Build. Test. Improve Your Trading Algorithms.
An integrated development environment for researching, coding, backtesting, and evaluating algorithmic strategies.
What Studio is
One workspace from trading idea to tested strategy
Strategy research usually spreads across spreadsheets, notebooks, scripts and screenshots. Quantzivo Studio is designed to keep it in one place: define rules, write code, run backtests with explicit assumptions, and review the results side by side.
- Visual rules or Python code, depending on how you prefer to work
- Transparent assumptions for costs, slippage and fills on every run
- Versioned strategies so you can trace what changed and why
- Simulated paper trading to observe a strategy forward in time, with no real money
Product status: Quantzivo Studio is in development. Features described here reflect the planned scope and may change before release.
Planned capabilities
Everything a strategy needs, in one environment
Each module is designed to work on its own and to hand its output to the next step without copying files around.
Strategy Builder
Compose entry, exit and risk rules from technical conditions without writing code.
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Coding Workspace
Write strategies in Python with templates, a clear structure and research notebooks.
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Backtesting Engine
Simulate strategies on historical data with configurable costs, slippage and fill timing.
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Strategy Analytics
Equity curves, trade lists, benchmark comparisons and period breakdowns for every run.
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Risk Metrics
Drawdowns, volatility and risk-adjusted ratios, shown alongside the assumptions behind them.
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Paper Trading
Run strategies forward on virtual portfolios with simulated orders. No broker, no real money.
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Developer Resources
Planned documentation, Python examples, code templates and integration guides.
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Research Notebooks
Explore data and prototype signals before promoting an idea into a full strategy.
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The research workflow
Six steps, each one checkable
Studio is organized around a disciplined research loop. Most ideas fail somewhere along it, and finding that out early is the point.
Idea
Write down the market behavior you expect and why it might persist. A clear hypothesis is easier to reject.
Rules
Translate the idea into explicit entry, exit and risk rules in the Strategy Builder.
Strategy Builder
Code
Extend or replace visual rules with Python when the logic needs more flexibility.
Algo developmentBacktest
Simulate the strategy on historical data with costs, slippage and realistic fill timing switched on.
Backtesting
Evaluate
Check drawdowns, risk-adjusted metrics, out-of-sample periods and sensitivity to parameters.
Metrics explainedPaper trade
Observe the strategy on new data in a simulated portfolio and compare it with the backtest.
Paper trading
Strategy analytics & risk
Results that show their assumptions
A return number on its own says little. Studio reports are designed to print the costs, slippage, fill model and data range next to every metric, so a result can be questioned and reproduced.
Equity curve, drawdown chart and full trade list- Risk-adjusted metrics compared with a benchmark
- Side-by-side comparison of strategy versions
Backtested and paper-traded results are hypothetical. They do not account for every real-world factor and do not indicate future performance.
Who it's for
Built for people who want to test before they trust
Studio is designed for research and learning. It suits anyone who wants to turn ideas into testable rules and understand the results honestly.
Students
Apply what you learn about markets and Python to strategies you can actually run and inspect.
Developers
Work in Python with a defined strategy structure instead of rebuilding a backtester from scratch.
Traders
Turn discretionary rules of thumb into explicit conditions and see how they held up historically.
Researchers
Run walk-forward tests, compare versions and keep a log of every experiment.
Finance professionals
Prototype systematic ideas and communicate them with clear, assumption-aware reports.
Educators
Demonstrate backtesting pitfalls and risk concepts with strategies students can modify themselves.
Part of the ecosystem
How Studio connects to the Academy and Quantzivo AI
Learn the concepts, build and test with them, then research the market context. Each platform is designed to hand off naturally to the next.
Learn the foundations
Courses cover the Python, indicators, backtesting methods and risk concepts that Studio puts into practice.
- Python Programming for Financial Markets
- Algorithmic Trading with Python
- Backtesting and bias awareness
Build and test
Turn lessons into working strategies, test them with explicit assumptions and track every version.
- Rules or Python, your choice
- Backtests with costs and slippage
- Simulated paper trading
Research the context
Use AI-assisted research to understand the technical, fundamental, news and macro backdrop behind a strategy's results.
- Technical and fundamental research
- News and sentiment summaries
- Explanations, not advice
Clear boundaries
Designed for research, not execution
Studio is a place to build and evaluate strategies. It is not a brokerage, and it does not place orders with real money.
| Capability | In Studio's planned scope | Notes |
|---|---|---|
| Rule-based strategy building | Planned | Visual entry, exit and risk rules with versioning |
| Python strategy coding | Planned | Templates, notebooks and a defined strategy structure |
| Historical backtesting | Planned | Configurable costs, slippage and fill timing; results are hypothetical |
| Paper trading | Planned | Simulated orders on virtual portfolios only |
| Live brokerage execution | - | Not offered. Studio does not connect to brokers or route real orders. |
| Buy or sell recommendations | - | Not offered. Studio provides tools for your own research, not investment advice. |
Studio FAQs
All FAQsQuestions about Quantzivo Studio
What the Studio is designed to do, what it will not do, and how to follow its progress.
Not yet. Studio is in development and the features on these pages describe its planned scope. Request early access to hear about previews as they become available.
No. The Strategy Builder is designed for rule-based strategies without code. Python is there for when you need more flexibility, and the Academy's Python courses can help you get there.
No. Studio is planned as a research and simulation environment. Paper trading uses simulated orders on virtual portfolios, and there is no brokerage connection or live order execution.
Not necessarily. Backtests are hypothetical and can be distorted by overfitting, look-ahead bias, survivorship bias and unrealistic cost assumptions. Studio is designed to make those risks visible, not to remove them.
Plans and pricing will be confirmed at launch. The pricing page shows the current planning outline.
Quantzivo Studio
Help shape Quantzivo Studio before it launches
Request early access to hear about previews, share the workflows you need, and see the Studio as it takes shape.