VectorBT PRO vs vectorbt: differences and migration

If you know the open-source vectorbt, what does VectorBT PRO add, what changes, and how do you move your code? This page compares vectorbt vs VectorBT PRO and shows a migration that reproduces the same backtest in both.

Short answer

VectorBT PRO takes the array-based research approach of vectorbt further: automate out-of-sample validation, simulate more detailed orders, scale searches across cores and machines, optimize portfolios, and work with AI coding assistants. It is the successor developed by the same author, with a redesigned API and a much broader set of research tools. The migration example below shows how to bring an existing strategy into PRO.

What stays familiar

Both editions represent each strategy configuration as a column of an array and run many of them at once. Both build on pandas accessors, broadcast inputs across assets and parameters, use an indicator factory with TA-Lib and other integrations, simulate portfolios from signals or orders with optional Numba callbacks, and analyze trades, drawdowns, and returns with Plotly charts. Your experience with vectorbt carries over, so you can build on familiar research patterns while adopting PRO's tools.

Why upgrade to VectorBT PRO?

PRO gives you more control over the backtest and more ways to develop, validate, and scale your research. Build on your existing indicator sweeps and signal strategies with:

Repeated research: run your own function over parameter combinations, divide the work into chunks, and resume completed tasks instead of starting a long search again.
More detailed simulation: add limit orders, scale out through stop ladders, or continue the same portfolio as new bars arrive.
Out-of-sample evaluation: select settings on training periods, test them on later periods, and compare results across splits and randomized baselines.
Help developing the code: use current examples, member support, and tools that let your coding assistant inspect the installed API and run a backtest.

You can bring your own data, indicators, and trading rules. Python remains the main high-level interface, and using PRO does not require learning Rust. The linked feature pages below show what each addition looks like in code.

What PRO adds

Area	VectorBT PRO adds
Data	More data sources, databases and file formats, and safe alignment across timeframes
Indicators and signals	Indicator expressions and more indicator libraries, and streaming indicators that update one bar at a time
Backtesting	Limit and stop-entry orders, stop ladders and time stops, leverage and contract multipliers, cash deposits, withdrawals, and dividends, and continuing a portfolio as new bars arrive
Analysis	Pattern search and event studies, MAE and MFE for every trade, and slicing and stacking or resampling whole portfolios
Strategy optimization and validation	Parameter sweeps over any function, walk-forward and purged cross-validation, and robustness tests
Portfolio optimization	Scheduled rebalancing and allocation functions, with external optimizers such as PyPortfolioOpt and Riskfolio-Lib
Performance	Chunking across threads, processes, and Ray, with resumable runs, and the `vectorbtpro-rust` crate for strategies written and run in Rust, without Python
Tooling and AI	Configuration files, a command-line interface, documentation search, an AI research assistant, and an MCP server for coding assistants

Cross-validation beyond basic splits

The community edition's rolling and expanding splitters divide arrays into windows. PRO adds a full cross-validation workflow: define rolling, expanding, calendar-based, purged, or custom splits, run your research function on them, select parameters on each training set, and evaluate the selected parameters on its test set. You can inspect the splits, apply purging and embargoing, and compare the resulting out-of-sample performance.

This turns a repeated process of splitting data, running searches, selecting settings, and collecting test results into a reusable workflow. See Walk-forward and cross-validation for worked examples.

PRO also includes the full source code, private documentation, tutorials, and cookbook recipes, and a Discord community for support and feature requests. The features overview covers these additions with examples.

Performance

PRO gives you several ways to scale a research workload: parallel kernels in Numba and Rust, chunked execution across threads, processes, and Ray, and backend selection from measured timings. Both editions support Numba and Rust kernels from Python. PRO builds on compiled calculations with tools for distributing work, controlling memory use, and reusing completed results. Beyond a Rust backend, PRO ships an entire native Rust crate, with simulators, steppers, and strategy callbacks that run in Rust programs without Python. How much faster a workload runs depends on its size and shape. The Benchmarks page shows measured results with their hardware and library versions.

Migrating code

The same strategy, a crossover of two exponential moving averages with a 10% trailing stop, in both editions on the same daily Bitcoin OHLC bars, saved to a CSV file. Passing open, high, and low lets the stop trigger inside a bar instead of only at the close:

Before: vectorbt

import pandas as pd
import vectorbt as vbt

ohlc = pd.read_csv("btc.csv", index_col=0, parse_dates=True)
fast = vbt.MA.run(ohlc["Close"], 10, ewm=True)
slow = vbt.MA.run(ohlc["Close"], 50, ewm=True)
entries = fast.ma_crossed_above(slow)
exits = fast.ma_crossed_below(slow)
pf = vbt.Portfolio.from_signals(
    ohlc["Close"],
    entries,
    exits,
    open=ohlc["Open"],
    high=ohlc["High"],
    low=ohlc["Low"],
    sl_stop=0.1,
    sl_trail=True,
    fees=0.001,
    freq="1D",
)
print(round(pf.total_return(), 4), round(pf.sharpe_ratio(), 4), pf.trades.count())

The direct port changes only what PRO renamed:

After: VectorBT PRO, direct port

import pandas as pd
import vectorbtpro as vbt

ohlc = pd.read_csv("btc.csv", index_col=0, parse_dates=True)
fast = vbt.MA.run(ohlc["Close"], 10, wtype="exp")  
slow = vbt.MA.run(ohlc["Close"], 50, wtype="exp")
entries = fast.ma_crossed_above(slow)
exits = fast.ma_crossed_below(slow)
pf = vbt.PF.from_signals(
    ohlc["Close"],
    entries,
    exits,
    open=ohlc["Open"],
    high=ohlc["High"],
    low=ohlc["Low"],
    tsl_stop=0.1,  
    fees=0.001,
    freq="1D",
)
print(round(pf.total_return, 4), round(pf.sharpe_ratio, 4), pf.trades.count())

The upgrade path loads the file into a data object, which keeps the columns together and hands them to indicators and the portfolio by name:

After: VectorBT PRO, with a data object

import vectorbtpro as vbt

data = vbt.CSVData.pull("btc.csv")  
fast = data.run(vbt.MA, 10, wtype="exp")  
slow = data.run(vbt.MA, 50, wtype="exp")
entries = fast.ma_crossed_above(slow)
exits = fast.ma_crossed_below(slow)
pf = vbt.PF.from_signals(data, entries, exits, tsl_stop=0.1, fees=0.001)  
print(round(pf.total_return, 4), round(pf.sharpe_ratio, 4), pf.trades.count())

Output of all three

3.6274 1.3232 20

All three versions returned 362.74% with a Sharpe ratio of 1.32 over 20 trades on daily Bitcoin bars from 2020 to 2024. Other common changes:

Import vectorbtpro instead of vectorbt. Both use the vbt alias, so install PRO in its own environment.
Data classes load with pull, as in vbt.YFData.pull("BTC-USD"). download still works as an alias.
When a call fails, vbt.phelp prints the current signature and documentation of any function.

Start by porting one small backtest on the same saved data, as above. Compare its signals, orders, and results before adding PRO features, since defaults and behavior can differ between the editions. The Developer tools page shows how to inspect signatures, source, and prepared inputs when adapting a more involved strategy.

Access

VectorBT PRO is distributed through a private GitHub repository to members, who also get the private documentation, tutorials, and Discord community. Monthly, 12-month, and lifetime plans are available with the same product and member benefits while active. A version you installed keeps working after your access ends. Access to the private repository, website, Discord, updates, and support ends with the paid term. The open-source vectorbt serves as the free trial. See Pricing and membership and the FAQ for details.

Migrating from vectorbt