Here are some resources for option trading:
**Backtesting Tools**
* OptionStack (backtesting tool, free trial)
* thinkorswim "thinkBack" (backtesting tool within a broker's platform)
* TradeMachine Pro (paid only, but has 30-day free trial)
* Tastylive backtesting webapp (free, with account registration)
**Technical Analysis**
* Daytrading Simulator article on TA for options
* Fidelity TA for options trading Webinar PDF
* Investopedia top technical indicators for options trading
* TDA three indicators to check before the trade
**Research & Indicators**
* Finlo (research and portfolio tracking, free 7-day trial)
* Predicting Alpha (volatility forecasting and strategy-focused dashboard, requires registration)
* Qwidgets customizable dashboard for researching options trades (free, requires registration)
* Simpler Trading (research, analysis, and education, mostly paid but some free resources)
**Screeners & Scanners**
* Barchart options screener
* CME Open Interest heatmap for CME options/futures products
* FDScanner
* Golden Sweeps (freemium web site)
* Market Chameleon options screener
* Option Samurai (paid subscription only)
* Option Sonar unusual activity scanner (free alerts are delayed, paid subscriptions are real-time)
**Option Trading Blogs, Video and Podcast Channels**
* InTheMoney YouTube channel
* Option Alpha YouTube channel
* Poor vs. Standard podcast
* projectoption YouTube channel
* Sky View Trading YouTube channel
* Start Options tutorial blog
* Tastytrade YouTube channel
* TrackingStockPundits options analysis and blogs
**Other Resources**
* ORATS backtesting blog
* Spintwig (mostly credit trading strategies)
* Thinkorswim's "How to Trade" section
- Python script for AI-powered code generation and improvement
- Supports multiple AI models (Claude, GPT-4, Llama)
- Iterative code improvement with diff visualization
- Automatic error detection and correction
- Generates complete project folder structure
- Creates README and requirements.txt files
- Colorful console output with progress indicators
- Option to input task description from a file
- Requires Python 3.7+ and installation of dependencies
- Usage: `python ai-coder.py` or `python ai-coder.py -f path/to/task_description.txt`
- Prompts user for task description, number of iterations, and AI model choice
- Generates project folder with final code, README, requirements.txt, .gitignore, and versions subfolder
- Customizable for AI models, iterations, error handling, and logging verbosity
- Quality depends on chosen AI model and task description clarity
- Requires internet connection for AI model communication
- Review and test generated code before production use
- MACHINA: CCTV viewer with realtime object tagger [WIP]
- Uses LLAVA, YOLO 11, OpenCV, and FAISS
- Connects to high-resolution RTSP stream, queues frames, and resamples for processing
- YOLO detects objects, assigns IDs based on coordinates, size, and timestamp
- Background thread makes LLM requests to Ollama server for object tagging
- Object matching uses center of detection boxes with 16px tolerance
- Test environment: 640x480 processing, 20ms inference time on GTX 1060
- Stream delays 1-2 seconds every ~10 minutes, frame skip mechanism implemented
- Prerequisites:
- Python 3.12.x
- Ollama server with LLAVA model
- Compatible PyTorch version
- Dependencies from requirements.txt
- Usage:
- S: snapshot
- R: start/stop recording
- Q: quit app
- Left mouse: select
- Middle mouse: zoom
- Right mouse: pan
- Project aims to create a complete headless security system
- TODO:
- Additional UI Layer
- RTS style object selection box with detailed information
- Detection of crowds, car crashes, police, ambulance, running humans
- Webhook callbacks for new/disappeared objects and movement after long stays
Pooltool is a general purpose billiards simulator crafted specifically for science and engineering.
Its core design principles focus on speed, flexibility, and the ease of visualization and analysis. With an interactive 3D interface, a robust API, and extensive documentation, pooltool aims to be a systemic tool in billiards-related research. Continuously evolving through active maintenance and bolstered by a growing community, this vision for pooltool emphasizes not just its current capabilities, but also its potential for growth and adaptation within billiards simulation.
Pooltool is peer-reviewed and published in the Journal of Open Source Software (JOSS) and pyOpenSci.
Transforming Multi-Sourced Text into Captivating Multi-Lingual Audio Conversations with GenAI
- Watch YouTube videos on how transformers and LLMs work:
- 3Blue1Brown's video on transformers
- Sebastian Raschka's "Build a Large Language Model From Scratch"
- Andrej Karpathy's "Neural Networks: Zero to Hero" playlist
- Start with understanding embeddings:
- Use SentenceTransformer to embed texts and visualize vectors
- Learn fundamentals of neural networks:
- Implement a basic feed-forward network (e.g. for MNIST)
- Understand backpropagation algorithm
- Study simpler language models:
- Character-level prediction with RNNs
- Word embeddings like word2vec
- Implement a small transformer from scratch
- Read architecture papers on models like T5, BERT, GPT-2
- Try recreating a minimal LLM implementation:
- Karpathy's llama2.c as a starting point
- Explore "Spreadsheets are all you need" Excel implementation
- Review detailed LLM architecture diagrams