semvec/pypi-packages huggingface dataset:
mongodb-controller 0.2.6
A Python module for managing fund time series data using MongoDB
Requires Python: >=3.11
Keywords: mongodb database fund time-series data-management

# MongoDB Fund Data Controller

A Python module for managing fund time series data using MongoDB. This module provides reliable data upload, management, and retrieval capabilities.

## Features

- Secure MongoDB connection management
- Reliable data upload and validation
- Efficient time series data querying
- Error handling and logging

## Version History

### 0.2.6 (2025-06-20)

- Added key retrieval functions for collections 3233, 3412, and 3421
- Enhanced MongoDB keys module with support for new collections

### 0.2.5 (2025-05-21)

- Added date range query functions for time series data
- Enhanced MongoDB keys module with support for new collections
- Improved data retrieval capabilities for collections 3233, 3412, and 3421

### 0.2.3 (2025-04-14)

- Removed Jupyter Notebook files from repository
- Fixed build issues to include all module components
- Ensured all utility functions are properly exported

### 0.2.2 (2025-04-14)

- Updated dependencies
- Fixed module imports
- Improved code organization
- Enhanced MongoDB collections support

### 0.2.1

- Initial public release

## Installation

```bash
pip install -r requirements.txt
```

## Project Structure

```
mongodb_controller/
├── doc/
│   ├── design.md
│   ├── context.md
│   └── commands-cascade.md
├── src/
│   └── mongodb_controller/
├── tests/
├── requirements.txt
└── README.md
```

## Use Cases & Implementation

### Enterprise Solutions

- **Cloud-Native RPA Framework**: Implemented within AWS Cloud Architecture, enabling automated fund data processing and reconciliation pipelines
- **Performance Analytics Engine**: Powers the enterprise-grade Fund Performance Calculator with real-time metrics and historical analysis capabilities
- **Portfolio Intelligence System**: Drives the Portfolio Analyzer platform, providing sophisticated risk assessment and allocation optimization
- **Financial Knowledge Base**: Underlies our Financial RAG (Retrieval-Augmented Generation) system, facilitating AI-powered investment insights

## Professional Background

### Author

**June Young Park**  
AI Management Development Team Lead  
LIFE Asset Management

### Expertise

- Data Cloud Architecture Design
- Financial Technology Integration
- Enterprise AI/ML Systems
- Investment Analytics Platforms

### Organization

**LIFE Asset Management**  
A premier investment management firm specializing in hedge fund and private equity solutions. Headquartered in the International Finance Center, Yeouido, South Korea.

_Focus Areas:_

- Quantitative Investment Strategies
- Corporate Value Enhancement
- Sustainable Shareholder Returns
- Innovation in Financial Technology

### Contact Information

- **Professional Email**: juneyoungpaak@gmail.com
- **Location**: TWO IFC 14F, Yeouido, Seoul, South Korea
PredictServe 0.1.0
A Python package for advanced data classification.
Requires Python: >=3.8

# ClassifyAPI 🚀

![Project Status](https://img.shields.io/badge/status-pre--alpha-orange)
![Language](https://img.shields.io/badge/python-3.9%2B-blue)
![Framework](https://img.shields.io/badge/framework-Flask%2FFastAPI-lightgrey)
![License](https://img.shields.io/badge/license-MIT-lightgrey)

> A lightweight, cloud-deployed web API for serving machine learning classification models.

## The Mission

This project serves as the practical application for the "Cloud Computing for Data Science" diploma curriculum. Its goal is to take a trained machine learning model and make it accessible to the world through a simple, robust API.

This project picks up where the [`AutoCleanSE`](https://github.com/chishxd/autocleanse) project leaves off. We will use the clean data produced by `AutoCleanSE` to train a model, and then we will build the infrastructure to deploy and serve that model in the cloud.

## Core Features (The Roadmap)

-   [ ] **API Framework:** A simple web server using Flask or FastAPI to define prediction endpoints.
-   [x] **Cloud Storage Integration:** Ability to connect to S3-compatible object storage (like MinIO or AWS S3) to retrieve assets.
-   [ ] **Model Serving:** Load a serialized ML model (e.g., a `.pkl` file) and use it to make live predictions.
-   [ ] **Cloud Deployment:** The entire application will be packaged and deployed to a cloud provider (e.g., as an AWS Lambda function).
-   [ ] **Containerization:** The application will be containerized using Docker/Podman for portability.

## Current Status

We have successfully established programmatic access to a local, S3-compatible MinIO server using `boto3`. This proves our ability to interact with cloud storage, which is the foundational first step.

### Local Setup for Cloud Simulation

This project uses **MinIO** to simulate AWS S3 locally. This allows for rapid, offline development.

<details>
<summary><b>Running the Local MinIO Server</b></summary>

```bash
# First, ensure the MinIO container exists. If not, create it.
# This command runs the server in the background.
podman run -d -p 9000:9000 -p 9001:9001 --name minio-server -v ~/minio-data:/data -e "MINIO_ROOT_USER=admin" -e "MINIO_ROOT_PASSWORD=password" minio/minio server /data --console-address ":9001"

# To start the server for a new work session:
podman start minio-server

# To stop the server:
podman stop minio-server
```
</details>

## Usage

The current test script demonstrates how to connect to the MinIO server.

```python
# cloud_test.py
import boto3

# --- Configuration ---
access_key = "admin"
secret_key = "password"
endpoint_url = "http://127.0.0.1:9000"
# ---

s3_client = boto3.client(
    's3',
    endpoint_url=endpoint_url,
    aws_access_key_id=access_key,
    aws_secret_access_key=secret_key
)

response = s3_client.list_buckets()
print("Successfully connected. Found buckets:")
for bucket in response['Buckets']:
    print(f"- {bucket['Name']}")
```

## License

Distributed under the MIT License.