# Daily OHLCV (with closing prices)

We can retrieve daily OHLCV data for specific tickers using various symbologies, by querying for the `DAY` Table.

For consolidated datasets such as for the US, each exchange's data and a composite are represented.

The  *\_DAILY* databases such as the sample *US_COMP_SAMPLE_DAILY* are optimized for querying long time periods,
while the Tick and Bar databases are optimized for querying an intraday time range.

Without Symbology:

```ipython3
import onetick.py as otp

data = otp.DataSource(db='US_COMP_SAMPLE_DAILY', tick_type='DAY')
df = otp.run(data,
             start=otp.dt(2024, 1, 1),
             end=otp.dt(2024, 4, 1),
             timezone='America/New_York',
             symbols='CSCO')
```

With Symbology:

```ipython3
data = otp.DataSource(db='US_COMP_SAMPLE_DAILY', tick_type='DAY')
df = otp.run(data,
             start=otp.dt(2024, 1, 1),
             end=otp.dt(2024, 4, 1),
             timezone='America/New_York',
             symbols='BSYM::::CSCO US Equity',
             symbol_date=otp.dt(2024, 1, 3))
df.head(5)
```

```myst-ansi
                 Time EXCHANGE    OPEN   HIGH    LOW  CLOSE   VOLUME  \
0 2024-01-02 20:15:00        A  50.460  50.83  50.27  50.55    62688   
1 2024-01-02 20:15:00        B  50.420  50.83  50.29  50.53    40416   
2 2024-01-02 20:15:00        C  50.505  50.80  50.26  50.52    82490   
3 2024-01-02 20:15:00        D  50.470  50.86  50.25  50.53  7404940   
4 2024-01-02 20:15:00        H  50.490  50.83  50.26  50.53   136771   

        VWAP  PRICE_OPENING_AUCTION  VOLUME_OPENING_AUCTION  \
0  50.420217                    NaN                       0   
1  50.508402                    NaN                       0   
2  50.477827                    NaN                       0   
3  50.551240                    NaN                       0   
4  50.464547                    NaN                       0   

   PRICE_CLOSING_AUCTION  VOLUME_CLOSING_AUCTION  VOLUME_MAIN_SESSION  \
0                    NaN                       0                61348   
1                    NaN                       0                40416   
2                    NaN                       0                82486   
3                    NaN                       0              6164089   
4                    NaN                       0               136771   

   VOLUME_PRE_MARKET  VOLUME_POST_MARKET  VOLUME_ODD_LOT  VOLUME_ROUND_LOT  \
0               1340                   0            4804             57884   
1                  0                   0            8160             32256   
2                  4                   0           17930             64560   
3               4769             1236082          478272           6926668   
4                  0                   0           23687            113084   

   VOLUME_OFF_EXCHANGE  OMDSEQ  
0                    0   30021  
1                    0   30022  
2                    0   30023  
3              7404940   30024  
4                    0   30025  
```

For US equities,
`USPRIM` stands for the primary exchange,
empty string for the composite,
and `N` for the New York Stock Exchange.

Most of the time you'll be looking for composite, which you can specify through a filter:

```ipython3
data = otp.DataSource(db='US_COMP_SAMPLE_DAILY', tick_type='DAY')
data = data[['CLOSE', 'VOLUME', 'EXCHANGE']]
data = data.where(data['EXCHANGE'] == '')
otp.run(data,
        start=otp.dt(2024, 1, 1),
        end=otp.dt(2024, 4, 1),
        timezone='America/New_York',
        symbols='BSYM::::CSCO US Equity',
        symbol_date=otp.dt(2024, 1, 3))
```

```myst-ansi
                  Time  CLOSE    VOLUME EXCHANGE
0  2024-01-02 20:15:00  50.51  20242939         
1  2024-01-03 20:15:00  50.51  20303875         
2  2024-01-04 20:15:00  50.08  18134121         
3  2024-01-05 20:15:00  50.09  13989287         
4  2024-01-08 20:15:00  50.46  18070293         
..                 ...    ...       ...      ...
56 2024-03-22 20:15:00  49.78  15022861         
57 2024-03-25 20:15:00  49.68  16191164         
58 2024-03-26 20:15:00  49.55  13842923         
59 2024-03-27 20:15:00  49.77  17230958         
60 2024-03-28 20:15:00  49.91  18139735         

[61 rows x 4 columns]
```