Algo Trading

Purpose

Apply GenAI to algorithmic trading by combining power-market price data, imbalance prices, weather forecasts, and grid congestion signals to generate, test, and optimize trading strategies faster than static rule-based approaches.

Primary users

Power traders, quantitative analysts, and energy portfolio/dispatch teams seeking systematic trading signals and rapid strategy iteration with embedded testing.

Where it fits (process/stage/trigger)

Used in pre-trade research and continuous strategy improvement, triggered by new market data, updated weather runs, emerging congestion patterns, or deteriorating live strategy performance.

Key capabilities / workflow

Ingests and aligns time-series inputs, derives explanatory signals from market, weather, and grid data, uses GenAI to propose strategy candidates and scenario variations, backtests on historical and synthetic datasets, iteratively refines strategy rules until performance and risk criteria are met, then produces deployable signals and a concise rationale.

Inputs

Market price time-series, imbalance prices, weather forecasts, grid congestion signals, and synthetic datasets for backtesting.

Outputs / Deliverables

Optimized trading strategy specification, backtesting and performance reports, scenario simulation summaries, and actionable trading signals with parameter settings for execution.

Value

Accelerates strategy discovery and validation, adapts rules to changing market regimes, improves decision speed with systematic testing, and reduces reliance on brittle static algorithms while strengthening traceability through documented backtest evidence.