Einstein Research — Edge Candidate Generator

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id: 'einstein-research-edge' name: 'einstein-research-edge' description: 'Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into strategy.yaml + metadata.json, or preflight-check interface compatibility (edge-finder-candidate/v1) before running pipeline backtests.' version: '1.0.0' author: 'DaVinci' last_amended_at: null trigger_patterns: [] pre_conditions: git_repo_required: false tools_available: [] expected_output_format: 'natural_language'

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Edge Research Ticket Generator

This skill formalizes the process of turning a trading hypothesis or anomaly into a structured, reproducible research ticket. It's the first step in the quantitative research pipeline, ensuring that ideas are well-defined and testable before any backtesting code is written.

When to Use This Skill

User has a trading idea or hypothesis (e.g., "I think stocks that do X tend to go up").

User observes a market anomaly and wants to investigate it systematically.

User wants to create a new candidate for the trade-strategy-pipeline.

Triggers: "research ticket," "new strategy idea," "test this hypothesis," "is this an edge?".

Workflow: From Idea to Pipeline-Ready Spec

Step 1: Idea Ingestion

The skill prompts the user for the core components of their idea:

Hypothesis: A clear, one-sentence statement of the proposed edge.

Entry Signal: The specific conditions that trigger a buy.

Exit Signal: The conditions that trigger a sell (e.g., target profit, stop-loss, time-based).

Universe: The group of stocks to test this on (e.g., S&P 500, Nasdaq 100).

Rationale: *Why* should this edge exist? (Behavioral, structural, etc.).

Step 2: Ticket Generation

The edge-generator CLI tool takes these inputs and creates a structured research ticket in Markdown format.

edge-generator create \
  --hypothesis "Stocks hitting a 52-week high with high volume have momentum." \
  --entry "Price > 52-week high AND Volume > 2x 50-day avg volume" \
  --exit "5-day hold OR 10% profit target OR 5% stop-loss" \
  --universe "sp500" \
  --rationale "Breakout momentum, high volume confirms institutional interest."

This generates a file like tickets/ER-2026-015_52_week_high_momentum.md.

Ticket Structure:

ID: ER-YYYY-NNN

Title: Short description of the idea.

Hypothesis: As provided.

Entry/Exit/Universe/Rationale: As provided.

Data Requirements: Lists the data needed (e.g., daily OHLCV, 52-week high, 50-day avg volume).

Priority Score: An initial score (0-100) based on uniqueness, rationale strength, and testability.

Step 3: Prioritization

The skill can rank all open tickets in the tickets/ directory to help decide what to research next.

edge-generator prioritize

This updates the priority scores based on factors like:

Novelty: How similar is this to previously tested (and failed) ideas?

Data Availability: Can this be tested with our current data sources?

Computational Cost: Is the backtest likely to be fast or slow?

Step 4: Export to Pipeline Spec

Once a ticket is prioritized and approved for research, this skill exports it to the format required by the trade-strategy-pipeline.

edge-generator export ER-2026-015

This creates a directory pipeline-candidates/ER-2026-015/ containing:

strategy.yaml: The machine-readable definition of the strategy.

    version: edge-finder-candidate/v1
    name: 52-Week High Momentum
    hypothesis: Stocks hitting a 52-week high with high volume have momentum.
    entry:
      - "price > high_52w"
      - "volume > 2 * avg_volume_50d"
    exit:
      - "hold_days == 5"
      - "pct_change >= 0.10"
      - "pct_change <= -0.05"
    universe: "sp500"
    

metadata.json: Additional context for the pipeline runner.

    {
      "ticketId": "ER-2026-015",
      "rationale": "Breakout momentum, high volume confirms institutional interest.",
      "priority": 85,
      "dataRequirements": ["daily_ohlcv", "high_52w", "avg_volume_50d"]
    }
    

Step 5: Handoff to Backtest Engine

The generated directory is now ready to be processed by the einstein-research-backtest-engine skill, which will execute the backtest based on the strategy.yaml spec.

Why This Is Important

Reproducibility: Every research effort starts with a formal, version-controlled definition.

Efficiency: Prevents wasted time on ill-defined ideas.

Systematic Process: Ensures a consistent and rigorous approach to alpha research.

Automation: The strategy.yaml format allows the backtesting process to be fully automated.

This skill is the gateway to the entire quantitative research pipeline, turning qualitative ideas into testable, machine-readable artifacts.