SFZ Capital delivers a complete algorithmic trading platform purpose-built for broker-dealers, prop trading desks, asset managers, and family offices. Deploy under your brand. Configure for your clients. Own the relationship.
Whether you're managing a trading desk, advising ultra-high-net-worth clients, or running a quantitative fund — SFZ Capital adapts to your operational structure.
White-label SFZ Capital's complete front- and backend under your brand. Your clients use institutional algo trading tools without knowing the infrastructure behind them. Full control over user flow, feature availability, and onboarding.
Equip your traders with a verified backtesting engine, visual strategy builder, and real-time risk controls. Monitor all desk activity from a unified admin terminal. Set firm-wide risk limits that override individual strategy parameters.
Deploy systematic strategies across client portfolios with consistent execution. Validate every strategy against institutional-grade backtesting before deployment. Full audit trail and performance attribution for regulatory reporting.
Give investment teams the tools to build, test, and monitor algorithmic strategies without external technology dependencies. Institutional-grade analytics with the privacy and control a family office demands.
Built to institutional standards from day one. Not retrofitted. Not approximated.
28-test verification suite covering intrabar stop-loss/take-profit mechanics, gap-fill simulation, same-bar resolution, commission and slippage modelling, trailing stops, NaN handling, and circuit breakers. Every edge case tested before deployment.
The signal evaluation engine (38 indicators, confluence gating, strength scoring) is strictly decoupled from the execution state machine. No shared state. No contamination between backtesting and live execution paths.
Intrabar SL/TP checking using bar OHLCV data, not close prices. Gap-through fill simulation. Walk-forward validation. Deflated Sharpe Ratio for overfitting detection. Monte Carlo robustness testing. 17-metric result suite.
Strict data isolation between internal simulation, paper trading, and live broker execution. Environment field on every data model. No state bleeding between environments.
Circuit breaker pattern for bot failure handling. ETag-based API polling. 10-second state caching with thread-safe locks. Atomic file writes for cache concurrency safety.
# SFZ Capital Execution Engine # 28/28 verification tests passing class ExecutionEngine: def check_stop_loss( self, bar, position ) -> ExitSignal | None: # Intrabar low check — not close price if bar['low'] <= position.stop_loss: # Gap-through handling exit_price = min( bar['open'], position.stop_loss ) return ExitSignal( price=exit_price, reason='SL_HIT', pnl=self.calc_pnl( position, exit_price ) ) return None
Define scope, licensing terms, and integration requirements. Typical timeline: 1-2 weeks.
API credentials exchange, sandbox environment access, and technical documentation handover.
Apply your brand identity — logo, colours, domain. Configure feature availability and user tier limits from the admin terminal.
Run parallel testing in sandbox before go-live. Full QA cycle with your technical team.
Your clients access a fully branded institutional algo trading platform. You own the relationship.
Fixed licensing fee for full platform access. No per-user costs. No revenue share. Predictable pricing that scales with your business, not ours.
For firms requiring on-premise deployment, custom SLA, dedicated support, or integration with proprietary systems.
Pricing available on request. All enquiries receive a response within 2 business days.
We work with a select number of institutional partners. Tell us about your firm and what you're looking to achieve.
sfz.algo@gmail.com
Response within 2 business days30-minute live walkthrough of the platform. No commitment required.
BOOK DEMO →Marburg, Germany
Serving institutional clients globallyTell us about your firm, the operating model you need to support, and the deployment path you're evaluating.