Test thousands of trading ideas in seconds, analyze portfolios across markets and timeframes, and uncover what works with minimal code. Built for both human researchers and AI agents, VectorBT combines rapid experimentation with a mature, battle-tested backtesting stack shaped by years of community use.
VectorBT is the open-source, community edition of VectorBT PRO, a state-of-the-art hybrid backtesting library.
- Fast, vectorized backtesting and strategy research on pandas/NumPy, accelerated with Numba
- Pandas-native API with custom accessors and high-performance operations
- Flexible broadcasting for multi-asset analysis and large parameter sweeps
- Rich indicator ecosystem with support for custom indicators and popular TA libraries (TA-Lib, Pandas TA, etc.)
- Portfolio backtesting with trades, positions, drawdowns, and performance analysis (incl. QuantStats)
- Signal-based tooling for generation, ranking, mapping, and distribution analysis
- Built-in data access (Yahoo Finance, CCXT, Alpaca, etc.), preprocessing, and random data generation
- Robustness testing, walk-forward optimization, and label generation for ML workflows
- Interactive visualization with Plotly, Jupyter widgets, and browser-friendly dashboards
- Automation support for scheduled updates and Telegram notifications
- Composable Python API suitable for rapid experimentation and AI agent-driven workflows
pip install -U vectorbtTo install optional dependencies as well:
pip install -U "vectorbt[full]"VectorBT lets you backtest strategies in just a few lines of Python.
- Profit from investing $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())19501.10906763755
- Buy when the 10-day SMA crosses above the 50-day SMA, and sell on the opposite crossover:
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())34417.80960086067
- Generate 1,000 strategies with random signals and test them on BTC and ETH:
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()
- For hyperparameter optimization fans: test 10,000 window combinations of a dual-SMA crossover strategy on BTC, ETH, and XRP:
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()
Inspect any strategy configuration by indexing with pandas:
print(pf[(10, 20, "ETH-USD")].stats())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:
pf[(10, 20, "ETH-USD")].plot().show()
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:
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)100%|██████████| 31/31 [00:21<00:00, 1.21it/s]
This is just the tip of the iceberg. Visit the website to learn more.
Candlestick Patterns (here)
Explore candlestick-pattern signals interactively and backtest them with VectorBT.
This work is fair-code distributed under the Apache 2.0 with Commons Clause 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.
Installing optional dependencies may be subject to a more restrictive license.
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.
