alphaear-predictor

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🇨🇳

Translation

Chinese

AlphaEar Predictor Skill

AlphaEar Predictor 技能

Overview

概述

This skill utilizes the Kronos model (via

KronosPredictorUtility

) to perform time-series forecasting and adjust predictions based on news sentiment.

本技能借助Kronos模型（通过

KronosPredictorUtility

）实现时序预测，并且可基于新闻情感调整预测结果。

Capabilities

功能

1. Forecast Market Trends

1. 预测市场趋势

1. Forecast Market Trends

1. 预测市场趋势

Workflow:

Generate Base Forecast: Use
```
scripts/kronos_predictor.py
```
(via
```
KronosPredictorUtility
```
) to generate the technical/quantitative forecast.
Adjust Forecast (Agentic): Use the Forecast Adjustment Prompt in
```
references/PROMPTS.md
```
to subjectively adjust the numbers based on latest news/logic.

Key Tools:

KronosPredictorUtility.get_base_forecast(df, lookback, pred_len, news_text)

: Returns

List[KLinePoint]

Example Usage (Python):

python

from scripts.utils.kronos_predictor import KronosPredictorUtility
from scripts.utils.database_manager import DatabaseManager

db = DatabaseManager()
predictor = KronosPredictorUtility()

工作流程：

生成基础预测：使用
```
scripts/kronos_predictor.py
```
（通过
```
KronosPredictorUtility
```
）生成技术/量化预测。
智能调整预测：使用
```
references/PROMPTS.md
```
中的预测调整提示词，基于最新新闻/逻辑对数值进行主观调整。

核心工具：

KronosPredictorUtility.get_base_forecast(df, lookback, pred_len, news_text)

：返回

List[KLinePoint]

。

（Python）使用示例：

python

from scripts.utils.kronos_predictor import KronosPredictorUtility
from scripts.utils.database_manager import DatabaseManager

db = DatabaseManager()
predictor = KronosPredictorUtility()

Forecast

forecast = predictor.predict("600519", horizon="7d") print(forecast)

undefined

forecast = predictor.predict("600519", horizon="7d") print(forecast)

undefined

Configuration

配置

This skill requires the Kronos model and an embedding model.

Kronos Model:
- Ensure
```
exports/models
```
  directory exists in the project root.
- Place trained news projector weights (e.g.,
```
kronos_news_v1.pt
```
  ) in
```
exports/models/
```
  .
- Or depend on the base model (automatically downloaded).
Environment Variables:
- ```
EMBEDDING_MODEL
```
  : Path or name of the embedding model (default:
```
sentence-transformers/all-MiniLM-L6-v2
```
  ).
- ```
KRONOS_MODEL_PATH
```
  : Optional path to override model loading.

本技能需要Kronos模型和一个嵌入模型。

Kronos模型：
- 确保项目根目录下存在
```
exports/models
```
  文件夹。
- 将训练好的新闻投射器权重（例如
```
kronos_news_v1.pt
```
  ）放到
```
exports/models/
```
  目录下。
- 或者依赖基础模型（会自动下载）。
环境变量：
- ```
EMBEDDING_MODEL
```
  ：嵌入模型的路径或名称（默认值：
```
sentence-transformers/all-MiniLM-L6-v2
```
  ）。
- ```
KRONOS_MODEL_PATH
```
  ：可选参数，用于覆盖模型加载路径的自定义路径。

Dependencies

依赖

```
torch
```
```
transformers
```
```
sentence-transformers
```
```
pandas
```
```
numpy
```
```
scikit-learn
```

```
torch
```
```
transformers
```
```
sentence-transformers
```
```
pandas
```
```
numpy
```
```
scikit-learn
```