semvec/pypi-packages huggingface dataset:
taranis-models 1.3.3.dev7
Taranis AI Models
Requires Python: <3.14,>=3.12

# Taranis AI Pydantic Models

This folder provides pydantic models for validation of data sent between Taranis AI services and offered to third party clients.

## Installation

It's recommended to use a uv to set up a virtual environment.

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
uv sync
```

If updating something and wanting to test it in `frontend` or `core` you can use `install_and_run_dev.sh` or something similar to the commands below:

```bash
uv sync --all-extras --frozen --python 3.13
uv pip install -e ../models

export UV_NO_SYNC=true
uv run pytest tests
```
pm-os 4.2.0
PM-OS: AI-powered Product Management Operating System
Requires Python: >=3.10
Keywords: ai, claude, llm, pm, product-management

# PM-OS

**AI-powered Product Management Operating System**

PM-OS is a comprehensive workflow system for Product Managers, integrating with Jira, Slack, GitHub, Google Workspace, and LLM providers to streamline daily work.

## Installation

```bash
# Basic installation
pip install pm-os

# With specific integrations
pip install pm-os[slack]       # Slack integration
pip install pm-os[jira]        # Jira integration
pip install pm-os[google]      # Google Workspace
pip install pm-os[github]      # GitHub integration
pip install pm-os[bedrock]     # AWS Bedrock LLM

# All integrations
pip install pm-os[all]
```

## Quick Start

```bash
# Initialize PM-OS (guided wizard)
pm-os init

# Check installation health
pm-os doctor

# Update to latest version
pm-os update
```

## Features

- **Guided Installation**: Interactive wizard configures everything
- **Daily Context Sync**: Aggregates Jira, Slack, Calendar, GitHub
- **Brain Knowledge Graph**: Entities, relationships, semantic search
- **Session Management**: Context preservation across sessions
- **Integration Hub**: Connects all your PM tools

## Documentation

See the [full documentation](https://pm-os.dev/docs) for detailed guides.

## License

MIT License - see [LICENSE](LICENSE) for details.
BackcastPro 0.6.4
トレーディング戦略のためのPythonバックテストライブラリ
Requires Python: >=3.9

# <img src="https://raw.githubusercontent.com/botterYosuke/BackcastPro/main/docs/img/logo.drawio.svg" alt="BackcastPro Logo" width="40" height="24"> BackcastPro

トレーディング戦略のためのPythonバックテストライブラリ。
**リプレイ型シミュレーター**で、1バーずつ時間を進めながらチャートと売買を可視化できます。

## インストール（Windows）

### PyPIから（エンドユーザー向け）

```powershell
python -m pip install BackcastPro
```

### 開発用インストール

```powershell
git clone <repository-url>
cd BackcastPro
python -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -e .
```

## 使用方法

### 基本的な使い方

```python
from BackcastPro import Backtest
import pandas as pd

# データ準備
df = pd.read_csv("AAPL.csv", index_col=0, parse_dates=True)
bt = Backtest(data={"AAPL": df}, cash=100000)

# 戦略関数
def my_strategy(bt):
    if bt.position == 0:
        bt.buy(tag="entry")
    elif bt.position > 0:
        bt.sell(tag="exit")

# ステップ実行
while not bt.is_finished:
    my_strategy(bt)
    bt.step()

# 結果を取得
results = bt.finalize()
print(results)
```

### 一括実行

```python
bt = Backtest(data={"AAPL": df}, cash=100000)
bt.set_strategy(my_strategy)
results = bt.run()
```

### marimo連携（リプレイ型シミュレーター）

```python
import marimo as mo

slider = mo.ui.slider(start=1, stop=len(bt.index), value=1, label="時間")
bt.goto(slider.value, strategy=my_strategy)

state = bt.get_state_snapshot()
info = mo.md(f"資産: ¥{state['equity']:,.0f} / 進捗: {state['progress'] * 100:.1f}%")

mo.vstack([slider, info])
```

## ドキュメント

- [ドキュメント一覧](https://botteryosuke.github.io/BackcastPro/)

## バグ報告 / サポート

- バグ報告や要望は GitHub Issues へ
- 質問は Discord コミュニティへ（[招待リンク](https://discord.gg/fzJTbpzE)）
- 使い方はドキュメントをご参照ください