{"repo":"JustinGuese/python_tradingbot_framework","free":true,"listed":false,"github":"https://github.com/JustinGuese/python_tradingbot_framework","clone":"git clone https://github.com/JustinGuese/python_tradingbot_framework.git","description":"Python algorithmic trading bot framework for Kubernetes: backtesting, hyperparameter optimization, 150+ technical analysis indicators (RSI, MACD, Bollinger Bands, ADX), portfolio management, PostgreSQL integration, Helm deployment, CronJob scheduling. Minimal overhead, production-ready, Yahoo Finance data.","language":"Jupyter Notebook","stars":35,"topics":["helm","kubernetes","python","quant","quantitative-finance","quantitative-trading","stocks-trading","trading","trading-algorithms","trading-strategies"],"license":"MIT","category":"trading","readme_excerpt":"🤖 Trading Bot Framework A Production-Ready, Kubernetes-Native Algorithmic Trading System kubectl create secret generic tradingbot-secrets --from-env-file=.env --namespace=tradingbots-2025 --dry-run=client -o yaml kubectl apply -f - This framework allows developers to build, backtest, and deploy automated trading strategies as Kubernetes CronJobs . It handles the \"boring stuff\"—data ingestion, technical analysis, database persistence, and portfolio tracking—so you can focus on the alpha. 🚀 Why this Framework? - Batteries Included : 150+ Technical Indicators (RSI, MACD, etc.) ready out of the box. - Infrastructure as Code : Native Helm charts for easy scaling on K8s. - Data Consistency : Built-in caching and PostgreSQL persistence for trade history and market data. - Backtesting to Production : One class handles local testing, hyperparameter optimization, and live execution. 🛠 System Architecture The system is designed to be lightweight and stateless. Each \"Bot\" is a containerized instance triggered by a schedule. 1. Ingestion : Fetches data from Yahoo Finance (with DB caching). 2. Analysis : Enriches data with the ta library (Technical Analysis). 3. Execution : BotClass manages the state of your portfolio in PostgreSQL. 4. Monitoring : Real-time performance tracking via the included Dashboard. ⚡ Quick Start 1. Requirements - Python 3.12+ (We recommend uv for speed) - Docker (for local DB) 2. Launch Local Environment 3. Your First Strategy Create a simple RSI Mean Reversion ","default_branch":null,"files":null,"tree":[],"storefront":"/r/JustinGuese","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/JustinGuese/python_tradingbot_framework/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}