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browser-use-agent wraps an llm into a web operator via async python, collecting dom snapshots and screenshots before issuing a single llm call to produce reasoning and action sequences executed over cdp. intended for form filling, scraping, and regression testing on non-deterministic pages.

e2b-sandbox-runtime provides isolated micro-vm execution for ai-generated code via python/typescript sdks. covers setup, basic usage patterns, and 42 constraints including fatal modes, but lacks step-by-step procedures for common tasks.

instructor-structured-output wraps pydantic models around llm calls across 20 providers via monkey-patching. covers schema injection, retry loops, and validation error recovery, but lacks concrete usage procedures and actionable troubleshooting steps.

pandas-ta-indicators wraps the pandas-ta library to compute technical analysis metrics (RSI, MACD, Bollinger Bands, KAMA) on multi-market data with visualization and parameter tuning. execution model and concrete procedures are absent; mostly metadata and constraint listings.

pyfolio-performance wraps pyfolio-reloaded to generate portfolio tear sheets (sharpe, drawdown, turnover, round-trip trades, sector attribution) from backtests. requires python 3.12+, uv, and doramagic ecosystem. covers a-share, hk, crypto markets with joinquant/eastmoney/baostock data sources.

openbb-terminal wraps multi-market financial data sourcing (stocks, crypto, commodities) with backtesting and technical analysis. execution requires explicit user intent matching on action verbs and market specification. actual step-by-step procedures are absent from this document.

finrl-rl-trading applies ensemble deep reinforcement learning (A2C, DDPG, PPO, TD3, SAC) for automated multi-market stock trading. executes backtesting and factor research across asia-pacific markets with strict semantic locks and anti-pattern guards.

ccxt-crypto-api wraps major cryptocurrency exchanges with order management, market data, and account monitoring, but lacks concrete procedural steps and instead relies on external reference files that are not included.

credit-lgd-model claims to build loss-given-default models for credit risk assessment but provides no actionable procedure, conflates trading pipelines with credit modeling, and lacks executable steps or output contracts.

quantlib-derivatives wraps QuantLib for pricing options, swaps, and bonds via SWIG bindings, with support for american finite-difference and multi-asset basket strategies, but lacks concrete execution procedures and step-by-step workflows.

financepy-derivatives wraps FinancePy for derivatives pricing, holiday calendars, and cash flow scheduling, but the skill.md conflates trading strategy execution with derivatives valuation and lacks concrete procedural steps.

MlFinLab 提供金融机器学习高级实现，包括信息驱动 bars（tick/volume/dollar/imbalance bars）、分数阶差分和回测工具，支持多市场因子研究与策略验证。

生成符合AMLSim格式的合成交易数据，将交易日志转换为用于反洗钱检测系统测试的模拟数据集，支持按银行ID分割账户、合并多源输出并生成交易网络图。

运行经典双均线交叉策略回测，事件驱动模拟信号生成与持仓，输出 PyFolio 绩效报告。

从 SEC EDGAR 批量抓取上市公司年报（10-K）和季报（10-Q）文件，支持按季度增量更新与本地缓存，适用于美股基本面分析和量化研究数据获取。。

提供基于Fava/Beancount的投资组合管理能力，支持税务亏损收割优化、资产配置分析与等价证券分组识别，辅助用户制定最优卖出策略。

提供多市场金融强化学习环境，支持PPO/DQN等DRL算法回测、Markowitz组合优化与实时模拟交易，适配Alpaca等券商接口。。

基于 LLM 的 A 股多智能体交易分析框架，支持批量选股对比、回测信号生成和因子研究，自带 OpenAI 兼容 API 适配器模板。

CrewAI 多智能体框架：role-goal-backstory 声明智能体，sequential / hierarchical 双流程，ReAct 与 OpenAI native function-calling 双 tool 循环，统一 Memory + 5 个原生 LLM provider + Lit C...

计算IFRS 9预期信用损失（ECL），支持Vasicek单因子前瞻性调整、Kaplan-Meier生存分析计算PD及贷款摊销计划生成，满足Basel III减值合规要求。

实现自托管式加密货币投资组合追踪，自动聚合多交易所和链上钱包资产，实时计算持仓损益并生成税务报告。

使用 BSM 和 Black 模型对欧式期权进行定价和 Greeks 计算，支持连续股息收益率调整。