184 lines
6.5 KiB
Markdown
184 lines
6.5 KiB
Markdown
# /research-strategy
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Research new trading strategies or scanner improvements, evaluate fit against
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the existing pipeline, write findings to `docs/iterations/research/`, and
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implement the top-ranked finding as a new scanner if it qualifies.
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Usage:
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- `/research-strategy` — autonomous mode: Claude picks research topics
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- `/research-strategy "topic"` — directed mode: research a specific strategy
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In CI (`CI=true`), stop before git operations — the workflow handles them.
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---
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## Step 1: Set Research Agenda
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**If a topic argument was provided** (`$ARGUMENTS` is not empty):
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- Research topic = `$ARGUMENTS`
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- Skip to Step 2.
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**Autonomous mode** (no argument):
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- Read `docs/iterations/LEARNINGS.md` and all scanner domain files in
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`docs/iterations/scanners/`
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- Identify the 3-5 highest-leverage research opportunities:
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- Scanners with low-confidence current understanding
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- Pending hypotheses marked with `- [ ]`
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- Gaps: signal types with no current scanner (e.g. dark pool flow,
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short interest changes, institutional 13F filings)
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- Rank by potential impact. Pick the top topic to research this run.
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- Print your agenda: "Researching: <topic> — Reason: <why this was prioritized>"
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## Step 2: Search
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Search the following sources for the research topic. For each source, look for:
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signal definition, entry/exit criteria, known statistical edge, known failure
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modes, data requirements.
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**Sources to search:**
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- Reddit: r/algotrading, r/quant, r/investing (site:reddit.com)
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- QuantifiedStrategies (site:quantifiedstrategies.com)
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- Alpha Architect (site:alphaarchitect.com)
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- CSS Analytics (site:cssanalytics.wordpress.com)
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- Hacker News: search hn.algolia.com for the topic
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- GitHub: search for "quant scanner <topic>" and "trading strategy <topic>"
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- SSRN: search quantitative finance papers on the topic
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- arXiv q-fin section
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Use WebSearch and WebFetch to retrieve actual content. Read at least 3-5
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distinct sources before forming a conclusion.
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## Step 3: Cross-Reference Existing Knowledge
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Check `docs/iterations/scanners/` and `docs/iterations/research/` for any
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prior work on this topic. Flag explicitly if this overlaps with:
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- An existing scanner (name it and the file)
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- A previously researched and discarded approach (cite the research file)
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- A pending hypothesis in an existing scanner file (cite it)
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## Step 4: Evaluate Fit
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Score the finding on four dimensions (each: ✅ pass / ⚠️ partial / ❌ fail):
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1. **Data availability**: Is the required data source already integrated in
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`tradingagents/dataflows/`? Check for existing API clients there.
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2. **Implementation complexity**: trivial (<2 hours) / moderate (2-8 hours) /
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large (>8 hours)
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3. **Signal uniqueness**: Low overlap with existing scanners = good.
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High overlap = flag as redundant.
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4. **Evidence quality**: backtested with statistics / qualitative analysis /
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anecdotal only
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**Auto-implement threshold** (all must pass for autonomous CI implementation):
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- Data availability: ✅ (data source already integrated)
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- Complexity: trivial or moderate
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- Uniqueness: low overlap
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- Evidence: qualitative or better
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## Step 5: Write Research File
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Save findings to `docs/iterations/research/` using filename format:
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`YYYY-MM-DD-<topic-slug>.md` where topic-slug is the topic lowercased with
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spaces replaced by hyphens.
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Use this template:
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```
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# Research: <Topic>
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**Date:** YYYY-MM-DD
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**Mode:** directed | autonomous
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## Summary
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<2-3 sentences on what was found>
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## Sources Reviewed
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- <source 1>: <key finding from this source>
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- <source 2>: <key finding from this source>
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...
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## Fit Evaluation
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| Dimension | Score | Notes |
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|-----------|-------|-------|
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| Data availability | ✅/⚠️/❌ | ... |
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| Complexity | trivial/moderate/large | ... |
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| Signal uniqueness | low/medium/high overlap | ... |
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| Evidence quality | backtested/qualitative/anecdotal | ... |
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## Recommendation
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Implement / Skip / Needs more data — <reason>
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## Proposed Scanner Spec (if recommending implementation)
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- **Scanner name:** `<name>`
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- **Data source:** `tradingagents/dataflows/<existing_file>.py`
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- **Signal logic:** <how to detect the signal, specific thresholds>
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- **Priority rules:** CRITICAL if X, HIGH if Y, MEDIUM otherwise
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- **Context format:** "<what to include in the candidate context string>"
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```
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Add an entry to `docs/iterations/LEARNINGS.md` under a `## Research` section
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(create the section if it doesn't exist):
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```
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| research/<filename> | research/<filename>.md | YYYY-MM-DD | <one-line summary> |
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```
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## Step 6: Implement (if threshold met)
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If the finding meets the auto-implement threshold from Step 4:
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1. Read `tradingagents/dataflows/discovery/scanner_registry.py` to understand
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the `@SCANNER_REGISTRY.register()` registration pattern.
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2. Read an existing simple scanner for the code pattern:
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`tradingagents/dataflows/discovery/scanners/earnings_calendar.py`
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3. Create `tradingagents/dataflows/discovery/scanners/<name>.py` following
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the same structure:
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- Class decorated with `@SCANNER_REGISTRY.register()`
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- `name` and `pipeline` class attributes
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- `scan(self, state)` method returning `List[Dict]`
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- Each dict must have keys: `ticker`, `source`, `context`, `priority`
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- Priority values: `"CRITICAL"`, `"HIGH"`, `"MEDIUM"`, `"LOW"`
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4. Check `tradingagents/dataflows/discovery/scanners/__init__.py` — if it
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imports scanners explicitly, add an import for the new one.
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If threshold is NOT met: write the research file only. Add this note at the
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top of the research file:
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```
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> **Auto-implementation skipped:** <reason — which threshold failed>
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```
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## Step 7: Commit (skip if CI=true)
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If the environment variable `CI` is set, stop here. The workflow handles git.
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Otherwise:
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```bash
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git add docs/iterations/research/ tradingagents/ docs/iterations/LEARNINGS.md
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```
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Run `git commit` with a message in the format:
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`research(<topic>): YYYY-MM-DD — <one-sentence summary of finding and action>`
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Then check for an existing open PR on branch `research/current`:
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```bash
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EXISTING=$(gh pr list --repo Aitous/TradingAgents --head research/current --state open --json number --jq '.[0].number // empty')
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```
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If one exists:
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```bash
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git push origin HEAD:research/current
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gh pr edit "$EXISTING" --repo Aitous/TradingAgents \
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--body "$(cat docs/iterations/LEARNINGS.md | head -30)"
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```
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If none exists:
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```bash
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git checkout -b research/current
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git push -u origin research/current
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gh pr create \
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--repo Aitous/TradingAgents \
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--title "research: new strategy findings — $(date +%Y-%m-%d)" \
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--body "$(cat docs/iterations/LEARNINGS.md | head -30)" \
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--label "automated,research" \
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--base main
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```
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