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‎.gitignore‎

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@@ -164,4 +164,8 @@ cython_debug/
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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test.sh
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<<<<<<< HEAD
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test.sh
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=======
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# test.sh
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>>>>>>> e0e18b93a48f51c1ed656a62155be25a128e460c

‎CLAUDE.md‎

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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Development Commands
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### Package Management
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This project uses `uv` for dependency management (migrated from Poetry).
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```bash
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# Install dependencies
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uv sync --all-extras --dev
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# Add a new dependency
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uv add <package-name>
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# Add a dev dependency
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uv add --dev <package-name>
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```
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### Testing
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```bash
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# Run all tests
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uv run pytest tests
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# Run tests quietly
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uv run pytest -q tests
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# Run specific test file
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uv run pytest tests/test_trading/test_orders.py
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# Run with test script (includes API keys)
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./test.sh
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# Run specific tests with test script
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./test.sh tests/test_trading/test_orders.py
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```
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### Code Quality
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```bash
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# Run linter
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uv run ruff check
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# Run linter with auto-fix
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uv run ruff check --fix
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# Format code
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uv run ruff format
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```
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## Architecture Overview
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### Core API Structure
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The repository implements a Python wrapper for the Alpaca Trading API with the following architecture:
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1. **Main Entry Point**: `PyAlpacaAPI` class in `src/py_alpaca_api/__init__.py`
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- Initializes with API key, secret, and paper trading flag
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- Provides access to `trading` and `stock` modules
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2. **Trading Module** (`src/py_alpaca_api/trading/`)
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- `account.py`: Account information, activities, and portfolio history
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- `orders.py`: Order management and execution
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- `positions.py`: Position tracking and management
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- `watchlists.py`: Watchlist CRUD operations
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- `market.py`: Market clock and calendar data
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- `news.py`: Financial news from Yahoo Finance and Benzinga
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- `recommendations.py`: Stock recommendations and sentiment analysis
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3. **Stock Module** (`src/py_alpaca_api/stock/`)
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- `assets.py`: Asset information retrieval
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- `history.py`: Historical stock data
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- `screener.py`: Stock screening for gainers/losers
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- `predictor.py`: Prophet-based stock prediction
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- `latest_quote.py`: Real-time quote data
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4. **Models** (`src/py_alpaca_api/models/`)
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- Dataclass models for all API entities
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- `model_utils.py`: Utility functions for data transformation
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- Consistent pattern: each model has a `from_dict()` function
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5. **HTTP Layer** (`src/py_alpaca_api/http/`)
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- `requests.py`: Centralized HTTP request handling with retry logic
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- Configurable retry strategies for resilient API communication
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### Key Design Patterns
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1. **Modular Architecture**: Each domain (trading, stock, models) is self-contained
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2. **Dataclass Models**: All API responses are converted to typed dataclasses
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3. **Centralized HTTP**: Single point for API communication with built-in resilience
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4. **Factory Pattern**: `from_dict()` methods for model instantiation
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### API Authentication
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- Requires `ALPACA_API_KEY` and `ALPACA_SECRET_KEY` environment variables
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- Supports both paper and live trading environments
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- Test script includes default paper trading credentials
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### External Dependencies
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- **Data Processing**: pandas, numpy
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- **Time Handling**: pendulum
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- **Stock Prediction**: prophet
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- **Web Scraping**: beautifulsoup4
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- **Market Data**: yfinance
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- **HTTP**: requests with caching and rate limiting
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## Testing Approach
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Tests are organized by module:
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- `test_http/`: HTTP request handling tests
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- `test_models/`: Model creation and transformation tests
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- `test_stock/`: Stock module functionality tests
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- `test_trading/`: Trading operations tests
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All tests require API credentials (can use paper trading credentials).

‎GEMINI.md‎

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# GEMINI.md
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This file provides guidance to Gemini (gemini.google.com) when working with code in this repository.
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## Development Commands
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### Package Management
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This project uses `uv` for dependency management (migrated from Poetry).
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```bash
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# Install dependencies
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uv sync --all-extras --dev
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14+
# Add a new dependency
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uv add <package-name>
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17+
# Add a dev dependency
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uv add --dev <package-name>
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```
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### Testing
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```bash
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# Run all tests
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uv run pytest tests
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26+
# Run tests quietly
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uv run pytest -q tests
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# Run specific test file
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uv run pytest tests/test_trading/test_orders.py
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# Run with test script (includes API keys)
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./test.sh
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# Run specific tests with test script
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./test.sh tests/test_trading/test_orders.py
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```
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### Code Quality
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```bash
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# Run linter
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uv run ruff check
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# Run linter with auto-fix
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uv run ruff check --fix
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# Format code
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uv run ruff format
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```
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## Architecture Overview
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### Core API Structure
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The repository implements a Python wrapper for the Alpaca Trading API with the following architecture:
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1. **Main Entry Point**: `PyAlpacaAPI` class in `src/py_alpaca_api/__init__.py`
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- Initializes with API key, secret, and paper trading flag
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- Provides access to `trading` and `stock` modules
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2. **Trading Module** (`src/py_alpaca_api/trading/`)
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- `account.py`: Account information, activities, and portfolio history
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- `orders.py`: Order management and execution
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- `positions.py`: Position tracking and management
64+
- `watchlists.py`: Watchlist CRUD operations
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- `market.py`: Market clock and calendar data
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- `news.py`: Financial news from Yahoo Finance and Benzinga
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- `recommendations.py`: Stock recommendations and sentiment analysis
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3. **Stock Module** (`src/py_alpaca_api/stock/`)
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- `assets.py`: Asset information retrieval
71+
- `history.py`: Historical stock data
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- `screener.py`: Stock screening for gainers/losers
73+
- `predictor.py`: Prophet-based stock prediction
74+
- `latest_quote.py`: Real-time quote data
75+
76+
4. **Models** (`src/py_alpaca_api/models/`)
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- Dataclass models for all API entities
78+
- `model_utils.py`: Utility functions for data transformation
79+
- Consistent pattern: each model has a `from_dict()` function
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81+
5. **HTTP Layer** (`src/py_alpaca_api/http/`)
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- `requests.py`: Centralized HTTP request handling with retry logic
83+
- Configurable retry strategies for resilient API communication
84+
85+
### Key Design Patterns
86+
87+
1. **Modular Architecture**: Each domain (trading, stock, models) is self-contained
88+
2. **Dataclass Models**: All API responses are converted to typed dataclasses
89+
3. **Centralized HTTP**: Single point for API communication with built-in resilience
90+
4. **Factory Pattern**: `from_dict()` methods for model instantiation
91+
92+
### API Authentication
93+
- Requires `ALPACA_API_KEY` and `ALPACA_SECRET_KEY` environment variables
94+
- Supports both paper and live trading environments
95+
- Test script includes default paper trading credentials
96+
97+
### External Dependencies
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- **Data Processing**: pandas, numpy
99+
- **Time Handling**: pendulum
100+
- **Stock Prediction**: prophet
101+
- **Web Scraping**: beautifulsoup4
102+
- **Market Data**: yfinance
103+
- **HTTP**: requests with caching and rate limiting
104+
105+
## Testing Approach
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Tests are organized by module:
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- `test_http/`: HTTP request handling tests
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- `test_models/`: Model creation and transformation tests
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- `test_stock/`: Stock module functionality tests
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- `test_trading/`: Trading operations tests
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All tests require API credentials (can use paper trading credentials).

‎pyproject.toml‎

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@@ -3,7 +3,7 @@ name = "py-alpaca-api"
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version = "0.1.0"
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description = "Add your description here"
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readme = "README.md"
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requires-python = ">=3.12"
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requires-python = ">=3.10"
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dependencies = [
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"beautifulsoup4>=4.12.3",
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"numpy>=2.1.1",

‎src/py_alpaca_api/stock/screener.py‎

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@@ -215,21 +215,22 @@ def _get_percentages(
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sceener_df = pd.DataFrame()
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for symbol in symbols_data.items():
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try:
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sym = symbol[0]
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last_day = symbol[1][-1]
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prev_day = symbol[1][-2]
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sym_data = {
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"symbol": sym,
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"change": round(
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((last_day["c"] - prev_day["c"]) / prev_day["c"]) * 100, 2
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),
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"price": last_day["c"],
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"volume": last_day["v"],
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"trades": last_day["n"],
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}
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sceener_df = pd.concat([sceener_df, pd.DataFrame([sym_data])])
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except TypeError or KeyError:
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if len(symbol[1]) > 1:
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sym = symbol[0]
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last_day = symbol[1][-1]
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prev_day = symbol[1][-2]
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sym_data = {
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"symbol": sym,
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"change": round(
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((last_day["c"] - prev_day["c"]) / prev_day["c"]) * 100, 2
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),
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"price": last_day["c"],
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"volume": last_day["v"],
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"trades": last_day["n"],
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}
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sceener_df = pd.concat([sceener_df, pd.DataFrame([sym_data])])
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except (TypeError, KeyError, IndexError):
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pass
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sceener_df.reset_index(drop=True, inplace=True)

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