Apply technical review fixes to sample code

- fetch_prices.py: keep full precision and stop when a ticker fails
  to download instead of overwriting prices.csv
- portfolio.py: reject gaps instead of dropna(), validate weights,
  include starting value in drawdown peaks, use the standard Sharpe
  ratio, and return NaN for zero volatility
- prices.csv: full-precision snapshot downloaded in October 2026
- README.md: current tutorial title and link

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

Author: martin-martin

python-stock-portfolio-analysis/README.md

@@ -1,9 +1,9 @@
-# Python for Finance: Analyze a Stock Portfolio With pandas
+# Python for Stock Analysis: Build a Portfolio Analyzer
 
-Companion materials for the Real Python tutorial.
+This folder provides the code examples for the Real Python tutorial [Python for Stock Analysis: Build a Portfolio Analyzer](https://realpython.com/python-stock-portfolio-analysis/).
 
 - `fetch_prices.py`: Downloads adjusted daily closing prices with yfinance and saves them to `prices.csv`
-- `prices.csv`: Snapshot of the prices used in the tutorial (2021-01-04 to 2025-12-31)
+- `prices.csv`: Snapshot of the prices used in the tutorial (2021-01-04 to 2025-12-31, downloaded in October 2026)
 - `portfolio.py`: The finished portfolio analyzer
 
-Run `python portfolio.py` to print the summary table and save `growth.png`.
+Install the dependencies with `python -m pip install -r requirements.txt`, then run `python portfolio.py` to print the summary table and save `growth.png`.

python-stock-portfolio-analysis/fetch_prices.py

@@ -1,10 +1,15 @@
+import sys
+
 import yfinance as yf
 
 TICKERS = ["AAPL", "JPM", "XOM", "JNJ", "SPY"]
 
 prices = yf.download(
     TICKERS, start="2021-01-01", end="2026-01-01", progress=False
 )["Close"]
-prices = prices[TICKERS].round(2)
+prices = prices.reindex(columns=TICKERS)
+failed = prices.columns[prices.isna().all()].tolist()
+if failed:
+    sys.exit(f"Download failed for {', '.join(failed)}")
 prices.to_csv("prices.csv")
 print(prices.tail())

python-stock-portfolio-analysis/portfolio.py

@@ -11,12 +11,19 @@ def load_prices(path="prices.csv"):
 
 
 def calculate_returns(prices):
-    return prices.pct_change().dropna()
+    if prices.isna().any().any():
+        raise ValueError("Found missing prices. Choose dates without gaps.")
+    if len(prices) < 3:
+        raise ValueError("You need at least three days of prices.")
+    return prices.pct_change().iloc[1:]
 
 
 def build_portfolio(returns, weights):
-    holdings = returns[list(weights)]
-    return holdings @ pd.Series(weights)
+    weights = pd.Series(weights)
+    if not (weights >= 0).all() or abs(weights.sum() - 1) > 1e-9:
+        raise ValueError("Weights must be non-negative and add up to 1.")
+    holdings = returns[weights.index]
+    return holdings @ weights
 
 
 def growth_of(returns, initial=10_000):
@@ -32,14 +39,22 @@ def annualized_volatility(returns):
     return returns.std() * TRADING_DAYS**0.5
 
 
-def max_drawdown(returns):
+def drawdowns(returns):
     growth = (1 + returns).cumprod()
-    return (growth / growth.cummax() - 1).min()
+    peak = growth.cummax().clip(lower=1)
+    return growth / peak - 1
+
+
+def max_drawdown(returns):
+    return drawdowns(returns).min()
 
 
 def sharpe_ratio(returns, risk_free_rate=0.0):
-    excess = annualized_return(returns) - risk_free_rate
-    return excess / annualized_volatility(returns)
+    daily_risk_free = (1 + risk_free_rate) ** (1 / TRADING_DAYS) - 1
+    excess = returns - daily_risk_free
+    if excess.std() == 0:
+        return float("nan")
+    return excess.mean() / excess.std() * TRADING_DAYS**0.5
 
 
 def summarize(returns, risk_free_rate=0.0):
@@ -55,13 +70,11 @@ def summarize(returns, risk_free_rate=0.0):
 
 
 def plot_growth(returns, filename="growth.png"):
-    growth = growth_of(returns)
-    drawdown = growth / growth.cummax() - 1
     fig, (top, bottom) = plt.subplots(
         2, 1, figsize=(10, 7), sharex=True, height_ratios=[3, 1]
     )
-    growth.plot(ax=top, title="Growth of $10,000")
-    drawdown.plot(ax=bottom, title="Drawdown", legend=False)
+    growth_of(returns).plot(ax=top, title="Growth of $10,000")
+    drawdowns(returns).plot(ax=bottom, title="Drawdown", legend=False)
     top.yaxis.set_major_formatter("${x:,.0f}")
     bottom.yaxis.set_major_formatter("{x:.0%}")
     fig.tight_layout()