---

name: daily-synthesis description: Phase 2 of the daily trade report. Converts the research phase's notes.md into a schema-valid report.json, honestly reflecting whatever research actually completed. allowed-tools: Bash, Read, Write, Edit, Glob, Grep

Daily synthesis phase

Convert the research log into reports/<date>/report.json, valid against scripts/report_schema.json.

DATE=$(TZ=America/New_York date +%F)
cat "reports/$DATE/candidates.jsonl"   # the real input
wc -c "reports/$DATE/notes.md"         # context and rejections

Step zero: write a valid report before anything else

Your first action is to write a schema-valid report.json with an empty recommendations array. Then improve it in place as you work.

You are on a timer too. A previous run read 38,000 characters of good research, started reasoning about it, and was cut off having written nothing — turning a strong research day into a total loss. A valid file on disk from minute one means the worst case is a thin report instead of no report.

DATE=$(TZ=America/New_York date +%F)
python - <<PY
import json, pathlib
from datetime import datetime
from zoneinfo import ZoneInfo
p = pathlib.Path("reports/$DATE/report.json")
p.write_text(json.dumps({
    "date": "$DATE",
    "generated_at_et": datetime.now(ZoneInfo("America/New_York")).isoformat(timespec="seconds"),
    "truncated": True,
    "data_quality_notes": "Synthesis in progress.",
    "market_context": {"summary": "Synthesis in progress.", "regime": "unknown"},
    "recommendations": [],
}, indent=2))
print("skeleton written")
PY

Rewrite it with real content as soon as you have your first finished recommendation, and again as you add each one. Do not hold the finished report in your head until the end.

Your job is to be a faithful editor, not a second researcher

The research phase may have been killed mid-sentence. Whatever is in notes.md is what you have. You may re-read files and run scripts/market_data.py to fill a missing current price or recompute a risk/reward ratio — that is it.

Do not invent candidates, prices, catalysts, or sources that are not in the notes. If the notes contain three usable candidates, publish three. If they contain zero, publish zero and explain why in data_quality_notes. An honest thin report is a correct output. A padded report is a failure that will be traded on.

Steps

1. Read candidates.jsonl first. Each line is a validated candidate, already in report shape. Where a symbol appears more than once, the last entry wins — research captures improved versions as it goes.

   Then read notes.md and prior_context.md for context, rejections, and position updates. notes.md uses whatever headings the research phase chose — do not expect a fixed format, and do not skip material because it is not laid out the way you expected.

   If candidates.jsonl is missing or thin but notes.md is substantial, the research phase was cut off before consolidating. Reconstruct what you can from the notes: any finding with a symbol, a direction, a level, and a source is a usable recommendation. Say in data_quality_notes that you reconstructed it, and be honest that levels not set during research are weaker. Reconstructing beats publishing nothing — a full page of researched findings and an empty report is the one outcome to avoid.

   A POSITION UPDATE becomes a normal recommendation for that symbol, with the revised levels and a thesis that opens by naming it as an update to an open position — for example, "Update to the 2026-08-05 long: thesis intact, raising the stop to 176." Position updates do not count against the 6–8 idea target; they are position management, not new risk.

2. Filter. Drop anything below conviction 3, anything missing a stop where config/strategy.md requires one, and anything under the reward-to-risk floor (2.0 swing, 1.5 intraday). Enforce the correlation cap: at most 3 ideas depending on the same driver.

3. Rank by conviction, then reward-to-risk, then catalyst proximity.

4. Refresh prices for the finalists with python scripts/market_data.py quote <symbols> so last_price is as current as the run allows. Set last_price: null if it cannot be fetched.

5. Recompute risk_reward from the final entry/target/stop rather than trusting the number in the notes. Drop any idea whose recomputed ratio falls below the floor.

6. Write report.json and validate it.

7. Verify before finishing:

python -c "
import json, jsonschema, pathlib, os
d = os.popen('TZ=America/New_York date +%F').read().strip()
r = json.loads(pathlib.Path(f'reports/{d}/report.json').read_text())
jsonschema.validate(r, json.loads(pathlib.Path('scripts/report_schema.json').read_text()))
print(f'valid — {len(r[\"recommendations\"])} recommendations')
"

Fix and re-validate until it passes. A schema-invalid file fails the publish step and the morning produces nothing.

Fields that carry the weight

catalyst.action is the field the reader acts on. It must say what to do relative to the event: enter before, wait for, trim into, avoid until. When the right move today is to not enter yet, set catalyst.wait: true — the publisher marks it ⏸ WAIT and holds it out of performance tracking.

entry.condition carries any precondition: only if CPI prints above consensus, only on a retest of 178 with volume. Do not bury a condition in the thesis where it will be missed.

thesis is two to three sentences, specific and falsifiable. Name the mechanism. "AI tailwinds" is not a thesis; "three consecutive hyperscaler capex raises with supply commentary pointing above consensus" is.

key_risk is the one thing most likely to break it, not a generic warning. "Market could go down" is filler.

sources must be URLs actually visited during research. Never synthesize a plausible-looking URL.

Truncation and honesty

Set truncated: true if the notes lack a RESEARCH COMPLETE block — that means the research phase was cut off. Then use data_quality_notes to say plainly what that cost:

Research phase hit its 60-minute cap during the falsification pass. Four candidates are fully specified; two others in the notes lacked stops and were dropped. Crypto and event markets were not reached this run.

Also record in data_quality_notes: sources that failed, prices that could not be fetched, and why the idea count is what it is if it is below target.

This field is what makes the report trustworthy over time. Write it as though the reader is deciding how much size to put on.