xone 0.0.17

Frequently used functions for financial data analysis

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Frequently used functions for financial data analysis

Installation

pip install xone

Utilities

In[1]: from xone import utils

Convert anything to list.

• If the input is clean, use tolist() directly. Some functions requires list as input, tolist() is to standardize all inputs for them.

In[2]: ticker = 'BHP AU'
In[3]: list_of_tickers = tolist(ticker)
In[4]: list_of_tickers

Out[4]: ['BHP AU']

In[5]: raw_price = [31.08, 31.10, 31.11, 31.07, 31.04, 31.04]
In[6]: price = utils.tolist(raw_price)
In[7]: price

Out[7]: [31.08, 31.10, 31.11, 31.07, 31.04, 31.04]

• If the input is list of tuples / lists / any weird combination, use flatten():

In[8]: raw_volume = [(10166, 69981), [14343, 10096], 11506, 9718]
In[9]: volume = utils.flatten(raw_volume)
In[10]: volume

Out[10]: [10166, 69981, 14343, 10096, 11506, 9718]

Order preserving DataFrame construction from list of dict.

Prior to Python 3.7, dict is not ordered like OrderedDict. Passing dict directly to DataFrame constructor will make the columns sorted by alphabets.

In[11]: import pandas as pd

In[12]: data_list = [
            dict(sid=1, symbol='1 HK', price=88.8),
            dict(sid=700, symbol='700 HK', price=350.),
        ]
In[13]: pd.DataFrame(data_list)

Out[13]:
   price  sid  symbol
0  88.80    1    1 HK
1 350.00  700  700 HK

to_frame makes sure the order of inputs will be kept:

In[14]: utils.to_frame(data_list)

Out[14]:
   sid  symbol  price
0    1    1 HK  88.80
1  700  700 HK 350.00

Files

In[15]: from xone import files

Automatic check and create path and save files:

In[16]: DATA_PATH = '/data/Bloomberg'
In[17]: data_file = f'{DATA_PATH}/{ticker.split()[-1]}/{ticker}/2018-09-10.parq'

In[18]: sample = pd.DataFrame(
            data=dict(price=price, volume=volume),
            index=pd.DatetimeIndex(
                start='2018-09-10T10:10:00', periods=6, freq='min'
            ).tz_localize('Australia/Sydney'),
        )
In[19]: sample

Out[19]: 
                           price  volume
2018-09-10 10:10:00+10:00  31.08   10166
2018-09-10 10:11:00+10:00  31.10   69981
2018-09-10 10:12:00+10:00  31.11   14343
2018-09-10 10:13:00+10:00  31.07   10096
2018-09-10 10:14:00+10:00  31.04   11506
2018-09-10 10:15:00+10:00  31.04    9718

create_folder checks folder existence and create all parent folders for the target folder.

In[20]: files.create_folder(data_file, is_file=True)
In[21]: sample.to_parquet(data_file)

Logs