我用 Python（使用 yfinance）构建了一个“Peter Lynch”股票分析器

Source: Dev.to

彼得·林奇，这位传奇的 Fidelity Magellan 基金经理，提出了一套他称为 “2‑Minute Drill.” 的系统化股票分析方法。他的体系将股票划分为六类，并为每一类应用特定的指标。

虽然林奇当年是用铅笔和纸手工完成这些工作，但我们可以使用 Python 和 yfinance 库来实现全自动化。

在本文中，我将展示如何构建 “The Lynchpad”——一个 Python 脚本，能够自动获取实时市场数据，并对你的投资组合运行林奇的决策树。

什么是彼得·林奇的决策树？

林奇的方法将股票分为六类：

林奇最重要的自动检查是 “库存警告”——如果库存增长速度快于销售，这表明需求在放缓，是一个红色警示。

用例：“Lynchpad”

此脚本演示了使 Python 成为投资分析理想工具的三大关键能力：

1. 外部库 – 导入 yfinance 自动获取实时股票数据。
2. 逻辑与自动化 – 自动应用 Lynch 复杂的“库存 vs. 销售”逻辑。
3. 人机交互 – 你在字典中定义股票代码和“故事”，代码负责完成计算。

Lynchpad 实时分析 FIG、DUOL、Z 和 KO 的表现

完整代码

将此代码复制到您的 Python 环境中。它会从 Yahoo Finance 获取实时数据，并自动应用林奇的决策树逻辑：

# ==========================================
#  THE LYNCHPAD: AUTOMATED STOCK CHECKLIST
# ==========================================
# 1. Install dependency if needed: pip install yfinance
import yfinance as yf
import pandas as pd

# --- USER INPUT SECTION ---
# Define your portfolio and the "Story" (Lynch's 2‑minute drill)
portfolio = {
    "FIG": {"category": "Fast Grower", "story": "Monopoly on design, AI integration coming."},
    "DUOL": {"category": "Fast Grower", "story": "Strong user retention, expanding to Math/Music."},
    "Z": {"category": "Turnaround", "story": "Housing market recovery play."},
    "KO": {"category": "Slow Grower", "story": "Defensive dividend play."},
}

def analyze_lynch_metrics(ticker_symbol, category):
    """Fetches data and checks specific Lynch warnings based on category."""
    stock = yf.Ticker(ticker_symbol)
    info = stock.info

    # 1. Basic Data
    price = info.get("currentPrice", 0)
    pe = info.get("trailingPE", 0)
    peg = info.get("pegRatio", 0)
    div_yield = (info.get("dividendYield", 0) or 0) * 100

    # 2. The "Cyclical/Inventory" Check (most important Lynch automated check)
    # Warning if Inventory is growing faster than Sales
    inventory_warning = "✅ OK"
    try:
        bs = stock.quarterly_balance_sheet
        fin = stock.quarterly_financials

        # Growth calculations (Current vs Previous Quarter)
        inv_curr, inv_prev = bs.loc["Inventory"].iloc[0], bs.loc["Inventory"].iloc[1]
        sales_curr, sales_prev = fin.loc["Total Revenue"].iloc[0], fin.loc["Total Revenue"].iloc[1]

        inv_growth = (inv_curr - inv_prev) / inv_prev
        sales_growth = (sales_curr - sales_prev) / sales_prev

        if inv_growth > sales_growth:
            inventory_warning = (
                f"⚠️ WARNING: Inv up {inv_growth:.1%} > Sales up {sales_growth:.1%}"
            )
    except Exception:
        inventory_warning = "N/A (No Inventory Data)"

    # 3. Category‑Specific Logic
    notes = []

    if category == "Fast Grower":
        if peg > 2.0:
            notes.append("❌ PEG is high (>2.0)")
        if pe > 40:
            notes.append("⚠️ P/E is very high")

    elif category == "Slow Grower":
        if div_yield < 2:
            notes.append("⚠️ Low dividend yield")
        else:
            notes.append("✅ Healthy dividend")

    # Add more category logic here as needed …

    return {
        "Ticker": ticker_symbol,
        "Price": f"${price}",
        "Category": category,
        "P/E": f"{pe:.1f}" if pe else "-",
        "PEG": peg,
        "Inv Warning": inventory_warning,
        "Auto‑Notes": "; ".join(notes),
    }

# --- EXECUTION ---
# Example usage (prints a summary for each ticker)
for ticker, data in portfolio.items():
    result = analyze_lynch_metrics(ticker, data["category"])
    print(result)

# The following fragment was present in the original article but appears incomplete.
# It is retained here unchanged for reference.

print(f"{'TICKER': 30:
        warnings.append("⚠️ High P/E")
    if price > 150:
        warnings.append("⚠️ Expensive")

    # Auto‑generated notes
    notes = []
    if eps and eps > 5:
        notes.append(f"✅ Strong EPS: {eps:.2f}")
    if pe and pe  20:
    notes.append(f"✅ Strong margins: {profit_margin:.1f}%")

超越彼得·林奇

虽然此示例遵循林奇的方法论，但实时 Python 环境适用于任何策略：

• 价值投资 – 实现 DCF 模型以计算内在价值。
• 动量交易 – 计算移动平均线、价格趋势。
• 股息投资 – 分析派息率和股息增长。
• 技术分析 – 计算 RSI、MACD、布林带等。

本文使用 CoilPad 编写，这是一个原生 macOS Python Playground。如果你喜欢这种自动化的股票分析方法，快来试试吧！