alfars 0.4.3

High-performance quant workflow with Rust core and Python bindings.

alfars: From epsilon to alpha.

Informed by data;
Enlightened by research;
Engineered with AI;
Executed with algorithms.

High-performance quant workflow with Rust core and Python bindings.

Features

Core Backtesting

• High Performance: Rust core with parallel computation (8-10x speedup)
• Flexible API: NumPy arrays and Pandas Series support
• Complete Features: qcut(N) grouping, long-short portfolios, IC calculation, factor analysis
• Alphalens Compatibility: Similar API design for easy migration
• Extensible: Modular design with custom weights, grouping, and commission models

Intelligent Factor Mining (v0.4.0)

• Expression System: AST-based expression builder for custom factor computation
• Lazy Evaluation: Polars-style delayed computation with query optimization
• Genetic Programming: Auto-discover high-performance factor expressions
• Dimension System: Type-safe factor expressions to prevent invalid calculations
• Persistence: Factor library management with search, caching, and versioning
• Meta-Learning: Intelligent GP parameter recommendations based on historical data

Interactive Lab (v0.4.0)

• One-Command Launch: alfars lab starts all services automatically
• Visual Backtest: Interactive charts for NAV, IC, and quantile returns
• Browser-based: Access via http://localhost:5173
• ClickHouse Support: Connect to ClickHouse for historical market data

Installation

Requirements

• Rust: 1.70+ (curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh)
• Python: 3.8+
• uv (recommended): pip install uv

Install from Source

# Clone repository
git clone https://github.com/EthanNOV56/alfa.rs.git
cd alfa.rs

# Option 1: Full installation with Python bindings
uv pip install -e .
maturin develop --release

# Option 2: Rust-only server (no Python extension needed)
cargo build --release --bin alfars-server

Using pip (future releases)

pip install alfars

Quick Start

Basic Usage

import numpy as np
import alfars as al

# Generate sample data
n_days, n_assets = 100, 200
factor = np.random.randn(n_days, n_assets)
returns = np.random.randn(n_days, n_assets) * 0.01 + factor * 0.005

# Run quantile backtest
result = al.quantile_backtest(
    factor=factor,
    returns=returns,
    quantiles=10,
    weight_method="equal",
    long_top_n=1,
    short_top_n=1,
    commission_rate=0.0003,
)

print(f"Long-Short Return: {result.long_short_cum_return:.4%}")
print(f"IC Mean: {result.ic_mean:.4f}")
print(f"IC IR: {result.ic_ir:.4f}")

Start Interactive Lab

# Option 1: Python FastAPI server (requires maturin develop first)
uv run python -m alfars.lab

# Option 2: Rust HTTP server (recommended - no Python dependency)
cargo run --release --bin alfars-server   # Start Rust backend (port 8000)
cd frontend && npm run dev                 # Start frontend (port 5173)

Then open http://localhost:5173 in your browser.

Genetic Programming Factor Mining

from alfars import GpEngine

# Create GP engine
gp = GpEngine(
    population_size=100,
    max_generations=50,
    max_depth=6,
)

# Set available columns
gp.set_columns(['open', 'high', 'low', 'close', 'volume'])

# Prepare data
data = {
    'close': close_prices,    # shape: (n_days, n_assets)
    'volume': volumes,
}
returns = next_day_returns

# Mine factors
factors = gp.mine_factors(data, returns, num_factors=10)

for expr_str, fitness in factors[:3]:
    print(f"Factor: {expr_str[:60]}... (fitness: {fitness:.4f})")

Expression System

from alfars import Expr, LazyFrame

# Build factor expressions
expr = (Expr.col("close") - Expr.col("open")) / Expr.col("open")
sqrt_expr = expr.abs().sqrt()

# Lazy evaluation
lf = LazyFrame.scan(data)
lf_with_factor = lf.with_columns([("my_factor", expr)])
result = lf_with_factor.collect()

Persistence & Meta-Learning

from alfars import PersistenceManager, MetaLearningAnalyzer

# Factor library
db = PersistenceManager("./factor_library")
db.save_factor(factor_metadata)
factors = db.search_factors(min_ic=0.1)

# Meta-learning recommendations
analyzer = MetaLearningAnalyzer()
analyzer.train(factors, history)
recommendations = analyzer.get_recommendations(target_complexity=4.5)
print(f"Recommended: {recommendations.recommended_functions}")

Performance Benchmarks

Data Size	Rust	Python	Speedup
100×200	5.2ms	42.1ms	8.1×
500×500	68.3ms	1.2s	17.6×
1000×1000	312ms	8.7s	27.9×

Test environment: AMD Ryzen 7 5800X, 32GB RAM

Project Structure

alfars/
├── Cargo.toml              # Rust project config
├── pyproject.toml          # Python project config
├── src/
│   ├── lib.rs             # Core + Python bindings
│   ├── al/                # Alpha file parser + factor store
│   ├── backtest/          # Backtest engine
│   ├── data/              # Data source abstraction
│   ├── expr/              # Expression system (AST, optimizer, registry)
│   ├── gp/                # Genetic programming + history + metalearning
│   ├── lazy/              # Lazy evaluation engine
│   ├── types/             # Series and DataFrame types
│   ├── persistence.rs     # Persistence manager
│   └── bin/server.rs      # Rust HTTP server
├── alfars/                # Python package
│   ├── __init__.py
│   ├── lab.py             # Interactive lab launcher
│   └── server.py          # FastAPI server
├── frontend/              # Interactive UI (Vite + TypeScript)
├── assets/                # Static assets (logo, etc.)
└── tests/                 # Test suite

Development

# Format code before committing
cargo fmt
ruff format

# Run tests
pytest tests/

# Build release
maturin build --release

Version History

v0.4.0 (Current)

• Interactive Lab: One-command alfars lab for visual factor research
• GP Parallelization: Rayon-based parallel fitness evaluation
• Improved GP Engine: Bug fixes for IC calculation, cumulative returns
• Dimension System: Type-safe factor expressions
• ClickHouse Integration: Direct database connectivity for historical data
• Rust HTTP Server: Standalone server without Python dependency

v0.2.0

• Expression system with AST-based builder
• Lazy evaluation engine (Polars-style)
• Genetic programming factor mining
• Persistence and factor library management
• Meta-learning recommendations

v0.1.0

• High-performance quantile backtesting
• Alphalens-compatible API
• NumPy/Pandas dual interface

License

MIT License

Acknowledgments

• alphalens - API design reference
• PyO3 - Rust-Python bindings
• ndarray - Array computing
• rayon - Data parallelism
• Polars - Lazy evaluation design