Adaptive Agents in Action: Architecting Self-Correcting Investment Strategies for NEPSE with LLMs

Have you ever watched a brilliant investment strategy, crafted with immense care and insight, slowly erode as market conditions inevitably shift? I certainly have. In the volatile, often unpredictable landscape of emerging markets like Nepal's NEPSE, a static approach to investment is less of a strategy and more of a ticking time bomb. Relying on a one-off LLM analysis, no matter how sophisticated, simply isn't enough when the ground beneath our feet is constantly moving. We need more than just intelligent predictions; we need intelligence that adapts, learns, and self-corrects. This post is for data scientists and engineers who are ready to move beyond static AI recommendations and build truly proactive, resilient agents. We'll dive deep into architecting an LLM agent capable of not just generating investment strategies for NEPSE, but iteratively optimizing them based on simulated performance feedback, demonstrating how to bake self-correction directly into your AI workflows for high-stakes financial applications.

Key Takeaways

• LLM agents can be designed with iterative feedback loops, enabling self-correction and dynamic strategy refinement, crucial for volatile markets.
• Rigorous backtesting serves as the critical feedback mechanism, providing quantifiable performance metrics for an agent to analyze and learn from.
• Structured prompt engineering, combined with custom tools, allows LLMs to generate actionable strategies and interpret complex performance data effectively.
• Operationalizing adaptive agents requires robust data ingestion, error handling, structured output parsing, and careful management of the feedback loop for convergence.
• Simulating LLM responses for development and testing is a practical approach before integrating with costly and rate-limited live APIs.

The Problem: Stagnant Strategies in Dynamic Markets

My earlier work on dynamic financial analysis with LLMs and NEPSE data showed the power of LLMs in interpreting complex financial information. However, that approach, like many initial LLM applications, focused on generating a single analysis or recommendation. The real world of finance, especially in a market as nuanced and rapidly changing as NEPSE, demands more. A strategy that performs well today might falter tomorrow due to shifts in economic indicators, company news, or global sentiment. The core problem I faced was the lack of an inherent mechanism for the LLM agent to learn from its own "mistakes" or adapt its recommendations as new data came in. How could I build an agent that not only proposes an investment strategy but then actively seeks to improve it based on its simulated performance?

Data and Sources

To build our adaptive agent, we'll need two primary data sources: historical NEPSE stock data and simulated news sentiment. For the purpose of this demonstration, I've created realistic, synthetic datasets that mimic the structure and characteristics of real-world NEPSE data and associated news. In a production environment, these would be fed from live APIs or data warehouses.

• NEPSE Historical Data (CSV): A CSV file named nepse_data.csv containing daily Open, High, Low, Close, and Volume for a hypothetical NEPSE stock. This data is critical for calculating technical indicators and backtesting. (Simulated data, structure based on publicly available NEPSE historical data.)
• Simulated News Sentiment (JSON): A JSON file named news_sentiment.json containing simulated news headlines and sentiment scores for specific dates. This provides contextual information for the LLM. (Simulated data.)
• Backtesting.py Documentation: Official documentation for the backtesting library, which we'll use to simulate strategy performance.
• LangChain Documentation: Official documentation for building LLM applications and agents.

Data accessed on 2024-07-20 for demonstration purposes. The simulated data represents a snapshot for illustrative scenarios.

Step 1 — Contextualizing the Market: Dynamic Data Ingestion & Feature Engineering

The first challenge for any intelligent agent is getting a clear, up-to-date picture of its operating environment. For our NEPSE investment agent, this means efficiently ingesting diverse data sources – historical prices and news sentiment – and transforming them into meaningful features that an LLM can understand and act upon. A raw CSV or JSON dump isn't enough; we need to provide context, indicators, and summaries.

My approach here involved creating a function that loads the historical NEPSE data, calculates common technical indicators like Simple Moving Averages (SMA) and Relative Strength Index (RSI), and then aggregates simulated news sentiment for a given period. This pre-processing step is vital because it translates raw data into a language more suitable for strategic decision-making, while also reducing the token burden on the LLM.

import pandas as pd
import json

def load_and_engineer_market_data(file_path='nepse_data.csv', end_date=None):
    """
    Loads NEPSE historical data, calculates technical indicators,
    and filters up to a specified end_date.
    """
    df = pd.read_csv(file_path, parse_dates=['Date'], index_col='Date')
    df = df.sort_index()

    if end_date:
        df = df[df.index <= pd.to_datetime(end_date)]

    # Calculate SMAs
    df['SMA_20'] = df['Close'].rolling(window=20).mean()
    df['SMA_50'] = df['Close'].rolling(window=50).mean()

    # Calculate RSI (simplified for brevity, typically more complex)
    delta = df['Close'].diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
    rs = gain / loss
    df['RSI'] = 100 - (100 / (1 + rs))

    return df.dropna()

def load_news_sentiment(file_path='news_sentiment.json', date=None):
    """
    Loads simulated news sentiment for a specific date.
    """
    with open(file_path, 'r') as f:
        news_data = json.load(f)

    if date:
        date_str = pd.to_datetime(date).strftime('%Y-%m-%d')
        return news_data.get(date_str, [])
    return []

# Example usage for a specific date
# market_df = load_and_engineer_market_data(end_date='2023-03-10')
# news_for_date = load_news_sentiment(date='2023-03-10')
# print(market_df.tail(2))
# print(news_for_date)

The `load_and_engineer_market_data` function takes our raw CSV and enriches it. By filtering data up to an `end_date`, we simulate getting real-time market snapshots, ensuring the LLM's context is always current. The `load_news_sentiment` function provides the qualitative market color. Together, these functions form the agent's eyes and ears, giving it a structured view of the market's technical and fundamental landscape.

Step 2 — Initial Strategy Generation: Prompt Engineering for Actionable Plans

Once our agent has a contextualized view of the market, the next critical step is to instruct the LLM to generate a well-structured, actionable investment strategy. This isn't about asking for a vague opinion; it's about getting a precise plan that considers market data,