Do I need programming experience?

No. Quantly uses a tree-based visual editor where you combine indicators, filters, allocation rules, and conditional logic. If you understand trading concepts like moving averages or RSI, you can build strategies without Python or notebooks.

How much historical data is available for backtesting?

Quantly provides 60+ years of historical market data on select tickers, going back to approximately 1962. You can run backtests, compare against benchmarks, and inspect metrics like Sharpe ratio, max drawdown, and cumulative return.

Can I connect a brokerage account?

Yes. Quantly supports automated execution through supported brokerages. See docs.quantly.trade/getting-started/supported-brokers for the current list. You can start with paper trading to validate your strategy, then connect a live account when you are ready.

What types of strategies can I build?

You can build momentum, mean-reversion, factor rotation, sector rotation, and multi-asset allocation strategies. The visual builder supports indicators (SMA, EMA, RSI, cumulative return), filters (top-N selection), conditional branches, and portfolio weighting methods.

How much does Quantly cost?

Quantly Pro is $25/month, or $240/year with annual billing (equivalent to $20/month when billed annually). One plan includes paper and live trading. See the pricing page for full details.

Trade like a quant.Without writing a line of code.

Q: What is Quantly?

Quantly is a visual quantitative trading platform that lets you design systematic strategies, backtest them across decades of historical market data, and automate paper or live execution - all without writing code.

Build, backtest, and automate rules-based strategies in one visual workspace.

Get started Try the live demo

Momentum Rotation

	Momentum Rotation	SPY
Today	+0.84%	+0.37%
Total Return	+353.80%	+135.96%
CAGR	+16.33%	+8.96%
Sharpe	1.21	0.62
Calmar	1.12	0.31
Max DD	-14.53%	-28.50%
Volatility	13.22%	16.00%
Win Rate	53.1%	51.1%

Performance and allocation data reflect simulated backtest results based on historical market prices. Past performance is not indicative of future returns.

Of daily price history on select tickers: 60+years
Typical backtest runtime, so you can iterate freely: <150ms
In every backtest tear sheet: 45+metrics
From first idea to a scheduled live strategy: 0lines of code

Describe a strategy in plain English. Get a working tree in seconds.

Tell the AI what you want to trade and when. It picks the indicators, writes the conditions, and builds the full strategy tree, ready to backtest and edit block by block.

Everything between an idea and a running portfolio. In one workspace, with nothing to host.

A visual editor for real strategy logic

Nest if/else branches, filters, and weighting rules in a tree that reads like your thesis. No Python, no notebooks.

Backtests in milliseconds

Change a rule and see the result before you lose the thought.

Backtest engine2,520 bars

125ms

Scheduled, hands-off execution

Pick a trade time for each strategy. Quantly rebalances on schedule in paper or live accounts and logs every order.

Click a tab to explore

A full tear sheet for every backtest

Year-by-year returns against SPY, monthly heatmaps, rolling risk, and drawdowns, without exporting to a spreadsheet.

Composable building blocks

Indicators plug straight into conditions, filters, and weights, so complex rotations stay readable.

From idea to execution in three steps. With no infrastructure to maintain.

Quantitative trading for everyone. Not just for people who maintain a research stack.

Systematic trading used to require a full research stack: Python notebooks, data pipelines, cron jobs, and broker APIs wired together by hand. Quantly replaces that with a single visual workspace where you design strategy logic, validate it against decades of market history, and deploy it to paper or live accounts.

Whether you are rotating between momentum leaders, running a mean-reversion basket, or allocating across sectors with conditional rules, the tree-based editor lets you express complex logic without maintaining infrastructure. Indicators like SMA, EMA, RSI, and cumulative return plug directly into filters, weighting methods, and if/else branches.

Once your strategy is validated, schedule daily runs and monitor equity curves, drawdowns, and trade logs from one portfolio dashboard. Connect a supported brokerage when you are ready to go from backtest to execution.

A do-it-yourself research stack compared with Quantly
DIY stack	Quantly
Python notebooks and scripts	Visual tree editor
Sourcing and cleaning price data	60+ years of history included
Hand-rolled backtest code	Backtests with 45+ metrics in milliseconds
Cron jobs on a server you maintain	Per-strategy schedules, managed for you
Broker API glue code	Supported brokerages, connected in minutes

Every live strategy keeps score. Live results are measured against the backtest that justified them.

View the track record Explore strategies

Backtest and live, side by side: See where live returns match the backtest and where they drift.
Tracking error you can see: Spot strategies that behave differently from their research before it costs you.
Learn from the community: Browse public strategies, compare their numbers, and save ideas to your watchlist.

One plan. Paper and live trading included.

$25/mo

Or $240 per year with annual billing.

Get started See full pricing

Visual strategy builder - no code required
Tree-based editor for indicators, filters, and allocation rules
Many technical indicators
Advanced backtesting with CAGR, Sharpe, and drawdown analytics
60+ years of historical market data
Benchmark comparisons (e.g. SPY) and equity curve charts
Automated daily execution in paper or live accounts
Portfolio dashboard with holdings and trade log
Connected brokerage accounts

Frequently asked questions. Everything else lives in the docs.

Can't find what you need? Read the documentation.