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Description

QuantRocket is a Python-based platform for researching, backtesting, and trading quantitative strategies. Built on Docker, QuantRocket can be deployed locally or to the cloud and has an open architecture that is flexible and extensible. It provides a JupyterLab environment, offers a suite of data integrations, and supports multiple backtesters: Zipline, the open-source backtester that originally powered Quantopian; Alphalens, an alpha factor analysis library; Moonshot, a vectorized backtester based on pandas; and MoonshotML, a walk-forward machine learning backtester.

Description

TradeSave+ is a subscription-based web application that merges a comprehensive trading journal with a macro-research workspace tailored for both retail and professional traders dealing in FX, indices, commodities, and cryptocurrencies. Users can log trades manually, import files from brokers or platforms like MT4, MT5, and TradingView, or utilize an MT5 Expert Advisor for automatic synchronization that captures entry and exit screenshots. They can analyze their trading performance through various metrics such as equity curves, win/loss ratios, expectancy statistics, drawdown monitoring, and detailed breakdowns of sessions and strategies across an unlimited number of live and backtesting journals. In addition to the journal, TradeSave+ features a Fundamentals desktop that evaluates approximately 51 instruments based on 18 macroeconomic indicators, along with tools like a currency-strength table, COT positioning data, live news summaries powered by AI, an economic calendar, and a unique Risk Sentiment composite that merges quantitative signals with AI-generated news into a comprehensive regime gauge ranging from -100 to +100. Furthermore, the application includes a daily pre-market Diary designed to enhance trading preparation through structured planning, checklists, and reflective practice, thereby supporting traders in their decision-making processes.