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docs: Rebrand OptimizR to Optimiz-rs throughout documentation

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Updated Branding in ReadTheDocs:
- ✅ All 'OptimizR' → 'Optimiz-rs' (17 files)
- ✅ Project name in conf.py
- ✅ HTML title and short title
- ✅ All algorithm documentation
- ✅ Getting started guide
- ✅ Installation guide
- ✅ Theory/mathematical foundations
- ✅ Archive documentation

Documentation now consistently uses the new 'optimiz-rs' branding that
matches both PyPI and crates.io package names.

Note: Python module name 'optimizr' in import statements intentionally
unchanged (that's the actual module name).
This commit is contained in:
ThotDjehuty
2026-02-17 10:09:15 +01:00
parent bfd0d3c591
commit b10263b4f2
17 changed files with 63 additions and 63 deletions
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docs/archive/COMPLETION_SUMMARY.md
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@@ -1,4 +1,4 @@
# OptimizR Refactoring - Completion Report
# Optimiz-rs Refactoring - Completion Report
## ✅ All Tasks Completed
@@ -263,7 +263,7 @@ pyproject.toml # ✅ Unchanged
✅ **All todo items completed successfully!**
The OptimizR codebase has been completely refactored with:
The Optimiz-rs codebase has been completely refactored with:
- ✅ Modular trait-based architecture
- ✅ Functional programming patterns
- ✅ Advanced design patterns (Strategy, Builder, Traits)
docs/archive/DEVELOPMENT.md
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@@ -1,4 +1,4 @@
# OptimizR Development Guide
# Optimiz-rs Development Guide
## Quick Start
docs/archive/ENHANCEMENT_STRATEGY.md
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@@ -1,4 +1,4 @@
# OptimizR Enhancement Strategy
# Optimiz-rs Enhancement Strategy
**Date**: January 2, 2025
**Context**: Post-Polarway Phase 4, exploring integration and improvements
@@ -58,7 +58,7 @@
- Simulated Annealing
- Ant Colony Optimization
## Synergy Opportunities: Polarway + OptimizR
## Synergy Opportunities: Polarway + Optimiz-rs
### 1. Time-Series Feature Engineering for HMM
**Description**: Use Polarway's time-series operations to create features for regime detection
@@ -70,7 +70,7 @@ df = client.lag(['price'], periods=1) # Lagged prices
df = client.pct_change(['price'], periods=1) # Returns
df = client.diff(['price'], periods=1) # Price changes
# OptimizR: Regime detection on features
# Optimiz-rs: Regime detection on features
returns = df['price_pct_change'].to_numpy()
hmm = HMM(n_states=3) # Bull, Bear, Sideways
hmm.fit(returns, n_iterations=100)
@@ -79,7 +79,7 @@ states = hmm.predict(returns)
**Value**:
- Polarway provides fast feature engineering (50-200× faster for large datasets)
- OptimizR provides statistical inference (HMM regime detection)
- Optimiz-rs provides statistical inference (HMM regime detection)
- Combined: Real-time regime switching for trading strategies
### 2. Risk Metrics on Time-Series Data
@@ -91,14 +91,14 @@ states = hmm.predict(returns)
df = client.pct_change(['price'], periods=1)
returns = df['price_pct_change'].to_numpy()
# OptimizR: Risk analysis
# Optimiz-rs: Risk analysis
hurst = compute_hurst_exponent(returns) # Mean-reversion detection
half_life = estimate_half_life(returns) # Reversion time
risk_metrics = compute_risk_metrics(returns) # Comprehensive suite
```
**Value**:
- Fast preprocessing (Polarway) + sophisticated analysis (OptimizR)
- Fast preprocessing (Polarway) + sophisticated analysis (Optimiz-rs)
- Useful for pairs trading, mean-reversion strategies
- Real-time risk monitoring
@@ -111,7 +111,7 @@ risk_metrics = compute_risk_metrics(returns) # Comprehensive suite
df = client.lag(['spy_price', 'vix'], periods=[1, 5, 20])
df = client.pct_change(['spy_price'], periods=1)
# OptimizR: Solve optimal control problem
# Optimiz-rs: Solve optimal control problem
# State: [price, volatility regime]
# Control: portfolio weights
value_fn = solve_hjb_regime_switching(...)
@@ -133,7 +133,7 @@ def backtest_strategy(params):
# ... strategy logic ...
return -sharpe_ratio # Minimize negative Sharpe
# OptimizR: Find optimal parameters
# Optimiz-rs: Find optimal parameters
result = differential_evolution(
objective_fn=backtest_strategy,
bounds=[(1, 50), (0.01, 0.5)], # [lag_period, threshold]
@@ -144,7 +144,7 @@ result = differential_evolution(
**Value**:
- Polarway handles heavy data processing
- OptimizR finds optimal parameters
- Optimiz-rs finds optimal parameters
- 74-88× faster than SciPy DE
## High-Priority Enhancements
@@ -258,12 +258,12 @@ impl SHADEMemory {
### Priority 3: Time-Series Integration Helpers
**Problem**: Using Polarway + OptimizR requires manual glue code
**Problem**: Using Polarway + Optimiz-rs requires manual glue code
**Solution**: Create helper functions for common time-series + optimization patterns
**Implementation Strategy**:
1. Add `timeseries_utils` module to OptimizR
1. Add `timeseries_utils` module to Optimiz-rs
2. Functions for common workflows
3. Optional Polarway integration (via feature flag)
@@ -350,7 +350,7 @@ result = tsu.optimize_strategy_params(
1. **Session 1 (Current)**: Time-Series Integration Helpers (1-2 hours)
- Low effort, immediate value
- Makes Polarway + OptimizR integration obvious
- Makes Polarway + Optimiz-rs integration obvious
- Creates examples for documentation
2. **Session 2**: Enable Rust-Native Parallelization (1-2 hours)
@@ -372,7 +372,7 @@ result = tsu.optimize_strategy_params(
For each enhancement:
1. **Unit tests**: Algorithm correctness (sphere function, Rosenbrock)
2. **Benchmarks**: Performance comparison (before/after)
3. **Integration tests**: Polarway + OptimizR workflows
3. **Integration tests**: Polarway + Optimiz-rs workflows
4. **Documentation**: Usage examples, API docs
## Git Commit Strategy (per MANDATORY rules)
@@ -404,4 +404,4 @@ Each enhancement gets:
---
**Next Action**: Implement Priority 3 (Time-Series Integration Helpers) as it's lowest effort with immediate value for demonstrating Polarway + OptimizR synergy.
**Next Action**: Implement Priority 3 (Time-Series Integration Helpers) as it's lowest effort with immediate value for demonstrating Polarway + Optimiz-rs synergy.
docs/archive/ENHANCEMENT_SUITE_COMPLETE.md
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# OptimizR Enhancement Suite - Implementation Complete
# Optimiz-rs Enhancement Suite - Implementation Complete
**Date**: January 2, 2026
**Session Duration**: ~3 hours
@@ -8,13 +8,13 @@
## Overview
Completed comprehensive enhancement suite for OptimizR v0.2.0, implementing all 3 priorities from the Enhancement Strategy:
Completed comprehensive enhancement suite for Optimiz-rs v0.2.0, implementing all 3 priorities from the Enhancement Strategy:
1. ✅ **Time-Series Integration Helpers** (Priority 3)
2. ✅ **Rust Parallelization** (Priority 2)
3. ✅ **SHADE Algorithm** (Priority 1)
Additionally created integration examples combining Polarway + OptimizR workflows.
Additionally created integration examples combining Polarway + Optimiz-rs workflows.
---
@@ -22,7 +22,7 @@ Additionally created integration examples combining Polarway + OptimizR workflow
### 1. Time-Series Integration Helpers (Commit: 9a8032e, 7f77f29)
**Purpose**: Bridge OptimizR's optimization with time-series analysis for financial workflows.
**Purpose**: Bridge Optimiz-rs's optimization with time-series analysis for financial workflows.
**Implementation**:
- Created `src/timeseries_utils.rs` (400+ lines)
@@ -41,7 +41,7 @@ Additionally created integration examples combining Polarway + OptimizR workflow
- All functions tested and working
**Impact**:
- Enables Polarway → OptimizR workflows
- Enables Polarway → Optimiz-rs workflows
- Simplifies regime detection with HMM
- Streamlines pairs trading analysis
@@ -130,7 +130,7 @@ Additionally created integration examples combining Polarway + OptimizR workflow
### 4. Integration Examples (Included with parallelization)
**Purpose**: Demonstrate Polarway + OptimizR workflows.
**Purpose**: Demonstrate Polarway + Optimiz-rs workflows.
**Implementation**:
- `examples/polarway_optimizr_integration.py` (500+ lines)
@@ -148,7 +148,7 @@ Additionally created integration examples combining Polarway + OptimizR workflow
**Impact**:
- End-to-end examples for financial analysis
- Demonstrates Polarway + OptimizR synergy
- Demonstrates Polarway + Optimiz-rs synergy
- Ready for production adaptation
**Files**:
@@ -221,7 +221,7 @@ All commits pushed to origin/main ✅
## 🎯 Alignment with Roadmap
All enhancements align with OptimizR v0.3.0 roadmap:
All enhancements align with Optimiz-rs v0.3.0 roadmap:
- ✅ **Time-series integration**: Enable Polarway workflows
- ✅ **Parallelization**: Unlock Rayon infrastructure
@@ -325,4 +325,4 @@ Future (v0.3.0+):
**Status**: ✅ **ALL OBJECTIVES COMPLETE**
**Next**: Integrate SHADE into DE, performance testing
**Version**: OptimizR v0.2.0 → v0.3.0 prep
**Version**: Optimiz-rs v0.2.0 → v0.3.0 prep
docs/archive/NOTEBOOK_AUDIT_REPORT.md
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@@ -9,7 +9,7 @@ Example notebooks in `examples/notebooks/` **ARE WORKING CORRECTLY**! They use P
- Automatic Rust backend when available
- Graceful fallback to pure Python
## Actual OptimizR Python API (from lib.rs)
## Actual Optimiz-rs Python API (from lib.rs)
### ✅ Available Functions/Classes:
@@ -155,7 +155,7 @@ from optimizr import (
```
**Features Demonstrated:**
- Direct comparison: OptimizR (Rust) vs Python libraries
- Direct comparison: Optimiz-rs (Rust) vs Python libraries
- Benchmarks against: hmmlearn, scipy, sklearn
- Performance metrics and speedup calculations
@@ -178,7 +178,7 @@ from optimizr import (
### Python Wrapper Design (Brilliant!)
OptimizR uses a **two-layer architecture**:
Optimiz-rs uses a **two-layer architecture**:
1. **Rust Core** (`src/` with PyO3):
- `HMMParams` class
@@ -237,7 +237,7 @@ This design is **excellent** because:
## Testing Summary
| Notebook | Status | OptimizR Features | Test Result |
| Notebook | Status | Optimiz-rs Features | Test Result |
|----------|--------|-------------------|-------------|
| 01_hmm_tutorial.ipynb | ✅ PASS | HMM (Rust) | All cells run |
| 02_mcmc_tutorial.ipynb | ✅ PASS | mcmc_sample | Imports OK |
@@ -277,7 +277,7 @@ This design is **excellent** because:
**Actual Status:** Notebooks use Python wrappers correctly
**What I Learned:**
1. OptimizR has excellent two-layer design
1. Optimiz-rs has excellent two-layer design
2. Python wrappers provide familiar OOP interface
3. Rust acceleration is transparent to users
4. Only 1 minor fix needed (random_state parameter)
docs/archive/PROJECT_SUMMARY.md
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@@ -1,8 +1,8 @@
# OptimizR Project Summary
# Optimiz-rs Project Summary
## What is OptimizR?
## What is Optimiz-rs?
OptimizR is a **general-purpose optimization library** that provides high-performance implementations of advanced algorithms in Rust with easy-to-use Python bindings. It's designed to be fast, reliable, and production-ready for open-source distribution.
Optimiz-rs is a **general-purpose optimization library** that provides high-performance implementations of advanced algorithms in Rust with easy-to-use Python bindings. It's designed to be fast, reliable, and production-ready for open-source distribution.
## Key Features
@@ -140,7 +140,7 @@ x_opt, f_min = differential_evolution(
## Differences from rust-hft-arbitrage-lab
| Aspect | rust-hft-arbitrage-lab | OptimizR |
| Aspect | rust-hft-arbitrage-lab | Optimiz-rs |
|--------|----------------------|----------|
| **Purpose** | HFT trading strategies | General optimization library |
| **Scope** | Trading-specific | Domain-agnostic |
@@ -165,7 +165,7 @@ x_opt, f_min = differential_evolution(
```bash
git init
git add .
git commit -m "Initial commit: OptimizR v0.1.0"
git commit -m "Initial commit: Optimiz-rs v0.1.0"
git remote add origin https://github.com/ThotDjehuty/optimiz-r.git
git push -u origin main
```
@@ -217,7 +217,7 @@ Based on benchmarks from rust-hft-arbitrage-lab:
## Marketing/Outreach
1. **Reddit**: r/rust, r/python, r/MachineLearning
2. **Hacker News**: "Show HN: OptimizR - Fast optimization algorithms in Rust"
2. **Hacker News**: "Show HN: Optimiz-rs - Fast optimization algorithms in Rust"
3. **Twitter/X**: Tweet with #rustlang #python
4. **PyPI**: Ensure good package description
5. **GitHub Topics**: optimization, rust, python, scientific-computing
docs/archive/REFACTORING.md
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# OptimizR Refactoring Summary
# Optimiz-rs Refactoring Summary
## Overview
This document summarizes the major refactoring applied to OptimizR to improve modularity, introduce functional programming patterns, implement design patterns, and add concurrency support.
This document summarizes the major refactoring applied to Optimiz-rs to improve modularity, introduce functional programming patterns, implement design patterns, and add concurrency support.
## Architecture Changes
@@ -369,7 +369,7 @@ impl ProposalStrategy for MyProposal {
## Conclusion
This refactoring significantly improves OptimizR's:
This refactoring significantly improves Optimiz-rs's:
- **Modularity**: Clear trait boundaries, easy to extend
- **Maintainability**: Builder patterns, functional utilities reduce boilerplate
- **Performance**: Parallel execution, memoization, lazy evaluation
docs/archive/SETUP_COMPLETE.md
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@@ -1,4 +1,4 @@
# OptimizR Setup Complete! ✅
# Optimiz-rs Setup Complete! ✅
## What Was Done
@@ -136,7 +136,7 @@ pytest tests/ -v -k HMM # Run HMM tests only
## Performance
OptimizR provides **50-100x speedup** over pure Python for:
Optimiz-rs provides **50-100x speedup** over pure Python for:
- HMM fitting (71x faster)
- MCMC sampling (71x faster)
- Differential Evolution (53x faster)
@@ -144,7 +144,7 @@ OptimizR provides **50-100x speedup** over pure Python for:
## Summary
The OptimizR project is now **fully functional** with:
The Optimiz-rs project is now **fully functional** with:
- ✅ Zero compilation errors
- ✅ All tests passing
- ✅ Docker support
docs/archive/TIMESERIES_HELPERS_IMPLEMENTATION.md
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@@ -1,7 +1,7 @@
# Time-Series Integration Helpers Implementation Summary
## Overview
Completed Priority 3 from Enhancement Strategy: Time-series integration helpers for OptimizR v0.3.0. These 6 helper functions bridge OptimizR's optimization capabilities with time-series analysis, particularly useful for regime-switching models and pairs trading strategies.
Completed Priority 3 from Enhancement Strategy: Time-series integration helpers for Optimiz-rs v0.3.0. These 6 helper functions bridge Optimiz-rs's optimization capabilities with time-series analysis, particularly useful for regime-switching models and pairs trading strategies.
## Implementation Details
@@ -15,7 +15,7 @@ Completed Priority 3 from Enhancement Strategy: Time-series integration helpers
- Volatility proxy: squared returns
- Lagged returns for each lag period
- Returns: Feature matrix (N-max_lag rows × (3 + num_lags) columns)
- Use case: Prepare price data for OptimizR's HMM regime detection
- Use case: Prepare price data for Optimiz-rs's HMM regime detection
2. **`rolling_hurst_exponent(returns: &[f64], window_size: usize) -> Vec<f64>`**
- Purpose: Detect mean-reversion vs trending behavior
@@ -146,7 +146,7 @@ import optimizr
prices = [100.0, 101.5, 99.8, 102.3, 103.7]
features = optimizr.prepare_for_hmm_py(prices, [1, 2])
# Use with OptimizR's HMM for regime detection
# Use with Optimiz-rs's HMM for regime detection
```
### Mean-Reversion Check
docs/source/algorithms/differential_evolution.md
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@@ -303,7 +303,7 @@ print(f"Function evaluations: {result.nfev}")
## Adaptive Control (jDE)
OptimizR implements **jDE** (self-adaptive DE), where the parameters $F$ and $CR$
Optimiz-rs implements **jDE** (self-adaptive DE), where the parameters $F$ and $CR$
evolve with the population:
$$
docs/source/algorithms/mcmc.md
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@@ -161,7 +161,7 @@ $$
This is symmetric, so Metropolis acceptance applies.
**This is what OptimizR implements.**
**This is what Optimiz-rs implements.**
---
docs/source/conf.py
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@@ -6,7 +6,7 @@ import sys
sys.path.insert(0, os.path.abspath('../../python'))
# -- Project information -----------------------------------------------------
project = 'OptimizR'
project = 'Optimiz-rs'
copyright = '2026, HFThot Research Lab'
author = 'HFThot Research Lab'
release = '0.3.0'
@@ -32,8 +32,8 @@ exclude_patterns = []
# -- Options for HTML output -------------------------------------------------
html_theme = 'furo' # Modern, clean theme
html_static_path = ['_static']
html_title = 'OptimizR Documentation'
html_short_title = 'OptimizR'
html_title = 'Optimiz-rs Documentation'
html_short_title = 'Optimiz-rs'
html_logo = 'logo_optimizrs.png'
html_favicon = 'logo_optimizrs.png'
docs/source/examples.md
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@@ -1,6 +1,6 @@
# Examples
Practical snippets for every OptimizR component.
Practical snippets for every Optimiz-rs component.
## Differential Evolution (global optimization)
docs/source/getting-started.md
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@@ -12,7 +12,7 @@ pip install -r docs/requirements.txt
pip install maturin numpy
```
## 2. Build and install OptimizR locally
## 2. Build and install Optimiz-rs locally
```bash
pip install .
@@ -26,7 +26,7 @@ maturin develop --release
python - <<'PY'
import optimizr
from optimizr import differential_evolution, HMM
print("OptimizR version:", optimizr.__version__)
print("Optimiz-rs version:", optimizr.__version__)
# Simple objective
f = lambda x: sum(v * v for v in x)
docs/source/index.rst
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@@ -1,6 +1,6 @@
.. OptimizR documentation master file
.. Optimiz-rs documentation master file
OptimizR Documentation
Optimiz-rs Documentation
======================
**High-performance optimization algorithms in Rust with Python bindings**
@@ -13,7 +13,7 @@ OptimizR Documentation
:target: https://github.com/ThotDjehuty/optimiz-r/blob/main/LICENSE
:alt: License
OptimizR provides blazingly fast, production-ready implementations of advanced optimization and statistical inference algorithms. Built with Rust for maximum performance and exposed to Python through PyO3, it delivers **50-100× speedup** over pure Python implementations.
Optimiz-rs provides blazingly fast, production-ready implementations of advanced optimization and statistical inference algorithms. Built with Rust for maximum performance and exposed to Python through PyO3, it delivers **50-100× speedup** over pure Python implementations.
.. toctree::
:maxdepth: 2
docs/source/installation.md
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## Install from PyPI
**Coming soon**: OptimizR will be available on PyPI.
**Coming soon**: Optimiz-rs will be available on PyPI.
```bash
pip install optimizr
docs/source/theory/mathematical_foundations.md
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@@ -1,6 +1,6 @@
# Mathematical Foundations
This page collects the core equations driving OptimizR’s Rust kernels, plus short intuition blurbs and micro-checks you can run in a notebook. For visuals and full walkthroughs, see the example notebooks in `examples/notebooks/`.
This page collects the core equations driving Optimiz-rs’s Rust kernels, plus short intuition blurbs and micro-checks you can run in a notebook. For visuals and full walkthroughs, see the example notebooks in `examples/notebooks/`.
## Differential Evolution (DE)
@@ -29,7 +29,7 @@ $$
\end{cases}
$$
**Self-adaptive jDE (used by OptimizR):**
**Self-adaptive jDE (used by Optimiz-rs):**
$$
F_i^{g+1} = \begin{cases}
F_{\min} + r_1 \cdot F_{\max} & r_2 < \tau_1,\\
@@ -52,7 +52,7 @@ $$
-\partial_t V(t,x) = \inf_{u\in\mathcal{U}} \Big[ \ell(x,u) + \nabla_x V(t,x)^{\top} b(x,u) + \tfrac12 \operatorname{Tr}\big(\sigma\sigma^{\top}(x,u) \, \nabla_x^2 V(t,x)\big) \Big],\quad V(T,x) = g(x).
$$
OptimizR uses finite differences with backward time-stepping and optional policy iteration. On a uniform grid $(t_n, x_j)$:
Optimiz-rs uses finite differences with backward time-stepping and optional policy iteration. On a uniform grid $(t_n, x_j)$:
$$
V^{n} = \min_{u}\Big\{ \ell(x_j,u)\,\Delta t + V^{n+1} + \nabla_x V^{n+1}\cdot b\,\Delta t + \tfrac12 \operatorname{Tr}(\sigma\sigma^{\top}\nabla_x^2 V^{n+1})\,\Delta t \Big\}.
$$
@@ -62,7 +62,7 @@ The control that attains the minimum yields the feedback policy $u^{\star}(x_j,
## Mean Field Games (1D solver)
OptimizR’s MFG module solves the coupled system for value $u$ and density $m$:
Optimiz-rs’s MFG module solves the coupled system for value $u$ and density $m$:
$$
\begin{aligned}
-\partial_t u(t,x) - \nu\,\partial_{xx} u(t,x) + H\big(x,\partial_x u(t,x), m(t,x)\big) &= 0,\\
@@ -100,7 +100,7 @@ For target density $\pi(x)$ and proposal $q(x'\mid x)$:
$$
\alpha(x \to x') = \min\Big(1, \frac{\pi(x')\, q(x \mid x')}{\pi(x)\, q(x' \mid x)}\Big).
$$
OptimizR uses symmetric Gaussian proposals (so $q$ cancels) by default, with optional bounds projection and burn-in.
Optimiz-rs uses symmetric Gaussian proposals (so $q$ cancels) by default, with optional bounds projection and burn-in.
**Heuristic:** Tune proposal std so acceptance is ~0.25–0.35 for moderate dimensions; see `examples/notebooks/02_mcmc.ipynb` for trace plots.