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AI-powered backtesting and strategy research for traders

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  22. 22FA

    Hey HN, we built an Econ+Finance database to let AI agents do investment research. We spend a lot of tokens to organize macro releases and SEC filings into a clean format, so that your agents have more context to do actual analysis. The problem AI agents are great at data analysis. But they become ineffective if most of their context window is spent on gathering and cleaning data, instead of validating hypotheses. Data in the wild is messy and rarely standardized. Definitions and measurements change over time. This problem is compounded by a fragmented data universe. Point solutions exist…

    Jul 2026 · github.com

  23. 23TE

    The qmrExchange project is an open-source financial markets exchange simulator that realistically mimics all the main components of modern trading venues. It allows us to test and quantify the behavior of different agents in a laboratory and isolated environment without the high noise-to-signal ratio that is otherwise unavoidable in live settings. By creating a completely functioning trading venue whose access is only granted to a finite and known number of agents or trading algorithms, qmrExchange enables analyzing causation and quantifying the impact of each agent in a way that is…

    2022 · github.com

  24. 24AF

    Hey HN! I've always been fascinated by financial markets. This month, I decided to build a tool to help with the research of stocks and cryptocurrencies. I'm using YFinance as a data source because it's free and provides a wide range of reliable market data. For sentiment analysis, I'm leveraging Google Trends to gauge public interest and sentiment over time. The tool, named Zenith, is a command-line utility with four main features: Market Analysis: Provides insights like moving averages, RSI, and volatility for selected stocks or cryptocurrencies. Sentiment Analysis: Uses Google Trends to…

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