vectorbt 0.28.4
Python library for backtesting and analyzing trading strategies at scale
Requires Python: >=3.10

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> [!TIP]
> *New in 0.28*:
>
> * Plotly 6 support
> * `ticker_kwargs` in `YFData`
> * Fixed Pandas TA dependency (→ [pandas-ta-classic](https://github.com/xgboosted/pandas-ta-classic)).

## :package: Installation

```sh
pip install -U vectorbt
```

To install optional dependencies as well:

```sh
pip install -U "vectorbt[full]"
```

## :sparkles: Usage

VectorBT lets you backtest strategies in just a few lines of Python.

* Profit from investing $100 in Bitcoin since 2014:

```python
import vectorbt as vbt

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

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

```plaintext
19501.10906763755
```

* Buy when the 10-day SMA crosses above the 50-day SMA, and sell on the opposite crossover:

```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)
print(pf.total_profit())
```

```plaintext
34417.80960086067
```

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

```python
import numpy as np

symbols = ["BTC-USD", "ETH-USD"]
data = vbt.YFData.download(symbols, missing_index="drop")
price = data.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()
```

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

* For hyperparameter optimization fans: test 10,000 window combinations of a dual-SMA crossover strategy on BTC, ETH, and XRP:

```python
symbols = ["BTC-USD", "ETH-USD", "XRP-USD"]
data = vbt.YFData.download(symbols, missing_index="drop")
price = data.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 = vbt.Portfolio.from_signals(price, entries, exits, size=np.inf, fees=0.001, freq="1D")

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="750" src="https://raw.githubusercontent.com/polakowo/vectorbt/master/docs/docs/assets/images/usage_dmac_heatmap.gif">

Inspect any strategy configuration by indexing with pandas:

```python
print(pf[(10, 20, "ETH-USD")].stats())
```

```plaintext
Start                          2017-11-09 00:00:00+00:00
End                            2026-01-03 00:00:00+00:00
Period                                2978 days 00:00:00
Start Value                                        100.0
End Value                                    1604.093789
Total Return [%]                             1504.093789
Benchmark Return [%]                          866.094127
Max Gross Exposure [%]                             100.0
Total Fees Paid                               204.226289
Max Drawdown [%]                               70.734951
Max Drawdown Duration                 1095 days 00:00:00
Total Trades                                          81
Total Closed Trades                                   80
Total Open Trades                                      1
Open Trade PnL                                -14.232533
Win Rate [%]                                       41.25
Best Trade [%]                                120.511071
Worst Trade [%]                               -27.772271
Avg Winning Trade [%]                          27.265519
Avg Losing Trade [%]                           -9.022864
Avg Winning Trade Duration    32 days 20:21:49.090909091
Avg Losing Trade Duration      8 days 16:51:03.829787234
Profit Factor                                   1.275515
Expectancy                                     18.979079
Sharpe Ratio                                    0.861945
Calmar Ratio                                    0.572758
Omega Ratio                                      1.20277
Sortino Ratio                                   1.301377
Name: (10, 20, ETH-USD), dtype: object
```

Same goes for plotting:

```python
pf[(10, 20, "ETH-USD")].plot().show()
```

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

It's not all about backtesting! VectorBT can also help with financial data analysis and visualization.

* Create a GIF that animates Bollinger Bands %B and bandwidth across multiple symbols:

```python
symbols = ["BTC-USD", "ETH-USD", "XRP-USD"]
data = vbt.YFData.download(symbols, period="6mo", missing_index="drop")
price = data.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(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]
```

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

This is just the tip of the iceberg. Visit the [website](https://vectorbt.dev/) to learn more.

## :link: Links

* [Website](https://vectorbt.dev/)
* [Colab Notebook](https://colab.research.google.com/drive/1ibqyrf6LPFlzRb6mkPpl3hxqL6ryNBXI?usp=sharing)

## :balance_scale: License

This work is [fair-code](http://faircode.io/) distributed under the [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) may use it for free. However, you may not sell products or services that are primarily this software.

If you have questions or want to request a license exception, please [contact the author](mailto:olegpolakow@vectorbt.pro).

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

## :star: Star History

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

## :warning: Disclaimer

This software is for educational purposes only. Do not risk money you cannot afford to lose.

USE THE SOFTWARE AT YOUR OWN RISK. THE AUTHORS AND ALL AFFILIATES ASSUME NO RESPONSIBILITY FOR YOUR TRADING RESULTS.