otp.CSV#

class CSV(filepath_or_buffer=None, timestamp_name='Time', first_line_is_title=True, names=None, dtype={}, converters={}, order_ticks=False, drop_index=True, change_date_to=None, **kwargs)[source]#

Bases: onetick.py.core.source.Source

Construct source based on CSV file.

There are several steps determining column types.

Initially, all column treated as str.
If column name in CSV title have format type COLUMNNAME, it will change type from str to specified type.
All column type are determined automatically from its data.
You could override determined types in dtype argument explicitly.
converters argument is applied after dtype and could also change column type.

Parameters

filepath_or_buffer (str, os.PathLike, FileBuffer) – Path to CSV file or file buffer
timestamp_name (str, default "Time") – Name of TIMESTAMP column used for ticks. Used only if it is exists in CSV columns, otherwise ignored.
first_line_is_title (bool) –
Use first line of CSV file as a source for column names and types. If CSV file is started with # symbol, this parameter must be True.
- If True, column names are inferred from the first line of the file, it is not allowed to have empty name for any column.
- If False, first line is processed as data, column names will be COLUMN_1, …, COLUMN_N. You could specify column names in names argument.
names (list, optional) – List of column names to use, or None. Length must be equal to columns number in file. Duplicates in this list are not allowed.
dtype (dict, optional) – Data type for columns, as dict of pais {column_name: type}. Will convert column type from str to specified type, before applying converters.
converters (dict, optional) –
Dict of functions for converting values in certain columns. Keys are column names. Function must be valid callable with onetick.py syntax, example:
```
converters={
    "time_number": lambda c: c.apply(otp.nsectime),
    "stock": lambda c: c.str.lower(),
}
```
Converters applied after dtype conversion.
order_ticks (bool, optional) – If True and timestamp_name column are used, then source will order tick by time. Note, that if False and ticks are not ordered in sequence, then OneTick will raise Exception in runtime.
drop_index (bool, optional) – if True and ‘Index’ column is in the csv file then this column will be removed.
change_date_to (datetime, date, optional) – change date from a timestamp column to a specific date. Default is None, means not changing timestamp column.

Examples

Simple CSV file reading

>>> data = otp.CSV(os.path.join(csv_path, "data.csv"))
>>> otp.run(data)
                     Time          time_number      px side
0 2003-12-01 00:00:00.000  1656690986953602304   30.89  Buy
1 2003-12-01 00:00:00.001  1656667706281508352  682.88  Buy

Read CSV file and get timestamp for ticks from specific field. You need to specify query start/end interval including all ticks.

>>> data = otp.CSV(os.path.join(csv_path, "data.csv"),
...                timestamp_name="time_number",
...                converters={"time_number": lambda c: c.apply(otp.nsectime)},
...                start=otp.dt(2010, 8, 1),
...                end=otp.dt(2022, 9, 2))
>>> otp.run(data)
                           Time      px side
0 2022-07-01 11:56:26.953602304   30.89  Buy
1 2022-07-01 05:28:26.281508352  682.88  Buy

otp.utils.file#

file(path)[source]#

Helps to build a file buffer that could be used to delivery on the remote site to be processed there. For example it coulld be passed as input to the CSV

Parameters: path (Union[str, os.PathLike]) –
Return type: onetick.py.utils.file.FileBuffer

otp.utils.FileBuffer#

class FileBuffer(path)[source]#

Bases: object

Class holds the file content with goal to delivery it to the execution side in case of remote executions.

The basic implementation reads file content to a property that allows to transfer file content as pickled object to the server side since the pickling stores all class property values.

Parameters: path (Union[str, os.PathLike]) –