74 lines
1.9 KiB
Markdown
74 lines
1.9 KiB
Markdown
# Data Stream Management Guide
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## Quick Commands
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### Check Stream Status
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```bash
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python check_stream.py status
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```
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### Generate Snapshot
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```bash
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python check_stream.py snapshot
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```
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## What You'll See
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### Stream Status Output
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- ✅ Dashboard is running
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- 💡 Guidance message
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- 📝 Data stream location note
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- Console output examples to look for
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### Snapshot Output
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- ✅ Snapshot saved: `data_snapshots/snapshot_YYYYMMDD_HHMMSS.json`
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- 📝 Note about data location
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## How It Works
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The script connects to your **running dashboard** instead of creating a new instance:
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1. **Checks if dashboard is running** at `http://127.0.0.1:8050`
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2. **Provides guidance** on where to find the data stream
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3. **Generates snapshots** with current timestamp and metadata
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## Where to Find Live Data
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The **data stream is active inside the dashboard console**. Look for output like:
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```
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OHLCV (1m): ETH/USDT | O:4335.67 H:4338.92 L:4334.21 C:4336.67 V:125.8
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TICK: ETH/USDT | Price:4336.67 Vol:0.0456 Side:buy
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DQN Prediction: BUY (conf:0.78)
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Training Exp: Action:1 Reward:0.0234 Done:False
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```
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## When Data Appears
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Data will show in the dashboard console when:
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1. **Market data is flowing** (OHLCV, ticks, COB)
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2. **Models are making predictions**
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3. **Training is active**
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## Snapshot Contents
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Snapshots contain:
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- Timestamp
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- Dashboard status
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- Empty data arrays (data is in dashboard console)
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- Note about checking console for live data
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## Usage Tips
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- **Start dashboard first**: `python run_clean_dashboard.py`
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- **Check status** to confirm dashboard is running
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- **Watch dashboard console** for live data stream
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- **Generate snapshots** to capture timestamps and metadata
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- **Wait for market activity** to see data populate
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## Files Created
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- `check_stream.py` - Status and snapshot commands
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- `data_snapshots/` - Directory for saved snapshots
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- `snapshot_*.json` - Timestamped snapshot files with metadata
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