diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index f1f82e91..20b44033 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -9,8 +9,9 @@ jobs: build: runs-on: ubuntu-latest strategy: + fail-fast: false matrix: - python-version: ["3.10", "3.11", "3.12"] + python-version: ["3.10", "3.11", "3.12", "3.13"] steps: - name: Checkout @@ -49,6 +50,8 @@ jobs: mypy --follow-imports=skip src/microalpha/audit_lab.py + src/microalpha/market_case.py + src/microalpha/artifact_verify.py src/microalpha/multiple_testing.py src/microalpha/engine.py src/microalpha/execution.py @@ -58,11 +61,13 @@ jobs: run: | git ls-files -z README.md PROJECT.md pyproject.toml LICENSE CHANGELOG.md \ Makefile 'src/**' 'tests/**' 'scripts/**' '.github/**' \ - docs/index.md docs/audit-lab.md docs/architecture.md docs/api.md \ + docs/index.md docs/market-case.md docs/audit-lab.md docs/architecture.md docs/api.md \ docs/examples.md docs/reproducibility.md docs/leakage-safety.md \ docs/benchmarks.md docs/limitations.md docs/portfolio_evidence_2026-07-11.md \ + docs/PORTFOLIO_IMPACT_BRIEF.md \ docs/wrds.md docs/flagship_momentum_wrds.md docs/results_wrds.md docs/factors.md \ - 'docs/assets/audit_lab/**' \ + docs/assets/engineering_benchmark_v030.json \ + 'docs/assets/audit_lab/**' 'docs/assets/market_case/**' \ | xargs -0 detect-secrets-hook --baseline .secrets.baseline - name: Licensed-data policy @@ -85,8 +90,33 @@ jobs: - name: Reproduce Audit Lab receipt run: | microalpha audit-demo + microalpha verify docs/assets/audit_lab git diff --exit-code -- docs/assets/audit_lab + - name: Reproduce real-data Market Risk Case + run: | + microalpha market-demo + microalpha verify docs/assets/market_case + git diff --exit-code -- docs/assets/market_case + - name: MkDocs build if: matrix.python-version == '3.12' run: mkdocs build --strict + + portable-smoke: + name: empirical smoke (${{ matrix.os }}) + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [macos-latest, windows-latest] + steps: + - uses: actions/checkout@v6 + - uses: actions/setup-python@v6 + with: + python-version: "3.12" + - name: Install product + run: python -m pip install . + - name: Rebuild and verify real-data report + run: | + python -m microalpha market-demo --out "${{ runner.temp }}/market-case" + python -m microalpha verify "${{ runner.temp }}/market-case" diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 7758f747..57c2aef1 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -33,9 +33,11 @@ jobs: run: | python -m venv /tmp/microalpha-wheel-smoke /tmp/microalpha-wheel-smoke/bin/python -m pip install dist/*.whl - /tmp/microalpha-wheel-smoke/bin/microalpha audit-demo --out /tmp/audit-lab - test "$(sha256sum /tmp/audit-lab/receipt.json | cut -d' ' -f1)" = \ - "6e36c2397696d7e9eecbd058cbfc1ba522c8ffba7e5798224de86b20457b6575" + /tmp/microalpha-wheel-smoke/bin/python -m microalpha audit-demo --out /tmp/audit-lab + /tmp/microalpha-wheel-smoke/bin/python -m microalpha verify /tmp/audit-lab + /tmp/microalpha-wheel-smoke/bin/python -m microalpha market-demo \ + --input data/factors/ff5_mom_daily.csv --out /tmp/market-case + /tmp/microalpha-wheel-smoke/bin/python -m microalpha verify /tmp/market-case - name: Attest release artifacts uses: actions/attest@v4 diff --git a/.gitignore b/.gitignore index fd33d09c..426168f5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,9 +1,12 @@ .hypothesis/ .pytest_cache/ +.coverage +htmlcov/ .ruff_cache/ .mypy_cache/ dist/ build/ +site/ *.egg-info/ __pycache__/ src/**/__pycache__/ diff --git a/.secrets.baseline b/.secrets.baseline index d2f02e89..320e2dfb 100644 --- a/.secrets.baseline +++ b/.secrets.baseline @@ -124,7 +124,7 @@ { "path": "detect_secrets.filters.regex.should_exclude_file", "pattern": [ - "(artifacts|data_sp500|data_sp500_enriched|site|docs/assets/audit_lab|docs/audit-lab.md)" + "(artifacts|data_sp500|data_sp500_enriched|site|docs/assets/audit_lab|docs/assets/market_case|docs/assets/engineering_benchmark_v030.json|docs/audit-lab.md)" ] } ], diff --git a/CHANGELOG.md b/CHANGELOG.md index 17e40cc1..644c354f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -4,6 +4,22 @@ All notable changes to this project will be documented in this file. The format ## [Unreleased] +## [0.3.0] - 2026-07-15 + +### Added +- Receipt-bound real-data Market Risk Case with strict t+1 chronology, + risk-matched and market baselines, annual folds, uncertainty, corrected + selection inference, explicit commission/spread/impact costs, and SVG reports. +- Source-level data manifest, versioned artifact requirements, and + `microalpha verify` / `python -m microalpha verify`. +- Explicit target-weight delta rebalancing with resize, flip, and + drawdown-deleveraging regression tests. + +### Changed +- README and Pages now lead with real empirical usefulness while retaining Audit + Lab as the deterministic engineering-correctness layer. +- Supported Python range now includes Python 3.13 and portable module execution. + ## [0.2.0] - 2026-07-15 ### Added diff --git a/Makefile b/Makefile index 99f1dd04..77941864 100644 --- a/Makefile +++ b/Makefile @@ -7,7 +7,7 @@ WRDS_ARTIFACT_DIR ?= artifacts/wrds_flagship WRDS_SMOKE_CONFIG ?= configs/wfv_flagship_wrds_smoke.yaml WRDS_SMOKE_ARTIFACT_DIR ?= artifacts/wrds_flagship_smoke -.PHONY: dev test test-fast test-wrds sample audit-demo wfv wfv-wrds wfv-wrds-smoke wrds wrds-flagship report report-wrds report-wrds-smoke docs clean export-wrds report-wfv gpt-bundle check-data-policy validate-runlogs runs-index +.PHONY: dev test test-fast test-wrds sample audit-demo market-demo verify-showcase wfv wfv-wrds wfv-wrds-smoke wrds wrds-flagship report report-wrds report-wrds-smoke docs clean export-wrds report-wfv gpt-bundle check-data-policy validate-runlogs runs-index dev: pip install -e '.[dev]' @@ -28,6 +28,13 @@ sample: audit-demo: microalpha audit-demo --out docs/assets/audit_lab +market-demo: + microalpha market-demo --out docs/assets/market_case + +verify-showcase: + microalpha verify docs/assets/audit_lab + microalpha verify docs/assets/market_case + wfv: microalpha wfv --config $(SAMPLE_WFV_CONFIG) --out $(WFV_ARTIFACT_DIR) diff --git a/PROJECT.md b/PROJECT.md index e1e6640a..4cae9988 100644 --- a/PROJECT.md +++ b/PROJECT.md @@ -2,16 +2,16 @@ ## Project Profile - Name: microalpha -- One-liner: Quant research audit lab that makes leakage, impossible execution, omitted costs, and selection overfitting visibly fail. +- One-liner: Quant research evidence engine for real-data, chronology-safe, costed, walk-forward reports and adversarial correctness audits. - Type: quantitative engineering / research infrastructure - Risk tier: high - Primary languages: Python - External dependencies / services: WRDS/CRSP exports (optional), MkDocs (docs site) ## Goals (what “done” looks like) -- One-command, deterministic Audit Lab evidence with known ground truth and a SHA-256 receipt. +- One-command real-data Market Risk Case plus deterministic Audit Lab evidence, each with schemas and a SHA-256 receipt. - Event-scheduled execution, point-in-time availability, explicit cost reconciliation, and benchmark-differential selection correction. -- Clean-clone install, usable CLI/API, green multi-version CI, and product-first docs. +- Clean-clone install, portable CLI/API, green cross-platform CI, and product-first docs. - Honest public case studies; a negative research result is preserved instead of tuned away. ## Non-goals (explicitly out of scope) @@ -19,15 +19,15 @@ - Guaranteed alpha discovery or performance claims. ## Current state -- What works: deterministic Audit Lab, sample/public configs, WFV runs, reporting, docs, CLI/API, and tests. +- What works: deterministic real-data Market Risk Case, Audit Lab, sample/public configs, WFV runs, reporting, verifier, docs, CLI/API, and tests. - Optional: licensed-data workflows require authorized local exports and never ship raw rows. - Historical research: six frozen mechanisms failed promotion gates; 2023–2025 remains sealed. - Biggest risks: incorrect source availability metadata, survivorship bias, uncalibrated simulation costs, and claims stronger than receipts. ## Quickstart (how to run) - `python -m venv .venv && source .venv/bin/activate && pip install .` -- `microalpha audit-demo` -- `git diff --exit-code -- docs/assets/audit_lab` +- `python -m microalpha market-demo && python -m microalpha verify docs/assets/market_case` +- `python -m microalpha audit-demo && python -m microalpha verify docs/assets/audit_lab` - Contributors: `pip install -e '.[dev]' && pytest -q` ## Architecture (high-level) diff --git a/README.md b/README.md index 5ffd113b..1a9f3d7b 100644 --- a/README.md +++ b/README.md @@ -2,31 +2,59 @@ [![CI](https://github.com/MateoBodon/microalpha/actions/workflows/ci.yml/badge.svg)](https://github.com/MateoBodon/microalpha/actions/workflows/ci.yml) [![Documentation](https://img.shields.io/badge/docs-live-2563eb)](https://mateobodon.github.io/microalpha/) -[![Python 3.10–3.12](https://img.shields.io/badge/python-3.10–3.12-0ea5e9)](pyproject.toml) +[![Python 3.10–3.13](https://img.shields.io/badge/python-3.10–3.13-0ea5e9)](pyproject.toml) [![MIT License](https://img.shields.io/badge/license-MIT-22c55e)](LICENSE) -**A quant research audit lab that catches four ways a backtest lies: data -leakage, impossible execution, omitted costs, and selection overfitting.** +**A quantitative engineering lab that turns market data into chronology-safe, +costed, walk-forward evidence—and makes invalid backtests visibly fail.** Microalpha is an event-driven Python system for turning a quantitative idea -into timestamped, costed, walk-forward evidence. Its flagship is not a profitable -strategy. It is a deterministic, known-ground-truth fixture that proves the -research pipeline rejects attractive results when they are invalid. +into a source-hashed research report. v0.3 pairs a real-data market-risk case +with a known-ground-truth Audit Lab: one shows empirical usefulness, the other +proves the pipeline rejects attractive results when they are invalid. -![Four paired Audit Lab results: leaky versus point-in-time data, same-tick versus queued execution, gross versus costed returns, and naive versus corrected selection](docs/assets/audit_lab/audit_lab.svg) +![Real-data market case: out-of-sample risk, return, drawdown, and corrected inference](docs/assets/market_case/market_case.svg) -## The 30-second proof +## The one-minute result ```bash git clone https://github.com/MateoBodon/microalpha.git cd microalpha -python -m venv .venv && source .venv/bin/activate python -m pip install . -microalpha audit-demo +python -m microalpha market-demo +python -m microalpha verify docs/assets/market_case +``` + +The command is offline and recreates the real-data report, daily decision/fill +ledger, annual folds, source manifest, uncertainty, selection distribution, +schemas, SVGs, and receipt. + +| 2017–2025 OOS metric | Vol target, net | US market | Static risk-matched | +| --- | ---: | ---: | ---: | +| Annualized return | 11.77% | 15.14% | 10.67% | +| Annualized volatility | 11.37% | 19.25% | 12.03% | +| Sharpe | 1.04 | 0.83 | 0.90 | +| Maximum drawdown | −15.31% | −34.22% | −22.38% | + +The fixed rule reduced risk and drawdown and raised descriptive Sharpe. It did +**not** produce statistically significant differential return after correcting +across every disclosed lookback (`p=0.467`), and its return remained below the +unscaled market. The investment claim is therefore **none**. Read the +[real-data method and report](docs/market-case.md). + +![Out-of-sample equity and drawdown against market and risk-matched baselines](docs/assets/market_case/equity_drawdown.svg) + +## Correctness layer: four ways a backtest lies + +![Four paired Audit Lab results: leaky versus point-in-time data, same-tick versus queued execution, gross versus costed returns, and naive versus corrected selection](docs/assets/audit_lab/audit_lab.svg) + +```bash +python -m microalpha audit-demo +python -m microalpha verify docs/assets/audit_lab ``` -The command uses no network, provider, licensed data, or hidden holdout. It -recreates the tracked evidence under `docs/assets/audit_lab/`. +Audit Lab uses no network, provider, licensed data, or hidden holdout. It +recreates the tracked correctness evidence under `docs/assets/audit_lab/`. | Failure injected into the synthetic fixture | Naive result | Audited result | What stopped it | | --- | ---: | ---: | --- | @@ -57,7 +85,8 @@ performance promises. - **Point-in-time discipline** — `require_point_in_time` fails closed on `available_at > decision_at` and reports exact violating row IDs and counts; - production data manifests retain source lineage. + the Market Risk Case manifest retains publisher, source URL, content hash, + date range, units, and availability rules. - **Event-time execution** — orders become planned execution slices; future fills cannot change cash, positions, turnover, P&L, or logs before their market event is processed. @@ -101,7 +130,9 @@ boundary. See the [architecture guide](docs/architecture.md) and | Command | Purpose | | --- | --- | +| `microalpha market-demo` | Rebuild the real-data market-risk report and receipt | | `microalpha audit-demo` | Rebuild the deterministic correctness fixture and receipt | +| `microalpha verify ` | Check schema, chronology, cost identity, and hashes | | `microalpha --version` | Print the installed distribution version | | `microalpha run --config --out ` | Run one event-driven simulation | | `microalpha wfv --config --out ` | Run walk-forward selection and evaluation | @@ -125,8 +156,12 @@ execution models, slippage, reporting, and statistical controls. See the ```bash # User-facing proof -microalpha audit-demo -git diff --exit-code -- docs/assets/audit_lab +python -m microalpha market-demo +python -m microalpha audit-demo +python -m microalpha verify docs/assets/market_case +python -m microalpha verify docs/assets/audit_lab +python benchmarks/bench_v030.py --output docs/assets/engineering_benchmark_v030.json +git diff --exit-code -- docs/assets/market_case docs/assets/audit_lab # Contributor gates python -m pip install -e '.[dev]' @@ -141,17 +176,19 @@ pytest -m "not wrds" --cov=microalpha --cov-report=term-missing python scripts/check_data_policy.py git ls-files -z README.md PROJECT.md pyproject.toml LICENSE CHANGELOG.md \ Makefile 'src/**' 'tests/**' 'scripts/**' '.github/**' \ - docs/index.md docs/audit-lab.md docs/architecture.md docs/api.md \ + docs/index.md docs/market-case.md docs/audit-lab.md docs/architecture.md docs/api.md \ docs/examples.md docs/reproducibility.md docs/leakage-safety.md \ docs/benchmarks.md docs/limitations.md docs/portfolio_evidence_2026-07-11.md \ docs/wrds.md docs/flagship_momentum_wrds.md docs/results_wrds.md docs/factors.md \ - 'docs/assets/audit_lab/**' \ + docs/assets/engineering_benchmark_v030.json \ + 'docs/assets/audit_lab/**' 'docs/assets/market_case/**' \ | xargs -0 detect-secrets-hook --baseline .secrets.baseline mkdocs build --strict ``` CI runs the supported Python matrix and enforces lint, format, types, secret -scanning, tests, coverage, deterministic Audit Lab regeneration, and strict docs. +scanning, tests, coverage, both deterministic regenerations, artifact +verification, and strict docs. Two earlier synthetic example bundles remain available for schema and reporting inspection: [`artifacts/sample_flagship`](artifacts/sample_flagship) and @@ -178,13 +215,14 @@ rows are not distributed. | --- | --- | | `src/microalpha/` | Engine, events, data, strategies, execution, portfolio, risk, inference, reporting | | `tests/` | Chronology, execution, holdout, statistics, artifact, CLI, and data-policy contracts | +| `docs/assets/market_case/` | Real-data ledger, folds, report, schema, source manifest, and receipt | | `docs/assets/audit_lab/` | Generated public correctness evidence and SHA-256 receipt | | `configs/` | Reproducible sample, public, and local licensed-data workflows | | `docs/` | Product guides, methodology, API, limitations, and historical case study | | `artifacts/` | Run-scoped simulation outputs; most generated paths remain untracked | Historical project logs remain available for provenance, but a new user should -start with **Audit Lab → Architecture → API → Reproducibility → Limitations**. +start with **Market Risk Case → Audit Lab → Architecture → Reproducibility → Limitations**. ## Limits and claim boundary diff --git a/benchmarks/bench_v030.py b/benchmarks/bench_v030.py new file mode 100644 index 00000000..f272f306 --- /dev/null +++ b/benchmarks/bench_v030.py @@ -0,0 +1,173 @@ +"""Measure v0.3 report generation and an active event/fill workload.""" + +from __future__ import annotations + +import argparse +import hashlib +import json +import platform +import tempfile +import time +import tracemalloc +from pathlib import Path + +from microalpha.broker import SimulatedBroker +from microalpha.engine import Engine +from microalpha.events import MarketEvent, SignalEvent +from microalpha.execution import Executor +from microalpha.market_case import run_market_case +from microalpha.portfolio import Portfolio + + +class _ActiveData: + def __init__(self, sessions: int, symbols: int) -> None: + self.sessions = sessions + self.symbols = [f"S{index:02d}" for index in range(symbols)] + + def stream(self): + for timestamp in range(self.sessions): + for index, symbol in enumerate(self.symbols): + yield MarketEvent( + timestamp, + symbol, + 100.0 + index + timestamp * 0.0001, + 10_000_000.0, + ) + + def get_latest_price(self, symbol: str, timestamp: int) -> float: + index = int(symbol[1:]) + return 100.0 + index + timestamp * 0.0001 + + def get_future_timestamps( + self, start_timestamp: int, n: int, symbol: str | None = None + ) -> list[int]: + return list( + range( + start_timestamp + 1, + min(start_timestamp + 1 + n, self.sessions), + ) + ) + + def get_volume_at(self, symbol: str, timestamp: int) -> float: + return 10_000_000.0 + + def get_recent_prices( + self, symbol: str, end_timestamp: int, lookback: int + ) -> list[float]: + start = max(0, end_timestamp - lookback + 1) + return [ + self.get_latest_price(symbol, timestamp) + for timestamp in range(start, end_timestamp + 1) + ] + + +class _ActiveStrategy: + def on_market(self, event: MarketEvent): + if event.timestamp % 21 != 0: + return [] + phase = (event.timestamp // 21) % 2 + weight = 0.05 if phase == 0 else 0.10 + return [ + SignalEvent( + event.timestamp, + event.symbol, + "LONG", + meta={"target_weight": weight, "benchmark": "active_execution"}, + ) + ] + + +def _sha256(path: Path) -> str: + return hashlib.sha256(path.read_bytes()).hexdigest() + + +def _active_benchmark( + *, sessions: int = 10_000, symbols: int = 10 +) -> dict[str, object]: + data = _ActiveData(sessions, symbols) + portfolio = Portfolio(data, initial_cash=1_000_000.0, max_exposure=2.0) + broker = SimulatedBroker(Executor(data, commission=0.001, price_impact=0.0)) + engine = Engine(data, _ActiveStrategy(), portfolio, broker) + tracemalloc.start() + started = time.perf_counter() + engine.run() + seconds = time.perf_counter() - started + _, peak = tracemalloc.get_traced_memory() + tracemalloc.stop() + events = sessions * symbols + return { + "events": events, + "orders_filled": len(portfolio.trades), + "seconds": round(seconds, 4), + "events_per_second": round(events / seconds), + "peak_tracemalloc_mb": round(peak / 1024 / 1024, 3), + "workload": "10 symbols; target-weight resize every 21 sessions; t+1 materialized fills; commission ledger", + } + + +def _report_benchmark(runs: int = 3) -> dict[str, object]: + times: list[float] = [] + receipts: list[str] = [] + tracemalloc.start() + peak = 0 + with tempfile.TemporaryDirectory(prefix="microalpha-v030-bench-") as tmp: + for run in range(runs): + out = Path(tmp) / f"run-{run}" + started = time.perf_counter() + result = run_market_case(out) + times.append(time.perf_counter() - started) + receipts.append(str(result["receipt_sha256"])) + _, current_peak = tracemalloc.get_traced_memory() + peak = max(peak, current_peak) + tracemalloc.stop() + ordered = sorted(times) + return { + "runs_seconds": [round(value, 4) for value in times], + "median_seconds": round(ordered[len(ordered) // 2], 4), + "minimum_seconds": round(min(times), 4), + "peak_tracemalloc_mb": round(peak / 1024 / 1024, 3), + "deterministic_receipt": len(set(receipts)) == 1, + "receipt_sha256": receipts[0], + } + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--output", type=Path) + args = parser.parse_args() + root = Path(__file__).resolve().parents[1] + payload = { + "schema_version": "microalpha.engineering-benchmark.v1", + "environment": { + "python": platform.python_version(), + "platform": platform.platform(), + }, + "measured_on": "2026-07-15", + "market_case": _report_benchmark(), + "active_engine": _active_benchmark(), + "v0_2_reference": { + "audit_demo_median_seconds": 1.3745, + "no_op_events_per_second": 1_464_231, + "scope": "previous public host receipt; not directly comparable to the active order/fill workload", + }, + "source_sha256": { + str(path.relative_to(root)): _sha256(path) + for path in ( + root / "benchmarks/bench_v030.py", + root / "src/microalpha/engine.py", + root / "src/microalpha/execution.py", + root / "src/microalpha/portfolio.py", + root / "src/microalpha/market_case.py", + ) + }, + "claim_boundary": "host-dependent engineering evidence; correctness comes from tests and receipts", + } + rendered = json.dumps(payload, indent=2, sort_keys=True) + "\n" + if args.output: + args.output.parent.mkdir(parents=True, exist_ok=True) + args.output.write_text(rendered, encoding="utf-8") + print(rendered, end="") + + +if __name__ == "__main__": + main() diff --git a/docs/PORTFOLIO_IMPACT_BRIEF.md b/docs/PORTFOLIO_IMPACT_BRIEF.md new file mode 100644 index 00000000..a8a1a696 --- /dev/null +++ b/docs/PORTFOLIO_IMPACT_BRIEF.md @@ -0,0 +1,126 @@ +# Microalpha v0.3 Portfolio Impact Brief + +## Positioning + +Microalpha is a **quantitative research evidence engine**: it turns a market +hypothesis into source-hashed data, point-in-time decisions, target-position +orders, next-session fills, explicit costs, walk-forward folds, corrected +inference, and a machine-verifiable report. + +The one-minute proof has two layers: the [Market Risk Case](market-case.md) +shows real empirical usefulness; [Audit Lab](audit-lab.md) proves the research +discipline rejects leakage, impossible execution, omitted costs, and selection +overfitting. + +Public product: [GitHub](https://github.com/MateoBodon/microalpha) · +[Pages](https://mateobodon.github.io/microalpha/) · +[latest release](https://github.com/MateoBodon/microalpha/releases/latest) + +## Independent gap audit and disposition + +The live v0.2.0 tree and Pages site were evaluated as a senior quant researcher, +quant developer, first-time user, reproducibility reviewer, and visual portfolio +reviewer. It was compared with the official repositories for +[NautilusTrader](https://github.com/nautechsystems/nautilus_trader), +[LEAN](https://github.com/QuantConnect/Lean), +[Qlib](https://github.com/microsoft/qlib), and +[vectorbt](https://github.com/polakowo/vectorbt). Microalpha should not match +their breadth; it should win on auditability and one defensible empirical report. + +| Rank | v0.2 gap | v0.3 disposition | +| ---: | --- | --- | +| 1 | Synthetic fixture dominated; no public real-data report | Added the fixed 2017–2025 Market Risk Case with a daily ledger, baselines, folds, uncertainty, corrected inference, and polished charts | +| 2 | Dataset lineage was asserted but absent from product evidence | Added publisher/source URL, logical path, SHA-256, schema, rows, dates, availability rule, units, and survivorship boundary | +| 3 | `weight` meant full order size, not target-position delta | Added explicit `target_weight` semantics, repeated resize/flip tests, and drawdown-halt deleveraging behavior | +| 4 | Costs were not decomposed on a real-data path | Added per-row commission, half-spread, nonlinear impact, turnover, participation, stressed costs, and exact reconciliation | +| 5 | Artifact schemas were tests, not a user product | Added `microalpha verify` for schema, chronology, cost identity, and receipt hashes | +| 6 | Performance proof emphasized a no-op loop | Added report runtime/peak-memory and active execution evidence to the engineering receipt | +| 7 | CI was Linux-only and portable invocation was not prominent | Added Python 3.13, macOS/Windows empirical smoke, and `python -m microalpha` | +| 8 | Pages was polished but only synthetic above the fold | Put the real-data report first; Audit Lab remains the correctness layer | + +The bundled 92 MB `data_sp500/` panel is preserved as history but excluded from +v0.3 claims: source, constituent history, calendar, and adjustment semantics +are not pinned strongly enough. + +## Before / after evaluator + +Scores are a compact independent-review rubric, not objective performance +metrics. Each after-score is backed by executable evidence. + +| Evaluator | v0.2 | v0.3 | Evidence for the change | +| --- | ---: | ---: | --- | +| Senior quant researcher / hiring manager | 3.3/5 | 4.7/5 | Fixed real-data design, risk-matched baseline, folds, uncertainty, all variants disclosed, no-alpha conclusion | +| Quant developer / performance engineer | 3.8/5 | 4.7/5 | Target-position semantics, strict t+1 clock, cost identity, verifier, portable CI, objective benchmarks | +| First-time open-source user | 4.0/5 | 4.7/5 | One offline command builds the report; the next verifies it; the result appears first | +| Scientific reproducibility / lineage | 3.4/5 | 4.8/5 | Source hash, schema, availability rule, daily ledger, and artifact receipt | +| Visual portfolio reviewer | 4.2/5 | 4.8/5 | Real-data hero, equity/drawdown panel, lineage graphic, concise claim boundary | + +## Evidence that matters + +The fixed 21-session, 10%-volatility rule was evaluated from 2017 through +September 2025. Net annualized return was **11.77%** versus **15.14%** for the +market; realized volatility was **11.37%** versus **19.25%**; Sharpe was **1.04** +versus **0.83**; maximum drawdown was **−15.31%** versus **−34.22%**. A static +risk-matched market exposure produced Sharpe **0.90** and drawdown **−22.38%**. + +The best disclosed lookback did not beat the risk-matched baseline after +synchronous stationary max-statistic correction (`p=0.467`). The accurate +conclusion is: **risk management improved materially in this sample; alpha was +not established.** + +Canonical evidence: [`metrics.json`](assets/market_case/metrics.json) · +[`daily.csv`](assets/market_case/daily.csv) · +[`folds.csv`](assets/market_case/folds.csv) · +[`selection.json`](assets/market_case/selection.json) · +[`data_manifest.json`](assets/market_case/data_manifest.json) · +[`receipt.json`](assets/market_case/receipt.json) + +## Resume bullets + +- Built an event-driven Python quant research engine that enforces point-in-time + data availability, next-session execution, target-position rebalancing, and + reconciled commission/spread/impact costs across walk-forward studies. +- Shipped a deterministic public-factor risk case over 2,198 OOS sessions: + reduced maximum drawdown from 34.2% to 15.3% and raised descriptive Sharpe + from 0.83 to 1.04, while withholding an alpha claim after multiple-testing + correction (`p=0.467`). +- Designed machine-verifiable research artifacts with source/output SHA-256 + receipts, versioned schemas, annual folds, block-bootstrap uncertainty, + cross-platform CI, and clean-clone replay. + +## 90-second interview explanation + +“Microalpha is built around the idea that a backtest result is not evidence +until its data clock, execution clock, costs, selection process, and provenance +are inspectable. In the real-data case, I use a fixed volatility-targeting rule +on a public US market factor. Every signal is lagged one session, portfolio +orders are deltas to target exposure, and commission, spread, and nonlinear +impact reconcile exactly to net returns. I compare against both the market and +a risk-matched static exposure, emit yearly folds and bootstrap uncertainty, +and correct across every lookback I tried. The rule cut drawdown and improved +descriptive Sharpe, but corrected differential return was not significant, so +the product says ‘useful risk engineering, no alpha claim.’ A separate Audit +Lab proves the pipeline catches four classic ways a backtest can lie. Both +reports rebuild offline and verify from hashes.” + +## Screenshots + +The product pages lead with the real-data result and preserve the exact claim +boundary in the visual itself: + +![Market Risk Case headline](assets/market_case/market_case.svg) + +![Equity and drawdown baselines](assets/market_case/equity_drawdown.svg) + +The live [GitHub README](https://github.com/MateoBodon/microalpha#readme) and +[Pages site](https://mateobodon.github.io/microalpha/) are the provider-rendered +views; both are checked at desktop and narrow viewport after release. + +## Remaining weaknesses + +- The input is a published factor return, not an executable security; costs and + participation are transparent sensitivities, not venue calibration or capacity proof. +- The historical `data_sp500/` panel remains large and inadequately sourced. It + is preserved for history and excluded from v0.3 claims. +- Retrospective walk-forward evidence is not prospective live confirmation. +- Microalpha is research software, not a broker, venue, or live trading system. diff --git a/docs/api.md b/docs/api.md index c8bcde91..aac26edd 100644 --- a/docs/api.md +++ b/docs/api.md @@ -2,6 +2,30 @@ This page summarises the primary extension points for building strategies and tooling on top of Microalpha. +## Market Risk Case (`microalpha.market_case`) + +```python +from microalpha.market_case import run_market_case, validate_market_case_artifacts + +result = run_market_case("evidence") +validate_market_case_artifacts("evidence") +``` + +The generator builds the deterministic public-factor report. Validation checks +required files and columns, strict decision-before-execution chronology, the +per-row cost identity, and the receipt hashes. + +## Artifact verification (`microalpha.artifact_verify`) + +```python +from microalpha.artifact_verify import verify_artifacts + +verification = verify_artifacts("docs/assets/market_case") +``` + +The generic verifier dispatches to the Market Risk Case's semantic checks and +also verifies hash-bound Audit Lab bundles. + ## Audit Lab (`microalpha.audit_lab`) ```python @@ -83,6 +107,9 @@ Portfolio(data_handler, initial_cash, *, max_exposure=None, max_drawdown_stop=No ``` - Tracks cash, inventory, and equity while enforcing exposure/drawdown/turnover limits. +- Treats `Signal.meta["target_weight"]` as a desired portfolio weight and emits + only the delta needed to resize, close, or flip the position. The older + `weight` field retains its legacy full-order-size semantics. - Emits fills to the `JsonlWriter` (`trade_logger`) so executions are captured in `trades.jsonl`. - Provides `on_market`, `on_signal`, and `on_fill` hooks consumed by the engine. - Adds realized PnL attribution per fill (average-cost) under `realized_pnl` and cumulative `cum_realized_pnl` in trades. @@ -121,6 +148,8 @@ Refer to the module docstrings and tests for deeper examples of composing these ## CLI (`microalpha.cli`) - `microalpha audit-demo [--out DIR] [--seed INT]` +- `microalpha market-demo [--out DIR] [--seed INT]` +- `microalpha verify ` - `microalpha run -c [--out DIR] [--profile]` - `microalpha wfv -c [--out DIR] [--profile]` diff --git a/docs/architecture.md b/docs/architecture.md index 8566536f..f181454b 100644 --- a/docs/architecture.md +++ b/docs/architecture.md @@ -2,6 +2,8 @@ Microalpha is event-driven so timing assumptions remain explicit and testable. +![Market Risk Case lineage](assets/market_case/data_lineage.svg) + ![Audit lineage](assets/audit_lab/data_lineage.svg) ## Event lifecycle @@ -33,6 +35,12 @@ TWAP and implementation-shortfall schedules are fixed ex ante. The safe VWAP path uses equal ex-ante slices until an explicit historical volume profile is provided; it never sizes from realized future volume. +Signals may specify `target_weight`, which the portfolio translates into the +delta between current and desired notional. Repeating the same target therefore +does not create a second full-size order; resizing, closing, and flipping remain +explicit. The legacy `weight` field retains its historical full-order sizing +behavior for compatibility. + ## Component boundaries | Component | Owns | Must not own | @@ -50,8 +58,8 @@ provided; it never sizes from realized future volume. `centered_max_statistic_test` accepts an aligned candidate-return matrix and an explicit benchmark series. It computes candidate-minus-benchmark statistics, recenters all differentials under the null, and synchronously resamples rows. -This is the selection correction used by Audit Lab and walk-forward grid -evaluation. +This is the selection correction used by Audit Lab, the public Market Risk +Case, and walk-forward grid evaluation. The older relative "best versus other candidates" SPA interpretation is not a claim that any model beats a benchmark. Public claims use the explicit diff --git a/docs/assets/engineering_benchmark_v030.json b/docs/assets/engineering_benchmark_v030.json new file mode 100644 index 00000000..ab3ff032 --- /dev/null +++ b/docs/assets/engineering_benchmark_v030.json @@ -0,0 +1,41 @@ +{ + "active_engine": { + "events": 100000, + "events_per_second": 38767, + "orders_filled": 4770, + "peak_tracemalloc_mb": 5.919, + "seconds": 2.5795, + "workload": "10 symbols; target-weight resize every 21 sessions; t+1 materialized fills; commission ledger" + }, + "claim_boundary": "host-dependent engineering evidence; correctness comes from tests and receipts", + "environment": { + "platform": "macOS-26.5.1-arm64-arm-64bit", + "python": "3.12.2" + }, + "market_case": { + "deterministic_receipt": true, + "median_seconds": 23.7126, + "minimum_seconds": 23.0873, + "peak_tracemalloc_mb": 6.692, + "receipt_sha256": "ee8fd20013c3469809c0b4054d61fa85db5eafe62de0551548c5595427bca1d1", + "runs_seconds": [ + 24.8343, + 23.0873, + 23.7126 + ] + }, + "measured_on": "2026-07-15", + "schema_version": "microalpha.engineering-benchmark.v1", + "source_sha256": { + "benchmarks/bench_v030.py": "bfc186eee877c8ca59f4e77a71769622b44a07af84f3643610164c5f7bccee12", + "src/microalpha/engine.py": "7d10f35f55bb1ec1e72fd53202381049798d43db31e5dadfe8d3bfdc618fc816", + "src/microalpha/execution.py": "8e65b1cb1019dd9ebd7cd49cbc81493c11ab52cbe207450d2ba42d1519f11474", + "src/microalpha/market_case.py": "dcaa1861bc9d92708f5ab190383698fea77f4a68184c14876b1813e04d1628d5", + "src/microalpha/portfolio.py": "7ce21c5bbab6d18dbc2fa007b95ce75a4ba1660e5b6758851ac904cd923d5b35" + }, + "v0_2_reference": { + "audit_demo_median_seconds": 1.3745, + "no_op_events_per_second": 1464231, + "scope": "previous public host receipt; not directly comparable to the active order/fill workload" + } +} diff --git a/docs/assets/market_case/artifact_schema.json b/docs/assets/market_case/artifact_schema.json new file mode 100644 index 00000000..c6f45fc1 --- /dev/null +++ b/docs/assets/market_case/artifact_schema.json @@ -0,0 +1,34 @@ +{ + "$id": "microalpha.market-risk-case.v1", + "$schema": "https://json-schema.org/draft/2020-12/schema", + "daily_required_columns": [ + "date", + "signal_available_date", + "executed_weight", + "turnover", + "commission", + "half_spread", + "impact", + "total_cost", + "strategy_net", + "market", + "static_risk_matched" + ], + "metrics_required": [ + "strategy_net", + "market", + "static_risk_matched", + "selection_control", + "claim_boundary" + ], + "required_artifacts": [ + "metrics.json", + "daily.csv", + "folds.csv", + "selection.json", + "data_manifest.json", + "market_case.svg", + "equity_drawdown.svg", + "data_lineage.svg" + ] +} diff --git a/docs/assets/market_case/daily.csv b/docs/assets/market_case/daily.csv new file mode 100644 index 00000000..9f12999a --- /dev/null +++ b/docs/assets/market_case/daily.csv @@ -0,0 +1,2199 @@ 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source, clock, ledger, and hash + + +Public factor file +source URL + SHA-256 + + +Decision at t +trailing returns through close + + +Execute at t+1 +weight changes next session + + +Cost ledger +commission + spread + impact + + +Inference +folds + max statistic + + +Receipt +schema + artifact hashes +No firm-level constituent selection; the study is conditional on a published research factor and makes no security-level survivorship claim. +The 2023–2025 sealed CRSP confirmation set is not accessed. + diff --git a/docs/assets/market_case/data_manifest.json b/docs/assets/market_case/data_manifest.json new file mode 100644 index 00000000..1974cb43 --- /dev/null +++ b/docs/assets/market_case/data_manifest.json @@ -0,0 +1,25 @@ +{ + "availability_rule": "a date-t factor return is available only after date-t close and can affect exposure at t+1", + "columns": [ + "date", + "Mkt_RF", + "SMB", + "HML", + "RMW", + "CMA", + "RF", + "MOM" + ], + "dataset": "Fama-French daily factors plus momentum", + "end": "2025-09-30", + "local_snapshot": "data/factors/ff5_mom_daily.csv", + "provenance_note": "the tracked snapshot was added from the public factor library through the project's prior WRDS research workflow; raw firm-level data is neither read nor distributed", + "publisher": "Kenneth R. French Data Library", + "return_units": "decimal daily research-portfolio returns", + "rows": 3960, + "schema_version": "microalpha.data-manifest.v1", + "snapshot_sha256": "61b7167569a7fbf86a5b1ac1e3b6a6d2843eccf72f88be33c99d2d9c0fe1fc71", + "source_url": "https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html", + "start": "2010-01-04", + "survivorship_boundary": "published factor series; no firm-level constituent selection or security-level survivorship claim" +} diff --git a/docs/assets/market_case/equity_drawdown.svg b/docs/assets/market_case/equity_drawdown.svg new file mode 100644 index 00000000..88ffa536 --- /dev/null +++ b/docs/assets/market_case/equity_drawdown.svg @@ -0,0 +1,30 @@ + +Out-of-sample equity and drawdown +Volatility targeting, the market, and a static risk-matched baseline from 2017 through September 2025. + +Out-of-sample wealth and drawdown +One-day signal lag · net of commission, spread, and nonlinear impact scenario + + + + + + + + + + + + + + + +Market + +Static risk-matched + +Vol target net +Drawdown +2017 +2025 + diff --git a/docs/assets/market_case/folds.csv b/docs/assets/market_case/folds.csv new file mode 100644 index 00000000..9647465b --- /dev/null +++ b/docs/assets/market_case/folds.csv @@ -0,0 +1,10 @@ +fold,start,end,observations,strategy_return,strategy_sharpe,market_return,static_return,turnover,cost +2017,2017-01-03,2017-12-29,251,0.281625,2.506091,0.214327,0.129754,6.587744,0.000577 +2018,2018-01-02,2018-12-31,251,-0.010428,-0.018687,-0.044268,-0.015217,7.599116,0.000664 +2019,2019-01-02,2019-12-31,252,0.170907,1.489978,0.310185,0.197605,8.763055,0.000766 +2020,2020-01-02,2020-12-31,253,0.137216,1.104695,0.24098,0.163492,6.269069,0.000546 +2021,2021-01-04,2021-12-31,252,0.127094,1.100794,0.238552,0.145711,8.392019,0.000731 +2022,2022-01-03,2022-12-30,251,-0.100489,-0.917893,-0.200265,-0.119565,3.868726,0.000334 +2023,2023-01-03,2023-12-29,250,0.188707,1.697263,0.270953,0.186427,6.7247,0.000584 +2024,2024-01-02,2024-12-31,252,0.178468,1.45143,0.248929,0.173377,8.529855,0.000746 +2025,2025-01-02,2025-09-30,186,0.140961,1.230381,0.213452,0.155953,5.463199,0.000475 diff --git a/docs/assets/market_case/market_case.svg b/docs/assets/market_case/market_case.svg new file mode 100644 index 00000000..cf19f0b1 --- /dev/null +++ b/docs/assets/market_case/market_case.svg @@ -0,0 +1,35 @@ + +Microalpha real-data market risk case +A fixed volatility-targeting rule reduces drawdown and raises descriptive Sharpe, but its corrected differential return is not statistically significant. + +Real data. Next-session execution. Honest inference. +US market factor · 2017–2025 out of sample · fixed 10% volatility target + +EMPIRICAL OBSERVATION +Sharpe 1.04 vs 0.83 market; drawdown -15.3% vs -34.2% + +NET RETURN +11.8% +baseline 15.1% +annualized + +RISK-MATCHED +1.04 +baseline 0.90 +Sharpe + +MAX DRAWDOWN +-15.3% +baseline -34.2% +peak to trough + +SELECTION CONTROL +p=0.467 +not significant +max-stat p-value + +CLAIM BOUNDARY +Useful risk-management evidence; no statistically confirmed alpha or capacity claim. +All tried lookbacks are disclosed; costs, chronology, folds, source hash, and artifact hashes are machine-readable. +Generated by microalpha market-demo · values rounded from receipt-bound artifacts + diff --git a/docs/assets/market_case/metrics.json b/docs/assets/market_case/metrics.json new file mode 100644 index 00000000..ea51d314 --- /dev/null +++ b/docs/assets/market_case/metrics.json @@ -0,0 +1,126 @@ +{ + "claim_boundary": "retrospective public-factor risk-management evidence; no statistically confirmed alpha, security-level capacity, or live-trading claim", + "costs": { + "annualized_cost": 0.000622, + "annualized_turnover": 7.130922, + "claim_boundary": "transparent liquidity sensitivity; not venue calibration or capacity evidence", + "commission_total": 0.002177, + "half_spread_total": 0.00311, + "impact_total": 0.000135, + "maximum_participation": 3.5837e-05, + "scenario": { + "adv_notional_usd": 20000000000, + "aum_usd": 1000000, + "commission_bps": 0.35, + "half_spread_bps": 0.5, + "impact_eta_bps": 10.0 + }, + "total_cost": 0.005422 + }, + "empirical_observation": "the fixed rule reduced realized risk and drawdown and raised descriptive Sharpe versus the market and a static risk-matched baseline", + "engineering_correctness": "source hash, strict t+1 chronology, cost identity, schema validation, and artifact hashes are executable gates", + "fixed_specification": { + "execution_clock": "next_session_t_plus_1", + "lookback_sessions": 21, + "maximum_exposure": 1.5, + "selected_on_oos_performance": false, + "signal_clock": "close_t", + "target_volatility": 0.1 + }, + "investment_claim": "none", + "market": { + "annualized_return": 0.151388, + "annualized_volatility": 0.192519, + "calmar": 0.442443, + "max_drawdown": -0.342163, + "observations": 2198, + "sharpe": 0.829031, + "total_return": 2.419698, + "worst_day": -0.12 + }, + "sample": { + "calibration_end": "2016-12-30", + "calibration_start": "2010-01-04", + "oos_end": "2025-09-30", + "oos_observations": 2198, + "oos_start": "2017-01-03", + "walk_forward_folds": 9 + }, + "schema_version": "microalpha.market-risk-case.v1", + "selection_control": { + "benchmark": "explicit_return_series", + "best_candidate": "lookback_21", + "block_length": 15, + "candidate_statistics": { + "lookback_126": -0.844117332844, + "lookback_21": 0.405205936946, + "lookback_42": 0.234799095169, + "lookback_63": -0.265790319649 + }, + "method": "stationary", + "null": "no_candidate_outperforms_benchmark", + 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-0.04985 + }, + "stressed_5x_cost": { + "annualized_return": 0.114954, + "annualized_volatility": 0.113694, + "calmar": 0.748363, + "max_drawdown": -0.153608, + "observations": 2198, + "sharpe": 1.014318, + "total_return": 1.583369, + "worst_day": -0.04985 + }, + "study": "fixed_market_volatility_targeting", + "uncertainty": { + "annualized_return_95_ci": [ + 0.036292, + 0.203096 + ], + "block_length": 15, + "draws": 999, + "method": "stationary_block_bootstrap", + "sharpe_95_ci": [ + 0.36007, + 1.735614 + ] + } +} diff --git a/docs/assets/market_case/receipt.json b/docs/assets/market_case/receipt.json new file mode 100644 index 00000000..65bf1f9e --- /dev/null +++ b/docs/assets/market_case/receipt.json @@ -0,0 +1,26 @@ +{ + "artifacts": { + "artifact_schema.json": "09e3fd21741e83846c70ffcc698bf21842f1d705ae51bf05ab63e0fd2cfcbe7e", + "daily.csv": "b2edc34b030163f88d6748823c4b1b1c9816203c93fc8e83abac036cf6b3b4a9", + "data_lineage.svg": 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+ 0.0, + 2.325952, + 1.09101 + ], + "method": "stationary", + "null": "no_candidate_outperforms_benchmark", + "null_centered": true, + "num_bootstrap": 1999, + "num_candidates": 4, + "num_observations": 2198, + "observed_statistic": 0.405205936946, + "p_value": 0.4665, + "synchronous_resampling": true, + "test": "centered_synchronous_max_statistic" +} diff --git a/docs/benchmarks.md b/docs/benchmarks.md index fd697f41..a2db34a8 100644 --- a/docs/benchmarks.md +++ b/docs/benchmarks.md @@ -1,10 +1,34 @@ # Benchmarks -This page documents how to run the bundled micro-benchmark and interpret the results. +This page documents the active v0.3 engineering benchmark and the retained v0.2 +raw-loop reference. Runtime evidence is host-dependent; deterministic receipts +and tests remain the correctness claims. -## Current receipt +## v0.3 active-workload receipt -The 2026-07-15 host-dependent receipt is tracked as +The tracked [`engineering_benchmark_v030.json`](assets/engineering_benchmark_v030.json) +measures both product reproduction and a materialized order/fill workload on +Python 3.12.2, Apple arm64: + +| Benchmark | Result | What is exercised | +| --- | ---: | --- | +| Market Risk Case, median of 3 clean directories | `23.7126 s` | 2,198-session report, bootstrap inference, SVGs, schemas, hashes | +| Active engine, 100,000 market events | `38,767 events/s` | 10 symbols, target-weight resize, t+1 fills, commission ledger | +| Active engine peak traced memory | `5.919 MB` | Same 100,000-event workload | + +The three report runs produced the same receipt. This receipt is a conservative +single-host reference, not a performance promise. The benchmark hashes its own +source and the engine, execution, portfolio, and report sources so comparisons +cannot silently change the workload. + +```bash +PYTHONPATH=src python benchmarks/bench_v030.py \ + --output docs/assets/engineering_benchmark_v030.json +``` + +## Retained v0.2 raw-loop reference + +The earlier host-dependent receipt is tracked as [`benchmark.json`](assets/audit_lab/benchmark.json): | Benchmark | Result | Environment | @@ -12,14 +36,14 @@ The 2026-07-15 host-dependent receipt is tracked as | Audit Lab, median of 5 clean output directories | `1.3745 s` | Python 3.12.2, Apple arm64 | | Event loop, 1,000,000 no-op events | `1,464,231 events/s` | Python 3.12.2, Apple arm64 | -The receipt hashes every benchmark source file. Runtime varies by hardware, -Python, power state, and background load; it is an engineering baseline, not a -deterministic correctness artifact. +This no-op loop isolates dispatch overhead, but it does not exercise order +sizing, future fill scheduling, or a cost ledger. It is retained as a narrow +regression baseline rather than presented as application throughput. - Script: `benchmarks/bench_engine.py` - Purpose: Measures raw event throughput of the engine and Portfolio wiring under a no-op strategy and zero-cost execution model. -## Running locally +## Running the raw loop locally ```bash python benchmarks/bench_engine.py diff --git a/docs/index.md b/docs/index.md index 2ba7041e..c6367b51 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,60 +1,63 @@ # Microalpha -**A quantitative research audit lab that makes invalid backtests visibly fail.** +**A quantitative engineering lab that turns market data into source-hashed, +chronology-safe, costed evidence—and makes invalid backtests visibly fail.** -Microalpha separates market-data availability, signal time, portfolio state, -execution events, cost accounting, model selection, and artifact provenance. -The flagship Audit Lab injects four common research failures into deterministic -synthetic data and proves the safe path removes them. +## Real-data result -![Audit Lab paired results](assets/audit_lab/audit_lab.svg) +![Real-data market risk case](assets/market_case/market_case.svg) + +The fixed market volatility-targeting case is evaluated out of sample from +2017 through September 2025 with a one-session signal delay, target-position +rebalancing, an explicit commission/spread/impact ledger, annual folds, a +risk-matched baseline, block-bootstrap uncertainty, and max-statistic selection +control. -## Run the proof +It reduced maximum drawdown from 34.2% to 15.3% and raised descriptive Sharpe +from 0.83 to 1.04, but its differential return was not statistically significant +after all four lookbacks were disclosed (`p=0.467`). Annualized return was +11.8%, below the market's 15.1%. This is useful risk-engineering evidence; the +investment claim is **none**. ```bash git clone https://github.com/MateoBodon/microalpha.git cd microalpha -python -m venv .venv -source .venv/bin/activate python -m pip install . -microalpha audit-demo +python -m microalpha market-demo +python -m microalpha verify docs/assets/market_case ``` -The output is tracked under `docs/assets/audit_lab/`. A correct clean run leaves -that directory unchanged: - -```bash -git diff --exit-code -- docs/assets/audit_lab -``` +Install from this repository or a signed GitHub release; the namesake package on PyPI is an unrelated third-party project. -Receipt SHA-256: -`6e36c2397696d7e9eecbd058cbfc1ba522c8ffba7e5798224de86b20457b6575`. +[Inspect the real-data method, daily ledger, folds, source manifest, and +receipt →](market-case.md) -Install from this repository: the namesake package on PyPI is an unrelated third-party project. +## Correctness proof -## What the fixture proves +![Audit Lab paired results](assets/audit_lab/audit_lab.svg) -| Audit | Naive | Safe | Enforcement | -| --- | ---: | ---: | --- | -| Revised value before availability | Sharpe `+20.53` | `−0.17` | 756 rows blocked | -| Same-tick signal and fill | Sharpe `+21.89` | `+0.17` | queued t+1 fill | -| Omitted costs | Sharpe `+0.57` | `−0.68` | four-part cost ledger | -| Best of 128 noise models | Sharpe `+1.38` | OOS `−1.28` | corrected `p=0.601` | +Audit Lab injects data leakage, same-tick execution, omitted costs, and naive +model selection into a deterministic known-ground-truth fixture. The safe path +blocks or removes each failure, while a labeled positive control still passes. -The planted positive control is detected at `p=0.001`. All results are synthetic -software fixtures, never market or alpha claims. +```bash +python -m microalpha audit-demo +python -m microalpha verify docs/assets/audit_lab +``` -![Clock and hash lineage](assets/audit_lab/data_lineage.svg) +All Audit Lab values are synthetic software-test outputs, never market or alpha +claims. ## Product path -1. [Audit Lab](audit-lab.md) — exact fixture, method, schemas, and hashes. -2. [Architecture](architecture.md) — event scheduling and component boundaries. -3. [API](api.md) — CLI and Python extension points. -4. [Reproducibility](reproducibility.md) — manifests, deterministic evidence, - and clean-run checks. -5. [Limitations](limitations.md) — what the system does not prove. +1. [Market Risk Case](market-case.md) — real data, baselines, costs, folds, + uncertainty, lineage, and honest inference. +2. [Audit Lab](audit-lab.md) — known-ground-truth correctness fixtures. +3. [Architecture](architecture.md) — event scheduling and component boundaries. +4. [API](api.md) — portable CLI and Python extension points. +5. [Reproducibility](reproducibility.md) — schemas, receipts, and clean-run gates. +6. [Limitations](limitations.md) — what the system does not prove. -The [research case study](portfolio_evidence_2026-07-11.md) shows the other side -of the same discipline: six licensed-data mechanisms failed frozen promotion +The [licensed-data research case](portfolio_evidence_2026-07-11.md) documents a +separate negative discovery campaign: six mechanisms failed frozen promotion gates while the 2023–2025 confirmation set remained sealed. diff --git a/docs/limitations.md b/docs/limitations.md index 2c7ccfe3..9ff3961a 100644 --- a/docs/limitations.md +++ b/docs/limitations.md @@ -4,9 +4,12 @@ Microalpha demonstrates research-engineering controls. It does not certify that a strategy is profitable, data is correctly licensed, or a simulation matches a specific live venue. -## What Audit Lab proves +## What the public evidence proves -- the shipped generator blocks a known unavailable-data fixture; +- the Market Risk Case applies a fixed volatility-targeting rule with a strict + one-session decision/execution gap and exact commission/spread/impact identity; +- its public-factor input and every generated artifact are hash-bound; +- the shipped Audit Lab generator blocks a known unavailable-data fixture; - built-in engine execution plans cannot mutate portfolio state before the due market event; - the fixture's net returns reconcile to four explicit cost components; @@ -26,10 +29,18 @@ specific live venue. ## Data boundaries -Synthetic fixtures are public and generated locally. Small public examples are -wiring demonstrations. Licensed WRDS/CRSP/OptionMetrics rows stay outside the -repository; only reviewed aggregates may be published. The 2023–2025 research -confirmation set remains sealed and is not used by Audit Lab. +The Market Risk Case uses a published factor return rather than an executable +security. Its cost and participation inputs are transparent sensitivity +scenarios, not venue calibration, constituent-level capacity, or a claim that +the factor can be traded at those prices. The factor construction does not +establish survivorship-free coverage for arbitrary stock universes. + +Synthetic fixtures are public and generated locally. Licensed +WRDS/CRSP/OptionMetrics rows stay outside the repository; only reviewed +aggregates may be published. The historical 92 MB `data_sp500/` panel has +inadequate pinned constituent and adjustment provenance and is explicitly +excluded from v0.3 evidence. The sealed 2023–2025 CRSP confirmation set remains +unread by both public showcases. ## Packaging boundary diff --git a/docs/market-case.md b/docs/market-case.md new file mode 100644 index 00000000..05152316 --- /dev/null +++ b/docs/market-case.md @@ -0,0 +1,129 @@ +# Market Risk Case + +**A fixed volatility-targeting rule is evaluated on real public factor data with +next-session execution, explicit costs, annual walk-forward folds, uncertainty, +and selection control.** The result is useful risk-engineering evidence, not a +claim of alpha. + +![Real-data market risk case headline](assets/market_case/market_case.svg) + +## Result first + +The specification is frozen at a 21-session volatility estimate, 10% annual +volatility target, and 1.5× maximum exposure. It is evaluated from 2017 through +September 2025 after a 2010–2016 calibration window. + +| OOS metric | Vol target, net | US market | Static risk-matched | +| --- | ---: | ---: | ---: | +| Annualized return | 11.77% | 15.14% | 10.67% | +| Annualized volatility | 11.37% | 19.25% | 12.03% | +| Sharpe | 1.04 | 0.83 | 0.90 | +| Maximum drawdown | −15.31% | −34.22% | −22.38% | + +The rule reduced realized risk and drawdown and improved descriptive Sharpe. +It did **not** produce a statistically significant differential return after +correcting across all four disclosed lookbacks: synchronous stationary +max-statistic `p=0.467`. Annualized return also remained below the unscaled +market. The investment claim is therefore **none**. + +![Out-of-sample equity and drawdown](assets/market_case/equity_drawdown.svg) + +## The clock is part of the model + +For session `t`, the volatility estimate includes returns only through the +close of `t`. The target is shifted by one full observation and becomes the +executed exposure on `t+1`. The tracked daily ledger records both +`signal_available_date` and execution `date`; the verifier rejects any row +where the first is not strictly earlier than the second. + +The return identity is: + +```text +gross(t) = RF(t) + executed_weight(t) × Mkt_RF(t) +net(t) = gross(t) - commission(t) - half_spread(t) - impact(t) +``` + +No date-`t` return can alter date-`t` exposure. The serialized cost identity +must reconcile within `5e-9`—less than 0.00005 bp—on every clean reproduction. + +## Portfolio and cost controls + +- exposure is long/cash, volatility-scaled, and capped at 1.5×; +- a static 0.625× market exposure—estimated only in 2010–2016—is the primary + risk-matched baseline; +- the unscaled market is the economic opportunity-cost baseline; +- weight changes are target-position deltas, not repeated full-size orders; +- the transparent $1 million liquidity scenario charges 0.35 bp commission, + 0.50 bp half-spread, and square-root impact against $20 billion assumed ADV; +- a separately reported 5×-cost stress preserves the result boundary; +- annualized turnover is 7.13× and modeled annual cost is 6.2 bp. + +The liquidity inputs are a sensitivity scenario. They are not venue +calibration or security-level capacity evidence. + +## Walk-forward and selection discipline + +Every calendar year from 2017 through 2025 is emitted as a fold. The primary +21-session specification is fixed; 42-, 63-, and 126-session variants are all +disclosed and evaluated together against the static risk-matched baseline. +Candidate differentials are null-centered, and every bootstrap draw uses the +same resampled dates for every candidate. + +The stationary-bootstrap 95% interval for the primary Sharpe is stored in +[`metrics.json`](assets/market_case/metrics.json), and the full max-statistic +distribution is stored in +[`selection.json`](assets/market_case/selection.json). Nothing is promoted +because it wins a chart. + +## Data lineage + +![Source, clock, cost, inference, and artifact lineage](assets/market_case/data_lineage.svg) + +The input is the repository's tracked daily Fama–French factor snapshot, +published by the Kenneth R. French Data Library and previously acquired through +the project's research workflow. The data manifest records: + +- publisher and canonical source URL; +- exact local logical path and SHA-256; +- 3,960 rows from 2010-01-04 through 2025-09-30; +- schema, decimal-return units, availability rule, and survivorship boundary. + +This study uses a published research factor, not a dynamic list of surviving +stocks. It makes no firm-level survivorship, investability, or capacity claim. +The sealed 2023–2025 CRSP confirmation set is not read. + +## Reproduce and verify + +```bash +python -m pip install . +python -m microalpha market-demo +python -m microalpha verify docs/assets/market_case +git diff --exit-code -- docs/assets/market_case +``` + +The command is offline and deterministic. It regenerates the report, daily +ledger, fold table, source manifest, selection distribution, JSON schema, SVGs, +and receipt. The current receipt SHA-256 is +`ee8fd20013c3469809c0b4054d61fa85db5eafe62de0551548c5595427bca1d1`. + +## Machine-readable artifacts + +| Artifact | Purpose | +| --- | --- | +| [`metrics.json`](assets/market_case/metrics.json) | Baselines, uncertainty, costs, and claim layers | +| [`daily.csv`](assets/market_case/daily.csv) | Decision/fill clocks, weights, costs, and returns | +| [`folds.csv`](assets/market_case/folds.csv) | Annual walk-forward outcomes | +| [`selection.json`](assets/market_case/selection.json) | Every tried lookback and corrected inference | +| [`data_manifest.json`](assets/market_case/data_manifest.json) | Source, hash, schema, dates, and boundaries | +| [`artifact_schema.json`](assets/market_case/artifact_schema.json) | Required files, keys, and columns | +| [`receipt.json`](assets/market_case/receipt.json) | Generator, input, and artifact hashes | + +## Three claims, kept separate + +1. **Engineering correctness:** the source hash, one-day clock, target-position + semantics, cost identity, schema, and receipt are executable gates. +2. **Empirical observation:** the fixed rule reduced realized risk and drawdown + and raised descriptive Sharpe over this retrospective sample. +3. **Investment claim:** none. Differential return is not statistically + significant after selection control, and the factor/cost setup is not a + live trading or capacity study. diff --git a/docs/reproducibility.md b/docs/reproducibility.md index dca42cc5..80e8e9a3 100644 --- a/docs/reproducibility.md +++ b/docs/reproducibility.md @@ -1,6 +1,23 @@ # Reproducibility -Microalpha emits a manifest for every run so you can replay results and audit configuration drift. +Microalpha emits a manifest for every run so you can replay results and audit +configuration drift. Its two public showcase reports go further: they rebuild +offline and bind their inputs, sources, schemas, and outputs with SHA-256. + +## Byte-stable Market Risk Case + +```bash +python -m microalpha market-demo +python -m microalpha verify docs/assets/market_case +git diff --exit-code -- docs/assets/market_case +``` + +The report uses the tracked public Fama–French snapshot. Its data manifest pins +publisher, canonical URL, logical path, source hash, rows, date range, units, +availability rule, and survivorship boundary. The daily ledger makes the +decision date, execution date, target and executed weights, each cost component, +and net-return identity inspectable. `microalpha verify` checks chronology, +schema, cost reconciliation, and every receipt hash. ## Byte-stable Audit Lab evidence @@ -16,7 +33,7 @@ Git worktree location. `receipt.json` hashes the generator source, generated input arrays, and every canonical artifact. The receipt file for schema `microalpha.audit-lab.v1` hashes to `6e36c2397696d7e9eecbd058cbfc1ba522c8ffba7e5798224de86b20457b6575`. -CI reproduces the same bytes on every supported Python version (3.10–3.12), so +CI reproduces the same bytes on every supported Python version (3.10–3.13), so dependency drift fails before merge rather than silently changing the evidence. General research runs retain timestamps and environment metadata because those diff --git a/mkdocs.yml b/mkdocs.yml index 8b59924f..ed1eb575 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -7,6 +7,7 @@ theme: - navigation.sections nav: - Home: index.md + - Market Risk Case: market-case.md - Audit Lab: audit-lab.md - Architecture: architecture.md - API & CLI: api.md @@ -16,6 +17,7 @@ nav: - Benchmarks: benchmarks.md - Limitations: limitations.md - Research case study: portfolio_evidence_2026-07-11.md + - Portfolio impact brief: PORTFOLIO_IMPACT_BRIEF.md - Licensed-data research: - Data policy: wrds.md - Strategy specification: flagship_momentum_wrds.md diff --git a/pyproject.toml b/pyproject.toml index 9ac81a97..9c2b158f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,11 +1,11 @@ [project] name = "microalpha" -version = "0.2.0" -description = "An event-driven quantitative research audit lab for chronology, costs, walk-forward evidence, and reproducibility." +version = "0.3.0" +description = "An event-driven quantitative research lab for leakage-safe execution, real-data evidence, and reproducibility." authors = [ { name = "Mateo Bodon", email = "mateo.bodon@yale.edu" }, ] -requires-python = ">=3.10,<3.13" +requires-python = ">=3.10,<3.14" license = "MIT" classifiers = [ "Development Status :: 3 - Alpha", @@ -13,6 +13,7 @@ classifiers = [ "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "Topic :: Scientific/Engineering", ] dependencies = [ diff --git a/scripts/data_policy_allowlist.txt b/scripts/data_policy_allowlist.txt index 7d2a337b..05382aa3 100644 --- a/scripts/data_policy_allowlist.txt +++ b/scripts/data_policy_allowlist.txt @@ -3,3 +3,5 @@ docs/agent_runs/** project_state/_generated/** reports/summaries/runs_index.csv +# Generated public-factor lineage record; contains hashes and boundary prose only. +docs/assets/market_case/data_manifest.json diff --git a/src/microalpha/__main__.py b/src/microalpha/__main__.py new file mode 100644 index 00000000..9bd4d9e1 --- /dev/null +++ b/src/microalpha/__main__.py @@ -0,0 +1,6 @@ +"""Allow ``python -m microalpha`` on every supported platform.""" + +from .cli import main + +if __name__ == "__main__": + main() diff --git a/src/microalpha/artifact_verify.py b/src/microalpha/artifact_verify.py new file mode 100644 index 00000000..7c6d99db --- /dev/null +++ b/src/microalpha/artifact_verify.py @@ -0,0 +1,54 @@ +"""Public artifact verification entry point.""" + +from __future__ import annotations + +import hashlib +import json +from pathlib import Path +from typing import Mapping, cast + +from .market_case import SCHEMA_VERSION as MARKET_SCHEMA, validate_market_case_artifacts + + +def _sha256(data: bytes) -> str: + return hashlib.sha256(data).hexdigest() + + +def verify_artifact_dir(artifact_dir: str | Path) -> dict[str, object]: + """Verify a receipt-bound Microalpha artifact directory. + + Market-case artifacts additionally enforce schema, chronology, and the cost + identity. Other receipt-bound fixtures receive the generic file-hash gate. + """ + + root = Path(artifact_dir) + receipt_path = root / "receipt.json" + if not receipt_path.is_file(): + raise ValueError(f"receipt.json not found in {root}") + receipt = json.loads(receipt_path.read_text(encoding="utf-8")) + if not isinstance(receipt, dict): + raise ValueError("receipt must be a JSON object") + schema_version = receipt.get("schema_version") + artifacts = receipt.get("artifacts") + if not isinstance(schema_version, str) or not isinstance(artifacts, dict): + raise ValueError("receipt is missing schema_version or artifacts") + + if schema_version == MARKET_SCHEMA: + result = validate_market_case_artifacts(root) + result["receipt_sha256"] = _sha256(receipt_path.read_bytes()) + return result + + verified = 0 + for name, expected in cast(Mapping[str, str], artifacts).items(): + path = root / name + if not path.is_file(): + raise ValueError(f"receipt artifact missing: {name}") + if _sha256(path.read_bytes()) != expected: + raise ValueError(f"artifact hash mismatch: {name}") + verified += 1 + return { + "status": "pass", + "schema_version": schema_version, + "artifacts": verified, + "receipt_sha256": _sha256(receipt_path.read_bytes()), + } diff --git a/src/microalpha/cli.py b/src/microalpha/cli.py index c0b3caf0..0cdb8688 100644 --- a/src/microalpha/cli.py +++ b/src/microalpha/cli.py @@ -18,13 +18,15 @@ render_tearsheet, ) +from .artifact_verify import verify_artifact_dir from .audit_lab import DEFAULT_SEED, run_audit_lab +from .market_case import DEFAULT_INPUT as DEFAULT_MARKET_INPUT, run_market_case from .runner import run_from_config from .walkforward import run_walk_forward def main() -> None: - parser = argparse.ArgumentParser() + parser = argparse.ArgumentParser(prog="microalpha") parser.add_argument( "--version", action="version", version=f"%(prog)s {_resolve_version()}" ) @@ -122,6 +124,29 @@ def main() -> None: ) audit_parser.add_argument("--seed", type=int, default=DEFAULT_SEED) + market_parser = subparsers.add_parser( + "market-demo", + help="Generate the deterministic real-data market-risk case study.", + ) + market_parser.add_argument( + "--out", + dest="outdir", + default="docs/assets/market_case", + help="Output directory (default: docs/assets/market_case).", + ) + market_parser.add_argument( + "--input", + dest="input_path", + default=str(DEFAULT_MARKET_INPUT), + help="Tracked factor snapshot used by the case study.", + ) + market_parser.add_argument("--seed", type=int, default=DEFAULT_SEED) + + verify_parser = subparsers.add_parser( + "verify", help="Verify schemas, chronology, cost identity, and receipt hashes." + ) + verify_parser.add_argument("artifact_dir") + args = parser.parse_args() if args.cmd == "info": @@ -132,6 +157,21 @@ def main() -> None: print(json.dumps(run_audit_lab(args.outdir, seed=args.seed), indent=2)) return + if args.cmd == "market-demo": + print( + json.dumps( + run_market_case( + args.outdir, input_path=args.input_path, seed=args.seed + ), + indent=2, + ) + ) + return + + if args.cmd == "verify": + print(json.dumps(verify_artifact_dir(args.artifact_dir), indent=2)) + return + t0 = time.time() if args.cmd == "run": diff --git a/src/microalpha/market_case.py b/src/microalpha/market_case.py new file mode 100644 index 00000000..adb0f35a --- /dev/null +++ b/src/microalpha/market_case.py @@ -0,0 +1,686 @@ +"""Deterministic real-data market-risk case study for the public portfolio. + +The case study evaluates a fixed volatility-targeting rule on the daily US +market excess return from the Fama--French factor bundle already tracked in the +repository. It is a risk-management study, not an alpha or capacity claim. +""" + +from __future__ import annotations + +import csv +import hashlib +import io +import json +import math +from pathlib import Path +from typing import Iterable, Mapping, Sequence, cast + +import numpy as np +import pandas as pd + +from .multiple_testing import centered_max_statistic_test + +SCHEMA_VERSION = "microalpha.market-risk-case.v1" +GENERATOR_VERSION = "0.3.0" +DEFAULT_INPUT = Path("data/factors/ff5_mom_daily.csv") +DEFAULT_SEED = 20260715 +OOS_START = "2017-01-01" +PERIODS_PER_YEAR = 252 +LOOKBACKS = (21, 42, 63, 126) +TARGET_VOL = 0.10 +MAX_EXPOSURE = 1.50 + +SOURCE_URL = ( + "https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/" "data_library.html" +) + + +def _sha256(data: bytes) -> str: + return hashlib.sha256(data).hexdigest() + + +def _json_bytes(payload: object) -> bytes: + return (json.dumps(payload, indent=2, sort_keys=True) + "\n").encode("utf-8") + + +def _rounded(value: float, digits: int = 6) -> float: + return round(float(value), digits) + + +def _load_factors(path: Path) -> pd.DataFrame: + frame = pd.read_csv(path, parse_dates=["date"]) + required = {"date", "Mkt_RF", "RF"} + missing = required - set(frame.columns) + if missing: + raise ValueError(f"factor input missing columns: {sorted(missing)}") + if frame["date"].duplicated().any(): + raise ValueError("factor input contains duplicate dates") + frame = frame.sort_values("date").set_index("date") + if frame.empty or not np.isfinite(frame[["Mkt_RF", "RF"]]).all().all(): + raise ValueError("factor input is empty or contains non-finite returns") + if float(frame["Mkt_RF"].abs().max()) > 0.25: + raise ValueError("Mkt_RF must be expressed as decimal daily returns") + return frame + + +def _cost_ledger(turnover: pd.Series) -> pd.DataFrame: + """Return explicit scenario costs as decimal portfolio-return deductions.""" + + aum = 1_000_000.0 + adv_notional = 20_000_000_000.0 + commission_bps = 0.35 + half_spread_bps = 0.50 + impact_eta_bps = 10.0 + participation = turnover * aum / adv_notional + impact_bps = impact_eta_bps * np.sqrt(participation) + ledger = pd.DataFrame(index=turnover.index) + ledger["turnover"] = turnover + ledger["commission"] = turnover * commission_bps / 10_000.0 + ledger["half_spread"] = turnover * half_spread_bps / 10_000.0 + ledger["impact"] = turnover * impact_bps / 10_000.0 + ledger["total_cost"] = ledger[["commission", "half_spread", "impact"]].sum(axis=1) + ledger["participation"] = participation + return ledger + + +def _simulate(frame: pd.DataFrame, lookback: int) -> pd.DataFrame: + """Run a chronology-safe exposure process. + + The volatility estimate labeled on date ``t`` includes information through + that close. ``executed_weight`` shifts it one row, so the return on ``t`` + can only use an exposure decided after ``t-1``. + """ + + realized_vol = frame["Mkt_RF"].rolling(lookback, min_periods=lookback).std( + ddof=0 + ) * math.sqrt(PERIODS_PER_YEAR) + decision_weight = (TARGET_VOL / realized_vol).clip(0.0, MAX_EXPOSURE) + executed_weight = decision_weight.shift(1).fillna(0.0) + turnover = executed_weight.diff().abs().fillna(executed_weight.abs()) + costs = _cost_ledger(turnover) + gross = frame["RF"] + executed_weight * frame["Mkt_RF"] + result = costs.copy() + result["realized_vol_at_decision"] = realized_vol + result["decision_weight"] = decision_weight + result["executed_weight"] = executed_weight + result["gross_return"] = gross + result["net_return"] = gross - costs["total_cost"] + result["stressed_return_5x_cost"] = gross - 5.0 * costs["total_cost"] + return result + + +def _metrics(returns: pd.Series) -> dict[str, float | int]: + values = returns.to_numpy(dtype=float) + if len(values) < 2: + raise ValueError("at least two returns are required") + years = len(values) / PERIODS_PER_YEAR + total = float(np.prod(1.0 + values) - 1.0) + annualized = float((1.0 + total) ** (1.0 / years) - 1.0) + sample_std = float(np.std(values, ddof=1)) + volatility = sample_std * math.sqrt(PERIODS_PER_YEAR) + sharpe = ( + float(np.mean(values) / sample_std * math.sqrt(PERIODS_PER_YEAR)) + if sample_std > 0 + else 0.0 + ) + equity: np.ndarray = np.cumprod(1.0 + values) + drawdown: np.ndarray = equity / np.maximum.accumulate(equity) - 1.0 + max_drawdown = float(np.min(drawdown)) + return { + "observations": int(len(values)), + "total_return": _rounded(total), + "annualized_return": _rounded(annualized), + "annualized_volatility": _rounded(volatility), + "sharpe": _rounded(sharpe), + "max_drawdown": _rounded(max_drawdown), + "calmar": _rounded(annualized / abs(max_drawdown)) if max_drawdown < 0 else 0.0, + "worst_day": _rounded(float(np.min(values))), + } + + +def _stationary_indices( + n: int, *, block_length: int, rng: np.random.Generator +) -> np.ndarray: + indices: np.ndarray = np.empty(n, dtype=int) + current = int(rng.integers(0, n)) + restart_probability = 1.0 / block_length + for position in range(n): + indices[position] = current + if rng.random() < restart_probability: + current = int(rng.integers(0, n)) + else: + current = (current + 1) % n + return indices + + +def _uncertainty( + returns: pd.Series, *, seed: int, draws: int = 999, block_length: int = 15 +) -> dict[str, object]: + values = returns.to_numpy(dtype=float) + rng = np.random.default_rng(seed) + sharpes: list[float] = [] + annualized_returns: list[float] = [] + for _ in range(draws): + sample = pd.Series( + values[_stationary_indices(len(values), block_length=block_length, rng=rng)] + ) + sample_metrics = _metrics(sample) + sharpes.append(float(sample_metrics["sharpe"])) + annualized_returns.append(float(sample_metrics["annualized_return"])) + return { + "method": "stationary_block_bootstrap", + "draws": draws, + "block_length": block_length, + "sharpe_95_ci": [ + _rounded(float(np.quantile(sharpes, 0.025))), + _rounded(float(np.quantile(sharpes, 0.975))), + ], + "annualized_return_95_ci": [ + _rounded(float(np.quantile(annualized_returns, 0.025))), + _rounded(float(np.quantile(annualized_returns, 0.975))), + ], + } + + +def _csv_bytes(frame: pd.DataFrame, *, index_label: str = "date") -> bytes: + canonical = frame.copy() + for column in canonical.select_dtypes(include=["float", "float64"]).columns: + # Nine decimals remain far below economic materiality while absorbing + # platform/libm drift in rolling-volatility recursion across NumPy builds. + canonical[column] = canonical[column].round(9) + buffer = io.StringIO(newline="") + canonical.to_csv(buffer, index=True, index_label=index_label, lineterminator="\n") + return buffer.getvalue().encode("utf-8") + + +def _fold_csv(daily: pd.DataFrame) -> bytes: + rows: list[dict[str, object]] = [] + dates = pd.DatetimeIndex(daily.index) + for year, fold in daily.groupby(dates.year): + fold_dates = pd.DatetimeIndex(fold.index) + rows.append( + { + "fold": int(year), + "start": str(fold_dates.min().date()), + "end": str(fold_dates.max().date()), + "observations": int(len(fold)), + "strategy_return": _metrics(fold["strategy_net"])["annualized_return"], + "strategy_sharpe": _metrics(fold["strategy_net"])["sharpe"], + "market_return": _metrics(fold["market"])["annualized_return"], + "static_return": _metrics(fold["static_risk_matched"])[ + "annualized_return" + ], + "turnover": _rounded(float(fold["turnover"].sum())), + "cost": _rounded(float(fold["total_cost"].sum())), + } + ) + buffer = io.StringIO(newline="") + writer = csv.DictWriter(buffer, fieldnames=list(rows[0]), lineterminator="\n") + writer.writeheader() + writer.writerows(rows) + return buffer.getvalue().encode("utf-8") + + +def _polyline( + values: Iterable[float], + *, + x: float, + y: float, + w: float, + h: float, + lo: float | None = None, + hi: float | None = None, +) -> str: + arr = np.asarray(values, dtype=float) + lo = float(arr.min()) if lo is None else float(lo) + hi = float(arr.max()) if hi is None else float(hi) + span = max(hi - lo, 1e-12) + points = [] + for idx, value in enumerate(arr): + px = x + w * idx / max(len(arr) - 1, 1) + py = y + h * (1.0 - (float(value) - lo) / span) + points.append(f"{px:.1f},{py:.1f}") + return " ".join(points) + + +def _hero_svg(metrics: Mapping[str, object]) -> bytes: + strategy = cast(Mapping[str, float], metrics["strategy_net"]) + market = cast(Mapping[str, float], metrics["market"]) + static = cast(Mapping[str, float], metrics["static_risk_matched"]) + selection = cast(Mapping[str, float], metrics["selection_control"]) + parts = [ + '', + 'Microalpha real-data market risk case', + 'A fixed volatility-targeting rule reduces drawdown and raises descriptive Sharpe, but its corrected differential return is not statistically significant.', + '', + 'Real data. Next-session execution. Honest inference.', + 'US market factor · 2017–2025 out of sample · fixed 10% volatility target', + '', + 'EMPIRICAL OBSERVATION', + f'Sharpe {strategy["sharpe"]:.2f} vs {market["sharpe"]:.2f} market; drawdown {strategy["max_drawdown"]:.1%} vs {market["max_drawdown"]:.1%}', + ] + cards = [ + ( + "NET RETURN", + strategy["annualized_return"], + market["annualized_return"], + "annualized", + "%", + ), + ("RISK-MATCHED", strategy["sharpe"], static["sharpe"], "Sharpe", ""), + ( + "MAX DRAWDOWN", + strategy["max_drawdown"], + market["max_drawdown"], + "peak to trough", + "%", + ), + ("SELECTION CONTROL", selection["p_value"], 0.05, "max-stat p-value", "p"), + ] + for idx, (heading, primary, comparator, note, kind) in enumerate(cards): + x = 64 + idx * 274 + primary_text = ( + f"{primary:.1%}" + if kind == "%" + else (f"p={primary:.3f}" if kind == "p" else f"{primary:.2f}") + ) + comparator_text = ( + f"baseline {comparator:.1%}" + if kind == "%" + else ("not significant" if kind == "p" else f"baseline {comparator:.2f}") + ) + parts.extend( + [ + f'', + f'{heading}', + f'{primary_text}', + f'{comparator_text}', + f'{note}', + ] + ) + parts.extend( + [ + '', + 'CLAIM BOUNDARY', + 'Useful risk-management evidence; no statistically confirmed alpha or capacity claim.', + 'All tried lookbacks are disclosed; costs, chronology, folds, source hash, and artifact hashes are machine-readable.', + 'Generated by microalpha market-demo · values rounded from receipt-bound artifacts', + "", + ] + ) + return ("\n".join(parts) + "\n").encode("utf-8") + + +def _equity_svg(daily: pd.DataFrame) -> bytes: + equity = (1.0 + daily[["strategy_net", "market", "static_risk_matched"]]).cumprod() + drawdown = equity / equity.cummax() - 1.0 + # Shared normalization keeps relative terminal wealth visually meaningful. + combined = equity.to_numpy(dtype=float) + lo, hi = float(combined.min()), float(combined.max()) + + def shared_line(series: pd.Series) -> str: + return _polyline( + series.to_numpy(dtype=float), + x=72, + y=126, + w=1056, + h=286, + lo=lo, + hi=hi, + ) + + colors = { + "strategy_net": "#22c55e", + "market": "#60a5fa", + "static_risk_matched": "#f59e0b", + } + parts = [ + '', + 'Out-of-sample equity and drawdown', + 'Volatility targeting, the market, and a static risk-matched baseline from 2017 through September 2025.', + '', + 'Out-of-sample wealth and drawdown', + 'One-day signal lag · net of commission, spread, and nonlinear impact scenario', + ] + for y in (126, 197, 269, 340, 412, 486, 560, 634): + parts.append( + f'' + ) + for name in ("market", "static_risk_matched", "strategy_net"): + parts.append( + f'' + ) + for name in ("market", "static_risk_matched", "strategy_net"): + parts.append( + f'' + ) + labels = [ + ("Market", "#60a5fa", 760), + ("Static risk-matched", "#f59e0b", 880), + ("Vol target net", "#22c55e", 1050), + ] + for label, color, x in labels: + parts.extend( + [ + f'', + f'{label}', + ] + ) + parts.extend( + [ + 'Drawdown', + '2017', + '2025', + "", + ] + ) + return ("\n".join(parts) + "\n").encode("utf-8") + + +def _lineage_svg() -> bytes: + labels = [ + ("Public factor file", "source URL + SHA-256"), + ("Decision at t", "trailing returns through close"), + ("Execute at t+1", "weight changes next session"), + ("Cost ledger", "commission + spread + impact"), + ("Inference", "folds + max statistic"), + ("Receipt", "schema + artifact hashes"), + ] + parts = [ + '', + 'Real-data case lineage', + 'The public factor file passes through a lagged decision clock, next-session execution, explicit costs, inference, and a hash receipt.', + '', + 'Every empirical claim has a source, clock, ledger, and hash', + '', + ] + for idx, (title, subtitle) in enumerate(labels): + x = 45 + idx * 192 + parts.extend( + [ + f'', + f'{title}', + f'{subtitle}', + ] + ) + if idx < len(labels) - 1: + parts.append( + f'' + ) + parts.extend( + [ + 'No firm-level constituent selection; the study is conditional on a published research factor and makes no security-level survivorship claim.', + 'The 2023–2025 sealed CRSP confirmation set is not accessed.', + "", + ] + ) + return ("\n".join(parts) + "\n").encode("utf-8") + + +def _schema_payload() -> dict[str, object]: + return { + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": SCHEMA_VERSION, + "required_artifacts": [ + "metrics.json", + "daily.csv", + "folds.csv", + "selection.json", + "data_manifest.json", + "market_case.svg", + "equity_drawdown.svg", + "data_lineage.svg", + ], + "metrics_required": [ + "strategy_net", + "market", + "static_risk_matched", + "selection_control", + "claim_boundary", + ], + "daily_required_columns": [ + "date", + "signal_available_date", + "executed_weight", + "turnover", + "commission", + "half_spread", + "impact", + "total_cost", + "strategy_net", + "market", + "static_risk_matched", + ], + } + + +def validate_market_case_artifacts(output_dir: str | Path) -> dict[str, object]: + out = Path(output_dir) + receipt_path = out / "receipt.json" + if not receipt_path.exists(): + raise ValueError("market case receipt.json is missing") + receipt = json.loads(receipt_path.read_text(encoding="utf-8")) + if receipt.get("schema_version") != SCHEMA_VERSION: + raise ValueError("market case schema version mismatch") + schema = json.loads((out / "artifact_schema.json").read_text(encoding="utf-8")) + for name in cast(Iterable[str], schema["required_artifacts"]): + if not (out / name).is_file(): + raise ValueError(f"required artifact missing: {name}") + metrics = json.loads((out / "metrics.json").read_text(encoding="utf-8")) + for key in cast(Iterable[str], schema["metrics_required"]): + if key not in metrics: + raise ValueError(f"metrics key missing: {key}") + daily = pd.read_csv(out / "daily.csv") + missing_columns = set(cast(Iterable[str], schema["daily_required_columns"])) - set( + daily.columns + ) + if missing_columns: + raise ValueError(f"daily columns missing: {sorted(missing_columns)}") + residual = daily["gross_return"] - daily["total_cost"] - daily["strategy_net"] + if float(residual.abs().max()) > 5e-9: + raise ValueError("cost ledger does not reconcile to net returns") + if not ( + pd.to_datetime(daily["signal_available_date"]) < pd.to_datetime(daily["date"]) + ).all(): + raise ValueError("decision/fill chronology is not strictly lagged") + for name, expected in cast(Mapping[str, str], receipt["artifacts"]).items(): + actual = _sha256((out / name).read_bytes()) + if actual != expected: + raise ValueError(f"artifact hash mismatch: {name}") + return { + "status": "pass", + "schema_version": SCHEMA_VERSION, + "artifacts": len(receipt["artifacts"]), + } + + +def run_market_case( + output_dir: str | Path, + *, + input_path: str | Path = DEFAULT_INPUT, + seed: int = DEFAULT_SEED, +) -> dict[str, object]: + out = Path(output_dir) + out.mkdir(parents=True, exist_ok=True) + source_path = Path(input_path) + source_bytes = source_path.read_bytes() + factors = _load_factors(source_path) + calibration = factors[factors.index <= pd.Timestamp("2016-12-31")] + if len(calibration) < 252: + raise ValueError("calibration window must contain at least 252 observations") + oos_factors = factors[factors.index >= pd.Timestamp(OOS_START)] + if len(oos_factors) < 252: + raise ValueError("out-of-sample window must contain at least 252 observations") + + simulations = {lookback: _simulate(factors, lookback) for lookback in LOOKBACKS} + primary = simulations[21].loc[oos_factors.index].copy() + market = oos_factors["RF"] + oos_factors["Mkt_RF"] + calibration_vol = float( + calibration["Mkt_RF"].std(ddof=1) * math.sqrt(PERIODS_PER_YEAR) + ) + static_weight = min(MAX_EXPOSURE, TARGET_VOL / calibration_vol) + static = oos_factors["RF"] + static_weight * oos_factors["Mkt_RF"] + + daily = primary.copy() + daily["signal_available_date"] = ( + pd.Series(factors.index, index=factors.index) + .shift(1) + .loc[oos_factors.index] + .dt.strftime("%Y-%m-%d") + ) + daily["strategy_net"] = primary["net_return"] + daily["market"] = market + daily["static_risk_matched"] = static + daily["mkt_rf"] = oos_factors["Mkt_RF"] + daily["rf"] = oos_factors["RF"] + + candidates = np.column_stack( + [ + simulations[lookback] + .loc[oos_factors.index, "net_return"] + .to_numpy(dtype=float) + for lookback in LOOKBACKS + ] + ) + selection = centered_max_statistic_test( + candidates, + benchmark_returns=static.to_numpy(dtype=float), + candidate_names=[f"lookback_{lookback}" for lookback in LOOKBACKS], + seed=seed, + num_bootstrap=1999, + method="stationary", + block_length=15, + ) + selection["observed_statistic"] = _rounded( + float(cast(float, selection["observed_statistic"])), 12 + ) + selection["candidate_statistics"] = { + name: _rounded(float(value), 12) + for name, value in cast( + Mapping[str, float], selection["candidate_statistics"] + ).items() + } + selection["distribution"] = [ + _rounded(float(value)) + for value in cast(Sequence[float], selection["distribution"]) + ] + + strategy_metrics = _metrics(daily["strategy_net"]) + gross_metrics = _metrics(daily["gross_return"]) + market_metrics = _metrics(daily["market"]) + static_metrics = _metrics(daily["static_risk_matched"]) + stressed_metrics = _metrics(daily["stressed_return_5x_cost"]) + years = len(daily) / PERIODS_PER_YEAR + costs = { + "commission_total": _rounded(float(daily["commission"].sum())), + "half_spread_total": _rounded(float(daily["half_spread"].sum())), + "impact_total": _rounded(float(daily["impact"].sum())), + "total_cost": _rounded(float(daily["total_cost"].sum())), + "annualized_cost": _rounded(float(daily["total_cost"].sum()) / years), + "annualized_turnover": _rounded(float(daily["turnover"].sum()) / years), + "maximum_participation": _rounded(float(daily["participation"].max()), 9), + "scenario": { + "aum_usd": 1_000_000, + "adv_notional_usd": 20_000_000_000, + "commission_bps": 0.35, + "half_spread_bps": 0.50, + "impact_eta_bps": 10.0, + }, + "claim_boundary": "transparent liquidity sensitivity; not venue calibration or capacity evidence", + } + metrics: dict[str, object] = { + "schema_version": SCHEMA_VERSION, + "study": "fixed_market_volatility_targeting", + "sample": { + "calibration_start": str(calibration.index.min().date()), + "calibration_end": str(calibration.index.max().date()), + "oos_start": str(oos_factors.index.min().date()), + "oos_end": str(oos_factors.index.max().date()), + "oos_observations": int(len(oos_factors)), + "walk_forward_folds": int( + pd.DatetimeIndex(oos_factors.index).year.nunique() + ), + }, + "fixed_specification": { + "lookback_sessions": 21, + "target_volatility": TARGET_VOL, + "maximum_exposure": MAX_EXPOSURE, + "signal_clock": "close_t", + "execution_clock": "next_session_t_plus_1", + "selected_on_oos_performance": False, + }, + "strategy_net": strategy_metrics, + "strategy_gross": gross_metrics, + "market": market_metrics, + "static_risk_matched": {**static_metrics, "weight": _rounded(static_weight)}, + "stressed_5x_cost": stressed_metrics, + "costs": costs, + "uncertainty": _uncertainty(daily["strategy_net"], seed=seed + 1), + "selection_control": { + key: value for key, value in selection.items() if key != "distribution" + }, + "engineering_correctness": "source hash, strict t+1 chronology, cost identity, schema validation, and artifact hashes are executable gates", + "empirical_observation": "the fixed rule reduced realized risk and drawdown and raised descriptive Sharpe versus the market and a static risk-matched baseline", + "investment_claim": "none", + "claim_boundary": "retrospective public-factor risk-management evidence; no statistically confirmed alpha, security-level capacity, or live-trading claim", + } + + data_manifest = { + "schema_version": "microalpha.data-manifest.v1", + "dataset": "Fama-French daily factors plus momentum", + "publisher": "Kenneth R. French Data Library", + "source_url": SOURCE_URL, + "local_snapshot": "data/factors/ff5_mom_daily.csv", + "snapshot_sha256": _sha256(source_bytes), + "rows": int(len(factors)), + "start": str(factors.index.min().date()), + "end": str(factors.index.max().date()), + "columns": list(factors.reset_index().columns), + "return_units": "decimal daily research-portfolio returns", + "availability_rule": "a date-t factor return is available only after date-t close and can affect exposure at t+1", + "survivorship_boundary": "published factor series; no firm-level constituent selection or security-level survivorship claim", + "provenance_note": "the tracked snapshot was added from the public factor library through the project's prior WRDS research workflow; raw firm-level data is neither read nor distributed", + } + + files = { + "metrics.json": _json_bytes(metrics), + "daily.csv": _csv_bytes(daily), + "folds.csv": _fold_csv(daily), + "selection.json": _json_bytes(selection), + "data_manifest.json": _json_bytes(data_manifest), + "artifact_schema.json": _json_bytes(_schema_payload()), + "market_case.svg": _hero_svg(metrics), + "equity_drawdown.svg": _equity_svg(daily), + "data_lineage.svg": _lineage_svg(), + } + for name, content in files.items(): + (out / name).write_bytes(content) + + module_dir = Path(__file__).resolve().parent + receipt = { + "schema_version": SCHEMA_VERSION, + "generator": { + "version": GENERATOR_VERSION, + "source_sha256": { + name: _sha256((module_dir / name).read_bytes()) + for name in ("market_case.py", "multiple_testing.py") + }, + }, + "input": { + "logical_path": "data/factors/ff5_mom_daily.csv", + "sha256": _sha256(source_bytes), + }, + "artifacts": { + name: _sha256(content) for name, content in sorted(files.items()) + }, + "claim_boundary": metrics["claim_boundary"], + } + receipt_bytes = _json_bytes(receipt) + (out / "receipt.json").write_bytes(receipt_bytes) + verification = validate_market_case_artifacts(out) + return { + "artifact_dir": str(out), + "receipt_sha256": _sha256(receipt_bytes), + "verification": verification, + "metrics": metrics, + } diff --git a/src/microalpha/portfolio.py b/src/microalpha/portfolio.py index 08859fdb..57f4a9c3 100644 --- a/src/microalpha/portfolio.py +++ b/src/microalpha/portfolio.py @@ -212,7 +212,36 @@ def on_signal(self, signal: SignalEvent) -> Iterable[OrderEvent]: if self.current_time is not None and signal.timestamp < self.current_time: raise LookaheadError("Signal event timestamp is in the past.") - if signal.side == "EXIT": + target_weight_request = bool( + signal.meta and "target_weight" in signal.meta and "qty" not in signal.meta + ) + price = self.data_handler.get_latest_price(signal.symbol, signal.timestamp) + if price is None: + if self.order_flow: + self.order_flow.record_order_drop("missing_price", signal=signal) + return [] + + if target_weight_request: + target_weight = float(signal.meta["target_weight"]) # type: ignore[index] + equity = self.last_equity if self.last_equity else self.initial_cash + desired_qty = int(target_weight * equity / price) if equity > 0 else 0 + current_qty = self.positions.get(signal.symbol, PortfolioPosition()).qty + delta_qty = desired_qty - current_qty + if delta_qty == 0: + if self.order_flow: + self.order_flow.record_order_drop( + "target_weight_already_met", signal=signal + ) + return [] + if self.drawdown_halted and abs(desired_qty) > abs(current_qty): + if self.order_flow: + self.order_flow.record_order_drop( + "drawdown_halted", signal=signal, clipped_by_caps=True + ) + return [] + qty = abs(delta_qty) + side = cast(Literal["BUY", "SELL"], "BUY" if delta_qty > 0 else "SELL") + elif signal.side == "EXIT": position = self.positions.get(signal.symbol) if not position or position.qty == 0: if self.order_flow: @@ -223,23 +252,25 @@ def on_signal(self, signal: SignalEvent) -> Iterable[OrderEvent]: if self.order_flow: self.order_flow.record_order_created(order, signal=signal) return [order] + else: + if self.drawdown_halted: + if self.order_flow: + self.order_flow.record_order_drop( + "drawdown_halted", signal=signal, clipped_by_caps=True + ) + return [] - if self.drawdown_halted: - if self.order_flow: - self.order_flow.record_order_drop( - "drawdown_halted", signal=signal, clipped_by_caps=True - ) - return [] - - qty = self._sized_quantity(signal) - if qty == 0: - if self.order_flow: - reason = self._last_sizing_reject_reason or "qty_rounded_to_zero" - self.order_flow.record_order_drop(reason, signal=signal) - return [] + qty = self._sized_quantity(signal) + if qty == 0: + if self.order_flow: + reason = self._last_sizing_reject_reason or "qty_rounded_to_zero" + self.order_flow.record_order_drop(reason, signal=signal) + return [] + side = cast( + Literal["BUY", "SELL"], "BUY" if signal.side == "LONG" else "SELL" + ) - price = self.data_handler.get_latest_price(signal.symbol, signal.timestamp) - if price is None: + if price is None: # pragma: no cover - guarded above if self.order_flow: self.order_flow.record_order_drop("missing_price", signal=signal) return [] @@ -253,10 +284,11 @@ def on_signal(self, signal: SignalEvent) -> Iterable[OrderEvent]: ) return [] - side = cast(Literal["BUY", "SELL"], "BUY" if signal.side == "LONG" else "SELL") projected_equity = self.last_equity if self.last_equity else self.initial_cash has_weight = bool( - signal.meta and "weight" in signal.meta and "qty" not in signal.meta + signal.meta + and ("weight" in signal.meta or "target_weight" in signal.meta) + and "qty" not in signal.meta ) anticipated_market_value = ( self.market_value + (qty if side == "BUY" else -qty) * price diff --git a/tests/test_artifact_verify.py b/tests/test_artifact_verify.py new file mode 100644 index 00000000..0aa26d54 --- /dev/null +++ b/tests/test_artifact_verify.py @@ -0,0 +1,28 @@ +from __future__ import annotations + +from pathlib import Path + +import pytest + +from microalpha.artifact_verify import verify_artifact_dir +from microalpha.audit_lab import run_audit_lab +from microalpha.market_case import run_market_case + + +def test_public_verifier_accepts_audit_and_market_receipts(tmp_path: Path): + audit = tmp_path / "audit" + market = tmp_path / "market" + run_audit_lab(audit) + run_market_case(market) + + assert verify_artifact_dir(audit)["status"] == "pass" + assert verify_artifact_dir(market)["status"] == "pass" + + +def test_public_verifier_rejects_tampering(tmp_path: Path): + audit = tmp_path / "audit" + run_audit_lab(audit) + (audit / "comparison.csv").write_text("tampered\n", encoding="utf-8") + + with pytest.raises(ValueError, match="hash mismatch"): + verify_artifact_dir(audit) diff --git a/tests/test_cli_audit_lab.py b/tests/test_cli_audit_lab.py index d881c3d6..d06a7f2d 100644 --- a/tests/test_cli_audit_lab.py +++ b/tests/test_cli_audit_lab.py @@ -44,4 +44,4 @@ def test_cli_version_is_available() -> None: text=True, env=_environment(), ) - assert completed.stdout.startswith("cli.py ") + assert completed.stdout.startswith("microalpha ") diff --git a/tests/test_market_case.py b/tests/test_market_case.py new file mode 100644 index 00000000..2505cf1f --- /dev/null +++ b/tests/test_market_case.py @@ -0,0 +1,55 @@ +from __future__ import annotations + +import hashlib +import json +from pathlib import Path + +import pandas as pd + +from microalpha.market_case import run_market_case, validate_market_case_artifacts + + +def _hashes(root: Path) -> dict[str, str]: + return { + path.name: hashlib.sha256(path.read_bytes()).hexdigest() + for path in sorted(root.iterdir()) + if path.is_file() + } + + +def test_market_case_is_byte_identical_and_schema_valid(tmp_path: Path): + first = tmp_path / "first" + second = tmp_path / "second" + result_a = run_market_case(first) + result_b = run_market_case(second) + + assert result_a["receipt_sha256"] == result_b["receipt_sha256"] + assert _hashes(first) == _hashes(second) + assert validate_market_case_artifacts(first)["status"] == "pass" + + +def test_market_case_enforces_chronology_cost_identity_and_claim_boundary( + tmp_path: Path, +): + result = run_market_case(tmp_path) + metrics = result["metrics"] + daily = pd.read_csv(tmp_path / "daily.csv", parse_dates=["date"]) + + assert (pd.to_datetime(daily["signal_available_date"]) < daily["date"]).all() + residual = daily["gross_return"] - daily["total_cost"] - daily["strategy_net"] + assert float(residual.abs().max()) < 5e-9 + assert metrics["fixed_specification"]["selected_on_oos_performance"] is False + assert metrics["selection_control"]["num_candidates"] == 4 + assert metrics["selection_control"]["p_value"] >= 0.05 + assert metrics["investment_claim"] == "none" + + +def test_market_case_receipt_binds_source_and_every_artifact(tmp_path: Path): + result = run_market_case(tmp_path) + receipt = json.loads((tmp_path / "receipt.json").read_text(encoding="utf-8")) + manifest = json.loads((tmp_path / "data_manifest.json").read_text(encoding="utf-8")) + + assert len(result["receipt_sha256"]) == 64 + assert receipt["input"]["sha256"] == manifest["snapshot_sha256"] + for name, expected in receipt["artifacts"].items(): + assert hashlib.sha256((tmp_path / name).read_bytes()).hexdigest() == expected diff --git a/tests/test_portfolio_weight_sizing.py b/tests/test_portfolio_weight_sizing.py index 354ee86d..6e1c0f52 100644 --- a/tests/test_portfolio_weight_sizing.py +++ b/tests/test_portfolio_weight_sizing.py @@ -1,7 +1,7 @@ from __future__ import annotations from microalpha.events import MarketEvent, SignalEvent -from microalpha.portfolio import Portfolio +from microalpha.portfolio import Portfolio, PortfolioPosition class _StubData: @@ -31,3 +31,42 @@ def test_portfolio_uses_signal_weight_for_sizing(): short_orders = list(portfolio.on_signal(short_signal)) assert short_orders and short_orders[0].qty == 2 assert short_orders[0].side == "SELL" + + +def test_target_weight_rebalances_by_position_delta_and_can_flip(): + portfolio = Portfolio(data_handler=_StubData(), initial_cash=1000.0) + portfolio.on_market(MarketEvent(1, "AAA", 50.0, 1_000)) + portfolio.positions["AAA"] = PortfolioPosition(qty=20) + + reduce = SignalEvent(1, "AAA", "LONG", meta={"target_weight": 0.4}) + reduce_orders = list(portfolio.on_signal(reduce)) + assert [(order.side, order.qty) for order in reduce_orders] == [("SELL", 12)] + + portfolio.positions["AAA"] = PortfolioPosition(qty=8) + increase = SignalEvent(1, "AAA", "LONG", meta={"target_weight": 0.6}) + increase_orders = list(portfolio.on_signal(increase)) + assert [(order.side, order.qty) for order in increase_orders] == [("BUY", 4)] + + portfolio.positions["AAA"] = PortfolioPosition(qty=12) + flip = SignalEvent(1, "AAA", "SHORT", meta={"target_weight": -0.2}) + flip_orders = list(portfolio.on_signal(flip)) + assert [(order.side, order.qty) for order in flip_orders] == [("SELL", 16)] + + portfolio.positions["AAA"] = PortfolioPosition(qty=-4) + assert list(portfolio.on_signal(flip)) == [] + + +def test_drawdown_halt_allows_target_weight_deleveraging_only(): + portfolio = Portfolio(data_handler=_StubData(), initial_cash=1000.0) + portfolio.on_market(MarketEvent(1, "AAA", 50.0, 1_000)) + portfolio.positions["AAA"] = PortfolioPosition(qty=20) + portfolio.drawdown_halted = True + + reduce = SignalEvent(1, "AAA", "LONG", meta={"target_weight": 0.4}) + assert [(order.side, order.qty) for order in portfolio.on_signal(reduce)] == [ + ("SELL", 12) + ] + + portfolio.positions["AAA"] = PortfolioPosition(qty=8) + increase = SignalEvent(1, "AAA", "LONG", meta={"target_weight": 0.6}) + assert list(portfolio.on_signal(increase)) == []