BUILD & PROVE

Write the rules once and use them everywhere.

Set the universe, signal, weighting and risk limits once, and the walk-forward backtest, the risk views and paper trading all read the same rules.

See how it works

Point-in-time data Cost-aware Out-of-sample

01 Thesis

“Rank US large caps on quality and momentum. Keep the book sector-neutral and control turnover.”

02 Portfolio logic

UniverseUS · top 500

SignalQuality 40% · momentum 60%

Book50 names · risk parity

ControlsSector neutral · max 4%

03 Walk-forward evidence

5 OOS folds

OOS Sharpe1.18

DSR0.94

PBO11%

Max DD−13.7%

Strategy BenchmarkOut-of-sample only

✓

Evidence readoutEdge survives costs. Turnover is the limiting constraint.

Capacity review

What you get

What Strategy Builder gives you.

Build it three ways

Use the visual builder, the sandboxed Python SDK, or let Vega draft the rules.

Catch the curve-fit early

Deflated Sharpe, universe warnings and the Probability of Backtest Overfitting flag a failing strategy.

From backtest to Aegis

Track a tested strategy's risk in Aegis, or paper-trade it with signals by email.

Alpha DSLStrategy SDKOptimizerRisk model

Zero code to full control

Click or code, and face the same gates.

Use the visual builder, write Python or export the signals.

From point-and-click to production Python.

Whichever route you take, the backtest and the risk views read the same rules.

No-code builder. Set factors, exits, optimizers and risk models.
Python SDK. Write the strategy as a class in a sandboxed runtime.
Export your work. Take the spec, the rule sheet or a Pine script.

strategy: momentum-x-sectoruniverse:   { region: US, top: 500 }signals:  - momentum(126) - momentum(21)  - filter: adx(14) > 20portfolio:  { long: 50, weighting: risk_parity }exits:      { trailing: 8%, invalidation: signal_flip }validate:   { walk_forward: 5y, costs: realistic, gates: [DSR, PBO] }