PRIVATE BETA · JUNE 2026

AI-ENABLED RESEARCH WORKFLOWS

Institutional-grade backtesting, in minutes.

See how your trading strategy performs across markets — with quant discipline and trader-friendly workflows.

Backtest360 is a cloud backtesting platform for systematic traders, quants and developers, available through Python, REST API and MCP, with a web UI and chat console rolling out.

✓No look-ahead shortcuts — point-in-time data controls on every run.

✓Realistic costs, slippage, stops, and validation discipline built into the research framework.

✓Run structured-rule backtests through a hosted API, MCP, or Python client today. Natural-language and UI research workflows are rolling out across the backtesting process during beta.

✓AI-assisted analysis helps explain results, identify weaknesses, interpret performance drivers, and refine strategy configuration.

Active beta users who test strategies and provide useful feedback may receive extended complimentary access and preferential launch terms after launch.

Illustrative output and interface shown. Current beta access is via API, MCP, and Python client workflows; actual screens may vary by strategy, asset class, and configuration.

JOIN THE PRIVATE BETA

Get started in minutes

Get an API key, connect to the hosted Backtest360 engine, and start testing structured-rule strategies with your own data.

Add role, market, and use case (optional) →

Active beta users who test strategies and provide useful feedback may receive extended complimentary access and preferential launch terms after launch.

Includes direct email support and a feedback route to the Backtest360 team.

200+ early signups

Institutional or team access? Contact us to discuss private beta access, team workflows, and post-launch plans.

BUILT FOR

◉SYSTEMATIC TRADERS▤QUANTS⬡DEVELOPERS◈PORTFOLIO MANAGERS▦RESEARCHERS◇INVESTMENT TEAMS

THE WORKFLOW

From idea to defensible backtest

Move from trading idea to tested strategy through hosted backtesting infrastructure — with every assumption made visible along the way.

Describe your strategy

Build strategy logic with conditional rules and 250+ built-in indicators, expressed via Python client, MCP, or hosted API. Natural-language and UI workflows are rolling out during beta.

Run a clean backtest

Submit the strategy to a hosted backtesting engine with realistic costs, slippage, lag rules, bias controls, and validation workflows designed to support out-of-sample discipline.

Understand the result

Compare against benchmarks, review metrics, drawdowns and trades, with AI-assisted insight into what happened and why.

Refine, compare and export

Save variants, compare backtest and validation-period outcomes, export results, and generate latest-signal outputs for saved strategies.

BUILT-IN RIGOUR

Built for serious strategy research

Bias controls

Engine-enforced lag-1 look-ahead protection
Point-in-time data controls
Strategy validation before execution
Bad-data checks for invalid prices
Out-of-sample discipline via validation workflows

Execution realism

Entry / exit timing assumptions
Commissions & slippage
Stops, leverage & re-entry rules
Position sizing modes
Fill models & lot sizing

Analysis depth

120+ performance metrics
Drawdowns & underwater curves
VaR / Expected Shortfall
Regime & factor diagnostics
Exposure & correlation

API-first infrastructure

Hosted REST API
Python client
MCP server for AI-agent workflows
Programmatic backtests
Latest-signal outputs
Built for research workflow integration

FOR DEVELOPERS

Backtesting infrastructure, one API key away.

Install the client, connect to the hosted engine, and run structured-rule backtests from Python, REST, or MCP.

Install

Python client on GitHub

Install the client and run structured-rule backtests from Python.

$ py -m pip install backtest360

THE PROBLEM

Most backtests are too easy to fool.

Smooth equity curves often hide look-ahead bias, unrealistic fills, missing costs, overfitting, and fragile assumptions.

Backtest360 is built to make those assumptions visible.

Bias & validation guardrails

Engine-enforced look-ahead protection
Point-in-time data controls
Lag rules and signal delays
Strategy and data validation checks
Workflow guardrails for validation discipline and overfitting risk

Realistic execution

Costs, slippage, impact
Stops & leverage rules
Execution timing and lag assumptions

Validation discipline

Research / validation separation
Date-range validation workflows
Robustness and sensitivity checks
Designed to reduce overfitting risk

Auditable outputs

Saved and reproducible backtest runs
Recorded inputs, settings, and assumptions
Gross-to-net performance breakdown
Export results and time series to Excel *

EVERY ASSUMPTION VISIBLE

Review the run behind the result

Backtest360 is designed to store each backtest as an inspectable research record — including the strategy definition, data used, execution assumptions, validation settings, output statistics, benchmark comparisons, validation-period outcomes, and AI diagnostics.

✓Archived run results

✓Data sources and versions

✓Strategy rules and parameters

✓Execution assumptions and costs applied

✓Bias checks and validation settings

✓Output statistics, risk metrics, and drawdowns

✓Search, filter, sort, and review prior results

✓Benchmark and saved-run comparisons

✓AI diagnostics and research notes

CONSOLE

↺ Replay

WHAT'S INCLUDED IN BETA

What's included in beta

Current beta access is via hosted API, MCP, and Python client workflows. The beta is focused on structured-rule backtesting, realistic execution assumptions, auditable research outputs, and API-first integration into systematic research workflows.

Strategy definition

Full conditional strategy logic
Nested rule trees across long and short entries and exits
250+ built-in indicators and transforms
Custom indicator support
Predefined strategy templates
Configurable rules, thresholds, indicators, stops, and sizing
Precomputed signal upload
Latest-signal output for saved strategies
Available via hosted REST API, MCP, and Python client workflows

Hosted backtesting engine

Hybrid bar-by-bar execution engine
Path-dependent stops, equity, and position state
Engine-enforced lag-1 look-ahead protection
Full backtest and latest-signal output modes
Long, short, and flip handling
Point-in-time data controls
Asset, session, and frequency auto-detection
Equities, ETFs, crypto, and FX *
9 timeframes from 1m to monthly
18 validated signal × execution combinations

AI-assisted research workflows *

AI-assisted backtest analysis
Strategy definition and configuration support
Framework guardrails and assumption checks
Performance, risk, and drawdown assessment
Metric explanation and statistical interpretation
Robustness prompts and improvement directions
Explainable guidance through the chat console

Risk & performance analytics

120+ metrics across 13 categories
Benchmark comparison and relative performance
Drawdowns and durations
Sharpe, Sortino, Calmar, VaR, and ES
Kelly, exposure, turnover, and trade statistics
Advanced volatility and distribution statistics
Standalone statistics endpoint
AI-assisted performance assessment *

Execution realism

Costs, commissions, and slippage
80 execution-mode variants
Exact, typical-price, adverse, and random fills
Fixed or volatility-scaled slippage
5 stop types: fixed, trailing, ATR, trailing-ATR, or none
3 re-entry modes: immediate, next signal, or cooldown
Position sizing, volatility targeting, and fractional Kelly
Gross / net / fees / slippage reconciliation

Data & workflow integration

Integrated historical market data for backtesting *
Upload your own CSV, Excel, or OHLCV data
Auto-parse and validation for missing or invalid prices
Cached inputs and repeatable runs
REST API, MCP, and Python client access
Exportable results and auditable run outputs

Includes direct email support and a feedback route to the Backtest360 team.

* Rolling out during beta or available subject to beta configuration.

PLANNED ENHANCEMENTS

Beta roadmap directions

Backtest360 is expanding from hosted API-first backtesting infrastructure into a broader professional research workflow. The areas below are planned development directions and may be prioritised, adjusted, or expanded based on beta-user feedback, technical requirements, and product sequencing.

These roadmap directions are not delivery commitments. Availability, timing, sequencing, and final scope may vary based on beta feedback, implementation priorities, and technical requirements. Suggestions from beta users are welcome.

LAUNCHING SOON

Ready to test your own strategy?

Join the private beta to get an API key, connect to the hosted Backtest360 engine, and start running professional-grade backtests with your own data.

Direct email support and a feedback loop with the team throughout the beta.