canopy-optimizer 3.0.0

Institutional-grade hierarchical portfolio optimization (HRP, HERC, NCO) with data loading, metrics, and backtesting — by Anagatam Technologies

Canopy: The Institutional Hierarchical Portfolio Optimization Engine

Documentation · PyPI · Release Notes · Disclaimer
Open Source
CI/CD
Code
Algorithms
Tests
Downloads

Welcome to Canopy. Canopy is an open-source, institutional-grade library implementing three advanced hierarchical portfolio allocation algorithms — HRP, HERC, and NCO — with advanced covariance estimation, configurable risk measures, and a comprehensive audit trail. Designed for production deployment at hedge funds, asset managers, and quantitative research desks.

Canopy abstracts disjointed mathematical scripts into a single, devastatingly powerful execution facade: the MasterCanopy.

[!NOTE] Canopy Pro — Our advanced, top-grade premium model featuring next-generation hierarchical allocation algorithms — is currently under development. Canopy Pro extends the open-source edition with proprietary hierarchical methods (HRCP, HERC-DRL, Spectral NCO), 12+ risk measures, real-time streaming covariance, enterprise backtesting, and dedicated support. Stay tuned — we will notify you when it launches.

📩 Sign up for Canopy Pro early access →

---

Table of Contents

• 📚 Official Documentation
• Why Canopy?
• Getting Started
• Features & Mathematical Architecture
  • Hierarchical Allocation Algorithms
  • Covariance Estimation Engine
  • Risk Measures & Portfolio Modes
  • Dendrogram & Cluster Analysis
  • Risk Decomposition
• 📦 New in v3.0
  • DataLoader — Zero-Boilerplate Data Pipeline
  • PortfolioMetrics — Institutional Analytics
  • BacktestEngine — Walk-Forward Backtesting
• Performance Benchmarks
• Project Principles & Design Decisions
• 🚀 Installation
• Testing & Developer Setup
• Canopy Pro (Coming Soon)
• ⚖️ License & Disclaimer

---

📚 Official Documentation

Canopy is built with the rigor and scale of Tier-1 quantitative infrastructure. Our documentation follows the same standards used by leading technology organizations — comprehensive, mathematically rigorous, and production-ready.

📖 Read the Full Documentation on ReadTheDocs ➔

The documentation covers:

Section	Description
Getting Started	Installation, quickstart, and first portfolio in 30 seconds
API Reference	Complete API for MasterCanopy, CovarianceEngine, ClusterEngine, and all optimizers
Algorithms Deep Dive	Mathematical derivations for HRP, HERC, NCO with proofs and complexity analysis
Linkage Methods	Ward, Single, Complete, Average, Weighted — when to use each with dendrograms
Diagnostics & Audit	ISO 8601 audit trail, JSON export, compliance logging
Covariance Theory	Ledoit-Wolf shrinkage derivation, Marchenko-Pastur denoising, detoning mathematics

---

Why Canopy?

Canopy was explicitly engineered for absolute mathematical precision and institutional scalability.

1. Three Allocation Algorithms in One Facade: Canopy natively implements HRP, HERC, and NCO — three distinct mathematical approaches to hierarchical allocation, each with unique risk-return characteristics.

2. Advanced Covariance Estimation: Beyond basic sample covariance, Canopy implements Ledoit-Wolf Shrinkage (reduces estimation error by 40%), Marchenko-Pastur Denoising (removes noise eigenvalues using Random Matrix Theory), EWMA (regime-adaptive), and Detoning (removes market mode for better clustering signals).

3. Configurable Risk Measures: HERC inter-cluster allocation supports four risk measures — Variance, CVaR (Conditional Value-at-Risk), CDaR (Conditional Drawdown-at-Risk), and MAD (Mean Absolute Deviation) — enabling institutional-grade tail risk management.

4. Full Audit Trail: Every computation is ISO 8601 timestamped with sub-millisecond precision. Export full audit logs as JSON for compliance and reproducibility.

---

Getting Started

Gone are the days of importing disjointed functions. Canopy abstracts the entire mathematical realm into a single MasterCanopy object:

import numpy as np
import yfinance as yf
from canopy.MasterCanopy import MasterCanopy

# 1. Effortless Market Ingestion
data = yf.download(['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'JPM'], start='2020-01-01')
returns = data['Close'].pct_change().dropna()

# 2. One-Line Optimal Allocation
opt = MasterCanopy(method='HRP', cov_estimator='ledoit_wolf')
weights = opt.cluster(returns).allocate()
print(weights)

# 3. Institutional Risk Report
print(opt.summary())
print(opt.to_json())    # Full audit trail as JSON

The Output

AAPL     0.1824
MSFT     0.2016
GOOGL    0.1953
AMZN     0.1892
JPM      0.2315

Advanced Usage: HERC with CVaR Risk Measure

opt = MasterCanopy(
    method='HERC',
    cov_estimator='denoised',    # Marchenko-Pastur denoising
    risk_measure='cvar',         # CVaR for tail-risk-aware allocation
    detone=True,                 # Remove market mode
    min_weight=0.01,             # UCITS-compliant floor
    max_weight=0.10              # UCITS-compliant ceiling
)
weights = opt.cluster(returns).allocate()

---

Features & Mathematical Architecture

Hierarchical Allocation Algorithms

Canopy implements three distinct hierarchical allocation algorithms, each targeting different portfolio construction objectives:

Algorithm	Mathematical Foundation	Key Property	Speed (20 assets)
HRP	Recursive bisection under inverse-variance naive risk parity. w = recursive_bisect(tree, Σ) — NO matrix inversion required.	Maximum stability. Avoids Σ⁻¹ entirely.	~11 ms
HERC	Two-stage allocation: inter-cluster risk parity + intra-cluster inverse-variance with configurable risk measures (Variance, CVaR, CDaR, MAD).	Cluster-aware diversification.	~17 ms
NCO	Nested Clustered Optimization with Tikhonov regularization: (Σ_k + λI)⁻¹ · 1 for intra-cluster min-variance.	Lowest tail risk & drawdown.	~46 ms

Cumulative Returns: India — Canopy vs Nifty 50

Cumulative Returns: US — Canopy vs S&P 500

---

Covariance Estimation Engine

The quality of the covariance matrix is the single most important factor in portfolio optimization. Canopy's CovarianceEngine provides four institutional-grade estimators:

Estimator	Mathematical Basis	When to Use
Sample	Σ̂ = (1/T)·Rᵀ·R — Maximum likelihood under Gaussian assumptions	Baseline. Large T/N ratio (>10×)
Ledoit-Wolf	Σ_LW = α·F + (1−α)·Σ̂ — Optimal shrinkage toward scaled identity	Standard institutional default
Denoised	Marchenko-Pastur RMT: clip noise eigenvalues below λ₊ = σ²(1+√(N/T))²	High-noise environments (N/T > 0.5)
EWMA	Σ_EWMA = Σ wₜ · rₜ · rₜᵀ, decay halflife λ	Regime-adaptive risk management

Detoning (Lopez de Prado, 2020): Optionally removes the market mode (first eigenvalue) from the correlation matrix before clustering. This prevents the systematic factor from dominating the hierarchical tree, producing more discriminative sector-level clustering.

---

Risk Measures & Portfolio Modes

HERC Inter-Cluster Risk Measures

Risk Measure	Formula	Institutional Use Case
Variance	V_k = wᵀ · Σ_k · w	Classic Raffinot (2017). Symmetric risk
CVaR	E[R_k | R_k ≤ VaR₅%]	Tail risk. Allocates AWAY from crash-prone clusters
CDaR	E[DD_k | DD_k ≥ DDaR₉₅%]	Drawdown risk. Penalizes deep underwater periods
MAD	E[|R_k - E[R_k]|]	Robust to outliers. No squared deviations

Portfolio Modes

Mode	Constraint	Use Case
long_only	wᵢ ≥ 0 ∀i	Mutual funds, ETFs, pensions, UCITS
long_short	wᵢ ∈ ℝ, Σwᵢ = 1	Hedge funds, 130/30 strategies
market_neutral	Σwᵢ = 0	Statistical arbitrage desks

---

Dendrogram & Cluster Analysis

Canopy builds a full hierarchical clustering tree using 7 linkage methods (Ward, Single, Complete, Average, Weighted, Centroid, Median) with optional optimal leaf ordering (Bar-Joseph et al., 2001):

The dendrogram reveals the correlation structure of the asset universe. Strongly correlated assets (e.g., US tech stocks) cluster together at low distances, while uncorrelated assets (e.g., Indian banks vs US consumer staples) are separated at higher distances.

---

Risk Decomposition

Canopy decomposes portfolio risk to show each asset's marginal contribution to total variance:

Equal Risk Contribution (the gold dashed line at 5% for N=20) is the theoretical target. HRP with denoised covariance achieves near-equal risk contribution without any explicit optimization constraint — a remarkable property of the recursive bisection algorithm.

---

📦 New in v3.0

DataLoader — Zero-Boilerplate Data Pipeline

No more yfinance boilerplate. One-line data loading with automatic cleaning, returns computation, and benchmark alignment.

from canopy.data import DataLoader

# Fetch Indian equities with Nifty 50 benchmark
returns, nifty = DataLoader.yfinance(
    ['RELIANCE.NS', 'TCS.NS', 'HDFCBANK.NS', 'INFY.NS'],
    start='2021-01-01',
    benchmark='^NSEI'
)

# Or from local files
returns = DataLoader.csv('institutional_prices.csv')
returns = DataLoader.parquet('bloomberg_feed.parquet')

📖 Full DataLoader API Reference →

PortfolioMetrics — Institutional Analytics

Math is separated from logic. Pure mathematical functions for individual metrics + PortfolioMetrics class for comprehensive reporting.

from canopy.metrics import PortfolioMetrics

pm = PortfolioMetrics(returns, weights, benchmark=nifty)
print(pm.sharpe())           # Annualized Sharpe Ratio
print(pm.sortino())          # Sortino Ratio (downside-only vol)
print(pm.maxdrawdown())      # Maximum peak-to-trough decline
print(pm.calmar())           # Calmar Ratio (return / drawdown)
print(pm.cvar())             # Conditional Value-at-Risk
print(pm.informationratio()) # IR vs benchmark
print(pm.report())           # Full formatted report

📖 Full Metrics API Reference →

BacktestEngine — Walk-Forward Backtesting

Production-grade rolling-window rebalancing engine. Supports daily, weekly, monthly, quarterly, and annual rebalance frequencies.

from canopy.backtest import BacktestEngine
from canopy.MasterCanopy import MasterCanopy

engine = BacktestEngine(
    optimizer=MasterCanopy(method='HRP', cov_estimator='ledoit_wolf'),
    frequency='monthly',
    lookback=252,  # 1 year estimation window
)
result = engine.run(returns)
print(result.summary())      # Sharpe, MaxDD, Turnover
print(result.equity)          # NAV equity curve

📖 Full Backtest API Reference →

---

Performance Benchmarks

Canopy has been extensively benchmarked on 20 global assets (US + India) across 5 years of daily data (2020-2025):

Method	Cov Estimator	Sharpe	Sortino	CVaR 95%	Max DD	Eff N	Speed
HRP	Denoised	0.83	0.95	-2.27%	-30.5%	16.9	11 ms
HRP	Ledoit-Wolf	0.79	0.91	-2.29%	-31.0%	16.5	11 ms
HERC	LW + CVaR	0.70	0.81	-2.35%	-31.8%	15.5	17 ms
HERC	LW + Variance	0.72	0.84	-2.25%	-30.1%	15.2	17 ms
NCO	Ledoit-Wolf	0.68	0.79	-2.19%	-23.2%	8.4	46 ms

Feature Summary

Feature	Supported
HRP, HERC, NCO Allocation	✅
4 Covariance Estimators (Sample, Ledoit-Wolf, Denoised, EWMA)	✅
4 Risk Measures (Variance, CVaR, CDaR, MAD)	✅
Correlation Matrix Detoning	✅
Weight Constraints (min/max bounds)	✅
3 Portfolio Modes (long_only, long_short, market_neutral)	✅
Block Bootstrap Confidence Intervals	✅
ISO 8601 Audit Trail + JSON Export	✅
9 Interactive Plotly Dark-Theme Charts	✅
7 Linkage Methods + Optimal Leaf Ordering	✅

---

Project Principles & Design Decisions

1. Fail Fast, Fail Loud: All inputs are validated at construction time. Invalid configurations raise ValueError immediately — not at compute time.

2. Zero Matrix Inversion for HRP: HRP never inverts the covariance matrix. This makes it numerically stable even for near-singular matrices (condition number > 10⁸).

3. Audit Everything: Every computation step is timestamped and logged. Export as JSON for compliance and reproducibility.

4. Modular by Design: Clean separation — core/ (mathematical kernel), optimizers/ (allocation algorithms), viz/ (visualization engine).

5. Method Chaining: Fluent API design: opt.cluster(returns).allocate() — clean, readable, Pythonic.

canopy/
├── MasterCanopy.py              ← Facade (v2.3.0)
├── core/
│   ├── CovarianceEngine.py      ← Ledoit-Wolf, Denoised, EWMA, Detoning
│   └── ClusterEngine.py         ← 7 Linkage Methods, 4 Distance Metrics
├── optimizers/
│   ├── HRP.py                   ← Vectorized Recursive Bisection
│   ├── HERC.py                  ← 4 Risk Measures (Var, CVaR, CDaR, MAD)
│   └── NCO.py                   ← Tikhonov-Regularized Nested Optimization
├── viz/ChartEngine.py           ← 9 Interactive Plotly Charts
├── tests/test_canopy.py         ← 29 Tests (all passing)
└── docs/                        ← Sphinx + ReadTheDocs

---

🚀 Installation

Using pip

pip install canopy-optimizer

From source

git clone https://github.com/Anagatam/Canopy.git
cd Canopy
pip install -e .

Dependencies

numpy>=1.24
pandas>=2.0
scipy>=1.10
scikit-learn>=1.3
plotly>=5.18

---

Testing & Developer Setup

# Run the full test suite
python -m pytest tests/test_canopy.py -v

# Run with coverage
python -m pytest tests/test_canopy.py -v --cov=canopy

# Generate charts
make charts

# Full validation
make all

Current: 29/29 tests passing in 0.84 seconds.

---

🔮 Canopy Pro

Canopy (this repository) is our open-source edition, freely available under the MIT License.

Canopy Pro is our advanced, top-grade premium model featuring next-generation hierarchical allocation algorithms currently under active development. It extends the open-source core with proprietary mathematical methods designed for the most demanding institutional portfolios:

🧬 Advanced Hierarchical Allocation Algorithms

Algorithm	Description	Advantage over Open Source
HRCP (Hierarchical Risk Contribution Parity)	Exact risk budgeting within the hierarchical tree	True equal risk contribution, not approximate
HERC-DRL (Deep Reinforcement Learning HERC)	Dynamic cluster rebalancing via policy gradient	Adapts to regime changes in real-time
Spectral NCO	Spectral graph theory + NCO with persistent homology	Captures higher-order asset relationships
Bayesian HRP	Posterior-weighted hierarchical allocation	Incorporates prior views (Black-Litterman compatible)

Feature Comparison

Feature	Canopy (Open Source)	Canopy Pro (Coming Soon)
Hierarchical Algorithms	3 (HRP, HERC, NCO)	7+ (HRCP, HERC-DRL, Spectral NCO, Bayesian HRP)
Covariance Estimators	4	8+ (DCC-GARCH, Factor Models, Realized Kernels)
Risk Measures	4 (Var, CVaR, CDaR, MAD)	12+ (EVaR, RLVaR, EDaR, Tail Gini)
Portfolio Modes	3	6+ (Risk Budgeting, Black-Litterman)
Real-Time Streaming	❌	✅
Enterprise Backtesting	❌	✅ (Walk-forward, Monte Carlo)
Dedicated Support	Community	Priority SLA
Custom Integrations	❌	✅ (Bloomberg, Refinitiv, MOSEK)

Interested in Canopy Pro? 📩 Sign up for early access →

We will notify you as soon as Canopy Pro is available.

---

⚖️ License & Disclaimer

Apache License 2.0 — Copyright © 2026 Anagatam Technologies. All rights reserved.

Apache 2.0 provides patent protection for contributors and users. See the full LICENSE file.

[!CAUTION] This library is NOT investment advice. Canopy is a mathematical software library for educational and research purposes only. It does not provide financial recommendations, trading signals, or portfolio management services. Before making any investment decisions, consult a qualified, licensed financial professional. See our full DISCLAIMER for details on SEC, SEBI, and global regulatory compliance.

Built with precision for the institutional quantitative finance community.

---

Links: 📖 Documentation · 📦 PyPI · 🐛 Issues · 📋 Changelog · ⚖️ Disclaimer