semvec/pypi-packages huggingface dataset:
prlearn 0.0.4
Parallel Reinforcement Learning library
Requires Python: <4.0.0,>=3.11.9

# PRLearn

PRLearn is a Python library for **P**arallel **R**einforcement **Learn**ing. It leverages multiprocessing to accelerate experience collection and agent training, making RL experimentation faster and more efficient.

## Key Features

- **Flexible architecture**: Easily extendable with custom agents, environments, and combiners.
- **Minimal dependencies**: Only Python 3.11+ and (optionally) multiprocess.
- **Parallel data collection and training**: Reduce training time via multiprocessing.
- **Agent combination**: Multiple strategies for aggregating agents (statistical, random, fixed, etc.).
- **Flexible scheduling**: Control training stages via ProcessActionScheduler.


## Installation

```sh
pip install prlearn
```
Or with multiprocess support:
```sh
pip install prlearn[multiprocess]
```

## Quick Start

### Define Your Agent

```python
from prlearn import Agent, Experience
from typing import Any, Dict, Tuple

class MyAgent(Agent):
    def action(self, state: Tuple[Any, Dict[str, Any]]) -> Any:
        observation, info = state
        # Action selection logic
        pass
    def train(self, experience: Experience):
        obs, actions, rewards, terminated, truncated, info = experience.get()
        # Training logic
        pass
```

### Use Trainer for Parallel Training

```python
import gymnasium as gym
from prlearn import Trainer
from prlearn.collection.agent_combiners import FixedStatAgentCombiner

env = gym.make("LunarLander-v2")
agent = MyAgent()

trainer = Trainer(
    agent=agent,
    env=env,
    n_workers=4,
    schedule=[
        ("finish", 1000, "episodes"),
        ("train_agent", 10, "episodes"),
    ],
    mode="parallel_learning",  # optional
    sync_mode="sync",          # optional
    combiner=FixedStatAgentCombiner("mean_reward"),  # optional
)

agent, result = trainer.run()
```

### Custom Environment

```python
from prlearn import Environment
from typing import Any, Dict, Tuple

class MyEnv(Environment):
    def reset(self) -> Tuple[Any, Dict[str, Any]]:
        # Reset logic
        return [[1, 2], [3, 4]], {"info": "description"}
    def step(self, action: Any) -> Tuple[Any, Any, bool, bool, Dict[str, Any]]:
        # Step logic
        return [[1, 2], [3, 4]], 1, False, False, {"info": "description"}
```

**See more usage examples in [docs/examples.md](docs/examples.md)**


## Extending

- **Custom agent**: Inherit from `Agent`, implement `action` and `train` methods.
- **Custom environment**: Inherit from `Environment`, implement `reset` and `step` methods.
- **Custom combiner**: Inherit from `AgentCombiner`, implement the `combine` method.


## Testing

To run tests:
```sh
pytest tests/
```

## License

MIT License. See [LICENSE](LICENSE).
tech-analysis 0.1.0
Pure Python technical analysis package
Requires Python: >=3.7

# tech_analysis

`tech_analysis` is a **pure Python**, dependency-free technical analysis library for financial markets. It includes a wide array of technical indicators, overlays, price action patterns, performance metrics, and risk management tools — designed for traders, quants, and researchers.

> No NumPy. No pandas. Just raw Python logic — fully portable and customizable.

---

## Features

- **Indicators**: RSI, MACD, ATR, CCI, TRIX, Bollinger Bands, ROC, Stochastic, etc.
- **Volume Tools**: OBV, PVT, MFI, Chaikin, Ease of Movement
- **Price Patterns**: is_inverted_hammer,
is_shooting_star,
is_morning_star,
is_evening_star,
is_bullish_harami,
is_bearish_harami,
is_three_white_soldiers,
is_three_black_crows,
is_tweezer_top,
is_tweezer_bottom,
is_piercing_line,
is_dark_cloud_cover,
is_rising_three_methods,
is_falling_three_methods,
is_inside_bar,
is_outside_bar,
is_marubozu,
is_inside_day_breakout,
break_of_structure,
- **Breadth Indicators**: Advance/Decline Line, A/D Ratio, New High-Low
- **Overlays**: SMA, EMA, Donchian Channels, MA Ribbons
- **Risk Management**: Kelly Criterion, Max Drawdown, Position Sizing, Monte Carlo
- **Performance Metrics**: Sharpe, Sortino, Calmar, CAGR, Beta, Volatility
- **forecast**: 

- **100% Pure Python**: No external packages required

---

## Installation

```bash
pip install tech_analysis
codeillusion 0.1.0
Code visualization and animation tool
Requires Python: >=3.7

# Codeillusion

**Codeillusion** is a Python package designed to animate code execution and visualize the evolution of variables step-by-step. It helps in teaching programming by making it more interactive and engaging.

## Installation

To install `codeillusion`, simply run:

```bash
pip install codeillusion
pakku 0.1.1
A lightweight Feature Rich Python package manager with GUI.
Requires Python: >=3.6


# Pakku

**Pakku** is a powerful, easy-to-use **Python Package Manager GUI** built with **PyQt5**.
It helps you **install**, **search**, **update**, **remove**, and **manage** Python packages visually --- with modern looks, real-time logs, and virtual environment management support.

No more memorizing pip commands! 📦🚀

---

## ✨ Features

- 📜 **View Installed Packages** with filtering and sorting
- 🔍 **Search PyPI Packages** and install directly
- ⚡ **Update / Remove** any package easily
- 🖥️ **Virtual Environment** management
- 🛠️ **Create / Import** `requirements.txt` files
- 📊 **System Information** display (disk space, site-packages path, etc.)
- 📝 **Persistent Logs** across sessions
- 🎨 **Modern Dark-Themed UI**

---

## 🚀 Installation

First, install Pakku using pip:

```bash
pip install pakku

```

(Or if you're using Kali Linux or similar protected systems: use `pipx` or a virtual environment.)

* * * * *

📟 Usage
--------

After installation, simply run:

```
pakku

```

The GUI will open!

✅ Works inside virtual environments\
✅ Auto-detects missing dependencies and installs them automatically

* * * * *

🔧 Requirements
---------------

-   Python 3.6+

-   PyQt5

-   requests

-   psutil

*(All automatically installed when you install Pakku)*

* * * * *

🛡️ Why Use Pakku?
-------------------

-   Beginner-friendly interface.

-   Handles real-time logs: See installations happening live.

-   Designed for professional and student developers.

-   No more complicated pip command errors.

-   Lightweight: Minimal overhead, fast startup.

* * * * *

💻 Local Development Setup
--------------------------

If you want to run directly from source:

```
git clone https://github.com/AlphaDarkmoon/pakku.git
cd Pakku
pip install -r requirements.txt
python -m pakku.main

```

* * * * *

📜 License
----------

MIT License.\
Feel free to use, modify, and contribute!

* * * * *

🤝 Contributing
---------------

Pull requests are welcome!\
Feel free to open an issue for any bug or suggestion.

* * * * *

📬 Author
---------

- [AlphaDarkmoon](https://github.com/AlphaDarkmoon)

* * * * *

⭐ Star this project on GitHub if you like it!\