8961.008555963961

```



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



```python

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()

```



```plaintext

16423.251963801864

```



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



```python

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](https://raw.githubusercontent.com/polakowo/vectorbt/master/docs/docs/assets/images/usage_rand_scatter.svg)



* 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:



```python

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:



```python

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

```



```plaintext

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:



```python

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

```



![dmac_portfolio.svg](https://raw.githubusercontent.com/polakowo/vectorbt/master/docs/docs/assets/images/usage_dmac_portfolio.svg)



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:



```python

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)

```



```plaintext

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 the [website](https://vectorbt.dev/) to learn more.



## Installation



```sh

pip install -U vectorbt

```



To also install optional dependencies:



```sh

pip install -U "vectorbt[full]"

```



## Colab Notebook



[Google Colaboratory](https://colab.research.google.com/drive/1ibqyrf6LPFlzRb6mkPpl3hxqL6ryNBXI?usp=sharing)



## License



This work is [fair-code](http://faircode.io/) distributed under [Apache 2.0 with Commons Clause](https://github.com/polakowo/vectorbt/blob/master/LICENSE.md) 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](mailto:[email protected]).



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



## Star History



[![Star History Chart](https://api.star-history.com/svg?repos=polakowo/vectorbt&type=Timeline)](https://star-history.com/#polakowo/vectorbt&Timeline)



## 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.