alvarobartt/investiny

🤏🏻 `investpy` but made tiny

🤏🏻 investiny is investpy but made tiny

investiny is faster, lighter, and easier to use than investpy.

investiny has been created due to the latest Investing.com changes in their API protection protocols, as now their main APIs are Cloudflare V2 protected. Anyway, there are still some APIs working fine, so this package has been created as a temporary replacement for investpy while we get to an agreement to continue the development of investpy. In the meantime, anyone can use investiny as I'm actively working on it, and ideally, it should support most of the functionality provided by investpy.

---

🤔 What are the differences with investpy?

investiny is faster, lighter and easier to use, but with reduced functionality for the moment. investiny lets you retrieve historical data from Investing.com through historical_data and search any available asset through search_assets, while investpy offers more functions to also retrieve technical indicators, economic calendars, dividends, etc. but those will come at some point to investiny too.

investiny introduces intraday data, so the specified intervals when retrieving historical data for any asset available at Investing.com goes from 1 minute to monthly data.

investpy uses Investing.com's APIs at https://www.investing.com/instruments/HistoricalDataAjax and at https://api.investing.com/api/financialdata/historical, that are Cloudflare protected and not working any more, as you'll end up getting blocked with 403 Forbidden HTTP code; while investiny is using https://tvc6.investing.com/, which seems to be more reliable right now according to the ran tests, as well as providing intraday data.

	Intraday Data	Any Range Historical Data	Search Assets/Quotes	Dividends	Economic Calendar	Technical Indicators	Economic News
investiny	✅	✅	✅	❌	❌	❌	❌
investpy	❌	✅	✅	✅	✅	✅	✅

---

🛠️ Installation

investiny requires Python 3.8+ and can be installed with pip as it follows:

pip install investiny

---

💻 Usage

Retrieve historical data from Investing.com using the Investing.com ID of the asset that you want to retrieve the data from.

from investiny import historical_data

data = historical_data(investing_id=6408, from_date="09/01/2022", to_date="10/01/2022") # Returns AAPL historical data as JSON (without date)

There's also a function to look for assets in Investing.com, that also lets you retrieve the Investing.com ID that you can, later on, use in historical_data as an input parameter.

from investiny import search_assets

results = search_assets(query="AAPL", limit=1, type="Stock", exchange="NASDAQ") # Returns a list with all the results found in Investing.com

As search_assets returns a list of results, you can check each of them, retrieve the ticker from the asset that you want to retrieve historical data from and pass it as a parameter to historical_data. So on, the combination of both functions should look like the following:

from investiny import historical_data, search_assets

search_results = search_assets(query="AAPL", limit=1, type="Stock", exchange="NASDAQ")
investing_id = int(search_results[0]["ticker"]) # Assuming the first entry is the desired one (top result in Investing.com)

data = historical_data(investing_id=investing_id, from_date="09/01/2022", to_date="10/01/2022")

⚠️ Disclaimer

Investing.com is a registered trademark of Investing.com, and its services are offered by Fusion Media Limited.

Neither investpy nor investiny is affiliated, endorsed, or vetted by Investing.com.

Both investpy and investiny are open-source packages that use Investing.com's available data, intended for research and educational purposes only.

You should refer to Investing.com's terms and conditions at https://www.investing.com/about-us/terms-and-conditions for details on your rights to use the actual data, as it is intended for personal use only.

investiny's Issues

problem with today's data

When you call today (10/10/2022):
data2 = historical_data(investing_id=curr_id,
from_date=st_date,
to_date=end_date)

where:
investing_id=1166004,from_date=10/01/2022,to_date=10/10/2022

you'll get json:
{'date': ['10/03/2022', '10/04/2022', '10/05/2022', '10/06/2022', '10/07/2022'], 'open': [7.0425000190735, 7.1424999237061, 7.2750000953674, 7.352499961853, 7.2849998474121], 'high': [7.0875000953674, 7.272500038147, 7.3274998664856, 7.3825001716614, 7.3550000190735], 'low': [7.0349998474121, 7.1199998855591, 7.2224998474121, 7.3049998283386, 7.2849998474121], 'close': [7.0599999427795, 7.2674999237061, 7.3274998664856, 7.335000038147, 7.3474998474121], 'volume': [1557937, 169883, 401434, 215784, 275430]}

so there are data for period: 10/03/2022 ... 10/07/2022. There is no data for 10/10/2022!

when you call it without to_date parameter

data = historical_data(investing_id=curr_id, 
                       from_date=st_date
                       ) 

, you'll get:

{'date': ['09/12/2022', '09/13/2022', '09/14/2022', '09/15/2022', '09/16/2022', '09/20/2022', '09/21/2022', '09/22/2022', '09/23/2022', '09/26/2022', '09/27/2022', '09/28/2022', '09/29/2022', '09/30/2022', '10/03/2022', '10/04/2022', '10/05/2022', '10/06/2022', '10/07/2022', '10/10/2022'], 'open': [7.4499998092651, 7.6100001335144, 7.5300002098083, 7.5900001525879, 7.3850002288818, 7.3499999046326, 7.3000001907349, 7.3425002098083, 7.2649998664856, 6.9800000190735, 7.0174999237061, 6.897500038147, 7.0799999237061, 7.0900001525879, 7.0425000190735, 7.1424999237061, 7.2750000953674, 7.352499961853, 7.2849998474121, 7.3600001335144], 'high': [7.5725002288818, 7.6599998474121, 7.6275000572205, 7.5999999046326, 7.3949999809265, 7.3899998664856, 7.4800000190735, 7.4099998474121, 7.2649998664856, 7.0549998283386, 7.039999961853, 7.0199999809265, 7.1574997901917, 7.1399998664856, 7.0875000953674, 7.272500038147, 7.3274998664856, 7.3825001716614, 7.3550000190735, 7.3800001144409], 'low': [7.4499998092651, 7.5275001525879, 7.4899997711182, 7.3699998855591, 7.3074998855591, 7.3150000572205, 7.3000001907349, 7.3249998092651, 7.0475001335144, 6.9749999046326, 6.9875001907349, 6.8775000572205, 7.0174999237061, 7.039999961853, 7.0349998474121, 7.1199998855591, 7.2224998474121, 7.3049998283386, 7.2849998474121, 7.3400001525879], 'close': [7.5650000572205, 7.5500001907349, 7.5749998092651, 7.4050002098083, 7.3625001907349, 7.3850002288818, 7.3375000953674, 7.3474998474121, 7.0650000572205, 6.9825000762939, 7.0050001144409, 6.9899997711182, 7.0700001716614, 7.0900001525879, 7.0599999427795, 7.2674999237061, 7.3274998664856, 7.335000038147, 7.3474998474121, 7.3800001144409], 'volume': [127182, 172399, 2245501, 107892, 143964, 265991, 547998, 227319, 201673, 541426, 511374, 251056, 272732, 7209297, 1557937, 169883, 401434, 215784, 275430, 8015]}

so now, probably both parameters are ignored and in such case there is the data for the whole month with todays data.

When I request data till today, I should get todays data as well. Could you please take a look on this?

python 3.8

Could you please add support for python 3.8

volume data

First of all thanks very much for this, Alvaro, your efforts are hugely appreciated!

Therefore I feel a bit bad asking for more, but am wondering whether it is possible to include volume data which was available for at least some of the indices?

Again many thanks!

Error 403

Hi,

I have just started receiving error 403... (alvarobartt/investpy#613). Can someone confirm that is a general issue or just something on my end?

Thank you,

What are options for webscraping investing.com?

@alvarobartt I have several tools that work to webscrape the webpage but then the website might change and was curious to know who here knows more front end dev work and we can create an alternate data source.

DM me for more details

solution for investiny 403

please use which is working api

import requests
from requests.structures import CaseInsensitiveDict
while True:
i=['1138417','17955','101810','160',]
for k in i:
url = f"https://tvc6.investing.com/9368e857cc51ddcae69108bd8a3b6d49/1664515691/56/56/23/history?symbol={k}&resolution=D&from=1633411692&to=1664515752"

    headers = CaseInsensitiveDict()
    headers["authority"] = "tvc4.investing.com"
    headers["accept"] = "*/*"
    headers["accept-language"] = "en-US,en;q=0.9"
    headers["content-type"] = "text/plain"
    headers["origin"] = "https://tvc-invdn-com.investing.com"
    headers["referer"] = "https://tvc-invdn-com.investing.com/"
    headers["sec-fetch-dest"] = "empty"
    headers["sec-fetch-mode"] = "cors"
    headers["sec-fetch-site"] = "same-site"
    headers["user-agent"] = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.5112.102 Safari/537.36"
    
    
    resp = requests.get(url, headers=headers)
    
    print(resp.status_code)
    time.sleep(1)
    if resp.status_code==200:
        print(resp.json())

some stock id's give an error

For some ids, historical_data gives this error:

historical_data(investing_id=40654,from_date=start_date_1, to_date=end_date_1)

also

historical_data(investing_id=26490,from_date=start_date_1, to_date=end_date_1)

for dates:
start_date_1 = '04/27/2000'
end_date_1 = '04/26/2012'

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
/tmp/ipykernel_16762/3915976534.py in <module>
----> 1 historical_data(investing_id=26490,from_date=start_date_1, to_date=end_date_1)

~/.local/lib/python3.10/site-packages/investiny/historical.py in historical_data(investing_id, from_date, to_date, interval)
     46     time_format = "%H:%M %m/%d/%Y" if isinstance(interval, int) else "%m/%d/%Y"
     47     output = {
---> 48         "date": [datetime.fromtimestamp(t).strftime(time_format) for t in data["t"]],  # type: ignore
     49         "open": data["o"],  # type: ignore
     50         "high": data["h"],  # type: ignore

KeyError: 't'

Examples from the README fail

This code:

from investiny import historical_data
data = historical_data(investing_id=6408, from_date="09/01/2022", to_date="10/01/2022") # Returns AAPL historical data as JSON (without date)
print(data)

fails:

Traceback (most recent call last):
  File "/usr/ports/finance/py-investiny/x.py", line 6, in <module>
    data = historical_data(investing_id=6408, from_date="09/01/2022", to_date="10/01/2022") # Returns AAPL historical data as JSON (without date)
  File "/usr/local/lib/python3.9/site-packages/investiny/historical.py", line 48, in historical_data
    info = investing_info(investing_id=investing_id)
  File "/usr/local/lib/python3.9/site-packages/investiny/info.py", line 41, in investing_info
    return request_to_investing(  # type: ignore
  File "/usr/local/lib/python3.9/site-packages/investiny/utils.py", line 36, in request_to_investing
    raise ConnectionError(
ConnectionError: Request to Investing.com API failed with error code: 403.

Issue with Date and Price mapping

from #40
Hi @alvarobartt .. updated investiny version however ran into some issues --

The output is missing 10/14 (Friday) and displaying 10/15, prices off by a day. This is for DXY.

Request to Investing.com API failed with error code: 403

here is my code:

from investiny import historical_data 

data = historical_data(investing_id=19256, from_date="01/01/2019", to_date="12/31/2019") #adese 19256 data-content-id üzerinden bulunuyor

here's out:

runfile('C:/Users/emrec/Desktop/TUBITAK 22 DOSYASI/stockrates/untitled2.py', wdir='C:/Users/emrec/Desktop/TUBITAK 22 DOSYASI/stockrates')
Traceback (most recent call last):

  File "C:\Users\emrec\AppData\Local\Programs\Python\Python39\lib\site-packages\spyder_kernels\py3compat.py", line 356, in compat_exec
    exec(code, globals, locals)

  File "c:\users\emrec\desktop\tubitak 22 dosyasi\stockrates\untitled2.py", line 3, in <module>
    data = historical_data(investing_id=6408, from_date="01/01/2019", to_date="12/31/2019") #adese 19256 data-content-id üzerinden bulunuyor

  File "C:\Users\emrec\AppData\Local\Programs\Python\Python39\lib\site-packages\investiny\historical.py", line 48, in historical_data
    info = investing_info(investing_id=investing_id)

  File "C:\Users\emrec\AppData\Local\Programs\Python\Python39\lib\site-packages\investiny\info.py", line 41, in investing_info
    return request_to_investing(  # type: ignore

  File "C:\Users\emrec\AppData\Local\Programs\Python\Python39\lib\site-packages\investiny\utils.py", line 36, in request_to_investing
    raise ConnectionError(

ConnectionError: Request to Investing.com API failed with error code: 403.

Request to Investing.com API failed with error code: 403

Hi I get below error when using investiny.
Request to Investing.com API failed with error code: 403

ERROR: Could not find a version that satisfies the requirement investiny

Getting an error when I try to install investing pip install investiny that the version isn't compatible, this looks different than most one line errors.

python3.9 is being used from conda as you can see below from my PyCharm IDE and per requirements >=python 3.8 from https://alvarobartt.github.io/investiny/requirements/ I should be ok. Is this related to #38 since I'm on MacOs and usually what causes issues for Linux systems translates to me on Unix.
#38

MacOs BigSur

Features to be implemented, if possible.

Glad if you can add a method to get the information followng pages in a stock.

1. Overview page info (like 52 wk Range ,P/E Ratio, Volume, Average Vol. (3m) etc)
2. Technical Anlysis page info (Like moving averages and rsi)

make compatible with python 3.8 and some improvements

• make compatible with python 3.8
• historical_price seems to return a dict of list like this:

{
'date': ['09/01/2022', '09/02/2022', '09/05/2022', '09/06/2022', '09/07/2022', '09/08/2022', '09/09/2022', '09/12/2022', '09/13/2022', '09/14/2022', '09/15/2022', '09/16/2022', '09/19/2022', '09/20/2022', '09/21/2022', '09/22/2022', '09/23/2022', '09/26/2022', '09/27/2022', '09/28/2022', '09/29/2022'], 
'open': [147.49000549316, 147.77000427246, 149.07000732422, 147.77000427246, 147.02000427246, 145.33000183105, 143.13999938965, 143.52000427246, 144.16999816895, 143.25999450684, 143.58000183105, 142.97999572754, 143.19000244141, 142.39999389648, 140.86000061035, 141.16000366211, 140.13999938965, 139.19999694824, 137.69999694824, 137, 138.33000183105], 
'high': [147.83000183105, 149.10000610352, 149.25, 149.02000427246, 147.88000488281, 145.80999755859, 144.47999572754, 144.55999755859, 144.4700012207, 143.86999511719, 143.63000488281, 143.24000549316, 143.25999450684, 142.58000183105, 141.97999572754, 142.02000427246, 140.99000549316, 139.2200012207, 139.28999328613, 138.74000549316, 138.49000549316], 
'low': [146.7799987793, 147.13999938965, 147.36999511719, 146.67999267578, 146.74000549316, 143.05999755859, 142.46000671387, 142.91000366211, 143, 142.64999389648, 142.74000549316, 142.14999389648, 142.17999267578, 140.5299987793, 140.72999572754, 139.89999389648, 138.44999694824, 137.41000366211, 136.61999511719, 135.52000427246, 136.2200012207], 
'close': [147.38999938965, 148.33000183105, 147.72999572754, 147.27000427246, 147.5299987793, 143.16000366211, 143.57000732422, 144.5, 143.38999938965, 143.63000488281, 143.2200012207, 142.72999572754, 142.61999511719, 140.69999694824, 141.19999694824, 140.02000427246, 139.47999572754, 138.86999511719, 137.13999938965, 137.91000366211, 137.5]
}

would be nice to have an option to return a pandas.DataFrame with date as index, kind of:

df = pd.DataFrame.from_dict(data, orient ='columns')
df = df.set_index('date')

                  open        high         low       close
date
09/01/2022  147.490005  147.830002  146.779999  147.389999
09/02/2022  147.770004  149.100006  147.139999  148.330002
09/05/2022  149.070007  149.250000  147.369995  147.729996
09/06/2022  147.770004  149.020004  146.679993  147.270004
09/07/2022  147.020004  147.880005  146.740005  147.529999
09/08/2022  145.330002  145.809998  143.059998  143.160004
09/09/2022  143.139999  144.479996  142.460007  143.570007
09/12/2022  143.520004  144.559998  142.910004  144.500000
09/13/2022  144.169998  144.470001  143.000000  143.389999
09/14/2022  143.259995  143.869995  142.649994  143.630005
09/15/2022  143.580002  143.630005  142.740005  143.220001
09/16/2022  142.979996  143.240005  142.149994  142.729996
09/19/2022  143.190002  143.259995  142.179993  142.619995
09/20/2022  142.399994  142.580002  140.529999  140.699997
09/21/2022  140.860001  141.979996  140.729996  141.199997
09/22/2022  141.160004  142.020004  139.899994  140.020004
09/23/2022  140.139999  140.990005  138.449997  139.479996
09/26/2022  139.199997  139.220001  137.410004  138.869995
09/27/2022  137.699997  139.289993  136.619995  137.139999
09/28/2022  137.000000  138.740005  135.520004  137.910004
09/29/2022  138.330002  138.490005  136.220001  137.500000

403 in search_assets as well

from investiny import search_assets
search_results = search_assets(query="EUR/USD", limit=1, type="FX")
Traceback (most recent call last):
File "", line 1, in
File "/home/pi/.local/lib/python3.11/site-packages/investiny/search.py", line 42, in search_assets
return request_to_investing(endpoint="search", params=params) # type: ignore
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/pi/.local/lib/python3.11/site-packages/investiny/utils.py", line 36, in request_to_investing
raise ConnectionError(
ConnectionError: Request to Investing.com API failed with error code: 403.

5000 rows limit

When I give a start and end date over 5000 days in difference, for example:

historical_data(investing_id=13928, from_date="09/01/2001", to_date="10/01/2022")

Free account suggestion

Dear alvarobartt,

investing.com will never help you with this open source project, because they hire cloudflare to ban every non-paying data bot from their server forever. Knowing this, it is up to us to find another solution.

Has anyone thought of working with a free account from investing.com ? It is possible to create one or more watch lists with a free account. With a free account you will receive a personal cookie. Using this cookie together with curl make it possible to get my watch_list data. Any problem at all, from 09:00 to 22:00 with a frequency of 5 minutes. The free account and personal cookie let you pass Cloudflare bot checker. I'm really fresh in python, but I managed to get this done.

I would like to help with this open source project, but someone first needs to introduce me to the investiny project.

People who supporting this idea, send a like

Possible way to fix 403 Error

Hi, @alvarobartt !
I've encountered the 403 Error problem today and found that using curl seem to be working fine, no 403 error.
And the only difference I can see is the headers ordering - requests shuffles headers, while curl preserves them as provided.
So I tried using urllib.request and it worked.

I'm using Python 3.10.5

Maybe this can solve all 403 errors in the project?

minimal working example:

import urllib.request

# take them from your browser, no cookies required
headers = {}

req = urllib.request.Request(f'https://sbcharts.investing.com/events_charts/us/222.json', b"", headers)
with urllib.request.urlopen(req) as response:
    response = response.read().decode()

`volume` is returned even when `n/\a

While tackling issue #40, I've spotted that the volume is being returned as part of historical_data as the current check is just making sure that the key exists, but not taking into consideration that the values inside are n/\a (None). See the screenshot below:

Still get 403 with tvc6 API

Hello, first thank you for the amazing project. When i tried examples of the documentation i still get 403 error :/, even with the new api.

I tried:

and received:

I am using Anaconda at this point, but driven by curiosity i tried open the api link on the browser and still get the 403 error, but after refresh all data are loaded correctly

EDIT: in the browser tvc4 had the same behavior. I think in some header or cookie problem, maybe ?

search_assets does not return data when exchange parameter is set

Searching for a stock in Indian Bombay Stock Exchange BSE like below

type = 'Stock'
exchange='BSE'
query = 'kotak'

search_results = search_assets(query=query, limit=1, type=type, exchange=exchange)
print(search_results)

returns empty list

[]

Searching for a stock in Indian National Stock Exchange NSE like below

type = 'Stock'
exchange='NSE'
query = 'kotak'

search_results = search_assets(query=query, limit=1, type=type, exchange=exchange)
print(search_results)

returns empty list

[]

However searching without providing any exchange parameter like following

type = 'Stock'
exchange=''
query = 'kotak'

search_results = search_assets(query=query, limit=1, type=type, exchange=exchange)
print(search_results)

returns 2 elements although the limit parameter is set to 1

[{'symbol': 'KTKM', 'full_name': 'NSE:KTKM', 'description': 'Kotak Mahindra Bank Ltd.', 'type': 'Stock', 'ticker': '18260', 'exchange': 'NSE'}, {'symbol': 'KTKM_p', 'full_name': 'BSE:KTKM_p', 'description': 'Kotak Mahindra Bank Ltd Preferred', 'type': 'Stock', 'ticker': '1180379', 'exchange': 'BSE'}]

Even more confusing is when limit is increased as in the following

type = 'Stock'
exchange=''
query = 'kotak'

search_results = search_assets(query=query, limit=4, type=type, exchange=exchange)
print(search_results)

3 results are returned

[{'symbol': 'KTKM', 'full_name': 'NSE:KTKM', 'description': 'Kotak Mahindra Bank Ltd.', 'type': 'Stock', 'ticker': '18260', 'exchange': 'NSE'}, {'symbol': 'KTKM_p', 'full_name': 'BSE:KTKM_p', 'description': 'Kotak Mahindra Bank Ltd Preferred', 'type': 'Stock', 'ticker': '1180379', 'exchange': 'BSE'}, {'symbol': 'KTKM', 'full_name': 'BSE:KTKM', 'description': 'Kotak Mahindra Bank Ltd.', 'type': 'Stock', 'ticker': '39573', 'exchange': 'BSE'}]

Does anyone know the significance of _p preferred? Should I search BSE history in normal or preferred?

Attention Needed

1. Search should return results related to the exchange searched in eg BSE, NSE
2. limit parameter has unintended effect when set to 1

Getting incorrect Date and Prices

I seem to be getting incorrect date and price mapping. I you check the images, 10/02/2022 and 10/09/22 are Sundays. Cross checked on investing.com and seems price output are moved back by a day.

** limited knowledge of running code, so might be doing something wrong at my end.. but will great if you take a look.

`exchange` param in `search_assets` has unexpected behaviour

Discussed in #33

^{Originally posted by longlie1109 October 12, 2022}
Is there a way to get the "exchange" in search_assets function ?
NASDAQ works fine but other market isn't work out for me

So as the discussion says, it seems that the results provided by search_assets contain a field named exchange which doesn't seem to be matching the actual filter for the search, which means that if the results contain e.g. exchange="RANDOM_EXCHANGE", if you use that filter in a next call to search_assets using the exchange param, it won't work, as the printed exchange is not the same one as the one used by the filter, even though those refer to the same.

Note that something similar happens with the type parameter, which was already solved at 8cf3e0e

How to get investing_id by the ticker?

Hi there! Could you explain how to get investing_id by the ticker? For example, how to get the historical data of GOOGL in NASDAQ exchange from 09/01/2022 to 10/01/2022?

Error code: 403 with investpy and investiny

Hello,
as I understand, the problem was not solved.
Are there any news?

ModuleNotFoundError: No module named 'pip'

When i do "pip install investiny" colab return the writing in the title. How can i fix?

Can not install this library

This library can not install using pip install investiny. It show following error message.

Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
ERROR: Could not find a version that satisfies the requirement investiny (from versions: none)
ERROR: No matching distribution found for investiny

Issue with Date and Price mapping

Opening new ticket per #31 @alvarobartt

Historical price query for US10Y returning incorrect Date and Price mapping.
Getting weekend date 10/08/22 (Sat) and missing 10/10/22(Mon) per the following -

Thanks for looking into this.

Request to Investing.com API failed with error code: 503.

Hello @alvarobartt

First of all, thank you for this amazing package, it is saving my month of work to check the historical valuations of my portfolio.

I just want to report that https://tvc4.investing.com/ when into maintenance yesterday and, since going back up, I am now constantly getting the error ```Request to Investing.com API failed with error code: 503.````

Was there any change into the API that you know of? Or some kind of limit implemented?

Thank you once more for your amazing work.

There is another api server from investing.com which works same

import requests
from requests.structures import CaseInsensitiveDict

url = "https://tvc6.investing.com/e44855091c7412b1c8473adb2d797c8b/1665109080/1/1/8/history?symbol=252&resolution=5&from=1664677140&to=1665109200"

headers = CaseInsensitiveDict()
headers["User-Agent"] = "Mozilla/5.0 (Windows NT 10.0; rv:91.0) Gecko/20100101 Firefox/91.0"
headers["Accept"] = "/"
headers["Accept-Language"] = "en-US,en;q=0.5"
headers["Referer"] = "https://tvc-invdn-com.investing.com/"
headers["Content-Type"] = "text/plain"
headers["Origin"] = "https://tvc-invdn-com.investing.com"
headers["Connection"] = "keep-alive"
headers["Sec-Fetch-Dest"] = "empty"
headers["Sec-Fetch-Mode"] = "cors"
headers["Sec-Fetch-Site"] = "same-site"

resp = requests.get(url, headers=headers)

print(resp.status_code)

Patiently waiting for search_quotes

I have download the list of all stocks on my countries exchange in two separate files ( two separate exchanges) from investing.com screener page by clicking on download results.

The downloaded csv contains the name and symbol but not the investing_id required for downloading the historical prices.

My idea is to loop through all name or symbol calling search_quotes to retrieve the investing_id from the search results and then store it some place safe.

Then I want to loop through the investing_id list to retrieve historical data.

Therefore patient.

pip install error in Linux

Installation in Windows machine went smoothly
That on Linux reported this error message. Anything I can do to fix it?

/usr/lib/python3/dist-packages/secretstorage/dhcrypto.py:15: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead
from cryptography.utils import int_from_bytes
/usr/lib/python3/dist-packages/secretstorage/util.py:19: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead
from cryptography.utils import int_from_bytes
Defaulting to user installation because normal site-packages is not writeable
ERROR: Could not find a version that satisfies the requirement investiny (from versions: none)
ERROR: No matching distribution found for investiny

Date column

HI there. I use historical_data function, I want to have Date column too, what should I do?

Issue with max from date and limit of max daily data

Is there a way to get max daily data for an Id... Currently there seems to be some limits. So for japan Germany 10 Y government spread (ID: 1079966). I get this as a result

Clearly 1301 rows for 22 years of data is a problem. The date jumps from 1998 to 2017.

The start date on investing.com is 01/11/2010. However while getting historical daily data they seem to have some kind of limit while downloading (Tried multiple tickers in excel. There seems to be some limit around 5400 points).

Is there a way to get max daily data which is available?

Not working for bonds

from investiny import historical_data, search_assets

search_results = search_assets(query='U.K. 10Y', limit=1, type="bond")
investing_id = int(search_results[0]["ticker"])

list index out of range

Solution with cloudscraper

It seems to work fine using cloudscraper instead of httpx

Just changed and added this :

    scraper = cloudscraper.create_scraper(
        browser={
            'browser': 'chrome',
            'platform': 'android',
            'desktop': False
        }
    )     

    r = scraper.get(url, params=params, headers=headers)

Date format

Any chance to use yyyy-mm-dd date format instead of m/d/Y?

Use trendet library with investiny historical data

I wanted to use the historical data for the trendet library, like in the code here https://github.com/alvarobartt/trendet

I had the error 'ConnectionError: ERR#0015: error 403, try again later. #600', but with investiny I was able to create the df finally, however the column names seem to be different now. In the code you have 'Up Trend' , 'Down Trend', 'Date'. But in the investiny I only saw 'open', 'high', 'low'. Am I using the wrong data? I got the one from investiny as follows:

from investiny import historical_data, search_assets

search_results = search_assets(query="AAPL", limit=1, type="Stock", exchange="NASDAQ")
investing_id = int(search_results[0]["ticker"]) # Assuming the first entry is the desired one (top result in Investing.com)

data = historical_data(investing_id=investing_id, from_date="09/01/2022", to_date="10/01/2022")

Send asynchronous requests to tvc6.investing.com to improve performance

In order to improve investiny's performance when retrieving historical data from Investing.com when the specified intervals imply retrieving data greater than 5000 points, we'll be using httpx with asyncio so as to make sure that those requests are run asynchronously rather than inside a for-loop.

We'll run a simple benchmark once done so as to compare both approaches to see which one performs better.

More information at https://www.twilio.com/blog/asynchronous-http-requests-in-python-with-httpx-and-asyncio courtesy of @sagnew