# AlphaBook Factor MCP (`neeenja/alphabook-factor`) Actor

Quant factor signal engine — remote MCP. 5 tools: get_value_factor, get_momentum_factor, get_low_vol_factor, get_size_factor, rank_factor_composite give factor scores, percentile ranks & composite rating (not raw fundamentals). A-shares/HK/US, no API key. Not advice.

- **URL**: https://apify.com/neeenja/alphabook-factor.md
- **Developed by:** [Neeen Ja](https://apify.com/neeenja) (community)
- **Categories:** MCP servers, AI
- **Stats:** 1 total users, 0 monthly users, 0.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $20.00 / 1,000 analysis calls

This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## AlphaBook Factor MCP

Quant factor signal engine as a remote MCP server. It gives your agent **factor scores, percentile ranks and a composite rating** — not a pile of raw fundamentals. Five tools cover value, momentum, low-volatility and size factors, plus a composite basket ranking. Value/size use a free valuation snapshot; momentum and low-vol are pure price-derived.

Works on China A-shares, HK and US through free public data. No API key. **Not investment advice.**

> **Honest boundary:** the free source returns a valuation *snapshot*, not a cross-sectional peer set. So value/size levels are approximated against typical A-share bands and clearly labelled as such — the tool never fabricates a peer percentile. Factor efficacy rotates over time; a high composite score is not a guarantee of outperformance.

### Tools

| Tool | Event | Returns |
|------|-------|---------|
| `get_value_factor` | tool-call $0.02 | Value score in \[-1,1] from PE/PB/PS vs typical bands |
| `get_momentum_factor` | tool-call $0.02 | 6-month & 12-1 momentum, qualitative read |
| `get_low_vol_factor` | tool-call $0.02 | Annualized realized volatility + low-vol score |
| `get_size_factor` | tool-call $0.02 | Market-cap band + size tilt score |
| `rank_factor_composite` | **screen-call $0.05** | Composite multi-factor ranking of up to 10 codes |

### Connect (MCP client)

```
https://neeenja--alphabook-factor.apify.actor/mcp
Authorization: Bearer <APIFY_TOKEN>
```

POST JSON-RPC: `initialize`, `tools/list`, `tools/call`. Example:

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": { "name": "get_value_factor", "arguments": { "code": "600519" } }
}
```

### Pricing

Pay-per-event. `initialize` and `tools/list` are free. Each analysis call is **$0.02**; a composite basket ranking (`rank_factor_composite`) is **$0.05** because it scores up to 10 symbols.

### Data sources & honest limits

- **Valuation:** East Money datacenter snapshot (PE/PB/PS, market cap). Snapshot only — no historical series.
- **Klines:** Tencent `gtimg.cn` (A/HK/US) and Yahoo (global). Free, no key.
- Missing data → `n/a`, never a fabricated `0`.
- Every output carries a `NOT_ADVICE` disclaimer.

***

## AlphaBook 因子 MCP

量化因子信号引擎，以远程 MCP 服务器形式提供。它给 agent 的是 **因子得分、分位与复合评分**，而非一堆原始基本面数字。五个工具覆盖价值、动量、低波动与规模因子，以及复合篮子排名。价值/规模用免费估值快照，动量/低波动纯价格派生。

支持 A 股、港股、美股，免费公开数据，无需 API Key。**不构成投资建议。**

> **诚实边界：** 免费源返回的是估值「快照」而非同行横截面，故价值/规模水位用典型 A 股经验带近似并如实标注——工具不会编造同行分位。因子有效性会轮动，复合分高≠一定跑赢。

### 工具

| 工具 | 计费事件 | 返回 |
|------|----------|------|
| `get_value_factor` | tool-call $0.02 | 价值得分（-1~1，来自 PE/PB/PS 相对典型区间） |
| `get_momentum_factor` | tool-call $0.02 | 6 个月 / 12-1 月动量及定性判读 |
| `get_low_vol_factor` | tool-call $0.02 | 年化已实现波动率 + 低波动得分 |
| `get_size_factor` | tool-call $0.02 | 市值区间 + 规模倾斜得分 |
| `rank_factor_composite` | **screen-call $0.05** | 最多 10 只标的的复合多因子排名 |

### 连接（MCP 客户端）

```
https://neeenja--alphabook-factor.apify.actor/mcp
Authorization: Bearer <APIFY_TOKEN>
```

发送 JSON-RPC：`initialize`、`tools/list`、`tools/call`。示例：

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": { "name": "get_value_factor", "arguments": { "code": "600519" } }
}
```

### 计费

按事件付费。`initialize` 与 `tools/list` 免费。每次分析调用 **$0.02**；复合篮子排名（`rank_factor_composite`）为 **$0.05**，因其最多评分 10 只标的。

### 数据源与诚实边界

- **估值：** 东方财富 datacenter 快照（PE/PB/PS、市值）。仅快照，无历史序列。
- **K 线：** 腾讯 `gtimg.cn`（A/港/美）与 Yahoo（全球），免费、无 Key。
- 数据缺失 → 返回 `n/a`，绝不编造 `0`。
- 所有输出均带 `NOT_ADVICE` 免责声明。

***

## AlphaBook 因子 MCP

量化因子訊號引擎，以遠端 MCP 伺服器形式提供。它給 agent 的是 **因子得分、分位與複合評分**，而非一堆原始基本面數字。五個工具涵蓋價值、動量、低波動與規模因子，以及複合籃子排名。價值/規模用免費估值快照，動量/低波動純價格派生。

支援 A 股、港股、美股，免費公開資料，無需 API Key。**不構成投資建議。**

> **誠實邊界：** 免費源回傳的是估值「快照」而非同行橫截面，故價值/規模水位用典型 A 股經驗帶近似並如實標註——工具不會編造同行分位。因子有效性會輪動，複合分高≠一定跑贏。

### 工具

| 工具 | 計費事件 | 回傳 |
|------|----------|------|
| `get_value_factor` | tool-call $0.02 | 價值得分（-1~1，來自 PE/PB/PS 相對典型區間） |
| `get_momentum_factor` | tool-call $0.02 | 6 個月 / 12-1 月動量及定性判讀 |
| `get_low_vol_factor` | tool-call $0.02 | 年化已實現波動率 + 低波動得分 |
| `get_size_factor` | tool-call $0.02 | 市值區間 + 規模傾斜得分 |
| `rank_factor_composite` | **screen-call $0.05** | 最多 10 檔標的的複合多因子排名 |

### 連線（MCP 用戶端）

```
https://neeenja--alphabook-factor.apify.actor/mcp
Authorization: Bearer <APIFY_TOKEN>
```

發送 JSON-RPC：`initialize`、`tools/list`、`tools/call`。範例：

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": { "name": "get_value_factor", "arguments": { "code": "600519" } }
}
```

### 計費

按事件付費。`initialize` 與 `tools/list` 免費。每次分析呼叫 **$0.02**；複合籃子排名（`rank_factor_composite`）為 **$0.05**，因其最多評分 10 檔標的。

### 資料來源與誠實邊界

- **估值：** 東方財富 datacenter 快照（PE/PB/PS、市值）。僅快照，無歷史序列。
- **K 線：** 騰訊 `gtimg.cn`（A/港/美）與 Yahoo（全球），免費、無需 Key。
- 資料缺失 → 回傳 `n/a`，絕不編造 `0`。
- 所有輸出均附 `NOT_ADVICE` 免責聲明。

# Actor input Schema

## Actor input object example

```json
{}
```

# Actor output Schema

## `mcpEndpoint` (type: `string`):

Streamable HTTP MCP endpoint of this Actor run. Connect an MCP client here (Authorization: Bearer \<APIFY_TOKEN>). POST JSON-RPC messages: initialize, tools/list, tools/call.

## `tools` (type: `string`):

Comma-separated list of the 6 tools: get_value_score, get_quality_score, get_momentum_factor, get_growth_score, get_size_factor, rank_factor_composite.

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {};

// Run the Actor and wait for it to finish
const run = await client.actor("neeenja/alphabook-factor").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {}

# Run the Actor and wait for it to finish
run = client.actor("neeenja/alphabook-factor").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{}' |
apify call neeenja/alphabook-factor --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,neeenja/alphabook-factor"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/KwxAtw2aD8OIG7htX/builds/TBDYPaD4ns5aftYad/openapi.json