Architecting Adaptive Agents: Self-Correcting Investment Strategies for NEPSE with Human-in-the-Loop Validation

Have you ever felt the sheer frustration of trying to make informed investment decisions in a market as dynamic and often opaque as Nepal's Stock Exchange (NEPSE)? I certainly have. The NEPSE is a beast of its own: volatile, driven by fragmented real-time data, and heavily influenced by a constant deluge of unstructured local news. While we've previously explored multi-tool agents for tasks like climate risk assessment, simply chaining tools together doesn't cut it when high-stakes financial capital is on the line. Today, I want to pull back the curtain and show you how I approached building an intelligent agent that not only processes diverse NEPSE-specific financial data but also critically evaluates its own outputs, identifies uncertainties, self-corrects its reasoning, and—crucially—integrates human expertise to generate reliable, actionable investment strategies amidst market volatility and information asymmetry. This post is for data scientists and engineers looking to architect robust, trustworthy AI systems for complex financial decision-making in emerging markets, where raw data is often messy and human oversight is non-negotiable.

Key Takeaways

• **Pydantic for Data Integrity**: Robust data ingestion and validation are non-negotiable for financial agents, especially with semi-structured external APIs. Pydantic models with custom validators provide a strong first line of defense against malformed data.
• **Multi-Tool Agent Design**: Equip your LLM agent with specialized, context-aware tools for data retrieval, technical analysis, and information synthesis, moving beyond generic chat capabilities.
• **Self-Correction via Prompt Engineering**: Implement explicit self-reflection and critique steps within the LLM's reasoning chain to allow the agent to identify inconsistencies, acknowledge uncertainty, and refine its own outputs before presenting a final recommendation.
• **Human-in-the-Loop Validation**: For high-stakes domains like finance, integrate a mandatory human review and feedback mechanism. This not only mitigates hallucination risk but also provides a crucial feedback loop for continuous agent improvement.
• **Orchestration for Trust**: A well-orchestrated agent workflow, combining data validation, tool execution, self-correction, and human oversight, is essential for building trust and reliability in autonomous decision-making systems.

The NEPSE Conundrum: Why Adaptive Agents?

The core problem we're tackling here is the inherent difficulty of making consistently good investment decisions in the NEPSE market. Unlike more mature markets, real-time, consolidated NEPSE data can be elusive. News often breaks via local portals or social media, requiring nuanced interpretation. A simple rule-based system falls short because market dynamics shift. A basic LLM, without guardrails, is prone to confidently hallucinating. We need an agent that doesn't just *answer* a query, but *investigates*, *analyzes*, *critiques its own findings*, and *seeks human validation* before recommending an action. This adaptive nature is paramount for navigating the unique challenges of Nepal's financial landscape.

Data and Sources

To demonstrate this agent, I've simulated responses from two types of external sources common in NEPSE analysis: a NEPSE market summary API and a financial news API. In a real-world scenario, you would integrate with actual data providers. For reproducibility and to keep the script runnable without external API keys, the data is embedded as static JSON strings within the script, mimicking realistic NEPSE market and news data structures.

• **Simulated NEPSE Market Summary**: Represents a snapshot of key indices and top movers.
• **Simulated Financial News Feed**: Contains headlines and snippets relevant to specific companies or sectors.
• **Pydantic Documentation**: Official Pydantic Documentation
• **LangChain Documentation**: LangChain Getting Started

Data accessed on 2026-10-01 (simulated for demonstration purposes).

Step 1: Building a Resilient Data Foundation with Pydantic

The first sub-problem in any data-driven system is reliable data ingestion. External APIs, especially in emerging markets, can be inconsistent. Schema changes, missing fields, or incorrect data types are common. If we feed malformed data to our agent, its analysis will be flawed from the start. My solution involves using Pydantic models to define expected data structures and validate incoming JSON responses rigorously.

Here's how I set up Pydantic models for NEPSE market data and news articles. Notice the custom validators for fields like `change_percent` to ensure data quality immediately upon ingestion.

from pydantic import BaseModel, ValidationError, validator
from typing import List, Optional, Dict
import json
import logging

logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')

class NepseMarketSummary(BaseModel):
    index: str
    current_value: float
    change_points: float
    change_percent: float
    volume_mn: float
    turnover_bn: float
    last_updated: str

    @validator('change_percent')
    def validate_change_percent(cls, v):
        if not (-100.0 <= v <= 100.0): # Assuming typical daily limits
            raise ValueError(f"Change percent {v} is out of realistic range.")
        return v

class FinancialNewsArticle(BaseModel):
    title: str
    source: str
    published_date: str
    summary: str
    sentiment: