edge-candidate-agent

1# Edge Candidate Agent
2 
3## Overview
4 
5Convert daily market observations into reproducible research tickets and Phase I-compatible candidate specs.
6Prioritize signal quality and interface compatibility over aggressive strategy proliferation.
7This skill can run end-to-end standalone, but in the split workflow it primarily serves the final export/validation stage.
8 
9## When to Use
10 
11- Convert market observations, anomalies, or hypotheses into structured research tickets.
12- Run daily auto-detection to discover new edge candidates from EOD OHLCV and optional hints.
13- Export validated tickets as `strategy.yaml` + `metadata.json` for `trade-strategy-pipeline` Phase I.
14- Run preflight compatibility checks for `edge-finder-candidate/v1` before pipeline execution.
15 
16## Prerequisites
17 
18- Python 3.9+ with `PyYAML` installed.
19- Access to the target `trade-strategy-pipeline` repository for schema/stage validation.
20- `uv` available when running pipeline-managed validation via `--pipeline-root`.
21 
22## Output
23 
24- `strategies/<candidate_id>/strategy.yaml`: Phase I-compatible strategy spec.
25- `strategies/<candidate_id>/metadata.json`: provenance metadata including interface version and ticket context.
26- Validation status from `scripts/validate_candidate.py` (pass/fail + reasons).
27- Daily detection artifacts:
28  - `daily_report.md`
29  - `market_summary.json`
30  - `anomalies.json`
31  - `watchlist.csv`
32  - `tickets/exportable/*.yaml`
33  - `tickets/research_only/*.yaml`
34 
35## Position in Split Workflow
36…