### Formula Syntax Examples for Factor Creation

Source: https://github.com/pandaai-tech/panda_factor/blob/main/README.md

Illustrates how to define quantitative factors using the formula syntax. Examples include calculating 20-day return rank, price-volume correlation, and a complex factor combining multiple operators. Formulas can span multiple lines using intermediate variables (as mentioned in the surrounding text).

```Formula
# 计算20日收益率排名
RANK((CLOSE / DELAY(CLOSE, 20)) - 1)

# 计算价格和成交量的相关性
CORRELATION(CLOSE, VOLUME, 20)

# 复杂因子示例
RANK((CLOSE / DELAY(CLOSE, 20)) - 1) *
STDDEV((CLOSE / DELAY(CLOSE, 1)) - 1, 20) *
IF(CLOSE > DELAY(CLOSE, 1), 1, -1)
```

--------------------------------

### Installing Project Modules in Editable Mode

Source: https://github.com/pandaai-tech/panda_factor/blob/main/README.md

Provides the command to install project sub-modules (like panda_common, panda_data, etc.) in editable mode using pip. This is recommended for development environments like VS Code or Cursor and should be executed from within each sub-module directory.

```Bash
pip install -e .
```

--------------------------------

### Example Complex Factor Calculation in Python

Source: https://github.com/pandaai-tech/panda_factor/blob/main/README.md

Provides a detailed example of implementing a complex quantitative factor in Python mode. It demonstrates accessing base data (close, volume, high, low) and using various built-in operators (DELAY, STDDEV, SUM, RANK, IF, SCALE) to calculate intermediate signals and synthesize a final factor value.

```Python
class ComplexFactor(Factor):
    def calculate(self, factors):
        close = factors['close']
        volume = factors['volume']
        high = factors['high']
        low = factors['low']
        
        # 计算20日收益率
        returns = (close / DELAY(close, 20)) - 1
        # 计算20日波动率
        volatility = STDDEV((close / DELAY(close, 1)) - 1, 20)
        # 计算价格区间
        price_range = (high - low) / close
        # 计算成交量比率
        volume_ratio = volume / DELAY(volume, 1)
        # 计算20日成交量均值
        volume_ma = SUM(volume, 20) / 20
        # 计算动量信号
        momentum = RANK(returns)
        # 计算波动率信号
        vol_signal = IF(volatility > DELAY(volatility, 1), 1, -1)
        # 合成最终因子
        result = momentum * vol_signal * SCALE(volume_ratio / volume_ma)
        return result
```

--------------------------------

### Python Project Dependencies List

Source: https://github.com/pandaai-tech/panda_factor/blob/main/panda_llm/requirements.txt

This snippet lists the core Python packages and their exact versions required to run the project. This format is typically used in a requirements.txt file for easy installation using pip.

```Python
fastapi==0.104.1
uvicorn==0.24.0
pydantic==2.4.2
aiohttp==3.9.1
pyyaml==6.0.1
python-dotenv==1.0.0
```

--------------------------------

### Utility Dependencies (Python)

Source: https://github.com/pandaai-tech/panda_factor/blob/main/requirements.txt

Lists general utility Python packages for logging (Loguru), YAML parsing (PyYAML), progress bars (tqdm), and package management (setuptools).

```Python
# Utilities
loguru>=0.6.0
PyYAML>=6.0
tqdm>=4.65.0
setuptools>=67.6.1
```

--------------------------------

### Trading and Market Data Dependencies (Python)

Source: https://github.com/pandaai-tech/panda_factor/blob/main/requirements.txt

Lists Python packages specifically for trading and accessing market data, including rqdatac and tqsdk.

```Python
# Trading & Market Data
rqdatac>=2.9.0
tqsdk>=3.2.10
```

--------------------------------

### Development and Testing Dependencies (Python)

Source: https://github.com/pandaai-tech/panda_factor/blob/main/requirements.txt

Lists Python packages used for development, testing, and interactive sessions, including pytest, pytest-asyncio, httpx, IPython, and openai.

```Python
# Development & Testing
pytest>=7.3.1
pytest-asyncio>=0.21.0
httpx>=0.24.0
IPython>=8.12.0
openai>=1.73.0
```

--------------------------------

### Web Framework Dependencies (Python)

Source: https://github.com/pandaai-tech/panda_factor/blob/main/requirements.txt

Lists Python packages required for building web applications, including FastAPI, Uvicorn, Flask, and Pydantic.

```Python
# Web Framework
fastapi>=0.95.0
uvicorn>=0.21.0
flask>=2.3.2
pydantic>=1.10.7
```

--------------------------------

### Database Dependencies (Python)

Source: https://github.com/pandaai-tech/panda_factor/blob/main/requirements.txt

Lists Python packages for database interactions, including MongoDB (PyMongo), Redis, and MySQL.

```Python
# Database
pymongo>=4.3.3
redis>=4.5.4
mysql-connector-python>=8.0.32
```

--------------------------------

### Accessing Generated Factors in Python

Source: https://github.com/pandaai-tech/panda_factor/blob/main/README.md

Demonstrates how to import the `panda_data` module, initialize it, and retrieve a specific factor by its name and date range. This code snippet is used to integrate factors produced by PandaFactor into external trading systems or strategies.

```Python
import panda_data

panda_data.init()
factor = panda_data.get_factor_by_name(factor_name="VH03cc651", start_date='20240320',end_date='20250325')
```

--------------------------------

### Task Scheduling Dependency (Python)

Source: https://github.com/pandaai-tech/panda_factor/blob/main/requirements.txt

Lists the Python package required for task scheduling, specifically APScheduler.

```Python
# Task Scheduling
apscheduler>=3.10.1
```

--------------------------------

### Defining a Custom Factor Class in Python

Source: https://github.com/pandaai-tech/panda_factor/blob/main/README.md

Defines the basic structure for creating a custom quantitative factor in Python mode. Factors must inherit from the `Factor` class and implement the `calculate` method, which takes `factors` (containing base data like close, volume) as input and must return a pandas Series with a multi-index ['symbol', 'date'].

```Python
class CustomFactor(Factor):
    def calculate(self, factors):
        return result
```

--------------------------------

### Data Processing Dependencies (Python)

Source: https://github.com/pandaai-tech/panda_factor/blob/main/requirements.txt

Lists Python packages for data manipulation, analysis, and visualization, such as NumPy, Pandas, SciPy, Statsmodels, Matplotlib, and Seaborn.

```Python
# Data Processing
numpy>=1.24.2
pandas>=2.0.0
scipy>=1.10.1
statsmodels>=0.13.5
matplotlib>=3.7.1
seaborn>=0.13.0
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.