glostat 1.6.1

GLOSTAT — Evidence-based probability predictor for global equities (개선된 TITAN의 open-source 진화형). Brier-calibrated multi-thesis ensemble. Information tool, not investment advice.

GLOSTAT — Evidence-based Probability Predictor for Global Equities

개선된 TITAN의 open-source 진화형 / Calibrated multi-horizon predictions with evidence chains.

Information tool. Not investment advice. Past calibration ≠ future performance.

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Status

v1.2 ACTIVE (2026-04-30) — KR calibration (L1) + DART API (L2) landed. Previous: v1.1 (2026-04-29) — KR support (K1). | v1.0 (2026-04-29) — Reframe of v0.7.

v0.6/v0.7 framed GLOSTAT as a decision engine (BUY/SELL action output) and concluded "8 thesis FAIL" against a Sharpe ≥ 0.8 / AUC ≥ 0.62 / OOS deg ≤ 30% gate. v1.0 reframes the same data as a prediction tool: probability distribution + 90% CI + per-thesis Brier-weighted contribution + evidence chain. The 8-thesis FAIL outcomes are now the first input rows of the calibration table — E_PEAD AUC 0.587, E_FOREIGN_REVERSAL OOS Sharpe 1.46, E_FOMC_DRIFT AUC 0.357 (anti-predictor) — all carry honest, sample-aware weights.

Read first: docs/post_mortem/SPRINT5_FAIL_post_mortem.md (the v0.6 honest diagnosis), then docs/ssot/PLAN_v1.0.md (the v1.0 spec), then docs/CALIBRATION.md (the empirical predictive strength table).

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What this project IS

• A calibrated probability predictor — outputs Prediction(p_up, p_up_lower, p_up_upper, contributing, ...) with Brier-derived ensemble weights per thesis.
• A deterministic hindcast harness — turns any thesis into a calibration row (Brier + AUC + Sharpe + OOS) with explicit IS/OOS split and reproducibility guarantees.
• A snapshot broker — every external data response persisted as a parquet shard + SQLite index + Merkle leaf, so any prediction can be replayed bit-for-bit months later.
• An open-source research framework — MIT, fork-friendly, designed so third-party thesis authors can plug in and contribute calibration data.
• A compliance gate that makes broadcast permanently impossible (ComplianceError on Telegram / mass-email entry points; INV-GS-024) and stamps a personal-use, not-investment-advice disclaimer on every prediction (INV-GS-104).
• A prompt registry that pins each LLM call to a sha256 so the prompt graph is auditable across versions.
• 45+ numbered invariants (INV-GS-001..105) with a 1:1 unit-test mapping and a machine-readable configs/invariants.yaml.

What this project IS NOT

• Not investment advice. Use at your own risk. Read the post-mortem first.
• Not a trading bot. No BUY/SELL action output (INV-GS-101). No target/stop/size output. The Prediction dataclass deliberately omits any field that prescribes action.
• Not an alpha-generating decision engine. v0.6 attempted that, failed honestly across 8 thesis. The framework's value is the **honest measurement
  • Brier weighting**, not a guaranteed alpha.
• Not a black-box predictor. Every Prediction carries a ThesisContribution chain with calibration window, n_samples, AUC, Brier weight, and source IDs.
• Not a broadcast tool. broadcast_telegram and mass_email are inert sentinels that always raise (INV-GS-024).
• Not a multi-user product. Personal use only.

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If you used TITAN, GLOSTAT v1.0 is its open-source global evolution

Dimension	TITAN	GLOSTAT v1.0
Markets	KR (KOSPI/KOSDAQ) only	Global (US, KR, FX, commodities, crypto)
Output	STRONG_BUY..STRONG_SELL action + directive + target/stop	Prediction(p_up, CI, contributing, evidence_hash)
Compliance	Telegram bot historically active	broadcast_telegram raises (INV-GS-024); per-prediction disclaimer (INV-GS-104)
Reproducibility	Local cache	Snapshot Broker (Merkle leaf + parquet shard + SQLite)
Calibration	Single B4 historical run (60.3% hit)	Quarterly recalibration → calibration_table.parquet
Weights	Heuristic engine ratios	Brier-score sigmoid weighting (sample-size aware)
Distribution	Private repo	MIT open-source
Honesty	"PEAD 60%"	"PEAD AUC 0.587, n=298, weight 0.18"
Scope discipline	All 9 engines on	Weak thesis auto-weight 0

GLOSTAT v1.0 inherits TITAN's engine-ensemble pattern and hindcast-first discipline, then layers on global coverage, calibrated probability output, formal reproducibility, and a hard compliance gate.

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Supported markets (v1.1)

Market	MIC	Status	Universe	Data sources
US large-cap	XNAS, XNYS	ACTIVE (v1.0)	S&P 500 Top 50 (sp500_top50.txt)	yfinance + SEC EDGAR
KR (KOSPI)	XKRX	ACTIVE (v1.1 K1)	KOSPI 200 (kospi200.txt)	yfinance (.KS) + Naver Finance
KOSDAQ	XKOS	partial (yfinance .KQ only; Naver pending)	—	yfinance (.KQ)
Crypto perp	BINANCE_PERP	research-only (Phase 1D)	BTC/ETH	CCXT
FX/Commodity ETFs	NYSE/CBOE	partial	(per-thesis)	yfinance + CFTC

KR predictions use E_FUNDAMENTAL_KR (yfinance .KS PER/ROE/dividend yield)

• E_FOREIGN_REVERSAL (Naver Finance 외인/기관 4-day reversal pattern, TITAN B4 port) + E_TIME (Ichimoku — universe-agnostic). See docs/KR_SUPPORT.md for the full guide.

# v1.1 K1: live KR prediction (no Bigdata MCP, $0 cost)
GLOSTAT_SEC_USER_AGENT="Your Name your@email" \
  glostat predict 096770   # SK Innovation
glostat predict 005930   # 삼성전자

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What we tested (8 thesis → calibration data, not failures)

The numbers below come from the v0.6/v0.7 hindcast runs preserved in cache/hindcast/ and reframed here as the v1.0 calibration baseline.

Thesis	Universe	n	AUC	Sharpe	OOS deg	v1.0 weight*
E_PEAD	US 50	298	0.587	+0.63	116%	0.18
E_FOREIGN_REVERSAL	KR 20	424	0.467	+0.58	0%	0.14
E_INSIDER_CLUSTER	US 19	11	0.339	+0.78	0%	0.05
E_COMMODITY_TS	Cmdy 10	517	0.489	+0.14	100%	0.06
E_SECTOR_ROTATION	US 11 sectors	174	0.470	-0.48	100%	0.00
E_FOMC_DRIFT	US 12	135	0.357	-1.34	100%	0.00
E_FX_CARRY	US/FX 8	135	0.400	-1.53	100%	0.00
E_FUNDING_CARRY	Crypto 2	4922	0.505	-0.23	457%	0.02

*Brier-derived weight (illustrative — actual values computed at run time). Full table + interpretation: docs/CALIBRATION.md.

The v0.6 verdict on the same data: "8 thesis FAIL → automatic shutdown." The v1.0 verdict: "8 calibrated signals, composite p_up exists with explicit confidence interval, weak/anti-predictive signals carry near-zero weight."

Both readings are honest. v1.0 is the more useful one.

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Architecture overview

                ┌─────────────────────────────────────────────────────┐
                │                CLI / library entry                   │
                │   glostat predict <ticker>      glostat calibrate    │
                └─────────────────────────────────────────────────────┘
                                       │
                ┌──────────────────────┼──────────────────────┐
                ▼                      ▼                      ▼
        DataRouter             Compliance Gate         PromptRegistry
        (phase-gated)          (broadcast=ERROR)       (sha256 per call)
                │
   ┌────────────┼────────────┬────────────┐
   ▼            ▼            ▼            ▼
yfinance    SEC EDGAR    CFTC/CCXT    Bigdata MCP   ← Phase 2+, blocked in MVP
   │            │            │            │
   └────────────┴────────────┴────────────┘
                │
                ▼
        Snapshot Broker  ───►  parquet shards + SQLite index + Merkle leaves
                │
                ▼
            Thesis modules  ───►  raw_score, direction, sources
                │
                ▼
   ┌─────────────────────────────────────┐
   │  predictor/composite.py             │
   │   composite_p_up()  +  Brier weights│   ← INV-GS-103
   └─────────────────────────────────────┘
                │
                ▼
            Prediction  ───►  p_up + CI + ThesisContribution[] + disclaimer
                │
                ▼  (calibration loop, quarterly)
       calibration_table.parquet
                │
                ▼
        docs/CALIBRATION.md (auto-regenerated)

Snapshot broker

from datetime import UTC, datetime
from pathlib import Path

from glostat.data.snapshot_broker import SnapshotBroker, SnapshotKey

broker = SnapshotBroker(root=Path("./snapshots"))
key = SnapshotKey(
    uaid="XNAS.AAPL",
    edge_type="tearsheet",
    ts_utc=datetime(2026, 4, 28, 12, 0, tzinfo=UTC),
    tool="yfinance.fundamentals",
    params_canon='{"period":"quarter"}',
)
record = broker.save_snapshot(key, {"per": 28.4, "roe": 0.21})
print(record.leaf.leaf_hash[:12], broker.audit_root()[:12])
broker.close()

Calibrated prediction (v1.0)

from glostat.predictor.composite import composite_p_up
from glostat.core.types import Prediction

prediction: Prediction = pipeline.predict("AAPL", horizon="5d")
print(f"p_up = {prediction.p_up:.3f}  90%CI=[{prediction.p_up_lower:.3f}, {prediction.p_up_upper:.3f}]")
for c in prediction.contributing:
    print(f"  {c.thesis_name:24} dir={c.direction:4} weight={c.brier_weight:.3f}  AUC={c.auc:.3f}  n={c.n_calibration_samples}")
print(prediction.disclaimer)

Compliance gate (cannot be bypassed)

from glostat.risk.compliance_gate import broadcast_telegram, ComplianceContext

broadcast_telegram(
    ctx=ComplianceContext(user_profile_hash="0" * 64, jurisdiction="US"),
    chat_ids=["@anyone"], message="anything",
)
# → glostat.risk.compliance_gate.ComplianceError: INV-GS-024 …

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Quickstart

Requires Python 3.14.

# clone + install (uv preferred)
git clone https://github.com/<you>/glostat.git
cd glostat
uv sync --extra dev

# verify
uv run pytest -q                                   # all unit tests
uv run python -c "import glostat; print(glostat.__version__)"   # → 1.0.0

# v1.0 prediction (mock data, no network)
uv run glostat predict AAPL --horizon 5d --mock

# canonical JSON output for downstream tooling
uv run glostat predict AAPL --horizon 5d --mock --json

# quarterly recalibration (re-run all thesis hindcasts → update table)
uv run glostat calibrate --all-thesis --window 365d
uv run glostat calibrate --update-table

Live mode requires GLOSTAT_SEC_USER_AGENT="Your Name [e-mail]" (SEC EDGAR mandates a contactable User-Agent — INV-GS-038).

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Reusable for your own thesis

The infrastructure is independent of which thesis you screen. To add a new thesis to the calibration table:

1. Write a thesis module. Subclass the Thesis protocol in src/glostat/experts/, return a typed (direction, raw_score, sources). See docs/EXAMPLES.md for a working template.
2. Register a data source if needed. Add a routing entry in src/glostat/data/data_router.py. The DataRouter enforces phase gating so paid sources stay blocked until you explicitly opt in.
3. Run the hindcast. Configure Hindcast, point at a universe, get an IS/OOS report with AUC, Sharpe, Brier.
4. Add a calibration row. Append the result to cache/calibration_table.parquet (one row per thesis-universe-horizon triple). The Brier-weighted ensemble picks the weight automatically.
5. PR with calibration data attached. New thesis PRs must include n ≥ 50, AUC, Sharpe, OOS deg and a calibration row. (INV-GS-026 + INV-GS-105.)

Full walkthrough: docs/EXAMPLES.md. Migration from v0.7: docs/MIGRATION_v0.7_TO_v1.0.md.

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Repo layout

src/glostat/
  core/         # Prediction (v1.0), ThesisContribution (NEW), Verdict (deprecated, kept for back-compat)
  data/         # snapshot broker, prompt registry, free-stack clients, phase-gated DataRouter
  experts/      # 11 thesis modules (PEAD, FOREIGN_REVERSAL, INSIDER_CLUSTER, FX_CARRY, …)
  predictor/    # NEW v1.0 — composite_p_up(), thesis_weight() (Brier sigmoid), calibration I/O
  gating/       # cost gate, regime gate (kept; used during hindcast as calibration mask)
  replay/       # hindcast harness, sprint4_gate (now calibration check), kill criteria
  risk/         # compliance gate (INV-GS-024 + INV-GS-104)

configs/
  invariants.yaml    # 45 numbered invariants (001..105), v0.6 deprecated entries flagged
  budget.yaml        # phase-gated budget caps (mvp $0)
  markets.yaml       # XNAS + XNYS + XKRX
  gating.yaml        # cost / regime / anti-herd parameters (decision-engine vintage; used as calibration mask only in v1.0)
  kill_criteria.yaml # narrowed v1.0 triggers (compliance, broker integrity, stale calibration)
  universes/

cache/
  calibration_table.parquet  # NEW v1.0 — quarterly-updated weights per thesis-universe-horizon
  hindcast/                  # phase1b + phase1c + phase1d historical reports

tests/                       # 506+ pytest tests, INV-GS-001..105 coverage
docs/
  ssot/                      # immutable plan history v0.1 → v0.7 + PLAN_v1.0.md (canonical)
  post_mortem/               # honest Sprint 5 FAIL diagnosis (v0.6)
  research/                  # design notes
  CALIBRATION.md             # NEW — per-thesis empirical predictive strength
  MIGRATION_v0.7_TO_v1.0.md  # NEW — developer migration guide
  EXAMPLES.md                # extending the framework

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Honest reading order

If you are evaluating whether to adopt or fork this:

1. docs/post_mortem/SPRINT5_FAIL_post_mortem.md — start here. The v0.6 framework worked; the alpha didn't. v1.0 turns that honest finding into the calibration baseline.
2. docs/ssot/PLAN_v1.0.md — canonical v1.0 spec. Section 0 explains the reframe rationale; Section 2 explains how the 8 FAIL outcomes become calibration data; Section 5 lists new INV-GS-101..105 and deprecated INV-GS-001/005/033.
3. docs/CALIBRATION.md — empirical predictive strength of every thesis currently in the calibration table.
4. docs/MIGRATION_v0.7_TO_v1.0.md — developer migration guide.
5. configs/invariants.yaml — the contract the framework enforces.
6. docs/EXAMPLES.md — practical extension recipes.

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Compliance disclaimer

GLOSTAT v1.0 is an information tool for personal use. Output is a probability distribution with explicit confidence intervals and source provenance — not an investment recommendation, not a securities solicitation, not financial advice. Past calibration data does not guarantee future predictive performance. Users are responsible for their own decisions.

broadcast_telegram and mass_email raise ComplianceError permanently and unconditionally (INV-GS-024). Every Prediction instance carries a non-empty disclaimer field, validated at construction time (INV-GS-104).

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Contributing

Issues and pull requests are welcome. Useful directions:

• New thesis modules that screen a different thesis (event-driven, cross-asset momentum, options-implied, factor-based). Must include calibration data.
• New data source clients (Polygon free tier, Tiingo, Stooq, FRED) routed through DataRouter with phase gating.
• Refinements to Brier weighting / sample-size guards in predictor/composite.py.
• Fixes / hardening of the snapshot broker, hindcast harness, or compliance gate.

PR template enforces:

• New thesis → calibration row in cache/calibration_table.parquet (INV-GS-026, INV-GS-105)
• No new INV-GS-101 violation (no BUY/SELL output)
• No new INV-GS-024/104 weakening (no broadcast, no missing disclaimer)

See CONTRIBUTING.md for coding style, INV-GS conventions, and templates.

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License

MIT — see LICENSE. Use it commercially, fork it, embed it, port it, just keep the copyright notice.

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Citing

If this framework helps your research or post-mortem write-up, a link back to this repository is appreciated. Cite the calibration table version (v1.x.0, quarter-bumped) so reproducibility is preserved.