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

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1. Degiro Connector

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

1.1. Contribution

⭐️ Hopefully you have starred the project ⭐️

You can contribute to the project in two ways :

Code and documentation : pull requests are welcome !

Feedback : feel free to open an issue or send me a message if you have a feedback or question.

1.2. Installation

# INSTALL
pip install degiro-connector

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

# UNINSTALL
pip uninstall degiro-connector

1.3. 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.
Favorites Add/Delete/Prioritize/Retrieve favorites lists.
FavoritesProducts Add/Update products associated with a favorite list.
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.
Underlyings List Underlyings for Futures and Options.

1.4. Table of contents

2. Real-time data

It is possible to fetch the following data from Degiro's API:

  • Charts series data
  • Streams of data in real-time

2.1. What are the workflows ?

CHARTS

To consume charts series :

A. Fetch charts : directly with your "user_token".
B. Convert to a DataFrame.

REAL-TIME

To consume real-time data-stream :

A. Connect : with your "user_token".
B. Subscribe to metrics.
C. Fetch these metrics.
D. Convert to a DataFrame.

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

2.2. 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.

2.3. How to get a Chart ?

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

For that you need to prepare a ChartRequest.

Here is a table with the available attributes for ChartRequest.

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 :

chart_fetcher = ChartFetcher(user_token=user_token)
chart_request = ChartRequest(
    culture="fr-FR",
    # override={
    #     "resolution": "P1D",
    #     "period": "P1W",
    # },
    period=Interval.P1D,
    requestid="1",
    resolution=Interval.PT60M,
    series=[
        "issueid:360148977",
        "price:issueid:360148977",
        "ohlc:issueid:360148977",
        "volume:issueid:360148977",
        # "vwdkey:AAPL.BATS,E",
        # "price:vwdkey:AAPL.BATS,E",
        # "ohlc:vwdkey:AAPL.BATS,E",
        # "volume:vwdkey:AAPL.BATS,E",
    ],
    tz="Europe/Paris",
)
chart = chart_fetcher.get_chart(
    chart_request=chart_request,
    raw=False,
)

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 models are described in this module : chart.py

For a more comprehensive example :

2.4. How to format a chart.series ?

A Chart object contains a list of series.

There is a SeriesFormatter to help you convertir a series into a polars.DataFrame.

df = SeriesFormatter.format(series=chart.series)
print(df)

Here are the result for different series.id.

  • issueid:360148977
issueId companyId name identifier ... windowPreviousClosePrice windowEndPrice
i64 i64 str str ... f64 f64
360148977 9245 Crédit issueid:3 ... 12.858 -0.00047
  • price:issueid:360148977
timestamp price
datetime[μs] f64
2023-12-29 09:00:00 12.858
2023-12-29 10:58:58.800 12.85
... ...
2023-12-29 17:28:58.800 12.83
2023-12-29 17:34:58.799999 12.852
  • volume:issueid:360148977
timestamp volume
datetime[μs] f64
2023-12-29 09:58:58.800 61627.0
2023-12-29 10:58:58.800 83854.0
2023-12-29 11:58:58.800 78240.0
2023-12-29 12:58:01.200 69263.0
2023-12-29 13:58:58.800 59040.0
2023-12-29 14:58:58.800 62831.0
2023-12-29 15:58:01.200 66681.0
2023-12-29 16:58:58.800 529315.0
2023-12-29 17:34:58.799999 1.317919e6
  • ohlc:issueid:360148977
timestamp open high low close
datetime[μs] f64 f64 f64 f64
2023-12-29 09:00:00 12.858 12.898 12.858 12.872
2023-12-29 10:00:00 12.872 12.876 12.84 12.85
2023-12-29 11:00:00 12.85 12.864 12.842 12.848
2023-12-29 12:00:00 12.844 12.884 12.844 12.87
2023-12-29 13:00:00 12.868 12.886 12.86 12.87
2023-12-29 14:00:00 12.872 12.902 12.868 12.886
2023-12-29 15:00:00 12.884 12.894 12.874 12.876
2023-12-29 16:00:00 12.876 12.882 12.854 12.858
2023-12-29 17:00:00 12.856 12.858 12.83 12.852

All the models are described in this module :

For a more comprehensive example :

2.5. How to find a product 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.

2.6. 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.

2.7. How to connect ?

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)

2.8. 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.

2.9. How to subscribe to a data-stream ?

If one needs the following data from the "AAPL" stock :

  • LastDate
  • LastTime
  • LastPrice
  • LastVolume

You can use this library to retrieve updates like this :

product_id LastPrice LastVolume LastDatetimeUTC request_duration_s response_datetime_utc
str f64 i64 datetime[μs] f64 datetime[μs]
360015751 34.6 1 2023-12-29 16:35:23 1.021337 2024-01-01 17:31:22.482618
AAPL.BATS,E 192.55 null 2023-12-29 20:59:59 1.021337 2024-01-01 17:31:22.482618

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'
        ],
    },
)

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 :

TickerFetcher.subscribe(
    ticker_request=ticker_request,
    session_id=session_id,
)

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

2.10. 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

2.11. 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

2.12. 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.

2.13. 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
# YOU NEED PANDAS AND PYARROW INSTALLED
pandas_df = df.to_pandas()

2.14. 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 TICKER 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)

2.15. 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,
    ),
}

2.16. 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 LastPrice LastVolume LastDatetimeUTC request_duration_s response_datetime_utc
str f64 i64 datetime[μs] f64 datetime[μs]
360015751 34.6 1 2023-12-29 16:35:23 1.021337 2024-01-01 17:31:22.482618
AAPL.BATS,E 192.55 null 2023-12-29 20:59:59 1.021337 2024-01-01 17:31:22.482618

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 :

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 :

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 :

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 : order.py

4.1. How to create an Order ?

The order creation is done in two steps :

  1. Checking : send the Order to the API to check if it is valid.
  2. 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 datetime Datetime of the response.
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 datetime Datetime of the response.

When the request fails, None is returned.

4.1.3 Example of combining these functions

order = Order(
    buy_sell=Action.BUY,
    order_type=OrderType.LIMIT,
    price=12.1,
    product_id=72160,
    size=1,
    time_type=TimeType.GOOD_TILL_DAY,
)
checking_response = trading_api.check_order(order=order)
confirmation_response = trading_api.confirm_order(
    confirmation_id=checking_response.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 :

account_update = trading_api.get_update(
    request_list=[
        UpdateRequest(
            option=UpdateOption.ORDERS,
            last_updated=0,
        ),
    ],
    raw=True,
)

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 :

account_update = trading_api.get_update(
    request_list=[
        UpdateRequest(
            option=UpdateOption.PORTFOLIO,
            last_updated=0,
        ),
    ],
    raw=True,
)

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 :

account_update = trading_api.get_update(
    request_list=[
        UpdateRequest(
            option=UpdateOption.TOTALPORTFOLIO,
            last_updated=0,
        ),
    ],
    raw=True,
)

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
orders_history = trading_api.get_orders_history(
    history_request=HistoryRequest(
        from_date=date(year=date.today().year, month=1, day=1),
        to_date=date.today(),
    ),
    raw=True,
)

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

5.6 How to retrieve Upcoming Payments ?

Here is how to get this data :

# FETCH DATA
upcoming_payments = trading_api.get_upcoming_payments()

For a more comprehensive example : upcoming_payments.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 :

account_overview = trading_api.get_account_overview(
    overview_request=OverviewRequest(
        from_date=date(year=date.today().year-1, month=1, day=1),
        to_date=date.today(),
    ),
    raw=False,
)

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 : AccountReport ?

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 :

report = trading_api.get_account_report(
    report_request=ReportRequest(
        country="FR",
        lang="fr",
        format=Format.CSV,
        from_date=date(year=date.today().year-1, month=1, day=1),
        to_date=date.today(),
    ),
    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.

Models definitions are available are in this module : account.py

For a more comprehensive example : 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
report = trading_api.get_position_report(
    report_request=ReportRequest(
        country="FR",
        lang="fr",
        format=Format.XLS,
        from_date=date(year=date.today().year-1, month=1, day=1),
        to_date=date.today(),
    ),
    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.

Models definitions are available are in this module : account.py

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 lookup products (search by name) ?

Text research on a financial product.

Here is how to get this data :

product_request = LookupRequest(
    search_text='APPLE',
    limit=10,
    offset=0,
    product_type_id=1,
)
products_lookup = trading_api.product_search(product_request=product_request)

For a more comprehensive example : product_lookup.py

7.3. How to search bonds ?

Here is how to get this data :

# SETUP REQUEST
product_request = BondsRequest(
    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(product_request=product_request)

For a more comprehensive example : product_search.py

7.4. How to search etfs ?

Here is how to get this data :

# SETUP REQUEST
product_request = ETFsRequest(
    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(product_request=product_request)

For a more comprehensive example : product_search.py

7.5. How to search funds ?

Here is how to get this data :

# SETUP REQUEST
product_request = FundsRequest(
    search_text='',
    offset=0,
    limit=100,
    require_total=True,
    sort_columns='name',
    sort_types='asc',
)

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

For a more comprehensive example : product_search.py

7.6. How to search futures ?

Here is how to get this data :

# SETUP REQUEST
product_request = FuturesRequest(
    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(product_request=product_request)

For a more comprehensive example : product_search.py

7.7. How to search leverageds ?

Here is how to get this data :

# SETUP REQUEST
product_request = LeveragedsRequest(
    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(product_request=product_request)

For a more comprehensive example : product_search.py

7.8. How to search options ?

Here is how to get this data :

# SETUP REQUEST
product_request = OptionsRequest(
    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(product_request=product_request)

For a more comprehensive example : product_search.py

7.9. How to search stocks ?

It contains information about available stocks.

Here is how to get this data :

# SETUP REQUEST
product_request = StocksRequest(
    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(product_request=product_request)

For a more comprehensive example : product_search.py

7.10. How to search warrants ?

Here is how to get this data :

# SETUP REQUEST
product_request = WarrantsRequest(
    search_text='',
    offset=0,
    limit=100,
    require_total=True,
    sort_columns='name',
    sort_types='asc',
)

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

For a more comprehensive example : product_search.py

7.11. How to search products from ids ?

Here is how to get this data :

product_info = trading_api.get_products_info(
    product_list=[96008, 1153605, 5462588],
    raw=False,
)

For a more comprehensive example : products_info.py

7.12. How to get my favorite products ?

Here is how to get this data :

favorites_batch = trading_api.get_favorite()

For a more comprehensive example : favorite_get.py

7.13. How to create a favorite list ?

Example :

favorite_id = trading_api.create_favorite(name="SOME_NAME")

For a more comprehensive example : favorite_create.py

7.14. How to delete a favorite list ?

Example :

success = trading_api.delete_favorite(id=1234567)

For a more comprehensive example : favorite_delete.py

7.15. How to move a favorite list ?

Example :

success = trading_api.move_favorite(
    list_id=1234567,
    position=1,
)

For a more comprehensive example : favorite_move.py

7.16. How to put favorite list products ?

Example :

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

For a more comprehensive example : favorite_put_product.py

7.17. How to delete favorite list products ?

Example :

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

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

7.18. How to get futures/options underlyings ?

Example :

underlying_list = trading_api.get_underlyings(
    underlyings_request= UnderlyingsRequest(
        future_exchange_id=1,
        # option_exchange_id=3,
    ),
    raw=False,
)

For a more comprehensive example : favorite_get_underlyings.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 :

agenda = trading_api.get_agenda(
    agenda_request=AgendaRequest(
        calendar_type=CalendarType.EARNINGS_CALENDAR,
        end_date=datetime.now(),
        start_date=datetime.now() - timedelta(days=1),
        offset=0,
        limit=25,
    ),
    raw=True,
)

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 AgendaRequest are in this module : agenda.py

For a more comprehensive example : agenda.py

8.8. How to get : EstimatesSummaries ?

Here is how to get this data :

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

Here are the available parameters for Agenda.Request :

Parameter Type Description
annual dict Indicators by year.
currency str currency, example EUR.
interim dict 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 : product.py

For a more comprehensive example : estimates_summaries.py

9. License

BSD-3-Clause License

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

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