# Guide: Connecting to Synology PostgreSQL from Local PC

This guide explains how to connect to your Synology's TimescaleDB from your local PC to work with historical 1m candle data.

## Prerequisites

### 1. Install PostgreSQL Client on Your PC

**Windows:**
```bash
# Download from: https://www.postgresql.org/download/windows/
# Or use chocolatey:
choco install postgresql
```

**Mac:**
```bash
brew install postgresql
# Or download Postgres.app from postgresapp.com
```

**Linux:**
```bash
# Ubuntu/Debian
sudo apt-get install postgresql-client

# Or use Docker:
docker run -it --rm postgres:15 psql --version
```

### 2. Install Python Dependencies

```bash
pip install asyncpg pandas numpy
# Or use requirements.txt from the project
pip install -r requirements.txt
```

## Step 1: Configure Synology for Remote Access

### Open PostgreSQL Port

1. **SSH into your Synology:**
   ```bash
   ssh admin@YOUR_SYNOLOGY_IP
   ```

2. **Edit PostgreSQL configuration:**
   ```bash
   # Find postgresql.conf (usually in /var/lib/postgresql/data/)
   sudo vim /var/lib/postgresql/data/postgresql.conf
   
   # Change:
   # listen_addresses = 'localhost'
   # To:
   listen_addresses = '*'
   ```

3. **Edit pg_hba.conf to allow remote connections:**
   ```bash
   sudo vim /var/lib/postgresql/data/pg_hba.conf
   
   # Add at the end:
   host    all             all             0.0.0.0/0               md5
   # Or for specific IP:
   host    all             all             YOUR_PC_IP/32           md5
   ```

4. **Restart PostgreSQL:**
   ```bash
   sudo systemctl restart postgresql
   # Or if using Docker:
   cd ~/btc_bot/docker && docker-compose restart timescaledb
   ```

### Configure Synology Firewall

1. Open **Control Panel** → **Security** → **Firewall**
2. Click **Edit Rules**
3. Create new rule:
   - **Ports:** Custom → TCP → 5433 (or your PostgreSQL port)
   - **Source IP:** Your PC's IP address (or allow all)
   - **Action:** Allow
4. Apply the rule

## Step 2: Test Connection

### Using psql CLI

```bash
# Replace with your Synology IP
export DB_HOST=192.168.1.100  # Your Synology IP
export DB_PORT=5433
export DB_NAME=btc_data
export DB_USER=btc_bot
export DB_PASSWORD=your_password

# Test connection
psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c "SELECT version();"
```

### Using Python

Create `test_connection.py`:

```python
import asyncio
import asyncpg

async def test():
    conn = await asyncpg.connect(
        host='192.168.1.100',      # Your Synology IP
        port=5433,
        database='btc_data',
        user='btc_bot',
        password='your_password'
    )
    
    version = await conn.fetchval('SELECT version()')
    print(f"Connected! PostgreSQL version: {version}")
    
    # Test candle count
    count = await conn.fetchval(
        "SELECT COUNT(*) FROM candles WHERE interval = '1m'"
    )
    print(f"Total 1m candles: {count:,}")
    
    await conn.close()

asyncio.run(test())
```

Run it:
```bash
python test_connection.py
```

## Step 3: Export Historical 1m Data

### Option A: Using psql (Quick Export)

```bash
psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c "
COPY (
    SELECT time, symbol, interval, open, high, low, close, volume 
    FROM candles 
    WHERE symbol = 'BTC' 
    AND interval = '1m' 
    AND time >= '2025-01-01'
    ORDER BY time
) TO STDOUT WITH CSV HEADER;
" > btc_1m_candles.csv
```

### Option B: Using Python (With Progress Bar)

Create `export_candles.py`:

```python
import asyncio
import asyncpg
import csv
from datetime import datetime, timezone
from tqdm import tqdm

async def export_candles(
    host: str,
    port: int,
    database: str,
    user: str,
    password: str,
    symbol: str = 'BTC',
    interval: str = '1m',
    start_date: str = '2025-01-01',
    output_file: str = 'candles.csv'
):
    """Export candles to CSV with progress bar"""
    
    conn = await asyncpg.connect(
        host=host, port=port, database=database,
        user=user, password=password
    )
    
    try:
        # Get total count
        total = await conn.fetchval("""
            SELECT COUNT(*) FROM candles 
            WHERE symbol = $1 AND interval = $2 AND time >= $3
        """, symbol, interval, datetime.fromisoformat(start_date).replace(tzinfo=timezone.utc))
        
        print(f"Exporting {total:,} candles...")
        
        # Export in batches
        with open(output_file, 'w', newline='') as f:
            writer = csv.writer(f)
            writer.writerow(['time', 'symbol', 'interval', 'open', 'high', 'low', 'close', 'volume'])
            
            async with conn.transaction():
                cursor = await conn.cursor(
                    "SELECT time, symbol, interval, open, high, low, close, volume "
                    "FROM candles WHERE symbol = $1 AND interval = $2 AND time >= $3 "
                    "ORDER BY time",
                    symbol, interval, datetime.fromisoformat(start_date).replace(tzinfo=timezone.utc)
                )
                
                with tqdm(total=total, unit='candles') as pbar:
                    while True:
                        rows = await cursor.fetch(1000)
                        if not rows:
                            break
                        for row in rows:
                            writer.writerow(row)
                        pbar.update(len(rows))
        
        print(f"✓ Exported to {output_file}")
        print(f"  Total rows: {total:,}")
        
    finally:
        await conn.close()

if __name__ == "__main__":
    asyncio.run(export_candles(
        host='192.168.1.100',      # Change to your Synology IP
        port=5433,
        database='btc_data',
        user='btc_bot',
        password='your_password',
        symbol='BTC',
        interval='1m',
        start_date='2025-01-01',
        output_file='btc_1m_candles.csv'
    ))
```

Run:
```bash
pip install tqdm
python export_candles.py
```

## Step 4: Calculate Indicators on PC

### Using pandas-ta (Recommended)

Create `calculate_indicators.py`:

```python
import pandas as pd
import pandas_ta as ta
from datetime import datetime

def calculate_indicators(input_file: str, output_file: str):
    """Calculate technical indicators from candle data"""
    
    # Read candles
    print(f"Reading {input_file}...")
    df = pd.read_csv(input_file)
    df['time'] = pd.to_datetime(df['time'])
    df = df.sort_values('time')
    
    print(f"Loaded {len(df):,} candles")
    
    # Calculate indicators
    print("Calculating indicators...")
    
    # Moving Averages
    df['ma44'] = ta.sma(df['close'], length=44)
    df['ma125'] = ta.sma(df['close'], length=125)
    df['ma200'] = ta.sma(df['close'], length=200)
    
    # RSI
    df['rsi'] = ta.rsi(df['close'], length=14)
    
    # Bollinger Bands
    bb = ta.bbands(df['close'], length=20, std=2)
    df['bb_upper'] = bb['BBU_20_2.0']
    df['bb_middle'] = bb['BBM_20_2.0']
    df['bb_lower'] = bb['BBL_20_2.0']
    
    # MACD
    macd = ta.macd(df['close'], fast=12, slow=26, signal=9)
    df['macd'] = macd['MACD_12_26_9']
    df['macd_signal'] = macd['MACDs_12_26_9']
    df['macd_histogram'] = macd['MACDh_12_26_9']
    
    # Save
    print(f"Saving to {output_file}...")
    df.to_csv(output_file, index=False)
    
    print(f"✓ Calculated {len(df.columns) - 8} indicators")
    print(f"  Output: {output_file}")
    
    return df

if __name__ == "__main__":
    df = calculate_indicators(
        input_file='btc_1m_candles.csv',
        output_file='btc_1m_with_indicators.csv'
    )
    
    # Show sample
    print("\nSample data:")
    print(df[['time', 'close', 'ma44', 'ma125', 'rsi']].tail(10))
```

Install dependencies:
```bash
pip install pandas pandas-ta
```

Run:
```bash
python calculate_indicators.py
```

### Performance Tips

- **Chunk processing** for very large files (> 1GB):
```python
# Process 100k rows at a time
chunksize = 100000
for chunk in pd.read_csv(input_file, chunksize=chunksize):
    # Calculate indicators for chunk
    pass
```

- **Use multiple cores:**
```python
from multiprocessing import Pool
# Parallelize indicator calculation
```

## Step 5: Import Indicators Back to Synology

### Option A: Direct SQL Insert (Fastest)

Create `import_indicators.py`:

```python
import asyncio
import asyncpg
import pandas as pd
from datetime import datetime
from tqdm import tqdm

async def import_indicators(
    host: str,
    port: int,
    database: str,
    user: str,
    password: str,
    input_file: str,
    batch_size: int = 1000
):
    """Import calculated indicators to Synology database"""
    
    # Read calculated indicators
    print(f"Reading {input_file}...")
    df = pd.read_csv(input_file)
    print(f"Loaded {len(df):,} rows with {len(df.columns)} columns")
    
    # Connect to database
    conn = await asyncpg.connect(
        host=host, port=port, database=database,
        user=user, password=password
    )
    
    try:
        # Get indicator columns (exclude candle data)
        indicator_cols = [c for c in df.columns if c not in 
                         ['time', 'symbol', 'interval', 'open', 'high', 'low', 'close', 'volume']]
        
        print(f"Importing {len(indicator_cols)} indicators: {indicator_cols}")
        
        # Prepare data
        symbol = df['symbol'].iloc[0]
        interval = df['interval'].iloc[0]
        
        total_inserted = 0
        
        with tqdm(total=len(df) * len(indicator_cols), unit='indicators') as pbar:
            for col in indicator_cols:
                # Prepare batch
                values = []
                for _, row in df.iterrows():
                    if pd.notna(row[col]):  # Skip NaN values
                        values.append((
                            row['time'],
                            symbol,
                            interval,
                            col,  # indicator_name
                            float(row[col]),
                            {}  # parameters (JSONB)
                        ))
                
                # Insert in batches
                for i in range(0, len(values), batch_size):
                    batch = values[i:i + batch_size]
                    await conn.executemany(
                        """
                        INSERT INTO indicators (time, symbol, interval, indicator_name, value, parameters)
                        VALUES ($1, $2, $3, $4, $5, $6)
                        ON CONFLICT (time, symbol, interval, indicator_name) DO UPDATE SET
                            value = EXCLUDED.value,
                            updated_at = NOW()
                        """,
                        batch
                    )
                    total_inserted += len(batch)
                    pbar.update(len(batch))
        
        print(f"\n✓ Imported {total_inserted:,} indicator values")
        
    finally:
        await conn.close()

if __name__ == "__main__":
    asyncio.run(import_indicators(
        host='192.168.1.100',
        port=5433,
        database='btc_data',
        user='btc_bot',
        password='your_password',
        input_file='btc_1m_with_indicators.csv',
        batch_size=1000
    ))
```

Run:
```bash
python import_indicators.py
```

### Option B: Using psql COPY (For Large Files)

```bash
# Convert to format for COPY command
python -c "
import pandas as pd
df = pd.read_csv('btc_1m_with_indicators.csv')
# Transform to long format for indicators table
indicators = []
for col in ['ma44', 'ma125', 'rsi', 'bb_upper', 'bb_lower']:
    temp = df[['time', 'symbol', 'interval', col]].copy()
    temp['indicator_name'] = col
    temp = temp.rename(columns={col: 'value'})
    indicators.append(temp)
result = pd.concat(indicators)
result = result[result['value'].notna()]
result.to_csv('indicators_for_import.csv', index=False)
"

# Upload and import on Synology
psql -h $DB_HOST -p $DB_PORT -U $DB_USER -d $DB_NAME -c "
COPY indicators (time, symbol, interval, indicator_name, value)
FROM '/path/to/indicators_for_import.csv' 
DELIMITER ',' CSV HEADER;
"
```

## Complete Workflow Script

Create `sync_indicators.py` for one-command workflow:

```python
#!/usr/bin/env python3
"""
Complete workflow: Export candles → Calculate indicators → Import to Synology
"""

import asyncio
import asyncpg
import pandas as pd
import pandas_ta as ta
from datetime import datetime, timezone
from tqdm import tqdm

class IndicatorSync:
    def __init__(self, host: str, port: int, database: str, user: str, password: str):
        self.db_config = {
            'host': host, 'port': port, 'database': database,
            'user': user, 'password': password
        }
    
    async def export_and_calculate(
        self,
        symbol: str = 'BTC',
        interval: str = '1m',
        start_date: str = '2025-01-01',
        indicators_config: dict = None
    ):
        """Main workflow"""
        
        print("="*70)
        print(f"INDICATOR SYNC: {symbol}/{interval}")
        print(f"Period: {start_date} to now")
        print("="*70)
        
        # Connect to database
        conn = await asyncpg.connect(**self.db_config)
        
        try:
            # Step 1: Export candles
            print("\n📥 Step 1: Exporting candles...")
            candles = await self._export_candles(conn, symbol, interval, start_date)
            print(f"   Exported {len(candles):,} candles")
            
            # Step 2: Calculate indicators
            print("\n⚙️  Step 2: Calculating indicators...")
            indicators_df = self._calculate_indicators(candles, indicators_config)
            print(f"   Calculated {len(indicators_df)} indicator values")
            
            # Step 3: Import indicators
            print("\n📤 Step 3: Importing to database...")
            await self._import_indicators(conn, indicators_df)
            print("   Import complete!")
            
            print("\n" + "="*70)
            print("✅ SYNC COMPLETE")
            print("="*70)
            
        finally:
            await conn.close()
    
    async def _export_candles(self, conn, symbol, interval, start_date):
        """Export candles from database"""
        rows = await conn.fetch(
            "SELECT time, symbol, interval, open, high, low, close, volume "
            "FROM candles WHERE symbol = $1 AND interval = $2 AND time >= $3 "
            "ORDER BY time",
            symbol, interval, datetime.fromisoformat(start_date).replace(tzinfo=timezone.utc)
        )
        return pd.DataFrame(rows, columns=['time', 'symbol', 'interval', 'open', 'high', 'low', 'close', 'volume'])
    
    def _calculate_indicators(self, candles_df, config=None):
        """Calculate technical indicators"""
        df = candles_df.copy()
        
        # Default indicators
        config = config or {
            'ma44': lambda d: ta.sma(d['close'], length=44),
            'ma125': lambda d: ta.sma(d['close'], length=125),
            'rsi': lambda d: ta.rsi(d['close'], length=14),
        }
        
        # Calculate each indicator
        for name, func in config.items():
            df[name] = func(df)
        
        # Transform to long format
        indicators = []
        indicator_cols = list(config.keys())
        
        for col in indicator_cols:
            temp = df[['time', 'symbol', 'interval', col]].copy()
            temp['indicator_name'] = col
            temp = temp.rename(columns={col: 'value'})
            temp = temp[temp['value'].notna()]
            indicators.append(temp)
        
        return pd.concat(indicators, ignore_index=True)
    
    async def _import_indicators(self, conn, indicators_df):
        """Import indicators to database"""
        # Convert to list of tuples
        values = [
            (row['time'], row['symbol'], row['interval'], row['indicator_name'], float(row['value']), {})
            for _, row in indicators_df.iterrows()
        ]
        
        # Insert in batches
        batch_size = 1000
        for i in tqdm(range(0, len(values), batch_size), desc="Importing"):
            batch = values[i:i + batch_size]
            await conn.executemany(
                """
                INSERT INTO indicators (time, symbol, interval, indicator_name, value, parameters)
                VALUES ($1, $2, $3, $4, $5, $6)
                ON CONFLICT (time, symbol, interval, indicator_name) DO UPDATE SET
                    value = EXCLUDED.value,
                    updated_at = NOW()
                """,
                batch
            )

# Usage
if __name__ == "__main__":
    sync = IndicatorSync(
        host='192.168.1.100',      # Your Synology IP
        port=5433,
        database='btc_data',
        user='btc_bot',
        password='your_password'
    )
    
    asyncio.run(sync.export_and_calculate(
        symbol='BTC',
        interval='1m',
        start_date='2025-01-01'
    ))
```

Run complete workflow:
```bash
python sync_indicators.py
```

## Troubleshooting

### Connection Refused
```
Error: connection refused
```
- Check if PostgreSQL is running: `docker ps | grep timescale`
- Verify port is open: `telnet SYNOLOGY_IP 5433`
- Check firewall rules on Synology

### Permission Denied
```
Error: password authentication failed
```
- Verify password is correct
- Check pg_hba.conf has proper entries
- Restart PostgreSQL after config changes

### Slow Performance
- Use batch inserts (1000 rows at a time)
- Process large datasets in chunks
- Consider using COPY command for very large imports
- Close cursor after use

### Memory Issues
- Don't load entire table into memory
- Use server-side cursors
- Process in smaller date ranges
- Use chunksize when reading CSV

## Security Notes

⚠️ **Important:**
- Use strong password for database user
- Limit pg_hba.conf to specific IPs when possible
- Use VPN if accessing over internet
- Consider SSL connection for remote access
- Don't commit passwords to git

## Next Steps

Once indicators are calculated and imported:

1. **Backtests will be fast** - Server just reads pre-calculated values
2. **Dashboard will load quickly** - No on-the-fly calculation needed
3. **Can add more indicators** - Just re-run sync with new calculations

## Alternative: SSH Tunnel (More Secure)

Instead of opening PostgreSQL port, use SSH tunnel:

```bash
# On your PC
ssh -L 5433:localhost:5433 admin@SYNOLOGY_IP

# Now connect to localhost:5433 (tunnels to Synology)
export DB_HOST=localhost
export DB_PORT=5433
python sync_indicators.py
```

This encrypts all traffic and doesn't require opening PostgreSQL port.

---

**Questions or issues?** Check the logs and verify each step works before proceeding to the next.