Alpha Canvas Research Workflows

Alpha Canvas is Corrai's workflow surface for turning alpha ideas into reproducible experiments. It is built for searches such as visual quant research workflow, strategy research canvas, factor research pipeline, AI backtesting workflow, and alpha research DAG.

The canvas is not a decorative no-code layer. It is a way to make research structure explicit: data enters, features transform it, signals produce decisions, execution rules create returns, and validation gates decide whether the result deserves attention.

Why a canvas exists

Quant research often starts in notebooks because notebooks are fast. The problem arrives later. A result that looks promising may depend on cells executed out of order, temporary data files, implicit costs, and undocumented parameter choices. When the same research is revisited weeks later, the team can no longer tell exactly what was tested.

Alpha Canvas exists to preserve the speed of exploration while making the workflow inspectable. Each node in the research DAG has a purpose:

• Data nodes declare source, market, field, frequency, and availability assumptions.
• Transform nodes define feature construction, normalization, lagging, winsorization, and ranking.
• Signal nodes convert factors into portfolio or trade instructions.
• Execution nodes declare next-bar fills, fees, slippage, sizing, turnover, and constraints.
• Validation nodes run walk-forward, purged CV, DSR, PBO, regime checks, and robustness tests.

The value is not that the graph looks tidy. The value is that the evidence is tied to the exact graph that produced it.

Research DAG versus Judge DAG

Corrai separates the creative workflow from the promotion workflow.

The Research DAG is where researchers and agents explore. It can branch, compare variants, test transformations, and discard weak paths quickly. The Judge DAG is more conservative. It freezes artifacts, reads the registered trial history, checks validation gates, and issues a verdict.

This distinction lets the product support AI-assisted exploration without letting exploration rewrite the rules of judgment. An agent can add a factor node. It cannot silently remove slippage or change the test split after seeing the result.

For the term itself, see Research DAG.

Evidence package output

When a workflow runs, the output is more than an equity curve. A Judge-ready evidence package includes:

• the hypothesis text and registered run identifier
• source datasets and version fingerprints
• feature definitions and lag assumptions
• execution assumptions, including fees and slippage
• all tested variants in the family
• walk-forward windows and out-of-sample results
• DSR, PBO, cost, drawdown, and regime diagnostics
• review status and promotion verdict

This is why Alpha Canvas is useful for reproducible backtesting, systematic trading research workflows, and quant strategy validation platforms. It records the process, not just the best chart.

What belongs on the canvas

Alpha Canvas is best for research that benefits from decomposition: factor pipelines, cross-sectional ranking, event studies, volatility signals, funding-rate strategies, market microstructure features, and multi-market robustness checks. It is not meant to replace every line of code. Researchers can still use code where code is the right tool; the canvas records how the artifact is connected to the rest of the experiment.

For the data side of these workflows, see Local-First Data Engine. For the validation philosophy, see Evidence-Based Alpha Validation.