diff --git a/app/services/fundamentals_derivation.py b/app/services/fundamentals_derivation.py index 3fada32..e5d203c 100644 --- a/app/services/fundamentals_derivation.py +++ b/app/services/fundamentals_derivation.py @@ -20,7 +20,7 @@ Rules: from __future__ import annotations from dataclasses import dataclass, field -from datetime import date, datetime, timezone +from datetime import date from typing import Any, Iterable _FP_TO_Q = {"Q1": 1, "Q2": 2, "Q3": 3, "FY": 4} @@ -30,12 +30,7 @@ TAPE_LEN = 4 # quarter-tape length # Duration (flow) fields differenced from YTD into discrete quarters + summed to TTM. _FLOW_FIELDS = ( - "revenue", - "net_income", - "operating_income", - "diluted_eps", - "cfo", - "capex", + "revenue", "net_income", "operating_income", "diluted_eps", "cfo", "capex", "depreciation_amortization", ) @@ -49,9 +44,7 @@ class MetricPoint: @dataclass class MetricSeries: value: float | None = None - history: list[MetricPoint] = field( - default_factory=list - ) # oldest -> newest, <= TAPE_LEN + history: list[MetricPoint] = field(default_factory=list) # oldest -> newest, <= TAPE_LEN period_end: date | None = None filed_date: date | None = None @@ -89,9 +82,7 @@ def derive(snapshots: Iterable[Any]) -> DerivedFundamentals: result.ttm_diluted_eps = _ttm(discrete["diluted_eps"], *latest) ttm_cfo = _ttm(discrete["cfo"], *latest) ttm_capex = _ttm(discrete["capex"], *latest) - result.ttm_fcf = ( - None if ttm_cfo is None or ttm_capex is None else ttm_cfo - ttm_capex - ) + result.ttm_fcf = None if ttm_cfo is None or ttm_capex is None else ttm_cfo - ttm_capex # tape = the CONSECUTIVE run of up to TAPE_LEN quarters ending at the latest, # stopping at a gap — so trend text never compares non-adjacent periods. @@ -99,9 +90,7 @@ def derive(snapshots: Iterable[Any]) -> DerivedFundamentals: result.metrics = { "revenue_growth_yoy": _yoy_growth_series(discrete["revenue"], selected, tape), "eps_growth_yoy": _yoy_growth_series(discrete["diluted_eps"], selected, tape), - "operating_margin": _margin_series( - discrete["operating_income"], discrete["revenue"], selected, tape - ), + "operating_margin": _margin_series(discrete["operating_income"], discrete["revenue"], selected, tape), "fcf_margin": _fcf_margin_series(discrete, selected, tape), "net_debt": _instant_series(selected, tape, _net_debt), "net_debt_to_ebitda": _leverage_series(selected, discrete, tape), @@ -113,27 +102,8 @@ def derive(snapshots: Iterable[Any]) -> DerivedFundamentals: return result -def derive_as_of(snapshots: Iterable[Any], as_of: datetime) -> DerivedFundamentals: - """Derive using only SEC filings accepted by the historical cutoff.""" - cutoff = _utc_datetime(as_of) - visible = ( - row - for row in snapshots - if (accepted := getattr(row, "accepted_at", None)) is not None - and _utc_datetime(accepted) <= cutoff - ) - return derive(visible) - - -def _utc_datetime(value: datetime) -> datetime: - if value.tzinfo is None: - return value.replace(tzinfo=timezone.utc) - return value.astimezone(timezone.utc) - - # -- period selection -------------------------------------------------------- - def _select_latest_per_period(snapshots: Iterable[Any]) -> dict[tuple[int, str], Any]: best: dict[tuple[int, str], Any] = {} for row in snapshots: @@ -156,9 +126,7 @@ def _ordered_quarters(selected: dict[tuple[int, str], Any]) -> list[tuple[int, i return sorted((fy, _FP_TO_Q[fp]) for (fy, fp) in selected) -def _consecutive_suffix( - quarters: list[tuple[int, int]], n: int -) -> list[tuple[int, int]]: +def _consecutive_suffix(quarters: list[tuple[int, int]], n: int) -> list[tuple[int, int]]: """The run of up to n quarters ending at the latest, walking back only through adjacent periods (stop at the first gap). Returned oldest -> newest.""" if not quarters: @@ -178,10 +146,7 @@ def _consecutive_suffix( # -- discrete + TTM ---------------------------------------------------------- - -def _discrete_quarters( - selected: dict[tuple[int, str], Any], field_name: str -) -> dict[tuple[int, int], float]: +def _discrete_quarters(selected: dict[tuple[int, str], Any], field_name: str) -> dict[tuple[int, int], float]: out: dict[tuple[int, int], float] = {} for (fy, fp), row in selected.items(): val = _discrete_value(selected, fy, fp, field_name) @@ -224,7 +189,6 @@ def _pct_change(cur: float | None, prior: float | None) -> float | None: # -- per-metric series (value at latest + tape history) ---------------------- - def _period_end(selected, fy: int, q: int) -> date | None: row = selected.get((fy, _Q_TO_FP[q])) return row.period_end if row is not None else None @@ -232,7 +196,7 @@ def _period_end(selected, fy: int, q: int) -> date | None: def _yoy_growth_series(dq, selected, tape) -> MetricSeries: pts = [] - for fy, q in tape: + for (fy, q) in tape: cur, prior = _ttm(dq, fy, q), _ttm(dq, fy - 1, q) pts.append(MetricPoint(_period_end(selected, fy, q), _pct_change(cur, prior))) return _series(pts) @@ -240,7 +204,7 @@ def _yoy_growth_series(dq, selected, tape) -> MetricSeries: def _margin_series(num_dq, den_dq, selected, tape) -> MetricSeries: pts = [] - for fy, q in tape: + for (fy, q) in tape: num, den = _ttm(num_dq, fy, q), _ttm(den_dq, fy, q) val = None if num is None or not den else num / den * 100.0 pts.append(MetricPoint(_period_end(selected, fy, q), val)) @@ -249,38 +213,24 @@ def _margin_series(num_dq, den_dq, selected, tape) -> MetricSeries: def _fcf_margin_series(discrete, selected, tape) -> MetricSeries: pts = [] - for fy, q in tape: - cfo, capex, rev = ( - _ttm(discrete["cfo"], fy, q), - _ttm(discrete["capex"], fy, q), - _ttm(discrete["revenue"], fy, q), - ) - val = ( - None - if cfo is None or capex is None or not rev - else (cfo - capex) / rev * 100.0 - ) + for (fy, q) in tape: + cfo, capex, rev = _ttm(discrete["cfo"], fy, q), _ttm(discrete["capex"], fy, q), _ttm(discrete["revenue"], fy, q) + val = None if cfo is None or capex is None or not rev else (cfo - capex) / rev * 100.0 pts.append(MetricPoint(_period_end(selected, fy, q), val)) return _series(pts) def _instant_series(selected, tape, fn) -> MetricSeries: - pts = [ - MetricPoint(_period_end(selected, fy, q), fn(selected.get((fy, _Q_TO_FP[q])))) - for (fy, q) in tape - ] + pts = [MetricPoint(_period_end(selected, fy, q), fn(selected.get((fy, _Q_TO_FP[q])))) for (fy, q) in tape] return _series(pts) def _leverage_series(selected, discrete, tape) -> MetricSeries: pts = [] - for fy, q in tape: + for (fy, q) in tape: row = selected.get((fy, _Q_TO_FP[q])) nd = _net_debt(row) - op, da = ( - _ttm(discrete["operating_income"], fy, q), - _ttm(discrete["depreciation_amortization"], fy, q), - ) + op, da = _ttm(discrete["operating_income"], fy, q), _ttm(discrete["depreciation_amortization"], fy, q) ebitda = None if op is None or da is None else op + da # Null when EBITDA <= 0: a negative denominator would flip polarity and a # "lower is better" read would rank a distressed issuer as favorable. @@ -291,7 +241,7 @@ def _leverage_series(selected, discrete, tape) -> MetricSeries: def _share_change_series(selected, tape) -> MetricSeries: pts = [] - for fy, q in tape: + for (fy, q) in tape: cur = _shares(selected.get((fy, _Q_TO_FP[q]))) prior = _shares(selected.get((fy - 1, _Q_TO_FP[q]))) pts.append(MetricPoint(_period_end(selected, fy, q), _pct_change(cur, prior))) diff --git a/app/services/fundamentals_research.py b/app/services/fundamentals_research.py deleted file mode 100644 index c1fbdb9..0000000 --- a/app/services/fundamentals_research.py +++ /dev/null @@ -1,176 +0,0 @@ -"""Pure scoring helpers for point-in-time fundamentals research. - -The runner converts a CIK-deduplicated cross-section into favorable 0..100 -factor ranks and three deliberately small composites. Historical valuation is -absent: stored bars are split-adjusted, while filing-time EPS and share counts -are not guaranteed to be on today's split basis. -""" - -from __future__ import annotations - -import math -from collections.abc import Mapping -from typing import Any - -MIN_CROSS_SECTION = 5 - -FACTOR_POLARITY: dict[str, bool] = { - "revenue_growth_yoy": True, - "eps_growth_yoy": True, - "operating_margin": True, - "fcf_margin": True, - "net_debt_to_ebitda": False, - "share_count_change_yoy": False, -} - -QUALITY_FACTORS = ( - "operating_margin", - "fcf_margin", - "net_debt_to_ebitda", - "share_count_change_yoy", -) -GROWTH_FACTORS = ("revenue_growth_yoy", "eps_growth_yoy") -SPLIT_SAFE_FACTOR_POLARITY: dict[str, bool] = { - "revenue_growth_yoy": True, - "operating_margin": True, - "fcf_margin": True, - "net_debt_to_ebitda": False, -} -SPLIT_SAFE_QUALITY_FACTORS = ( - "operating_margin", - "fcf_margin", - "net_debt_to_ebitda", -) -SPLIT_SAFE_GROWTH_FACTORS = ("revenue_growth_yoy",) -COMPOSITE_KEYS = ("quality", "growth", "balanced") - - -def raw_features(derived: Any) -> dict[str, float | None]: - """Extract the six research-eligible values from derived fundamentals.""" - metrics = getattr(derived, "metrics", {}) or {} - return { - key: _finite_or_none(getattr(metrics.get(key), "value", None)) - for key in FACTOR_POLARITY - } - - -def cross_section_scores( - features_by_issuer: Mapping[str, Mapping[str, Any]], - *, - min_cross_section: int = MIN_CROSS_SECTION, - split_safe: bool = False, -) -> dict[str, dict[str, float | None]]: - """Return favorable factor ranks and composites for every issuer. - - The default reproduces the original registered experiment. ``split_safe`` - excludes diluted-EPS growth and share-count change because filing-time - values are not comparable across stock splits without point-in-time split - factors. Its quality score needs two of three remaining inputs and its - growth score is revenue growth. Balanced always weights the two sub-scores - equally. - """ - factor_polarity = SPLIT_SAFE_FACTOR_POLARITY if split_safe else FACTOR_POLARITY - quality_factors = SPLIT_SAFE_QUALITY_FACTORS if split_safe else QUALITY_FACTORS - growth_factors = SPLIT_SAFE_GROWTH_FACTORS if split_safe else GROWTH_FACTORS - result = { - str(issuer): { - **{key: None for key in factor_polarity}, - **{key: None for key in COMPOSITE_KEYS}, - } - for issuer in features_by_issuer - } - - for factor, higher_is_better in factor_polarity.items(): - values = { - str(issuer): _finite_or_none(features.get(factor)) - for issuer, features in features_by_issuer.items() - } - ranks = favorable_percentiles( - values, - higher_is_better=higher_is_better, - min_count=min_cross_section, - ) - for issuer, rank in ranks.items(): - result[issuer][factor] = rank - - for scores in result.values(): - quality_values = _available(scores, quality_factors) - growth_values = _available(scores, growth_factors) - if len(quality_values) >= 2: - scores["quality"] = _mean(quality_values) - if growth_values: - scores["growth"] = _mean(growth_values) - if scores["quality"] is not None and scores["growth"] is not None: - scores["balanced"] = _mean( - [float(scores["quality"]), float(scores["growth"])] - ) - return result - - -def favorable_percentiles( - values_by_issuer: Mapping[str, Any], - *, - higher_is_better: bool, - min_count: int = MIN_CROSS_SECTION, -) -> dict[str, float | None]: - """Tie-aware favorable percentile for a deduplicated cross-section.""" - valid = { - str(issuer): float(value) - for issuer, value in values_by_issuer.items() - if _finite_or_none(value) is not None - } - result: dict[str, float | None] = {str(issuer): None for issuer in values_by_issuer} - if len(valid) < min_count: - return result - - for issuer, subject in valid.items(): - others = [value for key, value in valid.items() if key != issuer] - if higher_is_better: - worse = sum(value < subject for value in others) - else: - worse = sum(value > subject for value in others) - tied = sum(value == subject for value in others) - result[issuer] = round( - (worse + 0.5 * tied) / len(others) * 100.0, - 4, - ) - return result - - -def overlay_rank( - strategy_rank: Any, - fundamental_score: Any, - weight: float, - *, - missing_score: float = 50.0, -) -> float | None: - """Blend production rank with fundamentals without changing the gate.""" - base = _finite_or_none(strategy_rank) - if base is None: - return None - if not 0.0 <= weight <= 1.0: - raise ValueError("weight must be between 0 and 1") - score = _finite_or_none(fundamental_score) - if score is None: - score = float(missing_score) - return round((1.0 - weight) * base + weight * score, 4) - - -def _available(scores: Mapping[str, Any], keys: tuple[str, ...]) -> list[float]: - return [ - value for key in keys if (value := _finite_or_none(scores.get(key))) is not None - ] - - -def _mean(values: list[float]) -> float: - return round(sum(values) / len(values), 4) - - -def _finite_or_none(value: Any) -> float | None: - if ( - isinstance(value, (int, float)) - and not isinstance(value, bool) - and math.isfinite(value) - ): - return float(value) - return None diff --git a/docs/research/fundamentals-weight-backtest.md b/docs/research/fundamentals-weight-backtest.md index 10956a5..b2d626f 100644 --- a/docs/research/fundamentals-weight-backtest.md +++ b/docs/research/fundamentals-weight-backtest.md @@ -1,191 +1,74 @@ -# Point-in-time fundamentals weight backtest +# Fundamentals ranking-overlay research -Status: initial experiment completed; split-safe follow-up registered locally. -Running either protocol does not change production. +Status: completed 2026-07-23. Decision: keep production scoring and qualification unchanged. ## Question -Does reordering already-qualified long setups with SEC fundamentals improve the -production book's risk-adjusted return? The qualification gate, execution model, -position sizing, capacity, costs, ATR trail, and post-stop re-entry policy remain -unchanged. This isolates the incremental value of fundamentals as a ranking -overlay. +Does using point-in-time SEC fundamentals to reorder already-qualified long setups improve the production portfolio's risk-adjusted return? The experiments changed ranking only; qualification, execution, sizing, capacity, costs, ATR exits, and post-stop re-entry remained unchanged. -## Completed initial experiment +## Method -The control is the production 80/20 residual-momentum / volatility rank. The -runner tests three fundamental composites at weights 10%, 20%, 30%, and 40%: +- The control was the production 80/20 residual-momentum / volatility rank. +- SEC facts became visible only after `accepted_at`, using the newest visible accession per fiscal period. +- Portfolio simulations used daily entry opportunities, close fills, the production gate-reset re-entry policy, and a 30-session horizon. +- Train contained entries before 2024-01-01, validation covered 2024, and test began 2025-01-01. +- Missing composite scores were neutral at 50. +- Deflated Sharpe used the complete registered arm count for each experiment. -- Quality: operating margin, FCF margin, low net-debt/EBITDA, and low dilution. - At least two inputs must exist. -- Growth: revenue growth and diluted-EPS growth. At least one must exist. -- Balanced: equal weight to the quality and growth sub-scores. Both must exist. +The snapshot contained 511 tracked tickers, 507 unique CIKs, 30,494 SEC snapshot rows, and prices from 2021-06-24 through 2026-07-22. -Each raw metric is ranked favorably from 0 to 100 across the CIK-deduplicated -tracked universe. Missing composite scores are neutral at 50. The formula is: +## Initial experiment -`final rank = (1 - weight) * production rank + weight * fundamental rank` +The first registered matrix tested quality, growth, and balanced composites at 10%, 20%, 30%, and 40% weights: 13 trials including control. -There are 13 registered portfolio trials including the control. That complete -count is used by the Deflated Sharpe calculation. +No overlay passed the train and validation requirements. The most attractive full-period result, balanced at 10%, failed validation and improved test Sharpe by only 0.06. -No overlay passed the registered train and validation requirements. Review also -found that filing-time diluted EPS and shares are not guaranteed to use the same -split basis across periods. That makes EPS growth and share-count change unsafe -for historical ranking without point-in-time split factors. The initial result -remains an auditable rejection of its registered arms, but it is not evidence -that split-safe fundamentals have no value. +Review also found that filing-time diluted EPS and shares are not reliably comparable across stock splits. A snapshot audit found share-count changes above 25% for 90 of 461 issuers with comparable 2021+ periods, including recognizable split ratios for AMZN, GOOG, NVDA, CMG, and GE plus some obvious unit anomalies. Consequently, EPS growth and share-count change cannot be trusted for historical ranking without point-in-time split factors. -## Registered split-safe follow-up +The complete initial result is recoverable from Git commit `7f944d7`. -Run with `--protocol split-safe`. This is a smaller sensitivity experiment: +## Split-safe follow-up -- Quality: operating margin, FCF margin, and low net-debt/EBITDA. At least two - inputs must exist. +The follow-up excluded diluted-EPS growth and share-count change completely. It tested: + +- Quality: operating margin, FCF margin, and low net-debt/EBITDA, requiring at least two inputs. - Growth: revenue growth only. -- Balanced: equal weight to the quality and growth sub-scores. Both must exist. +- Balanced: equal quality and growth weights. - Overlay weights: 5%, 10%, and 15%. -Diluted-EPS growth and share-count change are excluded completely: they are not -ranked, do not enter composites, and do not appear in factor-IC output. Historical -P/E and FCF yield remain excluded for the same split-basis reason. Earnings -surprise remains excluded because the completed Dolt SUE study failed its -promotion bar for this strategy. +This produced 10 registered trials including control. Growth coverage among qualified candidates was 88.96%; lack of data was not the limiting factor. -There are 10 registered portfolio trials including the control. The split-safe -report uses 10 in its Deflated Sharpe calculation. It has a separate score cache -and `fundamentals-splitsafe-*` output prefix, so it cannot be confused with or -silently reuse the initial experiment's scores. +| Window | Control Sharpe | Revenue-growth 5% | Delta | +|---|---:|---:|---:| +| Train | 1.26 | 1.31 | +0.05 | +| Validation | 2.52 | 2.64 | +0.12 | +| Test | 1.99 | 1.99 | 0.00 | +| Full | 1.86 | 1.87 | +0.01 | -The test window has already been inspected during the initial experiment. Keep -the original date boundaries and development selection discipline, but treat the -follow-up as sensitivity evidence. Any promotion still requires forward paper -evidence. +Revenue growth at 5% mechanically passed the deliberately permissive "not worse" gate, but did not demonstrate an economically meaningful edge: -## Point-in-time rule +- Test CAGR rose from 54.4% to 56.1%, while full-period CAGR fell from 52.1% to 51.5%. +- Full-period trade overlap was 68.53%, so roughly one-third of selections changed for essentially unchanged Sharpe. +- Revenue-growth IC was 0.0006 in train, 0.0053 in test, and 0.0116 full-period with a full-period t-stat of 0.55. +- Growth weights of 10% and 15% deteriorated; quality and balanced composites failed. +- The test window had already been inspected, so this follow-up was sensitivity evidence rather than a fresh out-of-sample result. -Only SEC rows accepted before midnight America/New_York at the start of a signal -date are visible. This is conservative relative to the daily pre-market SEC -import and prevents same-day filings or later amendments from leaking backward. -The derivation then selects the newest visible accession per fiscal period. +The complete split-safe result is recoverable from Git commit `dba7ea7`. -Default windows are fixed before the first run: +## Decision -- Train: entry date before 2024-01-01 -- Validation: 2024-01-01 through 2024-12-31 -- Test: entry date on or after 2025-01-01 +- Do not add fundamental weight to production ranking or the automated qualification gate. +- Do not run another historical weight sweep on the same sample; it would add data-mining rather than new evidence. +- Keep fundamentals informational and user-facing in the UI. +- A5 source-parity and cutover work can proceed independently without changing scoring behavior. +- Treat historical EPS growth and share-count change as non-comparable across corporate actions until a split-aware solution or a conservative UI guard exists. -Do not move these boundaries after seeing results. The test window is used only -to check the single arm chosen from train and validation. Reports expose all -registered rows for auditability and correct multiple-testing accounting. +Revisit automated weighting only with materially better data, such as point-in-time split factors and historical constituent/delisting coverage, followed by genuinely new forward paper evidence. -## 1. Create the portable snapshot +## Limitations -Run this wherever the production PostgreSQL connection is already configured. -The exporter copies prices, the safe strategy settings, SEC snapshots, and Dolt -earnings rows. It does not copy credentials or unrelated system settings. +The snapshot uses today's tracked universe rather than historical membership and delisted securities, creating survivorship bias. Absolute CAGR and Sharpe must not be interpreted as unbiased live expectations. The relative comparison is useful, but the observed test window and short number of independent factor windows limit statistical power. -Windows PowerShell: +## Repository cleanup -```powershell -.venv\Scripts\python.exe scripts\create_backtest_snapshot.py ` - --output backtest_snapshots\fundamentals-backtest.sqlite ` - --force -``` - -Linux production host: - -```bash -.venv/bin/python scripts/create_backtest_snapshot.py \ - --output backtest_snapshots/fundamentals-backtest.sqlite \ - --force -``` - -Copy only the SQLite file to the MacBook. `scp`, a local network share, or an -encrypted USB drive are all fine. Do not copy `.env`. - -## 2. Prepare the MacBook - -Use the same Git commit as the machine that created the report. From the repo: - -```bash -python3.11 -m venv .venv -source .venv/bin/activate -python -m pip install --upgrade pip -python -m pip install -e '.[dev]' -chmod +x scripts/run_fundamentals_macbook.sh -``` - -Put the snapshot at `backtest_snapshots/fundamentals-backtest.sqlite`, or pass a -different path to the launcher. - -## 3. Run it - -The launcher now defaults to the registered `split-safe` follow-up: - -```bash -./scripts/run_fundamentals_macbook.sh \ - backtest_snapshots/fundamentals-backtest.sqlite -``` - -The launcher defaults to logical CPU count minus one. Override it if the laptop -gets too warm or memory pressure rises: - -```bash -WORKERS=8 ./scripts/run_fundamentals_macbook.sh \ - backtest_snapshots/fundamentals-backtest.sqlite -``` - -To reproduce the completed initial matrix instead, opt in explicitly: - -```bash -PROTOCOL=original ./scripts/run_fundamentals_macbook.sh \ - backtest_snapshots/fundamentals-backtest.sqlite -``` - -The first run builds two caches under `reports/.cache`: production candidate -replay and protocol-specific point-in-time fundamental scores. If interrupted, -rerun the same command; valid caches are reused. Cache keys include the protocol, -snapshot size, and mtime, so neither a protocol switch nor a new snapshot can -reuse incompatible scores. - -## 4. Bring the result back - -The final line names one ZIP such as: - -`reports/fundamentals-splitsafe-20260723-180000.zip` - -That ZIP contains: - -- the complete JSON report and reproducibility metadata; -- a readable Markdown summary; -- portfolio-arm CSV; -- factor-IC CSV; -- full-period trade CSV for every arm, allowing winner-concentration checks; -- this registered protocol. - -Copy the ZIP into this workspace or attach it in the conversation. The snapshot -itself is not needed for the first evaluation unless a result looks inconsistent. - -## Evaluation order - -1. Data coverage and the accepted-at range. -2. Individual factor IC: sign, magnitude, consistency, and cross-section size. -3. Composite IC in train, validation, and test. -4. Development selection made without the test window. -5. Test Sharpe, CAGR, drawdown, yearly returns, trial-corrected DSR, and trade - overlap versus control. -6. Sensitivity to a few dominant winners and whether the effect is economically - large enough to justify production complexity. - -The mechanical development bar requires train and validation Sharpe not below -control and validation drawdown no more than two percentage points worse. A test -pass is still research evidence, not automatic deployment. - -## Known limitation - -The snapshot contains today's tracked tickers, not historical constituent -membership or delisted names. This creates survivorship bias. The same biased -universe is used for control and overlays, so the local comparison is useful, -but its absolute Sharpe or CAGR must not be presented as an unbiased live -expectation. Forward paper performance remains the true out-of-sample check. +The experiment-only scorer, runner, Mac launcher, caches, tests, and expanded report bundles were removed after this decision. They remain recoverable from commits `eae4d34`, `34d6dda`, `7f944d7`, and `dba7ea7`. Production fundamentals derivation and ingestion remain unchanged. diff --git a/reports/fundamentals-overlay-20260723-135627-arms.csv b/reports/fundamentals-overlay-20260723-135627-arms.csv deleted file mode 100644 index 794ba30..0000000 --- a/reports/fundamentals-overlay-20260723-135627-arms.csv +++ /dev/null @@ -1,53 +0,0 @@ -arm,composite,weight,window,sharpe,sharpe_se,dsr,cagr_pct,max_drawdown_pct,calmar,trades,overlap_pct,top5_pnl_share_pct,avg_r_ex_top5 -control_w00,,0.0,train,1.26,0.764,0.4607,32.5,18.5,1.76,195,,67.83,0.2729 -control_w00,,0.0,validation,2.52,0.938,0.8309,71.3,14.8,4.82,106,,69.13,0.357 -control_w00,,0.0,test,1.99,0.81,0.7757,54.4,19.2,2.83,188,,62.82,0.1591 -control_w00,,0.0,full,1.86,0.49,0.9807,52.1,22.3,2.34,483,,38.74,0.4132 -quality_w10,quality,0.1,train,1.66,0.772,0.6629,45.4,16.9,2.68,183,57.5,63.51,0.2957 -quality_w10,quality,0.1,validation,2.44,0.941,0.8077,68.8,17.3,3.98,107,65.12,70.53,0.3518 -quality_w10,quality,0.1,test,1.7,0.805,0.6562,43.8,18.8,2.33,189,60.43,72.18,0.1424 -quality_w10,quality,0.1,full,1.91,0.493,0.9845,53.3,21.6,2.46,470,59.1,39.96,0.4105 -quality_w20,quality,0.2,train,1.64,0.77,0.6538,44.4,17.4,2.55,181,42.42,61.16,0.2545 -quality_w20,quality,0.2,validation,2.28,0.953,0.7551,59.4,18.6,3.19,111,53.9,79.91,0.3052 -quality_w20,quality,0.2,test,1.49,0.805,0.5562,35.9,18.6,1.93,189,44.44,80.74,0.136 -quality_w20,quality,0.2,full,1.75,0.493,0.9666,46.6,20.7,2.26,478,44.73,44.1,0.3938 -quality_w30,quality,0.3,train,1.69,0.767,0.6781,45.0,17.3,2.6,176,39.47,48.72,0.3464 -quality_w30,quality,0.3,validation,1.83,0.948,0.5869,42.9,18.8,2.28,111,51.75,83.6,0.2918 -quality_w30,quality,0.3,test,1.3,0.801,0.4621,28.9,18.6,1.56,189,41.73,94.0,0.1314 -quality_w30,quality,0.3,full,1.57,0.491,0.9297,39.1,19.6,2.0,469,41.25,43.46,0.4265 -quality_w40,quality,0.4,train,1.73,0.772,0.6954,46.5,18.2,2.55,190,31.85,48.84,0.3179 -quality_w40,quality,0.4,validation,1.58,0.944,0.4824,36.8,19.0,1.94,110,48.97,101.05,0.2815 -quality_w40,quality,0.4,test,1.18,0.812,0.4045,24.2,18.0,1.35,193,34.63,110.57,0.0845 -quality_w40,quality,0.4,full,1.39,0.494,0.8643,33.0,23.8,1.39,492,34.11,47.94,0.3366 -growth_w10,growth,0.1,train,1.24,0.769,0.4506,31.1,18.2,1.71,190,53.39,70.42,0.2227 -growth_w10,growth,0.1,validation,2.65,0.915,0.8695,74.6,11.6,6.41,103,74.17,67.49,0.4295 -growth_w10,growth,0.1,test,1.87,0.807,0.7297,52.4,18.9,2.77,197,61.09,69.9,0.1885 -growth_w10,growth,0.1,full,1.83,0.49,0.9776,51.5,21.6,2.39,485,58.43,42.2,0.4164 -growth_w20,growth,0.2,train,1.16,0.775,0.4105,27.3,17.8,1.53,189,42.75,83.96,0.1951 -growth_w20,growth,0.2,validation,1.99,0.945,0.6516,50.1,19.2,2.61,101,56.82,71.97,0.3987 -growth_w20,growth,0.2,test,1.59,0.8,0.6053,40.5,18.7,2.16,191,53.44,83.16,0.066 -growth_w20,growth,0.2,full,1.55,0.493,0.9232,39.3,21.1,1.86,477,49.07,45.99,0.3356 -growth_w30,growth,0.3,train,0.94,0.784,0.307,19.5,17.7,1.1,199,41.22,107.4,0.0981 -growth_w30,growth,0.3,validation,1.79,0.954,0.57,42.4,19.5,2.18,101,55.64,78.91,0.2995 -growth_w30,growth,0.3,test,1.32,0.8,0.472,31.5,18.3,1.72,198,47.33,92.89,0.066 -growth_w30,growth,0.3,full,1.29,0.496,0.8143,29.9,20.7,1.44,500,45.63,50.27,0.2876 -growth_w40,growth,0.4,train,1.08,0.785,0.3725,22.5,16.2,1.38,196,32.54,101.48,0.1686 -growth_w40,growth,0.4,validation,1.96,0.956,0.6383,48.1,19.3,2.5,101,55.64,80.82,0.2926 -growth_w40,growth,0.4,test,1.45,0.799,0.5368,36.3,17.8,2.03,193,50.59,84.67,0.0155 -growth_w40,growth,0.4,full,1.47,0.495,0.896,35.6,26.0,1.37,487,40.99,46.88,0.2959 -balanced_w10,balanced,0.1,train,1.66,0.773,0.6627,45.7,18.5,2.47,191,62.87,63.37,0.2536 -balanced_w10,balanced,0.1,validation,2.42,0.931,0.8044,64.1,17.4,3.69,105,64.84,75.23,0.4326 -balanced_w10,balanced,0.1,test,2.05,0.808,0.7978,57.6,18.9,3.04,186,56.49,62.34,0.2001 -balanced_w10,balanced,0.1,full,2.0,0.493,0.9903,57.4,21.4,2.69,471,58.21,38.56,0.4343 -balanced_w20,balanced,0.2,train,1.5,0.774,0.5842,40.3,16.2,2.49,186,48.25,69.56,0.2281 -balanced_w20,balanced,0.2,validation,1.94,0.945,0.6319,45.7,18.5,2.47,113,58.7,78.45,0.3889 -balanced_w20,balanced,0.2,test,1.99,0.805,0.7771,52.6,18.3,2.88,189,46.12,61.78,0.2105 -balanced_w20,balanced,0.2,full,1.71,0.493,0.96,45.6,19.3,2.37,481,47.18,37.87,0.4034 -balanced_w30,balanced,0.3,train,1.11,0.774,0.3854,26.5,17.1,1.55,201,37.98,82.7,0.1461 -balanced_w30,balanced,0.3,validation,1.9,0.94,0.6164,47.7,17.3,2.76,104,60.31,89.47,0.3487 -balanced_w30,balanced,0.3,test,1.54,0.799,0.5812,37.9,18.3,2.08,197,45.83,82.89,0.1937 -balanced_w30,balanced,0.3,full,1.52,0.492,0.9144,39.4,21.3,1.85,496,43.55,47.61,0.3639 -balanced_w40,balanced,0.4,train,1.16,0.782,0.4113,25.9,16.2,1.6,202,34.58,77.25,0.1727 -balanced_w40,balanced,0.4,validation,2.07,0.943,0.6827,56.2,18.4,3.06,103,50.36,80.3,0.2394 -balanced_w40,balanced,0.4,test,1.29,0.807,0.4574,30.0,17.8,1.68,202,46.62,91.39,0.1051 -balanced_w40,balanced,0.4,full,1.34,0.496,0.8401,32.3,26.4,1.22,507,40.83,51.04,0.3014 diff --git a/reports/fundamentals-overlay-20260723-135627-factor-ic.csv b/reports/fundamentals-overlay-20260723-135627-factor-ic.csv deleted file mode 100644 index 84c94f5..0000000 --- a/reports/fundamentals-overlay-20260723-135627-factor-ic.csv +++ /dev/null @@ -1,37 +0,0 @@ -window,signal,mean_ic,ic_t_stat,ic_positive_pct,mean_quintile_spread,weeks,avg_cross_section,reliable -train,share_count_change_yoy,0.0389,1.97,61.9,0.0029,21,435.5,True -train,net_debt_to_ebitda,0.0141,0.46,61.9,0.0098,21,183.9,True -train,balanced,0.0065,0.2,57.1,0.0007,21,381.2,True -train,quality,0.0038,0.19,47.6,-0.0033,21,396.6,True -train,revenue_growth_yoy,0.0006,0.02,47.6,0.0044,21,406.8,True -train,fcf_margin,-0.006,-0.29,38.1,-0.0044,21,365.3,True -train,growth,-0.0077,-0.26,47.6,0.003,21,442.5,True -train,eps_growth_yoy,-0.0152,-0.65,52.4,-0.004,21,370.2,True -train,operating_margin,-0.0288,-1.36,28.6,-0.0131,21,333.3,True -validation,growth,0.0558,1.21,55.6,0.0177,9,450.9,False -validation,revenue_growth_yoy,0.0485,0.95,55.6,0.0205,9,416.7,False -validation,balanced,0.0483,1.06,55.6,0.0126,9,387.9,False -validation,eps_growth_yoy,0.0473,1.44,55.6,0.014,9,393.8,False -validation,fcf_margin,0.0268,0.84,66.7,0.0003,9,368.0,False -validation,net_debt_to_ebitda,0.0066,0.12,55.6,0.0104,9,184.6,False -validation,quality,-0.0029,-0.14,44.4,-0.0002,9,401.0,False -validation,operating_margin,-0.0056,-0.18,44.4,-0.0058,9,338.3,False -validation,share_count_change_yoy,-0.0169,-0.52,55.6,-0.0098,9,439.9,False -test,eps_growth_yoy,0.0182,0.88,46.2,0.0071,13,415.7,True -test,share_count_change_yoy,0.0142,0.78,61.5,-0.0147,13,451.9,True -test,growth,0.011,0.32,46.2,0.0047,13,463.6,True -test,revenue_growth_yoy,0.0053,0.14,46.2,0.0046,13,429.3,True -test,net_debt_to_ebitda,-0.0082,-0.19,61.5,-0.0043,13,195.5,True -test,balanced,-0.0176,-0.47,38.5,-0.0147,13,402.2,True -test,quality,-0.0386,-1.59,30.8,-0.0257,13,419.6,True -test,operating_margin,-0.0436,-1.52,38.5,-0.0279,13,349.5,True -test,fcf_margin,-0.0535,-1.99,30.8,-0.0307,13,380.4,True -full,share_count_change_yoy,0.0161,1.16,54.8,-0.0052,42,441.3,True -full,growth,0.0123,0.65,54.8,0.0067,42,450.5,True -full,revenue_growth_yoy,0.0116,0.55,47.6,0.0069,42,415.5,True -full,eps_growth_yoy,0.008,0.58,57.1,0.003,42,388.5,True -full,balanced,0.0071,0.36,54.8,-0.0014,42,388.9,True -full,net_debt_to_ebitda,0.006,0.29,54.8,0.0037,42,187.9,True -full,quality,-0.0109,-0.85,45.2,-0.0092,42,404.5,True -full,fcf_margin,-0.0161,-1.04,35.7,-0.0123,42,370.5,True -full,operating_margin,-0.0252,-1.74,33.3,-0.017,42,339.2,True diff --git a/reports/fundamentals-overlay-20260723-135627-trades.csv b/reports/fundamentals-overlay-20260723-135627-trades.csv deleted file mode 100644 index 8bd2152..0000000 --- a/reports/fundamentals-overlay-20260723-135627-trades.csv +++ /dev/null @@ -1,6297 +0,0 @@ -arm,symbol,entry_date,exit_date,r,pnl,reason,hold,entry,exit,risk_dollars -control_w00,ANET,2022-06-24,2022-06-29,-1.0,-103.37492274806932,stop,3,24.96,, -control_w00,IRM,2022-06-24,2022-07-13,-1.0,-103.82251085231773,stop,12,49.46,, -control_w00,PANW,2022-06-24,2022-07-14,-1.0,-103.08685124345979,stop,13,85.12,, -control_w00,REGN,2022-06-24,2022-07-18,-1.0,-100.72422908655027,stop,15,612.49,, -control_w00,PFE,2022-06-24,2022-07-28,-0.7267195767195778,-67.80955966157887,trailing_stop,23,51.59,, -control_w00,DVN,2022-07-28,2022-08-04,-1.0,-110.40215607028928,stop,5,60.37,, -control_w00,LYV,2022-08-04,2022-08-05,-1.0,-64.1278927969482,stop,1,97.5,, -control_w00,FIX,2022-06-24,2022-08-08,4.201325540884595,161.84470748349145,time,30,83.02,, -control_w00,KLAC,2022-07-14,2022-08-09,1.768653049764865,170.17809309792054,trailing_stop,18,31.91,, -control_w00,COST,2022-06-29,2022-08-11,2.9468331939305483,214.45365557105163,time,30,469.84,, -control_w00,SNPS,2022-07-13,2022-08-19,3.9602255340482144,163.40596956553256,trailing_stop,27,303.07,, -control_w00,BLDR,2022-08-08,2022-08-22,-1.122579584551615,-58.25192067748323,stop,10,69.78,, -control_w00,TSLA,2022-07-13,2022-08-24,2.7455497956608825,266.4362786003152,time,30,237.04,, -control_w00,ANET,2022-07-14,2022-08-25,4.904280490428048,7.543221061125577,time,30,24.78,, -control_w00,ON,2022-07-18,2022-08-29,3.602333976165748,350.19821593514524,time,30,54.95,, -control_w00,EQT,2022-07-18,2022-08-29,3.4170006327778912,180.2553943590756,time,30,37.86,, -control_w00,MOS,2022-08-09,2022-08-31,0.3470723835869064,33.72695559697577,trailing_stop,16,54.01,, -control_w00,HAL,2022-08-29,2022-08-31,-1.2009690222153482,-145.33656232560782,stop,2,31.9,, -control_w00,TRGP,2022-08-29,2022-08-31,-1.3707729468599064,-32.178663631957505,stop,2,71.11,, -control_w00,MPC,2022-07-28,2022-09-01,1.4624855076464438,46.01328563425077,trailing_stop,25,89.59,, -control_w00,ALB,2022-08-05,2022-09-01,1.761405907546294,132.7932059646981,trailing_stop,19,237.99,, -control_w00,STLD,2022-08-31,2022-09-01,-1.0,-115.17243220050771,stop,1,80.72,, -control_w00,PANW,2022-08-22,2022-09-06,0.4934431444629017,19.78507742537393,trailing_stop,10,84.68,, -control_w00,COP,2022-08-24,2022-09-07,-1.0,-69.9660004581706,stop,9,110.52,, -control_w00,COR,2022-08-31,2022-09-13,-1.0,-0.9418641320251965,stop,8,146.56,, -control_w00,NUE,2022-09-07,2022-09-14,-0.903584595196936,-62.7842724289714,trailing_stop,5,135.61,, -control_w00,DVN,2022-08-19,2022-09-16,-0.6642417156528438,-77.07071881262542,trailing_stop,19,68.51,, -control_w00,ADM,2022-09-01,2022-09-16,-0.9351883715975945,-74.47729562644642,trailing_stop,10,87.58,, -control_w00,FANG,2022-08-25,2022-09-19,-1.1040854626822585,-2.529309057809067,stop,16,136.28,, -control_w00,F,2022-09-02,2022-09-20,-1.3973228860594182,-116.3024820727129,stop,11,15.16,, -control_w00,TRGP,2022-09-06,2022-09-22,-0.6548995062243557,-32.5832820672275,trailing_stop,12,68.05,, -control_w00,APA,2022-08-11,2022-09-23,0.060725186570144855,4.19356780088234,trailing_stop,30,34.84,, -control_w00,SLB,2022-08-31,2022-09-23,-1.0,-114.96428913064142,stop,16,38.15,, -control_w00,EOG,2022-09-13,2022-09-23,-1.4019702155902072,-2.0753977461644575,stop,8,122.91,, -control_w00,MOS,2022-09-14,2022-09-23,-1.0,-83.88289615312392,stop,7,53.9,, -control_w00,CVX,2022-09-20,2022-09-23,-1.058638522769647,-91.81325475353522,stop,3,156.28,, -control_w00,ADM,2022-09-20,2022-09-23,-1.0,-40.36248750539586,stop,3,86.75,, -control_w00,ABBV,2022-09-16,2022-09-30,-1.0,-70.19456897448859,stop,10,144.06,, -control_w00,TTD,2022-09-28,2022-10-07,-1.0,-102.38114083704865,stop,7,62.96,, -control_w00,ANET,2022-10-03,2022-10-10,-0.9355601948503821,-96.0713992101128,trailing_stop,5,28.96,, -control_w00,LVS,2022-09-30,2022-10-11,0.09268348362058872,6.282501612473824,trailing_stop,7,37.52,, -control_w00,APA,2022-10-07,2022-10-12,-1.0,-96.12189031587046,stop,3,42.52,, -control_w00,ABBV,2022-10-11,2022-10-28,0.28330042287682367,12.788008416617721,trailing_stop,13,141.51,, -control_w00,DLTR,2022-10-28,2022-11-03,-1.0,-59.888482502646454,stop,4,158.55,, -control_w00,AZO,2022-09-28,2022-11-09,3.537164772975563,267.60003991483717,time,30,2169.44,, -control_w00,XOM,2022-10-03,2022-11-14,4.807530677424784,458.89785304395775,time,30,91.92,, -control_w00,SLB,2022-10-03,2022-11-14,6.10176049526021,491.6423339467365,time,30,38.3,, -control_w00,MOS,2022-11-14,2022-11-17,-1.0,-122.17571054862532,stop,3,53.12,, -control_w00,PTC,2022-11-14,2022-11-17,-1.0,-10.790103841247136,stop,3,130.29,, -control_w00,ADM,2022-10-10,2022-11-21,2.6218468841419633,203.31552725089873,time,30,86.61,, -control_w00,HAL,2022-11-17,2022-11-21,-1.0,-101.36898867506869,stop,2,37.47,, 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-quality_w20,CVNA,2024-11-06,2024-12-18,-0.3099160512529215,-5.331786940526931,trailing_stop,29,47.79,, -quality_w20,DASH,2024-12-17,2024-12-18,-1.0,-228.07634405441235,stop,1,177.0,, -quality_w20,HOOD,2024-11-13,2024-12-20,1.228737088661061,348.38840583206905,trailing_stop,26,31.91,, -quality_w20,EBAY,2024-12-12,2024-12-30,-1.0,-238.45385593780787,stop,11,63.9,, -quality_w20,GM,2024-12-26,2025-01-02,-1.0,-257.00866971205267,stop,4,54.18,, -quality_w20,CEG,2025-01-03,2025-01-08,-1.0,-305.57618985636833,stop,3,252.4,, -quality_w20,GM,2025-01-06,2025-01-08,-1.0,-279.52409033788274,stop,2,53.53,, -quality_w20,NVDA,2024-12-27,2025-01-13,-0.9269712995424547,-283.4959613121089,trailing_stop,9,137.01,, -quality_w20,LDOS,2025-01-13,2025-01-23,-0.4143559290758687,-91.68680986485512,trailing_stop,7,152.64,, -quality_w20,WMB,2025-01-03,2025-01-27,0.09127940332759728,4.771016132751816,trailing_stop,14,56.6,, 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-quality_w20,FICO,2025-04-14,2025-04-21,-1.0,-276.8312767399805,stop,4,1932.74,, -quality_w20,AWK,2025-04-14,2025-04-25,-1.0,-230.31971739765314,stop,8,148.83,, -quality_w20,WMT,2025-04-11,2025-05-15,-0.0030145540319659503,-10.524571478963308,trailing_stop,23,92.8,, -quality_w20,FICO,2025-04-25,2025-05-20,0.6107829966069024,161.45855530815103,trailing_stop,17,1952.31,, -quality_w20,GEV,2025-04-09,2025-05-22,3.3770396605820623,898.934372904976,time,30,326.81,, -quality_w20,CVNA,2025-04-10,2025-05-23,2.7967452511711124,741.8610819408984,time,30,40.73,, -quality_w20,TPR,2025-05-20,2025-05-23,-1.2881983248615076,-398.0225141714293,stop,3,82.52,, -quality_w20,AXON,2025-04-11,2025-05-27,3.599070425381428,955.8871113196446,time,30,567.98,, -quality_w20,RKLB,2025-04-14,2025-05-28,3.2891976707110375,880.6165344939867,time,30,19.13,, -quality_w20,TSLA,2025-04-14,2025-05-28,2.878785375605082,769.8568898558385,time,30,252.35,, 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"exit_date": "2026-07-10" - } - ] - } - ], - "development_grades": { - "quality_w10": { - "pass": false, - "checks": { - "train_sharpe_not_worse": true, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": 0.4, - "validation_sharpe_delta": -0.08 - }, - "quality_w20": { - "pass": false, - "checks": { - "train_sharpe_not_worse": true, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": 0.38, - "validation_sharpe_delta": -0.24 - }, - "quality_w30": { - "pass": false, - "checks": { - "train_sharpe_not_worse": true, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": 0.43, - "validation_sharpe_delta": -0.69 - }, - "quality_w40": { - "pass": false, - "checks": { - "train_sharpe_not_worse": true, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": 0.47, - "validation_sharpe_delta": -0.94 - }, - "growth_w10": { - "pass": false, - "checks": { - "train_sharpe_not_worse": false, - "validation_sharpe_not_worse": true, - "validation_drawdown_within_2pp": true - }, - "train_sharpe_delta": -0.02, - "validation_sharpe_delta": 0.13 - }, - "growth_w20": { - "pass": false, - "checks": { - "train_sharpe_not_worse": false, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": -0.1, - "validation_sharpe_delta": -0.53 - }, - "growth_w30": { - "pass": false, - "checks": { - "train_sharpe_not_worse": false, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": -0.32, - "validation_sharpe_delta": -0.73 - }, - "growth_w40": { - "pass": false, - "checks": { - "train_sharpe_not_worse": false, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": -0.18, - "validation_sharpe_delta": -0.56 - }, - "balanced_w10": { - "pass": false, - "checks": { - "train_sharpe_not_worse": true, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": 0.4, - "validation_sharpe_delta": -0.1 - }, - "balanced_w20": { - "pass": false, - "checks": { - "train_sharpe_not_worse": true, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": 0.24, - "validation_sharpe_delta": -0.58 - }, - "balanced_w30": { - "pass": false, - "checks": { - "train_sharpe_not_worse": false, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": -0.15, - "validation_sharpe_delta": -0.62 - }, - "balanced_w40": { - "pass": false, - "checks": { - "train_sharpe_not_worse": false, - "validation_sharpe_not_worse": false, - "validation_drawdown_within_2pp": false - }, - "train_sharpe_delta": -0.1, - "validation_sharpe_delta": -0.45 - } - }, - "development_selection": null, - "final_check": null, - "completed_at": "2026-07-23T14:06:32.680439+00:00" -} \ No newline at end of file diff --git a/reports/fundamentals-overlay-20260723-135627.md b/reports/fundamentals-overlay-20260723-135627.md deleted file mode 100644 index 0be183d..0000000 --- a/reports/fundamentals-overlay-20260723-135627.md +++ /dev/null @@ -1,123 +0,0 @@ -# Point-in-time fundamentals overlay research - -Generated: 2026-07-23T14:06:10.423614+00:00 - -## Protocol - -- Train ends before **2024-01-01**. -- Validation runs until **2025-01-01**. -- Test starts at that date and is not used to select the arm. -- Qualification is unchanged; fundamentals only reorder qualified longs. -- SEC filings become visible at midnight New York time after acceptance. -- Pre-registered portfolio trials for DSR: **13**. - -## Data warnings - -- Current tracked universe only: historical constituent membership and delisted names are unavailable, so absolute results have survivorship bias. -- Historical valuation is excluded because split-adjusted bars cannot be safely combined with filing-time EPS and shares without split factors. -- Earnings surprise is excluded because the completed SUE study already failed its promotion bar for this strategy. -- The test window remains research evidence, not a pristine future sample; live paper performance is still the final out-of-sample check. - -## Factor IC - -| window | signal | IC | t | positive | quintile spread | weeks | N | -|---|---|---:|---:|---:|---:|---:|---:| -| train | share_count_change_yoy | 0.0389 | 1.97 | 61.9 | 0.0029 | 21 | 435.5 | -| train | net_debt_to_ebitda | 0.0141 | 0.46 | 61.9 | 0.0098 | 21 | 183.9 | -| train | balanced | 0.0065 | 0.2 | 57.1 | 0.0007 | 21 | 381.2 | -| train | quality | 0.0038 | 0.19 | 47.6 | -0.0033 | 21 | 396.6 | -| train | revenue_growth_yoy | 0.0006 | 0.02 | 47.6 | 0.0044 | 21 | 406.8 | -| train | fcf_margin | -0.006 | -0.29 | 38.1 | -0.0044 | 21 | 365.3 | -| train | growth | -0.0077 | -0.26 | 47.6 | 0.003 | 21 | 442.5 | -| train | eps_growth_yoy | -0.0152 | -0.65 | 52.4 | -0.004 | 21 | 370.2 | -| train | operating_margin | -0.0288 | -1.36 | 28.6 | -0.0131 | 21 | 333.3 | -| validation | growth | 0.0558 | 1.21 | 55.6 | 0.0177 | 9 | 450.9 | -| validation | revenue_growth_yoy | 0.0485 | 0.95 | 55.6 | 0.0205 | 9 | 416.7 | -| validation | balanced | 0.0483 | 1.06 | 55.6 | 0.0126 | 9 | 387.9 | -| validation | eps_growth_yoy | 0.0473 | 1.44 | 55.6 | 0.014 | 9 | 393.8 | -| validation | fcf_margin | 0.0268 | 0.84 | 66.7 | 0.0003 | 9 | 368 | -| validation | net_debt_to_ebitda | 0.0066 | 0.12 | 55.6 | 0.0104 | 9 | 184.6 | -| validation | quality | -0.0029 | -0.14 | 44.4 | -0.0002 | 9 | 401 | -| validation | operating_margin | -0.0056 | -0.18 | 44.4 | -0.0058 | 9 | 338.3 | -| validation | share_count_change_yoy | -0.0169 | -0.52 | 55.6 | -0.0098 | 9 | 439.9 | -| test | eps_growth_yoy | 0.0182 | 0.88 | 46.2 | 0.0071 | 13 | 415.7 | -| test | share_count_change_yoy | 0.0142 | 0.78 | 61.5 | -0.0147 | 13 | 451.9 | -| test | growth | 0.011 | 0.32 | 46.2 | 0.0047 | 13 | 463.6 | -| test | revenue_growth_yoy | 0.0053 | 0.14 | 46.2 | 0.0046 | 13 | 429.3 | -| test | net_debt_to_ebitda | -0.0082 | -0.19 | 61.5 | -0.0043 | 13 | 195.5 | -| test | balanced | -0.0176 | -0.47 | 38.5 | -0.0147 | 13 | 402.2 | -| test | quality | -0.0386 | -1.59 | 30.8 | -0.0257 | 13 | 419.6 | -| test | operating_margin | -0.0436 | -1.52 | 38.5 | -0.0279 | 13 | 349.5 | -| test | fcf_margin | -0.0535 | -1.99 | 30.8 | -0.0307 | 13 | 380.4 | -| full | share_count_change_yoy | 0.0161 | 1.16 | 54.8 | -0.0052 | 42 | 441.3 | -| full | growth | 0.0123 | 0.65 | 54.8 | 0.0067 | 42 | 450.5 | -| full | revenue_growth_yoy | 0.0116 | 0.55 | 47.6 | 0.0069 | 42 | 415.5 | -| full | eps_growth_yoy | 0.008 | 0.58 | 57.1 | 0.003 | 42 | 388.5 | -| full | balanced | 0.0071 | 0.36 | 54.8 | -0.0014 | 42 | 388.9 | -| full | net_debt_to_ebitda | 0.006 | 0.29 | 54.8 | 0.0037 | 42 | 187.9 | -| full | quality | -0.0109 | -0.85 | 45.2 | -0.0092 | 42 | 404.5 | -| full | fcf_margin | -0.0161 | -1.04 | 35.7 | -0.0123 | 42 | 370.5 | -| full | operating_margin | -0.0252 | -1.74 | 33.3 | -0.017 | 42 | 339.2 | - -## Portfolio arms - -| arm | window | Sharpe | SE | DSR | CAGR | MaxDD | Calmar | trades | overlap | -|---|---|---:|---:|---:|---:|---:|---:|---:|---:| -| control_w00 | train | 1.26 | 0.764 | 0.4607 | 32.5 | 18.5 | 1.76 | 195 | — | -| control_w00 | validation | 2.52 | 0.938 | 0.8309 | 71.3 | 14.8 | 4.82 | 106 | — | -| control_w00 | test | 1.99 | 0.81 | 0.7757 | 54.4 | 19.2 | 2.83 | 188 | — | -| control_w00 | full | 1.86 | 0.49 | 0.9807 | 52.1 | 22.3 | 2.34 | 483 | — | -| quality_w10 | train | 1.66 | 0.772 | 0.6629 | 45.4 | 16.9 | 2.68 | 183 | 57.5 | -| quality_w10 | validation | 2.44 | 0.941 | 0.8077 | 68.8 | 17.3 | 3.98 | 107 | 65.12 | -| quality_w10 | test | 1.7 | 0.805 | 0.6562 | 43.8 | 18.8 | 2.33 | 189 | 60.43 | -| quality_w10 | full | 1.91 | 0.493 | 0.9845 | 53.3 | 21.6 | 2.46 | 470 | 59.1 | -| quality_w20 | train | 1.64 | 0.77 | 0.6538 | 44.4 | 17.4 | 2.55 | 181 | 42.42 | -| quality_w20 | validation | 2.28 | 0.953 | 0.7551 | 59.4 | 18.6 | 3.19 | 111 | 53.9 | -| quality_w20 | test | 1.49 | 0.805 | 0.5562 | 35.9 | 18.6 | 1.93 | 189 | 44.44 | -| quality_w20 | full | 1.75 | 0.493 | 0.9666 | 46.6 | 20.7 | 2.26 | 478 | 44.73 | -| quality_w30 | train | 1.69 | 0.767 | 0.6781 | 45 | 17.3 | 2.6 | 176 | 39.47 | -| quality_w30 | validation | 1.83 | 0.948 | 0.5869 | 42.9 | 18.8 | 2.28 | 111 | 51.75 | -| quality_w30 | test | 1.3 | 0.801 | 0.4621 | 28.9 | 18.6 | 1.56 | 189 | 41.73 | -| quality_w30 | full | 1.57 | 0.491 | 0.9297 | 39.1 | 19.6 | 2 | 469 | 41.25 | -| quality_w40 | train | 1.73 | 0.772 | 0.6954 | 46.5 | 18.2 | 2.55 | 190 | 31.85 | -| quality_w40 | validation | 1.58 | 0.944 | 0.4824 | 36.8 | 19 | 1.94 | 110 | 48.97 | -| quality_w40 | test | 1.18 | 0.812 | 0.4045 | 24.2 | 18 | 1.35 | 193 | 34.63 | -| quality_w40 | full | 1.39 | 0.494 | 0.8643 | 33 | 23.8 | 1.39 | 492 | 34.11 | -| growth_w10 | train | 1.24 | 0.769 | 0.4506 | 31.1 | 18.2 | 1.71 | 190 | 53.39 | -| growth_w10 | validation | 2.65 | 0.915 | 0.8695 | 74.6 | 11.6 | 6.41 | 103 | 74.17 | -| growth_w10 | test | 1.87 | 0.807 | 0.7297 | 52.4 | 18.9 | 2.77 | 197 | 61.09 | -| growth_w10 | full | 1.83 | 0.49 | 0.9776 | 51.5 | 21.6 | 2.39 | 485 | 58.43 | -| growth_w20 | train | 1.16 | 0.775 | 0.4105 | 27.3 | 17.8 | 1.53 | 189 | 42.75 | -| growth_w20 | validation | 1.99 | 0.945 | 0.6516 | 50.1 | 19.2 | 2.61 | 101 | 56.82 | -| growth_w20 | test | 1.59 | 0.8 | 0.6053 | 40.5 | 18.7 | 2.16 | 191 | 53.44 | -| growth_w20 | full | 1.55 | 0.493 | 0.9232 | 39.3 | 21.1 | 1.86 | 477 | 49.07 | -| growth_w30 | train | 0.94 | 0.784 | 0.307 | 19.5 | 17.7 | 1.1 | 199 | 41.22 | -| growth_w30 | validation | 1.79 | 0.954 | 0.57 | 42.4 | 19.5 | 2.18 | 101 | 55.64 | -| growth_w30 | test | 1.32 | 0.8 | 0.472 | 31.5 | 18.3 | 1.72 | 198 | 47.33 | -| growth_w30 | full | 1.29 | 0.496 | 0.8143 | 29.9 | 20.7 | 1.44 | 500 | 45.63 | -| growth_w40 | train | 1.08 | 0.785 | 0.3725 | 22.5 | 16.2 | 1.38 | 196 | 32.54 | -| growth_w40 | validation | 1.96 | 0.956 | 0.6383 | 48.1 | 19.3 | 2.5 | 101 | 55.64 | -| growth_w40 | test | 1.45 | 0.799 | 0.5368 | 36.3 | 17.8 | 2.03 | 193 | 50.59 | -| growth_w40 | full | 1.47 | 0.495 | 0.896 | 35.6 | 26 | 1.37 | 487 | 40.99 | -| balanced_w10 | train | 1.66 | 0.773 | 0.6627 | 45.7 | 18.5 | 2.47 | 191 | 62.87 | -| balanced_w10 | validation | 2.42 | 0.931 | 0.8044 | 64.1 | 17.4 | 3.69 | 105 | 64.84 | -| balanced_w10 | test | 2.05 | 0.808 | 0.7978 | 57.6 | 18.9 | 3.04 | 186 | 56.49 | -| balanced_w10 | full | 2 | 0.493 | 0.9903 | 57.4 | 21.4 | 2.69 | 471 | 58.21 | -| balanced_w20 | train | 1.5 | 0.774 | 0.5842 | 40.3 | 16.2 | 2.49 | 186 | 48.25 | -| balanced_w20 | validation | 1.94 | 0.945 | 0.6319 | 45.7 | 18.5 | 2.47 | 113 | 58.7 | -| balanced_w20 | test | 1.99 | 0.805 | 0.7771 | 52.6 | 18.3 | 2.88 | 189 | 46.12 | -| balanced_w20 | full | 1.71 | 0.493 | 0.96 | 45.6 | 19.3 | 2.37 | 481 | 47.18 | -| balanced_w30 | train | 1.11 | 0.774 | 0.3854 | 26.5 | 17.1 | 1.55 | 201 | 37.98 | -| balanced_w30 | validation | 1.9 | 0.94 | 0.6164 | 47.7 | 17.3 | 2.76 | 104 | 60.31 | -| balanced_w30 | test | 1.54 | 0.799 | 0.5812 | 37.9 | 18.3 | 2.08 | 197 | 45.83 | -| balanced_w30 | full | 1.52 | 0.492 | 0.9144 | 39.4 | 21.3 | 1.85 | 496 | 43.55 | -| balanced_w40 | train | 1.16 | 0.782 | 0.4113 | 25.9 | 16.2 | 1.6 | 202 | 34.58 | -| balanced_w40 | validation | 2.07 | 0.943 | 0.6827 | 56.2 | 18.4 | 3.06 | 103 | 50.36 | -| balanced_w40 | test | 1.29 | 0.807 | 0.4574 | 30 | 17.8 | 1.68 | 202 | 46.62 | -| balanced_w40 | full | 1.34 | 0.496 | 0.8401 | 32.3 | 26.4 | 1.22 | 507 | 40.83 | - -## Mechanical selection - -- No overlay passed the train + validation requirements. - -Production remains unchanged pending human review. diff --git a/reports/fundamentals-overlay-20260723-135627.zip b/reports/fundamentals-overlay-20260723-135627.zip deleted file mode 100644 index 776004d..0000000 Binary files a/reports/fundamentals-overlay-20260723-135627.zip and /dev/null differ diff --git a/reports/fundamentals-splitsafe-20260723-153520-arms.csv b/reports/fundamentals-splitsafe-20260723-153520-arms.csv deleted file mode 100644 index b7ede3d..0000000 --- a/reports/fundamentals-splitsafe-20260723-153520-arms.csv +++ /dev/null @@ -1,41 +0,0 @@ -arm,composite,weight,window,sharpe,sharpe_se,dsr,cagr_pct,max_drawdown_pct,calmar,trades,overlap_pct,top5_pnl_share_pct,avg_r_ex_top5 -control_w00,,0.0,train,1.26,0.764,0.5133,32.5,18.5,1.76,195,,67.83,0.2729 -control_w00,,0.0,validation,2.52,0.938,0.8618,71.3,14.8,4.82,106,,69.13,0.357 -control_w00,,0.0,test,1.99,0.81,0.8122,54.4,19.2,2.83,188,,62.82,0.1591 -control_w00,,0.0,full,1.86,0.49,0.986,52.1,22.3,2.34,483,,38.74,0.4132 -quality_w05,quality,0.05,train,1.5,0.767,0.6354,40.3,16.7,2.42,192,72.0,69.73,0.2241 -quality_w05,quality,0.05,validation,2.43,0.939,0.8392,67.4,17.3,3.89,106,73.77,71.95,0.3914 -quality_w05,quality,0.05,test,1.67,0.807,0.6889,43.1,19.2,2.24,191,64.78,71.88,0.1701 -quality_w05,quality,0.05,full,1.81,0.492,0.9816,49.7,22.0,2.26,482,68.12,40.52,0.3944 -quality_w10,quality,0.1,train,1.68,0.768,0.7191,46.1,16.7,2.75,195,64.56,63.19,0.1846 -quality_w10,quality,0.1,validation,1.95,0.948,0.6828,47.9,17.8,2.69,112,61.48,74.11,0.3579 -quality_w10,quality,0.1,test,1.57,0.809,0.6436,39.7,18.8,2.12,193,56.79,76.49,0.131 -quality_w10,quality,0.1,full,1.72,0.493,0.9714,46.0,19.3,2.39,491,59.67,39.37,0.3583 -quality_w15,quality,0.15,train,1.4,0.773,0.5848,35.8,15.3,2.33,189,47.69,67.32,0.2364 -quality_w15,quality,0.15,validation,1.85,0.932,0.6467,45.0,18.4,2.44,111,57.25,79.6,0.3738 -quality_w15,quality,0.15,test,1.78,0.804,0.7361,46.3,18.8,2.47,195,49.61,66.98,0.1186 -quality_w15,quality,0.15,full,1.66,0.491,0.963,43.4,19.7,2.2,484,48.31,38.77,0.3822 -growth_w05,growth,0.05,train,1.31,0.768,0.5392,32.7,17.9,1.83,192,72.0,69.9,0.3036 -growth_w05,growth,0.05,validation,2.64,0.918,0.893,73.6,11.6,6.33,103,75.63,68.23,0.447 -growth_w05,growth,0.05,test,1.99,0.8,0.8152,56.1,18.9,2.96,190,64.35,67.21,0.1646 -growth_w05,growth,0.05,full,1.87,0.489,0.9869,51.5,21.3,2.41,481,68.53,42.14,0.4654 -growth_w10,growth,0.1,train,1.11,0.775,0.4362,26.3,17.8,1.47,197,53.73,86.35,0.218 -growth_w10,growth,0.1,validation,2.64,0.918,0.893,73.6,11.6,6.33,103,75.63,68.23,0.447 -growth_w10,growth,0.1,test,1.92,0.808,0.7886,54.1,17.3,3.13,192,55.1,68.64,0.2284 -growth_w10,growth,0.1,full,1.77,0.492,0.9776,48.9,20.2,2.42,490,56.94,42.63,0.4153 -growth_w15,growth,0.15,train,1.07,0.772,0.4156,24.6,18.0,1.37,194,52.55,88.03,0.2015 -growth_w15,growth,0.15,validation,2.02,0.93,0.7123,52.6,19.1,2.76,100,58.46,70.05,0.3237 -growth_w15,growth,0.15,test,1.61,0.809,0.6618,42.3,17.3,2.45,191,55.97,78.59,0.2064 -growth_w15,growth,0.15,full,1.39,0.492,0.8915,34.7,20.6,1.69,487,54.95,47.05,0.3551 -balanced_w05,balanced,0.05,train,1.2,0.771,0.4822,30.2,20.9,1.45,198,73.13,74.32,0.2634 -balanced_w05,balanced,0.05,validation,2.25,0.947,0.7861,62.1,20.0,3.11,106,70.97,76.48,0.3761 -balanced_w05,balanced,0.05,test,2.02,0.802,0.8244,55.6,18.9,2.94,185,78.47,66.73,0.1759 -balanced_w05,balanced,0.05,full,1.76,0.492,0.9765,48.3,21.9,2.2,481,73.69,42.66,0.412 -balanced_w10,balanced,0.1,train,1.34,0.77,0.5545,34.1,17.0,2.01,189,68.42,66.41,0.2941 -balanced_w10,balanced,0.1,validation,1.82,0.93,0.6349,43.1,19.3,2.23,110,58.82,83.18,0.4047 -balanced_w10,balanced,0.1,test,2.0,0.811,0.8152,55.9,18.5,3.03,187,57.56,66.25,0.2341 -balanced_w10,balanced,0.1,full,1.71,0.492,0.9703,46.0,19.9,2.31,476,59.57,41.92,0.46 -balanced_w15,balanced,0.15,train,1.35,0.766,0.5599,34.3,15.5,2.21,182,56.43,65.25,0.276 -balanced_w15,balanced,0.15,validation,1.89,0.931,0.6627,45.3,17.8,2.54,111,55.0,79.37,0.3649 -balanced_w15,balanced,0.15,test,1.83,0.808,0.755,48.7,18.5,2.64,190,50.0,65.58,0.1869 -balanced_w15,balanced,0.15,full,1.59,0.49,0.9503,41.0,19.9,2.06,474,52.88,39.94,0.4303 diff --git a/reports/fundamentals-splitsafe-20260723-153520-factor-ic.csv b/reports/fundamentals-splitsafe-20260723-153520-factor-ic.csv deleted file mode 100644 index 576a68b..0000000 --- a/reports/fundamentals-splitsafe-20260723-153520-factor-ic.csv +++ /dev/null @@ -1,29 +0,0 @@ -window,signal,mean_ic,ic_t_stat,ic_positive_pct,mean_quintile_spread,weeks,avg_cross_section,reliable -train,net_debt_to_ebitda,0.0141,0.46,61.9,0.0098,21,183.9,True -train,growth,0.0006,0.02,47.6,0.0044,21,406.8,True -train,revenue_growth_yoy,0.0006,0.02,47.6,0.0044,21,406.8,True -train,fcf_margin,-0.006,-0.29,38.1,-0.0044,21,365.3,True -train,balanced,-0.0075,-0.2,47.6,-0.003,21,305.9,True -train,quality,-0.0185,-0.72,47.6,-0.0092,21,318.2,True -train,operating_margin,-0.0288,-1.36,28.6,-0.0131,21,333.3,True -validation,balanced,0.0597,1.18,55.6,0.013,9,312.9,False -validation,growth,0.0485,0.95,55.6,0.0205,9,416.7,False -validation,revenue_growth_yoy,0.0485,0.95,55.6,0.0205,9,416.7,False -validation,fcf_margin,0.0268,0.84,66.7,0.0003,9,368.0,False -validation,quality,0.0144,0.6,44.4,-0.0019,9,323.1,False -validation,net_debt_to_ebitda,0.0066,0.12,55.6,0.0104,9,184.6,False -validation,operating_margin,-0.0056,-0.18,44.4,-0.0058,9,338.3,False -test,growth,0.0053,0.14,46.2,0.0046,13,429.3,True -test,revenue_growth_yoy,0.0053,0.14,46.2,0.0046,13,429.3,True -test,net_debt_to_ebitda,-0.0082,-0.19,61.5,-0.0043,13,195.5,True -test,balanced,-0.0318,-0.67,46.2,-0.0123,13,318.9,True -test,operating_margin,-0.0436,-1.52,38.5,-0.0279,13,349.5,True -test,fcf_margin,-0.0535,-1.99,30.8,-0.0307,13,380.4,True -test,quality,-0.055,-1.63,30.8,-0.0288,13,332.2,True -full,growth,0.0116,0.55,47.6,0.0069,42,415.5,True -full,revenue_growth_yoy,0.0116,0.55,47.6,0.0069,42,415.5,True -full,net_debt_to_ebitda,0.006,0.29,54.8,0.0037,42,187.9,True -full,balanced,-0.0042,-0.18,47.6,-0.0032,42,311.2,True -full,fcf_margin,-0.0161,-1.04,35.7,-0.0123,42,370.5,True -full,quality,-0.0242,-1.45,38.1,-0.014,42,323.6,True -full,operating_margin,-0.0252,-1.74,33.3,-0.017,42,339.2,True diff --git a/reports/fundamentals-splitsafe-20260723-153520-trades.csv b/reports/fundamentals-splitsafe-20260723-153520-trades.csv deleted file mode 100644 index 0cf81ce..0000000 --- a/reports/fundamentals-splitsafe-20260723-153520-trades.csv +++ /dev/null @@ -1,4830 +0,0 @@ -arm,symbol,entry_date,exit_date,r,pnl,reason,hold,entry,exit,risk_dollars -control_w00,ANET,2022-06-24,2022-06-29,-1.0,-103.37492274806932,stop,3,24.96,, -control_w00,IRM,2022-06-24,2022-07-13,-1.0,-103.82251085231773,stop,12,49.46,, -control_w00,PANW,2022-06-24,2022-07-14,-1.0,-103.08685124345979,stop,13,85.12,, -control_w00,REGN,2022-06-24,2022-07-18,-1.0,-100.72422908655027,stop,15,612.49,, -control_w00,PFE,2022-06-24,2022-07-28,-0.7267195767195778,-67.80955966157887,trailing_stop,23,51.59,, -control_w00,DVN,2022-07-28,2022-08-04,-1.0,-110.40215607028928,stop,5,60.37,, -control_w00,LYV,2022-08-04,2022-08-05,-1.0,-64.1278927969482,stop,1,97.5,, -control_w00,FIX,2022-06-24,2022-08-08,4.201325540884595,161.84470748349145,time,30,83.02,, -control_w00,KLAC,2022-07-14,2022-08-09,1.768653049764865,170.17809309792054,trailing_stop,18,31.91,, -control_w00,COST,2022-06-29,2022-08-11,2.9468331939305483,214.45365557105163,time,30,469.84,, -control_w00,SNPS,2022-07-13,2022-08-19,3.9602255340482144,163.40596956553256,trailing_stop,27,303.07,, -control_w00,BLDR,2022-08-08,2022-08-22,-1.122579584551615,-58.25192067748323,stop,10,69.78,, -control_w00,TSLA,2022-07-13,2022-08-24,2.7455497956608825,266.4362786003152,time,30,237.04,, -control_w00,ANET,2022-07-14,2022-08-25,4.904280490428048,7.543221061125577,time,30,24.78,, -control_w00,ON,2022-07-18,2022-08-29,3.602333976165748,350.19821593514524,time,30,54.95,, -control_w00,EQT,2022-07-18,2022-08-29,3.4170006327778912,180.2553943590756,time,30,37.86,, -control_w00,MOS,2022-08-09,2022-08-31,0.3470723835869064,33.72695559697577,trailing_stop,16,54.01,, -control_w00,HAL,2022-08-29,2022-08-31,-1.2009690222153482,-145.33656232560782,stop,2,31.9,, -control_w00,TRGP,2022-08-29,2022-08-31,-1.3707729468599064,-32.178663631957505,stop,2,71.11,, -control_w00,MPC,2022-07-28,2022-09-01,1.4624855076464438,46.01328563425077,trailing_stop,25,89.59,, -control_w00,ALB,2022-08-05,2022-09-01,1.761405907546294,132.7932059646981,trailing_stop,19,237.99,, -control_w00,STLD,2022-08-31,2022-09-01,-1.0,-115.17243220050771,stop,1,80.72,, -control_w00,PANW,2022-08-22,2022-09-06,0.4934431444629017,19.78507742537393,trailing_stop,10,84.68,, -control_w00,COP,2022-08-24,2022-09-07,-1.0,-69.9660004581706,stop,9,110.52,, -control_w00,COR,2022-08-31,2022-09-13,-1.0,-0.9418641320251965,stop,8,146.56,, -control_w00,NUE,2022-09-07,2022-09-14,-0.903584595196936,-62.7842724289714,trailing_stop,5,135.61,, -control_w00,DVN,2022-08-19,2022-09-16,-0.6642417156528438,-77.07071881262542,trailing_stop,19,68.51,, -control_w00,ADM,2022-09-01,2022-09-16,-0.9351883715975945,-74.47729562644642,trailing_stop,10,87.58,, -control_w00,FANG,2022-08-25,2022-09-19,-1.1040854626822585,-2.529309057809067,stop,16,136.28,, -control_w00,F,2022-09-02,2022-09-20,-1.3973228860594182,-116.3024820727129,stop,11,15.16,, -control_w00,TRGP,2022-09-06,2022-09-22,-0.6548995062243557,-32.5832820672275,trailing_stop,12,68.05,, -control_w00,APA,2022-08-11,2022-09-23,0.060725186570144855,4.19356780088234,trailing_stop,30,34.84,, -control_w00,SLB,2022-08-31,2022-09-23,-1.0,-114.96428913064142,stop,16,38.15,, -control_w00,EOG,2022-09-13,2022-09-23,-1.4019702155902072,-2.0753977461644575,stop,8,122.91,, -control_w00,MOS,2022-09-14,2022-09-23,-1.0,-83.88289615312392,stop,7,53.9,, -control_w00,CVX,2022-09-20,2022-09-23,-1.058638522769647,-91.81325475353522,stop,3,156.28,, -control_w00,ADM,2022-09-20,2022-09-23,-1.0,-40.36248750539586,stop,3,86.75,, -control_w00,ABBV,2022-09-16,2022-09-30,-1.0,-70.19456897448859,stop,10,144.06,, -control_w00,TTD,2022-09-28,2022-10-07,-1.0,-102.38114083704865,stop,7,62.96,, -control_w00,ANET,2022-10-03,2022-10-10,-0.9355601948503821,-96.0713992101128,trailing_stop,5,28.96,, -control_w00,LVS,2022-09-30,2022-10-11,0.09268348362058872,6.282501612473824,trailing_stop,7,37.52,, -control_w00,APA,2022-10-07,2022-10-12,-1.0,-96.12189031587046,stop,3,42.52,, -control_w00,ABBV,2022-10-11,2022-10-28,0.28330042287682367,12.788008416617721,trailing_stop,13,141.51,, -control_w00,DLTR,2022-10-28,2022-11-03,-1.0,-59.888482502646454,stop,4,158.55,, -control_w00,AZO,2022-09-28,2022-11-09,3.537164772975563,267.60003991483717,time,30,2169.44,, 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true, - "validation_sharpe": 2.64 - }, - "final_check": { - "arm_id": "growth_w05", - "pass": true, - "checks": { - "test_sharpe_not_worse": true, - "test_drawdown_within_2pp": true - }, - "test_sharpe_delta": 0.0, - "note": "Research evidence only; passing does not change production." - }, - "completed_at": "2026-07-23T15:36:24.717421+00:00" -} \ No newline at end of file diff --git a/reports/fundamentals-splitsafe-20260723-153520.md b/reports/fundamentals-splitsafe-20260723-153520.md deleted file mode 100644 index d6a6130..0000000 --- a/reports/fundamentals-splitsafe-20260723-153520.md +++ /dev/null @@ -1,105 +0,0 @@ -# Split-safe fundamentals overlay sensitivity - -Generated: 2026-07-23T15:36:07.807809+00:00 - -## Protocol - -- Research protocol: **split-safe**. -- Train ends before **2024-01-01**. -- Validation runs until **2025-01-01**. -- Test starts at that date and is not used to select the arm. -- Qualification is unchanged; fundamentals only reorder qualified longs. -- SEC filings become visible at midnight New York time after acceptance. -- Pre-registered portfolio trials for DSR: **10**. - -## Data warnings - -- Current tracked universe only: historical constituent membership and delisted names are unavailable, so absolute results have survivorship bias. -- Diluted-EPS growth and share-count change are excluded because filing-time values are not split-comparable without point-in-time split factors. -- Earnings surprise is excluded because the completed SUE study already failed its promotion bar for this strategy. -- The test window has already been observed; this follow-up is sensitivity evidence and live paper performance remains the final out-of-sample check. - -## Factor IC - -| window | signal | IC | t | positive | quintile spread | weeks | N | -|---|---|---:|---:|---:|---:|---:|---:| -| train | net_debt_to_ebitda | 0.0141 | 0.46 | 61.9 | 0.0098 | 21 | 183.9 | -| train | growth | 0.0006 | 0.02 | 47.6 | 0.0044 | 21 | 406.8 | -| train | revenue_growth_yoy | 0.0006 | 0.02 | 47.6 | 0.0044 | 21 | 406.8 | -| train | fcf_margin | -0.006 | -0.29 | 38.1 | -0.0044 | 21 | 365.3 | -| train | balanced | -0.0075 | -0.2 | 47.6 | -0.003 | 21 | 305.9 | -| train | quality | -0.0185 | -0.72 | 47.6 | -0.0092 | 21 | 318.2 | -| train | operating_margin | -0.0288 | -1.36 | 28.6 | -0.0131 | 21 | 333.3 | -| validation | balanced | 0.0597 | 1.18 | 55.6 | 0.013 | 9 | 312.9 | -| validation | growth | 0.0485 | 0.95 | 55.6 | 0.0205 | 9 | 416.7 | -| validation | revenue_growth_yoy | 0.0485 | 0.95 | 55.6 | 0.0205 | 9 | 416.7 | -| validation | fcf_margin | 0.0268 | 0.84 | 66.7 | 0.0003 | 9 | 368 | -| validation | quality | 0.0144 | 0.6 | 44.4 | -0.0019 | 9 | 323.1 | -| validation | net_debt_to_ebitda | 0.0066 | 0.12 | 55.6 | 0.0104 | 9 | 184.6 | -| validation | operating_margin | -0.0056 | -0.18 | 44.4 | -0.0058 | 9 | 338.3 | -| test | growth | 0.0053 | 0.14 | 46.2 | 0.0046 | 13 | 429.3 | -| test | revenue_growth_yoy | 0.0053 | 0.14 | 46.2 | 0.0046 | 13 | 429.3 | -| test | net_debt_to_ebitda | -0.0082 | -0.19 | 61.5 | -0.0043 | 13 | 195.5 | -| test | balanced | -0.0318 | -0.67 | 46.2 | -0.0123 | 13 | 318.9 | -| test | operating_margin | -0.0436 | -1.52 | 38.5 | -0.0279 | 13 | 349.5 | -| test | fcf_margin | -0.0535 | -1.99 | 30.8 | -0.0307 | 13 | 380.4 | -| test | quality | -0.055 | -1.63 | 30.8 | -0.0288 | 13 | 332.2 | -| full | growth | 0.0116 | 0.55 | 47.6 | 0.0069 | 42 | 415.5 | -| full | revenue_growth_yoy | 0.0116 | 0.55 | 47.6 | 0.0069 | 42 | 415.5 | -| full | net_debt_to_ebitda | 0.006 | 0.29 | 54.8 | 0.0037 | 42 | 187.9 | -| full | balanced | -0.0042 | -0.18 | 47.6 | -0.0032 | 42 | 311.2 | -| full | fcf_margin | -0.0161 | -1.04 | 35.7 | -0.0123 | 42 | 370.5 | -| full | quality | -0.0242 | -1.45 | 38.1 | -0.014 | 42 | 323.6 | -| full | operating_margin | -0.0252 | -1.74 | 33.3 | -0.017 | 42 | 339.2 | - -## Portfolio arms - -| arm | window | Sharpe | SE | DSR | CAGR | MaxDD | Calmar | trades | overlap | -|---|---|---:|---:|---:|---:|---:|---:|---:|---:| -| control_w00 | train | 1.26 | 0.764 | 0.5133 | 32.5 | 18.5 | 1.76 | 195 | — | -| control_w00 | validation | 2.52 | 0.938 | 0.8618 | 71.3 | 14.8 | 4.82 | 106 | — | -| control_w00 | test | 1.99 | 0.81 | 0.8122 | 54.4 | 19.2 | 2.83 | 188 | — | -| control_w00 | full | 1.86 | 0.49 | 0.986 | 52.1 | 22.3 | 2.34 | 483 | — | -| quality_w05 | train | 1.5 | 0.767 | 0.6354 | 40.3 | 16.7 | 2.42 | 192 | 72 | -| quality_w05 | validation | 2.43 | 0.939 | 0.8392 | 67.4 | 17.3 | 3.89 | 106 | 73.77 | -| quality_w05 | test | 1.67 | 0.807 | 0.6889 | 43.1 | 19.2 | 2.24 | 191 | 64.78 | -| quality_w05 | full | 1.81 | 0.492 | 0.9816 | 49.7 | 22 | 2.26 | 482 | 68.12 | -| quality_w10 | train | 1.68 | 0.768 | 0.7191 | 46.1 | 16.7 | 2.75 | 195 | 64.56 | -| quality_w10 | validation | 1.95 | 0.948 | 0.6828 | 47.9 | 17.8 | 2.69 | 112 | 61.48 | -| quality_w10 | test | 1.57 | 0.809 | 0.6436 | 39.7 | 18.8 | 2.12 | 193 | 56.79 | -| quality_w10 | full | 1.72 | 0.493 | 0.9714 | 46 | 19.3 | 2.39 | 491 | 59.67 | -| quality_w15 | train | 1.4 | 0.773 | 0.5848 | 35.8 | 15.3 | 2.33 | 189 | 47.69 | -| quality_w15 | validation | 1.85 | 0.932 | 0.6467 | 45 | 18.4 | 2.44 | 111 | 57.25 | -| quality_w15 | test | 1.78 | 0.804 | 0.7361 | 46.3 | 18.8 | 2.47 | 195 | 49.61 | -| quality_w15 | full | 1.66 | 0.491 | 0.963 | 43.4 | 19.7 | 2.2 | 484 | 48.31 | -| growth_w05 | train | 1.31 | 0.768 | 0.5392 | 32.7 | 17.9 | 1.83 | 192 | 72 | -| growth_w05 | validation | 2.64 | 0.918 | 0.893 | 73.6 | 11.6 | 6.33 | 103 | 75.63 | -| growth_w05 | test | 1.99 | 0.8 | 0.8152 | 56.1 | 18.9 | 2.96 | 190 | 64.35 | -| growth_w05 | full | 1.87 | 0.489 | 0.9869 | 51.5 | 21.3 | 2.41 | 481 | 68.53 | -| growth_w10 | train | 1.11 | 0.775 | 0.4362 | 26.3 | 17.8 | 1.47 | 197 | 53.73 | -| growth_w10 | validation | 2.64 | 0.918 | 0.893 | 73.6 | 11.6 | 6.33 | 103 | 75.63 | -| growth_w10 | test | 1.92 | 0.808 | 0.7886 | 54.1 | 17.3 | 3.13 | 192 | 55.1 | -| growth_w10 | full | 1.77 | 0.492 | 0.9776 | 48.9 | 20.2 | 2.42 | 490 | 56.94 | -| growth_w15 | train | 1.07 | 0.772 | 0.4156 | 24.6 | 18 | 1.37 | 194 | 52.55 | -| growth_w15 | validation | 2.02 | 0.93 | 0.7123 | 52.6 | 19.1 | 2.76 | 100 | 58.46 | -| growth_w15 | test | 1.61 | 0.809 | 0.6618 | 42.3 | 17.3 | 2.45 | 191 | 55.97 | -| growth_w15 | full | 1.39 | 0.492 | 0.8915 | 34.7 | 20.6 | 1.69 | 487 | 54.95 | -| balanced_w05 | train | 1.2 | 0.771 | 0.4822 | 30.2 | 20.9 | 1.45 | 198 | 73.13 | -| balanced_w05 | validation | 2.25 | 0.947 | 0.7861 | 62.1 | 20 | 3.11 | 106 | 70.97 | -| balanced_w05 | test | 2.02 | 0.802 | 0.8244 | 55.6 | 18.9 | 2.94 | 185 | 78.47 | -| balanced_w05 | full | 1.76 | 0.492 | 0.9765 | 48.3 | 21.9 | 2.2 | 481 | 73.69 | -| balanced_w10 | train | 1.34 | 0.77 | 0.5545 | 34.1 | 17 | 2.01 | 189 | 68.42 | -| balanced_w10 | validation | 1.82 | 0.93 | 0.6349 | 43.1 | 19.3 | 2.23 | 110 | 58.82 | -| balanced_w10 | test | 2 | 0.811 | 0.8152 | 55.9 | 18.5 | 3.03 | 187 | 57.56 | -| balanced_w10 | full | 1.71 | 0.492 | 0.9703 | 46 | 19.9 | 2.31 | 476 | 59.57 | -| balanced_w15 | train | 1.35 | 0.766 | 0.5599 | 34.3 | 15.5 | 2.21 | 182 | 56.43 | -| balanced_w15 | validation | 1.89 | 0.931 | 0.6627 | 45.3 | 17.8 | 2.54 | 111 | 55 | -| balanced_w15 | test | 1.83 | 0.808 | 0.755 | 48.7 | 18.5 | 2.64 | 190 | 50 | -| balanced_w15 | full | 1.59 | 0.49 | 0.9503 | 41 | 19.9 | 2.06 | 474 | 52.88 | - -## Mechanical selection - -- Development-selected arm: **growth_w05**. The test result is reported only as a final check. -- Final check: `{'arm_id': 'growth_w05', 'pass': True, 'checks': {'test_sharpe_not_worse': True, 'test_drawdown_within_2pp': True}, 'test_sharpe_delta': 0.0, 'note': 'Research evidence only; passing does not change production.'}` - -Production remains unchanged pending human review. diff --git a/reports/fundamentals-splitsafe-20260723-153520.zip b/reports/fundamentals-splitsafe-20260723-153520.zip deleted file mode 100644 index f1ee622..0000000 Binary files a/reports/fundamentals-splitsafe-20260723-153520.zip and /dev/null differ diff --git a/scripts/create_backtest_snapshot.py b/scripts/create_backtest_snapshot.py index f357ed6..e49b7b6 100644 --- a/scripts/create_backtest_snapshot.py +++ b/scripts/create_backtest_snapshot.py @@ -1,9 +1,9 @@ -"""Create a portable local SQLite snapshot for offline backtest research. +"""Create a minimal local SQLite snapshot for offline backtest research. -Copies the data required by the production backtest and fundamentals research: +Copies only the data required by app.services.backtest_service.run_backtest: tickers, OHLCV bars, SPY benchmark closes, and the activation / recommendation / -paper-exit settings the run reads, immutable SEC snapshots, and Dolt earnings -events. Other system settings are skipped to avoid copying secrets locally. +paper-exit settings the run reads. Other system settings are intentionally +skipped to avoid copying secrets into local snapshot files. """ from __future__ import annotations @@ -54,9 +54,7 @@ def _parse_args() -> argparse.Namespace: help="SQLite snapshot path to create.", ) parser.add_argument("--batch-size", type=int, default=5000) - parser.add_argument( - "--force", action="store_true", help="Overwrite an existing snapshot file." - ) + parser.add_argument("--force", action="store_true", help="Overwrite an existing snapshot file.") return parser.parse_args() @@ -67,7 +65,6 @@ async def _copy_table( *, batch_size: int, where=None, - row_transform=None, ) -> int: table = model.__table__ columns = list(table.columns) @@ -90,8 +87,6 @@ async def _copy_table( stream = await source.stream(stmt.execution_options(yield_per=batch_size)) async for partition in stream.partitions(batch_size): rows = [dict(row._mapping) for row in partition] - if row_transform is not None: - rows = [row_transform(row) for row in rows] if not rows: continue await dest.execute(insert(table), rows) @@ -110,8 +105,6 @@ async def _main() -> None: from app.database import Base import app.models # noqa: F401 - registers all metadata tables from app.models.benchmark_price import BenchmarkPrice - from app.models.earnings_event import EarningsEvent - from app.models.fundamental_snapshot import FundamentalSnapshot from app.models.ohlcv import OHLCVRecord from app.models.settings import SystemSetting from app.models.ticker import Ticker @@ -130,12 +123,8 @@ async def _main() -> None: connect_args={"server_settings": {"default_transaction_read_only": "on"}}, ) dest_engine = create_async_engine(_sqlite_url(output)) - SourceSession = async_sessionmaker( - source_engine, class_=AsyncSession, expire_on_commit=False - ) - DestSession = async_sessionmaker( - dest_engine, class_=AsyncSession, expire_on_commit=False - ) + SourceSession = async_sessionmaker(source_engine, class_=AsyncSession, expire_on_commit=False) + DestSession = async_sessionmaker(dest_engine, class_=AsyncSession, expire_on_commit=False) print(f"Source: {_hide_password(source_url)}") print(f"Snapshot: {output}") @@ -146,9 +135,7 @@ async def _main() -> None: async with SourceSession() as source, DestSession() as dest: counts = { - "tickers": await _copy_table( - source, dest, Ticker, batch_size=args.batch_size - ), + "tickers": await _copy_table(source, dest, Ticker, batch_size=args.batch_size), "system_settings": await _copy_table( source, dest, @@ -165,30 +152,9 @@ async def _main() -> None: SystemSetting.key.like("paper_%"), ), ), - "benchmark_prices": await _copy_table( - source, dest, BenchmarkPrice, batch_size=args.batch_size - ), - "ohlcv_records": await _copy_table( - source, dest, OHLCVRecord, batch_size=args.batch_size - ), + "benchmark_prices": await _copy_table(source, dest, BenchmarkPrice, batch_size=args.batch_size), + "ohlcv_records": await _copy_table(source, dest, OHLCVRecord, batch_size=args.batch_size), } - # Import-run provenance is operational metadata, not a research input. - # Null it so the portable snapshot needs no data_import_runs rows. - async with SourceSession() as source, DestSession() as dest: - counts["fundamental_snapshots"] = await _copy_table( - source, - dest, - FundamentalSnapshot, - batch_size=args.batch_size, - row_transform=lambda row: {**row, "import_run_id": None}, - ) - counts["earnings_events"] = await _copy_table( - source, - dest, - EarningsEvent, - batch_size=args.batch_size, - row_transform=lambda row: {**row, "import_run_id": None}, - ) finally: await source_engine.dispose() await dest_engine.dispose() diff --git a/scripts/run_fundamentals_macbook.sh b/scripts/run_fundamentals_macbook.sh deleted file mode 100755 index 97042c4..0000000 --- a/scripts/run_fundamentals_macbook.sh +++ /dev/null @@ -1,56 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -ROOT="$(git rev-parse --show-toplevel)" -cd "$ROOT" - -SNAPSHOT="${1:-backtest_snapshots/fundamentals-backtest.sqlite}" -PROTOCOL="${PROTOCOL:-split-safe}" -WORKERS="${WORKERS:-$(sysctl -n hw.logicalcpu 2>/dev/null || echo 8)}" -if [[ "$WORKERS" -gt 1 ]]; then - WORKERS=$((WORKERS - 1)) -fi - -if [[ -x .venv/bin/python ]]; then - PYTHON=.venv/bin/python -else - PYTHON="${PYTHON:-python3}" -fi - -if [[ ! -f "$SNAPSHOT" ]]; then - echo "Snapshot not found: $SNAPSHOT" >&2 - exit 1 -fi - -case "$PROTOCOL" in - split-safe) - PREFIX="fundamentals-splitsafe" - SCORE_CACHE="reports/.cache/fundamentals-splitsafe-scores.pkl" - ;; - original) - PREFIX="fundamentals-overlay" - SCORE_CACHE="reports/.cache/fundamentals-scores.pkl" - ;; - *) - echo "Unsupported PROTOCOL: $PROTOCOL (use split-safe or original)" >&2 - exit 1 - ;; -esac - -STAMP="$(date -u +%Y%m%d-%H%M%S)" -OUT="reports/${PREFIX}-${STAMP}.json" - -echo "Snapshot: $SNAPSHOT" -echo "Workers: $WORKERS" -echo "Protocol: $PROTOCOL" -echo "Output: $OUT" - -"$PYTHON" scripts/run_fundamentals_research.py "$SNAPSHOT" \ - --protocol "$PROTOCOL" \ - --workers "$WORKERS" \ - --candidate-cache reports/.cache/fundamentals-candidates.pkl \ - --fundamentals-cache "$SCORE_CACHE" \ - --out "$OUT" - -echo -echo "Bring this file back for review: ${OUT%.json}.zip" diff --git a/scripts/run_fundamentals_research.py b/scripts/run_fundamentals_research.py deleted file mode 100644 index a5095dc..0000000 --- a/scripts/run_fundamentals_research.py +++ /dev/null @@ -1,1214 +0,0 @@ -"""Point-in-time fundamentals rank-overlay research. - -The production qualification gate is unchanged. The runner first measures -30-session factor IC, then reorders already-qualified candidates with quality, -growth, or balanced fundamental ranks. It supports the original registered -matrix and a split-safe follow-up sensitivity. It writes JSON, Markdown, CSV, -and a portable ZIP bundle. -""" - -from __future__ import annotations - -import argparse -import asyncio -import csv -import hashlib -import json -import multiprocessing -import os -import pickle -import subprocess -import sys -import zipfile -from collections import defaultdict -from concurrent.futures import ProcessPoolExecutor, as_completed -from datetime import date, datetime, time, timezone -from pathlib import Path -from types import SimpleNamespace -from typing import Any -from zoneinfo import ZoneInfo - -from sqlalchemy import func, select -from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine - -ROOT = Path(__file__).resolve().parents[1] -if str(ROOT) not in sys.path: - sys.path.insert(0, str(ROOT)) - -CACHE_VERSION = "fundamentals-overlay-v1" -NY = ZoneInfo("America/New_York") -COMPOSITES = ("quality", "growth", "balanced") -ORIGINAL_PROTOCOL = "original" -SPLIT_SAFE_PROTOCOL = "split-safe" -WEIGHTS = (0.10, 0.20, 0.30, 0.40) -SPLIT_SAFE_WEIGHTS = (0.05, 0.10, 0.15) - - -def _arm_matrix(weights: tuple[float, ...]) -> tuple[dict[str, Any], ...]: - """Build the control plus three bounded overlay families.""" - return ( - { - "id": "control_w00", - "label": "Production 80/20 momentum-volatility rank", - "composite": None, - "weight": 0.0, - }, - *tuple( - { - "id": f"{composite}_w{round(weight * 100):02d}", - "label": f"{composite.title()} overlay {round(weight * 100)}%", - "composite": composite, - "weight": weight, - } - for composite in COMPOSITES - for weight in weights - ), - ) - - -ARMS = _arm_matrix(WEIGHTS) -N_TRIALS = len(ARMS) -SPLIT_SAFE_ARMS = _arm_matrix(SPLIT_SAFE_WEIGHTS) -SPLIT_SAFE_N_TRIALS = len(SPLIT_SAFE_ARMS) - - -def _parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("snapshot") - parser.add_argument( - "--workers", type=int, default=max(1, multiprocessing.cpu_count() - 1) - ) - parser.add_argument("--out", default=None) - parser.add_argument( - "--candidate-cache", default="reports/.cache/fundamentals-candidates.pkl" - ) - parser.add_argument( - "--fundamentals-cache", default="reports/.cache/fundamentals-scores.pkl" - ) - parser.add_argument("--train-end", default="2024-01-01") - parser.add_argument("--test-start", default="2025-01-01") - parser.add_argument( - "--protocol", - choices=(ORIGINAL_PROTOCOL, SPLIT_SAFE_PROTOCOL), - default=ORIGINAL_PROTOCOL, - ) - parser.add_argument("--allow-spawn", action="store_true") - parser.add_argument("--quiet", action="store_true") - return parser.parse_args() - - -def _sqlite_url(path: Path) -> str: - return f"sqlite+aiosqlite:///{path.resolve().as_posix()}" - - -def _default_out(protocol: str = ORIGINAL_PROTOCOL) -> Path: - stamp = datetime.now().strftime("%Y%m%d-%H%M%S") - label = ( - "fundamentals-splitsafe" - if protocol == SPLIT_SAFE_PROTOCOL - else "fundamentals-overlay" - ) - return Path("reports") / f"{label}-{stamp}.json" - - -def _snapshot_hash(path: Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(chunk) - return digest.hexdigest() - - -def _cache_key(snapshot: Path, suffix: dict[str, Any]) -> dict[str, Any]: - stat = snapshot.stat() - return { - "version": CACHE_VERSION, - "snapshot": str(snapshot.resolve()), - "size": stat.st_size, - "mtime_ns": stat.st_mtime_ns, - **suffix, - } - - -def _load_cache(path: Path, key: dict[str, Any]) -> Any | None: - if not path.exists(): - return None - with path.open("rb") as handle: - payload = pickle.load(handle) # noqa: S301 - trusted local cache - return payload.get("value") if payload.get("key") == key else None - - -def _save_cache(path: Path, key: dict[str, Any], value: Any) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - with path.open("wb") as handle: - pickle.dump( - {"key": key, "value": value}, handle, protocol=pickle.HIGHEST_PROTOCOL - ) - - -def _git_commit() -> str | None: - try: - return subprocess.run( - ["git", "rev-parse", "HEAD"], - cwd=ROOT, - capture_output=True, - check=True, - text=True, - ).stdout.strip() - except (OSError, subprocess.CalledProcessError): - return None - - -async def _load_snapshot(snapshot: Path, quiet: bool) -> dict[str, Any]: - from app.models.earnings_event import EarningsEvent - from app.models.fundamental_snapshot import FundamentalSnapshot - from app.models.ohlcv import OHLCVRecord - from app.models.ticker import Ticker - from app.services import backtest_service as bt - from app.services.admin_service import get_activation_config - from app.services.paper_trade_service import get_exit_policy - from app.services.recommendation_service import get_recommendation_config - - engine = create_async_engine(_sqlite_url(snapshot), pool_pre_ping=True) - session_factory = async_sessionmaker( - engine, class_=AsyncSession, expire_on_commit=False - ) - try: - async with session_factory() as db: - config = await get_recommendation_config(db) - activation = await get_activation_config(db) - exit_config = await get_exit_policy(db) - benchmark = await bt._load_benchmark_closes_for_backtest( - db, days=None, refresh=False - ) - tickers = list( - (await db.execute(select(Ticker).order_by(Ticker.symbol))).scalars() - ) - snapshots = list( - ( - await db.execute( - select(FundamentalSnapshot).order_by( - FundamentalSnapshot.cik, - FundamentalSnapshot.accepted_at, - ) - ) - ).scalars() - ) - earnings_count = int( - ( - await db.execute(select(func.count()).select_from(EarningsEvent)) - ).scalar_one() - ) - price_bounds = ( - await db.execute( - select( - func.min(OHLCVRecord.date), - func.max(OHLCVRecord.date), - func.count(), - ) - ) - ).one() - prices: dict[str, tuple] = {} - for index, ticker in enumerate(tickers, 1): - columns = await bt._fetch_columns(db, ticker.symbol) - if columns is not None: - prices[ticker.symbol] = columns - if not quiet and index % 50 == 0: - print(f"loaded prices {index}/{len(tickers)}", flush=True) - finally: - await engine.dispose() - - by_cik: dict[str, list[Any]] = defaultdict(list) - for row in snapshots: - by_cik[str(row.cik)].append(row) - ticker_rows = [ - { - "symbol": ticker.symbol, - "cik": str(ticker.cik) if ticker.cik else None, - "sic": ticker.sic, - } - for ticker in tickers - ] - audit = { - "tickers": len(tickers), - "tickers_with_prices": len(prices), - "tickers_with_cik": sum(row["cik"] is not None for row in ticker_rows), - "unique_ciks": len({row["cik"] for row in ticker_rows if row["cik"]}), - "fundamental_rows": len(snapshots), - "fundamental_ciks": len(by_cik), - "accepted_at_min": min((row.accepted_at for row in snapshots), default=None), - "accepted_at_max": max((row.accepted_at for row in snapshots), default=None), - "earnings_rows": earnings_count, - "price_date_min": price_bounds[0], - "price_date_max": price_bounds[1], - "price_rows": int(price_bounds[2] or 0), - } - return { - "config": config, - "activation": activation, - "exit_config": exit_config, - "benchmark": benchmark, - "ticker_rows": ticker_rows, - "prices": prices, - "snapshots_by_cik": dict(by_cik), - "audit": audit, - } - - -def _representatives( - ticker_rows: list[dict], prices: dict[str, tuple] -) -> dict[str, str]: - reps: dict[str, str] = {} - for row in ticker_rows: - cik = row.get("cik") - symbol = str(row["symbol"]) - if not cik or symbol not in prices: - continue - if cik not in reps or symbol < reps[cik]: - reps[cik] = symbol - return reps - - -def _build_candidates( - snapshot: Path, - data: dict[str, Any], - args: argparse.Namespace, -) -> tuple[list[dict], int]: - from app.services import backtest_service as bt - from scripts import run_research_matrix as shared - - cache_path = Path(args.candidate_cache) - key = _cache_key(snapshot, {"kind": "daily-production-candidates"}) - cached = _load_cache(cache_path, key) - if cached is not None: - if not args.quiet: - print(f"loaded candidate cache {cache_path}", flush=True) - return list(cached["qualified"]), int(cached["entry_candidate_count"]) - - prices = data["prices"] - workers = max(1, min(args.workers, max(1, multiprocessing.cpu_count() - 1))) - replay_rows: list[dict] = [] - replay_start = date(1900, 1, 1) - - def replay_one(symbol: str, columns: tuple) -> list[dict]: - return bt._replay_candidates_for_period( - symbol, - columns, - data["config"], - data["activation"], - data["benchmark"], - replay_start, - "daily", - True, - True, - ) - - if workers == 1: - for index, (symbol, columns) in enumerate(prices.items(), 1): - replay_rows.extend(replay_one(symbol, columns)) - if not args.quiet and index % 25 == 0: - print(f"replay {index}/{len(prices)}", flush=True) - else: - context = bt._mp_context() or multiprocessing.get_context("spawn") - with ProcessPoolExecutor(max_workers=workers, mp_context=context) as pool: - futures = { - pool.submit( - bt._replay_candidates_for_period, - symbol, - columns, - data["config"], - data["activation"], - data["benchmark"], - replay_start, - "daily", - True, - True, - ): symbol - for symbol, columns in prices.items() - } - for index, future in enumerate(as_completed(futures), 1): - replay_rows.extend(future.result()) - if not args.quiet and index % 25 == 0: - print(f"replay {index}/{len(futures)}", flush=True) - - setups = [row for row in replay_rows if not row.get("_rank_only")] - observations = [row for row in replay_rows if row.get("_universe_rank_observation")] - ranks = shared._live_universe_rank_map( - observations, - data["benchmark"], - bt.STRATEGY_RANK_MOMENTUM_WEIGHT, - ) - cutoff = float(data["activation"].get("min_momentum_percentile", 80.0)) - qualified: list[dict] = [] - for setup in setups: - if setup.get("direction") != "long": - continue - identity = (str(setup["symbol"]), str(setup["date"])) - rank = ranks.get(identity) - if rank is None: - continue - candidate = { - key: value - for key, value in setup.items() - if not key.startswith("_universe_") - } - candidate[bt.PRODUCTION_PERCENTILE_KEY] = rank["momentum_percentile"] - candidate[bt.VOL_PERCENTILE_KEY] = rank["volatility_percentile"] - candidate[bt.RESIDUAL_HIGH_VOL_BLEND_80_20_KEY] = rank["strategy_rank"] - candidate["qualified"] = bt._momentum_qualifies(candidate, cutoff) - if candidate["qualified"]: - qualified.append(candidate) - - if not qualified: - raise RuntimeError("no qualified long candidates after replay") - value = {"qualified": qualified, "entry_candidate_count": len(setups)} - _save_cache(cache_path, key, value) - if not args.quiet: - print(f"wrote candidate cache {cache_path}", flush=True) - return qualified, len(setups) - - -def _weekly_factor_dates( - representatives: dict[str, str], prices: dict[str, tuple] -) -> set[date]: - from app.services import backtest_service as bt - - dates: set[date] = set() - for symbol in representatives.values(): - columns = prices[symbol] - records = [ - SimpleNamespace(date=date.fromordinal(int(value))) for value in columns[0] - ] - for index in bt._weekly_asof_indices(records): - if index >= bt.MIN_LOOKBACK - 1 and index + bt.HORIZON < len(records): - dates.add(records[index].date) - return dates - - -def _utc(value: datetime) -> datetime: - if value.tzinfo is None: - return value.replace(tzinfo=timezone.utc) - return value.astimezone(timezone.utc) - - -def _coverage_summary(rows: list[dict[str, int]]) -> dict[str, Any]: - if not rows: - return {} - keys = sorted(rows[0]) - result: dict[str, Any] = {"dates": len(rows)} - for key in keys: - values = sorted(row[key] for row in rows) - middle = len(values) // 2 - median = ( - values[middle] - if len(values) % 2 - else (values[middle - 1] + values[middle]) / 2 - ) - result[key] = { - "min": values[0], - "median": median, - "max": values[-1], - } - return result - - -def _build_scores( - snapshot: Path, - dates: set[date], - representatives: dict[str, str], - snapshots_by_cik: dict[str, list[Any]], - split_safe: bool, - args: argparse.Namespace, -) -> tuple[dict[str, dict[str, dict[str, float | None]]], dict[str, Any]]: - from app.services import fundamentals_derivation as derivation - from app.services import fundamentals_research as research - - factor_polarity = ( - research.SPLIT_SAFE_FACTOR_POLARITY if split_safe else research.FACTOR_POLARITY - ) - ordered_dates = sorted(dates) - date_fingerprint = hashlib.sha256( - "|".join(value.isoformat() for value in ordered_dates).encode() - ).hexdigest() - cache_path = Path(args.fundamentals_cache) - key = _cache_key( - snapshot, - { - "kind": "point-in-time-scores", - "score_profile": SPLIT_SAFE_PROTOCOL if split_safe else ORIGINAL_PROTOCOL, - "date_fingerprint": date_fingerprint, - "availability": "accepted before signal-date midnight America/New_York", - }, - ) - cached = _load_cache(cache_path, key) - if cached is not None: - if not args.quiet: - print(f"loaded fundamentals cache {cache_path}", flush=True) - return cached["scores"], cached["coverage"] - - eligible_ciks = sorted(set(representatives) & set(snapshots_by_cik)) - rows_by_cik = { - cik: sorted(snapshots_by_cik[cik], key=lambda row: _utc(row.accepted_at)) - for cik in eligible_ciks - } - positions = {cik: 0 for cik in eligible_ciks} - visible = {cik: [] for cik in eligible_ciks} - current_features: dict[str, dict[str, float | None]] = {} - scores_by_date: dict[str, dict[str, dict[str, float | None]]] = {} - coverage_rows: list[dict[str, int]] = [] - - for date_index, signal_date in enumerate(ordered_dates, 1): - cutoff = datetime.combine(signal_date, time.min, tzinfo=NY).astimezone( - timezone.utc - ) - for cik in eligible_ciks: - rows = rows_by_cik[cik] - position = positions[cik] - changed = False - while position < len(rows) and _utc(rows[position].accepted_at) <= cutoff: - visible[cik].append(rows[position]) - position += 1 - changed = True - positions[cik] = position - if changed: - current_features[cik] = research.raw_features( - derivation.derive(visible[cik]) - ) - scores = research.cross_section_scores( - current_features, - split_safe=split_safe, - ) - scores_by_date[signal_date.isoformat()] = scores - coverage_rows.append( - { - key: sum(row.get(key) is not None for row in scores.values()) - for key in (*factor_polarity, *research.COMPOSITE_KEYS) - } - ) - if not args.quiet and date_index % 100 == 0: - print(f"fundamentals dates {date_index}/{len(ordered_dates)}", flush=True) - - coverage = _coverage_summary(coverage_rows) - value = {"scores": scores_by_date, "coverage": coverage} - _save_cache(cache_path, key, value) - if not args.quiet: - print(f"wrote fundamentals cache {cache_path}", flush=True) - return scores_by_date, coverage - - -def _factor_diagnostics( - representatives: dict[str, str], - prices: dict[str, tuple], - scores_by_date: dict[str, dict[str, dict[str, float | None]]], - factor_keys: tuple[str, ...], - train_end: date, - test_start: date, -) -> dict[str, list[dict]]: - from app.services import backtest_service as bt - - signal_keys = (*factor_keys, *COMPOSITES) - observations: list[dict[str, Any]] = [] - for cik, symbol in representatives.items(): - columns = prices[symbol] - ordinals, _opens, _highs, _lows, closes, _volumes = columns - records = [ - SimpleNamespace(date=date.fromordinal(int(value))) for value in ordinals - ] - for index in bt._weekly_asof_indices(records): - forward_index = index + bt.HORIZON - if index < bt.MIN_LOOKBACK - 1 or forward_index >= len(records): - continue - if closes[index] <= 0: - continue - signal_date = records[index].date - score = scores_by_date.get(signal_date.isoformat(), {}).get(cik, {}) - forward = closes[forward_index] / closes[index] - 1.0 - iso = signal_date.isocalendar() - for key in signal_keys: - value = score.get(key) - if value is not None: - observations.append( - { - "signal": key, - "date": signal_date, - "week": (iso.year, iso.week), - "value": float(value), - "forward": float(forward), - "symbol": symbol, - } - ) - - windows = { - "train": lambda value: value < train_end, - "validation": lambda value: train_end <= value < test_start, - "test": lambda value: value >= test_start, - "full": lambda _value: True, - } - result: dict[str, list[dict]] = {} - for window, predicate in windows.items(): - collected: dict = defaultdict(lambda: defaultdict(list)) - for row in observations: - if predicate(row["date"]): - collected[row["signal"]][row["week"]].append( - { - "val": row["value"], - "fwd": row["forward"], - "symbol": row["symbol"], - } - ) - result[window] = bt._signal_evaluation(collected) - return result - - -def _attach_overlay_ranks( - candidates: list[dict], - ticker_rows: list[dict], - scores_by_date: dict[str, dict[str, dict[str, float | None]]], - arms: tuple[dict[str, Any], ...], -) -> dict[str, Any]: - from app.services import backtest_service as bt - from app.services import fundamentals_research as research - - symbol_to_cik = { - str(row["symbol"]): row.get("cik") for row in ticker_rows if row.get("cik") - } - covered = {composite: 0 for composite in COMPOSITES} - for candidate in candidates: - cik = symbol_to_cik.get(str(candidate["symbol"])) - score = scores_by_date.get(str(candidate["date"]), {}).get(cik, {}) - for composite in COMPOSITES: - value = score.get(composite) - candidate[f"fund_{composite}"] = value - if value is not None: - covered[composite] += 1 - base = candidate.get(bt.RESIDUAL_HIGH_VOL_BLEND_80_20_KEY) - for arm in arms: - if arm["composite"] is None: - continue - candidate[_ranking_key(arm)] = research.overlay_rank( - base, - score.get(str(arm["composite"])), - float(arm["weight"]), - ) - total = len(candidates) - return { - composite: { - "candidates": count, - "pct": round(count / total * 100.0, 2) if total else 0.0, - } - for composite, count in covered.items() - } - - -def _ranking_key(arm: dict[str, Any]) -> str: - from app.services import backtest_service as bt - - if arm["composite"] is None: - return bt.RESIDUAL_HIGH_VOL_BLEND_80_20_KEY - return "fund_overlay_{}_{:02d}".format( - arm["composite"], round(float(arm["weight"]) * 100) - ) - - -def _window(arm: dict[str, Any], name: str) -> dict[str, Any] | None: - return next( - (row for row in arm.get("windows", []) if row.get("window") == name), - None, - ) - - -def _trade_overlap(subject: set[str], control: set[str]) -> dict[str, Any]: - union = subject | control - return { - "overlap_pct": round(len(subject & control) / len(union) * 100.0, 2) - if union - else 100.0, - "added": len(subject - control), - "removed": len(control - subject), - } - - -def _winner_concentration(details: list[dict[str, Any]]) -> dict[str, Any]: - pnls = sorted( - (float(row["pnl"]) for row in details if row.get("pnl") is not None), - reverse=True, - ) - rs = sorted( - (float(row["r"]) for row in details if row.get("r") is not None), - reverse=True, - ) - top_pnl = sum(pnls[:5]) - total_pnl = sum(pnls) - remaining_rs = rs[5:] - return { - "top5_pnl": round(top_pnl, 2) if pnls else None, - "net_pnl_ex_top5": round(total_pnl - top_pnl, 2) if pnls else None, - "top5_share_of_positive_net_pct": ( - round(top_pnl / total_pnl * 100.0, 2) if total_pnl > 0 else None - ), - "avg_r_ex_top5": ( - round(sum(remaining_rs) / len(remaining_rs), 4) if remaining_rs else None - ), - } - - -def _run_arm( - arm: dict[str, Any], - candidates: list[dict], - data: dict[str, Any], - train_end: date, - test_start: date, - n_trials: int, -) -> tuple[dict[str, Any], dict[str, set[str]]]: - from app.services import backtest_service as bt - from scripts import run_research_matrix as shared - - strategy = next( - row for row in bt.PORTFOLIO_MONITOR_STRATEGIES if row.get("is_production") - ) - entry = bt._entry_variant_config(str(strategy["entry_variant"])) - if entry is None: - raise RuntimeError("production entry configuration missing") - exit_config = data["exit_config"] - exit_policy = bt.LIVE_EXIT_MODE_TO_SIM.get( - str(exit_config.get("mode", "atr_trailing")), "atr_trail3" - ) - hold_days = int(exit_config.get("hold_days", 30)) - trail = float(exit_config.get("atr_multiplier", bt.ATR_TRAIL_MULTIPLIER)) - ranking_key = _ranking_key(arm) - reentry = bt._make_gate_reset_reentry_fn( - candidates, - data["prices"], - cadence="daily", - ranking_key=ranking_key, - ) - windows: list[dict[str, Any]] = [] - trades_by_window: dict[str, set[str]] = {} - full_trade_details: list[dict[str, Any]] = [] - for name, start, end in ( - ("train", None, train_end), - ("validation", train_end, test_start), - ("test", test_start, None), - ("full", None, None), - ): - sim = bt._simulate_portfolio( - candidates, - data["prices"], - data["benchmark"], - exit_policy, - hold_days, - ranking_key=ranking_key, - max_positions=int(entry["max_positions"]), - risk_per_trade=float(entry["risk_per_trade"]), - atr_trail_multiplier=trail, - post_stop_reentry_fn=reentry, - start_date=start, - end_date=end, - fill_mode=bt.FILL_MODE_CLOSE, - include_trades=True, - ) - if sim is None: - windows.append({"window": name, "error": "no trades"}) - trades_by_window[name] = set() - continue - shared._assert_calendar_truncation(sim, hold_days, bt.FILL_MODE_CLOSE) - details = sim.pop("trade_details", []) - sim["winner_concentration"] = _winner_concentration(details) - if name == "full": - full_trade_details = details - trades_by_window[name] = { - "{}:{}".format(row.get("symbol"), row.get("entry_date")) for row in details - } - for heavy in ("equity_curve", "benchmark_curve", "reentry_events"): - sim.pop(heavy, None) - dsr = bt.deflated_sharpe_ratio( - sim.get("sharpe"), - sim.get("sharpe_se"), - n_trials, - n_returns=sim.get("n_returns"), - return_skew=sim.get("return_skew"), - return_kurtosis=sim.get("return_kurtosis"), - ) - windows.append({"window": name, "dsr": dsr, **sim}) - return ( - { - "id": arm["id"], - "label": arm["label"], - "composite": arm["composite"], - "weight": arm["weight"], - "ranking_key": ranking_key, - "windows": windows, - "full_trade_details": full_trade_details, - }, - trades_by_window, - ) - - -def _development_grade(control: dict, arm: dict) -> dict[str, Any]: - control_train = _window(control, "train") or {} - control_validation = _window(control, "validation") or {} - arm_train = _window(arm, "train") or {} - arm_validation = _window(arm, "validation") or {} - required = ( - control_train.get("sharpe"), - control_validation.get("sharpe"), - control_validation.get("max_drawdown_pct"), - arm_train.get("sharpe"), - arm_validation.get("sharpe"), - arm_validation.get("max_drawdown_pct"), - ) - if any(value is None for value in required): - return {"pass": False, "reason": "missing train or validation statistic"} - checks = { - "train_sharpe_not_worse": arm_train["sharpe"] >= control_train["sharpe"], - "validation_sharpe_not_worse": ( - arm_validation["sharpe"] >= control_validation["sharpe"] - ), - "validation_drawdown_within_2pp": ( - arm_validation["max_drawdown_pct"] - <= control_validation["max_drawdown_pct"] + 2.0 - ), - } - return { - "pass": all(checks.values()), - "checks": checks, - "train_sharpe_delta": round(arm_train["sharpe"] - control_train["sharpe"], 4), - "validation_sharpe_delta": round( - arm_validation["sharpe"] - control_validation["sharpe"], 4 - ), - } - - -def _final_check(control: dict, selected: dict | None) -> dict[str, Any] | None: - if selected is None: - return None - control_test = _window(control, "test") or {} - selected_test = _window(selected, "test") or {} - values = ( - control_test.get("sharpe"), - control_test.get("max_drawdown_pct"), - selected_test.get("sharpe"), - selected_test.get("max_drawdown_pct"), - ) - if any(value is None for value in values): - return {"pass": False, "reason": "missing test statistic"} - checks = { - "test_sharpe_not_worse": selected_test["sharpe"] >= control_test["sharpe"], - "test_drawdown_within_2pp": ( - selected_test["max_drawdown_pct"] <= control_test["max_drawdown_pct"] + 2.0 - ), - } - return { - "arm_id": selected["id"], - "pass": all(checks.values()), - "checks": checks, - "test_sharpe_delta": round(selected_test["sharpe"] - control_test["sharpe"], 4), - "note": "Research evidence only; passing does not change production.", - } - - -def _fmt(value: Any) -> str: - if value is None: - return "—" - return f"{value:.4g}" if isinstance(value, float) else str(value) - - -def _markdown(report: dict[str, Any]) -> str: - protocol_id = report.get("research_protocol", ORIGINAL_PROTOCOL) - title = ( - "Split-safe fundamentals overlay sensitivity" - if protocol_id == SPLIT_SAFE_PROTOCOL - else "Point-in-time fundamentals overlay research" - ) - lines = [ - f"# {title}", - "", - "Generated: {}".format(report.get("generated_at")), - "", - "## Protocol", - "", - "- Research protocol: **{}**.".format(protocol_id), - "- Train ends before **{}**.".format(report["splits"]["train_end"]), - "- Validation runs until **{}**.".format(report["splits"]["test_start"]), - "- Test starts at that date and is not used to select the arm.", - "- Qualification is unchanged; fundamentals only reorder qualified longs.", - "- SEC filings become visible at midnight New York time after acceptance.", - "- Pre-registered portfolio trials for DSR: **{}**.".format(report["n_trials"]), - "", - "## Data warnings", - "", - ] - lines.extend(f"- {warning}" for warning in report.get("warnings", [])) - lines.extend( - [ - "", - "## Factor IC", - "", - "| window | signal | IC | t | positive | quintile spread | weeks | N |", - "|---|---|---:|---:|---:|---:|---:|---:|", - ] - ) - for window, rows in report.get("factor_ic", {}).items(): - for row in rows: - lines.append( - "| {} | {} | {} | {} | {} | {} | {} | {} |".format( - window, - row.get("signal"), - _fmt(row.get("mean_ic")), - _fmt(row.get("ic_t_stat")), - _fmt(row.get("ic_positive_pct")), - _fmt(row.get("mean_quintile_spread")), - row.get("weeks"), - _fmt(row.get("avg_cross_section")), - ) - ) - lines.extend( - [ - "", - "## Portfolio arms", - "", - "| arm | window | Sharpe | SE | DSR | CAGR | MaxDD | Calmar | trades | overlap |", - "|---|---|---:|---:|---:|---:|---:|---:|---:|---:|", - ] - ) - for arm in report.get("arms", []): - for row in arm.get("windows", []): - overlap = row.get("selection_vs_control", {}).get("overlap_pct") - lines.append( - "| {} | {} | {} | {} | {} | {} | {} | {} | {} | {} |".format( - arm["id"], - row.get("window"), - _fmt(row.get("sharpe")), - _fmt(row.get("sharpe_se")), - _fmt(row.get("dsr")), - _fmt(row.get("cagr_pct")), - _fmt(row.get("max_drawdown_pct")), - _fmt(row.get("calmar")), - _fmt(row.get("trades")), - _fmt(overlap), - ) - ) - selection = report.get("development_selection") - lines.extend(["", "## Mechanical selection", ""]) - if selection: - lines.append( - "- Development-selected arm: **{}**. ".format(selection["arm_id"]) - + "The test result is reported only as a final check." - ) - lines.append("- Final check: `{}`".format(report.get("final_check"))) - else: - lines.append("- No overlay passed the train + validation requirements.") - lines.extend(["", "Production remains unchanged pending human review.", ""]) - return "\n".join(lines) - - -def _write_outputs(report: dict[str, Any], out: Path, *, bundle: bool) -> None: - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(report, indent=2, default=str), encoding="utf-8") - markdown_path = out.with_suffix(".md") - markdown_path.write_text(_markdown(report), encoding="utf-8") - - arms_csv = out.with_name(f"{out.stem}-arms.csv") - with arms_csv.open("w", newline="", encoding="utf-8") as handle: - writer = csv.writer(handle) - writer.writerow( - [ - "arm", - "composite", - "weight", - "window", - "sharpe", - "sharpe_se", - "dsr", - "cagr_pct", - "max_drawdown_pct", - "calmar", - "trades", - "overlap_pct", - "top5_pnl_share_pct", - "avg_r_ex_top5", - ] - ) - for arm in report.get("arms", []): - for row in arm.get("windows", []): - writer.writerow( - [ - arm["id"], - arm["composite"], - arm["weight"], - row.get("window"), - row.get("sharpe"), - row.get("sharpe_se"), - row.get("dsr"), - row.get("cagr_pct"), - row.get("max_drawdown_pct"), - row.get("calmar"), - row.get("trades"), - row.get("selection_vs_control", {}).get("overlap_pct"), - row.get("winner_concentration", {}).get( - "top5_share_of_positive_net_pct" - ), - row.get("winner_concentration", {}).get("avg_r_ex_top5"), - ] - ) - - factor_csv = out.with_name(f"{out.stem}-factor-ic.csv") - with factor_csv.open("w", newline="", encoding="utf-8") as handle: - writer = csv.writer(handle) - writer.writerow( - [ - "window", - "signal", - "mean_ic", - "ic_t_stat", - "ic_positive_pct", - "mean_quintile_spread", - "weeks", - "avg_cross_section", - "reliable", - ] - ) - for window, rows in report.get("factor_ic", {}).items(): - for row in rows: - writer.writerow( - [ - window, - row.get("signal"), - row.get("mean_ic"), - row.get("ic_t_stat"), - row.get("ic_positive_pct"), - row.get("mean_quintile_spread"), - row.get("weeks"), - row.get("avg_cross_section"), - row.get("reliable"), - ] - ) - - trades_csv = out.with_name(f"{out.stem}-trades.csv") - trade_columns = [ - "arm", - "symbol", - "entry_date", - "exit_date", - "r", - "pnl", - "reason", - "hold", - "entry", - "exit", - "risk_dollars", - ] - with trades_csv.open("w", newline="", encoding="utf-8") as handle: - writer = csv.DictWriter(handle, fieldnames=trade_columns, extrasaction="ignore") - writer.writeheader() - for arm in report.get("arms", []): - for trade in arm.get("full_trade_details", []): - writer.writerow({"arm": arm["id"], **trade}) - - if bundle: - bundle_path = out.with_suffix(".zip") - with zipfile.ZipFile(bundle_path, "w", zipfile.ZIP_DEFLATED) as archive: - for path in (out, markdown_path, arms_csv, factor_csv, trades_csv): - archive.write(path, arcname=path.name) - protocol = ROOT / "docs" / "research" / "fundamentals-weight-backtest.md" - if protocol.exists(): - archive.write(protocol, arcname=protocol.name) - - -async def _main() -> None: - from app.services import fundamentals_research as research - - args = _parse_args() - split_safe = args.protocol == SPLIT_SAFE_PROTOCOL - arms = SPLIT_SAFE_ARMS if split_safe else ARMS - n_trials = len(arms) - factor_keys = tuple( - ( - research.SPLIT_SAFE_FACTOR_POLARITY - if split_safe - else research.FACTOR_POLARITY - ).keys() - ) - snapshot = Path(args.snapshot) - out = Path(args.out) if args.out else _default_out(args.protocol) - if not snapshot.exists(): - raise SystemExit(f"snapshot not found: {snapshot}") - if args.workers < 1: - raise SystemExit("--workers must be positive") - train_end = date.fromisoformat(args.train_end) - test_start = date.fromisoformat(args.test_start) - if train_end >= test_start: - raise SystemExit("--train-end must be earlier than --test-start") - - os.environ["BACKTEST_SNAPSHOT_OFFLINE"] = "1" - if args.allow_spawn: - os.environ["BACKTEST_ALLOW_SPAWN"] = "1" - - data = await _load_snapshot(snapshot, args.quiet) - audit = data["audit"] - if audit["fundamental_rows"] == 0 or audit["fundamental_ciks"] < 5: - raise SystemExit( - "snapshot lacks usable fundamental_snapshots; create a fresh export " - "with scripts/create_backtest_snapshot.py" - ) - representatives = _representatives(data["ticker_rows"], data["prices"]) - candidates, entry_candidate_count = _build_candidates(snapshot, data, args) - factor_dates = _weekly_factor_dates(representatives, data["prices"]) - all_dates = factor_dates | { - date.fromisoformat(str(candidate["date"])) for candidate in candidates - } - scores_by_date, score_coverage = _build_scores( - snapshot, - all_dates, - representatives, - data["snapshots_by_cik"], - split_safe, - args, - ) - candidate_coverage = _attach_overlay_ranks( - candidates, data["ticker_rows"], scores_by_date, arms - ) - factor_ic = _factor_diagnostics( - representatives, - data["prices"], - scores_by_date, - factor_keys, - train_end, - test_start, - ) - - warnings = [ - "Current tracked universe only: historical constituent membership and " - "delisted names are unavailable, so absolute results have survivorship bias.", - "Earnings surprise is excluded because the completed SUE study already " - "failed its promotion bar for this strategy.", - "The test window has already been observed; this follow-up is sensitivity " - "evidence and live paper performance remains the final out-of-sample check.", - ] - if split_safe: - warnings.insert( - 1, - "Diluted-EPS growth and share-count change are excluded because filing-time " - "values are not split-comparable without point-in-time split factors.", - ) - else: - warnings.insert( - 1, - "Historical valuation is excluded because split-adjusted bars cannot be " - "safely combined with filing-time EPS and shares without split factors.", - ) - - report: dict[str, Any] = { - "generated_at": datetime.now(timezone.utc).isoformat(), - "research_protocol": args.protocol, - "git_commit": _git_commit(), - "snapshot": str(snapshot.resolve()), - "snapshot_sha256": _snapshot_hash(snapshot), - "splits": { - "train_end": train_end.isoformat(), - "test_start": test_start.isoformat(), - "windows": { - "train": f"entry date < {train_end.isoformat()}", - "validation": ( - f"{train_end.isoformat()} <= entry date < {test_start.isoformat()}" - ), - "test": f"entry date >= {test_start.isoformat()}", - }, - }, - "n_trials": n_trials, - "pre_registered_arms": list(arms), - "protocol": { - "id": args.protocol, - "qualification": "unchanged production gate; rank overlay only", - "cadence": "daily", - "fill_mode": "close; production near-close proxy", - "horizon_sessions": 30, - "filing_availability": ( - "accepted_at before signal-date midnight America/New_York; " - "conservative match for the daily pre-market SEC import" - ), - "missing_fundamental_score": 50.0, - "factor_keys": list(factor_keys), - "split_sensitive_metrics_excluded": ( - ["eps_growth_yoy", "share_count_change_yoy"] if split_safe else [] - ), - "selection": ( - "highest validation Sharpe among arms with train and validation " - "Sharpe not below control and validation drawdown within 2pp" - ), - "production_mutation": False, - }, - "warnings": warnings, - "data_audit": audit, - "strategy_config": { - "recommendation": data["config"], - "activation": data["activation"], - "exit": data["exit_config"], - }, - "score_cross_section_coverage": score_coverage, - "qualified_candidate_coverage": candidate_coverage, - "entry_candidate_count": entry_candidate_count, - "qualified_candidates": len(candidates), - "factor_ic": factor_ic, - "arms": [], - "development_grades": {}, - "development_selection": None, - "final_check": None, - } - _write_outputs(report, out, bundle=False) - - trade_sets: dict[str, dict[str, set[str]]] = {} - control: dict[str, Any] | None = None - for arm in arms: - if not args.quiet: - print("running {}".format(arm["id"]), flush=True) - result, arm_trades = _run_arm( - arm, candidates, data, train_end, test_start, n_trials - ) - trade_sets[str(arm["id"])] = arm_trades - if control is None: - control = result - else: - for row in result["windows"]: - name = str(row["window"]) - row["selection_vs_control"] = _trade_overlap( - arm_trades.get(name, set()), - trade_sets["control_w00"].get(name, set()), - ) - report["development_grades"][str(arm["id"])] = _development_grade( - control, result - ) - report["arms"].append(result) - _write_outputs(report, out, bundle=False) - - if control is None: - raise RuntimeError("control arm did not run") - eligible = [ - arm - for arm in report["arms"][1:] - if report["development_grades"].get(arm["id"], {}).get("pass") - ] - selected = max( - eligible, - key=lambda arm: ( - float((_window(arm, "validation") or {}).get("sharpe") or -999.0), - -float(arm["weight"]), - ), - default=None, - ) - if selected is not None: - report["development_selection"] = { - "arm_id": selected["id"], - "chosen_without_test": True, - "validation_sharpe": (_window(selected, "validation") or {}).get("sharpe"), - } - report["final_check"] = _final_check(control, selected) - report["completed_at"] = datetime.now(timezone.utc).isoformat() - _write_outputs(report, out, bundle=True) - print(f"wrote {out}", flush=True) - bundle_path = out.with_suffix(".zip") - print("wrote {}".format(bundle_path), flush=True) - - -if __name__ == "__main__": - asyncio.run(_main()) diff --git a/tests/unit/test_fundamentals_derivation.py b/tests/unit/test_fundamentals_derivation.py index a6fc804..ca249f4 100644 --- a/tests/unit/test_fundamentals_derivation.py +++ b/tests/unit/test_fundamentals_derivation.py @@ -2,7 +2,7 @@ from __future__ import annotations -from dataclasses import dataclass, replace +from dataclasses import dataclass from datetime import date, datetime, timezone import pytest @@ -38,9 +38,7 @@ _ENDS = { # period_end per (fy, quarter index 0..3) } -def _year( - fy, discretes: dict[str, list[float]], instants: dict[str, list] | None = None -): +def _year(fy, discretes: dict[str, list[float]], instants: dict[str, list] | None = None): """Build 4 snapshot rows (Q1,Q2,Q3,FY) with YTD-cumulative flow fields from the given per-quarter discrete values; instants set as-is per quarter.""" rows = [] @@ -57,38 +55,22 @@ def _year( def _two_years(): rev25 = [100, 110, 120, 130] rev26 = [110, 121, 132, 143] # +10% each quarter YoY - rows = _year( - 2025, - { - "revenue": rev25, - "operating_income": [x * 0.2 for x in rev25], - "diluted_eps": [1.0, 1.1, 1.2, 1.3], - "cfo": [x * 0.25 for x in rev25], - "capex": [x * 0.05 for x in rev25], - "depreciation_amortization": [x * 0.05 for x in rev25], - }, - instants={ - "shares_outstanding": [1000, 1000, 1000, 1000], - "cash_and_st_investments": [40] * 4, - "total_debt": [140] * 4, - }, - ) - rows += _year( - 2026, - { - "revenue": rev26, - "operating_income": [x * 0.2 for x in rev26], - "diluted_eps": [1.1, 1.21, 1.32, 1.43], - "cfo": [x * 0.25 for x in rev26], - "capex": [x * 0.05 for x in rev26], - "depreciation_amortization": [x * 0.05 for x in rev26], - }, - instants={ - "shares_outstanding": [900, 900, 900, 900], - "cash_and_st_investments": [50] * 4, - "total_debt": [150] * 4, - }, - ) + rows = _year(2025, { + "revenue": rev25, + "operating_income": [x * 0.2 for x in rev25], + "diluted_eps": [1.0, 1.1, 1.2, 1.3], + "cfo": [x * 0.25 for x in rev25], + "capex": [x * 0.05 for x in rev25], + "depreciation_amortization": [x * 0.05 for x in rev25], + }, instants={"shares_outstanding": [1000, 1000, 1000, 1000], "cash_and_st_investments": [40] * 4, "total_debt": [140] * 4}) + rows += _year(2026, { + "revenue": rev26, + "operating_income": [x * 0.2 for x in rev26], + "diluted_eps": [1.1, 1.21, 1.32, 1.43], + "cfo": [x * 0.25 for x in rev26], + "capex": [x * 0.05 for x in rev26], + "depreciation_amortization": [x * 0.05 for x in rev26], + }, instants={"shares_outstanding": [900, 900, 900, 900], "cash_and_st_investments": [50] * 4, "total_debt": [150] * 4}) return rows @@ -115,11 +97,9 @@ def test_net_debt_leverage_and_share_dilution(): # net debt = total_debt - cash = 150 - 50 = 100 (latest instant) assert d.metrics["net_debt"].value == pytest.approx(100.0) # EBITDA TTM = TTM operating_income + TTM D&A; net_debt/ebitda - op_ttm = 506 * 0.2 # 101.2 - da_ttm = 506 * 0.05 # 25.3 - assert d.metrics["net_debt_to_ebitda"].value == pytest.approx( - 100.0 / (op_ttm + da_ttm), rel=1e-6 - ) + op_ttm = 506 * 0.2 # 101.2 + da_ttm = 506 * 0.05 # 25.3 + assert d.metrics["net_debt_to_ebitda"].value == pytest.approx(100.0 / (op_ttm + da_ttm), rel=1e-6) # shares 900 vs 1000 a year earlier -> -10% (buyback) assert d.metrics["share_count_change_yoy"].value == pytest.approx(-10.0, abs=1e-6) @@ -150,9 +130,7 @@ def test_net_debt_requires_both_components(): r.total_debt = None d = fd.derive(rows) assert d.metrics["net_debt"].value is None - assert ( - d.metrics["net_debt_to_ebitda"].value is None - ) # net debt null -> leverage null + assert d.metrics["net_debt_to_ebitda"].value is None # net debt null -> leverage null def test_leverage_null_when_ebitda_nonpositive(): @@ -162,23 +140,15 @@ def test_leverage_null_when_ebitda_nonpositive(): r.depreciation_amortization = 1 d = fd.derive(rows) assert d.metrics["net_debt"].value == pytest.approx(100.0) # net debt still valid - assert d.metrics["net_debt_to_ebitda"].value is None # but leverage nulled + assert d.metrics["net_debt_to_ebitda"].value is None # but leverage nulled def test_tape_stops_at_a_gap(): - rows = [ - r - for r in _two_years() - if not (r.fiscal_year == 2026 and r.fiscal_period == "Q1") - ] + rows = [r for r in _two_years() if not (r.fiscal_year == 2026 and r.fiscal_period == "Q1")] d = fd.derive(rows) hist = d.metrics["operating_margin"].history # consecutive suffix ending at FY2026: Q2, Q3, FY (not compressed across the Q1 gap) - assert [p.period_end for p in hist] == [ - date(2026, 3, 31), - date(2026, 6, 30), - date(2026, 9, 30), - ] + assert [p.period_end for p in hist] == [date(2026, 3, 31), date(2026, 6, 30), date(2026, 9, 30)] def test_yoy_growth_null_when_prior_nonpositive(): @@ -190,48 +160,14 @@ def test_yoy_growth_null_when_prior_nonpositive(): assert d.metrics["eps_growth_yoy"].value is None # loss->profit is not a % -def test_derive_as_of_excludes_future_amendment(): - rows = _two_years() - original = next( - row for row in rows if row.fiscal_year == 2026 and row.fiscal_period == "FY" - ) - amendment = replace( - original, - accepted_at=datetime(2027, 1, 1, tzinfo=UTC), - revenue=999999, - ) - before = fd.derive_as_of([*rows, amendment], datetime(2026, 12, 31, tzinfo=UTC)) - after = fd.derive_as_of([*rows, amendment], datetime(2027, 1, 2, tzinfo=UTC)) - assert before.metrics["revenue_growth_yoy"].value == pytest.approx(10.0) - assert after.metrics["revenue_growth_yoy"].value != pytest.approx(10.0) - - -def test_derive_as_of_treats_sqlite_naive_acceptance_as_utc(): - rows = _two_years() - rows[0].accepted_at = datetime(2025, 1, 1) - result = fd.derive_as_of(rows, datetime(2027, 1, 1, tzinfo=UTC)) - assert result.latest_period_end == date(2026, 9, 30) - - def test_amendment_selection_newest_accepted_wins(): rows = _two_years() # an amendment to FY2026 FY restates revenue YTD higher, accepted later - amended = Snap( - 2026, - "FY", - date(2026, 9, 30), - date(2026, 11, 1), - datetime(2027, 1, 1, tzinfo=UTC), - revenue=999999, - operating_income=100, - diluted_eps=1.43, - cfo=100, - capex=10, - depreciation_amortization=25, - shares_outstanding=900, - cash_and_st_investments=50, - total_debt=150, - ) + amended = Snap(2026, "FY", date(2026, 9, 30), date(2026, 11, 1), + datetime(2027, 1, 1, tzinfo=UTC), revenue=999999, + operating_income=100, diluted_eps=1.43, cfo=100, capex=10, + depreciation_amortization=25, shares_outstanding=900, + cash_and_st_investments=50, total_debt=150) d = fd.derive(rows + [amended]) # Q4 revenue discrete now uses the amended YTD(FY)=999999 minus YTD(Q3)=363 # so TTM/growth reflects the amendment, proving newest accepted_at won. diff --git a/tests/unit/test_fundamentals_research.py b/tests/unit/test_fundamentals_research.py deleted file mode 100644 index 7d7e563..0000000 --- a/tests/unit/test_fundamentals_research.py +++ /dev/null @@ -1,97 +0,0 @@ -from __future__ import annotations - -import math - -import pytest - -from app.services import fundamentals_research as research - - -def test_favorable_percentiles_are_tie_aware(): - ranks = research.favorable_percentiles( - {"a": 3, "b": 3, "c": 3, "d": 3, "e": 3}, - higher_is_better=True, - ) - assert set(ranks.values()) == {50.0} - - -def test_lower_is_better_flips_the_rank(): - ranks = research.favorable_percentiles( - {"a": 1, "b": 2, "c": 3, "d": 4, "e": 5}, - higher_is_better=False, - ) - assert ranks["a"] == 100.0 - assert ranks["e"] == 0.0 - - -def test_invalid_and_thin_cross_sections_stay_null(): - ranks = research.favorable_percentiles( - {"a": 1, "b": 2, "c": math.nan, "d": None, "e": 5}, - higher_is_better=True, - ) - assert all(value is None for value in ranks.values()) - - -def test_composites_use_equal_subgroup_weighting(): - features = { - str(index): { - "operating_margin": index, - "fcf_margin": index, - "net_debt_to_ebitda": 6 - index, - "share_count_change_yoy": 6 - index, - "revenue_growth_yoy": index, - "eps_growth_yoy": index, - } - for index in range(1, 6) - } - scores = research.cross_section_scores(features) - assert scores["5"]["quality"] == 100.0 - assert scores["5"]["growth"] == 100.0 - assert scores["5"]["balanced"] == 100.0 - assert scores["3"]["balanced"] == 50.0 - - -def test_split_safe_composites_ignore_eps_and_share_count(): - def features(unsafe_multiplier: int): - return { - str(index): { - "operating_margin": index, - "fcf_margin": index, - "net_debt_to_ebitda": 6 - index, - "revenue_growth_yoy": index, - "eps_growth_yoy": unsafe_multiplier * (6 - index), - "share_count_change_yoy": unsafe_multiplier * index, - } - for index in range(1, 6) - } - - baseline = research.cross_section_scores(features(1), split_safe=True) - distorted = research.cross_section_scores(features(1_000_000), split_safe=True) - - assert distorted == baseline - assert baseline["5"]["quality"] == 100.0 - assert baseline["5"]["growth"] == 100.0 - assert baseline["5"]["balanced"] == 100.0 - assert "eps_growth_yoy" not in baseline["5"] - assert "share_count_change_yoy" not in baseline["5"] - - -def test_split_safe_quality_still_requires_two_comparable_inputs(): - features = { - str(index): { - "operating_margin": index, - "revenue_growth_yoy": index, - } - for index in range(1, 6) - } - scores = research.cross_section_scores(features, split_safe=True) - assert all(row["quality"] is None for row in scores.values()) - assert scores["5"]["growth"] == 100.0 - assert scores["5"]["balanced"] is None - - -def test_overlay_uses_neutral_missing_score_and_validates_weight(): - assert research.overlay_rank(90, None, 0.2) == 82.0 - assert research.overlay_rank(90, 100, 0.2) == 92.0 - with pytest.raises(ValueError, match="weight"): - research.overlay_rank(90, 50, 1.1) diff --git a/tests/unit/test_fundamentals_research_runner.py b/tests/unit/test_fundamentals_research_runner.py deleted file mode 100644 index 16352e7..0000000 --- a/tests/unit/test_fundamentals_research_runner.py +++ /dev/null @@ -1,102 +0,0 @@ -from __future__ import annotations - -import zipfile - -from scripts import run_fundamentals_research as runner - - -def _window(name, sharpe, drawdown): - return { - "window": name, - "sharpe": sharpe, - "sharpe_se": 0.2, - "dsr": 0.8, - "cagr_pct": 10.0, - "max_drawdown_pct": drawdown, - "calmar": 1.0, - "trades": 20, - } - - -def test_arm_matrix_is_bounded_and_pre_registered(): - assert runner.N_TRIALS == 13 - assert runner.ARMS[0]["id"] == "control_w00" - assert {arm["weight"] for arm in runner.ARMS[1:]} == {0.1, 0.2, 0.3, 0.4} - assert {arm["composite"] for arm in runner.ARMS[1:]} == { - "quality", - "growth", - "balanced", - } - - -def test_split_safe_matrix_is_smaller_and_trial_corrected(): - assert runner.SPLIT_SAFE_N_TRIALS == 10 - assert runner.SPLIT_SAFE_ARMS[0]["id"] == "control_w00" - assert {arm["weight"] for arm in runner.SPLIT_SAFE_ARMS[1:]} == { - 0.05, - 0.10, - 0.15, - } - assert {arm["composite"] for arm in runner.SPLIT_SAFE_ARMS[1:]} == { - "quality", - "growth", - "balanced", - } - - -def test_split_safe_output_has_a_distinct_name(): - assert runner._default_out(runner.SPLIT_SAFE_PROTOCOL).name.startswith( - "fundamentals-splitsafe-" - ) - - -def test_development_grade_does_not_read_test_window(): - control = { - "windows": [ - _window("train", 1.0, 10.0), - _window("validation", 1.0, 10.0), - _window("test", 9.0, 1.0), - ] - } - arm = { - "windows": [ - _window("train", 1.1, 10.0), - _window("validation", 1.2, 11.0), - _window("test", -9.0, 90.0), - ] - } - assert runner._development_grade(control, arm)["pass"] is True - - -def test_output_bundle_is_self_contained(tmp_path): - report = { - "generated_at": "2026-07-23T00:00:00Z", - "splits": {"train_end": "2024-01-01", "test_start": "2025-01-01"}, - "n_trials": 13, - "warnings": ["survivorship bias"], - "factor_ic": {"full": []}, - "arms": [], - "development_selection": None, - "final_check": None, - } - output = tmp_path / "result.json" - runner._write_outputs(report, output, bundle=True) - with zipfile.ZipFile(output.with_suffix(".zip")) as archive: - names = set(archive.namelist()) - assert { - "result.json", - "result.md", - "result-arms.csv", - "result-factor-ic.csv", - "result-trades.csv", - } <= names - - -def test_winner_concentration_exposes_top_five_dependence(): - details = [ - {"pnl": value, "r": value / 10} for value in (100, 90, 80, 70, 60, -10, -20) - ] - result = runner._winner_concentration(details) - assert result["top5_pnl"] == 400 - assert result["net_pnl_ex_top5"] == -30 - assert result["avg_r_ex_top5"] == -1.5