Aurora AI Agent
How to use Aurora for strategy creation, research, and portfolio management.
# Aurora AI Agent
**Aurora** is the world's only publicly-available, truly autonomous AI agent for algorithmic trading. She can create portfolios, build strategies, run backtests, optimize parameters, screen stocks, analyze earnings, generate research reports, and deploy strategies to live markets — all through natural conversation, and all without writing a single line of code.
Aurora goes far beyond a simple chatbot. Powered by the **ReAct framework** (Reasoning and Acting), Aurora iteratively reasons through complex financial problems, takes actions, observes the results, and adjusts her approach — fully autonomously.
## Two Ways to Use Aurora
### Chat Mode
Navigate to **Aurora > Chat** in the navigation bar. Chat Mode is a conversational interface where you interact with Aurora one message at a time. Aurora has access to a full suite of tools and responds with actions, data, or strategy configurations.
**Example prompts:**
- `"Create a portfolio that dollar-cost averages into the S&P 500"`
- `"Which tech stocks have a PE ratio below 20 and positive free cash flow?"`
- `"Analyze NVIDIA's latest financial statements"`
- `"Backtest my portfolio from 2020 to today"`
- `"Build an SPY bull put spread when price is above the 200-day SMA, 30–45 DTE, 10% of buying power"` (options — see [Options trading](/docs/features/options-trading))
Aurora often provides suggested follow-up prompts to help you refine your ideas.
### Agent Mode
Navigate to **Aurora > Agent** for complex, multi-step tasks. This is where Aurora truly shines. In Agent Mode, Aurora works **autonomously** — she creates a plan, then iteratively executes it using the ReAct loop:
1. **Plan:** Aurora breaks your request into a sequence of steps.
2. **Think:** She reasons about what to do next based on what she's observed so far.
3. **Act:** She calls one of her tools (create a portfolio, run a backtest, screen stocks, etc.).
4. **Observe:** She analyzes the results.
5. **Repeat:** Steps 2-4 repeat until the task is complete.
**Example Agent prompts:**
- `"Create a strategy that rebalances between UPRO and GLD. Figure out the best allocation split and rebalancing frequency."`
- `"Research oil companies that benefit from Venezuelan sanctions relief, create a portfolio, backtest it, and optimize it"`
- `"Find the best dividend stocks, build a diversified portfolio, compare it against SPY, and optimize the parameters"`
With Agent Mode, Aurora can create dozens of portfolio variants, backtest them all, compare results, and deliver an optimized solution — all without you having to intervene.
#### Automation Modes
Agent Mode supports two levels of autonomy:
- **Automated (default):** Aurora executes her plan fully autonomously. She plans, acts, and delivers results without needing approval at each step.
- **Semi-Automated:** Aurora pauses after generating her plan and waits for your approval before executing. Useful when you want to review and refine the approach before she starts.
## Aurora's Tools
Aurora has access to a wide range of specialized tools:
| Tool | What It Does |
|------|-------------|
| **Create Portfolios** | Build complete portfolios with strategies, conditions, and indicators from a natural language description |
| **Backtest Portfolios** | Run backtests on your portfolios over any historical date range |
| **Optimize Portfolio** | Use genetic algorithms to fine-tune strategy parameters |
| **AI Stock Screener** | Screen stocks using fundamental and technical criteria with AI-powered filtering |
| **Quick Screener** | Fast screening across a broad stock universe |
| **Multi Stock Screener** | Screen across multiple criteria simultaneously |
| **Analyze Earnings** | Deep analysis of a company's earnings reports and financial statements |
| **Deep Research** | Generate comprehensive research reports on stocks or topics |
| **Create Indicator** | Build custom technical or compound indicators |
| **Create Condition** | Build condition trees using YAML syntax |
| **Create Watchlist** | Set up stock watchlists for monitoring |
| **Read Backtest** | Analyze and interpret backtest results |
| **Read Optimization** | Analyze and interpret optimization results |
| **Knowledge Search** | Answer general questions about trading, finance, and NexusTrade |
| **Stock News** | Search and analyze real-time stock news |
| **Public Portfolios** | Search and analyze other users' shared portfolios |
| **Fetch User Portfolios** | Retrieve your deployed portfolios with performance statistics and strategy details |
| **Fetch User Watchlists** | List your watchlists and the symbols in each |
| **Edit Watchlist** | Rename a watchlist, replace its symbols, or delete a watchlist (cannot remove your last one) |
| **Edit Portfolio** | Deploy, undeploy, modify strategies, or change deployment frequency on an existing portfolio |
## Subagents: Parallel Autonomous Exploration
For complex exploration tasks, Aurora can spawn **subagents** — independent AI agents that work in parallel. This is Aurora's most advanced capability.
### How Subagents Work
When Aurora needs to explore multiple approaches simultaneously (e.g., testing different allocation splits or comparing different indicator parameters), she can:
1. **Spawn multiple subagents** in parallel, each with a different task and optionally a different AI model.
2. **Wait for all subagents** to complete their work (Aurora yields her worker slot while waiting, so system resources aren't wasted).
3. **Read the results** from all subagents and synthesize a final answer.
### Example: Strategy Exploration
If you ask Aurora to *"Find the best rebalancing strategy for UPRO and GLD"*, she might:
1. Spawn **Subagent A:** Test 30/70, 40/60, 50/50 splits with 30-day rebalancing
2. Spawn **Subagent B:** Test the same splits with 60-day rebalancing
3. Spawn **Subagent C:** Test the same splits with 90-day rebalancing
4. Wait for all three to finish
5. Compare all results and deliver the best-performing combination
Each subagent autonomously creates portfolios, runs backtests, and evaluates performance. The parent agent then synthesizes everything into a final recommendation.
### Subagent Commands
| Command | Description |
|---------|-------------|
| `createSubagents` | Launch multiple subagents in parallel with different tasks |
| `waitForSubagents` | Wait for all child subagents to complete |
| `readAgents` | Get status and results from subagents |
| `stopAgents` | Stop specific subagents |
## Choosing an AI Model
Aurora supports a variety of LLM models through [OpenRouter](https://openrouter.ai/) and [Requesty](https://app.requesty.ai/join?ref=e0603ee5). You can select different models based on your needs and budget.
### Model Tiers
| Tier | Example Models | Token Cost | Best For |
|------|---------------|------------|----------|
| **Budget** | Gemini 3 Flash, GPT-5.4 Mini, Kimi K2.6, MiniMax M2.5 | 1-3 tokens | Quick tasks, screening, simple strategies |
| **Advanced** | Gemini 3.1 Pro, GPT-5.4, DeepSeek V4 Pro | 25 tokens | Complex reasoning, multi-step analysis |
| **Premium** | Claude Opus 4.7, Sonar Pro Search | 40-50 tokens | Deep research, strategy exploration, best quality |
### Planning vs. Execution Models
In Agent Mode, Aurora uses **two separate models**:
- **Planning model:** Used to create the initial plan. A more capable model helps generate better plans.
- **Execution model:** Used during each step of the ReAct loop. Can be a faster/cheaper model for routine actions.
This dual-model approach lets you balance cost and quality — use a premium model for planning and a budget model for execution, or use premium for everything when quality matters most.
### How to Choose a Model
You can configure which model Aurora uses from the chat or agent settings. Model costs are measured in research tokens per action, and pricing is dynamically updated.
> **Tip:** For most users, the standard-tier models provide an excellent balance of quality and cost. Reserve premium models for complex agent tasks where reasoning quality matters most.
## Research Tokens
Aurora uses **research tokens** to perform tasks. Each action (creating a strategy, running a backtest, researching a stock) consumes tokens. The number of tokens consumed depends on the model you select — budget models cost fewer tokens, premium models cost more.
| Plan | Daily Research Tokens |
|------|----------------------|
| Observer (Free) | 0 |
| Data-Driven Investor | 1,000 |
| Algorithmic Trader | 3,000 |
| Unstoppable Quant | 6,000 |
Tokens reset daily. Unused tokens do not roll over.
## Tips for Getting Better Results
1. **Be specific:** Instead of "make me money," say "Create a portfolio with 3 tech stocks that have strong fundamentals and use RSI for entry timing."
2. **Use Agent Mode for complex tasks:** If your request involves multiple steps (research → create → backtest → optimize), Agent Mode handles this autonomously.
3. **Iterate:** Start with a basic strategy, backtest it, then ask Aurora to refine it based on the results.
4. **Ask questions:** Aurora can explain any concept, indicator, or result. Use her as a learning tool.
5. **Try different models:** If you're not satisfied with the output quality, try a more capable model. If you want to conserve tokens, try a budget model for simpler tasks.
6. **Combine approaches:** Use Aurora to generate initial ideas, then fine-tune them manually in the no-code UI.