stockula 0.8.0

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Stockula

Stockula is a comprehensive Python trading platform that provides tools for technical analysis, backtesting, data fetching, and price forecasting. Built with modern Python practices, it integrates popular financial libraries to offer a complete solution for quantitative trading strategy development.

• Stockula
  • ✨ Features
  • 🚀 Quick Start
    • Installation
    • Basic Usage
    • Configuration Example
    • Backtest-Optimized Allocation
    • Forecast Evaluation
  • 📚 Documentation
    • 🏁 Getting Started
    • 📖 User Guide
    • 🔧 API Reference
    • 🛠️ Development
    • 🔍 Help
  • 🏗️ Architecture
  • 📋 Requirements
    • Key Dependencies
  • 🎨 Rich CLI Examples
    • Progress Tracking
    • Results Tables
    • Ticker-Level Backtest Results
    • Strategy Summaries
  • 🚀 CI/CD
    • Continuous Integration
    • Commit Guidelines
  • 🔗 Links
  • 📝 License
  • 🤝 Contributing
    • Development Setup

✨ Features

• 📊 Technical Analysis: 40+ indicators (SMA, EMA, RSI, MACD, Bollinger Bands, etc.)
• 🔄 Backtesting: Test trading strategies with realistic broker costs and commission structures
• 📈 Data Fetching: Real-time and historical market data via yfinance with intelligent SQLite caching
• 🔮 Price Forecasting: Automated time series forecasting using AutoTS with two modes:
  • Future prediction mode: Forecast N days from today
  • Historical evaluation mode: Train/test split with accuracy metrics (RMSE, MAE, MAPE)
• 🎨 Rich CLI Interface: Beautiful progress bars, tables, and colored output
• 🗄️ Database Caching: Automatic SQLite caching for offline analysis and fast data access
• 🚀 Modern Python: Built with uv for fast package management and Pydantic for configuration

🚀 Quick Start

Installation

1. Install uv (if not already installed):

   curl -LsSf https://astral.sh/uv/install.sh | sh
   

2. Clone and install:

   git clone https://github.com/mkm29/stockula.git
   cd stockula
   uv sync
   

Basic Usage

# Analyze a single stock
uv run python -m stockula.main --ticker AAPL

# Run with configuration file
cp examples/config.simple.yaml .config.yaml
uv run python -m stockula.main

# Run specific analysis modes
uv run python -m stockula.main --ticker GOOGL --mode ta        # Technical analysis
uv run python -m stockula.main --ticker MSFT --mode backtest  # Backtesting (results sorted by return, highest first)
uv run python -m stockula.main --ticker NVDA --mode forecast  # Forecasting (results sorted by return, highest first)

Configuration Example

data:
  start_date: "2023-01-01"
  end_date: null

portfolio:
  initial_capital: 100000
  allocation_method: equal_weight
  tickers:
    - symbol: AAPL
      quantity: 10
    - symbol: GOOGL
      quantity: 5

backtest:
  initial_cash: 10000.0
  broker_config:
    name: "robinhood"  # Zero commission + TAF
  strategies:
    - name: smacross
      parameters:
        fast_period: 10
        slow_period: 20

Backtest-Optimized Allocation

Stockula includes an advanced allocation strategy that uses historical backtesting to optimize portfolio allocation:

# config.yaml for backtest-optimized allocation
portfolio:
  initial_capital: 100000
  allocation_method: backtest_optimized
  tickers:
    - symbol: AAPL
      category: TECH
      quantity: 0  # Placeholder - will be calculated
    - symbol: SPY
      category: INDEX
      quantity: 0  # Placeholder - will be calculated
    - symbol: GLD
      category: COMMODITY
      quantity: 0  # Placeholder - will be calculated
    - symbol: NVDA
      category: MOMENTUM
      quantity: 0  # Placeholder - will be calculated

# Configure backtest optimization
backtest_optimization:
  train_start_date: "2023-01-01"
  train_end_date: "2023-12-31"
  test_start_date: "2024-01-01"
  test_end_date: "2024-06-30"
  ranking_metric: "Return [%]"    # Default, can also use "Sharpe Ratio", etc.
  min_allocation_pct: 2.0
  max_allocation_pct: 25.0
  initial_allocation_pct: 2.0

The BacktestOptimizedAllocator will:

1. Test 11 different trading strategies on each asset using training data
2. Select the best-performing strategy for each asset
3. Evaluate performance on test data
4. Allocate capital based on test performance (higher return = larger allocation by default)

See the Allocation Strategies documentation for more details.

Note: Currently, the backtest_optimized allocation method requires placeholder quantities in the config. Full CLI integration is planned for a future release.

Forecast Evaluation

When running forecasts in evaluation mode (with train/test split), Stockula provides accuracy metrics:

               Portfolio Value
┏━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ Metric          ┃ Date       ┃ Value      ┃
┡━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ Observed Value  │ 2025-04-01 │ $20,000.00 │
│ Predicted Value │ 2025-04-30 │ $20,201.99 │
│ Accuracy        │ 2025-04-30 │ 92.4190%   │
└─────────────────┴────────────┴────────────┘

How Accuracy is Calculated:

Portfolio accuracy is calculated as: Accuracy = 100% - MAPE

Where MAPE (Mean Absolute Percentage Error) measures the average percentage difference between predicted and actual prices. For example:

• If a stock's MAPE is 6.11%, its accuracy is 93.89%
• The portfolio accuracy is the average of all individual stock accuracies

This provides an intuitive measure where:

• 100% = Perfect prediction
• 90%+ = Excellent forecast
• 80-90% = Good forecast
• <80% = Consider improving model or data

📚 Documentation

For comprehensive documentation, visit our MkDocs Documentation Site:

🏁 Getting Started

• Installation Guide - Detailed setup instructions
• Quick Start - Common workflows and examples
• Configuration - Complete configuration reference

📖 User Guide

• Architecture Overview - System design and data flow
• Data Fetching - Market data and caching system
• Technical Analysis - 40+ indicators and usage
• Backtesting - Strategy testing with realistic costs
• Allocation Strategies - Portfolio allocation methods including backtest optimization
• Forecasting - AutoTS time series prediction
• Forecasting Models - Fast & full financial model details
• Rich CLI Features - Enhanced command-line interface

🔧 API Reference

• Strategies API - Built-in and custom trading strategies
• Broker Configuration - TODO Commission structures and fee models
• Data Models - TODO Pydantic models and validation
• Database API - TODO SQLite operations and CLI

🛠️ Development

• Testing - Comprehensive testing guide, strategy, and coverage
• CI/CD - Continuous integration and deployment with GitHub Actions

🔍 Help

• Troubleshooting - Common issues and solutions

🏗️ Architecture

graph TB
    subgraph "User Interface"
        CLI[CLI main.py]
        Config[Configuration<br/>.config.yaml]
    end

    subgraph "Core Domain"
        Factory[Domain Factory]
        Portfolio[Portfolio]
        Asset[Asset]
    end

    subgraph "Allocation Module"
        Allocator[Base Allocator]
        STD[Standard Allocator]
        OPT[Backtest Optimized<br/>Allocator]
    end

    subgraph "Data Layer"
        Fetcher[Data Fetcher<br/>yfinance wrapper]
        DB[(SQLite Database<br/>stockula.db)]
    end

    subgraph "Analysis Modules"
        TA[Technical Analysis<br/>finta]
        BT[Backtesting<br/>strategies]
        FC[Forecasting<br/>AutoTS]
    end

    CLI --> Config
    Config --> Factory
    Factory --> Portfolio
    Factory --> Allocator
    STD --> Allocator
    OPT --> Allocator
    OPT --> BT
    TA --> Fetcher
    BT --> Fetcher
    FC --> Fetcher
    Fetcher --> DB

    style CLI fill:#2196F3,stroke:#1976D2,color:#fff
    style Config fill:#4CAF50,stroke:#388E3C,color:#fff
    style DB fill:#FF9800,stroke:#F57C00,color:#fff

📋 Requirements

• Python: 3.13 or higher
• Operating System: macOS, Linux, or Windows
• Memory: 8GB RAM recommended
• Storage: 100MB free space

Key Dependencies

• pandas: Data manipulation and analysis
• yfinance: Yahoo Finance data fetching
• finta: Financial technical analysis indicators
• backtesting: Strategy backtesting framework
• autots: Automated time series forecasting
• rich: Enhanced CLI formatting with progress bars and tables
• pydantic: Data validation and settings management

🎨 Rich CLI Examples

Progress Tracking

⠋ Backtesting SMACROSS on AAPL... ━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 85% 0:00:02

Results Tables

                           Portfolio Composition
┏━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ Ticker ┃ Category    ┃ Quantity ┃ Allocation % ┃      Value ┃ Status    ┃
┡━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━┩
│ SPY    │ INDEX       │     1.00 │         0.7% │    $637.10 │ Hold Only │
│ VOO    │ INDEX       │     1.00 │         0.6% │    $585.58 │ Hold Only │
│ DFUSX  │ INDEX       │     1.00 │         0.0% │     $42.40 │ Hold Only │
│ FSKAX  │ INDEX       │     1.00 │         0.2% │    $175.65 │ Hold Only │
│ FSMDX  │ INDEX       │   199.00 │         7.8% │  $7,279.42 │ Hold Only │
│ FXAIX  │ INDEX       │     1.00 │         0.2% │    $221.98 │ Hold Only │
│ NVDA   │ MOMENTUM    │     1.00 │         0.2% │    $173.50 │ Tradeable │
│ AMD    │ MOMENTUM    │     1.00 │         0.2% │    $166.47 │ Tradeable │
│ TSM    │ MOMENTUM    │     1.00 │         0.3% │    $245.60 │ Tradeable │
│ AAPL   │ MOMENTUM    │     1.00 │         0.2% │    $213.88 │ Tradeable │
│ MSFT   │ MOMENTUM    │     1.00 │         0.5% │    $513.71 │ Tradeable │
│ GOOGL  │ MOMENTUM    │     1.00 │         0.2% │    $193.18 │ Tradeable │
│ AMZN   │ MOMENTUM    │     1.00 │         0.2% │    $231.44 │ Tradeable │
│ META   │ MOMENTUM    │     1.00 │         0.8% │    $712.68 │ Tradeable │
│ TSLA   │ MOMENTUM    │     1.00 │         0.3% │    $316.06 │ Tradeable │
│ PLTR   │ MOMENTUM    │   469.00 │        79.7% │ $74,477.20 │ Tradeable │
│ LIDR   │ SPECULATIVE │  1631.00 │         7.7% │  $7,233.48 │ Tradeable │
│ OPEN   │ SPECULATIVE │     1.00 │         0.0% │      $2.54 │ Tradeable │
│ SOFI   │ SPECULATIVE │     1.00 │         0.0% │     $21.20 │ Tradeable │
│ IONQ   │ SPECULATIVE │     1.00 │         0.0% │     $43.17 │ Tradeable │
└────────┴─────────────┴──────────┴──────────────┴────────────┴───────────┘

Ticker-Level Backtest Results

                             Ticker-Level Backtest Results
┏━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━┓
┃ Ticker ┃ Strategy       ┃   Return ┃ Sharpe Ratio ┃ Max Drawdown ┃ Trades ┃ Win Rate ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━┩
│ NVDA   │ VIDYA          │  +27.67% │         1.50 │       -3.43% │      0 │      N/A │
│ NVDA   │ SMACROSS       │  +44.60% │         0.64 │      -38.09% │      9 │    44.4% │
│ NVDA   │ DOUBLEEMACROSS │  +27.67% │         1.50 │       -3.43% │      0 │      N/A │
│ NVDA   │ VAMA           │  +41.34% │         0.59 │      -42.46% │      9 │    33.3% │
│ NVDA   │ ER             │  +60.37% │         1.20 │      -14.22% │     10 │    40.0% │
| ...    │ ...            │      ... │         ...  │         ...  │    ... │      ... │
└────────┴────────────────┴──────────┴──────────────┴──────────────┴────────┴──────────┘

Strategy Summaries

Strategy summaries are displayed in descending order by "Return During Period" (highest returns first):

╭─────────────────────────────────────────────────── STRATEGY: DOUBLEEMACROSS ────────────────────────────────────────────────────╮
│                                                                                                                                 │
│  Parameters: Default                                                                                                            │
│  Broker: robinhood (zero-commission)                                                                                            │
│                                                                                                                                 │
│  Portfolio Value at Start Date: $19,997.94                                                                                      │
│  Portfolio Value at End (Backtest): $30,261.55                                                                                  │
│                                                                                                                                 │
│  Strategy Performance:                                                                                                          │
│    Average Return: +51.32%                                                                                                      │
│    Winning Stocks: 12                                                                                                           │
│    Losing Stocks: 2                                                                                                             │
│    Total Trades: 22                                                                                                             │
│                                                                                                                                 │
│  Return During Period: $10,263.61 (+51.32%)                                                                                     │
│                                                                                                                                 │
│  Detailed report saved to: results/reports/strategy_report_doubleemacross_20250727_221642.json                                  │
│                                                                                                                                 │
╰─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯

🚀 CI/CD

Continuous Integration

Stockula uses GitHub Actions for automated testing and deployment:

• Testing (test.yml): Runs on all pull requests and pushes to main

  • Linting: Code style checks with ruff
  • Unit Tests: Fast, isolated tests with coverage reporting
  • Integration Tests: Currently disabled, will test with SQLite database

• Release Management (release-please.yml): Automated versioning and releases

  • Monitors commits using Conventional Commits
  • Creates release PRs automatically
  • Publishes to PyPI on release

• Docker Builds (docker-build.yml): Multi-platform container images

  • Triggers on version tags (v*)
  • Builds for linux/amd64 and linux/arm64/v8
  • Publishes to GitHub Container Registry

Commit Guidelines

This project uses Conventional Commits:

• feat: New features
• fix: Bug fixes
• chore: Maintenance tasks
• docs: Documentation updates
• test: Test additions or changes

🔗 Links

• 📖 Full Documentation: docs/
• 🐛 Issue Tracker: GitHub Issues
• 💡 Feature Requests: GitHub Discussions

📝 License

MIT License - see LICENSE file for details.

🤝 Contributing

Contributions are welcome! Please see our Contributing Guide for development setup and guidelines.

Development Setup

1. Install pre-commit hooks:

   uv run pre-commit install
   

2. Run tests and linting:

   # Run tests
   uv run pytest
   
   # Run linting (consistent with CI)
   uv run lint
   
   # Or run individual commands
   uv run ruff check src tests
   uv run ruff format --check src tests
   
   # Fix linting issues
   uv run ruff check src tests --fix
   uv run ruff format src tests
   

3. Manual pre-commit run:

   uv run pre-commit run --all-files
   

This project uses:

• Conventional Commits for commit messages
• Release Please for automated releases
• pre-commit for code quality checks

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📚 For detailed documentation, examples, and API references, visit our comprehensive documentation site.