Add MkDocs documentation with Material theme and mkdocstrings #29

Copilot · 2025-12-02T13:30:04Z

Adds comprehensive API documentation using MkDocs + Material theme with automatic docstring extraction via mkdocstrings.

Changes

Configuration

mkdocs.yml - Material theme with dark/light mode, code copy, search, navigation tabs
pyproject.toml - Added docs optional dependency group

Documentation Structure (`docs/`)

Getting Started: Installation guide, quickstart with examples
API Reference: All indicator categories with mkdocstrings auto-generation
- Overlap (SMA, EMA, BBANDS, SAR, etc.)
- Momentum (RSI, MACD, STOCH, ADX, etc.)
- Volume, Volatility, Cycle, Statistics, Math, Price Transform
- Pattern Recognition (60+ candlestick patterns, chart patterns)
- Streaming indicators, Pandas extension
Examples: Complete usage examples with trading strategies
Performance: Benchmarks comparing NumPy vs Numba backends
Accuracy: TA-Lib compatibility verification

CI/CD

.github/workflows/docs.yml - Build on PR, deploy to GitHub Pages on main merge

README

Added documentation badge and links

Usage

# Install docs dependencies
pip install -e ".[docs]"

# Build locally
mkdocs serve

# Or build static site
mkdocs build

Documentation will be available at https://deepentropy.github.io/numta after merge.

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Original prompt

Objectif

Ajouter une documentation API complète et moderne pour numta en utilisant MkDocs + Material + mkdocstrings, et améliorer les docstrings existantes.

Tâches

1. Configuration MkDocs

Créer mkdocs.yml avec:

Theme Material avec mode sombre/clair

Plugin mkdocstrings pour génération auto depuis docstrings

Navigation organisée par catégorie d'indicateurs

Recherche intégrée

Code copy buttons

2. Structure de Documentation

Créer la structure docs/:
docs/
├── index.md                    # Page d'accueil avec overview
├── getting-started/
│   ├── installation.md         # Guide d'installation
│   └── quickstart.md           # Guide de démarrage rapide
├── api/
│   ├── index.md               # Vue d'ensemble API
│   ├── overlap.md             # SMA, EMA, BBANDS, WMA, DEMA, TEMA, KAMA, MAMA, T3, TRIMA, SAR
│   ├── momentum.md            # RSI, MACD, STOCH, ADX, CCI, MFI, WILLR, MOM, ROC, etc.
│   ├── volume.md              # OBV, AD, ADOSC
│   ├── volatility.md          # ATR, NATR, TRANGE
│   ├── cycle.md               # HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDLINE, HT_TRENDMODE
│   ├── statistics.md          # LINEARREG, STDDEV, VAR, CORREL, BETA, TSF
│   ├── math.md                # MAX, MIN, SUM, MINMAX, etc.
│   ├── price_transform.md     # MEDPRICE, TYPPRICE, WCLPRICE, MIDPOINT, MIDPRICE
│   ├── patterns.md            # Candlestick patterns (CDL*) + Chart patterns
│   ├── streaming.md           # Streaming indicators
│   └── pandas.md              # Pandas extension (.ta accessor)
├── examples/
│   └── basic_usage.md         # Exemples d'utilisation
├── performance.md             # Benchmarks et performance
└── accuracy.md                # Comparaison accuracy vs TA-Lib
3. Améliorer les Docstrings

Améliorer les docstrings dans les fichiers source (style Google) pour les fonctions principales:

src/numta/api/overlap.py

SMA, EMA, WMA, DEMA, TEMA, KAMA, MAMA, T3, TRIMA, BBANDS, SAR, SAREXT, MA

Exemple de docstring améliorée:
def SMA(close: np.ndarray, timeperiod: int = 30) -> np.ndarray:
    """
    Simple Moving Average.
    
    Calculate the arithmetic mean of the last `timeperiod` values. The SMA
    is a lagging indicator commonly used to identify trend direction.
    
    Args:
        close: Array of closing prices (float64).
        timeperiod: Number of periods for the moving average. Default is 30.
    
    Returns:
        Array of SMA values. The first (timeperiod - 1) values are NaN.
    
    Examples:
        >>> import numpy as np
        >>> import numta
        >>> close = np.array([44.0, 44.5, 43.5, 44.5, 44.0])
        >>> numta.SMA(close, timeperiod=3)
        array([  nan,   nan, 44.  , 44.17, 44.  ])
    
    See Also:
        EMA: Exponential Moving Average (more weight to recent prices)
        WMA: Weighted Moving Average
        DEMA: Double Exponential Moving Average
    
    Note:
        Optimized with Numba JIT compilation when available.
    """
src/numta/api/momentum_indicators.py

RSI, MACD, STOCH, STOCHF, ADX, CCI, MFI, etc.

src/numta/api/volume_indicators.py

OBV, AD, ADOSC

src/numta/api/volatility_indicators.py

ATR, NATR, TRANGE

src/numta/api/cycle_indicators.py

HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDLINE, HT_TRENDMODE

4. Mise à jour pyproject.toml

Ajouter les dépendances docs:
[project.optional-dependencies]
docs = [
    "mkdocs>=1.5.0",
    "mkdocs-material>=9.0.0",
    "mkdocstrings[python]>=0.24.0",
    "mkdocs-gen-files>=0.5.0",
    "mkdocs-literate-nav>=0.6.0",
]
5. GitHub Actions pour Documentation

Créer .github/workflows/docs.yml pour:

Build de la documentation sur chaque PR

Déploiement automatique sur GitHub Pages lors du merge sur main

6. README update

Ajouter un lien vers la documentation dans le README.md et un badge.

Critères d'Acceptation

mkdocs.yml configuré correctement

Structure docs/ créée avec contenu

Docstrings améliorées pour au moins les fonctions principales (overlap, momentum)

pyproject.toml mis à jour avec dépendances docs

GitHub Actions workflow pour docs

Documentation buildable localement avec mkdocs serve

This pull request was created as a result of the following prompt from Copilot chat.

Objectif

Ajouter une documentation API complète et moderne pour numta en utilisant MkDocs + Material + mkdocstrings, et améliorer les docstrings existantes.

Tâches

1. Configuration MkDocs

Créer mkdocs.yml avec:

Theme Material avec mode sombre/clair

Plugin mkdocstrings pour génération auto depuis docstrings

Navigation organisée par catégorie d'indicateurs

Recherche intégrée

Code copy buttons

2. Structure de Documentation

Créer la structure docs/:
docs/
├── index.md                    # Page d'accueil avec overview
├── getting-started/
│   ├── installation.md         # Guide d'installation
│   └── quickstart.md           # Guide de démarrage rapide
├── api/
│   ├── index.md               # Vue d'ensemble API
│   ├── overlap.md             # SMA, EMA, BBANDS, WMA, DEMA, TEMA, KAMA, MAMA, T3, TRIMA, SAR
│   ├── momentum.md            # RSI, MACD, STOCH, ADX, CCI, MFI, WILLR, MOM, ROC, etc.
│   ├── volume.md              # OBV, AD, ADOSC
│   ├── volatility.md          # ATR, NATR, TRANGE
│   ├── cycle.md               # HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDLINE, HT_TRENDMODE
│   ├── statistics.md          # LINEARREG, STDDEV, VAR, CORREL, BETA, TSF
│   ├── math.md                # MAX, MIN, SUM, MINMAX, etc.
│   ├── price_transform.md     # MEDPRICE, TYPPRICE, WCLPRICE, MIDPOINT, MIDPRICE
│   ├── patterns.md            # Candlestick patterns (CDL*) + Chart patterns
│   ├── streaming.md           # Streaming indicators
│   └── pandas.md              # Pandas extension (.ta accessor)
├── examples/
│   └── basic_usage.md         # Exemples d'utilisation
├── performance.md             # Benchmarks et performance
└── accuracy.md                # Comparaison accuracy vs TA-Lib
3. Améliorer les Docstrings

Améliorer les docstrings dans les fichiers source (style Google) pour les fonctions principales:

src/numta/api/overlap.py

SMA, EMA, WMA, DEMA, TEMA, KAMA, MAMA, T3, TRIMA, BBANDS, SAR, SAREXT, MA

Exemple de docstring améliorée:
def SMA(close: np.ndarray, timeperiod: int = 30) -> np.ndarray:
    """
    Simple Moving Average.
    
    Calculate the arithmetic mean of the last `timeperiod` values. The SMA
    is a lagging indicator commonly used to identify trend direction.
    
    Args:
        close: Array of closing prices (float64).
        timeperiod: Number of periods for the moving average. Default is 30.
    
    Returns:
        Array of SMA values. The first (timeperiod - 1) values are NaN.
    
    Examples:
        >>> import numpy as np
        >>> import numta
        >>> close = np.array([44.0, 44.5, 43.5, 44.5, 44.0])
        >>> numta.SMA(close, timeperiod=3)
        array([  nan,   nan, 44.  , 44.17, 44.  ])
    
    See Also:
        EMA: Exponential Moving Average (more weight to recent prices)
        WMA: Weighted Moving Average
        DEMA: Double Exponential Moving Average
    
    Note:
        Optimized with Numba JIT compilation when available.
    """
src/numta/api/momentum_indicators.py

RSI, MACD, STOCH, STOCHF, ADX, CCI, MFI, etc.

src/numta/api/volume_indicators.py

OBV, AD, ADOSC

src/numta/api/volatility_indicators.py

ATR, NATR, TRANGE

src/numta/api/cycle_indicators.py

HT_DCPERIOD, HT_DCPHASE, HT_PHASOR, HT_SINE, HT_TRENDLINE, HT_TRENDMODE

4. Mise à jour pyproject.toml

Ajouter les dépendances docs:
[project.optional-dependencies]
docs = [
    "mkdocs>=1.5.0",
    "mkdocs-material>=9.0.0",
    "mkdocstrings[python]>=0.24.0",
    "mkdocs-gen-files>=0.5.0",
    "mkdocs-literate-nav>=0.6.0",
]
5. GitHub Actions pour Documentation

Créer .github/workflows/docs.yml pour:

Build de la documentation sur chaque PR

Déploiement automatique sur GitHub Pages lors du merge sur main

6. README update

Ajouter un lien vers la documentation dans le README.md et un badge.

Critères d'Acceptation

mkdocs.yml configuré correctement

Structure docs/ créée avec contenu

Docstrings améliorées pour au moins les fonctions principales (overlap, momentum)

pyproject.toml mis à jour avec dépendances docs

GitHub Actions workflow pour docs

Documentation buildable localement avec mkdocs serve

✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.

Co-authored-by: deepentropy <8287111+deepentropy@users.noreply.github.com>

chatgpt-codex-connector · 2025-12-02T13:49:49Z

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Copilot

Pull request overview

This PR adds comprehensive MkDocs documentation with the Material theme and automatic API documentation via mkdocstrings. The documentation structure is well-organized with getting started guides, API reference, examples, performance benchmarks, and accuracy verification.

Key Changes

MkDocs configuration with Material theme, dark/light mode, code copy, and navigation features
Comprehensive documentation structure covering all indicator categories
GitHub Actions workflow for building and deploying to GitHub Pages
Documentation dependencies added to pyproject.toml

Reviewed changes

Copilot reviewed 22 out of 22 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
mkdocs.yml	MkDocs configuration with Material theme, mkdocstrings plugin, and organized navigation
pyproject.toml	Added docs dependency group with mkdocs, material theme, and mkdocstrings
.github/workflows/docs.yml	CI/CD workflow for building docs on PRs and deploying to GitHub Pages
README.md	Added documentation badge and links to documentation site
docs/index.md	Homepage with overview, highlights, and quick example
docs/getting-started/installation.md	Installation guide with optional dependencies
docs/getting-started/quickstart.md	Quick start guide with basic usage examples
docs/api/*.md	API reference pages for all indicator categories using mkdocstrings
docs/examples/basic_usage.md	Comprehensive usage examples including trading strategies
docs/performance.md	Performance benchmarks and optimization tips
docs/accuracy.md	TA-Lib compatibility verification and accuracy test results

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Copilot · 2025-12-02T13:54:01Z

docs/api/pandas.md

+
+# Stochastic returns two columns
+df.ta.stoch(append=True)
+# Adds: STOCHk_5_3_0_3_0, STOCHd_5_3_0_3_0


The Stochastic column names in the comment are incorrect. The pandas extension uses STOCH_SLOWK_{fastk}_{slowk}_{slowd} and STOCH_SLOWD_{fastk}_{slowk}_{slowd} format. With default parameters (fastk=5, slowk=3, slowd=3), the columns should be:

# Adds: STOCH_SLOWK_5_3_3, STOCH_SLOWD_5_3_3

Suggested change

# Adds: STOCHk_5_3_0_3_0, STOCHd_5_3_0_3_0

# Adds: STOCH_SLOWK_5_3_3, STOCH_SLOWD_5_3_3

Copilot · 2025-12-02T13:54:01Z

docs/api/pandas.md

+
+# Bollinger Bands returns three columns
+df.ta.bbands(append=True)
+# Adds: BBU_5_2, BBM_5_2, BBL_5_2


The Bollinger Bands column names are missing the decimal point. The pandas extension formats the column names with the float values, so with default parameters (timeperiod=5, nbdevup=2.0, nbdevdn=2.0), the columns should be:

# Adds: BBU_5_2.0, BBM_5, BBL_5_2.0

Suggested change

# Adds: BBU_5_2, BBM_5_2, BBL_5_2

# Adds: BBU_5_2.0, BBM_5, BBL_5_2.0

Copilot · 2025-12-02T13:54:02Z

docs/api/pandas.md

+## Working with Different Column Names
+
+If your DataFrame uses different column names:
+
+```python
+df = pd.DataFrame({
+    'price': prices,
+    'hi': highs,
+    'lo': lows,
+    'vol': volumes
+})
+
+# Specify column mappings
+df.ta.set_columns(close='price', high='hi', low='lo', volume='vol')
+
+# Now indicators use the correct columns
+df.ta.sma(timeperiod=20)
+```


The documentation references a non-existent df.ta.set_columns() method. The pandas extension automatically detects OHLCV columns from standard names (case-insensitive). For non-standard column names, users should use the column parameter on individual indicator calls instead. This section should be removed or rewritten to explain the actual auto-detection behavior and the column parameter.

Copilot · 2025-12-02T13:54:02Z

docs/api/pandas.md

+```python
+# MACD returns three columns
+df.ta.macd(append=True)
+# Adds: MACD_12_26_9, MACDsignal_12_26_9, MACDhist_12_26_9


The MACD column names in the comment are inconsistent with the actual implementation. The pandas extension uses MACDSignal and MACDHist (capitalized), but the comment shows MACDsignal and MACDhist (lowercase). Update the comment to match the actual column names:

# Adds: MACD_12_26_9, MACDSignal_12_26_9, MACDHist_12_26_9

Suggested change

# Adds: MACD_12_26_9, MACDsignal_12_26_9, MACDhist_12_26_9

# Adds: MACD_12_26_9, MACDSignal_12_26_9, MACDHist_12_26_9

Initial plan

ec4559a

Copilot AI assigned Copilot and deepentropy Dec 2, 2025

Copilot started work on behalf of deepentropy December 2, 2025 13:30 View session

Copilot AI and others added 2 commits December 2, 2025 13:42

Add comprehensive MkDocs documentation with Material theme

8f79e96

Co-authored-by: deepentropy <8287111+deepentropy@users.noreply.github.com>

Address code review feedback for docs

3550a27

Co-authored-by: deepentropy <8287111+deepentropy@users.noreply.github.com>

Copilot AI changed the title ~~[WIP] Add complete API documentation using MkDocs and mkdocstrings~~ Add MkDocs documentation with Material theme and mkdocstrings Dec 2, 2025

Copilot AI requested a review from deepentropy December 2, 2025 13:49

Copilot finished work on behalf of deepentropy December 2, 2025 13:49

deepentropy marked this pull request as ready for review December 2, 2025 13:49

Copilot AI review requested due to automatic review settings December 2, 2025 13:49

Copilot started reviewing on behalf of deepentropy December 2, 2025 13:50 View session

Copilot finished reviewing on behalf of deepentropy December 2, 2025 13:52

Copilot AI reviewed Dec 2, 2025

View reviewed changes

deepentropy merged commit 1d42b55 into main Dec 2, 2025
15 checks passed

deepentropy deleted the copilot/add-api-documentation-mkdocs branch December 2, 2025 13:55

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	# Adds: STOCHk_5_3_0_3_0, STOCHd_5_3_0_3_0
	# Adds: STOCH_SLOWK_5_3_3, STOCH_SLOWD_5_3_3

	# Adds: BBU_5_2, BBM_5_2, BBL_5_2
	# Adds: BBU_5_2.0, BBM_5, BBL_5_2.0

	# Adds: MACD_12_26_9, MACDsignal_12_26_9, MACDhist_12_26_9
	# Adds: MACD_12_26_9, MACDSignal_12_26_9, MACDHist_12_26_9

Add MkDocs documentation with Material theme and mkdocstrings #29

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Changes

Configuration

Documentation Structure (docs/)

CI/CD

README

Usage

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Objectif

Tâches

1. Configuration MkDocs

2. Structure de Documentation

3. Améliorer les Docstrings

src/numta/api/overlap.py

src/numta/api/momentum_indicators.py

src/numta/api/volume_indicators.py

src/numta/api/volatility_indicators.py

src/numta/api/cycle_indicators.py

4. Mise à jour pyproject.toml

5. GitHub Actions pour Documentation

6. README update

Critères d'Acceptation

Objectif

Tâches

1. Configuration MkDocs

2. Structure de Documentation

3. Améliorer les Docstrings

src/numta/api/overlap.py

src/numta/api/momentum_indicators.py

src/numta/api/volume_indicators.py

src/numta/api/volatility_indicators.py

src/numta/api/cycle_indicators.py

4. Mise à jour pyproject.toml

5. GitHub Actions pour Documentation

6. README update

Critères d'Acceptation

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Pull request overview

Key Changes

Reviewed changes

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Copilot AI commented Dec 2, 2025 •

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Documentation Structure (`docs/`)

`src/numta/api/overlap.py`

`src/numta/api/momentum_indicators.py`

`src/numta/api/volume_indicators.py`

`src/numta/api/volatility_indicators.py`

`src/numta/api/cycle_indicators.py`

`src/numta/api/overlap.py`

`src/numta/api/momentum_indicators.py`

`src/numta/api/volume_indicators.py`

`src/numta/api/volatility_indicators.py`

`src/numta/api/cycle_indicators.py`