https://github.com/quantbai/phandas
A multi-factor quantitative trading framework for cryptocurrency markets.
https://github.com/quantbai/phandas
cryptocurrency quantitative-trading trading-framework
Last synced: 5 months ago
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A multi-factor quantitative trading framework for cryptocurrency markets.
- Host: GitHub
- URL: https://github.com/quantbai/phandas
- Owner: quantbai
- License: bsd-3-clause
- Created: 2025-08-11T06:17:09.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2026-01-11T21:52:07.000Z (5 months ago)
- Last Synced: 2026-01-12T01:23:49.868Z (5 months ago)
- Topics: cryptocurrency, quantitative-trading, trading-framework
- Language: Python
- Homepage: https://phandas.streamlit.app/
- Size: 1.38 MB
- Stars: 94
- Watchers: 0
- Forks: 15
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](#english) [](#繁體中文)
## English
A multi-factor quantitative trading framework for cryptocurrency markets.
### Overview
Phandas is a streamlined toolkit for alpha factor research and backtesting in cryptocurrency markets. Design factors with 60+ operators, test with dollar-neutral backtesting, and analyze with professional metrics.
### Try it now
[**Web Demo**](https://phandas.streamlit.app/) - Experience Phandas directly in your browser. No installation required.
### Key Features
- **Data Fetching**: Multi-source OHLCV data (Binance, OKX)
- **Factor Engine**: 60+ time-series and cross-sectional operators
- **Neutralization**: Vector projection & regression-based orthogonalization
- **Backtesting**: Dollar-neutral strategies with full/partial rebalancing
- **Performance Metrics**: Sharpe, Sortino, Calmar, Max Drawdown, VaR, PSR
- **Factor Analysis**: IC, IR, correlation, coverage, turnover
- **MCP Integration**: AI agents (Claude) can directly access Phandas
### Installation
```bash
pip install phandas
```
### Quick Start
```python
from phandas import *
# Fetch market data
panel = fetch_data(
symbols=['ETH', 'SOL', 'ARB', 'OP', 'POL', 'SUI'],
timeframe='1d',
start_date='2023-01-01',
sources=['binance'],
)
# Extract factors
close = panel['close']
volume = panel['volume']
open = panel['open']
# Construct momentum factor
momentum_20 = (close / close.ts_delay(20)) - 1
# Neutralize against volume
factor = vector_neut(rank(momentum_20), rank(-volume))
# Backtest strategy
result = backtest(
entry_price_factor=open,
strategy_factor=factor,
transaction_cost=(0.0003, 0.0003)
)
result.plot_equity()
```
### AI Integration via MCP
Use Phandas with AI IDEs (Cursor, Claude Desktop) directly—no coding required.
**Setup for Cursor (Recommended)**
1. `pip install phandas`
2. Open Cursor → Settings → Tools & MCP → **New MCP Server**
3. Paste the JSON config below, save and restart
```json
{
"mcpServers": {
"phandas": {
"command": "python",
"args": ["-m", "phandas.mcp_server"]
}
}
}
```
**Available Tools (4 Functions)**
- `fetch_market_data`: Get OHLCV data for symbols
- `list_operators`: Browse all 50+ factor operators
- `read_source`: View source code of any function
- `execute_factor_backtest`: Backtest custom factor expressions
---
## 繁體中文
一個專為加密貨幣市場設計的多因子量化交易框架。
### 概述
Phandas 是一個精簡的加密貨幣因子研究與回測工具。提供 60+ 運算子設計因子、美元中性回測、專業績效指標分析。
### 立即體驗
[**網頁演示**](https://phandas.streamlit.app/) - 直接在瀏覽器中體驗 Phandas，無需安裝。
### 核心功能
- **資料獲取**：多源 OHLCV 資料（Binance、OKX）
- **因子引擎**：60+ 時間序列與橫截面運算子
- **因子中性化**：向量投影與迴歸正交化
- **回測引擎**：美元中性策略、全/部分調倉
- **績效指標**：夏普比、Sortino、Calmar、最大回撤、VaR、PSR
- **因子分析**：IC、IR、相關性、覆蓋率、換手率
- **MCP 集成**：AI 代理（Claude）可直接調用 Phandas
### 安裝
```bash
pip install phandas
```
### 快速開始
```python
from phandas import *
# 獲取市場資料
panel = fetch_data(
symbols=['ETH', 'SOL', 'ARB', 'OP', 'POL', 'SUI'],
timeframe='1d',
start_date='2023-01-01',
sources=['binance'],
)
# 提取因子
close = panel['close']
volume = panel['volume']
open = panel['open']
# 構建動量因子
momentum_20 = (close / close.ts_delay(20)) - 1
# 對成交量進行中性化
factor = vector_neut(rank(momentum_20), rank(-volume))
# 回測策略
result = backtest(
entry_price_factor=open,
strategy_factor=factor,
transaction_cost=(0.0003, 0.0003)
)
result.plot_equity()
```
### AI 集成（MCP 支援）
在 AI IDE（Cursor、Claude Desktop）中直接使用 Phandas—無需編碼。
**Cursor 設定（推薦）**
1. `pip install phandas`
2. 開啟 Cursor → Settings → Tools & MCP → **New MCP Server**
3. 貼上下方 JSON 配置，儲存並重啟
```json
{
"mcpServers": {
"phandas": {
"command": "python",
"args": ["-m", "phandas.mcp_server"]
}
}
}
```
**可用工具（4 個函數）**
- `fetch_market_data`: 獲取代幣 OHLCV 資料
- `list_operators`: 瀏覽 50+ 因子運算子
- `read_source`: 查看任何函數的源代碼
- `execute_factor_backtest`: 回測自訂因子表達式
---
## Documentation | 文檔
- [Full Docs](https://phandas.readthedocs.io/) - Complete API reference
- [Operators Guide](https://phandas.readthedocs.io/guide/operators_guide.html) - 50+ operators
- [MCP Setup](https://phandas.readthedocs.io/mcp_setup.html) - AI IDE integration
---
## Community & Support | 社群與支持
- **Discord**: [Join us - Phantom Management](https://discord.gg/TcPHTSGMdH)
- **GitHub Issues**: [Report bugs or request features](https://github.com/quantbai/phandas/issues)
## License
This project is licensed under the BSD 3-Clause License - see [LICENSE](LICENSE) file for details.