Backtesting software that runs on your own computer

By John, builder of Wise Apple · Updated August 27, 2026

You ran out of backtest credits on a Tuesday, halfway through a parameter sweep, and the next tier was more than you wanted to pay to find out whether an idea was bad. That is the moment most people start searching for something that runs locally.

"Local" is a property, not a product. It means the computation happens on hardware you own, which has consequences for cost, speed, privacy, and — less obviously — for whether you do the honest tests at all. This page is about those consequences, both directions, and then an honest survey of the three families of backtesting tool: cloud platforms, desktop applications, and code libraries. Wise Apple is one entry in that survey. It gets a section, not the page.

What local actually buys you

No quotas, no credits, no meter

When compute is yours, the two-hundredth backtest costs what the first did. This sounds like a cost argument and it is mostly a behaviour argument. The tests that separate a real edge from a flattering one — the parameter neighbourhood, the cost multiplier, walk-forward over ten windows, a permutation test with fifty shuffles — are all "run it many more times". Under a meter, they are the tests you skip.

No queue

Shared infrastructure schedules your job among everyone else's. Local means the run starts when you click. For a single backtest that is a convenience; for an iterative loop of change-run-read, it is the difference between a workflow and a wait.

Nothing leaves the room

A strategy that is never uploaded raises no question about who can read it, what the platform's terms permit, or what happens to it if the platform changes hands. That is not an accusation against any platform. It is a category of question that local tools do not have to answer.

It works offline

On a plane, in a cabin, during an outage. Minor until it matters.

What local costs you

1. Your hardware is the ceiling. A laptop is not a cluster. Heavy machine learning on long intraday histories takes as long as your CPU takes.
2. Data is your problem, or the tool's. Cloud platforms curate data centrally. Local tools either ship it, sync it, or leave you to find and check it yourself.
3. Nothing to share. Cloud platforms have communities, forums and libraries of shared strategies. Local tools are quieter.

The three families, honestly

Cloud-first platforms

QuantConnect, and the many hosted strategy builders aimed at crypto bots. Strengths: broad curated data, execution to brokers or exchanges, communities, and nothing to install. Costs: metered compute, your strategy on their servers, and — in the no-code ones especially — a tendency to hide the honest settings behind the equity curve. The QuantConnect page covers the best of this family in detail.

Local desktop applications

AmiBroker, and the strategy testers inside broker platforms. Strengths: fast, mature, your machine, one-time licences in some cases. Costs: usually Windows-only, usually scripting-driven, and the honest-backtest discipline is entirely on you — a desktop engine will run a leaked backtest as confidently as a clean one. The AmiBroker page and the Mac and Linux survey cover the platform question.

Code libraries

Backtrader, backtesting.py, vectorbt, and the do-it-yourself pandas script. Strengths: free, local, endlessly flexible, completely unopinionated. Costs: completely unopinionated. Nothing stops you filling at the signal bar's close, fitting a scaler on the full series, or splitting at random, and the library reports the result with total confidence. The no-code guide makes the case that the programming is the easy part and the method is what the libraries do not teach.

Where the families land

Data, and what local means for it

The most common objection to local tools is data: cloud platforms curate it, and a laptop does not. It is a fair objection with three honest answers. Code libraries leave data entirely to you, which means the five checks — adjustments, survivorship, gaps, sessions, interval construction — are your job every time. Desktop applications typically import from files or vendor plugins, with the same checks owed. And a local tool can still receive curated data — sync it once, keep it on the machine, compute against it offline — which is the arrangement that gets the curation without the meter. "Local" describes where the computation happens, not where the data came from.

A quick decision tree

1. Need a broker connection, options or futures data, or a cluster? Cloud platform, and accept the meter.
2. Write code, and want unlimited flexibility? Code library, with the honesty checklist as a discipline.
3. Have a workflow in a mature Windows tool? Desktop application, virtualised if you are on Mac or Linux.
4. None of those, and your ideas are single-instrument rules or models on stocks, ETFs or crypto? A local no-code lab, judged on the six settings.

Where Wise Apple sits

Wise Apple is a local, no-code research lab that runs in the browser. Training and backtesting execute on your own machine via WebAssembly, so runs are unlimited and nothing about a strategy or its results is uploaded. Ten-plus years of candles for stocks, ETFs and crypto are loaded for you, with CSV and provider-key import for your own data. The honest settings — next-bar fills, Fees, Slippage and Market Impact, a chronological split with Embargo Bars, the Walk-Forward Window — are defaults rather than options, and nineteen models across six families sit on the bench with Ensemble Voting to combine them. Every trade is inspectable candle by candle and the report exports to CSV. Live signals go to you — Telegram, Discord, email, SMS, webhook — from an alert node you own.

The parts that should give you pause

It is early, built by one person, and a small fraction of the roadmap has shipped. It tests one instrument at a time, so portfolio and cross-sectional strategies are outside it. It places no trades and sells no signals. Its charting is basic. Its ceiling is lower than a programming language's, by design — the trade for not needing one. The cloud platforms have communities it does not; the desktop engines have speed it does not; the libraries have flexibility it does not. If any of those is what you need most, the honest recommendation is elsewhere on this page.