# Where Are the Prompt Templates in AutoHedge and How to Use Them

> Find AutoHedge prompt templates in prompts.py and learn how to use them with workers.py to configure your autonomous trading agents.

- Repository: [Swarms/AutoHedge](https://github.com/The-Swarm-Corporation/AutoHedge)
- Tags: how-to-guide
- Published: 2026-09-08

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**AutoHedge stores all LLM prompt definitions in [`autohedge/prompts.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/prompts.py) and consumes them through [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py) to configure autonomous trading agents.**

The AutoHedge project by The-Swarm-Corporation orchestrates multiple AI agents for algorithmic trading using the swarms framework. Understanding how to locate and manipulate these **AutoHedge prompt templates** is essential for customizing agent behavior and injecting dynamic market data into the trading pipeline.

## Locating the Prompt Templates in AutoHedge

All prompt definitions live in a single module: **[`autohedge/prompts.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/prompts.py)**. This file contains the complete corpus of system instructions and dynamic templates used by the Director, Quant, Sentiment, Risk, and Execution agents.

According to the AutoHedge source code, this centralization allows consistent prompt versioning across the autonomous hedge fund architecture. The file defines both static system prompts that establish agent identity and templated prompts containing `{placeholder}` tokens for runtime formatting.

## System Prompts vs. Template Prompts

AutoHedge distinguishes between two prompt categories:

### System Prompts

System prompts define the role, objectives, and operational constraints for each autonomous agent. These are plain Python string constants imported by **[`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py)** and passed directly to the `swarms.Agent` constructor.

For example, the `DIRECTOR_PROMPT` configures the orchestration layer that manages trade flow between specialized workers.

### Template Prompts

Template prompts contain variable placeholders like `{stock}`, `{thesis}`, and `{quant_analysis}`. These strings are designed for Python’s `str.format()` method, allowing agents to inject real-time market data and previous analysis results into new requests.

Key templates include `RISK_ASSESSMENT_PROMPT` and `EXECUTION_ORDER_PROMPT`, which require dynamic values to generate actionable trading instructions.

## How to Use AutoHedge Prompt Templates

### Assigning System Prompts to Agents

Import constants from [`autohedge/prompts.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/prompts.py) and pass them to the `system_prompt` parameter when instantiating agents in [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py) or your custom scripts:

```python
from autohedge.prompts import DIRECTOR_PROMPT
from swarms import Agent

director = Agent(
    agent_name="Trading-Director",
    system_prompt=DIRECTOR_PROMPT + "\nCurrent date: 2024-01-15",
    model_name="gpt-4.1",
    max_loops=1,
)

```

This pattern, as implemented in [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py), establishes the foundational identity of each agent in the pipeline.

### Formatting Dynamic Template Prompts

For prompts requiring runtime data, use the `format()` method to populate placeholders. The `RISK_ASSESSMENT_PROMPT` expects variables like `stock`, `thesis`, and `quant_analysis`:

```python
from autohedge.prompts import RISK_ASSESSMENT_PROMPT

stock = "AAPL"
thesis = "Long on AAPL due to strong earnings."
quant_analysis = """{
    "technical_score": 0.85,
    "volume_score": 0.72,
    "trend_strength": 0.78,
    "volatility": 0.12,
    "probability_score": 0.81,
    "key_levels": {"support": 150.0, "resistance": 165.0, "pivot": 157.5}
}"""

prompt = RISK_ASSESSMENT_PROMPT.format(
    stock=stock,
    thesis=thesis,
    quant_analysis=quant_analysis,
)

```

The resulting prompt contains fully populated risk analysis instructions ready for agent consumption.

### Chaining Agents with Formatted Prompts

After formatting, pass the prompt to specialized agents imported from [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py):

```python
from autohedge.workers import risk_agent

risk_result = risk_agent.run(prompt)
print(risk_result)

```

This workflow enables the Director agent to coordinate multi-stage analysis where quantitative output feeds into risk assessment through dynamically formatted **AutoHedge prompt templates**.

## Summary

- **AutoHedge prompt templates** reside exclusively in [`autohedge/prompts.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/prompts.py), serving as the single source of truth for all agent instructions.
- **System prompts** establish agent roles and are assigned via the `system_prompt` parameter in `swarms.Agent` constructors within [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py).
- **Template prompts** utilize Python `str.format()` to inject real-time variables like stock tickers and technical analysis data.
- The architecture separates prompt definition ([`prompts.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/prompts.py)) from agent instantiation ([`workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/workers.py)), enabling modular customization of the trading pipeline.

## Frequently Asked Questions

### What file contains all the prompt definitions in AutoHedge?

All prompt definitions are centralized in **[`autohedge/prompts.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/prompts.py)**. This module exports string constants for both system prompts and dynamic templates used across the Director, Quant, Risk, and Execution agents.

### How do I customize a system prompt for a specific agent?

Import the desired constant from `autohedge.prompts` and modify it before passing to the `Agent` constructor. For example, append context-specific instructions to `DIRECTOR_PROMPT` when initializing the agent in [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py) or your implementation.

### Can I add new variables to existing template prompts?

Yes. Modify the string in [`autohedge/prompts.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/prompts.py) to include new `{placeholder}` tokens, then supply corresponding keyword arguments to the `format()` method when generating prompts. Ensure downstream agents in [`autohedge/workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/autohedge/workers.py) are updated to provide the new data fields.

### Which agents use template prompts versus static system prompts?

According to the source code, all agents receive static system prompts during initialization in [`workers.py`](https://github.com/The-Swarm-Corporation/AutoHedge/blob/main/workers.py). Template prompts are typically used for inter-agent communication where the Director or Quant agents generate formatted requests containing `RISK_ASSESSMENT_PROMPT` or `EXECUTION_ORDER_PROMPT` populated with current market data.