How the Quant Agent Performs Technical Analysis in AutoHedge: LLM-Driven Quantitative Workflows

The Quant Agent performs technical analysis by orchestrating LLM-driven evaluations defined in autohedge/prompts.py, converting qualitative market theses into structured JSON outputs containing technical scores, trend strength metrics, and probability estimates for downstream risk assessment.

In the AutoHedge trading system by The-Swarm-Corporation, the Quant Agent serves as the specialized quantitative analysis module that bridges qualitative market insights with data-driven trading decisions. Unlike traditional technical analysis tools that rely on hardcoded indicators, this agent leverages prompt templates to instruct language models in evaluating market conditions, statistical patterns, and risk metrics. Understanding how the Quant Agent performs technical analysis reveals a unique architecture where LLM prompts, rather than explicit algorithms, drive the computational assessment of trade viability.

The Quant Agent's Architecture and Purpose

The Quant Agent operates as a Quantitative Analysis AI within AutoHedge's multi-agent architecture. According to the source code in autohedge/prompts.py, the agent's responsibilities are explicitly defined in the QUANT_PROMPT template (lines 24-33), which mandates four critical functions:

• Technical Indicator Analysis: Evaluating moving averages, RSI, and Bollinger Bands
• Statistical Pattern Evaluation: Identifying mean-reversion and momentum characteristics
• Risk Metric Calculation: Computing Value-at-Risk, Expected Shortfall, and Greeks
• Trade Success Probability: Deriving likelihood estimates for trade execution

This prompt-based architecture means the actual analytical computation occurs within the language model when executing the rendered prompts, making the Quant Agent a lightweight orchestration layer around LLM capabilities.

The Technical Analysis Workflow

When the AutoHedge system evaluates a potential trade, the Quant Agent executes a seven-step workflow defined in autohedge/workers.py and driven by the QUANT_ANALYSIS_PROMPT template (lines 74-90):

1. Receive Market Thesis: The Director Agent provides a qualitative outlook including price targets and key factors.

2. Render Analysis Prompt: The system populates QUANT_ANALYSIS_PROMPT with the stock ticker and Director's thesis.

3. Execute Technical Indicator Analysis: The LLM evaluates technical signals and returns a normalized technical_score between 0 and 1.

4. Assess Statistical Patterns: Historical data analysis produces volume_score and trend_strength metrics.

5. Calculate Risk Metrics: Volatility calculations and risk assessments populate the volatility field.

6. Estimate Success Probability: The model synthesizes inputs into a probability_score (0-1 scale).

7. Return Structured Data: A JSON payload includes key_levels (support, resistance, pivot) for downstream agents.

Output Schema and Quantitative Metrics

The QUANT_ANALYSIS_PROMPT in autohedge/prompts.py (lines 74-90) enforces a strict JSON output schema that standardizes the Quant Agent's technical analysis. This structure ensures consistent data transfer to the Risk and Execution agents:

• technical_score: Normalized float (0-1) representing technical signal strength
• volume_score: Metric indicating liquidity and volume confirmation
• trend_strength: Quantified momentum measurement
• volatility: Risk assessment derived from price variance calculations
• probability_score: Combined metric estimating trade success likelihood
• key_levels: Dictionary containing support, resistance, and pivot price points

This schema transforms unstructured market analysis into machine-readable quantitative data that the Risk Agent consumes for position sizing and the Execution Agent uses for order placement.

Code Implementation and Integration

Implementing the Quant Agent requires rendering the prompt templates and parsing the structured LLM responses. The following examples demonstrate the practical implementation using AutoHedge's prompt definitions.

Rendering the Quant Analysis Prompt

from autohedge.prompts import QUANT_ANALYSIS_PROMPT

stock = "AAPL"
director_thesis = (
    "Long outlook on Apple due to strong earnings and bullish technicals. "
    "Key factors: rising 50-day MA, RSI in oversold region, breakout above resistance."
)

prompt = QUANT_ANALYSIS_PROMPT.format(
    stock=stock,
    thesis=director_thesis,
)

# model is your LLM client (e.g., OpenAI, Anthropic)

quant_response = model.complete(prompt)
print(quant_response)   # JSON with technical_score, probability_score, etc.

Parsing Structured Results

import json

quant_data = json.loads(quant_response)

print(f"Technical score: {quant_data['technical_score']:.2f}")
print(f"Trend strength: {quant_data['trend_strength']:.2f}")
print(f"Probability of success: {quant_data['probability_score']:.2%}")
print("Key levels:", quant_data["key_levels"])

Feeding the Risk Assessment Pipeline

from autohedge.prompts import RISK_ASSESSMENT_PROMPT

risk_prompt = RISK_ASSESSMENT_PROMPT.format(
    stock=stock,
    thesis=director_thesis,
    quant_analysis=quant_response,
)

risk_output = model.complete(risk_prompt)
print(risk_output)  # Structured risk metrics for position sizing

Summary

• The Quant Agent uses prompt templates (QUANT_PROMPT and QUANT_ANALYSIS_PROMPT) in autohedge/prompts.py to instruct LLMs in performing technical analysis.
• It transforms qualitative Director theses into structured JSON outputs containing technical scores, trend strength, volatility metrics, and probability estimates.
• The workflow follows a seven-step process from thesis receipt through risk calculation to structured data return.
• Output includes normalized scores (0-1 scale) for technical signals and success probability, plus discrete price levels for support and resistance.
• The agent serves as an orchestration layer that feeds quantitative insights to downstream Risk and Execution agents in the AutoHedge pipeline.

Frequently Asked Questions

How does the Quant Agent differ from traditional technical analysis libraries?

Unlike libraries such as TA-Lib that execute deterministic mathematical functions, the Quant Agent performs technical analysis through LLM prompt completion. The agent renders natural language instructions via QUANT_ANALYSIS_PROMPT, directing the language model to interpret market conditions and return structured quantitative assessments rather than calculating indicators through explicit code.

What specific metrics does the Quant Agent output?

According to the JSON schema defined in autohedge/prompts.py (lines 74-90), the Quant Agent outputs technical_score, volume_score, trend_strength, volatility, probability_score, and key_levels (containing support, resistance, and pivot points). All scores are normalized between 0 and 1 to ensure consistency across different asset classes and market conditions.

How is the Quant Agent integrated with other agents in AutoHedge?

The Quant Agent receives input from the Director Agent (qualitative market thesis) and provides output to the Risk Agent and Execution Agent. In autohedge/workers.py, the orchestration layer passes the Director's thesis into QUANT_ANALYSIS_PROMPT, then feeds the resulting JSON into RISK_ASSESSMENT_PROMPT for position sizing before final trade execution.

Can the Quant Agent perform real-time technical analysis?

The Quant Agent's performance depends on the underlying LLM's inference speed and the freshness of data provided in the Director's thesis. While the prompt architecture in autohedge/prompts.py supports real-time evaluation, the system requires external data pipelines to supply current price, volume, and indicator values within the thesis parameter before prompt rendering occurs.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}