# OpenClaw Trading Examples

This directory contains example scripts demonstrating various features of OpenClaw Trading.

## Quick Start

### 1. Basic Economic Tracking (01_quickstart.py)

Demonstrates the core economic tracking system:

- Creating an economic tracker
- Checking survival status
- Calculating decision costs
- Simulating trades
- Tracking balance history

```bash
python examples/01_quickstart.py
```

### 2. Workflow Demo (02_workflow_demo.py)

Shows how to use the LangGraph workflow system:

- Creating a trading workflow
- Running parallel analysis
- Getting trading signals
- Handling workflow results

```bash
python examples/02_workflow_demo.py
```

### 3. Factor Market (03_factor_market.py)

Demonstrates the factor market system:

- Browsing available factors
- Purchasing factors
- Using factors in analysis
- Factor unlocking mechanism

```bash
python examples/03_factor_market.py
```

### 4. Learning System (04_learning_system.py)

Shows how to use the learning system:

- Browsing available courses
- Enrolling agents in courses
- Completing courses
- Applying learned skills

```bash
python examples/04_learning_system.py
```

### 5. Work-Trade Balance (05_work_trade_balance.py)

Demonstrates the work-trade balance mechanism:

- Monitoring agent performance
- Switching to work mode
- Earning through work
- Returning to trading

```bash
python examples/05_work_trade_balance.py
```

### 6. Portfolio Risk Management (06_portfolio_risk.py)

Shows portfolio-level risk management:

- Managing multiple positions
- Calculating portfolio risk
- Risk-adjusted position sizing
- Stop-loss and take-profit

```bash
python examples/06_portfolio_risk.py
```

## Running All Examples

To run all examples at once:

```bash
# Make executable
chmod +x examples/run_all.sh

# Run all
./examples/run_all.sh
```

Or manually:

```bash
for script in examples/0*.py; do
    echo "Running $script..."
    python "$script"
    echo ""
done
```

## Custom Examples

### Creating a Custom Agent

```python
from openclaw.agents.base import BaseAgent

class MyCustomAgent(BaseAgent):
    def analyze(self, symbol: str):
        # Your analysis logic here
        return {"signal": "buy", "confidence": 0.8}

# Usage
agent = MyCustomAgent("my_agent", initial_capital=1000.0)
result = agent.analyze("AAPL")
```

### Running a Backtest

```python
from openclaw.backtest.engine import BacktestEngine

engine = BacktestEngine()
engine.configure(
    symbols=["AAPL"],
    start_date="2023-01-01",
    end_date="2023-12-31",
    initial_capital=10000.0
)

results = engine.run()
print(f"Total Return: {results.total_return:.2%}")
```

### Using the Workflow

```python
from openclaw.workflow.trading_workflow import TradingWorkflow

workflow = TradingWorkflow(
    symbol="AAPL",
    initial_capital=1000.0,
    enable_parallel=True
)

result = await workflow.run()
print(f"Signal: {result['signal']}")
print(f"Confidence: {result['confidence']:.2%}")
```

## Jupyter Notebook Tutorials

For interactive tutorials, see the `notebooks/` directory:

1. `01_getting_started.ipynb` - Introduction to OpenClaw
2. `02_agent_comparison.ipynb` - Comparing different agents
3. `03_backtesting.ipynb` - Backtesting strategies
4. `04_custom_strategies.ipynb` - Creating custom strategies

To start Jupyter:

```bash
jupyter notebook notebooks/
```

## Prerequisites

Ensure you have OpenClaw installed:

```bash
pip install -e "."
```

Or set PYTHONPATH:

```bash
export PYTHONPATH=/path/to/openclaw/src:$PYTHONPATH
```

## Additional Resources

- [Full Documentation](../docs/)
- [API Reference](../docs/source/api.rst)
- [Architecture Guide](../docs/source/architecture.rst)