readme updates

README.md

@@ -4,7 +4,7 @@ emoji: 📚
 colorFrom: yellow
 colorTo: purple
 sdk: streamlit
-sdk_version: 1.42.2
+sdk_version: 1.45.1
 app_file: app.py
 pinned: false
 license: apache-2.0
@@ -14,20 +14,58 @@ short_description: 'Stock trading application'
 # Stock Trader Application
 
 ## Overview
-Stock Trader is an interactive web application built with Streamlit that allows you to monitor stock market data, execute trades using a paper trading account through Alpaca, analyze market sentiment, and visualize stock performance. The application combines real-time market data, news sentiment analysis, and automated trading capabilities in an easy-to-use interface.
+Stock Trader is an interactive web application built with Streamlit that allows you to monitor stock market data, execute trades using a paper trading account through Alpaca, analyze market sentiment, and visualize stock performance. It is hosted on Hugging Face
 
 
-## Features
-- **Real-time Stock Data**: Track current prices and market status
-- **Manual Trading**: Buy and sell stocks with customizable quantities
-- **Sentiment Analysis**: Analyze news sentiment for stocks using multiple sources
-- **Automated Trading**: Background process trades stocks based on sentiment analysis
-- **Portfolio Visualization**: View your holdings and portfolio value in real-time
-- **Market Insights**: Monitor top volume stocks with interactive charts
+## Prompt and LLM-Generated Code
+I used the [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) model with another LLM chat application I also built on Hugging Face Spaces to generate the initial codebase. The LLM chat app is here: https://huggingface.co/spaces/Intel/intel-ai-enterprise-inference. It takes advantage of a [Intel® AI for Enterprise Inference](https://github.com/opea-project/Enterprise-Inference) with an OpenAI-compatible API key and model endpoint, hosted on cloud provider [Denvr Dataworks](https://www.denvrdata.com/intel).  
 
-## Prompt
-I used a DeepSeek distilled Llama model with the following chat application to build the initial codebase: https://huggingface.co/spaces/Intel/intel-ai-enterprise-inference.
-Here is the prompt I wrote:
+If you are interested in looking at which models are currently available, you can ping the endpoint with the instructions below. This is standard boilerplate OpenAI code to check on your models that you can use with any OpenAI-compatible API key and model endpoint. First, make sure to save your API key and model endpoint in your environment variables first.
+```bash
+export OPENAI_API_KEY="your-api-key"
+export BASE_URL="https://api.inference.denvrdata.com/v1/"
+```
+
+Here is the Python code to check on the models available on your endpoint:
+```python
+from openai import OpenAI
+import os
+
+api_key = os.environ["OPENAI_API_KEY"]
+base_url = os.environ["BASE_URL"]
+client = OpenAI(api_key=api_key, base_url=base_url)
+models= client.models.list()
+model_names = [model.id for model in models] 
+print(f'client model_names = {model_names}')
+```
+
+You should get back something like:
+
+
+```bash
+client model_names = ['deepseek-ai/DeepSeek-R1-Distill-Llama-8B', 'mistralai/Mixtral-8x7B-Instruct-v0.1', 'tiiuae/Falcon3-10B-Instruct', 'BAAI/bge-base-en-v1.5', 'mistralai/Mistral-7B-Instruct-v0.1', 'inceptionai/jais-adapted-70b-chat', 'mistralai/Mistral-7B-Instruct-v0.2', 'tiiuae/Falcon3-7B-Instruct', 'meta-llama/Llama-3.2-1B-Instruct', 'meta-llama/Meta-Llama-3.1-8B-Instruct', 'meta-llama/Llama-3.3-70B-Instruct', 'ALLaM-AI/ALLaM-7B-Instruct-preview', 'meta-llama/Llama-3.2-3B-Instruct', 'meta-llama/Llama-3.1-70B-Instruct', 'deepseek-ai/DeepSeek-R1-Distill-Llama-70B']
+```
+The models supported on Denvr at the time of writing are highlighted below. New models are always being added.
+
+| Model |
+|------------|
+| [ALLaM-AI/ALLaM-7B-Instruct-preview](https://huggingface.co/ALLaM-AI/ALLaM-7B-Instruct-preview) |
+| [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) |
+| [deepseek-ai/DeepSeek-R1-Distill-Llama-70B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-70B) |
+| [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) |
+| [inceptionai/jais-adapted-70b-chat](https://huggingface.co/inceptionai/jais-adapted-70b-chat) |
+| [meta-llama/Llama-3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) |
+| [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) |
+| [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) |
+| [meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) |
+| [meta-llama/Meta-Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) |
+| [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) |
+| [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) |
+| [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) |
+| [tiiuae/Falcon3-10B-Instruct](https://huggingface.co/tiiuae/Falcon3-10B-Instruct) |
+| [tiiuae/Falcon3-7B-Instruct](https://huggingface.co/tiiuae/Falcon3-7B-Instruct) |
+
+Here is the initial prompt I gave to the DeepSeek model to generate the stock trading application:
 
 ```text
 You are an expert Python software engineer and financial technology developer. Generate a fully functional Python stock trading application that uses the `yfinance` and `alpaca` packages to fetch stock data and automatically buys and sells stocks (a day-trader) based on sentiment analysis of news headlines. The app should:
@@ -44,12 +82,11 @@ You are an expert Python software engineer and financial technology developer. G
 10. Create a simple web UI for the program. I prefer streamlit.
 11. Include comments, a short README describing how to run the app, and a requirements.txt file for what packages are required.
 12. Use only free frameworks and APIs. Don’t use any paid ones.
-
-
 ```
 
-## Installation
+After generating the initial code, I took that and modified it to my needs to arrive at what you would find in [app.py](app.py).
 
+## Installing the Application
 ### Prerequisites
 - Python 3.8 or higher
 - [UV](https://github.com/astral-sh/uv) package manager (for dependency management)
@@ -99,13 +136,12 @@ ALPHA_VANTAGE_API_KEY = "your_alpha_vantage_api_key"
 
 Or you can add your secrets directly to the Hugging Face Space Settings if you are deploying the application on Hugging Face.
 
-6. Launch the application
+6. To launch the application locally with streamlit, you can run:
 ```bash
 streamlit run app.py
 ```
 
 ## Using the Application
-
 ### Manual Trading
 1. **Enter a Stock Symbol**: Type a valid stock ticker symbol (e.g., AAPL, MSFT) in the sidebar.
    
@@ -126,22 +162,24 @@ The main area displays charts for top volume stocks, showing historical price mo
 - Hover over data points for precise values
 
 ### Automated Trading
-The application includes an automated trading feature that runs in the background:
-1. Analyzes sentiment for top volume stocks
+The application includes an automated trading feature that runs in the background in a loop. You can open up the Logs on the Hugging Face Spaces app if you have deployed it there, or look at the terminal output if you deployed it locally. 
+
+1. Analyzes sentiment for top 10 volume stocks
 2. Executes buy orders for stocks with positive sentiment (if there is sufficient buying power)
 3. Executes sell orders for stocks with negative sentiment (if a position is held)
 4. Holds positions for stocks with neutral sentiment
 
-## Troubleshooting
-
 ### Common Issues
-- **API Rate Limits**: If charts or data fail to load, you may have exceeded API rate limits. Wait a few minutes and try again.
+- **API Rate Limits**: If charts or data fail to load, you may have exceeded API rate limits. 
+  - **News API**: Limited to 100 requests per day on the free tier
+  - **Alpha Vantage**: Limited to 5 API requests per minute and 500 requests per day on the free tier
 - **Invalid Stock Symbols**: Ensure you're entering valid ticker symbols when trading.
-- **Market Hours**: Some features may behave differently when markets are closed.
+- **Market Hours**: Some features may behave differently when markets are closed, though you should be able to still buy and sell stocks. The actual execution of the trade will only occur once the market opens again.
+
+### Follow Up
+
+Connect to LLMs on Intel® Gaudi® accelerators with just an endpoint and an OpenAI-compatible API key, courtesy of cloud-provider Denvr Dataworks: https://www.denvrdata.com/intel
 
-### API Limitations
-- **News API**: Limited to 100 requests per day on the free tier
-- **Alpha Vantage**: Limited to 5 API requests per minute and 500 requests per day on the free tier
+Chat with 6K+ fellow developers on the Intel DevHub Discord: https://discord.gg/kfJ3NKEw5t
 
-## License
-This project is licensed under the Apache 2.0 License.
+Connect with me on LinkedIn: https://linkedin.com/in/bconsolvo