alphaear-predictor
Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.
Best use case
alphaear-predictor is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.
Teams using alphaear-predictor should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/alphaear-predictor/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How alphaear-predictor Compares
| Feature / Agent | alphaear-predictor | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Market prediction skill using Kronos. Use when user needs finance market time-series forecasting or news-aware finance market adjustments.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# AlphaEar Predictor Skill
## Overview
This skill utilizes the Kronos model (via `KronosPredictorUtility`) to perform time-series forecasting and adjust predictions based on news sentiment.
## Capabilities
### 1. Forecast Market Trends
### 1. Forecast Market Trends
**Workflow:**
1. **Generate Base Forecast**: Use `scripts/kronos_predictor.py` (via `KronosPredictorUtility`) to generate the technical/quantitative forecast.
2. **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()
# Forecast
forecast = predictor.predict("600519", horizon="7d")
print(forecast)
```
## Configuration
This skill requires the **Kronos** model and an embedding model.
1. **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).
2. **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.
## Dependencies
- `torch`
- `transformers`
- `sentence-transformers`
- `pandas`
- `numpy`
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