py-alpha-lib 0.2.0

Alpha Library: A high-performance rolling window calculation library implemented in Rust with Python bindings. Used for financial data analysis and factor research.

alpha-lib

High-performance quantitative finance algorithm library, implemented in Rust with Python bindings (PyO3).

Provides efficient rolling-window calculations commonly used in factor-based quantitative trading.

Performance

Benchmarked on Alpha 101, 4000 stocks x 261 trading days (1,044,000 data points per factor):

Implementation	Factors	Data Load	Compute	Total	Speedup
pandas	75	31.2s	2,643s	2,675s (44min)	1x
polars_ta	81	0.3s	58s	58s	46x
alpha-lib	101	0.3s	3.6s	3.9s	729x

See COMPARISON.md for per-factor timing and correctness analysis.

Installation

pip install py-alpha-lib

Usage

Context Settings

Control computation behavior via alpha.set_ctx():

• groups — Number of securities in the data array. Each group is processed independently and in parallel. Required for cross-sectional operations like RANK.

• start — Starting index for calculation (default: 0).

• end — Ending index for calculation (default: len(data)). end can be used when you want to calculate only a part of the data. for example, when back test iteratively.

• flags — Bitwise flags:

  • FLAG_SKIP_NAN (1): Skip NaN values in rolling windows.
  • FLAG_STRICTLY_CYCLE (2): Return NaN until window is full (matches pandas rolling() default).
  • Combine with |: flags=FLAG_SKIP_NAN | FLAG_STRICTLY_CYCLE

  import alpha
  import numpy as np
  
  data = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], dtype=np.float64)
  
  # 3-period moving average (partial results during warm-up)
  result = alpha.MA(data, 3)
  # [1.  1.5 2.  3.  4.  5.  6.  7.  8.  9.]
  
  # Strict mode: NaN until window is full
  alpha.set_ctx(flags=alpha.FLAG_STRICTLY_CYCLE)
  result = alpha.MA(data, 3)
  # [nan nan 2.  3.  4.  5.  6.  7.  8.  9.]
  
  # Skip NaN values
  alpha.set_ctx(flags=alpha.FLAG_SKIP_NAN)
  data_nan = np.array([1, 2, np.nan, 4, 5, 6, 7, 8, 9, 10], dtype=np.float64)
  result = alpha.MA(data_nan, 3)
  # [1.  1.5 nan 3.  4.5 5.  6.  7.  8.  9.]
  

Example 1: Plug and Play

import alpha
from alpha.context import ExecContext

# ExecContext auto-infers groups from securityid/tradetime columns
# and calls alpha.set_ctx(groups=...) automatically
data = pl.read_csv("data.csv").sort(["securityid", "tradetime"])
ctx = ExecContext(data)

# Call operators directly on numpy arrays
close = data["close"].to_numpy()
ma20 = alpha.MA(close, 20)
rank = alpha.RANK(close)       # cross-sectional rank (groups auto-configured)
corr = alpha.CORR(close, data["vol"].to_numpy().astype(float), 10)

Data layout: flat 1D array [stock1_day1, stock1_day2, ..., stockN_dayM], sorted by security then time. The groups parameter tells the library where each stock's data begins.

Example 2: Factor Expression Transpiler

Convert factor expressions to Python code, then run:

python -m alpha.lang examples/wq101/alpha101.txt

# 3. Use generated code
from alpha.context import ExecContext
from factors import alpha_001

data = pl.read_csv("data.csv").sort(["securityid", "tradetime"])
ctx = ExecContext(data)  # auto-infers groups
result = alpha_001(ctx)

Factor expression to Python code

You can convert factor expressions to Python code using the lang module. For example:

python -m alpha.lang examples/wq101/alpha101.txt

This will read the factor expressions from examples/wq101/alpha101.txt and generate corresponding Python code using alpha-lib functions.

After generating the code, you may need to adjust the code

• Fix type conversions between float and bool.
• Add context settings if needed.

Benchmarking and Full Examples

GTJA Alpha 191

Implementation of 190/191 factors from the GTJA (国泰君安) Alpha 191 factor set in examples/gtja191/:

Metric	Value
Computable	190 / 191
Compute time	~4.5s (4000 stocks × 261 days)
Avg per factor	24ms

python -m examples.gtja191.al 143     # run specific factor
python -m examples.gtja191.al          # run all factors

WorldQuant Alpha 101

Full implementation of 101 Formulaic Alphas in examples/wq101/:

• al/ — alpha-lib implementation (Rust backend)
• pd_/ — pandas reference (DolphinDB port)
• pl_/ — polars_ta reference

examples/wq101/main.py --with-al 1 2 3 4 # Run specific factors
examples/wq101/main.py --with-al -s 1 -e 102 # Run all factors
examples/wq101/main.py --with-pd --with-al -s 1 -e 15 # Compare with pandas

Benchmark scripts in benchmarks/.

Supported Algorithms

Naming Rules:

• Function starts with CC_ means it is a cross-commodity/cross-security/cross-group operation.
• Function without prefix means it is a rolling window operation.

Name	Description
BACKFILL	Forward-fill NaN values with the last valid observation
BARSLAST	Calculate number of bars since last condition true
BARSSINCE	Calculate number of bars since first condition true
BINS	Discretize the input into n bins, the ctx.groups() is the number of groups
CC_RANK	Calculate rank percentage cross group dimension, the ctx.groups() is the number of groups Same value are averaged
CC_ZSCORE	Calculate cross-sectional Z-Score across groups at each time step
CORR	Time Series Correlation in moving window on self
CORR2	Calculate two series correlation over a moving window
COUNT	Calculate number of periods where condition is true in passed periods window
COUNT_NANS	Count number of NaN values in a rolling window
COV	Calculate Covariance over a moving window
CROSS	For 2 arrays A and B, return true if A[i-1] < B[i-1] and A[i] >= B[i] alias: golden_cross, cross_ge
DMA	Exponential Moving Average current = weight * current + (1 - weight) * previous
EMA	Exponential Moving Average (variant of well-known EMA) weight = 2 / (n + 1)
ENTROPY	Calculate rolling Shannon entropy over a moving window
FRET	Future Return
GROUP_RANK	Calculate rank percentage within each category group at each time step
GROUP_ZSCORE	Calculate Z-Score within each category group at each time step
HHV	Find highest value in a preceding periods window
HHVBARS	The number of periods that have passed since the array reached its periods period high
INTERCEPT	Linear Regression Intercept
KURTOSIS	Calculate rolling sample excess Kurtosis over a moving window
LLV	Find lowest value in a preceding periods window
LLVBARS	The number of periods that have passed since the array reached its periods period low
LONGCROSS	For 2 arrays A and B, return true if previous N periods A < B, Current A >= B
LWMA	Linear Weighted Moving Average
MA	Simple Moving Average, also known as arithmetic moving average
MIN_MAX_DIFF	Calculate rolling min-max difference (range) over a moving window
MOMENT	Calculate rolling k-th central moment over a moving window
NEUTRALIZE	Neutralize the effect of a categorical variable on a numeric variable
PRODUCT	Calculate product of values in preceding periods window
RANK	Calculate rank in a sliding window with size periods
RCROSS	For 2 arrays A and B, return true if A[i-1] > B[i-1] and A[i] <= B[i] alias: death_cross, cross_le
REF	Right shift input array by periods, r[i] = input[i - periods]
REGBETA	Calculate Regression Coefficient (Beta) of Y on X over a moving window
REGRESI	Calculate Regression Residual of Y on X over a moving window
RLONGCROSS	For 2 arrays A and B, return true if previous N periods A > B, Current A <= B
SCAN_ADD	Conditional cumulative add: r[t] = r[t-1] + (cond[t] ? input[t] : 0)
SCAN_MUL	Conditional cumulative multiply: r[t] = r[t-1] * (cond[t] ? input[t] : 1)
SKEWNESS	Calculate rolling sample Skewness over a moving window
SLOPE	Linear Regression Slope
SMA	Exponential Moving Average (variant of well-known EMA) weight = m / n
STDDEV	Calculate Standard Deviation over a moving window
SUM	Calculate sum of values in preceding periods window
SUMBARS	Calculate number of periods (bars) backwards until the sum of values is greater than or equal to amount
SUMIF	Calculate sum of values in preceding periods window where condition is true
VAR	Calculate Variance over a moving window
WEIGHTED_DELAY	Calculate weighted delay (exponentially weighted lag)
ZSCORE	Calculate rolling Z-Score over a moving window

Full function signatures: python/alpha/algo.md

Development

Requirements:

• Rust (latest stable)
• Python 3.11+
• maturin

# Build and install in development mode
maturin develop --release

# Run tests
cargo test

Vibe Coding

When adding new algorithms with LLM assistance, provide the function list as context. Use the skill add_algo.md for guided implementation.

This project is a co-created by Gemini (through Antigravity) and Claude (from tic-top).