polakowo/vectorbt

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Trading research at scale.

VectorBT is a Python toolkit for trading research. Explore market data, build indicators and signals, and compare thousands of ideas across assets and timeframes. NumPy arrays, Numba acceleration, and an optional Rust engine let you run large experiments in a few lines of Python.

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Move from exploring price patterns to evaluating strategies in one workflow. Inspect trades, fees, drawdowns, and portfolio performance, then explore the results with interactive charts. Built for traders, researchers, and AI agents, VectorBT brings data analysis, strategy simulation, and visualization together in a composable Python API.

VectorBT is the open-source community edition of VectorBT PRO. PRO extends the library with parallelization, additional data integrations, portfolio optimization, pattern recognition, event projections, limit orders, leverage, and over 100 other features, with new features added weekly.

See VectorBT vs PRO or browse PRO feature examples.

Features

Fast, vectorized backtesting Research strategies at scale with pandas, NumPy, and Numba. Backtesting examples →	Optional Rust engine Use precompiled kernels for supported operations without JIT compilation. Rust engine →
Pandas-native API Work with custom accessors and high-performance operations directly on pandas objects. Pandas integration →	Flexible broadcasting Analyze multiple assets and sweep large parameter grids with flexible array broadcasting. Broadcasting API →
Rich indicator ecosystem Build custom indicators and use integrations for TA-Lib, Pandas TA, and more. Indicator features →	Portfolio backtesting Analyze trades, drawdowns, and performance, with QuantStats integration. Portfolio analysis →
Signal tooling Generate, rank, map, and analyze the distribution of trading signals. Signal features →	Built-in data access Fetch market data, preprocess it, and generate synthetic time series. Data features →
Robustness testing Run walk-forward optimization and generate labels for machine learning workflows. Robustness testing →	Interactive visualization Explore results with Plotly charts, Jupyter widgets, and browser dashboards. Plotting features →
Automation tools Schedule data updates and send Telegram notifications. Scheduling and notifications →	Composable Python API Combine building blocks for rapid experimentation and AI agent-driven workflows. Python workflows →

Installation

pip install -U vectorbt

To install the optional Rust engine:

pip install -U "vectorbt[rust]"

To install all optional integrations (TA-Lib, Pandas TA, etc.):

pip install -U "vectorbt[full]"

To install all optional integrations together with the Rust engine:

pip install -U "vectorbt[full,rust]"

Examples

Invest $100 in Bitcoin since 2014

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())

Output:

19501.10906763755

Trade a dual-SMA crossover strategy

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())

Output:

34417.80960086067

[!TIP] VectorBT PRO

Simulate limit orders, use leverage, and model futures with contract multipliers.

Generate 1,000 random strategies

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()

Output:

Test 10,000 dual-SMA window combinations

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()

Output:

[!TIP] VectorBT PRO

Use random search to sample a large parameter grid, or conditional parameters to exclude combinations such as a fast MA window longer than the slow MA window. The cross-validation tutorial shows how to select parameters on training data and evaluate them on later periods.

Inspect any strategy configuration

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

Output:

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

Plot any strategy configuration

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

Output:

Animate Bollinger Bands across multiple symbols

VectorBT goes beyond backtesting, with tools for financial data analysis and visualization:

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, writer_kwargs=dict(loop=0))

Output:

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

Visit the website for more examples, documentation, and guides.

Example apps

Candlestick Patterns

Explore candlestick patterns interactively and backtest their signals with VectorBT.

Links

• Website
• Docker images
• Colab notebook

License

This work is fair-code distributed under the Apache 2.0 with Commons Clause license.

The source code is publicly available, 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.

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

Contributors

Thanks to everyone who has contributed to VectorBT!

Star History

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 affiliates assume no responsibility for your trading results.