This is yet another library to access Degiro's API.

These details have not been verified by PyPI

Project links

Project description

1. Degiro Connector

This is yet another library to access Degiro's API.

1.1. Which features ?

Here are the features you can access through this library :

Endpoint	Feature(s)
AccountCashReport	Export cash movements in a specific format : CSV, HTML, PDF or XLS.
AccountInfo	Retrieve a table containing : "clientId" and Currencies.
AccountOverview	Retrieve all the cash movements between two dates.
Agenda	Crucial events regarding products : Dividend, Economic, Earnings, Holiday, IPO or Split.
Bonds ETFs Funds Futures Leverageds Lookup Options Stocks Warrants	Search list of products according their name, type and other criterias. For instance all the stocks from NASDAQ 100.
Chart	Retrieve chart data.
ClientDetails	Retrieve a table containing : "clientId", "intAccount" and other account information.
CompanyProfile	Retrieve a company's profile using its ISIN code.
CompanyRatios	Retrieve a company's ratios using its ISIN code.
Config	Retrieve a table containing : "clientId" and URLs which are constitutive of Degiro's API.
EstimatesSummaries	Retrieve a company's estimates summaries using its ISIN code.
FavouritesList	Add/Delete/Retrieve favourites lists.
FavouritesListProducts	Add/Update favourites lists content.
FinancialStatements	Retrieve a company's financial statements using its ISIN code.
LatestNews	Retrieve latest news about all the companies.
LoginQuotecast	Establish a connection for quotecast operations.
LoginTrading	Establish a connection for trading operations.
LogoutTrading	Destroy previously established connection for trading operations.
NewsByCompany	Retrieve news related to a specific company.
Order	Create, update, delete an Order.
OrderHistory	Retrieve all Orders created between two dates.
Orders	List pending Orders.
Portfolio	List products in your Portfolio.
ProductsConfig	Retrieve a table containing : useful parameters to filter products.
ProductsInfo	Search for products using their ids.
Quotecasts	Fetch real-time data on financial products. For instance the real-time stock Price.
TopNewsPreview	Retrieve some news preview about all the companies.
TotalPorfolio	Retrieve aggregated information about your assets.
TransactionsHistory	Retrieve all Transactions created between two dates.

1.2. How to install ?

pip install degiro-connector

1.3. How to upgrade ?

pip install --no-cache-dir --upgrade degiro-connector

1.4. How to uninstall ?

pip uninstall degiro-connector

2. Real-time data

It is possible to fetch a stream of data in real-time from Degiro's API.

For instance if one needs the following data from the "AAPL" stock :

LastDate
LastTime
LastPrice
LastVolume

You can use this library to retrieve updates like this :

LastDate    LastTime    LastPrice LastVolume
2020-11-13  22:00:00    119.26    4697040

For a list of available metrics, see the example in section 2.6.

2.1. What are the workflows ?

This is the workflow for consuming real-time data-stream :

A. Find your "user_token".
B. Setup the API object with your "user_token".
C. Connect.
D. Subscribe to data-stream.
E. Fetch data-stream.

This is the worflow for consuming charts :

A. Find your "user_token".
B. Setup the API object with your "user_token".
C. Fetch charts.

All the details of these steps are explained in the rest of this section.

2.2. What are the credentials ?

The only credential you need in order to fetch real-time data and charts is the :

user_token

Beware, these two identifiers are not the same thing :

user_token : used to fetch real-time data and charts.
int_account : used for some trading operations.

2.3. How to find your : user_token ?

You can find your "user_token" inside one of these tables :

"Config" : attribute "clientId"
"ClientDetails" : attribute "id"

See sections related to "Config" and "ClientDetails" tables.

2.4. How to login ?

In order to fetch data you need to establish a connection.

You can use the following code to connect :

session_id = TickerFetcher.get_session_id(user_token=YOUR_USER_TOKEN)

2.5. Is there a timeout ?

Connection timeout is around 15 seconds.

Which means a connection will cease to work after this timeout.

This timeout is reset each time you use this connection to :

Subscribe to a metric (for instance a stock Price)
Fetch the data-stream

So if you use it nonstop (in a loop) you won't need to reconnect.

2.6. How to subscribe to a data-stream ?

To subscribe to a data-stream you need to setup a TickerRequest.

A Request has the following parameters :

Parameter	Type	Description
request_type	str	The value "subscription" or "unsubscription".
request_map	dict	Map of products and metrics to subscribe/unsubscribe to.

Here is an example of a request :

ticker_request = TickerRequest(
    request_type="subscription",
    request_map={
        "360015751": [
            'LastDate',
            'LastTime',
            'LastPrice',
            'LastVolume',
            'AskPrice',
            'AskVolume',
            'LowPrice',
            'HighPrice',
            'BidPrice',
            'BidVolume'
        ],
        "AAPL.BATS,E": [
            'LastDate',
            'LastTime',
            'LastPrice',
            'LastVolume',
            'AskPrice',
            'AskVolume',
            'LowPrice',
            'HighPrice',
            'BidPrice',
            'BidVolume'
        ],
    },
)
TickerFetcher.subscribe(
    ticker_request=ticker_request,
    session_id=session_id,
)

In this example these are the vwd_id of the products from which you want Real-time data :

360015751
AAPL.BATS,E

See the section related to vwd_id for more information.

Once you have built this Request object you can send it to Degiro's API like this :

quotecast_api.subscribe(request=request)

For more comprehensive examples : realtime_poller.py / realtime_one_shot.py

2.7. How to unsubscribe to a data-stream ?

To remove metrics from the data-stream you need to setup a TickerRequest.

If you try to unsubscribe to a metric to which you didn't subscribed previously it will most likely have no impact.

A Request has the following parameters :

Parameter	Type	Description
request_type	str	The value "subscription" or "unsubscription".
request_map	dict	Map of products and metrics to subscribe/unsubscribe to.

Here is an example of a request :

ticker_request = TickerRequest(
    request_type="unsubscription",
    request_map={
        "360015751": [
            'LastDate',
            'LastTime',
            'LastPrice',
            'LastVolume',
            'AskPrice',
            'AskVolume',
            'LowPrice',
            'HighPrice',
            'BidPrice',
            'BidVolume'
        ],
        "AAPL.BATS,E": [
            'LastDate',
            'LastTime',
            'LastPrice',
            'LastVolume',
            'AskPrice',
            'AskVolume',
            'LowPrice',
            'HighPrice',
            'BidPrice',
            'BidVolume'
        ],
    },
)
TickerFetcher.subscribe(
    ticker_request=ticker_request,
    session_id=session_id,
)

For more comprehensive examples : realtime_poller.py / realtime_one_shot.py

2.8. How to fetch the data ?

You can use the following code :

session_id = TickerFetcher.get_session_id(user_token=user_token)
logger = TickerFetcher.build_logger()
session = TickerFetcher.build_session()
ticker = TickerFetcher.fetch_ticker(
    session_id=session_id,
    session=session,
    logger=logger,
)

For a more comprehensive example : realtime_poller.py

2.9. How to use this data ?

Received data is a Quotecast object with the following properties :

Parameter	Type	Description
json_data	dict	Dictionary representation of what Degiro's API has sent.
metadata	Metadata	Containing the "response_datetime" and "request_duration".

Here is how to access these properties :

json_text = ticker.json_text
response_datetime = ticker.response_datetime
request_duration= ticker.request_duration

Notes:

The API sometimes might return an empty Quotecast message.
The API often returns a subset of the requested metrics, e.g. only 'LastPrice'. This should be considered when appending consecutive data responses.

2.10. Which are the available data types ?

This library provides the tools to convert Degiro's JSON data into something more programmer-friendly.

Here is the list of available data types :

Type	Description
list[Metric]	List of Metric, which is a Pydantic BaseModel.
polars.DataFrame	DataFrame from the library Polars.

There are integrated method to turn polars.DataFrame into Python dict/list or pandas.DataFrame.

Here is how to build each type :

# BUILD `LIST[METRIC]`
ticker_to_metric_list = TickerToMetricList()
metric_list = ticker_to_metric_list.parse(ticker=ticker)

# BUILD `POLARS.DATAFRAME`
ticker_to_df = TickerToDF()
polars_df = ticker_to_df.parse(ticker=ticker)

# BUILD `LIST[DICT]`
python_list = polars_df.to_dicts()

# BUILD PANDAS.DATAFRAME
pandas_df = df.to_pandas()

2.11. What is a Ticker ?

The generated Ticker contains :

Parameter	Type	Description
json_text	str	The json message received
response_datetime	datetime	Datetime of the response.
request_duration	timedelta	Duration of the request.

A Ticker is Pydantic BaseModel, it can serialize and deserialize into json.

Here is how to manipulate a Ticker object :

# GET ICKER PARAMETERS
json_text = ticker.json_text
response_datetime = ticker.response_datetime
request_duration= ticker.request_duration

# SERIALIZE/DESERIALIZE
ticker_json = Ticker.model_dump_json()
ticker_json = Ticker.model_validate_json(json_data=ticker_json)

2.12. What is inside the `list[Metric]` ?

The list[Metric] is the parsed version of the json message received from Degiro's API.

A Metric contains the following parameters :

Parameter	Type	Description
metric_type	MetricType	str
product_id	str	The product identifier in the Quotecast API.
value	str	float

Example - list[Metric] :

metric_list = [
    Metric(
        product_id=360114899,
        metric_type="LastDate",
        value="2020-11-06",
    ),
    Metric(
        product_id=360114899,
        metric_type="LastTime",
        value="17:36:17",
    ),
    Metric(
        product_id=360114899,
        metric_type="LastPrice",
        value=70.0,
    ),
    Metric(
        product_id=360114899,
        metric_type="LastVolume",
        value=100,
    ),
    Metric(
        product_id=360015751,
        metric_type="LastDate",
        value="2020-11-06",
    ),
    Metric(
        product_id=360015751,
        metric_type="LastTime",
        value="17:36:17",
    ),
    Metric(
        product_id=360015751,
        metric_type="LastPrice",
        value=22.99,
    ),
    Metric(
        product_id=360015751,
        metric_type="LastVolume",
        value=470,
    ),
}

2.13. What is inside the DataFrame ?

In addition to whatever metrics you have chosen to subscribe to (see the example in section 2.6), the DataFrame will contain the following columns :

Column	Description
product_id	Product identifier, for instance "AAPL.BATS,E" for APPLE stock.
response_datetime	Datetime at which the data was received.
request_duration	Duration of the request used to fetch the data.

Example - DataFrame :

       product_id response_datetime  request_duration    LastDate  LastTime LastPrice LastVolume
0   360114899  2020-11-08 12:00:27          1.022489  2020-11-06  17:39:57      70.0        100
1   360015751  2020-11-08 12:00:27          1.022489  2020-11-06  17:36:17     22.99        470

2.14. How to get chart data ?

You can fetch an object containing the same data than in Degiro's website graph.

For that you need to prepare a Chart.Request object.

Here is a table with the available attributes for Chart.Request.

Parameter	Type	Description
requestid	str	It sends you back whatever string you put here, you can set it to : "1".
resolution	Chart.Resolution	Resolution of the chart like : Chart.Resolution.PT1M.
culture	str	Country code like : "en-US" or "fr-FR".
period	Chart.Period	Period of the chart, like : Chart.Period.P1D.
series	repeated string	Data to get like : ['issueid:36014897', 'price:issueid:360148977'].
tz	str	Timezone like : "Europe/Paris"

Example of code :

request = Chart.Request()
request.culture = "fr-FR"
request.period = Chart.Interval.PT1H
request.requestid = "1"
request.resolution = Chart.Interval.P1D
# request.series.append("issueid:360148977")
# request.series.append("price:issueid:360148977")
request.series.append("ohlc:issueid:360148977")
# request.series.append("volume:issueid:360148977")
# request.series.append("vwdkey:AAPL.BATS,E")
# request.series.append("price:vwdkey:AAPL.BATS,E")
# request.series.append("ohlc:vwdkey:AAPL.BATS,E")
# request.series.append("volume:vwdkey:AAPL.BATS,E")
request.tz = "Europe/Paris"

# FETCH DATA
chart = quotecast_api.get_chart(
    request=request,
    override={
        "resolution": "P1D",
        "period": "P1W",
    },
    raw=True,
)

The issueid parameter is the vwd_id of the product from which you want the Chart data.

See the section related to vwd_id for more information.

All the options for the enumerations are available in this file : quotecast.proto

For a more comprehensive examples :

2.15. How to find a : vwd_id ?

In operations related to Quotecast, Degiro uses the vwd_id to identify a product.

Which means that if you want a Chart or Real-time data for a specific product : you first need to find this product's vwd_id.

This two identifiers are not the same :

Identifier	API name(s)	Description
id	str	Id used identify a product in `Trading` related endpoints.
vwd_id	issueid vwdId vwdIdSecondary	Id used identify a product in `Quotecast` (`Chart` and `Real-time data`) related endpoint.

Here are some methods you can use to fetch a product's vwd_id :

product_search
get_products_info

The method product_search let you use the name or other attributes of a product to fetch it's vwd_id.

The method get_products_info let you use a product's id to fetch it's vwd_id.

3. Trading connection

This library is divided into two modules :

quotecast : to consume real-time financial data.
trading : to manage your Degiro's account.

The module quotecast is described in the section related to real-time data.

The rest of this document will only refer to the module : trading.

3.1. How to login ?

In order to use the module trading.api you need to establish a connection.

Check the section related to int_account to understand how to get yours.

Here is how to connect :

# SETUP CREDENTIALS
credentials = Credentials(
    username = YOUR_USERNAME,
    password = YOUR_PASSWORD,
    int_account = YOUR_INT_ACCOUNT,  # OPTIONAL FOR LOGIN
)

# SETUP TRADING API
trading_api = API(credentials=credentials)

# ESTABLISH CONNECTION
trading_api.connect()

For a more comprehensive example : connection.py

3.2. How to logout ?

Once you no longer need to use the API you can destroy your connection.

You can use the following code to disconnect :

# DESTROY CONNECTION
trading_api.logout()

For a more comprehensive example : logout.py

3.3. What are the credentials ?

Some credentials are required to use Degiro's trading API.

Here are these credentials :

Parameter	Type	Description
username	str	Username used to log into Degiro's website.
password	str	Password used to log into Degiro's website.
int_account	int	OPTIONAL : unique identifier of the account : used by Degiro's server.
totp_secret_key	str	OPTIONAL : used for Two-factor Authentication (2FA).
one_time_password	str	OPTIONAL : used for Two-factor Authentication (2FA).

Check the section related to int_account to understand how to get yours.

Check the section related to 2FA if you want to know more about these two parameters :

totp_secret_key
one_time_password

3.4. How to find your : int_account ?

To get your int_acccount you can run this example : client_details_table.py

See section related to ClientDetails table for more details.

This int_acccount is required to do most of the trading operations available in this connector.

Here are some operations for which your int_acccount is not required :

Connection
Fetch table : ClientDetails
Fetch table : Config

Beware, these two identifiers are not the same thing :

user_token : used to fetch real-time data and charts.
int_account : used for some trading operations.

3.5. How to use 2FA ?

First I will briefly explain what is : Two-Factor Authentication (2FA).

I recommend to skip a few paragraphs if you already know what is 2FA.

In a standard connection you are providing two parameters:

username
password

If you use Two-Factor Authentication (2FA) you need an extra parameter:

one_time_password

This one_time_password has a validity of 30 secondes and is generated using a totp_secret_key code.

Usually you use an app like ‎Google Authenticator to store this totp_secret_key and generate the one_time_password.

The totp_secret_key is stored inside the QRCode which is displayed when you enable 2FA on Degiro's website

To use 2FA with this library you have two solutions.

SOLUTION 1

Provide your totp_secret_key : the library will use it to generate a new one_time_password at each connection.

So you won't have to type your one_time_password manually at each connection.

This is the proper way.

See the section about totp_secret_key to understand how to get yours.

Here is an example of connection with the totp_secret_key :

# SETUP CREDENTIALS
credentials = Credentials(
    username=YOUR_USERNAME,
    password=YOUR_PASSWORD,
    int_account=YOUR_INT_ACCOUNT,  # OPTIONAL FOR LOGIN
    totp_secret_key=YOUR_2FA_SECRET_KEY,  # ONLY IF 2FA IS ENABLED
)

# SETUP TRADING API
trading_api = API(credentials=credentials)

# ESTABLISH CONNECTION
trading_api.connect()

A complete example here : connection_2fa.py

SOLUTION 2

Provide a new one_time_password at each connection.

Here is an example of connection with the one_time_password :

# SETUP CREDENTIALS
credentials = Credentials(
    username=YOUR_USERNAME,
    password=YOUR_PASSWORD,
    int_account=YOUR_INT_ACCOUNT,  # OPTIONAL FOR LOGIN
    one_time_password=YOUR_2FA_OTP,  # ONLY IF 2FA IS ENABLED
)

# SETUP TRADING API
trading_api = API(credentials=credentials)

# ESTABLISH CONNECTION
trading_api.connect()

A complete example here : connection_otp.py

3.6. How to find your : totp_secret_key ?

The parameter totp_secret_key is only required if you have enabled 2FA on Degiro's website.

When you try to activate 2FA on Degiro's website, it displays a QRCode.

This QRCode changes at each activation.

A QRCode is a picture which can be converted into a text.

You can download this QRCode and use a tool to extract the text from it.

This extracted text will look like this :

otpauth://totp/DEGIRO:YOUR_USERNAME?algorithm=SHA1&issuer=DEGIRO&secret=YOUR_TOPT_SECRET_KEY&digits=6&period=30

Has you can guess the "totp_secret_key" is in this part :

secret=YOUR_TOPT_SECRET_KEY

Here is an example of script that extracts the text from a QRCode : qrcode.py

3.7. How to find your : one_time_password ?

The parameter one_time_password is the password you type when you log in the website using 2FA.

Usually you get it through an app like Google Authenticator.

It is preferable to use the parameter totp_secret_key instead of one_time_password.

3.8. Is there a timeout ?

A connection for trading operations has a timeout of 30 minutes. It's defined in seconds as constant timeouts.TRADING_TIMEOUT.

The timeout period is started when the TradingAPI.connect() results successfully.

Every time an operation is made to this TradingAPI connection, the timeout for this connection will be reset.

If a TradingAPI connection is left unused during the timeout period, the connection will expire. Every consecutive function call to the TraderAPI will then throw a TimeoutError exception.

A connection timeout might occur when your trading strategy only performs a few trades per day, e.g. once per hour. So when finally a buy opportuntiy occurs, check_order():

# FETCH CHECKING_RESPONSE
checking_response = trading_api.check_order(order=order)

Will fail with TimeoutError:

TimeoutError: Connection has probably expired.

3.9. How to manage : TimeoutError ?

HANDLE EXCEPTION

It's recommended to catch the TimeoutError on every function call to the TradingAPI.

When it's detected, it's sufficient to call connect() again, followed by repeating the original call that threw the exception. Example for the check_order() call:

try:
    checking_response = trading_api.check_order(order=order)
except TimeoutError:
    logging.warning("TradingAPI session did timeout, reconnecting for new session ID...")
    trading_api.connect()
    checking_response = trading_api.check_order(order=order)

This shows the following in the log, while the order is checked successfully after a successful reconnect:

WARNING - TradingAPI session did timeout, reconnecting for new session ID...
INFO - get_session_id:response_dict: {'isPassCodeEnabled': False, 'locale': 'nl_NL', 'redirectUrl': 'https://trader.degiro.nl/trader/', 'sessionId': '2BADBBEF3****', 'status': 0, 'statusText': 'success'}
INFO - confirmation_id: "053df7cf-****"
response_datetime {
  seconds: 1643801134
  nanos: 715765000
}

REFRESH TIMEOUT

As mentioned before, the timeout will be reset after each call to the TradingAPI. This provides the opportunity to make a periodic function call, for example every 10 minutes to get_update().

However, this might interfere with your other logic and might not be robust over time when DeGiro decides to decrease the timeout on their server.

Therefor it's strongly recommended to always incorporate the exception handling for TimeoutError as indicated in the example above.

CHANGE PERIOD

It's possible to change the timeout period while creating the TradingAPI. Just add a ModelConnection object for parameter connection_storage:

# SETUP TRADING API
trading_api = TradingAPI(
    credentials=credentials,
    connection_storage=ModelConnection(
        timeout=600,
    )
)

# Connect:
trading_api.connect()

The connection will now expire after 10 minutes (10 * 60 s).

4. Order

Creating and updating of orders is done with an Order object. Here are the parameters:

Parameter	Type	Description
id	str	Optional for `update_order()`. It's the `order_id` of the created `Order` as returned by `confirm_order()`.
action	`Order.Action`	Whether you want to : `BUY` or `SELL`.
order_type	`Order.OrderType`	Type of order : `LIMIT`, `STOP_LIMIT`, `MARKET` or `STOP_LOSS`.
price	float	Limit price of the order. Optional for the following `order_type` : `LIMIT` and `STOPLIMIT`.
product_id	int	Identifier of the product concerned by the order.
size	float	Size of the order.
stop_price	float	Stop price of the order. Optional for the following `order_type` : `STOPLIMIT` and `STOPLOSS`
time_type	`Order.TimeType`	Duration of the order : `GOOD_TILL_DAY` or `GOOD_TILL_CANCELED`

The full description of an Order is available here : trading.proto

4.1. How to create an Order ?

The order creation is done in two steps :

Checking : send the Order to the API to check if it is valid.
Confirmation : confirm the creation of the Order.

Keeping these two steps (instead of reducing to one single "create" function) provides more options.

4.1.1 Check order

Use the check_order() function of the Trading API:

Request parameters

Parameter	Type	Description
order	`Order`	The order to create with all necessary parameters.

Response parameters

On a succesfull request, a dict with the following parameters is returned:

Parameter	Type	Description
confirmation_id	str	Id necessary to confirm the creation of the Order by the `confirm_order()` function.
response_datetime	`Timestamp`	A `Timestamp` represents a point in time independent of any time zone or calendar, represented as seconds (`seconds`) and fractions of seconds at nanosecond resolution (`nanos`) in UTC Epoch time. A `Timestamp` can be converted to a RFC 3339 date string by using `ToJsonString()`. Note: This `Timestamp` is created by Degiro Connector on reception of the API response, not by DeGiro server.
free_space_new	float	New free space (balance) if the Order is confirmed.
transaction_fees	float	Transaction fees that will be applied to the Order.
show_ex_ante_report_link	bool	?

When the request fails, None is returned.

4.1.2 Confirm order

Use the confirm_order() function of the Trading API:

Request parameters

Parameter	Type	Description
confirmation_id	str	The confirmtation id from the `check_order()` response.
order	`Order`	The same order from the `check_order()` request.

Response parameters

On a succesfull request, a dict with the following parameters is returned:

Parameter	Type	Description
order_id	str	A unique id of the accepted order. This id is required to update or delete the pending order.
response_datetime	`Timestamp`	A `Timestamp` represents a point in time independent of any time zone or calendar, represented as seconds (`seconds`) and fractions of seconds at nanosecond resolution (`nanos`) in UTC Epoch time. A `Timestamp` can be converted to a RFC 3339 date string by using `ToJsonString()`. Note: This `Timestamp` is created by Degiro Connector on reception of the API response, not by DeGiro server.

When the request fails, None is returned.

4.1.3 Example of combining these functions

# SETUP ORDER
order = Order(
    action=Order.Action.BUY,
    order_type=Order.OrderType.LIMIT,
    price=10,
    product_id=71981,
    size=1,
    time_type=Order.TimeType.GOOD_TILL_DAY,
)

# FETCH CHECKING_RESPONSE
checking_response = trading_api.check_order(order=order)

# EXTRACT CONFIRMATION_ID
confirmation_id = checking_response.confirmation_id

# SEND CONFIRMATION
confirmation_response = trading_api.confirm_order(
    confirmation_id=confirmation_id,
    order=order,
)

For a more comprehensive example : order.py

4.2. How to update an Order ?

To modify a specific Order, you need to set it up with the order_id from the confirm_order() response and use the update_order() function of the Trading API:

Request parameters

Parameter	Type	Description
order	`Order`	The order to update with all necessary parameters, including `id` as returned by `confirm_order()`.

Response parameters

On a succesfull request, a bool with the value True is returned.

When the request fails, None is returned. A valid reason is that the pending order has been already executed on the exchange, and this order_id no longer exists.

Here is an example:

# ORDER SETUP
order = Order(
    id=YOUR_ORDER_ID,   # `order_id` from `confirm_order()` response
    action=Order.Action.BUY,
    order_type=Order.OrderType.LIMIT,
    price=10.60,
    product_id=71981,
    size=1,
    time_type=Order.TimeType.GOOD_TILL_DAY,
)

# UPDATE ORDER
succcess = trading_api.update_order(order=order)

4.3. How to delete an Order ?

To delete a specific Order you just need the order_id from the confirm_order() response and use the delete_order() function of the Trading API:

Request parameters

Parameter	Type	Description
order_id	str	The unique id of the accepted order as returned by `confirm_order()`.

Response parameters

On a succesfull request, a bool with the value True is returned.

When the request fails, None is returned. A valid reason is that the pending order has been already executed on the exchange, and this order_id no longer exists.

Here is an example:

# DELETE ORDER
succcess = trading_api.delete_order(order_id=YOUR_ORDER_ID) # `order_id` from `confirm_order()` response

5. Portfolio

5.1. How to retrieve pending Orders ?

This is how to get the list of Orders currently created but not yet executed or deleted :

request_list = Update.RequestList()
request_list.values.extend([
    Update.Request(option=Update.Option.ORDERS, last_updated=0),
])

update = trading_api.get_update(request_list=request_list)
update_dict = pb_handler.message_to_dict(message=update)
orders_df = pd.DataFrame(update_dict['orders']['values'])

Example : Orders

   product_id      time_type  price  size                                    id  ...  action  order_type stop_price retained_order  sent_to_exchange
0           0  GOOD_TILL_DAY      2     3  202cb962-ac59-075b-964b-07152d234b70  ...     BUY       LIMIT         16             17                18

For a more comprehensive example : update.py

5.2. How to get the Portfolio ?

This is how to list the stocks/products currently in the portfolio :

request_list = Update.RequestList()
request_list.values.extend([
    Update.Request(option=Update.Option.PORTFOLIO, last_updated=0),
])

update = trading_api.get_update(request_list=request_list)
update_dict = pb_handler.message_to_dict(message=update)
portfolio_df = pd.DataFrame(update_dict['portfolio']['values'])

For a more comprehensive example : update.py

Note: In order to resolve product IDs to Human readable names, see 7.12. How to search products from ids ?

5.3. How to get the TotalPortfolio ?

This is how to get aggregated data about the portfolio :

request_list = Update.RequestList()
request_list.values.extend([
    Update.Request(option=Update.Option.TOTALPORTFOLIO, last_updated=0),
])

update = trading_api.get_update(request_list=request_list)
update_dict = pb_handler.message_to_dict(message=update)
total_portfolio_df = pd.DataFrame(update_dict['total_portfolio']['values'])

Example : DataFrame

   degiroCash  flatexCash  totalCash  totalDepositWithdrawal  todayDepositWithdrawal  ...  reportNetliq  reportOverallMargin  reportTotalLongVal  reportDeficit  marginCallStatus
0           0           1          2                       3                       4  ...            16                   17                  18             19    NO_MARGIN_CALL

For a more comprehensive example : update.py

5.4. How to retrieve the OrdersHistory ?

This method returns data about passed orders between two dates.

The result contains a list of "Orders" objects with the following attributes :

Parameter	Type	Description
created	str	RFC 3339 Datetime, example : "2020-10-06T20:07:18+02:00".
orderId	str	MD5 HASH, example : "098f6bcd-4621-d373-cade-4e832627b4f6"
productId	int	Id of the product example : 65156
size	float	Size of the order, example : 10.0000
price	float	Price of the order, example : 8.6800
buysell	str	"B" or "S"
orderTypeId	int	see 3.Order
orderTimeTypeId	int	see 3.Order
stopPrice	float	Price like : 0.0000
totalTradedSize	int	-
type	str	"CREATE", "DELETE" or "MODIFY"
status	str	"CONFIRMED"
last	str	RFC 3339 Datetime, example : "2020-10-06T20:07:18+02:00".
isActive	bool	-

Here is how to get this data :

# SETUP REQUEST
from_date = OrdersHistory.Request.Date(year=2020,month=11,day=15)
to_date = OrdersHistory.Request.Date(year=2020,month=10,day=15)
request = OrdersHistory.Request(from_date=from_date, to_date=to_date)

# FETCH DATA
orders_history = trading_api.get_orders_history(request=request)

For a more comprehensive example : orders_history.py

5.5. How to retrieve the TransactionsHistory ?

Here is how to get this data :

# SETUP REQUEST
from_date = TransactionsHistory.Request.Date(year=2020,month=11,day=15)
to_date = TransactionsHistory.Request.Date(year=2020,month=10,day=15)
request = TransactionsHistory.Request(from_date=from_date, to_date=to_date)

# FETCH DATA
transactions_history = trading_api.get_transactions_history(request=request)

For a more comprehensive example : transactions_history.py

6. Account

6.1. How to retrieve the table : Config ?

The config table contains the following informations :

Parameter	Type	Description
sessionId	str	Current session id.
clientId	int	Unique Degiro's Account identifier also called "userToken"
tradingUrl	str	-
paUrl	str	-
reportingUrl	str	-
paymentServiceUrl	str	-
productSearchUrl	str	-
dictionaryUrl	str	-
productTypesUrl	str	-
companiesServiceUrl	str	-
i18nUrl	str	-
vwdQuotecastServiceUrl	str	-
vwdNewsUrl	str	-
vwdGossipsUrl	str	-
taskManagerUrl	str	-
refinitivNewsUrl	str	-
refinitivAgendaUrl	str	-
refinitivCompanyProfileUrl	str	-
refinitivCompanyRatiosUrl	str	-
refinitivFinancialStatementsUrl	str	-
refinitivClipsUrl	str	-
landingPath	str	-
betaLandingPath	str	-
mobileLandingPath	str	-
loginUrl	str	-

Here is how to get this table :

# FETCH DATA
config_table = trading_api.get_config()

# EXTRACT SOME DATA
user_token = config_table['clientId']
session_id = config_table['sessionId']

For a more comprehensive example : config_table.py

6.2. How to retrieve the table : ClientDetails ?

The ClientDetails table contains information about the current Degiro Account.

Parameter	Type
id	int
intAccount	int
loggedInPersonId	int
clientRole	str
effectiveClientRole	str
contractType	str
username	str
displayName	str
email	str
firstContact.firstName	str
firstContact.lastName	str
firstContact.displayName	str
firstContact.nationality	str
firstContact.gender	str
firstContact.dateOfBirth	str
firstContact.placeOfBirth	str
firstContact.countryOfBirth	str
address.streetAddress	str
address.streetAddressNumber	str
address.zip	str
address.city	str
address.country	str
cellphoneNumber	str
locale	str
language	str
culture	str
bankAccount.bankAccountId	int
bankAccount.bic	str
bankAccount.iban	str
bankAccount.status	str
flatexBankAccount.bic	str
flatexBankAccount.iban	str
memberCode	str
isWithdrawalAvailable	bool
isAllocationAvailable	bool
isIskClient	bool
isCollectivePortfolio	bool
isAmClientActive	bool
canUpgrade	bool

Here is how to get this table :

# FETCH DATA
client_details_table = trading_api.get_client_details()

# EXTRACT SOME DATA
int_account = client_details_table['data']['intAccount']
user_token = client_details_table['data']['id']

For a more comprehensive example : client_details_table.py

6.3. How to retrieve the table : AccountInfo ?

The AccountInfo table contains the following information about currencies.

Parameter	Type
clientId	int
baseCurrency	str
currencyPairs	dict
marginType	str
cashFunds	dict
compensationCapping	float

Here is how to get this table :

account_info_table = trading_api.get_account_info()

For a more comprehensive example : account_info_table.py

6.4. How to get the table : AccountOverview ?

It will provide a list of cash movements.

Here is how to get this data :

# SETUP REQUEST
from_date = AccountOverview.Request.Date(year=2020,month=11,day=15)
to_date = AccountOverview.Request.Date(year=2020,month=10,day=15)
request = AccountOverview.Request(from_date=from_date, to_date=to_date)

# FETCH DATA
account_overview = trading_api.get_account_overview(request=request)

For a more comprehensive example : account_overview.py

Each cash movement contains this kind of parameters :

Parameter	Type
date	str
valueDate	str
id	int
orderId	str
description	str
productId	int
currency	str
change	float
balance	dict
unsettledCash	float
total	float

6.5. How to export the table : CashAccountReport ?

It will export a list of cash movements in a specific format.

Available formats :

CSV
HTML
PDF
XLS

Here is how to get this content in CSV format :

# SETUP REQUEST
from_date = CashAccountReport.Request.Date(year=2020,month=11,day=15)
to_date = CashAccountReport.Request.Date(year=2020,month=10,day=15)
request = CashAccountReport.Request(
    format=CashAccountReport.Format.CSV,
    country='FR',
    lang='fr',
    from_date=from_date,
    to_date=to_date,
)

# FETCH DATA
cash_account_report = trading_api.get_cash_account_report(
    request=request,
    raw=False,
)

Here are the available parameters for CashAccountReport.Request :

Parameter	Type	Description
format	CashAccountReport.Format	Wanted format : `CSV` `HTML` `PDF` `XLS`
country	str	Country name, like : `FR`
lang	int	Language, like : `fr`
from_date	CashAccountReport.Request.Date	Events starting after this date.
to_date	CashAccountReport.Request.Date	Events before this date.

Exact definitions of CashAccountReport and CashAccountReport.Request are in this file : trading.proto

For a more comprehensive example : cash_account_report.py

6.6. How to export the table : PositionReport ?

It will export a list of cash movements in a specific format.

Available formats :

CSV
HTML
PDF
XLS

Here is how to get this content in CSV format :

# SETUP REQUEST
to_date = CashAccountReport.Request.Date(year=2020,month=10,day=15)
request = CashAccountReport.Request(
    format=CashAccountReport.Format.CSV,
    country='FR',
    lang='fr',
    to_date=to_date,
)

# FETCH DATA
position_report = trading_api.get_position_report(
    request=request,
    raw=False,
)

Here are the available parameters for PositionReport.Request :

Parameter	Type	Description
format	PositionReport.Format	Wanted format : `CSV` `HTML` `PDF` `XLS`
country	str	Country name, like : `FR`
lang	int	Language, like : `fr`
to_date	PositionReport.Request.Date	Events before this date.

Exact definitions of PositionReport and PositionReport.Request are in this file : trading.proto

For a more comprehensive example : position_report.py

7. Products

7.1. How to get the table : ProductsConfig ?

This table contains useful parameters to filter products.

Here are the parameters which are inside this table :

Parameter	Type
stockCountries	list
bondExchanges	list
bondIssuerTypes	list
eurexCountries	list
futureExchanges	list
optionExchanges	list
combinationExchanges	list
cfdExchanges	list
exchanges	list
indices	list
regions	list
countries	list
productTypes	list
etfFeeTypes	list
investmentFundFeeTypes	list
optionAggregateTypes	list
leveragedAggregateTypes	list
etfAggregateTypes	list
investmentFundAggregateTypes	list
lookupSortColumns	list
stockSortColumns	list
bondSortColumns	list
cfdSortColumns	list
etfSortColumns	list
futureSortColumns	list

Here is how to get this data :

# FETCH DATA
products_config = trading_api.get_products_config()

For a more comprehensive example : products_config.py

7.2. How to get my favourite products ?

Here is how to get this data :

# FETCH DATA
favourites_list = trading_api.get_favourites_list()

For a more comprehensive example : favourites_list.py

7.3. How to lookup products (search by name) ?

Text research on a financial product.

Here is how to get this data :

# SETUP REQUEST
request = ProductSearch.RequestLookup(
    search_text='APPLE',
    limit=10,
    offset=0,
    product_type_id=1,
)

# FETCH DATA
products_lookup = trading_api.product_search(request=request)

For a more comprehensive example : product_lookup.py

7.4. How to search bonds ?

Here is how to get this data :

# SETUP REQUEST
request = ProductSearch.RequestBonds(
    bond_issuer_type_id=0,
    bond_exchange_id=710,

    search_text='',
    offset=0,
    limit=100,
    require_total=True,
    sort_columns='name',
    sort_types='asc',
)

# FETCH DATA
bond_list = trading_api.product_search(request=request)

For a more comprehensive example : product_search.py

7.5. How to search etfs ?

Here is how to get this data :

# SETUP REQUEST
request = ProductSearch.RequestETFs(
    popular_only=False,
    input_aggregate_types='',
    input_aggregate_values='',

    search_text='',
    offset=0,
    limit=100,
    require_total=True,
    sort_columns='name',
    sort_types='asc',
)

# FETCH DATA
etf_list = trading_api.product_search(request=request)

For a more comprehensive example : product_search.py

7.6. How to search funds ?

Here is how to get this data :

# SETUP REQUEST
request = ProductSearch.RequestFunds(
    search_text='',
    offset=0,
    limit=100,
    require_total=True,
    sort_columns='name',
    sort_types='asc',
)

# FETCH DATA
fund_list = trading_api.product_search(request=request)

For a more comprehensive example : product_search.py

7.7. How to search futures ?

Here is how to get this data :

# SETUP REQUEST
request = ProductSearch.RequestFutures(
    future_exchange_id=1,
    underlying_isin='FR0003500008',

    search_text='',
    offset=0,
    limit=100,
    require_total=True,
    sort_columns='name',
    sort_types='asc',
)

# FETCH DATA
fund_list = trading_api.product_search(request=request)

For a more comprehensive example : product_search.py

7.8. How to search leverageds ?

Here is how to get this data :

# SETUP REQUEST
request = ProductSearch.RequestLeverageds(
    popular_only=False,
    input_aggregate_types='',
    input_aggregate_values='',

    search_text='',
    offset=0,
    limit=100,
    require_total=True,
    sort_columns='name',
    sort_types='asc',
)

# FETCH DATA
etf_list = trading_api.product_search(request=request)

For a more comprehensive example : product_search.py

7.9. How to search options ?

Here is how to get this data :

# SETUP REQUEST
request = ProductSearch.RequestOptions(
    input_aggregate_types='',
    input_aggregate_values='',
    option_exchange_id=3,
    underlying_isin='FR0003500008',

    search_text='',
    offset=0,
    limit=100,
    require_total=True,
    sort_columns='expirationDate,strike',
    sort_types='asc,asc',
)

# FETCH DATA
option_list = trading_api.product_search(request=request)

For a more comprehensive example : product_search.py

7.10. How to search stocks ?

It contains information about available stocks.

Here is how to get this data :

# SETUP REQUEST
request = ProductSearch.RequestStocks(
    index_id=122001,    # NASDAQ 100
    exchange_id=663,    # NASDAQ
                        # You can either use `index_id` or `exchange id`
                        # See which one to use in the `ProductsConfig` table
    is_in_us_green_list=True,
    stock_country_id=846, # US

    search_text='',
    offset=0,
    limit=100,
    require_total=True,
    sort_columns='name',
    sort_types='asc',
)

# FETCH DATA
stock_list = trading_api.product_search(request=request)

For a more comprehensive example : product_search.py

7.11. How to search warrants ?

Here is how to get this data :

# SETUP REQUEST
request = ProductSearch.RequestWarrants(
    search_text='',
    offset=0,
    limit=100,
    require_total=True,
    sort_columns='name',
    sort_types='asc',
)

# FETCH DATA
warrant_list = trading_api.product_search(request=request)

For a more comprehensive example : product_search.py

7.12. How to search products from ids ?

Here is how to get this data :

# SETUP REQUEST
request = ProductsInfo.Request()
request.products.extend([96008, 1153605, 5462588])

# FETCH DATA
products_info = trading_api.get_products_info(
    request=request,
    raw=True,
)

For a more comprehensive example : products_info.py

7.13. How to create a favourite list ?

Example :

favorite_list_id = trading_api.create_favourite_list(name="SOME_NAME")

For a more comprehensive example : favourite_list_create.py

7.14. How to delete a favourite list ?

Example :

success = trading_api.delete_favourite_list(id=1234567)

For a more comprehensive example : favourite_list_delete.py

7.15. How to put favourite list products ?

Example :

success = trading_api.put_favourite_list_product(
    id=1234567,
    product_id=1234567,
)

For a more comprehensive example : favourite_list_put_product.py

7.16. How to delete favourite list products ?

Example :

success = trading_api.delete_favourite_list_product(
    id=1234567,
    product_id=1234567,
)

For a more comprehensive example : [favourite_list_delete product.py](examples/trading/favourite_list_delete product.py)

8. Companies

8.1. How to get : CompanyProfile ?

Here is how to get this data :

# FETCH DATA
company_profile = trading_api.get_company_profile(
    product_isin='FR0000131906',
)

For a more comprehensive example : company_profile.py

8.2. How to get : CompanyRatios ?

This table contains information about the company.

Here are the parameters which are inside this table :

Parameter	Type
totalFloat	str
sharesOut	str
consRecommendationTrend	dict
forecastData	dict
currentRatios	dict

Here is how to get this data :

# FETCH DATA
company_ratios = trading_api.get_company_ratios(
    product_isin='FR0000131906',
)

For a more comprehensive example : company_ratios.py

8.3. How to get : FinancialStatements ?

Here is how to get this data :

# FETCH DATA
financials_statements = trading_api.get_financials_statements(
    product_isin='FR0000131906',
)

For a more comprehensive example : financial_statements.py

8.4. How to get : LatestNews ?

Here is how to get this data :

# SETUP REQUEST
request = LatestNews.Request(
    offset=0,
    languages='en,fr',
    limit=20,
)

# FETCH DATA
latest_news = trading_api.get_latest_news(
    request=request,
    raw=True,
)

For a more comprehensive example : latest_news.py

8.5. How to get : TopNewsPreview ?

Here is how to get this data :

# FETCH DATA
top_news_preview = trading_api.get_top_news_preview(raw=True)

For a more comprehensive example : top_news_preview.py

8.6. How to get : NewsByCompany ?

Here is how to get this data :

# SETUP REQUEST
request = NewsByCompany.Request(
    isin='NL0000235190',
    limit=10,
    offset=0,
    languages='en,fr',
)

# FETCH DATA
news_by_company = trading_api.get_news_by_company(
    request=request,
    raw=True,
)

For a more comprehensive example : news_by_company.py

8.7. How to get : Agenda ?

Here is how to get this data :

# SETUP REQUEST
request = Agenda.Request()
request.start_date.FromJsonString('2021-06-21T22:00:00Z')
request.end_date.FromJsonString('2021-11-28T23:00:00Z')
request.calendar_type = Agenda.CalendarType.DIVIDEND_CALENDAR
request.offset = 0
request.limit = 25

# FETCH DATA
agenda = trading_api.get_agenda(
    request=request,
    raw=False,
)

Here are the available parameters for Agenda.Request :

Parameter	Type	Description
calendar_type	Agenda.CalendarType	Type of agenda : `DIVIDEND_CALENDAR` `ECONOMIC_CALENDAR` `EARNINGS_CALENDAR` `HOLIDAY_CALENDAR` `IPO_CALENDAR` `SPLIT_CALENDAR`
offset	int	-
limit	int	-
order_by_desc	bool	-
start_date	Timestamp	Events starting after this date.
end_date	Timestamp	Events before this date.
company_name	str	Filter used on the events description.
countries	str	Comma separated list of countries like : `FR,US`
classifications	str	Comma separated list of sectors like : `GovernmentSector,ExternalSector`
units	str	Comma separated list of units like : `Acre,Barrel`

Exact definitions of Agenda and Agenda.Request are in this file : trading.proto

For a more comprehensive example : agenda.py

8.8. How to get : EstimatesSummaries ?

Here is how to get this data :

# SETUP REQUEST
request = Agenda.Request()
request.start_date.FromJsonString('2021-06-21T22:00:00Z')
request.end_date.FromJsonString('2021-11-28T23:00:00Z')
request.calendar_type = Agenda.CalendarType.DIVIDEND_CALENDAR
request.offset = 0
request.limit = 25

# FETCH DATA
product_isin = "FR0000131906"
estimates_summaries = trading_api.get_estimates_summaries(
    product_isin=product_isin,
    raw=False,
)

Here are the available parameters for Agenda.Request :

Parameter	Type	Description
annual	google.protobuf.Struct	Indicators by year.
currency	str	currency, example `EUR`.
interim	google.protobuf.Struct	Indicators by quarter.
lastRetrieved	str	Last Retrieved, example : `2021-12-31T20:07:30.939Z`.
lastUpdated	str	Last updated,, example : `2021-02-18T01:30:00Z`.
preferredMeasure	str	Preferred measure, example : `EPS`.
ric	str	Reuters Instrument Code, example : `BOUY.PA`.

Exact definition of EstimatesSummaries is in this file : trading.proto

For a more comprehensive example : estimates_summaries.py

9. Contributing

Pull requests are welcome.

Feel free to open an issue or send me a message if you have a question.

10. License

BSD-3-Clause License

Project details

These details have not been verified by PyPI

Project links

Release history Release notifications | RSS feed

3.0.23

Oct 27, 2024

3.0.22

Jul 13, 2024

3.0.21

Jun 14, 2024

3.0.20

Jun 13, 2024

3.0.19

May 23, 2024

3.0.18

Apr 26, 2024

3.0.17

Jan 24, 2024

3.0.16

Jan 15, 2024

3.0.15

Jan 10, 2024

3.0.14

Jan 7, 2024

3.0.13

Jan 3, 2024

3.0.12

Jan 2, 2024

3.0.11

Jan 1, 2024

3.0.9

Jan 1, 2024

3.0.8

Jan 1, 2024

3.0.6

Dec 30, 2023

3.0.5

Dec 30, 2023

3.0.4

Dec 30, 2023

This version

3.0.3

Dec 30, 2023

3.0.2

Dec 30, 2023

3.0.1

Dec 30, 2023

3.0.0

Dec 30, 2023

2.0.23

Jan 12, 2023

2.0.22

Oct 3, 2022

2.0.21

Jun 1, 2022

2.0.20

May 8, 2022

2.0.19

Apr 2, 2022

2.0.18

Mar 9, 2022

2.0.17

Feb 19, 2022

2.0.16

Feb 6, 2022

2.0.15

Jan 30, 2022

2.0.14

Jan 18, 2022

2.0.13

Dec 24, 2021

2.0.12

Dec 17, 2021

2.0.11

Dec 9, 2021

2.0.10

Dec 8, 2021

2.0.9

Nov 27, 2021

2.0.8

Nov 20, 2021

2.0.7

Nov 17, 2021

2.0.6

Nov 11, 2021

2.0.5

Nov 6, 2021

2.0.4

Nov 4, 2021

2.0.3

Oct 13, 2021

2.0.2

Oct 2, 2021

2.0.1

Sep 28, 2021

2.0.0

Sep 28, 2021

1.0.10

Sep 6, 2021

1.0.9

Jul 29, 2021

1.0.8

Jul 29, 2021

1.0.7

Jul 29, 2021

1.0.6

Jul 29, 2021

1.0.5

Jul 21, 2021

1.0.4

Jul 10, 2021

1.0.3

Jul 2, 2021

1.0.2

Jun 22, 2021

1.0.1

Jun 22, 2021

1.0.0

Jun 2, 2021

0.1.3

May 30, 2021

0.1.1

May 28, 2021

0.1.0

May 21, 2021

0.0.11

May 19, 2021

0.0.9

Apr 16, 2021

0.0.8

Mar 29, 2021

0.0.7

Feb 25, 2021

0.0.6

Feb 18, 2021

0.0.4

Feb 14, 2021

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

degiro_connector-3.0.3.tar.gz (80.6 kB view details)

Uploaded Dec 30, 2023 Source

Built Distribution

degiro_connector-3.0.3-py3-none-any.whl (115.9 kB view details)

Uploaded Dec 30, 2023 Python 3

File details

Details for the file degiro_connector-3.0.3.tar.gz.

File metadata

Download URL: degiro_connector-3.0.3.tar.gz
Upload date: Dec 30, 2023
Size: 80.6 kB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: poetry/1.7.1 CPython/3.12.1 Linux/6.2.0-1018-azure

File hashes

Hashes for degiro_connector-3.0.3.tar.gz
Algorithm	Hash digest
SHA256	`bdd27ebd22da6852371c3bba5aefa95d1b3f39d1ecae0de8e9ecd611b859d35a`
MD5	`7dc9d76b99a63d9277cd030a356e887a`
BLAKE2b-256	`eed3f3c17c4e2f76545417f78b5b76244c99815f51849279687190f3a335aef3`

See more details on using hashes here.

File details

Details for the file degiro_connector-3.0.3-py3-none-any.whl.

File metadata

Download URL: degiro_connector-3.0.3-py3-none-any.whl
Upload date: Dec 30, 2023
Size: 115.9 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: poetry/1.7.1 CPython/3.12.1 Linux/6.2.0-1018-azure

File hashes

Hashes for degiro_connector-3.0.3-py3-none-any.whl
Algorithm	Hash digest
SHA256	`0c01664f6eecd1bf9ebf5d8a17a731a6211525ca0df0108eb8a3faea9e5ec8f4`
MD5	`ae417952c7dfb58748640e176e840a8c`
BLAKE2b-256	`fa3e835d10ddb2414cba44c08b16522a970e1f81bf0586b127e490b29310051b`

See more details on using hashes here.

degiro-connector 3.0.3

Navigation

Verified details

Maintainers

Unverified details

Project links

Meta

Classifiers

Project description

1. Degiro Connector

1.1. Which features ?

1.2. How to install ?

1.3. How to upgrade ?

1.4. How to uninstall ?

1.5. Table of contents

2. Real-time data

2.1. What are the workflows ?

2.2. What are the credentials ?

2.3. How to find your : user_token ?

2.4. How to login ?

2.5. Is there a timeout ?

2.6. How to subscribe to a data-stream ?

2.7. How to unsubscribe to a data-stream ?

2.8. How to fetch the data ?

2.9. How to use this data ?

2.10. Which are the available data types ?

2.11. What is a Ticker ?

2.12. What is inside the list[Metric] ?

2.13. What is inside the DataFrame ?

2.14. How to get chart data ?

2.15. How to find a : vwd_id ?

3. Trading connection

3.1. How to login ?

3.2. How to logout ?

3.3. What are the credentials ?

3.4. How to find your : int_account ?

3.5. How to use 2FA ?

3.6. How to find your : totp_secret_key ?

3.7. How to find your : one_time_password ?

3.8. Is there a timeout ?

3.9. How to manage : TimeoutError ?

4. Order

4.1. How to create an Order ?

4.1.1 Check order

Request parameters

Response parameters

4.1.2 Confirm order

Request parameters

Response parameters

4.1.3 Example of combining these functions

4.2. How to update an Order ?

Request parameters

Response parameters

4.3. How to delete an Order ?

Request parameters

Response parameters

5. Portfolio

5.1. How to retrieve pending Orders ?

5.2. How to get the Portfolio ?

5.3. How to get the TotalPortfolio ?

5.4. How to retrieve the OrdersHistory ?

5.5. How to retrieve the TransactionsHistory ?

6. Account

6.1. How to retrieve the table : Config ?

6.2. How to retrieve the table : ClientDetails ?

6.3. How to retrieve the table : AccountInfo ?

6.4. How to get the table : AccountOverview ?

6.5. How to export the table : CashAccountReport ?

6.6. How to export the table : PositionReport ?

7. Products

7.1. How to get the table : ProductsConfig ?

7.2. How to get my favourite products ?

7.3. How to lookup products (search by name) ?

7.4. How to search bonds ?

7.5. How to search etfs ?

7.6. How to search funds ?

7.7. How to search futures ?

7.8. How to search leverageds ?

7.9. How to search options ?

7.10. How to search stocks ?

2.12. What is inside the `list[Metric]` ?