chore: lock polars version to 1.37.1 and update readme docs

Author: quantbai

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2026 Phantom Management
+Copyright (c) 2026 quantbai
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -2,11 +2,11 @@
 
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 [![Python 3.11+](https://img.shields.io/badge/python-3.11+-blue.svg)](https://www.python.org/downloads/)
-[![Polars](https://img.shields.io/badge/Polars-1.35+-green.svg)](https://pola.rs/)
+[![Polars](https://img.shields.io/badge/Polars-1.37.1-green.svg)](https://pola.rs/)
 
 High-performance multi-factor quantitative framework built on Polars.
 
-Named after **ELVES** (Emission of Light and Very Low Frequency perturbations due to Electromagnetic Pulse Sources), the atmospheric lightning phenomenon that occurs at extreme speed.
+Named after **ELVES**, the atmospheric lightning phenomenon that occurs at extreme speed.
 
 ## Features
 
@@ -24,16 +24,27 @@ pip install elvers
 ## Quick Start
 
 ```python
-from elvers import load, col, collect_all
-from elvers import ts_mean, ts_rank, rank, zscore
+from elvers import load
+from elvers import ts_rank, rank, zscore, group_neutralize
 
-load("data.csv")
+# 1. Load data into a managed Panel
+panel = load("crypto_data.csv")
 
-close = col("close")
+# 2. Access factors via panel indexing
+close = panel["close"]
+volume = panel["volume"]
+
+# 3. Define factors using professional operators
 momentum = ts_rank(close, 20)
 alpha = zscore(rank(momentum))
 
-result = collect_all(alpha)
+# 4. Advanced: Neutralization
+# Neutralize alpha against volume groups
+final_alpha = group_neutralize(alpha, panel["group"])
+
+# 5. Compute results
+result = panel.collect(final_alpha)
+print(result)
 ```
 
 ## License
@@ -42,6 +53,6 @@ MIT License - see [LICENSE](LICENSE) for details.
 
 ## Author
 
-Phantom Management
+quantbai
 
 

elvers/__init__.py

@@ -4,72 +4,58 @@
 Ultra-minimal, maximum performance WQ-style factor expressions.
 Built on Polars for lightning-fast factor research.
 
-Author: Phantom Management
+Author: quantbai
 """
 
-__version__ = "0.1.0"
-__author__ = "Phantom Management"
+__version__ = "0.1.1"
+__author__ = "quantbai"
 
-from .core import (
-    Factor,
-    load,
-    lf,
-    col,
-    collect,
-    collect_all,
-    to_csv,
-    to_pandas,
-)
+from .core import Factor
+from .io import load, Panel, PanelInfo
 
 from .ops import (
-    add, sub, mul, div, reverse,
+    add, subtract, multiply, divide, reverse,
 
     ts_delay, ts_delta, ts_mean, ts_sum, ts_std_dev,
     ts_min, ts_max, ts_median, ts_rank, ts_skewness, ts_kurtosis,
     ts_zscore, ts_corr, ts_covariance, ts_product,
     ts_arg_max, ts_arg_min, ts_decay_linear, ts_av_diff, ts_scale,
-    ts_var, ts_quantile_val, ts_cv, ts_autocorr,
-    ts_return, ts_log_return, ts_pct_change,
-    ts_count_nulls, ts_step,
-    ts_ewm_mean, ts_ewm_std, ts_ewm_var,
+    ts_quantile, ts_cv, ts_autocorr,
+    ts_count_nans,
 
-    rank, zscore, mean, median, sum_, std,
+    rank, zscore, mean, median,
     scale, normalize, quantile, spread, signal,
 
-    log, ln, sqrt, abs_, sign, power, signed_power,
+    log, ln, sqrt, sign, power, signed_power,
     inverse, s_log_1p, maximum, minimum, where,
-    clip, nan_to_value, fill_null,
 
-    vector_neut, regression_neut, demean, market_neut,
-    group_demean, group_rank, group_zscore, group_scale,
-    group_normalize, winsorize, mad_winsorize,
+    vector_neut, regression_neut,
+    group_neutralize, group_rank, group_zscore, group_scale,
+    group_normalize,
 )
 
 __all__ = [
     "__version__",
     "__author__",
     "Factor",
-    "load", "lf", "col", "collect", "collect_all", "to_csv", "to_pandas",
+    "load", "Panel", "PanelInfo",
 
-    "add", "sub", "mul", "div", "reverse",
+    "add", "subtract", "multiply", "divide", "reverse",
 
     "ts_delay", "ts_delta", "ts_mean", "ts_sum", "ts_std_dev",
     "ts_min", "ts_max", "ts_median", "ts_rank", "ts_skewness", "ts_kurtosis",
     "ts_zscore", "ts_corr", "ts_covariance", "ts_product",
     "ts_arg_max", "ts_arg_min", "ts_decay_linear", "ts_av_diff", "ts_scale",
-    "ts_var", "ts_quantile_val", "ts_cv", "ts_autocorr",
-    "ts_return", "ts_log_return", "ts_pct_change",
-    "ts_count_nulls", "ts_step",
-    "ts_ewm_mean", "ts_ewm_std", "ts_ewm_var",
+    "ts_quantile", "ts_cv", "ts_autocorr",
+    "ts_count_nans",
 
-    "rank", "zscore", "mean", "median", "sum_", "std",
+    "rank", "zscore", "mean", "median",
     "scale", "normalize", "quantile", "spread", "signal",
 
-    "log", "ln", "sqrt", "abs_", "sign", "power", "signed_power",
+    "log", "ln", "sqrt", "sign", "power", "signed_power",
     "inverse", "s_log_1p", "maximum", "minimum", "where",
-    "clip", "nan_to_value", "fill_null",
 
-    "vector_neut", "regression_neut", "demean", "market_neut",
-    "group_demean", "group_rank", "group_zscore", "group_scale",
-    "group_normalize", "winsorize", "mad_winsorize",
+    "vector_neut", "regression_neut",
+    "group_neutralize", "group_rank", "group_zscore", "group_scale",
+    "group_normalize",
 ]

elvers/core/__init__.py

@@ -1,15 +1,7 @@
 """Core module exports."""
 
 from .factor import Factor
-from .context import load, lf, col, collect, collect_all, to_csv, to_pandas
 
 __all__ = [
     "Factor",
-    "load",
-    "lf",
-    "col",
-    "collect",
-    "collect_all",
-    "to_csv",
-    "to_pandas",
 ]