Meet the team
See all 1 on GitHub →
Commit activity
Last 12 weeks · 0 commits
Community health
1 of 6 standards met
25
✓README○License○Contributing○Code of Conduct○Issue Template○PR Template
Recent PRs & issues
Quiet · 9 in progress · Last activity 3y ago
See all on GitHub →
Update packages Add pubsub emulator to serve Add firebase emulator conifg
This commit is focused on using puppeteer to better handle twitter scraping. Instead of just taking a screenshot of the entire page it filters by the div's and takes the text from only tre first 3 or so tweets in cramer's feed. This should hopefully get you more accurate results. I've also changed the model version you're sending the tweets to, been a couple of improvements there.
My name is Luis, I'm a big-data machine-learning developer, I'm a fan of your work, and I usually check your updates. I was afraid that my savings would be eaten by inflation. I have created a powerful tool that based on past technical patterns (volatility, moving averages, statistics, trends, candlesticks, support and resistance, stock index indicators). All the ones you know (RSI, MACD, STOCH, Bolinger Bands, SMA, DEMARK, Japanese candlesticks, ichimoku, fibonacci, williansR, balance of power, murrey math, etc) and more than 200 others. The tool creates prediction models of correct trading points (buy signal and sell signal, every stock is good traded in time and direction). For this I have used big data tools like pandas python, stock market libraries like: tablib, TAcharts ,pandas_ta... For data collection and calculation. And powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM. With the models trained with the selection of the best technical indicators, the tool is able to predict trading points (where to buy, where to sell) and send real-time alerts to Telegram or Mail. The points are calculated based on the learning of the correct trading points of the last 2 years (including the change to bear market after the rate hike). I think it could be useful to you, to improve, I would like to share it with you, and if you are interested in improving and collaborating I am also willing, and if not file it in the box. If tou want, Please read the readme , and in case of any problem you can contact me , If you are convinced try to install it with the documentation. https://github.com/Leci37/LecTrade/tree/develop I appreciate the feedback
Recent fixes
Summary Modernize deployment from Firebase to Docker with continuous service mode for 24/7 operation on Raspberry Pi or any Docker-enabled device. Changes ✅ Docker containerization - Full Dockerfile with ARM support ✅ Continuous service - Built-in scheduler using node-cron (no external cron needed) ✅ Raspberry Pi ready - Optimized for Pi deployment ✅ Self-documenting scripts - Simple deployment workflow ✅ Updated dependencies - node-cron, dotenv added ✅ Cleaned up docs - Removed verbose documentation, kept README concise Deployment Trading Strategy Bot runs Monday-Friday at 10:00 AM ET (configurable via env var). Paper trading enabled by default. Testing ✅ Tested locally on Mac ✅ Deployed and running on Raspberry Pi ✅ Service confirmed healthy with proper scheduling 🤖 Generated with Claude Code
Structured data for AI agents
Repository: fireship-io/cramer-algo-trader. Description: Trade Stocks with Node.js, Alpaca, and GPT-3 Stars: 297, Forks: 75. Primary language: JavaScript. Languages: JavaScript (100%). Open PRs: 9, open issues: 1. Last activity: 3y ago. Community health: 25%. Top contributors: codediodeio.