Minor webapp update

README.md

@@ -105,11 +105,11 @@ This project constructs and backtests a **sector-neutralized momentum factor** a
 > 5. **Decompose** portfolio risk into systematic vs. idiosyncratic components via PCA (eigendecomposition + random matrix theory)
 > 6. **Stress test** (notebook 06) by generating synthetic markets and re-running the backtest across alternative histories
 
-The project is structured in three parts, all complete:
+The project is structured in three parts:
 
-* **Part I — Data and Factor Analysis** (notebooks 01–03) — *complete*
-* **Part II — Backtest and Risk Decomposition** (notebooks 04–05) — *complete*
-* **Part III — Synthetic Markets and Stress Testing** (notebook 06) — *complete*
+* **Part I — Data and Factor Analysis** (notebooks 01–03)
+* **Part II — Backtest and Risk Decomposition** (notebooks 04–05)
+* **Part III — Synthetic Markets and Stress Testing** (notebook 06)
 
 ---
 
@@ -181,7 +181,7 @@ This part builds the data matrix $\mathbf{R}$, diagnoses individual factor vecto
 
 ### 1. Data Overview and Market Statistics
 
-We use a cached snapshot of S&P 500 constituents and download adjusted close prices. Because that snapshot is still based on a modern S&P 500 membership list, it introduces **survivorship bias** — names that went bankrupt or were delisted between 2005 and today won't appear. In linear-algebra terms: the columns of $\mathbf{R}$ are a non-random subset of all stocks that existed; the columns we *don't* see are exactly the ones that went to zero, biasing returns upward. Notebook 04 includes a sensitivity analysis for this.
+We use a cached snapshot of S&P 500 constituents and download adjusted close prices. Because that snapshot is still based on a modern S&P 500 membership list, it introduces **survivorship bias** — names that went bankrupt or were delisted between 2005 and today won't appear. In linear-algebra terms: the columns of $\mathbf{R}$ are a non-random subset of all stocks that existed; the columns we *don't* see are exactly the ones that went to zero or got delisted, biasing returns upward. Notebook 04 includes a sensitivity analysis for this.
 
 Key findings:
 * **Universe breadth** rises from ~385 to ~501 stocks over the sample — but the column set is fixed to *today's* constituents, so this counts how many of today's survivors had price data in month $t$. The matrix isn't truly "getting wider"; its survivor-only columns fill in over time.
@@ -355,7 +355,7 @@ frd.example.com {
 }
 ```
 
-The data stays **mounted read-only** (`./data:/app/data`), mirroring the `.gitignore`. The app validates the required CSV artifacts at startup and tells you to run notebooks `01 -> 06` if anything is missing or malformed.
+The data stays **mounted read-only** (`./data:/app/data`). The app validates the required CSV artifacts at startup and tells you to run notebooks `01 -> 06` if anything is missing or malformed.
 
 ---
 

frd/research.py

@@ -133,9 +133,25 @@ def form_decile_portfolios(
 
 
 def decile_long_returns(signal_df: pd.DataFrame, ret_df: pd.DataFrame, decile: float = 0.1) -> pd.Series:
-    """Top-decile equal-weight long-only monthly returns."""
-    port = form_decile_portfolios(signal_df, ret_df, decile=decile)
-    return port["long"] if "long" in port else pd.Series(dtype=float)
+    """Top-decile equal-weight long-only monthly returns (signal at t, return at t+1).
+
+    Vectorized for speed — the webapp's live stress-test button calls this once
+    per click. Ranks cross-sectionally per month, takes the top decile, and
+    averages next-month returns. (Semantically equivalent to the per-month loop
+    in ``form_decile_portfolios`` but without computing holdings/turnover.)
+    """
+    ranks = signal_df.rank(axis=1, pct=True)               # NaN stays NaN
+    rvals = ranks.values[:-1]                               # signal at t   (T-1, N)
+    next_ret = ret_df.values[1:]                            # return at t+1 (T-1, N)
+    with np.errstate(invalid="ignore", all="ignore"):
+        thr = np.nanquantile(rvals, 1 - decile, axis=1)     # per-row (1-decile) quantile of valid scores
+    top = (rvals >= thr[:, None]) & ~np.isnan(rvals)
+    valid = (~np.isnan(rvals)).sum(axis=1)
+    contrib = np.where(top, next_ret, np.nan)
+    with np.errstate(invalid="ignore"):
+        long_ret = np.nanmean(contrib, axis=1)
+    long_ret = np.where(valid >= 50, long_ret, np.nan)
+    return pd.Series(long_ret, index=np.arange(1, signal_df.shape[0]), dtype=float)
 
 
 def _is_datetime_like(index: pd.Index) -> bool:

webapp/app.py

@@ -23,7 +23,7 @@ from typing import Any
 import numpy as np
 import pandas as pd
 from fastapi import FastAPI, HTTPException
-from fastapi.responses import HTMLResponse
+from fastapi.responses import HTMLResponse, PlainTextResponse
 from fastapi.staticfiles import StaticFiles
 from sklearn.decomposition import PCA
 
@@ -223,7 +223,7 @@ def _json_safe(o: Any) -> Any:
     return o
 
 
-@app.get("/")
+@app.api_route("/", methods=["GET", "HEAD"])
 def index():
     html = (STATIC_DIR / "index.html").read_text(encoding="utf-8")
     css_v = _file_version(STATIC_DIR / "app.css")
@@ -233,6 +233,12 @@ def index():
     return HTMLResponse(content=html)
 
 
+@app.get("/robots.txt", response_class=PlainTextResponse)
+def robots():
+    # Disallow crawling — this is an interactive demo, not a site to index.
+    return "User-agent: *\nDisallow: /\n"
+
+
 @app.get("/api/health")
 def health():
     s = app.state