Uzi Skill Stock Analyzer

# skills/scripts/run_real_test.py is the main entry pointimport subprocess
result = subprocess.run(    ["python", "skills/deep-analysis/scripts/run_real_test.py",     "--ticker", "002273",     "--market", "A",      # A | HK | US     "--output", "./reports/"],    capture_output=True, text=True)print(result.stdout)

# Each dimension has its own fetcher with multi-source fallbackfrom skills.deep_analysis.scripts.lib.stock_features import StockFeatureEngine
engine = StockFeatureEngine(ticker="002273", market="A")features = engine.fetch_all()   # returns dict of 108 normalized features
# Key feature groups:print(features["valuation"])    # PE, PB, PS, EV/EBITDA, PCFprint(features["growth"])       # Revenue/profit YoY, QoQ, 3Y CAGRprint(features["quality"])      # ROE, ROIC, gross margin, FCF yieldprint(features["technical"])    # MA, MACD, RSI, volume ratio, ATRprint(features["sentiment"])    # North-bound flow, margin balance, short interestprint(features["governance"])   # Insider ownership, pledge ratio, audit opinion

from skills.deep_analysis.scripts.lib.investor_criteria import INVESTOR_REGISTRYfrom skills.deep_analysis.scripts.lib.investor_evaluator import InvestorEvaluator
evaluator = InvestorEvaluator(features=features)
# Score all 51 investorsresults = evaluator.evaluate_all()
for investor in results:    print(f"{investor['name']:12s} | Score: {investor['score']:3d} | "          f"Stance: {investor['stance']:8s} | "          f"Triggered: {investor['triggered_rules']}")

# Evaluate one investor persona against loaded featuresresult = evaluator.evaluate_single("巴菲特", features)
# Example output structure:# {#   "name": "巴菲特",#   "score": 62,#   "stance": "neutral",#   "summary": "观望：护城河 27/40 可见；但 ROE 5 年最低 6.7%，达标率仅 0/5",#   "triggered_rules": [#       {"rule": "asset_debt_ratio < 0.4", "met": True,  "label": "资产负债率 30% 保守"},#       {"rule": "roe_5y_min > 0.15",      "met": False, "label": "ROE 5 年最低 6.7%"},#   ]# }

from skills.deep_analysis.scripts.lib.fin_models import DCFModel
dcf = DCFModel(    ticker="002273",    market="A",    # A-share defaults (override as needed):    risk_free_rate=0.025,      # rf = 2.5%    equity_risk_premium=0.06,  # ERP = 6%    tax_rate=0.25,             # China corporate tax    terminal_growth=0.025,     # Gordon Growth g = 2.5%)
valuation = dcf.run()
print(f"WACC:            {valuation['wacc']:.2%}")print(f"Intrinsic Value: ¥{valuation['intrinsic_value']:.2f}")print(f"Current Price:   ¥{valuation['current_price']:.2f}")print(f"Safety Margin:   {valuation['safety_margin']:.1%}")print(f"Sensitivity:\n{valuation['sensitivity_table']}")  # 5×5 DataFrame

from skills.deep_analysis.scripts.lib.fin_models import CompsModel
comps = CompsModel(ticker="002273", market="A")result = comps.run()
# result["peer_table"] → DataFrame with PE/PB/EV-EBITDA for each peer# result["percentiles"] → where subject sits vs peers (0–100)# result["implied_targets"] → target prices from each multipleprint(result["peer_table"].to_string())print(f"PE Percentile: {result['percentiles']['pe']:.0f}th")print(f"Implied target (PE-based): ¥{result['implied_targets']['pe']:.2f}")

from skills.deep_analysis.scripts.lib.research_workflow import ICMemo
memo = ICMemo(ticker="002273", market="A", features=features)output = memo.generate()
# Sections: executive_summary, investment_thesis, risk_factors,#           scenario_analysis, valuation_bridge, catalysts,#           portfolio_fit, recommendationprint(output["scenario_analysis"])# Bull: ¥26.95 (p=30%)  Base: ¥20.73 (p=50%)  Bear: ¥14.51 (p=20%)

# Example: fetch_realtime_price.py internal logic (simplified)PRICE_SOURCES = [    ("eastmoney_push2", fetch_eastmoney),   # Primary    ("xueqiu",          fetch_xueqiu),      # Fallback 1    ("tencent",         fetch_tencent),     # Fallback 2    ("sina",            fetch_sina),        # Fallback 3    ("baidu",           fetch_baidu),       # Fallback 4]
for source_name, fetch_fn in PRICE_SOURCES:    try:        data = fetch_fn(ticker)        if data and data.get("price"):            return data    except Exception as e:        log.warning(f"{source_name} failed: {e}, trying next...")
raise DataFetchError(f"All price sources failed for {ticker}")

# From investor_criteria.py — each investor is a dataclass with:# - group: A–G# - style: value / growth / macro / technical / china_value / youzi / quant# - rules: list of Rule objects (field, operator, threshold, weight, label)# - skip_markets: markets this investor ignores (e.g. 赵老哥 skips US)# - override_conditions: context-based manual overrides
INVESTOR_GROUPS = {    "A": ["巴菲特", "格雷厄姆", "芒格", "费雪", "邓普顿", "卡拉曼"],          # Classic Value    "B": ["林奇", "欧奈尔", "蒂尔", "木头姐"],                                # Growth    "C": ["索罗斯", "达里奥", "霍华德马克斯", "德鲁肯米勒", "罗伯逊"],        # Macro Hedge    "D": ["利弗莫尔", "米内尔维尼", "达瓦斯", "江恩"],                        # Technical    "E": ["段永平", "张坤", "朱少醒", "谢治宇", "冯柳", "邓晓峰"],            # China Value    "F": ["章盟主", "赵老哥", "炒股养家", "佛山无影脚", "北京炒家", "鑫多多",           # ... 17 more 游资 personas],                                       # A-share 游资    "G": ["西蒙斯", "索普", "大卫·肖"],                                       # Quant Systems}
# Agent override examples (from SKILL.md):# - Buffett analyzing Apple → agent knows BRK #1 holding → force bullish override# - 赵老哥 analyzing US stock → agent skips (游资 don't trade US)# - 木头姐 analyzing 白酒 → agent applies "not disruptive innovation" → bearish override

# A sample of Buffett's rules from investor_criteria.py:BUFFETT_RULES = [    Rule(field="roe_5y_avg",        op=">=", threshold=0.15,  weight=10, label="ROE 5年均值≥15%"),    Rule(field="roe_5y_min",        op=">=", threshold=0.15,  weight=10, label="ROE 5年最低≥15%"),    Rule(field="debt_to_equity",    op="<=", threshold=0.5,   weight=8,  label="负债权益比≤0.5"),    Rule(field="gross_margin",      op=">=", threshold=0.4,   weight=8,  label="毛利率≥40%"),    Rule(field="fcf_yield",         op=">=", threshold=0.05,  weight=7,  label="自由现金流收益≥5%"),    Rule(field="moat_score",        op=">=", threshold=30,    weight=12, label="护城河评分≥30/40"),    Rule(field="pe_ratio",          op="<=", threshold=25,    weight=6,  label="PE≤25x"),    Rule(field="insider_ownership", op=">=", threshold=0.1,   weight=5,  label="内部人持股≥10%"),    # ... more rules]

# Correct ticker formats:# HK:  "00700.HK"  or  "0700.HK"   (with .HK suffix)# US:  "AAPL"  or  "TSLA"          (plain uppercase)# A:   "002273"  or  "600519"       (6-digit code)# A:   "贵州茅台"  or  "水晶光电"    (Chinese name also works)