AI Stock Sentiment Predictor

Below is a high-level yet technically detailed problem formulation for creating a **Stock Sentiment Predictor** AI Widget on Abyss, abysshub.com. This document aims to guide developers in implementing the solution while highlighting advanced logic considerations and best practices for a robust FinTech application. --- ## 1. Overview ### Widget Objective A **Stock Sentiment Predictor** Widget that: 1. **Scrapes financial news articles** for a given stock ticker and date range. 2. **Applies GPT-4 (or other large language models) and classical sentiment analysis** to generate a quantitative sentiment score (ranging from 1–10). 3. **Outputs**: - A **CSV** with per-article sentiment scores and metadata (e.g., publication date, headline, source). - A **graph** (PNG/SVG) visualizing the sentiment trend over the selected time range. ### Intended Users - **Retail Investors**: Quick insight into market sentiment. - **Financial Analysts**: Additional layer of data-driven signals for research. --- ## 2. Interface Requirements Below are the Abyss interface elements this Widget will require: 1. **Ticker Symbol** - **Type**: Text Field - **Environment Variable**: `ticker_symbol` - **Example**: “AAPL”, “TSLA”, “AMZN” - **Purpose**: Identifies which stock to analyze. 2. **Date Range** - **Type**: Two Date Pickers or a single date range field (depending on implementation preference). - **Environment Variables**: - `start_date` (YYYY-MM-DD) - `end_date` (YYYY-MM-DD) - **Purpose**: Bounds for scraping relevant financial news. The user will fill these fields via the Widget UI in Abyss. The environment variables will be accessible in your `[login to view URL]` script using `[login to view URL]("var_name")`. --- ## 3. Key Files and Project Structure 1. **`[login to view URL]` (Required)** - Main entry point for executing the sentiment predictor logic. - Orchestrates: - Input acquisition (ticker symbol, date range). - News scraping. - GPT-4 / classical sentiment analysis. - Output generation (CSV + chart). - **Example Layout**: ```python import os from sentiment_pipeline import run_sentiment_analysis def main(): # Read inputs ticker_symbol = [login to view URL]('ticker_symbol', 'AAPL') start_date = [login to view URL]('start_date', '2025-01-01') end_date = [login to view URL]('end_date', '2025-01-31') # Run pipeline csv_output_path, chart_output_path = run_sentiment_analysis( ticker_symbol, start_date, end_date ) # Move or confirm final files are in 'output' folder # (If run_sentiment_analysis already places them there, no extra step needed.) if __name__ == "__main__": main() ``` 2. **`[login to view URL]`** - Must list all Python dependencies, including: - **requests** or **httpx** (for API calls). - **beautifulsoup4** or **newspaper3k** (for web scraping, if applicable). - **openai** or another LLM integration library (for GPT-4). - **matplotlib**, **plotly**, or **altair** (for chart generation). - Additional libraries for CSV handling or data manipulation, e.g., **pandas**. 3. **`[login to view URL]` (Optional)** - For system-level packages like `poppler-utils` if you need PDF extraction or any other system dependency. 4. **`output/` folder** - Must exist in the root directory. - All final files (CSV, chart image) must be placed here. 5. **Additional Python Files** - e.g., `[login to view URL]` containing the main analysis logic, helper modules for scraping, analysis, and chart generation. --- ## 4. Advanced Logic Details 1. **News Scraping** - You can integrate with: - **News APIs** (e.g., NewsAPI, Finnhub, or Yahoo Finance data endpoints). - **Web scraping libraries** for site-specific scraping. - **Filtering**: Only scrape articles that reference the target ticker or relevant financial phrases. - **Batch Processing**: If many articles are returned, consider batching requests or scraping in pages to avoid hitting memory limits (512 MB max). 2. **Sentiment Analysis** - **LLM-Based**: Use GPT-4, o1, DeepSeek r1, or Claude 3.5 to classify the sentiment for each article: - You might pass the article headline and summary to an LLM, prompting for a sentiment score between 1 and 10, along with a short rationale. - **Classical Approach**: Optionally combine LLM output with: - **VADER** or **TextBlob** for an aggregate or weighted sentiment score. - **Hybrid Score**: Weighted average: \[ \text{score} = w_{\text{GPT}} \cdot \text{score}_{\text{GPT}} + w_{\text{VADER}} \cdot \text{score}_{\text{VADER}} \] This method can increase reliability by leveraging multiple analysis approaches. 3. **Predictive Trends** - **Time Decay Weighting**: Articles closer to the `end_date` might have more influence on the final aggregated sentiment. - **Rolling Average**: Smooth out day-to-day fluctuations and highlight overall sentiment trends. 4. **Output Generation** - **CSV**: - Name: `[login to view URL]` - Fields: `[date, headline, sentiment_score, source]` - Placed in `output/` folder. - **Graph**: - Line chart (e.g., **matplotlib** or **plotly**) showing daily average sentiment from `start_date` to `end_date`. - Name: `[login to view URL]` (or `.svg`) - Placed in `output/` folder. 5. **Performance & Memory Constraints** - Keep total usage under **512 MB**: - Use streaming or partial reading for large data sets. - Offload large computations if needed or limit the article volume. - **CPU**: Max 4 vCPUs / 4 GB RAM ensures your approach must be efficient. --- ## 5. Step-by-Step Execution Flow 1. **Initialize** - `main()` in `[login to view URL]` reads environment variables (`ticker_symbol`, `start_date`, `end_date`). 2. **Acquire Data** - Query news API or scrape relevant articles in date range. - Store raw data in memory or a local file (watch memory usage). 3. **Sentiment Analysis** - For each article (headline/summary/body): - Pass text to GPT-4 or alternative LLM for a sentiment rating (1–10). - Optionally, run classical sentiment on the same text. - Combine results with advanced weighting logic. 4. **Aggregate & Format** - Save results into a `pandas` DataFrame or similar structure. - Compute average sentiment per day (for chart). 5. **Generate Outputs** - Write final data to `output/[login to view URL]`. - Produce a line chart (`matplotlib` or other library) and save as `output/[login to view URL]`. 6. **Completion** - `[login to view URL]` exits, ensuring the `output/` folder contains: - **CSV** (with detailed article-level sentiment). - **Chart** (visualizing time-series sentiment trend). --- ## 6. Testing, Deployment, and Sharing 1. **Local Testing** - Locally set environment variables: ```bash export ticker_symbol='AAPL' export start_date='2025-01-01' export end_date='2025-01-31' python [login to view URL] ``` - Verify outputs in `output/`. 2. **Test on Abyss** - Upload code to Abyss, ensure `[login to view URL]` and `[login to view URL]` is in root. - Fill interface fields, run “TEST RUN WIDGET.” - Check logs and `output/` in Abyss for correct generation. 3. **Deployment** - Once passing the test run, click “SHARE AND DEPLOY.” - Provide example outputs (sample CSV + chart) to demonstrate functionality. 4. **Ongoing Maintenance** - If you update logic (e.g., new weighting strategy), re-test and re-deploy. --- ## 7. Potential Error Handling & Debugging - **API Rate Limits / Connection Errors**: - Catch exceptions, retry or gracefully skip articles if an API fails. - **Memory Exceeded**: - Limit articles, do incremental processing. - **No Output**: - Always ensure at least one file is written to `output/`, even if no articles are found (e.g., “No articles found for this date range.”). --- ## Conclusion This **Stock Sentiment Predictor** AI Widget on Abyss demonstrates an advanced, data-driven FinTech tool that merges **web scraping**, **LLM-based** sentiment evaluation, and **visual analytics**. By carefully structuring your Python project, respecting Abyss’s interface requirements, and optimizing for performance, you can offer an intuitive application for retail investors and financial analysts to gauge market sentiment and make data-driven decisions.