Wolf Finance

Money-Making Overview

Systematic value investing compounds at 15-25% annually. A $50K portfolio following this framework generates $7.5K-12.5K/year. With position sizing and margin of safety, drawdowns stay under 20%. One good investment thesis can return 2-10x over 3-5 years.

Revenue Streams

1. Personal Portfolio — compound your own capital
2. Investment Research ($2K-10K/report) — sell theses to funds/family offices
3. Portfolio Management (0.5-1.5% AUM) — manage for others
4. Newsletter/Picks ($19-97/mo) — publish investment ideas

First Action in 60 Minutes

#!/usr/bin/env bash
# Portfolio health check
mkdir -p ~/wolf-finance/{holdings,research,theses,reviews}

echo "=== Portfolio Health Check ==="
echo "1. List ALL current holdings with cost basis"
echo "2. Run 7-factor Quality Screen (eliminate weak positions):"
echo "   - Declining revenue (3yr) -> RED FLAG"
echo "   - Increasing debt/equity -> RED FLAG"
echo "   - Negative free cash flow -> RED FLAG"
echo "   - Insider selling >50% -> RED FLAG"
echo "   - Losing market share -> RED FLAG"
echo "   - Regulatory risk -> RED FLAG"
echo "   - Overvalued (P/E > 3x industry) -> RED FLAG"
echo "3. Flag any position with 3+ red flags for review"
echo "4. Check thesis: has anything fundamentally changed?"
echo "5. Decision: HOLD, INCREASE, or EXIT each position"

Anti-Rationalization Table

Output Format

On completion: "Portfolio: [N] positions, $[N] value, [N] red flags, [N] thesis updates needed, [N] actions taken"

Wolf Finance

When to Use

Trigger phrases:

• "wolf finance"

• "Use when working with wolf finance"

• Analyzing any financial asset (equities, crypto, forex, commodities, derivatives)

• Building investment theses with evidence-tiered backing (T1/T2/T3)

• Running pre-trade risk gates before position entry

• Portfolio construction and risk management across asset classes

• Institutional-grade reporting for investment committees

• Deep company research and valuation

• Management quality assessment and due diligence

• Quality screening of investment candidates

When NOT to Use

• For personal financial advice (consult a licensed advisor)
• When the analysis requires real-time market data you do not have
• For tax or legal decisions (consult professionals)

Overview

Wolf Finance provides finance operations with accuracy and compliance. Integrates comprehensive investment research frameworks drawn from Buffett, Munger, Duan Yongping, and Li Lu — from quality screening through deep company research to final decision memorandum.

Workflow

# Example: Portfolio risk calculation
def calculate_risk(returns: list[float]) -> dict:
    import statistics
    mean = statistics.mean(returns)
    stdev = statistics.stdev(returns)
    sharpe = mean / stdev if stdev > 0 else 0
    return {"mean": mean, "stdev": stdev, "sharpe_ratio": sharpe}

1. Gather data — Collect financial data from authoritative sources
2. Analyze — Apply financial models and calculations
3. Validate — Cross-check results against benchmarks
4. Report — Generate clear, actionable financial reports
5. Recommend — Provide data-driven suggestions

Key Metrics

• Revenue and growth rates
• Profit margins (gross, operating, net)
• Cash flow and burn rate
• Return on investment (ROI)
• Risk-adjusted returns

Compliance

• Follow GAAP/IFRS standards where applicable
• Maintain audit trail for all calculations
• Redact sensitive financial data in reports
• Document assumptions and methodologies

Anti-Rationalization Table

Financial Data Standards

Every key financial data point MUST come from two independent sources. Flag discrepancies > 1%.

Data source priority by market:

Error calculation:

Error rate = |source1 - source2| / source1 x 100%

Common discrepancy causes: GAAP vs Non-GAAP, exchange rate timing, fiscal year definitions, consolidation scope, data lag.

Mandatory calculation verification — use tooling, never LLM mental math:

• Market cap: python3 tools/financial_rigor.py verify-market-cap --price {price} --shares {shares} --reported {reported_mcap} --currency {currency}
• Valuation: python3 tools/financial_rigor.py verify-valuation --price {price} --eps {eps} --bvps {bvps} --fcf-per-share {fcf} --dividend {dividend}
• Cross-validation: python3 tools/financial_rigor.py cross-validate --field {field} --values '{"source1": val, "source2": val}' --unit {unit}
• Three-scenario valuation: python3 tools/financial_rigor.py three-scenario --price {price} --eps {eps} --shares {shares} --growth {opt} {base} {pess} --pe {opt_pe} {base_pe} {pess_pe} --years 3

Investment Research Framework

An 8-module systematic investment research process combining the frameworks of Buffett, Munger, Duan Yongping, and Li Lu.

Step 0: AI Research Bias Awareness

Before starting, rate the company's "AI-researchability" to identify potential data bias:

Bias self-check (maintain throughout research):

• Is my "certainty" from business quality or data volume?
• Would halving available data change my conclusion?
• Is my output highly similar to market consensus? Where is my information advantage?
• Am I underestimating the possibility that a great business has little public data?

Step 1: Data Collection

Collect using parallel research agents:

1. Revenue structure: segment revenue, growth rates, gross margins
2. Financials: 5yr revenue, net income, margins, FCF, cash position
3. Competitive landscape: market share, key competitors
4. Business model & moat: core competitive advantages
5. Technology: core tech stack, R&D investment
6. Management: CEO background, ownership, key decisions
7. Industry: TAM, growth forecasts
8. Risk factors: geopolitical, regulatory, supply chain
9. Current valuation: market cap, PE, PS, PEG, EV/Revenue
10. Bull and bear case core arguments

Cross-validate every key data point using the Financial Data Standards section above and the financial_rigor.py tool suite. Never rely on LLM mental arithmetic for calculations.

Common error prevention:

• Market cap unit: HKD bn vs RMB bn vs USD bn — easy to misplace a zero
• FCF definition: capex scope varies (leases, acquisitions)
• Debt scope: whether operating lease liabilities are included
• Ownership: AB-share companies — economic interest != voting rights

Step 2: Business Essence Analysis (Duan Yongping "Right Business")

• Define the business in one sentence
• Revenue structure decomposition (table)
• 5-year profitability trends (table)
• Business model canvas: one-time sale vs subscription/recurring? Hardware vs software vs platform?
• Ecosystem stickiness / customer lock-in strength
• Gross margin vs peers — explain why high or low
• Operating leverage analysis
• Duan Yongping-style question: What makes this business good? If you could describe it in one sentence, what is it?

Step 3: Moat Assessment (Buffett "Economic Moat")

Evaluate each moat type:

Analyze moat trend: wider or narrower in the past 5 years? Outlook for the next 5 years.

Buffett-style question: Will this moat still exist in 10 years? What could destroy it?

Step 4: Reverse Thinking & Risk Checklist (Munger "Invert, Always Invert")

• List every way this company could fail (table: path / probability / impact)
• Historical analogies: find companies in similar positions — what happened?
• Cross-disciplinary analysis: network effect theory, tech adoption curves, game theory
• Bias check: narrative bias, anchoring, survivorship bias
• Collect the bear case core arguments
• Munger-style question: Where am I most likely to be wrong? Why would smart people not buy / short this stock?

Step 5: Management Assessment (Duan Yongping "Right People" + Buffett "Management Integrity")

• CEO/founder key decision review (table: time / decision / outcome / score)
• Capital allocation ability: R&D returns, M&A success rate, buyback timing
• Shareholder alignment: management ownership, compensation structure, insider selling records
• Organizational capability: team stability, key person risk
• Culture characteristics
• Duan Yongping-style question: If the CEO retired tomorrow, could this company still compete?

Step 6: Industry & Civilization Trend (Li Lu "Civilization Evolution Framework")

• Is the industry in a "civilization-level paradigm shift"?
• Historical technology revolution analogy (steam/electricity/internet/AI)
• TAM growth curve and ceiling analysis
• Company position in the industry value chain
• Technology roadmap risk
• Customer/supplier concentration analysis
• Li Lu-style question: Looking back 20 years from now, is this company "the Standard Oil of this era" or "a flash-in-the-pan 3Com"?

Step 7: Valuation & Margin of Safety (Buffett "Intrinsic Value" + Duan Yongping "Right Price")

• Current market pricing (key valuation metrics table) — MUST verify with tooling
• Reverse DCF: what growth does the current price imply?
• Three-scenario DCF valuation — MUST calculate with tooling, never mental math
• Comparison with own historical valuation
• Comparison with peer valuation
• Duan Yongping-style question: If the stock market closed for 5 years tomorrow, would you still be happy to own at this price?

Step 8: Comprehensive Decision Memorandum

Summary table:

Decision table:

Report must include:

1. Information richness grade (A/B/C) and AI research limitations statement at the top
2. Distinguish "AI analysis confidence" (depends on data availability) from "investment certainty" (depends on business quality)
3. For C-grade companies: include "questions needing primary verification" list
4. Data spot-check before release using tools/report_audit.py

Value Investing Pre-Buy Checklist

A 6-gate checklist adapted from the Buffett-Munger framework. Each gate scored *1-5.

Gate 1: Can I Understand This Business? (Circle of Competence)

• Can you explain how the company makes money in one sentence?
• What will it most likely still be doing in 10 years?
• What key variables determine success or failure?
• Is your understanding from deep research or hearsay?

Hard veto: if you cannot clearly describe how it makes money, mark "outside competence — no analysis."

Gate 2: Is This a Good Business? (Economic Characteristics)

Scoring: *1-5 based on how many standards are met and whether trends are improving or deteriorating.

Gate 3: Is the Moat Deep Enough? (Competitive Advantage)

Check each type:

Additional test: If a competitor had $10B, could they replicate this business?

Gate 4: Can Management Be Trusted? (People Factor)

Hard veto: severe integrity issues.

Gate 5: Is the Price Cheap Enough? (Margin of Safety)

Three-scenario valuation — MUST use tooling, never mental math:

python3 tools/financial_rigor.py three-scenario \
  --price {price} --eps {eps} --shares {shares} \
  --growth {opt} {base} {pess} --pe {opt_pe} {base_pe} {pess_pe} --currency {currency}

• If wrong, what is maximum downside at current price?
• Would you add if the stock halved?

Gate 6: Position Sizing & Decision Discipline

• Is FOMO driving the decision?
• Are you buying only because someone recommended it?
• Could you accept a 5-year trading halt?
• Can the buy thesis be written in under 200 words?

Final: Mirror Test

Write:

"I am buying ___ at ___ because:

1. The business essence is ___, I understand it;
2. Its moat is ___ and it is widening/narrowing;
3. Management is ___ worthy/unworthy of trust;
4. Current price represents ___ of intrinsic value, ___ sufficient/insufficient margin of safety;
5. Even if I am wrong, downside is controllable/uncontrollable because ___."

Cannot complete in 5 sentences = do not buy.

Quick Veto List

• Cannot clearly explain how the company makes money
• 3 consecutive years of negative FCF with no sign of improvement
• Management integrity issues
• Competitive advantage being irreversibly eroded
• Thesis requires "a greater fool" to pay more later
• Cannot afford the investment going to zero
• Main reason to buy is "everyone else is buying" or "it has been going up"
• Cannot write the buy thesis in under 200 words

Quality Screen: 7-Factor Elimination Criteria

Quickly filter out non-first-class companies using 7 hard criteria with 3 exemption rules.

The 7 Criteria

Exemption Rules

A: Strategic Investment Period (exempts Rule 1)

• Listed < 10 years
• Gross margin > 30% (proves business model has pricing power)
• Last 2 years operating CF positive (proves self-sustaining)

B: Active Low-Margin Strategy (exempts Rule 6)

• Gross margin > 30% (can earn but chooses not to)
• Last 2 years net margin back above 5% or in clear uptrend

C: High-Turnover Thin-Margin Model (exempts Rules 4 and 6)

• ROE > 20%
• Operating CF / Net income > 1.0
• Business model is "membership / platform commission / high-turnover thin-margin"

Notes

• Banks/insurance: Rule 3 (interest coverage) does not apply
• REITs: use core operating profit ROE instead
• Cyclicals: use full-cycle averages (cover at least one peak and one trough)
• Short listing history (< 5yr): use all available data, flag "insufficient data window"
• Data deficiency: mark as "data insufficient" rather than passing/failing

Passing screen does not equal "good investment" — it means the company survived first-pass elimination. Further research on business model sustainability, management, valuation, and competitive dynamics is still required.

Management Deep Dive

Deep management quality assessment when standard management scoring is uncertain (*** or below) or management is the core investment thesis.

Framework

1. Key Person Identification

Identify CEO, CFO, founder (if not CEO), controlling shareholder, other key executives. Distinguish "who makes decisions" from "who has the title."

2. CEO Capability Assessment

Strategic vision: Review CEO public statements (letters to shareholders, earnings calls, interviews, social media) over the past 5 years. Extract predictions and compare with actual outcomes.

• Has the CEO made correct judgments ahead of the market?
• Has the CEO stayed calm when everyone was overly optimistic?
• Independent thinking vs following consensus?

Execution ability:

3. Integrity Assessment (Most Important)

Promise vs delivery tracking — extract specific commitments from past 3 years:

Track record:

Crisis behavior: Search for the company's major crises. How did management react?

• Proactive communication or avoidance?
• Internal attribution or external blame?
• Do the hard but right thing or pander to short-term markets?

Stakeholder attitudes:

4. Capital Allocation Ability

M&A record:

Buyback record: Check valuation at time of buyback vs current. Dividend record: Payout ratio vs FCF, sustainability. New business investment: Area, cumulative spend, current status, return.

Scoring:

5. Governance Structure

• Dual-class share structure / super-voting rights
• Founder/controller ownership percentage
• Independent director independence
• Executive compensation vs net profit and peers
• Related-party transactions fairness

6. CEO Exit Scenario

7. Comprehensive Scoring

Key Principles

• Integrity is a veto — incompetence can be learned, character cannot be fixed
• Watch actions, not words — what management does, not what they say
• Crisis reveals truth — anyone is a good CEO in good times; tough times reveal real skill
• Capital allocation is the final exam — making money is easy, deploying it well is hard
• Never fall in love with management — stay objective, even admirable people make big mistakes

Investment Portfolio Management

Portfolio-level investment thesis tracking, drift analysis, and structured portfolio review. These systems are modeled after the disciplined buy-and-hold processes used by Buffett, Li Lu, and Duan Yongping — methods that transform portfolio management from reactive guessing into systematic oversight.

When to Use

• Managing multi-position investment portfolio with thesis-backed rationale
• Tracking investment thesis evolution quarter-over-quarter
• Detecting thesis drift — separating fact changes from price changes and wording changes
• Running structured portfolio review and rebalancing
• Stress-testing portfolio against concentration, correlation, and macro scenarios

When NOT to Use

• Intraday trading or short-term momentum decisions
• When you lack structured thesis documentation (baseline required for drift detection)
• For personal financial planning unrelated to portfolio positions

Investment Thesis Tracker

The thesis tracker is a buy-and-hold discipline system that enforces documented reasoning before entry and systematic re-validation through quarterly check-ins.

Design principle: Most investors stop at research -> buy -> pray. Missing post-entry tracking causes reluctance to sell, panic-selling on drawdowns, and forgetting why you bought. The system answers one question at every check: Would you still buy this today if you did not own it?

Two modes:

Mode A — Build Thesis:

1. Collect current price, valuation (PE/PB/dividend yield), latest financial data via WebSearch
2. Validate valuation using tools/financial_rigor.py verify-valuation
3. Core thesis — answers these 5 questions in <=200 characters:
   • What is the business and how does it make money?
   • What is the moat and is it widening or stable?
   • Why is management trustworthy?
   • What discount to intrinsic value offers the margin of safety?
   • Why is downside risk controllable if wrong?
4. Decompose into 3-7 testable assumptions, each with verification method and frequency
5. Define red-line conditions (any triggers immediate re-evaluation): management integrity failure, core revenue decline 2+ quarters, moat breached, regulatory regime change, insider dumping
6. Record valuation anchor (buy price, PE, market cap, intrinsic value, margin of safety)
7. Save to reports/{company}-thesis.md

Mode B — Track Thesis:

1. Load existing thesis file
2. Gather latest data via WebSearch (quarterly results, major events, current price, insider trades)
3. Check each assumption against latest evidence — mark Green (valid), Yellow (weakening), Red (damaged), Black (broken)
4. Check red-line list — any triggered red-line gets flagged in report with action recommendation
5. Update valuation anchor
6. Compute thesis health score: 10 - (black_countx3) - (red_countx2) - (yellow_countx1) - (redline_triggersx5), min 1 max 10
7. Append check record to thesis file tracking table

Key principles for thesis tracking:

• Write sell conditions before buying — decisions made when calm are better than those made in panic
• Theses must be verifiable — "great company" is not a thesis, "ROE > 25% and trending up" is
• When a red-line triggers, act — "let us wait and see" is how large losses start
• Thesis broken != price down — a 30% price drop does not automatically mean sell; a broken thesis does
• Be honest about mistakes — admit when the thesis was wrong, do not hold out of ego

Thesis Drift Detection

Separates genuine evidence changes from price noise and stylistic rewording by comparing two thesis snapshots across five fixed dimensions.

Design principle: The hardest thing about holding long-term positions is distinguishing:

• Fact changes — revenue, margins, competitive landscape, management behavior, capital allocation verifiably changed
• Price changes — market sentiment or valuation multiple shifted, but the business itself did not change
• Wording changes — two reports express things differently but underlying evidence and judgment are the same

Three modes:

Mode A — Compare specified reports:

1. Read both reports; extract date, company, core thesis, assumptions, red-lines, valuation anchor, management quality assessment, competition moat assessment, current action
2. Normalize evidence across both reports into a single comparison table
3. Validate all numerical changes using tools/financial_rigor.py — no LLM mental math
4. Judge each of 5 dimensions as Improved / Unchanged / Weakened:
   • Valuation anchor: intrinsic value, PE/PB/FCF Yield, margin of safety, target price range
   • Core assumptions list: revenue growth, margins, cash flow, users/orders/capacity
   • Red-line list: integrity, regulation, business decline, competition breakthrough, management anomaly
   • Management quality: integrity, capital allocation, buybacks/dividends, execution, shareholder friendliness
   • Competitive moat: market share, pricing power, network effects, cost advantage, substitution threat
5. Every non-Unchanged conclusion must cite specific new evidence (earnings line items, regulatory filings, news events, price vs fundamentals distinction)
6. If no evidence explains the change, judge Unchanged or Cannot Determine

Mode B — Auto snapshot comparison: Find old and new thesis snapshots in reports/{company}-thesis*.md; verify same company, different dates; execute Mode A.

Mode C — Missing baseline handling: State explicitly that drift detection cannot run without a historical baseline. Guide user to first build a thesis via the Thesis Tracker. Do not fabricate an old thesis from memory or market impression.

Key principles for drift detection:

• Evidence over wording — paraphrasing is not drift; only fact changes count
• Fundamentals over price — price changes only affect the valuation anchor, not business quality
• Red-lines have priority over cheap valuation — a triggered red-line is not neutralized by a low PE
• Every conclusion must trace to specific evidence

Portfolio Review

A structured 7-step portfolio review process that treats portfolio management as a separate discipline from stock-picking.

Design principle: Researching companies is only half of investing. The other half is portfolio-level decisions: position sizing, funding source (new money vs swap), correlation with existing holdings, opportunity cost — every dollar should go where it earns the most.

Seven steps:

Step 1: Parse positions — Normalize input holdings into standard table (ticker, quantity, cost basis, current price, market value, weight, P&L). Support both proportional and unit formats. Load saved portfolio if available (reports/portfolio-latest.md).

Step 2: Get current data — Use parallel sub-agents via WebSearch for each position: current price and valuation (PE, PB, dividend yield), latest quarter financial changes, major recent events, analyst consensus. Validate with tools/financial_rigor.py verify-valuation.

Step 3: Single-position health check — For each position answer three questions:

1. Would you still buy at the current price if you did not own it?
2. Could you hold for 5 years without trading?
3. Is the buy thesis still intact?

Step 4: Portfolio-level analysis:

• Concentration: largest position (<40%), top 3 (50-80%), total holdings (5-15), cash (10-30%)
• Correlation: identify hidden risk resonance — same industry, same country/currency, same macro exposure, supply chain adjacency
• Opportunity cost: rank all positions by expected annual return x certainty. The lowest-ranked should beat cash (risk-free rate ~4%). If not, sell and hold cash
• Stress test: global recession, US-China escalation, interest rate spike, tech bubble burst — directional + rough magnitude per position

Step 5: Optimization suggestions:

• Specific rebalance actions (add/reduce/clear/hold/new) with current vs suggested weight and rationale
• Cash management recommendation

Step 6: Output report with executive summary covering: portfolio health rating (Outstanding / Good / Needs Attention / Severe), single most important action to take, biggest current risk

Step 7: Save portfolio file to reports/portfolio-latest.md with holdings table, review date, rebalance log, next review reminder

Key principles for portfolio review:

• Every dollar has an opportunity cost — holding a mediocre stock costs you the chance to own a great one
• Concentration is not risk; ignorance is — holding 3 deeply understood positions is safer than 30 you barely know
• Cash is a position — when no good opportunities exist, holding cash is not shameful
• Portfolio-level > individual stock level — a good stock in the wrong position size still hurts you
• Review quarterly, do not trade daily

Process

1. Prepare — Gather requirements, verify prerequisites, set up environment
2. Execute — Run wolf finance workflow with configured parameters
3. Verify — Validate output meets requirements, document results

Verification

• All steps executed successfully
• Results validated against acceptance criteria
• Error handling tested with edge cases
• Documentation updated with findings
• Financial data cross-validated from 2+ independent sources
• All calculations verified with financial_rigor.py tooling
• Research bias grade assigned (A/B/C)