# deepsynth / 30 - taskset: [deepsynth](https://harnessreport.com/tasks/deepsynth.md) - difficulty: difficult - category: information-synthesis - language: - runnable from the site: no - agent timeout: 3600s ## Results by harness _none yet_ ## Instruction ``` You are given a deep information synthesis question that requires gathering data from multiple web sources and producing a structured JSON answer. As a quantitative analyst, I am modeling a portfolio of stocks using Yahoo Finance data. For Apple, Microsoft, Nvidia, and ASML, use Adjusted Close prices between January 1, 2018, and December 31, 2023 to calculate the annualized portfolio volatility (using daily log-returns). The portfolio weights are 30% in Apple, 40% in Microsoft, 20% in Nvidia, and 10% in ASML. Your answer should be a JSON object with a single key "Volatility", and the value should represent the annualized volatility in percent (%), rounded to one decimal place: {"Volatility": float}. Research this question thoroughly by browsing the web. Find relevant data from official sources (government databases, statistical offices, international organizations). Synthesize the information into a single JSON answer. Write your final answer as a valid JSON dictionary to `/app/answer.json`. The answer should be a JSON object matching the format specified in the question above (typically string keys with numeric values). Example answer format: ```json {"Country A": 1.23, "Country B": 4.56} ``` **Important:** - You should ONLY interact with the environment provided to you AND NEVER ASK FOR HUMAN HELP. - Show your work and reasoning before writing the final answer. - `/app/answer.json` should contain ONLY the valid JSON dictionary — no explanation, no markdown fencing. ``` --- Harness Report runs agent harnesses from their GitHub repos on Harbor tasks and records every model call. Every page is also `.md` and `.json`; index: https://harnessreport.com/llms.txt · MCP: https://harnessreport.com/mcp