docs: remove external platform references, self-contained spec

Replace all third-party comparison notes with elvers' own design rationale.
Rank convention now includes concrete example output for 5 instruments.
ts_product documents why sign-magnitude is necessary without referencing
other implementations.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: quantbai

CLAUDE.md

@@ -36,7 +36,7 @@ elvers/
 tests/
   conftest.py              Test fixtures (make_ts, make_factor)
   test_*.py                One test file per operator module
-OPERATORS.md               Operator specification: numerical conventions, per-operator behavior, WQ comparison
+OPERATORS.md               Operator specification: numerical conventions, per-operator behavior, design rationale
 CLAUDE.md                  Development standards (this file)
 ```
 

OPERATORS.md

@@ -1,8 +1,7 @@
 # Elvers Operator Specification
 
 Complete reference for all operators. Each entry documents signature, mathematical
-definition, null/edge-case behavior, and design rationale where elvers diverges
-from common platforms.
+definition, null/edge-case behavior, and design rationale.
 
 ---
 
@@ -40,10 +39,6 @@ operation:
 | add / subtract | 0 | null treated as 0 |
 | multiply | 1 | null treated as 1 |
 
-> **vs WQ:** WorldQuant uses `NaN` terminology and `filter=false` default.
-> elvers is identical in behavior but uses `null` throughout and spells
-> the parameter as `filter=True` (Python convention).
-
 ### Division by Zero
 
 All divisions are guarded at `abs(denominator) < 1e-10`, returning null.
@@ -74,10 +69,10 @@ variance or standard deviation documents which ddof it uses.
 - Null handling: nulls are excluded from ranking and remain null in output.
 - Formula: `average_rank / count_of_non_null`
 
-> **vs WQ:** WorldQuant `rank(x)` outputs range **[0, 1]** inclusive of zero
-> (`Rank(x) = (0.5, 0.25, 0.75, 1, 0)` in their docs). elvers intentionally
-> excludes zero because a rank of 0 is ambiguous -- it could mean "missing" or
-> "lowest". Range (0, 1] ensures every ranked value is unambiguously present.
+Design rationale: a rank of 0 is ambiguous -- it could mean "missing" or
+"lowest". Range (0, 1] ensures every ranked value is unambiguously present
+and distinguishable from null. For 5 instruments, ranks are
+`(0.2, 0.4, 0.6, 0.8, 1.0)`, not `(0, 0.25, 0.5, 0.75, 1.0)`.
 
 **Time-series `ts_rank(x, window)`:**
 - Output range: (0, 1] (same convention).
@@ -103,9 +98,9 @@ from contaminating signals.
 3. If any value in the window is zero, the product is 0.
 4. If any value is null, it propagates (min_samples applies).
 
-> **vs WQ:** WorldQuant does not document negative-value handling for
-> `ts_product`. A naive `exp(sum(log(x)))` implementation silently returns
-> NaN/null for negative inputs. elvers handles this correctly.
+A naive `exp(sum(log(x)))` implementation silently fails for negative inputs
+because `log(x)` is undefined for `x < 0`. The sign-magnitude approach avoids
+this by operating on absolute values and tracking the sign separately.
 
 ---
 
@@ -441,9 +436,8 @@ Cross-sectional rank normalized to (0, 1].
 - Null: excluded from ranking, remain null
 - Formula: `average_rank / count_of_non_null`
 
-> **vs WQ:** `Rank(x) = (0.5, 0.25, 0.75, 1, 0)` in WQ for input `(4,3,6,10,2)`.
-> elvers returns `(0.6, 0.4, 0.8, 1.0, 0.2)` -- no zero, strictly positive.
-> This avoids ambiguity between "rank zero" and "missing data".
+For input `(4, 3, 6, 10, 2)` across 5 instruments, elvers returns
+`(0.6, 0.4, 0.8, 1.0, 0.2)` -- strictly positive, no zero.
 
 ### `zscore(x)`
 

README.md

@@ -27,7 +27,7 @@ Polars-native factor computation engine for quantitative research. All operators
 | Std/Variance | ddof=0 (population). Corr/Cov use ddof=1 (sample). |
 | Rolling warmup | All `ts_*` operators: first `window-1` values are null. |
 
-Full conventions, WQ comparison notes, and per-operator specifications:
+Full conventions and per-operator specifications:
 **[OPERATORS.md](OPERATORS.md)**
 
 ## Installation