All traders sometimes need to get historical data of stocks for further price analysis and charting. Most often this data is supplied for paid or you must spend a lot of time manually uploading data from special sites.
But there are many online services that provide APIs to get stock price data automatically. One of this service is Alpha Vantage. The main data source for this service is the NASDAQ exchange. Detailed documentation on working with Alpha Vantage API here: https://www.alphavantage.co/documentation/
AVStockParser is a simple library that can be use as python module or console CLI program. AVStockParser request time series with stock history data in .json-format from www.alphavantage.co and convert into Pandas dataframe or .csv file with OHLCV-candlestick in every strings. You will get a table that contains columns of data in the following sequence: "date", "time", "open", "high", "low", "close", "volume". One line is a set of data for plotting one candlestick. In addition, you can draw simple interactive chart of candlesticks.
See russian readme here (инструкция на русском здесь): https://github.com/Tim55667757/AVStockParser/blob/master/README_RU.md
The easiest way is to install via PyPI:
pip install avstockparser
After that, you can check the installation with the command:
pip show avstockparser
Alpha Vantage service use authentication with api key. Request free api key at this page: https://www.alphavantage.co/support/#api-key
Api key is a alphanumeric string token. You must send token with every request to server. When you work with AVStockParser just use this flag --api-key "your token here" or set apiKey variable for method AVParseToPD(apiKey="your token here".
Get help:
avstockparser --help
Output:
usage: python AVStockParser.py [some options] [one command]
Alpha Vantage data parser. Get, parse, and save stock history as .csv-file or
Pandas dataframe. See examples: https://tim55667757.github.io/AVStockParser
optional arguments:
-h, --help show this help message and exit
--api-key API_KEY Option (required): Alpha Vantage service's api key.
Request free api key at this page:
https://www.alphavantage.co/support/#api-key
--ticker TICKER Option (required): stock ticker, e.g., 'GOOGL' or
'YNDX'.
--output OUTPUT Option: full path to .csv output file. Default is None
mean that function return only Pandas dataframe.
--period PERIOD Option: values can be 'TIME_SERIES_INTRADAY',
'TIME_SERIES_DAILY', 'TIME_SERIES_WEEKLY',
'TIME_SERIES_MONTHLY'. Default: 'TIME_SERIES_INTRADAY'
means that api returns intraday stock history data
with pre-define interval. More examples:
https://www.alphavantage.co/documentation/
--interval INTERVAL Option: '1min', '5min', '15min', '30min' or '60min'.
This is intraday period used only with
--period='TIME_SERIES_INTRADAY' key. Default: '60min'
means that api returns stock history with 60 min
interval.
--size SIZE Option: how many last candles returns for history.
Values can be 'full' or 'compact'. This parameter used
for 'outputsize' AV api parameter. Default: 'compact'
means that api returns only 100 values of stock
history data.
--retry RETRY Option: number of connections retry for data request
before raise exception. Default is 3.
--debug-level DEBUG_LEVEL
Option: showing STDOUT messages of minimal debug
level, e.g., 10 = DEBUG, 20 = INFO, 30 = WARNING, 40 =
ERROR, 50 = CRITICAL.
--parse Command: get, parse, and save stock history as Pandas
dataframe or .csv-file if --output key is defined.
--render Command: use PriceGenerator module to render
interactive chart from parsed data. This key only used
with --parse key.
Let us try to get daily candlesticks of YNDX stock into file YNDX1440.csv. The command may be like this:
avstockparser --debug-level 10 --api-key "your token here" --ticker YNDX --period TIME_SERIES_DAILY --size full --output YNDX1440.csv --parse
If successful, you should get a log output like the following:
AVStockParser.py L:184 DEBUG [2020-12-25 01:03:13,459] Alpha Vantage data parser started: 2020-12-25 01:03:13
AVStockParser.py L:51 DEBUG [2020-12-25 01:03:13,459] Request to Alpha Vantage: [https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=YNDX&outputsize=full&apikey=***]
AVStockParser.py L:55 DEBUG [2020-12-25 01:03:13,459] Trying (1) to send request...
AVStockParser.py L:119 INFO [2020-12-25 01:03:15,013] It was received 2415 candlesticks data from Alpha Vantage service
AVStockParser.py L:120 INFO [2020-12-25 01:03:15,013] Showing last 3 rows with Time Zone: 'US/Eastern':
AVStockParser.py L:123 INFO [2020-12-25 01:03:15,018] date time open high low close volume
AVStockParser.py L:123 INFO [2020-12-25 01:03:15,018] 2412 2020.12.22 00:00 67.2800 67.4400 66.3000 67.1100 1002761
AVStockParser.py L:123 INFO [2020-12-25 01:03:15,018] 2413 2020.12.23 00:00 67.7300 68.7700 67.6400 67.6900 822039
AVStockParser.py L:123 INFO [2020-12-25 01:03:15,018] 2414 2020.12.24 00:00 68.1800 68.2900 67.1700 67.6500 359133
AVStockParser.py L:127 INFO [2020-12-25 01:03:15,027] Stock history saved to .csv-formatted file [./YNDX1440.csv]
AVStockParser.py L:229 DEBUG [2020-12-25 01:03:15,027] All Alpha Vantage data parser operations are finished success (summary code is 0).
AVStockParser.py L:234 DEBUG [2020-12-25 01:03:15,028] Alpha Vantage data parser work duration: 0:00:01.568581
AVStockParser.py L:235 DEBUG [2020-12-25 01:03:15,028] Alpha Vantage data parser finished: 2020-12-25 01:03:15
For key --period you can use values: TIME_SERIES_DAILY, TIME_SERIES_WEEKLY and TIME_SERIES_MONTHLY to get daily, weekly, and monthly candlesticks. Default is TIME_SERIES_INTRADAY.
In another example let us try to get intraday hourly candlesticks of MMM stock into file MMM60.csv. The command may be like this:
avstockparser --debug-level 10 --api-key "your token here" --ticker MMM --period TIME_SERIES_INTRADAY --interval 60min --size compact --output MMM60.csv --parse
If successful, you will receive something like this output:
AVStockParser.py L:184 DEBUG [2020-12-25 01:09:44,601] Alpha Vantage data parser started: 2020-12-25 01:09:44
AVStockParser.py L:51 DEBUG [2020-12-25 01:09:44,601] Request to Alpha Vantage: [https://www.alphavantage.co/query?function=TIME_SERIES_INTRADAY&symbol=MMM&interval=60min&outputsize=compact&apikey=***]
AVStockParser.py L:55 DEBUG [2020-12-25 01:09:44,601] Trying (1) to send request...
AVStockParser.py L:119 INFO [2020-12-25 01:09:45,542] It was received 100 candlesticks data from Alpha Vantage service
AVStockParser.py L:120 INFO [2020-12-25 01:09:45,542] Showing last 3 rows with Time Zone: 'US/Eastern':
AVStockParser.py L:123 INFO [2020-12-25 01:09:45,551] date time open high low close volume
AVStockParser.py L:123 INFO [2020-12-25 01:09:45,551] 97 2020.12.23 16:00 174.5700 175.0200 173.9550 174.0200 332340
AVStockParser.py L:123 INFO [2020-12-25 01:09:45,551] 98 2020.12.23 17:00 173.9900 173.9900 173.9600 173.9600 316682
AVStockParser.py L:123 INFO [2020-12-25 01:09:45,551] 99 2020.12.23 18:00 173.9900 173.9900 173.9900 173.9900 1410
AVStockParser.py L:127 INFO [2020-12-25 01:09:45,555] Stock history saved to .csv-formatted file [./MMM60.csv]
AVStockParser.py L:229 DEBUG [2020-12-25 01:09:45,555] All Alpha Vantage data parser operations are finished success (summary code is 0).
AVStockParser.py L:234 DEBUG [2020-12-25 01:09:45,555] Alpha Vantage data parser work duration: 0:00:00.953904
AVStockParser.py L:235 DEBUG [2020-12-25 01:09:45,555] Alpha Vantage data parser finished: 2020-12-25 01:09:45
The file ./MMM60.csv will be completely similar and include the same columns but with hourly data: "date", "time", "open", "high", "low", "close", "volume":
2020.12.11,09:00,171.9100,171.9100,171.5500,171.8300,1663
2020.12.11,10:00,172.0100,173.6800,172.0100,173.5900,214065
2020.12.11,11:00,173.5800,173.9770,172.6700,173.0800,268294
...
2020.12.23,16:00,174.5700,175.0200,173.9550,174.0200,332340
2020.12.23,17:00,173.9900,173.9900,173.9600,173.9600,316682
2020.12.23,18:00,173.9900,173.9900,173.9900,173.9900,1410
Key --size may be full (Alpha Vantage service return a lot of history candles) or compact (only last 100 candles).
Key --interval use only with --period TIME_SERIES_INTRADAY. Intraday intervals of history candles may be only 1min, 5min, 15min, 30min or 60min.
Also, you can draw an interactive chart (see: PriceGenerator library) using --render key after --parse key:
avstockparser --debug-level 10 --api-key "your token here" --ticker YNDX --period TIME_SERIES_DAILY --size compact --output YNDX1440.csv --parse --render
After running the command above, you will get three files:
YNDX1440.csv— .csv-file containing prices (example: ./media/YNDX1440.csv);index.html— price chart and statistics drawn using the Bokeh library and saved to an .html-file (example: ./media/index.html);index.html.md— statistics as simple text, saved in markdown format (example: ./media/index.html.md).
Let us look only one simple example of requesting history of IBM stock as Pandas dataframe:
from avstockparser.AVStockParser import AVParseToPD as Parser
# Requesting historical candles and save the data into a Pandas dataframe variable.
# If the variable output is not specified, the module only returns data in Pandas dataframe format.
df = Parser(
reqURL=r"https://www.alphavantage.co/query?",
apiKey="demo",
output=None,
ticker="IBM",
period="TIME_SERIES_INTRADAY",
interval="5min",
size="full",
retry=2,
)
print(df)
Output is like this:
AVStockParser.py L:119 INFO [2020-12-25 01:49:51,612] It was received 2001 candlesticks data from Alpha Vantage service
AVStockParser.py L:120 INFO [2020-12-25 01:49:51,612] Showing last 3 rows with Time Zone: 'US/Eastern':
AVStockParser.py L:123 INFO [2020-12-25 01:49:51,616] date time open high low close volume
AVStockParser.py L:123 INFO [2020-12-25 01:49:51,616] 1998 2020.12.23 17:35 124.1000 124.1000 124.1000 124.1000 1571
AVStockParser.py L:123 INFO [2020-12-25 01:49:51,617] 1999 2020.12.23 18:45 124.0500 124.0500 124.0000 124.0000 278
AVStockParser.py L:123 INFO [2020-12-25 01:49:51,617] 2000 2020.12.23 20:00 123.9100 123.9100 123.9100 123.9100 225
date time open high low close volume
0 2020.11.25 06:50 124.3500 124.3500 124.2000 124.2000 1218
1 2020.11.25 07:05 124.2000 124.2000 124.2000 124.2000 150
2 2020.11.25 07:10 124.1000 124.1000 124.1000 124.1000 100
3 2020.11.25 07:35 124.0000 124.0000 124.0000 124.0000 510
4 2020.11.25 08:05 123.8201 124.2000 123.8200 123.8200 1414
... ... ... ... ... ... ... ...
1996 2020.12.23 17:05 123.9100 123.9100 123.9100 123.9100 100
1997 2020.12.23 17:20 123.9000 123.9000 123.9000 123.9000 1146
1998 2020.12.23 17:35 124.1000 124.1000 124.1000 124.1000 1571
1999 2020.12.23 18:45 124.0500 124.0500 124.0000 124.0000 278
2000 2020.12.23 20:00 123.9100 123.9100 123.9100 123.9100 225
[2001 rows x 7 columns]
I wish you success in the automation of exchange trading! ;)