01
Make the rules yours.
Describe your strategy or bring Python. Start with a simple allocation, then add signals, constraints, or other models.
Plain language → inspectable code
A Portfolio Visualizer alternative
Your next backtest.
Your own rules.
Turn your research into a model you can inspect, adapt, and keep using. Custom allocation rules, saved runs, and a direct connection to your AI agent.
No card required. Credit expires after 7 days.
Builds and runs are metered. See usage pricing.
ENSEMBLE / ModelsInteractive preview
Sample workspace
COREBalanced Core
- Annualized return
- 4.5%
- Volatility
- 5.6%
- Max drawdown
- -11.3%
Growth of 100Hypothetical backtest
2011-08-012026-09-25
Look-through allocation2026-09-25
IEF51.4%
VTI17.1%
VXUS12.5%
CTA9.5%
GLD9.4%
The underlying assets, across both child models. Historical sample weights, not a recommendation.
Balanced CoreEqual risk contribution
Global EquityVTI + VXUSInverse volatility
DiversifiersIEF + GLD + CTAInverse volatility
- Lookback
- 60 days
- Rebalance
- Monthly
- Execution
- Next close
Combine reusable models and inspect the rules behind the allocation.
2 child models5 assetsDaily price data
From an idea to something you can use
A backtest is the beginning.
Give your strategy a home, with the tools to understand it and the flexibility to make it your own.
02
Keep the whole picture.
Inspect results alongside code, inputs, and data assumptions. Change a parameter and keep both runs for comparison.
Code + inputs + run history
03
Continue wherever you work.
Open the same saved model in the web app, with your AI agent, or through API and CLI. Enable scheduled refreshes when ready.
Web · MCP · API · CLI
Find your fitChoose the workflow
Choose the workflow
you want to grow into.
Portfolio Visualizer
A toolkit for portfolio analysis.
A useful fit for predefined analysis tools, including portfolio backtesting, factor analysis, and Monte Carlo planning.
Explore its current tools ENSEMBLE
A workspace for your own models.
- Express custom rules and inspect their code
- Keep models, versions, and research runs
- Build with your agent and continue in the app
Different tools for different work. ENSEMBLE focuses on allocation models and historical backtests; it does not provide standalone Monte Carlo planning or factor regression. Independent of, and not endorsed by, Portfolio Visualizer. Tools checked October 7, 2026.
Start with one questionWhat would you
What would you
like to test?
Your first model can be simple. Describe the assets, the weights, and when to rebalance.
See a worked 60/40 exampleAn example starting point
“Build a portfolio with 60% SPY and 40% IEF. Rebalance monthly. Show the backtest, drawdowns, and allocation.”
Saved modelInspectable codeHistorical results
Start with $100 credit No card required. Credit expires after 7 days.
Builds and runs are metered. See usage pricing.
Before you start
A few useful answers.
Research software. Your decisions.
ENSEMBLE does not place trades.
When is ENSEMBLE a good fit?
Choose ENSEMBLE when you want custom allocation rules, inspectable model code, retained research runs, or access through an AI agent, API, or CLI. It focuses on allocation models and historical backtests, rather than standalone Monte Carlo planning or factor regression.
Do I need to write code?
No. Describe your strategy in plain language, or use a connected AI agent. You can also bring Python. Review the implementation, data coverage, and assumptions before relying on the results. Backtests are hypothetical and do not predict future returns.
What does it cost to try?
Start with $100 in credit, valid for 7 days, with no card required. Builds and runs use metered credit; this is not unlimited free use. Continued usage requires available credit or funding. See the pricing page for optional plans and current usage rates.
Can I recreate an existing portfolio?
You can describe its assets, weights, and rebalance rules to build a new model. There is no automatic Portfolio Visualizer import. Results can differ because of data history, execution timing, fees, and other assumptions, so compare those before comparing returns.