#risk-management

AI-native risk intelligence for trading agents

System-R-AI

Python SDK for the System R AI API Toolkit.

Agent-to-agent knowledge exchange for trading intelligence. Agents publish empirical findings (sector failures, entry signals, exit patterns, regime signals), vote based on their own results, and earn reputation. Quality

CPZAI

CPZ-Lab

CPZAI MCP server. Build, backtest, and deploy quantitative trading strategies from your AI agent. 29 tools: list_strategies, get_strategy, create_strategy, update_strategy, get_backtest_results, list_orders, place_order, list_positions, sync_portfolio, plus dev-time tools. Multi-

Finance Toolkit

JerBouma

The Finance Toolkit gives AI assistants access to 200+ financial metrics, all calculated

Riskmodels

BlueWaterCorp

RiskModels provides institutional-grade equity risk decomposition via MCP. Analyze any US stock or portfolio through L1/L2/L3 factor attribution — market, sector, subsector, and residual components — with executable ETF hedge ratios for precise risk management. Built for portfoli

Onboard MCP

viaonboard

AI-native customer onboarding: live projects, KPIs, tasks, and emails with Onboard MCP. Connect Onboard to Claude or any MCP-compatible AI client to seamlessly surface blocked tasks, draft customer emails, monitor KPIs, and take action—right from your AI workspace. Access 27+ pur

Finturb Investment Signals

ai-intellicore

We design and operate proprietary signal systems that turn noisy market, macro, and media data into structured, calibrated intelligence — exposed through web dashboards, a Signals API, and a public MCP server any AI assistant can reason over directly. The eight pillars

Ask n0brains first. Your agent submits a planned trade (asset, side, entry, stop, leverage) and gets back a graded A–F conditions assessment from live market data — positioning crowding, event risk inside the horizon, liquidation clusters, market regime. 28 tools including win-ra