A Python-based framework for back testing, optimizing and identifying cryptocurrency trading strategies using historical data.

This Python script implements and backtests a multi-asset trading strategy on a portfolio. It leverages common technical indicators to generate buy and sell signals and simulates portfolio performance over historical data.

This Python script implements a backtesting simulation for a simple trading strategy using two common technical indicators: the 200-day Simple Moving Average (SMA) and a 2-period Relative Strength Index (RSI). The strategy is designed to identify and capitalize on potential buy and sell signals based on the confluence of these indicators.

Quant-grade toolkits for algorithmic traders and system developers: realistic backtesting, pattern detection, Elliott Wave, ML pipelines, intelligent stop-loss, and safe live trading infrastructure with monitoring and emergency protection.