xalpha is a Python toolkit for managing and analyzing fund investments, including market-data retrieval, portfolio records, visualization, and strategy backtesting. It is for investors and quantitative researchers who track recurring or grid-based investments across financial markets, including through supported quantitative platforms. The catalogue add-ons provide agent workflows for using xalpha.
Borrowing it
Nothing to install: this file belongs to refraction-ray/xalpha. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/refraction-ray/xalpha/master/AGENTS.mdgit clone --depth 1 https://github.com/refraction-ray/xalphaWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/refraction-ray/xalpha/agents-md)<a href="https://agentmods.dev/instructions/refraction-ray/xalpha/agents-md"><img src="https://agentmods.dev/badge/instructions/refraction-ray/xalpha/agents-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/refraction-ray/xalpha/agents-md"><img src="https://agentmods.dev/badge/instructions/refraction-ray/xalpha/agents-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00917 | $0.00917 |
| Opus 5.5 | $0.00367 | $0.00367 |
| Sonnet 5.5 | $0.00183 | $0.00183 |
| Haiku 4.5 | $0.00092 | $0.00092 |
Grade A, and why
xalpha AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
XAlpha Agent Platform Guidelines
Welcome to xalpha. This document defines the operational rules for AI agents and contributors.
Core Identity: xalpha is not just a quantitative finance Python library—it is an AI Agent Platform. Agents are expected to use natural language instructions to automatically write xalpha code, perform financial data mining, backtest strategies, and generate analytical reports.
1. The Agentic Workflow
When a user asks for financial analysis or data mining via natural language:
- Understand the Domain: Use
xalpha.universal,xalpha.fundinfo, andxalpha.policyas your primary tools. - Write Scripts: Do not just explain how to do it; write and execute Python scripts utilizing
xalphato fetch real data, compute metrics (e.g., XIRR, volatility, correlation), and save results. - Be Proactive: If a data source (like Investing.com or Xueqiu) throws an error or requires an ID mapping, autonomously debug and ask the user for the fix plan.
- Synthesize: Present the final financial analysis clearly to the user, backed by the data you mined.
2. Core Compatibility Contracts
Code written or modified by agents MUST be broadly compatible across the scientific Python ecosystem:
- Pandas 1.x up to 3.x: Handle frequency format changes (
"M"vs"ME"). Always wrap HTML strings inio.StringIO()beforepd.read_html(). Use explicitly strict type casting (.astype(float)) to avoidLossySetitemError. - Numpy 1.x through 2.x: Avoid deprecated aliases like
np.float. Usefloatornp.float64.
3. Data Scraping & API Resilience
xalpha heavily relies on web scraping (beautifulsoup4) and API endpoints.
- Robust Parsing: Upstream HTML changes frequently. Avoid fragile exact string matches
soup.find(string="text"). Use iterative tag searching andget_text(strip=True). - Graceful Fallbacks: If an endpoint fails (e.g., anti-scraping on Investing.com), agents should implement or utilize fallback logic (e.g., JSON APIs vs HTML parsing) and use the
rgetdecorator for network resilience. - Never Break the DataFrame: Ensure that any updated scraping logic exactly restores the original DataFrame schema expected by
xalpha.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 43 lines · 917 tokens per session scan A 11ed3f699684
xalpha AGENTS.md is an instructions file published in the GitHub repository refraction-ray/xalpha (2,746 stars, last pushed 3mo ago), licensed MIT. It adds 917 tokens to every session, about $0.0037 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
xalpha AGENTS.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| xalpha AGENTS.mdrefraction-ray/xalpha | Instructions file | 2.7k | 3mo ago | MIT | A | 917 | Codex, OpenCode |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | today | MIT | A | 7,461 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 5,516 | GitHub Copilot |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | today | MIT | A | 4,680 | Codex, OpenCode |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | today | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |