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Risk Informed Investment Approach (RIIA) with Agentic AI

In the fast-paced financial world, investors face a constant barrage of challenges: massive amounts of raw data, sudden market volatility, and the psychological hurdles of greed, fear, and FOMO (Fear of Missing Out). . This becomes a perfect use case to build data science model around proper risk management and then make it accessible through Agentic AI approach. This also helps great deal with keeping emotions out of the game and focus on quantitative signals. Risk Informed Investment Approach (RIIA) is designed to cut through such noise, offering an risk information platform that prioritizes positive sharpe ratio and restrict maximum portfolio drawdown.

Project Goal : Consistency Over Chaos

The primary business goal of RIIA is to minimize risk while maintaining steady performance. To measure success, the project focuses on two key financial metrics

  • Sharpe Ratio: Targeting a return factor over the risk-free rate of greater than 1
  • Maximum Drawdown: Keeping portfolio losses below 10%


The Architecture: From Data to Decision

RIIA operates through a sophisticated multi-layered pipeline that integrates a Data Science Layer with an Agentic AI Layer.

1. The Data Pipeline

The foundation is built on a massive public dataset spanning 2010 to 2025, including daily OHLC (Open, High, Low, Close) and volume data.

  • Feature Engineering: The system analyzes 33 distinct features, including 21 technical indicators and 6 market perception features.

  • Champion Pattern Selection: RIIA identifies high-probability chart patterns like the Bullish Flag, HnS, DoubleTop, and DoubleBottom, using F2-scores to prioritize reliability and recall.

2. The Agentic AI Layer

RIIA moves beyond simple automation by employing a suite of specialized AI Agents, each mimicking a professional role in a financial firm:

  1. Research Analyst: Monitors macro-economic trends.
  2. Sentiment Analyst: Tracks greed/fear indicators
  3. Technical Analyst: Analyzes candlestick patterns and charts.
  4. Strategy & Scenario Analysts: Determine investment approaches and evaluate adding  to existing positions.
  5. Execution & Outcome Analysts: Handle the execution and review pass/fail investments to refine future learning.

  • Flow Diagram
Below is the flow diagram and communication layer with Agentic AI. It follows CRISP-DM data science methodology.

 

Deployment and Tech Stack

The project is built on a modern stack and is discoverable to Agentic AI through the Model Context Protocol (MCP):

  • Data Science: Python (pandas, matplotlib), Random Forest, and DDQN Reinforcement Learning.

  • Agentic AI: FastMCP, FastAPI, Pydantic AI for workflows, and integration with the Claude Desktop tool.

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