Skill: clawdiri-ai/einstein-research-edge-dv (version 0.1.0)

--- 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' --- # 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. ```bash 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. ```bash 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`. ```bash edge-generator export ER-2026-015 ``` This creates a directory [REDACTED_SECRET] containing: - **`strategy.yaml`**: The machine-readable definition of the strategy. ```yaml 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. ```json { "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.


Security scan verdicts — ClawScan: suspicious | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (HIGH)