python_tradingbot_framework
Unverified Mean-rev strategy on Indices by JustinGuese. BotFinder score 18 out of 100.
Python algorithmic trading bot framework for Kubernetes: backtesting, hyperparameter optimization, 150+ technical analysis indicators (RSI, MACD, Bollinger Bands, ADX), portfolio m
Source: github
BotFinder analysis pending.
python_tradingbot_framework
🤖 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. 🎯 The Target: Alpha vs QQQ, Not Return Bots are judged by their annualised alpha against QQQ: the daily return left over after removing the bot's QQQ exposure (beta), along with that alpha's t-stat. Raw return and Sha
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