semvec/pypi-packages huggingface dataset:
apilot 0.1.32
AI-Driven Qutan, Open to All
License: MIT
Requires Python: >=3.10

# APilot - AI-Driven Quantitative Trading Platform

<p align="center">
    <img src ="https://img.shields.io/badge/version-0.1.2-blueviolet.svg"/>
    <img src ="https://img.shields.io/badge/python-3.10|3.11|3.12-blue.svg" />
    <img src ="https://img.shields.io/badge/license-MIT-green.svg" />
</p>

## Overview

APilot is a high-performance quantitative trading framework focused on cryptocurrency and stock markets, developed by the AlphaPilot.tech team. The framework supports both strategy backtesting and live trading, providing a comprehensive solution for quantitative traders.

Official website: [www.alphapilot.tech](https://www.alphapilot.tech)

## Key Features

- **Event-driven architecture**: Built for high-performance, real-time trading systems
- **Multiple trading strategies**: Price Action strategies, Factor strategies (in development)
- **Professional execution algorithms**: BestLimit, TWAP algorithms
- **Comprehensive backtesting**: Accurate simulation with detailed performance analytics
- **Multi-exchange support**: Currently focusing on Binance, with more to come
- **Live trading capability**: Execute strategies in real-time with risk management
- **Extensible framework**: Easy to add new strategies, data sources, and exchanges

## Strategy Types

- **Price Action (PA) strategies**: Support for trend following, mean reversion, and other classic price action strategies
- **Factor strategies**: Quantitative strategies based on multi-factor models (in development)

## Technical Architecture

### Design Principles

- **Core Module**: Contains all abstract interfaces and core data structures
  - Abstract base classes (BaseEngine, BaseGateway, etc.)
  - Data models (OrderData)
  - Constant definitions (Direction, Interval, etc.)
  - Basic event system

- **Feature Modules**: Specific implementations for different domains
  - `execution/gateway/` - Exchange API implementations
  - `engine/` - Specific engine implementations
  - `strategy/` - Trading strategy templates and implementations
  - `performance/` - Performance calculation and reporting

## Installation



## Quick Start

### Backtesting a Strategy




### Running Live Trading


## Getting Started with Development

For detailed documentation on developing with APilot, please refer to our [Development Guide](docs/development_guide.md).

## Testing

```bash
# Run all tests
python -m pytest tests/

# Run specific test file
python -m pytest tests/test_bar_generator.py
```

## Contributing

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

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
sqltesty 0.2.0
Test SQL files using DuckDB and CSV fixtures
License: MIT
Requires Python: >=3.12

# SQLTest

A command-line tool to test SQL queries using DuckDB and CSV fixtures.

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## Installation

```bash
pip install .
```

## Usage

The `sqltest` command looks for SQL files in your project and tests them against fixture data.

```bash
# Run all SQL tests with default settings
sqltest

# Specify a custom test directory
sqltest --test-dir=custom/tests/path

# Run with verbose output
sqltest --verbose
```

The verbose output now includes enhanced error highlighting with color coding:
- Green for matching values and passed tests
- Red for mismatched values and failed tests
- Yellow for structural differences

Key features of the enhanced output:
- **Vertical alignment** of differences for improved visual clarity
- **Row-by-row comparison** that clearly shows which specific rows have issues
- **Color-coded output** to quickly identify discrepancies
- **Detailed column analysis** to understand patterns in the data

This makes it easier to identify and fix issues in your SQL queries, especially when dealing with complex datasets.

## Test Structure

- Place your SQL files anywhere in your repository
- Create a test directory (default: `tests/sqltest`)
- For each SQL file, create a subdirectory with the same name (without extension)
- Within each subdirectory, place:
  - `output.csv`: The expected output of your SQL query
  - Other CSV files named after tables referenced in your SQL query

### Example Structure

```
/your-project
  /some-dir
    query1.sql
    query2.sql
  /tests
    /sqltest
      /query1
        customers.csv     # Table data
        products.csv      # Table data
        output.csv        # Expected query result
      /query2
        table1.csv        # Table data
        output.csv        # Expected query result
```

## Development

```bash
# Install development dependencies
pip install -e ".[dev]"

# Run tests
pytest
```

If you want to run the examples, just do

```
cd examples

sqltest
```