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Retrial dashboard interface displaying risk-adjusted portfolio performance metrics

AI-driven data intelligence

Clarity through complexity, reported every day

Retrial processes large volumes of market data on a continuous basis and converts it into risk-adjusted performance metrics, so decisions are grounded in evidence rather than sentiment.

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The dashboard surfaces drawdown, volatility-adjusted returns and model confidence intervals, refreshed on a daily cycle rather than real-time noise.

Data vs. insight

Markets now generate more data than any analyst can reasonably interpret

  • Statistical noise. Price action across thousands of assets produces signals that look meaningful but carry little predictive value once isolated.
  • Predictive lag. By the time a trend is visible to a human observer, a meaningful part of the opportunity has often already passed.
  • Emotional decision-making. Volatile markets reward patience, yet manual monitoring tends to encourage reactive, short-term choices.
Raw data volume
Usable signal
Actionable insight

The Retrial engine

Three functions work together, and each one is retrained as conditions change

No single model is treated as final. Retrial continuously re-examines its own output against new data, which is where the platform takes its name.

01 — Real-time analysis

Continuous data analysis

Market, on-chain and order-book data are ingested and normalised continuously, allowing the system to detect structural shifts rather than isolated price moves.

02 — Predictive modelling

Pattern recognition at scale

Models are trained to identify multi-variable correlations across assets and timeframes that are difficult for a human analyst to track manually.

03 — Risk mitigation

Deterministic risk controls

Position sizing and exposure limits follow fixed, documented rules, so risk management does not depend on discretionary judgement during periods of stress.

Retrial daily reporting process showing data verification and audit preparation

Transparency by design

Every 24 hours, you receive an audit-ready account of performance

Reporting is not generated on request or adjusted retrospectively. Each cycle produces a fixed record of positions, returns and risk exposure at the point it was captured.

  • A daily report is issued covering gross and net returns, drawdown, and current exposure by asset category.
  • Underlying calculations are logged, so figures can be reconciled against raw data if required.
  • Fee structures and decision logic are disclosed in documentation; there is no undisclosed model behaviour.

Applied use

The same engine supports different investment approaches

Portfolio diversification

Reducing concentration risk across correlated assets

Retrial monitors correlation between holdings as it shifts over time, flagging when a portfolio that appears diversified is in practice exposed to a single underlying risk factor.

Average cross-asset correlation0.41
Effective diversification score6.8 / 10
Flagged concentration events (30d)3
Rebalancing trigger threshold±5.0%
Rebalancing actions (30d)7
Realised drawdown vs. benchmark-2.3%

Automated rebalancing

Maintaining target allocation during volatility

When exposures drift beyond pre-set thresholds, the system rebalances according to fixed rules, which limits drawdown during periods of sudden market movement without requiring manual intervention.

Historical backtesting

Validating strategy behaviour before live deployment

Model logic is tested against historical market cycles, including prior periods of high volatility, so performance characteristics are understood before capital is allocated.

Backtest windowmulti-cycle
Worst observed drawdown-18.4%
Recovery period (median)41 days

Frequently asked

Direct answers to common technical questions

How is account and data security handled?

Data in transit and at rest is encrypted using industry-standard protocols. API keys used for exchange connections are stored with restricted, read-only permissions wherever the exchange supports this, and withdrawal access is never requested.

Where does the underlying market data come from?

Data is sourced through direct API integrations with established exchanges and market data providers. Each feed is validated against at least one secondary source before being used in model calculations, reducing the risk of relying on a single point of failure.

Is the AI's decision-making process a black box?

Risk-management protocols are deterministic and documented: position limits, exposure caps and rebalancing triggers follow fixed rules rather than opaque discretionary logic. Predictive components are retrained on a scheduled basis, and the criteria for retraining are recorded in your daily reports.

Review the platform before committing capital

Request access to see live daily reporting and historical backtesting for a sample portfolio configuration.

Request platform access

No payment details are required to request access. You will receive a sample daily report within the evaluation period.