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/research-strategy

Research new trading strategies or scanner improvements, evaluate fit against the existing pipeline, write findings to docs/iterations/research/, and implement the top-ranked finding as a new scanner if it qualifies.

Usage:

/research-strategy — autonomous mode: Claude picks research topics
/research-strategy "topic" — directed mode: research a specific strategy

In CI (CI=true), stop before git operations — the workflow handles them.

Step 1: Set Research Agenda

If a topic argument was provided ($ARGUMENTS is not empty):

Research topic = $ARGUMENTS
Skip to Step 2.

Autonomous mode (no argument):

Read docs/iterations/LEARNINGS.md and all scanner domain files in docs/iterations/scanners/
Identify the 3-5 highest-leverage research opportunities:
- Scanners with low-confidence current understanding
- Pending hypotheses marked with - [ ]
- Gaps: signal types with no current scanner (e.g. dark pool flow, short interest changes, institutional 13F filings)
Rank by potential impact. Pick the top topic to research this run.
Print your agenda: "Researching: — Reason: "

Step 2: Search

Search the following sources for the research topic. For each source, look for: signal definition, entry/exit criteria, known statistical edge, known failure modes, data requirements.

Sources to search:

Reddit: r/algotrading, r/quant, r/investing (site:reddit.com)
QuantifiedStrategies (site:quantifiedstrategies.com)
Alpha Architect (site:alphaarchitect.com)
CSS Analytics (site:cssanalytics.wordpress.com)
Hacker News: search hn.algolia.com for the topic
GitHub: search for "quant scanner " and "trading strategy "
SSRN: search quantitative finance papers on the topic
arXiv q-fin section

Fetching content — always use Jina Reader:

For every URL you want to read, prepend https://r.jina.ai/ to get clean, LLM-ready markdown with JS rendering included. Examples:

# Instead of fetching a URL directly:
WebFetch("https://r.jina.ai/https://www.reddit.com/r/algotrading/search/?q=momentum&sort=top")
WebFetch("https://r.jina.ai/https://alphaarchitect.com/2023/01/momentum-investing/")
WebFetch("https://r.jina.ai/https://arxiv.org/abs/2401.12345")

For arXiv, prefer the structured API for discovery, then Jina for full text:

WebFetch("https://export.arxiv.org/api/query?search_query=ti:<topic>+cat:q-fin&max_results=5")

For Reddit, after finding post URLs via WebSearch, fetch the full thread via Jina:

WebFetch("https://r.jina.ai/https://www.reddit.com/r/algotrading/comments/<id>/")

Read at least 3-5 distinct sources before forming a conclusion.

Step 3: Cross-Reference Existing Knowledge

Check docs/iterations/scanners/ and docs/iterations/research/ for any prior work on this topic. Flag explicitly if this overlaps with:

An existing scanner (name it and the file)
A previously researched and discarded approach (cite the research file)
A pending hypothesis in an existing scanner file (cite it)

Step 4: Evaluate Fit

Score the finding on four dimensions (each: ✅ pass / ⚠️ partial / ❌ fail):

Data availability: Is the required data source already integrated in tradingagents/dataflows/? Check for existing API clients there.
Implementation complexity: trivial (<2 hours) / moderate (2-8 hours) / large (>8 hours)
Signal uniqueness: Low overlap with existing scanners = good. High overlap = flag as redundant.
Evidence quality: backtested with statistics / qualitative analysis / anecdotal only

Auto-implement threshold (all must pass for autonomous CI implementation):

Data availability: ✅ (data source already integrated)
Complexity: trivial or moderate
Uniqueness: low overlap
Evidence: qualitative or better

Step 5: Write Research File

Save findings to docs/iterations/research/ using filename format: YYYY-MM-DD-<topic-slug>.md where topic-slug is the topic lowercased with spaces replaced by hyphens.

Use this template:

# Research: <Topic>

**Date:** YYYY-MM-DD
**Mode:** directed | autonomous

## Summary
<2-3 sentences on what was found>

## Sources Reviewed
- <source 1>: <key finding from this source>
- <source 2>: <key finding from this source>
...

## Fit Evaluation
| Dimension | Score | Notes |
|-----------|-------|-------|
| Data availability | ✅/⚠️/❌ | ... |
| Complexity | trivial/moderate/large | ... |
| Signal uniqueness | low/medium/high overlap | ... |
| Evidence quality | backtested/qualitative/anecdotal | ... |

## Recommendation
Implement / Skip / Needs more data — <reason>

## Proposed Scanner Spec (if recommending implementation)
- **Scanner name:** `<name>`
- **Data source:** `tradingagents/dataflows/<existing_file>.py`
- **Signal logic:** <how to detect the signal, specific thresholds>
- **Priority rules:** CRITICAL if X, HIGH if Y, MEDIUM otherwise
- **Context format:** "<what to include in the candidate context string>"

Add an entry to docs/iterations/LEARNINGS.md under a ## Research section (create the section if it doesn't exist):

| research/<filename> | research/<filename>.md | YYYY-MM-DD | <one-line summary> |

Step 6: Implement (if threshold met)

If the finding meets the auto-implement threshold from Step 4:

Read tradingagents/dataflows/discovery/scanner_registry.py to understand the @SCANNER_REGISTRY.register() registration pattern.
Read an existing simple scanner for the code pattern: tradingagents/dataflows/discovery/scanners/earnings_calendar.py
Create tradingagents/dataflows/discovery/scanners/<name>.py following the same structure:
- Class decorated with @SCANNER_REGISTRY.register()
- name, pipeline, and strategy class attributes
- scan(self, state) method returning List[Dict]
- Each dict must have keys: ticker, source, context, priority
- Priority values: "CRITICAL", "HIGH", "MEDIUM", "LOW"
- All tunable parameters must use self.scanner_config.get("param", default) so they can be overridden from config without touching scanner code
Add an import in tradingagents/dataflows/discovery/scanners/__init__.py: from . import <name> # noqa: F401
Add a config entry to tradingagents/default_config.py under discovery.scanners.<name>. This is mandatory — without it the scanner cannot be enabled/disabled or tuned from config, and is invisible to the settings UI. Include every parameter that self.scanner_config.get() reads:
```
"<name>": {
    "enabled": True,
    "pipeline": "<pipeline>",
    "limit": 10,
    "<param>": <default>,  # one entry per scanner_config.get() call
},
```

If threshold is NOT met: write the research file only. Add this note at the top of the research file:

> **Auto-implementation skipped:** <reason — which threshold failed>

Step 7: Commit (skip if CI=true)

If the environment variable CI is set, stop here. The workflow handles git.

Otherwise:

git add docs/iterations/research/ tradingagents/ docs/iterations/LEARNINGS.md

Run git commit with a message in the format: research(<topic>): YYYY-MM-DD — <one-sentence summary of finding and action>

Then check for an existing open PR on branch research/current:

EXISTING=$(gh pr list --repo Aitous/TradingAgents --head research/current --state open --json number --jq '.[0].number // empty')

If one exists:

git push origin HEAD:research/current
gh pr edit "$EXISTING" --repo Aitous/TradingAgents \
  --body "$(cat docs/iterations/LEARNINGS.md | head -30)"

If none exists:

git checkout -b research/current
git push -u origin research/current
gh pr create \
  --repo Aitous/TradingAgents \
  --title "research: new strategy findings — $(date +%Y-%m-%d)" \
  --body "$(cat docs/iterations/LEARNINGS.md | head -30)" \
  --label "automated,research" \
  --base main

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