Quant Observability Platform
From backtest to paper trading to live execution—see what's actually happening at every stage.
Your backtest shows 40% returns. Paper trading looks promising. Then live performance disappoints. The problem isn't your strategy—it's that you can't see where things break down. We fix that.
The Strategy Lifecycle
Every algorithmic strategy passes through three phases. At each phase, there are things you can't see—until it's too late.
Backtest
Historical simulation shows promising returns. But hidden errors inflate your expectations.
Blind spots:
- • Lookahead bias in feature calculation
- • Survivorship bias in asset universe
- • Overfitting to historical noise
- • Unrealistic execution assumptions
Paper Trading
Simulated live trading with real-time data. Results look good—but the simulation lies.
Blind spots:
- • Perfect fills that won't happen live
- • No market impact modeling
- • Data quality issues masked
- • Latency not representative
Live Trading
Real capital, real markets. Performance disappoints—and you don't know why.
Blind spots:
- • Execution slippage eating returns
- • Data feed issues causing bad signals
- • Risk limits breached without warning
- • No attribution for underperformance
Observability closes these gaps. See what's happening at every stage—before it costs you money.
Observability at Every Stage
Different phases require different visibility. Our platform gives you the right observability at each stage of the strategy lifecycle.
Backtest Observability
Validate methodology before committing capital.
- Automated bias detection (lookahead, survivorship)
- Data leakage tracing
- Overfitting metrics and alerts
- Execution assumption validation
- Regime analysis and parameter sensitivity
Paper Trading Observability
Bridge the gap between simulation and reality.
- Realistic execution simulation
- Data feed quality monitoring
- Signal generation tracking
- Latency and timing analysis
- Backtest vs paper comparison
Live Trading Observability
Know what's happening in real-time.
- Execution slippage measurement
- Fill rate and venue analytics
- Risk limits and kill-switches
- Real-time P&L attribution
- Anomaly detection and alerts
Get Started
From initial assessment to full observability coverage—here's how we work together.
Assessment
We evaluate your infrastructure, data sources, and strategies to identify where observability will have the highest impact.
Integration
Connect your backtesting systems, data feeds, and execution layer. We handle the technical integration.
Configure
Set up dashboards, alerts, and validation rules specific to your strategies and risk requirements.
Iterate
Use observability insights to continuously improve your strategies, execution, and data pipelines.
What You'll See
Observability means understanding why things happen, not just what happened. Here's what our platform reveals.
- Why your backtest results won't hold up
- See exactly where lookahead bias creeps in. Trace data leakage through your feature pipeline. Understand which parameters are overfit to noise. Get confidence intervals on your performance metrics, not just point estimates.
- When your data quality degrades
- See gaps in your market data before they corrupt signals. Catch anomalies and bad prints in real-time. Track distribution drift in your features. Know the blast radius when an upstream source has issues.
- Where your execution costs come from
- See slippage broken down by strategy, asset, venue, and time of day. Understand which orders have the most market impact. Compare venues and routing decisions. Quantify the cost of latency in your specific context.
- How your risk exposure evolves
- See drawdowns developing before they breach limits. Understand correlations across strategies in real-time. Track exposure and leverage at every level. Get alerts that trigger on trajectory, not just thresholds.
Frequently Asked Questions
What is quant observability?
Observability for algorithmic trading means having visibility into your strategy's behavior across backtesting, paper trading, and live execution. This includes surfacing data quality issues, flagging potential methodology errors, monitoring execution quality, and understanding why live performance differs from backtest results.
Who is this platform for?
Quant researchers developing new strategies, developers building trading infrastructure, and portfolio managers monitoring live execution. We work with family offices, prop trading firms, and hedge funds of all sizes.
Do you offer implementation services?
Yes. Beyond our platform, we provide hands-on engineering support for integration, calibration, and ongoing refinement. Our team has built trading infrastructure at firms managing billions in AUM. We help you tune the platform to your specific strategies and infrastructure.
What problems does the platform help surface?
Divergence between backtest and live results. Data quality anomalies that need investigation. Execution patterns that warrant attention. Risk exposures approaching limits. The platform surfaces issues for review—your team applies judgment about which require action.
How is this different from building in-house?
Building robust observability infrastructure requires significant engineering investment. Our platform gives you a foundation immediately, while our services team can help integrate it with your existing systems and refine it as you learn what patterns matter for your specific strategies.
What does onboarding look like?
We start with a technical assessment of your current infrastructure and identify where observability will have the highest impact. From there, we deploy the platform, integrate with your data sources, and configure initial alerts and dashboards. Expect ongoing refinement as you learn which checks are most valuable for your use cases.