This is a developer's methodology log, not an analyst's manifesto. Where an established method exists, I name it. Where I built something by intuition, I say so. The honesty grade on each item is the point — not the polish.
Honesty Grades
Items that directly implement or are inspired by established academic papers or standard techniques.
9 binary checks on financial statement quality (0-9).
Academic Basis
Joseph Piotroski, "Value Investing: The Use of Historical Financial Statement Information to Separate Winners from Losers", Journal of Accounting Research, 2000.
Grade Note
F7 (share dilution) is approximated via EPS/NI direction comparison — we lack historical shares_outstanding data.
Key Formula
Profitability (4): NI > 0, OCF > 0, ROA improving YoY, OCF > NI. Leverage (3): Debt ratio down, Current ratio up, No dilution. Efficiency (2): Gross margin up, Asset turnover up.
Limitations
8-point checklist for growth stocks with fundamental backing (0-8).
Academic Basis
Partha Mohanram, "Separating Winners from Losers among Low Book-to-Market Stocks using Financial Statement Analysis", Review of Accounting Studies, 2005.
Grade Note
Stability checks include a direction-aware pass (consistent uptrend overrides variance) — this extension is not in the original paper.
Key Formula
Profitability (3): ROA, OCF/Assets, OCF > NI vs sector median. Stability (2): ROA variance, Revenue growth variance. Investment (3): R&D/Assets, CapEx/Assets, SG&A/Assets vs sector median.
Limitations
7 factors (C·A·N·S·L·I·M) each scored 0-100, heuristically adapted from O'Neil.
Academic Basis
William O'Neil, "How to Make Money in Stocks", 1988. Originally a screening tool, not a scoring system.
Grade Note
Scoring 0-100 per factor is our interpretation. I (Institutional) uses KR/TW institutional flow data, which differs from US mutual fund ownership in the original.
Limitations
14-period Relative Strength Index.
Academic Basis
J. Welles Wilder Jr., "New Concepts in Technical Trading Systems", 1978.
Limitations
On-Balance Volume — cumulative volume tracking price direction.
Academic Basis
Joseph Granville, "Granville's New Key to Stock Market Profits", 1963.
Limitations
Relative Strength percentile rank within sector.
Academic Basis
O'Neil RS rating methodology. Formula: (count_below / (n-1)) × 99.
Limitations
Standard Pearson correlation used for cross-market lead-lag analysis.
Academic Basis
Karl Pearson (1896). Standard econometric lead-lag technique.
Grade Note
The JP→TW→KR ripple relationship discovered through this analysis may reflect a shared semiconductor cycle rather than true causation.
Limitations
Additive smoothing for small-sample sentiment ratios.
Academic Basis
Laplace additive smoothing. Formula: (positive + 2.5) / (total + 5).
Limitations
Logistic function for smooth 0-100 normalization without hard clamping.
Academic Basis
Logistic function (Verhulst, 1838). Center and steepness parameters are domain-tuned.
Grade Note
The function shape is standard; the center/steepness parameters are chosen by intuition, not calibrated.
Limitations
Fact-axis composite using weighted geometric mean of GPU rental price and DC construction pipeline scores. CapEx news sentiment is separated into a narrative axis.
Academic Basis
OECD, "Handbook on Constructing Composite Indicators", 2008 — compensability problem: weighted arithmetic mean assumes full compensability, so a deficit in one axis is fully offset by a surplus in another. Geometric mean is less compensatory and penalizes low values, preventing one axis from masking another (the approach adopted by the UNDP Human Development Index in 2010).
Grade Note
Migrated 2026-06 from weighted arithmetic mean (gpu·0.4 + dc·0.3 + capex·0.3), which let CapEx news sentiment (a narrative input) dominate ~67% of the composite and mask an extreme DC pipeline reading. Separating sentiment follows OECD guidance that "highly different dimensions" should not be aggregated compensatorily. Internal precedent: the Narrative-vs-Fact Divergence card already separates sentiment from fact.
Key Formula
overall = (gpu^0.4 × dc^0.3)^(1/0.7), each axis clamped to [1, 99] to avoid log(0). CapEx sentiment returned separately; gap = fact − narrative.
Limitations
28-day moving-average baseline per company×keyword; current value flagged new/stable/surge/drop by deviation from baseline.
Academic Basis
Standard moving-average baseline / deviation detection. Threshold-based anomaly flagging.
Grade Note
Evergreen keywords (e.g. CUDA) are tracked, not hidden — baseline deviation is the signal (surge = reinforcement, drop = pullback). Both directions matter.
Key Formula
baseline = mean(last 28 observed days); deviation% = (current − baseline) / baseline × 100. States: |dev| ≤ THRESHOLD → stable; > +THRESHOLD → surge; < −THRESHOLD → drop; <28 days → collecting.
Limitations
Items built by intuition, where similarities to known methodology were discovered after the fact.
Percentile-based min-max scaling with winsorizing at P5/P95.
Academic Basis
Winsorized normalization (Winsor; Ruppert, "Statistics and Data Analysis for Financial Engineering", 2004).
Grade Note
Built by intuition (clip extremes, scale to 0-100), later found to match standard winsorized normalization.
Key Formula
(return - P5) / (P95 - P5) × 100, clamped to [0, 100].
Limitations
Excludes broad market selloff days from signal computation.
Academic Basis
Conceptually similar to detoning / market-common-component removal (Lopez de Prado, "Machine Learning for Asset Managers", 2020, Ch. 2).
Grade Note
A heuristic threshold (5+ tickers with ≥1% drop = market day, exclude) that later matched the concept of common-component removal.
Limitations
Short-term fear-greed gauge (0-100) combining 3 weighted layers.
Academic Basis
Inspired by CNN Fear & Greed Index (7-indicator composite sentiment gauge).
Grade Note
CNN uses equal weights; we use differentiated weights (Market 40%, Supply 35%, Smart Money 25%) with fixed-range min-max instead of z-score.
Key Formula
Layer weights: Market Temperature 40%, Supply Chain Temperature 35%, Smart Money 25%.
Limitations
Weighted composite indicator construction used across CPI, Tension, Divergence, HBM Tightness.
Academic Basis
OECD, "Handbook on Constructing Composite Indicators: Methodology and User Guide", 2008.
Grade Note
We follow the general pattern (normalize → weight → aggregate) but weights are set by domain intuition, not PCA or expert panel.
Limitations
Product/tech keywords extracted from job titles and surfaced when newly appearing or surging, then cross-checked against the news corpus.
Academic Basis
Inspired by internal topic-surge detection (tag/n-gram surge). News cross-check labels keywords leading (hiring yes / news no) vs coincident (hiring yes / news yes).
Grade Note
News cross-check is a LABEL, not a filter — a keyword appearing in hiring but not yet in news is kept and flagged "leading" (highest value), not discarded.
Limitations
Items built on intuition where no matching academic method has been found. This is honesty, not weakness.
Bitcoin hashrate used as a heuristic signal for GPU demand cycles.
Academic Basis
No academic source found.
Limitations
Maps DRAM price direction × inventory direction to 8 fixed risk scores.
Academic Basis
No academic source found.
Grade Note
Score thresholds (±7.5, ±15, ±5) were set by intuition without backtesting. Flat-band boundary behavior is not validated.
Limitations
The "5 tickers" cutoff for identifying market-wide selloff days.
Academic Basis
No academic source found.
Grade Note
"5" is an undocumented magic number — a hard cut that ignores the underlying distribution.
Limitations
AI datacenter MW (construction/proposed, build-stage weighted) propagated equally to 4 supply-chain nodes (memory, power_infra, networking, packaging).
Academic Basis
No academic source found. Reuses internal hyperscaler-as-demand-source rule (big-tech capex sets big-tech ticker neutral, propagates demand to supply chain).
Grade Note
Equal 1/N split across 4 nodes is a placeholder — real part intensity differs (memory/power likely larger). Node coefficients not yet applied. Status weights (construction 1.0, proposed 0.3, operational/unknown excluded) are intuition-set.
Limitations
Rule-based extraction of MW and construction/completion dates from free-text note field.
Academic Basis
No academic source found. Regex/rule-based: GW×1000, total>phase>single, "potential/expansion" excluded as future capacity.
Grade Note
Raised MW total 3,519 → 39,673 (11×) by surfacing large projects previously unstructured (e.g. Fermi 11GW). Existing confirmed MW never overwritten; extracted values flagged inferred.
Limitations
Weighted sum of 4 components (0-100). Overall investment attractiveness score per stock.
Formula
raw = (CAN SLIM × 0.40) + (Trend × 0.25) + (Quality × 0.25) − (Risk × 0.10) + 5
Components
| CAN SLIM | 40% | Growth, supply/demand, leadership |
| Trend | 25% | EMA structure, RSI, ADX, 3M momentum |
| Quality | 25% | ROE, operating margin, D/E, revenue growth |
| Risk | -10% | ATR, 52w drawdown, D/E, net margin, BB%B |
Grade Thresholds
≥75: STRONG · ≥60: WATCH · ≥45: NEUTRAL · ≥30: CAUTION · <30: AVOID
Limitations
CapEx sustainability of 4 AI hyperscalers (Alphabet, Microsoft, Amazon, Meta). -50 to +50.
Structure
| Base Pressure | 0 ~ +25 | CAPEX/OCF percentile vs 9yr history |
| GPU Signal | -10 ~ +10 | 30d rental price trend |
| DRAM/DSI Signal | -15 ~ +15 | Inventory-price interaction |
Limitations
Weighted 3-axis composite (0-100). Higher = tighter HBM supply.
Axes
| Hyperscaler CAPEX | ~36% | Demand pressure |
| HBM Supply Allocation | ~36% | SK Hynix, Samsung, Micron |
| TSMC Packaging Load | ~28% | CoWoS bottleneck proxy |
HBM ASP Direction is currently excluded — HBM contract pricing is not publicly disclosed. DDR5 spot is accumulating as a proxy candidate.
This document is a living methodology log, updated with each new feature. Last updated: 2026-06-18.
All data and analysis on this site are for informational purposes only and do not constitute investment advice. Investment decisions should be made based on your own judgment and responsibility.