# Data Stream Management Guide

## Quick Commands

### Check Stream Status
```bash
python check_stream.py status
```

### Generate Snapshot
```bash
python check_stream.py snapshot
```

## What You'll See

### Stream Status Output
- ✅ Dashboard is running
- 💡 Guidance message
- 📝 Data stream location note
- Console output examples to look for

### Snapshot Output
- ✅ Snapshot saved: `data_snapshots/snapshot_YYYYMMDD_HHMMSS.json`
- 📝 Note about data location

## How It Works

The script connects to your **running dashboard** instead of creating a new instance:

1. **Checks if dashboard is running** at `http://127.0.0.1:8050`
2. **Provides guidance** on where to find the data stream
3. **Generates snapshots** with current timestamp and metadata

## Where to Find Live Data

The **data stream is active inside the dashboard console**. Look for output like:

```
OHLCV (1m): ETH/USDT | O:4335.67 H:4338.92 L:4334.21 C:4336.67 V:125.8
TICK: ETH/USDT | Price:4336.67 Vol:0.0456 Side:buy
DQN Prediction: BUY (conf:0.78)
Training Exp: Action:1 Reward:0.0234 Done:False
```

## When Data Appears

Data will show in the dashboard console when:
1. **Market data is flowing** (OHLCV, ticks, COB)
2. **Models are making predictions**
3. **Training is active**

## Snapshot Contents

Snapshots contain:
- Timestamp
- Dashboard status
- Empty data arrays (data is in dashboard console)
- Note about checking console for live data

## Usage Tips

- **Start dashboard first**: `python run_clean_dashboard.py`
- **Check status** to confirm dashboard is running
- **Watch dashboard console** for live data stream
- **Generate snapshots** to capture timestamps and metadata
- **Wait for market activity** to see data populate

## Files Created

- `check_stream.py` - Status and snapshot commands
- `data_snapshots/` - Directory for saved snapshots
- `snapshot_*.json` - Timestamped snapshot files with metadata