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Use when working on dataset materialization, model training, backtesting, scan-edge signals, paper-trading workflows, or experiment artifacts in polymarket-tmax-lab.
Mit Codex oder Claude installieren Kopieren Sie diesen Prompt, fügen Sie ihn in Codex, Claude oder einen anderen Assistant ein und lassen Sie die Skill-Seite prüfen und installieren.
Use when running the LGBM autoresearch loop, editing candidate YAML specs, gating new variants, or promoting winners in polymarket-tmax-lab.
Use when working on bootstrap-lab, warehouse management, seed export or restore, legacy raw/parquet cleanup, or canonical data layout in polymarket-tmax-lab.
Use when working on bootstrap-lab, warehouse management, seed export or restore, legacy raw/parquet cleanup, or canonical data layout in polymarket-tmax-lab.
Use when working on dataset materialization, model training, backtesting, scan-edge signals, paper-trading workflows, or experiment artifacts in polymarket-tmax-lab.
Use when running the LGBM autoresearch loop, editing candidate YAML specs, gating new variants, or promoting winners in polymarket-tmax-lab.
Use when changing Polymarket discovery, temperature market filtering, rule parsing, settlement source handling, station mapping, or official truth adapters.
Basierend auf der SOC-Berufsklassifikation
| name | pmtmax-research-loop |
| description | Use when working on dataset materialization, model training, backtesting, scan-edge signals, paper-trading workflows, or experiment artifacts in polymarket-tmax-lab. |
Use this skill for the research and trading simulation loop.
AGENTS.md.docs/agent-skills/research-loop.mddocs/codebase/modeling.mddocs/codebase/backtest-execution.mdNEVER record paper-trader output when book_source is missing or fixture-only — those rows are diagnostics and must not pollute forward_paper_trades.json.
Before running paper-trader, ALWAYS verify real temperature markets exist:
import httpx
r = httpx.get('https://gamma-api.polymarket.com/markets?active=true&limit=50', timeout=10)
data = r.json()
temp = [m for m in data if any(k in m.get('question','').lower() for k in ['temperature','highest','celsius','°c','°f'])]
print(f'Active temperature markets: {len(temp)}') # Must be > 0
After paper-trader, check book_source in artifacts/workspaces/ops_daily/signals/v2/paper_signals_latest.json:
book_source contains "clob"book_source is missing or fixture-onlyTemperature markets open daily — real prices available ~KST 09:00. Run paper-trader after markets open.
forward_paper_trades.json was reset 2026-04-22 after non-CLOB priced trades were found.
weather_train with weather_real; it must not include Polymarket ids, rules, price history, CLOB books, or publish evidence.collect-weather-training should now be treated as a monitored batch step:
keep stderr progress enabled unless a wrapper needs quiet output, and prefer
short HTTP bounds (--http-timeout-seconds 15 --http-retries 1) for free-tier
Open-Meteo historical-forecast collection. Keep --max-consecutive-429 2;
two consecutive Open-Meteo 429 responses are a daily-limit hit, so cancel
the remaining chunk and record rate-limit-cancelled in checker state.scripts/pmtmax-workspace weather_train uv run python scripts/run_weather_train_queue_agent.py
It advances the next 7-day checker queue and auto-refreshes
gaussian_emos pretrain once the row-gap threshold is reached. It defaults
to the two-consecutive-429 cancel rule.weather_train gold has
11,499 rows. Full successful ranges are 2024-01-03..2024-05-24,
2024-06-01..2024-12-30, and 2026-01-01..2026-01-14; retry-only/free-path
daily-limit ranges include 2025-01-07..2025-01-27 and
2026-01-22..2026-01-28. Read live details from
checker/weather_train_status.md.checker/; update
checker/weather_train_status.md and checker/weather_train_collection_log.md
after every run.checker/; update checker/historical_price_status.md and
checker/historical_price_collection_log.md after every recovery run.weather_train queue
collection in parallel with historical_real backfill-price-history because
those jobs use separate workspace roots. Do not overlap multiple mutating
historical_real jobs; finish backfill-price-history before
materialize-backtest-panel, and finish panel rebuild before benchmark or
publish gates.scripts/pmtmax-workspace historical_real uv run python scripts/run_historical_price_recovery_agent.py
It advances one checker shard by default and records the latest panel-ready
decision rows plus the latest backtest priced_decision_rows anchor.scripts/pmtmax-workspace historical_real uv run python scripts/run_model_research_agent.py
It keeps one active autoresearch run aligned to the current dataset/panel
signatures, auto-creates the next small candidate YAML when needed, and
tracks train/step/gate/paper/promote state under checker/model_research_*.historical_real / real_market only.--model values when
materializing from the warehouse. The base config can default to GFS-only;
compare source feature counts before training so a row-count increase is not
mistaken for a multi-source dataset.book_source=fixture; missing CLOB books must be missing_book, not tradable signals.synthetic_ market ids, fixture forecasts, and fabricated books are trust violations in canonical research.build-dataset should still be run with an explicit --markets-path configs/market_inventory/full_training_set_snapshots.json.
Without it the CLI now auto-defaults to that same inventory (printing a yellow warning) when the file exists, and a
shrinkage guard (refuses to overwrite when the new frame is <50% of the existing row count) blocks accidental shrink.
Only if the default inventory is missing does it fall back to the ~12 bundled example rows.full_training_set_snapshots.json is a checked-in training inventory. It is not the auto-refreshed canonical historical backlog.configs/market_inventory/historical_temperature_snapshots.json first and then intentionally regenerate full_training_set_snapshots.json from that curated inventory.historical_training_set* / historical_backtest_panel overwrite requires --allow-canonical-overwrite.--output-name values for experiments and rely on the automatic artifacts/recovery/ backup when promoting canonical output.scan-edge MUST include --min-model-prob 0.05 --max-model-prob 0.95.0 9 * * * for scripts/daily_experiment.sh; 0 0 * * * is only the equivalent on a UTC host.scripts/run_price_check.sh and redirect to an absolute logs/price_check.log path. Do not put raw uv run python scripts/log_gamma_prices.py commands directly in crontab.train-advanced --model-name lgbm_emos --variant <variant>.
Passing --pretrained-weather-model injects weather_pretrain_* features at
fit/predict time, not just sidecar lineage. The first high_neighbor_oof
quick eval with active injection worsened CRPS_C from 0.8004 to 0.8241,
and the delta_only follow-up still worsened to 0.8111; do not promote
weather-pretrain feature candidates without a stronger candidate-specific gate.historical_training_set_curated_multisource_20260425
restored all four NWP source feature groups and improved canonical baseline
holdout CRPS_C (0.8004 -> 0.6749), but on the curated holdout it worsened
CRPS_C versus GFS-only (2.0559 -> 2.8771) despite better Brier/DirAcc due
to under-dispersed, cold-biased tails. Do not promote before variance/tail
calibration.scripts/run_tail_calibration_experiment.py
produced diagnostic wrappers under
artifacts/workspaces/historical_real/models/curated_multisource_tailcal_20260426/.
Balanced tailcal reduced curated holdout CRPS_C to 1.7195 and kept baseline
holdout near canonical baseline at 0.8043, but the root signal was
zero-Celsius NWP daily-max sentinels (32F / 0C) marked available. Treat
this as diagnostic; fix feature validity upstream before promotion.historical_training_set_curated_multisource_sentinelfix_20260426 removed
all-source zero-C sentinel rows (1083 -> 0) and retrained
lgbm_emos/high_neighbor_oof, but curated holdout CRPS_C remained worse than
same-holdout GFS-only (2.7690 vs 2.1103).scripts/run_source_gating_experiment.py trained binary/regret-weighted
train-side gates between sentinel-fix multi-source and GFS-only. Best curated
holdout CRPS_C was only 2.7607 vs raw primary 2.7690 and GFS-only
2.1103; baseline holdout stayed safe (0.4932 best vs raw 0.4965).
Gate fallback use stayed near 2% on curated holdout, so this is diagnostic
only. Next experiment should target source-disagreement/region-driven variance
calibration or proper OOF stacking, not simple binary fallback gating.scripts/run_disagreement_calibration_experiment.py showed variance-only
source-disagreement calibration improves curated holdout CRPS_C to 2.2342
but still loses to GFS-only 2.1103. Positive primary-vs-GFS disagreement
mean shrink plus variance calibration improves curated holdout CRPS_C to
1.3153 with Brier 0.0748, but worsens baseline holdout CRPS_C to
0.5627 from raw 0.4965. Treat as the strongest diagnostic wrapper so far,
not promotion. Next serious candidate should be a non-holdout-tuned LGBM
disagreement feature variant or proper OOF stacker.2,602
snapshots. That verified backlog was promoted into
full_training_set_snapshots.json as a sharded manifest plus
full_training_set_snapshots.d/part-000..003.json; canonical v2
historical_training_set now has 7,794 rows / 2,598 markets and zero
all-source daily-max sentinels. Canonical v2 historical_backtest_panel has
68,550 token rows with 30,615 ok, 37,501 missing, and 434 stale. No
two-consecutive-429 cancellation occurred in discovery120, and follow-up
discovery160 appended 0 URLs. A later broader discovery appended 22
URL-manifest entries that still need snapshot promotion. This is dataset
promotion, not model promotion.uv run python scripts/quick_eval.py (champion baseline + OOF variants).observation-report, observation-shadow, observation-daemon, approve-live-candidate.paper-multimodel-report, execution-sensitivity-report, market-bottleneck-report.execution-watchlist-playbook.exact_public intraday -> documented research intraday -> METAR fallback, target-day only.station-dashboard, station-dashboard-daemon.artifacts/workspaces/ops_daily/signals/v2/execution_watchlist_playbook.json when present and raises Tier A ask-threshold alerts without changing live guardrails.station-cycle, station-daemon.artifacts/workspaces/ops_daily/benchmarks/v2/benchmark_summary.jsonartifacts/public_models/champion.json.lgbm_emos / high_neighbor_oof.pmtmax-autoresearch when you are exploring new lgbm_emos candidates around the current champion or the run manifest baseline.publish_gate.decision=GO are invalid; republish with publish-champion after a real recent-core GO instead of using or repairing them manually.scripts/pmtmax-workspace weather_train uv run pmtmax collect-weather-training --city Seoul --date-from 2024-01-01 --date-to 2024-01-07 --http-timeout-seconds 15 --http-retries 1 --missing-only
scripts/pmtmax-workspace weather_train uv run pmtmax train-weather-pretrain --model-name gaussian_emosscripts/pmtmax-workspace historical_real uv run pmtmax build-dataset --markets-path configs/market_inventory/full_training_set_snapshots.json --forecast-missing-only --allow-canonical-overwritescripts/pmtmax-workspace historical_real uv run pmtmax materialize-backtest-panel --markets-path configs/market_inventory/full_training_set_snapshots.json --allow-canonical-overwritescripts/pmtmax-workspace historical_real uv run pmtmax summarize-price-history-coverage --markets-path configs/market_inventory/full_training_set_snapshots.json
scripts/pmtmax-workspace historical_real uv run pmtmax backfill-price-history --markets-path configs/market_inventory/full_training_set_snapshots.json --only-missing --price-no-cache --limit-markets 25
scripts/pmtmax-workspace historical_real uv run pmtmax materialize-backtest-panel --markets-path configs/market_inventory/full_training_set_snapshots.json --allow-canonical-overwriteuv run pmtmax bootstrap-lab --forecast-missing-onlyscripts/pmtmax-workspace historical_real uv run pmtmax backfill-forecasts --markets-path configs/market_inventory/historical_temperature_snapshots.json --strict-archive --missing-onlyscripts/pmtmax-workspace historical_real uv run pmtmax backfill-forecasts --markets-path configs/market_inventory/historical_temperature_snapshots.json --strict-archive --missing-only --single-run-horizon market_open --single-run-horizon previous_evening --single-run-horizon morning_ofscripts/pmtmax-workspace historical_real uv run pmtmax materialize-training-set --markets-path configs/market_inventory/historical_temperature_snapshots.json --model ecmwf_ifs025 --model ecmwf_aifs025_single --model kma_gdps --model gfs_seamless --output-name <variant>scripts/pmtmax-workspace historical_real uv run python scripts/run_tail_calibration_experiment.pyscripts/pmtmax-workspace historical_real uv run python scripts/run_source_gating_experiment.pyscripts/pmtmax-workspace historical_real uv run python scripts/run_disagreement_calibration_experiment.pyscripts/run_recent_core_benchmark_local.sh --model-name lgbm_emos --variant <variant> --retrain-stride 1scripts/pmtmax-workspace recent_core_eval uv run python scripts/run_recent_core_benchmark.py --model-name lgbm_emos --variant <variant> --retrain-stride 10 --backtest-last-n 60 --prebuilt-dataset-path data/workspaces/historical_real/parquet/gold/v2/historical_training_set.parquet --prebuilt-panel-path data/workspaces/historical_real/parquet/gold/v2/historical_backtest_panel.parquet --prebuilt-last-n-market-days 90data/workspaces/historical_real/parquet/gold/v2/historical_training_set.parquetartifacts/workspaces/<workspace>/models/v2/artifacts/public_models/champion.jsonartifacts/workspaces/ops_daily/signals/v2/scan_edge_latest.jsonartifacts/workspaces/ops_daily/signals/v2/live_pilot_queue.jsonartifacts/workspaces/ops_daily/signals/v2/forward_paper_trades.jsonlogs/daily_experiment.logscripts/run_price_check.shlogs/price_check.logconfigs/paper-exploration.yamlconfigs/paper-all-supported-horizon-policy.yamlartifacts/workspaces/ops_daily/signals/v2/execution_watchlist_playbook.json