pyoptima 0.0.5

Portfolio optimization with a sklearn-style API and config-driven workflows. Multiple objectives, constraints, and solvers.

PyOptima

Portfolio optimization with a sklearn-style API and config-driven workflows

PyOptima is a Python package for portfolio optimization. Use a composable, sklearn-style API in code or run from YAML/JSON configs. Many objectives (min volatility, max Sharpe, efficient frontier, risk parity, CVaR, etc.), constraints (sector caps, cardinality, turnover), and solvers (IPOPT, HiGHS, others via Pyomo).

Features

• sklearn-style API – PortfolioOptimizer(objective=..., constraints=[...]).solve(**data) with get_params / set_params / clone
• Config-driven – PortfolioOptimizer.from_config(path).solve() or run_from_config(path) for any template
• Objectives – min_volatility, max_sharpe, efficient_return, efficient_risk, max_utility, risk_parity, min_cvar, Black-Litterman, and more
• Constraints – Weight bounds, sector caps/mins, cardinality, turnover, per-asset bounds
• Solvers – IPOPT (default for portfolio), HiGHS, CBC, GLPK, Gurobi via Pyomo
• IO – read_portfolio_csv, read_portfolio_json, pandas-friendly result (weights_to_dataframe())
• ETL – Batch optimization and ETL output formatting for pipelines

Installation

Core Library

pip install pyoptima

With Optional Dependencies

# API server
pip install pyoptima[api]

# UI (Next.js frontend)
pip install pyoptima[ui]

# All optional dependencies
pip install pyoptima[api,ui]

Solvers

PyOptima uses Pyomo, which supports multiple solvers:

• IPOPT (default) - Quadratic/nonlinear optimization (required for portfolio optimization)
• HiGHS - Linear and mixed-integer programming (included via pip)
• CBC - Linear/integer programming
• GLPK - Linear programming
• Gurobi - Commercial solver (requires license)

Installation:

• HiGHS: Automatically installed with PyOptima (pip install pyoptima)
• IPOPT: Requires IPOPT C++ libraries first, then pip install pyoptima (includes cyipopt)
  • Install IPOPT libraries: conda install -c conda-forge ipopt (recommended)
  • Or use system packages: sudo apt-get install coinor-libipopt-dev (Ubuntu/Debian)
  • PyOptima automatically uses the ipopt_v2 interface via cyipopt for better performance
• See Solver Installation Guide for details

Quick Start

Config-driven (recommended)

Config schema: template, data, solver. For portfolio, data includes objective, expected_returns, covariance_matrix.

1. Create a config file (portfolio.json):

{
  "template": "portfolio",
  "data": {
    "objective": "min_volatility",
    "expected_returns": [0.12, 0.11, 0.15],
    "covariance_matrix": [
      [0.04, 0.01, 0.02],
      [0.01, 0.05, 0.01],
      [0.02, 0.01, 0.06]
    ],
    "symbols": ["AAPL", "MSFT", "GOOGL"],
    "weight_bounds": [0, 1]
  },
  "solver": { "name": "ipopt" }
}

2. Run from Python

from pyoptima import run_from_config, PortfolioOptimizer, load_config_file

# One-shot (any template: portfolio, knapsack, lp, ...)
result = run_from_config("portfolio.json")
print(result["weights"], result["portfolio_return"], result["portfolio_volatility"])

# Or: build optimizer from config, then solve (portfolio only)
opt = PortfolioOptimizer.from_config("portfolio.json")
result = opt.solve()  # uses config data

3. CLI

pyoptima optimize portfolio.json

Code-only (sklearn-style)

from pyoptima import PortfolioOptimizer

opt = PortfolioOptimizer(objective="min_volatility", max_weight=0.4)
result = opt.solve(
    expected_returns=[0.12, 0.11, 0.15],
    covariance_matrix=[[0.04, 0.01, 0.02], [0.01, 0.05, 0.01], [0.02, 0.01, 0.06]],
    symbols=["AAPL", "MSFT", "GOOGL"],
)
print(result.weights, result.portfolio_return, result.portfolio_volatility)

ETL Integration

PyOptima provides first-class integration with pycharter ETL pipelines. Use pyoptima.etl.optimize_batch directly as a pycharter custom_function:

pycharter Configuration (No Bridge Code Needed)

# transform.yaml
custom_function:
  module: pyoptima.etl
  function: optimize_batch
  mode: batch
  kwargs:
    objective: min_volatility
    solver: ipopt

Python Usage

from pyoptima.etl import optimize_batch

# ETL input format
data = [{
    "job_id": "growth-2025-01-06",
    "symbols": ["AAPL", "MSFT", "GOOGL"],
    "covariance_matrix": {
        "matrix": [[0.04, 0.01, 0.02], [0.01, 0.05, 0.01], [0.02, 0.01, 0.06]],
        "symbols": ["AAPL", "MSFT", "GOOGL"]
    },
    "expected_returns": {"AAPL": 0.12, "MSFT": 0.11, "GOOGL": 0.15},
}]

# Optimize - returns ETL-ready output
results = optimize_batch(data, objective="min_volatility", solver="ipopt")

# Output format ready for loading
print(results[0]["job_id"])        # "growth-2025-01-06"
print(results[0]["weights"])       # {"AAPL": 0.25, "MSFT": 0.40, "GOOGL": 0.35}
print(results[0]["status"])        # "optimal"
print(results[0]["volatility"])    # 0.142

Key features: pycharter custom_function support, input normalization (dict/list/numpy/pandas), ETL-ready output (job_id, weights, status, metrics), all portfolio objectives, per-record error handling.

See ETL Integration Guide for complete details.

Constraints

PyOptima supports advanced portfolio constraints beyond basic weight bounds:

Available Constraints

• Sector Caps - Limit total exposure to specific sectors
• Sector Minimums - Enforce minimum exposure to sectors
• Cardinality - Maximum number of non-zero positions
• Turnover Limits - Control trading from current portfolio state
• Per-Asset Bounds - Individual min/max bounds per asset
• Minimum Position Size - Conditional holding requirements

Example

from pyoptima import PortfolioOptimizer, SectorCaps, SumToOne, WeightBounds

opt = PortfolioOptimizer(
    objective="min_volatility",
    constraints=[
        WeightBounds(0.05, 0.40),
        SumToOne(),
        SectorCaps({"Technology": 0.50}, asset_sectors={"AAPL": "Technology", "MSFT": "Technology"}),
    ],
)
result = opt.solve(expected_returns=..., covariance_matrix=..., symbols=...)

Or pass constraints as kwargs in config data:

# In config data
constraints = dict(
    # Sector constraints
    sector_caps={"Technology": 0.40, "Financials": 0.30},
    asset_sectors={
        "AAPL": "Technology",
        "MSFT": "Technology",
        "JPM": "Financials"
    },
    
    # Position limits
    max_positions=15,
    min_position_size=0.02,
    
    # Turnover control
    max_turnover=0.30,
    current_weights={"AAPL": 0.20, "MSFT": 0.30, ...},
    
    # Per-asset bounds
    per_asset_bounds={
        "AAPL": (0.05, 0.30),  # Min 5%, Max 30%
        "MSFT": (0.10, 0.40)
    }
)

See Constraints Documentation for detailed examples and best practices.

Available Methods

PyOptima provides 21 portfolio optimization methods organized by category:

Efficient Frontier Methods

• min_volatility - Minimize portfolio volatility
• max_sharpe - Maximize Sharpe ratio
• max_quadratic_utility - Maximize quadratic utility
• efficient_risk - Maximize return for target volatility
• efficient_return - Minimize volatility for target return

Black-Litterman Methods

• black_litterman_max_sharpe - BL with max Sharpe objective
• black_litterman_min_volatility - BL with min volatility objective
• black_litterman_quadratic_utility - BL with quadratic utility objective

Conditional Value at Risk (CVaR)

• min_cvar - Minimize CVaR
• efficient_cvar_risk - Maximize return for target CVaR
• efficient_cvar_return - Minimize CVaR for target return

Conditional Drawdown at Risk (CDaR)

• min_cdar - Minimize CDaR
• efficient_cdar_risk - Maximize return for target CDaR
• efficient_cdar_return - Minimize CDaR for target return

Semivariance Methods

• min_semivariance - Minimize downside variance
• efficient_semivariance_risk - Maximize return for target semivariance
• efficient_semivariance_return - Minimize semivariance for target return

Hierarchical Risk Parity (HRP)

• hierarchical_min_volatility - HRP with min volatility
• hierarchical_max_sharpe - HRP with max Sharpe

Critical Line Algorithm (CLA)

• cla_min_volatility - CLA for min volatility
• cla_max_sharpe - CLA for max Sharpe

See examples/ directory for complete, runnable examples of each method.

Data Format Support

PyOptima automatically detects and converts various data formats:

Covariance Matrix Formats

• Nested dict (ETL format): {"matrix": [[...], [...]], "symbols": [...]}
• Flat dict: {"AAPL": {"AAPL": 0.04, "MSFT": 0.01}, ...}
• NumPy array: np.array([[0.04, 0.01], [0.01, 0.05]])
• Pandas DataFrame: pd.DataFrame(..., index=symbols, columns=symbols)

Expected Returns Formats

• Flat dict: {"AAPL": 0.12, "MSFT": 0.11}
• Pandas Series: pd.Series([0.12, 0.11], index=["AAPL", "MSFT"])
• NumPy array: np.array([0.12, 0.11])

Symbols are automatically aligned between covariance matrix and expected returns.

Documentation

• ETL Integration Guide - Using PyOptima with ETL pipeline
• Constraints Documentation - Advanced constraint types and examples
• Usage Guide - Detailed usage examples

API Reference

Config-driven API

from pyoptima import run_from_config, load_config_file, OptimizationConfig

# One-shot from path or dict
result = run_from_config("portfolio.json")
config = load_config_file("portfolio.json")

# Config shape: template, data, solver (see Quick Start)

PortfolioOptimizer (sklearn-style + from_config)

from pyoptima import PortfolioOptimizer

# Build from config (portfolio only); solve uses config data
opt = PortfolioOptimizer.from_config("min_volatility.json")
result = opt.solve()

# Or solve with different data
result2 = opt.solve(expected_returns=er2, covariance_matrix=cov2, symbols=symbols)

Result Format

Results are returned as dictionaries:

{
    "weights": {"AAPL": 0.30, "MSFT": 0.40, ...},
    "portfolio_return": 0.12,
    "portfolio_volatility": 0.15,
    "sharpe_ratio": 0.80,
    "status": "optimal",
    "objective_value": 0.04,
    "message": "Optimization successful"
}

ETL Integration

from pyoptima.etl import optimize_batch

# ETL (pycharter custom_function)
results = optimize_batch(
    data=[{"expected_returns": {...}, "covariance_matrix": {...}}],
    objective="min_volatility",
    max_weight=0.40,
    solver="ipopt"
)

Constraints via kwargs

# Pass constraints as kwargs to optimize_batch
results = optimize_batch(
    data,
    objective="min_volatility",
    sector_caps={"Technology": 0.40},
    asset_sectors={"AAPL": "Technology", ...},
    max_assets=10,
    max_turnover=0.30,
    current_weights={"AAPL": 0.20, ...},
    max_weight=0.30,
)

CLI Usage

Optimization

# Optimize from config file
pyoptima optimize config.json

# With output file
pyoptima optimize config.json --output results.json --pretty

API Server

# Start API server
pyoptima api --host 0.0.0.0 --port 8000

# With auto-reload (development)
pyoptima api --host 0.0.0.0 --port 8000 --reload

UI

# Development mode
pyoptima ui dev

# Build for production
pyoptima ui build

# Serve built UI
pyoptima ui serve

Development Setup

# Clone repository
cd pyoptima

# Install in development mode
pip install -e ".[api,ui]"

# Run tests
pytest tests/

# Run API server
pyoptima api --host 0.0.0.0 --port 8000

# Run UI (development)
pyoptima ui dev

Requirements

• Python 3.10+
• Pyomo (for optimization)
• NumPy, Pandas (for data handling)
• Pydantic >= 2.0.0 (for configuration)
• Optional: FastAPI, Uvicorn (for API server)
• Optional: Next.js, React (for UI)

Project Structure

pyoptima/
├── pyoptima/           # Core package
│   ├── methods/        # Optimization methods (registry)
│   ├── constraints/    # Constraint system
│   ├── adapters/       # Data format adapters
│   ├── integration/   # ETL integration
│   ├── utils/          # Utilities (data formats, Pyomo helpers)
│   └── models/         # Configuration models
├── api/                # FastAPI server (optional)
├── ui/                 # Next.js UI (optional)
├── examples/           # Example configurations and scripts
├── tests/              # Test suite
└── docs/               # Documentation

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add some amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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