vnquant package

1. Introduction

This project offers comprehensive financial information and advanced visualization tools for the Vietnam stock market to researchers. Specifically, it provides extensive data, including historical prices, finacial, business, and cashflow reports for individual or multiple symbols over the same period. This enables investors to conduct in-depth quantitative analyses and forecasting. Additionally, the available stock prices can be utilized to create visualizations with advanced metrics such as Bollinger Bands and Relative Strength Index (RSI), aiding in identifying optimal buying and selling points.

2. Setting:

2.1. Setup on local machine

This project is in developing process, So it is only distributed on github channel. To install requiring you open the command line and type the below commands:

git clone https://github.com/phamdinhkhanh/vnquant
cd vnquant
python setup.py install

you must install git command line in your computer to run above command.

2.2. Google colab

To use package in google colab, you have to mount point to google drive folder first and setup the same as in local machine. Reference to google colab - vnquant example for detail.

3. Visualization: (0.0.2)

from version 0.0.2 vnquant enable to you visualize stock price from any symbols code at source cafe or vnd or pandas data frame which have OHLC type. OHLC type meaning that your data frame columns is enough ['open', 'high', 'low', 'close'] list. Below is general syntax of visualization function supported on vnquant package.

import vnquant.plot as pl
pl.vnquant_candle_stick(data,
                        title=None,
                        xlab='Date', ylab='Price',
                        start_date=None, end_date=None,
                        colors=['blue', 'red'],
                        width=800, height=600,
                        show_advanced=[],
                        data_source='cafe',
                        **kargs)

Arguments

• data: is pandas data frame of OHLC type or OHLCV type, or string symbol of any VietNam stock index. in case symbol, data is automatically cloned from open source.
• title: General title of candle stick chart. If data is a symbol, title is going to be created based on symbol and cloned datetime interval.
• xlab: x label. Default Date.
• ylab: y label. Default Price.
• start_date: start date. Default None. Must to be declared when data is symbol.
• end_date: end date. Default None. Must to be declared when data is symbol.
• colors: list colors defines increasing and decreasing color stick candle in order.
• width: with of plot frame. Default 800px
• height: height of plot frame. Default 600px
• show_advanced: list of advanced stock index to show up. Each element belongs to ['volume', 'macd', 'rsi'].
• data_source: invalid when use symbol intead of data frame. Source to clone data, 'VND' or 'CAFE'.

3.1. Visualization from source VND or CAFE

In this way, you can visualize stock price clone from VND or CAFE source by pass symbol, start_date, end_date into module as below:

from vnquant import plot as plt
plt.vnquant_candle_stick(
    data='VND',
    title='VND symbol from 2019-09-01 to 2019-11-01',
    xlab='Date', ylab='Price',
    start_date='2019-09-01',
    end_date='2019-11-01',
    data_source='CAFE',
    show_advanced=['volume', 'macd', 'rsi'],
    width=1600,
    height=800
)

You can suppress volume by set up show_vol=False. Result as below:

3.2. Visualization from data frame

Data frame must be OHLC or OHLCV type. OHLC type when it includes ['open','high','low','close'] and OHLCV is ['open','high','low','close','volume'] or ['open','high','low','close','volume_match'] and index is DateTime. In case your data frame have columns with the same function, you should accordingly rename its.

from vnquant import plot as plt

plt.vnquant_candle_stick(
    data = data,
    title='Your data',
    ylab='Date', xlab='Price',
    show_advanced=['volume', 'macd', 'rsi']
)

To check whether data_vnd frame is OHLC or OHLCV type you can try:

from vnquant import utils
print(utils._isOHLC(data_vnd))
print(utils._isOHLCV(data_vnd))

Return True mean data frame is adapted types.

4. Clone Stock Prices: (0.1.2)

You can load the prices of one or more stocks in specific time interval according to syntax as below.

import vnquant.data as dt

loader = dt.DataLoader(
  symbols: Union[str, list], 
  start: Optional[Union[str, datetime]]=None,
  end: Optional[Union[str, datetime]]=None, 
  data_source: str='CAFE', 
  minimal: bool=True,
  table_style: str='levels')

For example:

loader = DataLoader(
  symbols='VND', 
  start='2018-01-10', end='2018-02-15', 
  data_source='CAFE', 
  minimal=True, 
  table_style='levels')

loader.download()

Arguments

• symbols (Union[str, list]): A single stock symbol as a string or multiple stock symbols as a list of strings. The stock symbols regularly include 3 upper case letters except several special index such as: E1VFVN30, FUEVN100 for both data_source cafe and vnd, HNX-INDEX, HNX30-INDEX, UPCOM-INDEX for cafe.
• start (Optional[Union[str, datetime]], default=None): The start date for the data. Can be a string in the format 'YYYY-MM-DD' or a datetime object.
• end (Optional[Union[str, datetime]], default=None): The end date for the data. Can be a string in the format 'YYYY-MM-DD' or a datetime object.
• data_source (str, default='CAFE'): The data source to be used for downloading stock data. Currently supports 'CAFE' and 'VND'.
• minimal (bool, default=True): If True, returns a minimal set of columns. If False, returns all available columns.
• table_style (str, default='levels'): The style of the returned table. Options are 'levels', 'prefix', and 'stack'.

• 'levels': Returns the DataFrame with multi-level colums of symbols and list of basic arguments like ['high', 'low', 'open', 'close', 'adjust', 'volume', 'value']
• 'prefix': Adds the stock symbol as a prefix to each column name.
• 'stack': Returns the DataFrame and add one column 'code' to clarify each record belong to what stock symbol.

4.1. Clone one stock:

There are three return formats supported in the latest version 0.1.2.

• Multiple-level: It will return a table with mutilple column indexes with two main levels Arguments (including many basical stock indexes) and Symbols (including list of stock codes).

import vnquant.data as dt
loader = dt.DataLoader('VND', '2018-02-02','2018-04-02')
data = loader.download()
data.head()

Attributes	high	low	open	close	avg	volume
Symbols	VND	VND	VND	VND	VND	VND
date
2018-02-02	28.95	27.60	28.5	28.95	28.28	1700670.0
2018-02-05	28.45	26.95	28.1	26.95	27.68	2150120.0
2018-02-06	26.95	25.10	25.1	26.40	25.25	3129690.0
2018-02-07	28.20	27.50	27.5	28.20	27.99	1985120.0
2018-02-08	29.20	27.70	28.0	28.00	28.47	943260.0

• Prefix: The stock code is appended before each index to become a column name.

import vnquant.data as dt
loader = dt.DataLoader(['VND', 'FPT'], '2018-02-02','2018-04-02', table_style='prefix')
data = loader.download()
data.head()

	VND_code	FPT_code	VND_high	FPT_high	VND_low	FPT_low	VND_open	FPT_open	VND_close	FPT_close	VND_adjust	FPT_adjust	VND_volume_match	FPT_volume_match	VND_value_match	FPT_value_match
date
2018-04-02	VND	FPT	29.80	61.7	29.10	61.0	29.10	61.5	29.55	61.5	6.52	21.98	2141980.0	2194820.0	6.320100e+10	1.347410e+11
2018-03-30	VND	FPT	29.50	61.3	28.75	59.4	29.00	59.6	29.05	60.7	6.41	21.69	1688000.0	2434830.0	4.925300e+10	1.474940e+11
2018-03-29	VND	FPT	29.00	59.7	28.25	59.0	28.50	59.4	29.00	59.5	6.40	21.26	1294580.0	827280.0	3.717900e+10	4.915900e+10
2018-03-28	VND	FPT	28.65	59.4	27.60	58.8	27.75	59.1	28.65	58.9	6.32	21.05	1429910.0	660440.0	4.012300e+10	3.901500e+10
2018-03-27	VND	FPT	28.55	60.1	27.75	59.0	28.55	59.9	28.00	59.5	6.18	21.26	2589800.0	993260.0	7.289400e+10	5.916600e+10

• Stack: It will add one more column named code to demonstrate the stock symbols.

import vnquant.data as dt
loader = dt.DataLoader(['VND', 'FPT'], '2018-02-02','2018-04-02', table_style='stack')
data = loader.download()
data.head(4)

	code	high	low	open	close	adjust	volume_match	value_match
date
2018-04-02	FPT	61.7	61.00	61.5	61.50	21.98	2194820.0	1.347410e+11
2018-04-02	VND	29.8	29.10	29.1	29.55	6.52	2141980.0	6.320100e+10
2018-03-30	FPT	61.3	59.40	59.6	60.70	21.69	2434830.0	1.474940e+11
2018-03-30	VND	29.5	28.75	29.0	29.05	6.41	1688000.0	4.925300e+10

4.2. Clone more stocks

We need to set up symbols as a list.

loader = dt.DataLoader(symbols=["VND", "VCB"], start="2018-01-10", end="2018-02-15", minimal=True, data_source="cafe")
data = loader.download()
data.head()

Attributes	high		low		open		close		avg		volume
Symbols	VND	VCB	VND	VCB	VND	VCB	VND	VCB	VND	VCB	VND	VCB
date
2018-01-10	27.75	59.2	27.10	57.3	27.55	58.3	27.50	58.0	27.52	58.08	1466780.0	2842830.0
2018-01-11	27.50	58.8	26.80	57.2	27.30	57.5	27.20	58.8	27.21	58.04	1260720.0	1766240.0
2018-01-12	28.20	59.4	27.35	58.0	27.45	58.8	27.60	58.0	27.76	58.63	1730170.0	2525840.0
2018-01-15	28.40	60.0	27.35	57.0	27.60	58.0	28.25	60.0	28.11	58.76	1273740.0	2217420.0
2018-01-16	28.40	60.3	27.90	58.8	28.10	59.3	28.25	60.0	28.14	59.64	1163350.0	2218380.0

4.3. Clone full information:

To get more the others information about volume and value beside basical fields, we need to declare minimal=False (default True).

loader = dt.DataLoader(symbols=["VND"], start="2018-01-10", end="2018-02-15", minimal=False)
data = loader.download()
data.head()

Attributes	change_perc1	change_perc2	open	high	low	close	avg	volume_match	volume_reconcile	volume
Symbols	VND	VND	VND	VND	VND	VND	VND	VND	VND	VND
date
2018-01-10	0.00	0.000000	27.55	27.75	27.10	27.50	27.52	1382780.0	84000.0	1466780.0
2018-01-11	-0.30	0.010909	27.30	27.50	26.80	27.20	27.21	1260720.0	0.0	1260720.0
2018-01-12	0.40	0.014706	27.45	28.20	27.35	27.60	27.76	1730170.0	0.0	1730170.0
2018-01-15	0.65	0.023551	27.60	28.40	27.35	28.25	28.11	1273740.0	0.0	1273740.0
2018-01-16	0.00	0.000000	28.10	28.40	27.90	28.25	28.14	1077350.0	86000.0	1163350.0

Through this project, i hope you make your work being more covinient and easy by applying them. Though try hard, but there are many drawback, kindly comment and send me feed back to implement my project.

5. Get finance, cashflow, business and basic index reports (0.0.3)

In version 0.0.3 you can download finance, cashflow, business and basic index reports with class vnquant.data.FinanceLoader. Currently, we only support you clone one symbol per each time. To use this class you import as bellow:

import vnquant.data as dt
loader = dt.FinanceLoader(symbol = 'VND', 
                          start = '2019-06-02',
                          end = '2021-12-31')

Arguments

• symbol: a string indicates the stock name. The stock symbol in regular includes 3 upper case letters.
• start: start date time with format yyyy-mm-dd.
• end: end date time with format yyyy-mm-dd.

5.1. Get finance report

Function get_finan_report() will help you get these finance indexes. For example:

import vnquant.data as dt
loader = dt.FinanceLoader('VND', '2019-06-02','2021-12-31')
data_finan = loader.get_finan_report()
data_finan.head()

	2021-06	2021-03	2020-12	2020-09	2020-06	2020-03	2019-12	2019-09	2019-06
index
Tài sản ngắn hạn	21351441834807	17347112129802	12793253609747	11585099253268	11479005695043	10851511570130	11239350733660	11059560981795	10590145635691
Tài sản tài chính ngắn hạn	21339567743512	17332349291032	12770938291296	11570420145910	11465268994352	10826493556341	11222476803929	11036102039564	10550582164047
Tiền và các khoản tương đương tiền	1153684350307	590663521250	595786368281	175314778804	185532378242	368330609085	613548205346	298144380199	413837038988
Tiền	778884350307	460708745512	509970753138	141314778804	148847077857	346330609085	611548205346	158744380199	252137038988
Tiền gửi của người đầu tư về giao dịch chứng khoán	0	0	0	0	0	0	0	0	0

5.2. Get business report

To get business report of each symbol, you use get_business_report() function as below:

import vnquant.data as dt
loader = dt.FinanceLoader('VND', '2019-06-02','2021-12-31', data_source='VND', minimal=True)
data_bus = loader.get_business_report()
data_bus.head()

	2021-06	2021-03	2020-12	2020-09	2020-06	2020-03	2019-12	2019-09	2019-06
index
Lãi từ các tài sản tài chính ghi nhận thông qua lãi/lỗ ( FVTPL)	384484114460	446903481873	348445083732	177846582328	126873424297	112699410946	69471930133	100788177914	96112568053
Lãi bán các tài sản tài chính PVTPL	187329856826	258061271830	321502700913	126020773731	120870780627	121996865833	77348559746	70332267851	62535937574
Chênh lệch tăng đánh giá lại các TSTC thông qua lãi/lỗ	192578271638	188425144043	-8160509655	33713693597	-848309517	-10832424389	-13633761575	28457177453	15139720013
Cổ tức, tiền lãi phát sinh từ tài sản tài chính PVTPL	4575985996	417066000	35102892474	18112115000	6850953187	1534969502	5757131962	1998732610	18436910466
Lãi từ các khoản đầu tư nắm giữ đến ngày đáo hạn	24948288940	108779296202	98753552528	84000482631	84108399683	105941161289	107029644615	100310037665	120061106620

5.3. Get cashflow report

Function get_cashflow_report() shall support to clone cashflow report:

import vnquant.data as dt
loader = dt.FinanceLoader('VND', '2019-06-02','2021-12-31', data_source='VND', minimal=True)
data_cash = loader.get_cashflow_report()
data_cash.head()

	2021-06	2021-03	2020-09	2020-06	2020-03	2019-12	2019-09	2019-06
index
Điều chỉnh cho các khoản	117772457799	102102918502	39938806717	-2179656198	189614451308	77119446501	115901816119	310400347299
Chi phí khấu hao tài sản cố định	7121178573	5117724702	5200320730	4817922370	5082314726	4956658731	5024428635	5218835903
Phân bổ lợi thế thương mại	0	0	0	0	0	0	0	0
Dự phòng giảm giá các khoản đầu tư ngắn hạn, dài hạn	15351994938	7673582615	-50406427024	-97302010585	74769599906	-47666085953	-1958363587	194803059759
Lãi, lỗ chênh lệch tỷ giá hối đoái chưa thực hiện	2291430861	0	-855383375	-855383375	0	-535741671	-136318575	136318575

5.4. Get basic index report

This function provide to you basic and important index of each symbol such as: ROA, ROE, Net Profit Marget, Net Revenue Growth, Profit After tax Growth

import vnquant.data as dt
loader = dt.FinanceLoader('VND', '2019-06-02','2021-12-31', data_source='VND', minimal=True)
data_basic = loader.get_basic_index()
data_basic.head()

	2020-12	2019-12
index
Profit After Tax Growth (YoY)	0.810405	0.025519
Net Revenue Growth (YoY)	0.421240	-0.023797
Net Profit Margin (Yr)	0.324553	0.254787
ROE last 4 quarters	0.195974	0.123374
ROA last 4 quarters	0.053917	0.033224