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dhan

Dhan historical candle download.

Dhan is the one broker here that splits its candles across two endpoints: daily bars come from /v2/charts/historical and everything intraday from /v2/charts/intraday, with differently formatted dates. Both are POSTs carrying a JSON body, and both answer columnar - one parallel array per field rather than a list of rows - so the response is transposed into bars here.

Three things were settled against the live API on 2026-09-12 rather than taken from the documentation.

Dhan serves exactly five intraday resolutions: 1, 5, 15, 25 and 60 minutes. Asking for any other number is not refused, it returns an empty array, so an unsupported interval looks exactly like a market holiday. INTERVALS therefore lists only what Dhan actually serves.

An empty window and a window before the instrument's history begins are reported differently. A valid instrument with nothing to show - a Saturday - answers 200 with empty arrays. A window that predates the instrument's first bar answers 400 with DH-907, which reads like an error and is not one; it is the same "there is nothing here" in different clothes, so it is parsed as an empty window. Retiring a series on DH-907 would kill RELIANCE the moment its backward walk stepped past 2002.

Daily bars are capped by nothing: a single request for 1990 to today returns the instrument's entire history. Intraday requests are refused beyond ninety days, with a message that says so.

Classes:

Name Description
DhanCandles

Downloads Dhan's historical candles.

DhanCandles 

DhanCandles(api=None)

Bases: BrokerCandles

Downloads Dhan's historical candles.

Dhan candle downloader.

  • api is an authenticated DhanAPI. One is constructed when not supplied, which establishes a session if the stored one has expired.

Example programs

examples/stock_brokers/instruments/historical/dhan/DhanCandles/example_1_intraday_and_daily_windows.py
"""Fetches one intraday window and one daily window of RELIANCE bars from Dhan, and turns Dhan's columnar answer into bars.

Dhan serves intraday and daily candles from two endpoints that take the same body with differently formatted dates: the intraday one wants a time of day on each date and an interval code, the daily one wants bare dates. It answers with one array per field rather than one object per bar, and `parse_response` transposes them.

A `DhanCandles` constructor opens a PostgreSQL connection and logs in to Dhan when the stored session has expired, so the program builds the object without running its constructor, as `test_runs/candle_parse.py` does, and gives it a stand-in for `DhanAPI`. The stand-in records every POST and answers with arrays shaped like Dhan's recorded responses, wrapped in the `status_code` and `data` envelope the real API class returns. Notice that intraday bars keep the UTC offset their epoch decodes to, so 03:45 UTC is the 09:15 India time bar, while the daily bar, stamped with an epoch that decodes to 18:30 UTC, lands on midnight India time on 2002-01-01, and that a cash instrument's answer has no `open_interest` array, so open interest is None.

Run it from the project root:

    python examples/stock_brokers/instruments/historical/dhan/DhanCandles/example_1_intraday_and_daily_windows.py
"""

import datetime

from stock_brokers.instruments.historical.dhan import (
    DhanCandles,
)


class RecordedDhanAPI:
    """A stand-in for `DhanAPI` that answers each endpoint with one recorded columnar response.

    Attributes:
        requests (list): The (url, body) pairs it was sent.
    """

    def __init__(self):
        """Builds the stand-in with no requests recorded.

        Returns:
            None: This method returns nothing.
        """
        self.requests = []

    def post(self, url, json=None):
        """Records a POST request and answers it with recorded arrays.

        Args:
            url (str): The endpoint the downloader called.
            json (dict): The request body it sent.

        Returns:
            dict: The response envelope, with the arrays under `data`.
        """
        self.requests.append((url, json))
        if url.endswith('/intraday'):
            data = {
                'timestamp': [
                    1788839100.0,
                    1788839400.0,
                ],
                'open': [
                    1306.8,
                    1303.1,
                ],
                'high': [
                    1306.8,
                    1304.0,
                ],
                'low': [
                    1300.0,
                    1301.0,
                ],
                'close': [
                    1303.1,
                    1302.0,
                ],
                'volume': [
                    174555.0,
                    98434.0,
                ],
            }
        else:
            data = {
                'timestamp': [
                    1009823400.0,
                ],
                'open': [
                    55.73,
                ],
                'high': [
                    56.46,
                ],
                'low': [
                    55.31,
                ],
                'close': [
                    55.75,
                ],
                'volume': [
                    688738.0,
                ],
            }
        return {
            'status_code': 200,
            'data': data,
        }


class IntradayAndDailyWindowsExample:
    """Fetches and parses one intraday and one daily window.

    Attributes:
        api (RecordedDhanAPI): The stand-in Dhan API.
        candles (DhanCandles): The downloader being shown.
    """

    def __init__(self):
        """Builds the downloader around the stand-in API without opening a database connection.

        Returns:
            None: This method returns nothing.
        """
        self.api = RecordedDhanAPI()
        self.candles = object.__new__(DhanCandles)
        self.candles._api = self.api

    def fetch_and_print(self, interval, start_date, end_date):
        """Fetches one window of RELIANCE bars and prints the request and the bars.

        Args:
            interval (str): The stored interval name.
            start_date (datetime.date): The first day of the window.
            end_date (datetime.date): The last day of the window.

        Returns:
            None: This method returns nothing.
        """
        payload = self.candles.fetch_candles('2885|NSE_EQ|EQUITY', interval, start_date, end_date)
        url, body = self.api.requests[-1]
        print(f'{interval}: {url}')
        print(f'  body: {body}')
        for bar in self.candles.parse_response(payload, interval):
            bar_time, open_price, high, low, close, volume, open_interest = bar
            print(f'  {bar_time.isoformat()} open {open_price} high {high} low {low} close {close} volume {volume} oi {open_interest}')

    def run(self):
        """Fetches the intraday window and then the daily one.

        Returns:
            None: This method returns nothing.
        """
        self.fetch_and_print('5minute', datetime.date(2026, 9, 8), datetime.date(2026, 9, 8))
        self.fetch_and_print('day', datetime.date(2002, 1, 1), datetime.date(2002, 1, 31))


if __name__ == '__main__':
    IntradayAndDailyWindowsExample().run()

It prints:

5minute: https://api.dhan.co/v2/charts/intraday
  body: {'securityId': '2885', 'exchangeSegment': 'NSE_EQ', 'instrument': 'EQUITY', 'oi': True, 'interval': '5', 'fromDate': '2026-09-08 00:00:00', 'toDate': '2026-09-08 23:59:59'}
  2026-09-08T03:45:00+00:00 open 1306.8 high 1306.8 low 1300.0 close 1303.1 volume 174555.0 oi None
  2026-09-08T03:50:00+00:00 open 1303.1 high 1304.0 low 1301.0 close 1302.0 volume 98434.0 oi None
day: https://api.dhan.co/v2/charts/historical
  body: {'securityId': '2885', 'exchangeSegment': 'NSE_EQ', 'instrument': 'EQUITY', 'oi': True, 'fromDate': '2002-01-01', 'toDate': '2002-01-31'}
  2002-01-01T00:00:00+05:30 open 55.73 high 56.46 low 55.31 close 55.75 volume 688738.0 oi None
examples/stock_brokers/instruments/historical/dhan/DhanCandles/example_2_identifiers_and_refusals.py
"""Lists Dhan's instruments as download identifiers, reads what an identifier says about its instrument, and classifies Dhan's refusals.

Dhan's security id is not unique on its own: 2885 is RELIANCE in the NSE cash segment and an old currency option in another. So `instruments` joins the security id, Dhan's exchange segment name and the instrument type into one identifier, leaving out any segment Dhan does not chart. The program gives the downloader a stand-in database connection whose cursor returns four rows as the instrument master query would, one of them in NSE's commodity segment, which Dhan lists but cannot chart and which is therefore dropped.

`identifier_parts` takes an identifier apart again and `series_context` reads its exchange and segments; the latter is a classmethod, so it needs no downloader at all.

Last, the program fetches through a stand-in for `DhanAPI` that raises the `DhanAPIException` each of three refusals produces. `Data_Error` is Dhan's answer for an empty window, so it comes back as no bars rather than an error; `Input_Exception` naming missing fields is what an expired contract gets, so the series is retired; and `Invalid_Authentication` means the session is dead. The downloader is built without its constructor, as in `test_runs/candle_parse.py`, so nothing connects to PostgreSQL or logs in.

Run it from the project root:

    python examples/stock_brokers/instruments/historical/dhan/DhanCandles/example_2_identifiers_and_refusals.py
"""

import datetime

from stock_brokers.api.dhan import (
    DhanAPIException,
)
from stock_brokers.instruments.historical.base import (
    CandleError,
)
from stock_brokers.instruments.historical.dhan import (
    DhanCandles,
)


class InstrumentMasterCursor:
    """A stand-in database cursor that answers the instrument master query with fixed rows.

    Attributes:
        rows (list): The rows `fetchall` returns.
    """

    def __init__(self, rows):
        """Holds the rows to answer with.

        Args:
            rows (list): Tuples of (exchange, segment, security id, instrument, expiry).

        Returns:
            None: This method returns nothing.
        """
        self.rows = rows

    def __enter__(self):
        """Enters the `with` block.

        Returns:
            InstrumentMasterCursor: This cursor.
        """
        return self

    def __exit__(self, exception_type, exception, traceback):
        """Leaves the `with` block without suppressing an exception.

        Args:
            exception_type (type | None): The exception's class, if one was raised.
            exception (BaseException | None): The exception, if one was raised.
            traceback (types.TracebackType | None): Its traceback, if one was raised.

        Returns:
            bool: Always False.
        """
        return False

    def execute(self, query):
        """Accepts the query without running it.

        Args:
            query (str): The SQL the downloader sent.

        Returns:
            None: This method returns nothing.
        """

    def fetchall(self):
        """Returns the fixed rows.

        Returns:
            list: The rows.
        """
        return self.rows


class InstrumentMasterConnection:
    """A stand-in PostgreSQL connection over a few rows of Dhan's instrument master.

    Attributes:
        rows (list): The rows every cursor answers with.
    """

    def __init__(self):
        """Builds the connection with four instrument master rows.

        Returns:
            None: This method returns nothing.
        """
        self.rows = [
            (
                'NSE',
                'E',
                '2885',
                'EQUITY',
                None,
            ),
            (
                'NSE',
                'I',
                '13',
                'INDEX',
                None,
            ),
            (
                'MCX',
                'M',
                '565899',
                'FUTCOM',
                '2026-10-19',
            ),
            (
                'NSE',
                'M',
                '801234',
                'OPTFUT',
                '2026-10-27',
            ),
        ]

    def cursor(self):
        """Opens a cursor.

        Returns:
            InstrumentMasterCursor: A cursor over the rows.
        """
        return InstrumentMasterCursor(self.rows)

    def commit(self):
        """Accepts a commit.

        Returns:
            None: This method returns nothing.
        """


class RefusingDhanAPI:
    """A stand-in for `DhanAPI` that refuses with a chosen Dhan error.

    Attributes:
        error_type (str): Dhan's error type to refuse with.
        error_message (str): Dhan's error message to refuse with.
    """

    def __init__(self, error_type, error_message):
        """Holds the refusal to raise.

        Args:
            error_type (str): Dhan's error type, such as `Data_Error`.
            error_message (str): Dhan's error message.

        Returns:
            None: This method returns nothing.
        """
        self.error_type = error_type
        self.error_message = error_message

    def post(self, url, json=None):
        """Refuses a POST request.

        Args:
            url (str): The endpoint the downloader called.
            json (dict): The request body it sent.

        Returns:
            dict: Never returns.

        Raises:
            DhanAPIException: Always, carrying the chosen type and message.
        """
        raise DhanAPIException(code=self.error_type, message=self.error_message)


class IdentifiersAndRefusalsExample:
    """Lists identifiers, reads them, and classifies three refusals.

    Attributes:
        candles (DhanCandles): The downloader being shown.
    """

    def __init__(self):
        """Builds the downloader around the stand-in connection without running its constructor.

        Returns:
            None: This method returns nothing.
        """
        self.candles = object.__new__(DhanCandles)
        self.candles._connection = InstrumentMasterConnection()

    def show_identifiers(self):
        """Lists the instruments and reads each identifier.

        Returns:
            None: This method returns nothing.
        """
        instruments = self.candles.instruments()
        print(f'Instruments to download: {len(instruments)} of 4 rows')
        for identifier, instrument_type, expiry in instruments:
            security_id, exchange_segment, _ = self.candles.identifier_parts(identifier)
            context = DhanCandles.series_context(identifier)
            print(f'{identifier}: security id {security_id} in {exchange_segment}, exchange {context.exchange}, {len(context.segments)} segments to search, expiry {expiry}')

    def show_refusals(self):
        """Fetches through three refusing stand-ins and prints what each became.

        Returns:
            None: This method returns nothing.
        """
        refusals = [
            (
                'Data_Error',
                'System is unable to fetch data due to incorrect parameters or no data present',
            ),
            (
                'Input_Exception',
                'Missing required fields, bad values for parameters etc.',
            ),
            (
                'Invalid_Authentication',
                'Client ID or user generated access token is invalid or expired.',
            ),
        ]
        for error_type, error_message in refusals:
            self.candles._api = RefusingDhanAPI(error_type, error_message)
            try:
                payload = self.candles.fetch_candles(
                    '52175|NSE_FNO|FUTSTK',
                    '5minute',
                    datetime.date(2024, 1, 1),
                    datetime.date(2024, 3, 30),
                )
                outcome = f'empty window, {len(self.candles.parse_response(payload, "5minute"))} bars'
            except CandleError as error:
                outcome = type(error).__name__
            print(f'{error_type}: {outcome}')

    def run(self):
        """Runs both parts in turn.

        Returns:
            None: This method returns nothing.
        """
        self.show_identifiers()
        self.show_refusals()


if __name__ == '__main__':
    IdentifiersAndRefusalsExample().run()

It prints:

Instruments to download: 3 of 4 rows
2885|NSE_EQ|EQUITY: security id 2885 in NSE_EQ, exchange nse, 6 segments to search, expiry None
13|IDX_I|INDEX: security id 13 in IDX_I, exchange None, 8 segments to search, expiry None
565899|MCX_COMM|FUTCOM: security id 565899 in MCX_COMM, exchange mcx, 16 segments to search, expiry 2026-10-19
Data_Error: empty window, 0 bars
Input_Exception: CandleInstrumentUnknown
Invalid_Authentication: CandleAuthenticationError

Methods:

Name Description
instruments

Every instrument Dhan has ever published, identified the way its charting API needs.
identifier_parts

Split a stored Dhan identifier into the fields a request needs.
series_context

Read the exchange and segment out of a Dhan identifier.
fetch_candles

Fetch one window of Dhan candles.
parse_response

Turn Dhan's columnar response into bars.
priority_for

Where a series sits in the queue. Lower is worked first.
seed

Make sure every instrument and interval this broker serves has a progress row.
claim

The next series due for a window, or None when there is nothing to do.
next_window

The date range to ask for next, as a (start, end, direction) triple.
relogin

Establish a fresh session and rebuild everything built on the old one.
after_relogin

Re-establish anything a downloader keeps on top of the broker session. Nothing by default.
visit

Fetch one window for one series and store it, advancing the watermark.
run

Work the queue until stopped, out of work, or out of time.
summary

How far the download has got, for a status display.
close

Close the database connection.
Source code in stock_brokers/instruments/historical/dhan.py 
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def __init__(self, api=None):
    """
    Dhan candle downloader.

    - `api` is an authenticated `DhanAPI`. One is constructed when not supplied, which
      establishes a session if the stored one has expired.
    """
    super().__init__()
    if api is None:
        from stock_brokers.api.utilities.session import ensure_session
        from stock_brokers.api.dhan import DhanAPI

        # Through ensure_session rather than straight to DhanAPI, so this shares the Redis
        # lock and the login rate limiter with any other process using ensure_session. This
        # process runs for weeks and will be alive across an overnight token expiry, when
        # another process may be logging in too.
        ensure_session(self.BROKER_NAME, logger=self._logger)
        api = DhanAPI()
    self._api = api

instruments 

instruments()

Every instrument Dhan has ever published, identified the way its charting API needs.

Dhan's security id is not unique on its own: id 2885 is Reliance in the NSE cash segment and a 2025 EURINR option in the currency one, and the instrument master repeats ids this way some ten thousand times. So the identifier stored in the progress table is the security id, the exchange segment and the instrument name joined by bars, which is exactly the three fields the request body carries, and fetch_candles splits it again.

Instruments in a segment Dhan does not chart are left out rather than registered and retired one request at a time.

Example usage

From example_2_identifiers_and_refusals.py
instruments = self.candles.instruments()
Source code in stock_brokers/instruments/historical/dhan.py 
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def instruments(self):
    """
    Every instrument Dhan has ever published, identified the way its charting API needs.

    Dhan's security id is not unique on its own: id 2885 is Reliance in the NSE cash segment
    and a 2025 EURINR option in the currency one, and the instrument master repeats ids this
    way some ten thousand times. So the identifier stored in the progress table is the
    security id, the exchange segment and the instrument name joined by bars, which is
    exactly the three fields the request body carries, and `fetch_candles` splits it again.

    Instruments in a segment Dhan does not chart are left out rather than registered and
    retired one request at a time.
    """
    query = f"""
        select distinct on (i.exch_id, i.segment, i."{self.TOKEN_COLUMN}")
               i.exch_id, i.segment, i."{self.TOKEN_COLUMN}",
               i."{self.TYPE_COLUMN}", i."{self.EXPIRY_COLUMN}"
        from {self.BROKER_NAME}.instruments i
        where i."{self.TOKEN_COLUMN}" is not null and i."{self.TOKEN_COLUMN}" <> ''
        order by i.exch_id, i.segment, i."{self.TOKEN_COLUMN}", i.download_date desc
    """
    with self._connection.cursor() as cursor:
        cursor.execute(query)
        rows = cursor.fetchall()
    self._connection.commit()

    instruments = []
    for exchange, segment, token, instrument_type, expiry in rows:
        exchange_segment = EXCHANGE_SEGMENTS.get((exchange, segment))
        if exchange_segment is None or not instrument_type:
            continue
        instruments.append((f"{token}|{exchange_segment}|{instrument_type}",
                            instrument_type, expiry))
    return instruments

identifier_parts staticmethod 

identifier_parts(identifier)

Split a stored Dhan identifier into the fields a request needs.

The identifier is built in instruments and taken apart here and nowhere else, so the two cannot drift apart.

Parameters:

Name Type Description Default
identifier str

For example "2885|NSE_EQ|EQUITY".

 required

Returns:

Type Description
tuple

The security id, the exchange segment and the instrument type.

Example usage

From example_2_identifiers_and_refusals.py
security_id, exchange_segment, _ = self.candles.identifier_parts(identifier)
Source code in stock_brokers/instruments/historical/dhan.py 
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@staticmethod
def identifier_parts(identifier):
    """
    Split a stored Dhan identifier into the fields a request needs.

    The identifier is built in `instruments` and taken apart here and nowhere else, so the two
    cannot drift apart.

    Args:
        identifier (str): For example "2885|NSE_EQ|EQUITY".

    Returns:
        tuple: The security id, the exchange segment and the instrument type.
    """
    security_id, exchange_segment, instrument_type = str(identifier).split("|")
    return security_id, exchange_segment, instrument_type

series_context classmethod 

series_context(identifier)

Read the exchange and segment out of a Dhan identifier.

Parameters:

Name Type Description Default
identifier str

For example "2885|NSE_EQ|EQUITY".

 required

Returns:

Type Description
SeriesContext

The security id, with the exchange and segments its segment names.

Example usage

From example_2_identifiers_and_refusals.py
context = DhanCandles.series_context(identifier)
Source code in stock_brokers/instruments/historical/dhan.py 
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@classmethod
def series_context(cls, identifier):
    """
    Read the exchange and segment out of a Dhan identifier.

    Args:
        identifier (str): For example "2885|NSE_EQ|EQUITY".

    Returns:
        SeriesContext: The security id, with the exchange and segments its segment names.
    """
    security_id, exchange_segment, _ = cls.identifier_parts(identifier)
    exchange, family = cls.EXCHANGE_SEGMENTS.get(exchange_segment, (None, None))
    if family is None:
        return SeriesContext(security_id)
    if exchange is None:
        return SeriesContext(security_id, None, segments_of(["nse", "bse"], family))
    return SeriesContext(security_id, exchange, segments_of([exchange], family),
                         exchange_token=security_id)

fetch_candles 

fetch_candles(token, interval, start_date, end_date)

Fetch one window of Dhan candles.

The two endpoints take the same body but differently formatted dates, the intraday one wanting a time of day as well, which is why they are built separately.

  • token is the securityId|exchangeSegment|instrument identifier from instruments.
  • interval is the stored interval name.
  • start_date and end_date bound the window, inclusive.

Example usage

From example_1_intraday_and_daily_windows.py
payload = self.candles.fetch_candles('2885|NSE_EQ|EQUITY', interval, start_date, end_date)
From example_2_identifiers_and_refusals.py
payload = self.candles.fetch_candles(
    '52175|NSE_FNO|FUTSTK',
    '5minute',
    datetime.date(2024, 1, 1),
    datetime.date(2024, 3, 30),
)
Source code in stock_brokers/instruments/historical/dhan.py 
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def fetch_candles(self, token, interval, start_date, end_date):
    """
    Fetch one window of Dhan candles.

    The two endpoints take the same body but differently formatted dates, the intraday one
    wanting a time of day as well, which is why they are built separately.

    - `token` is the `securityId|exchangeSegment|instrument` identifier from `instruments`.
    - `interval` is the stored interval name.
    - `start_date` and `end_date` bound the window, inclusive.
    """
    security_id, exchange_segment, instrument_type = self.identifier_parts(token)
    body = {"securityId": security_id, "exchangeSegment": exchange_segment,
            "instrument": instrument_type, "oi": True}

    if is_intraday(interval):
        url = INTRADAY_URL
        body["interval"] = self.INTERVALS[interval]
        body["fromDate"] = f"{start_date.isoformat()} 00:00:00"
        body["toDate"] = f"{end_date.isoformat()} 23:59:59"
    else:
        url = DAILY_URL
        body["fromDate"] = start_date.isoformat()
        body["toDate"] = end_date.isoformat()

    try:
        response = self._api.post(url=url, json=body)
    except Exception as exception:
        classified = self._classify(exception)
        if classified is None:
            # An empty window, dressed as an error. Answered with no bars rather than raised.
            return {}
        raise classified from exception

    return (response or {}).get("data") or {}

_classify staticmethod 

_classify(exception)

Decide what a Dhan refusal means for the series being downloaded.

Dhan names its conditions in the body, and the API class turns that into an exception whose code is the error type and whose message is the error message. Returning None says the refusal is not a failure at all but an empty window.

DH-905, Dhan's catch-all input error, is the one that retires a series: it is what an expired contract answers. It is also what a window wider than Dhan allows answers, and that would be a bug in this module rather than a dead instrument, so the message is read before the series is given up on.

  • exception is what the API call raised.
Source code in stock_brokers/instruments/historical/dhan.py 
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@staticmethod
def _classify(exception):
    """
    Decide what a Dhan refusal means for the series being downloaded.

    Dhan names its conditions in the body, and the API class turns that into an exception
    whose code is the error type and whose message is the error message. Returning None says
    the refusal is not a failure at all but an empty window.

    `DH-905`, Dhan's catch-all input error, is the one that retires a series: it is what an
    expired contract answers. It is also what a window wider than Dhan allows answers, and
    that would be a bug in this module rather than a dead instrument, so the message is read
    before the series is given up on.

    - `exception` is what the API call raised.
    """
    # The API class raises with Dhan's error type as the code and its message as the message,
    # and both end up in the exception's text. The numeric DH-9xx code does not, so the type
    # names are what is matched: Rate_Limit, Invalid_Authentication, Data_Error and the rest.
    text = str(exception)
    lowered = text.lower()

    if "rate_limit" in lowered or "too many requests" in lowered:
        return CandleThrottled(text[:200])
    if ("invalid_authentication" in lowered or "invalid_access" in lowered
            or "user_account" in lowered):
        return CandleAuthenticationError(text[:200])
    if "data_error" in lowered or "no data present" in lowered:
        return None
    if "input_exception" in lowered:
        if "at a time" in lowered or "days" in lowered:
            # The window this module asked for is wider than Dhan allows. Retrying the same
            # window will not help, but retiring the instrument would be wrong: the fault is
            # in MAXIMUM_WINDOW_DAYS, so it is left to fail loudly and back off.
            return exception
        return CandleInstrumentUnknown(text[:200])
    return exception

parse_response 

parse_response(payload, interval)

Turn Dhan's columnar response into bars.

Dhan answers with one array per field rather than one object per bar, so the arrays are transposed here. Open interest is absent for cash instruments rather than present and zero, so it is read defensively.

Dhan's timestamps are epoch seconds, and its daily bars already land on midnight India time; they are normalised anyway, so every broker's day bar in this project is produced the same way.

  • payload is the data object from the response.
  • interval is the stored interval name.

Example usage

From example_1_intraday_and_daily_windows.py
self.candles.parse_response(payload, interval)
From example_2_identifiers_and_refusals.py
outcome = f'empty window, {len(self.candles.parse_response(payload, "5minute"))} bars'
Source code in stock_brokers/instruments/historical/dhan.py 
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def parse_response(self, payload, interval):
    """
    Turn Dhan's columnar response into bars.

    Dhan answers with one array per field rather than one object per bar, so the arrays are
    transposed here. Open interest is absent for cash instruments rather than present and
    zero, so it is read defensively.

    Dhan's timestamps are epoch seconds, and its daily bars already land on midnight India
    time; they are normalised anyway, so every broker's day bar in this project is produced
    the same way.

    - `payload` is the `data` object from the response.
    - `interval` is the stored interval name.
    """
    payload = payload or {}
    times = payload.get("timestamp") or []
    opens = payload.get("open") or []
    highs = payload.get("high") or []
    lows = payload.get("low") or []
    closes = payload.get("close") or []
    volumes = payload.get("volume") or []
    open_interests = payload.get("open_interest") or []

    bars = []
    for index, stamp in enumerate(times):
        if index >= min(len(opens), len(highs), len(lows), len(closes), len(volumes)):
            break
        bar_time = datetime.datetime.fromtimestamp(stamp, datetime.timezone.utc)
        if not is_intraday(interval):
            bar_time = daily_bar_time(bar_time)
        open_interest = open_interests[index] if index < len(open_interests) else None
        bars.append((bar_time, opens[index], highs[index], lows[index], closes[index],
                     volumes[index], open_interest))
    return bars

priority_for 

priority_for(instrument_type, expiry, interval)

Where a series sits in the queue. Lower is worked first.

The ordering is the difference between useful data in days and useful data in months. Cash instruments come first because they have the deepest history and are wanted most; expired options come last because there are tens of thousands of them, each holding a few weeks of data, and most will be refused outright. Coarse intervals beat fine ones within each tier, since a day bar covers a year in one request and a minute bar covers two months.

  • instrument_type is the broker's own type string.
  • expiry is the contract expiry, or None for cash.
  • interval is the stored interval name.

Example usage

From example_1_one_window_of_daily_bars.py
priority = self.downloader.priority_for(instrument_type, expiry, interval)
Source code in stock_brokers/instruments/historical/base.py 
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def priority_for(self, instrument_type, expiry, interval):
    """
    Where a series sits in the queue. Lower is worked first.

    The ordering is the difference between useful data in days and useful data in months.
    Cash instruments come first because they have the deepest history and are wanted most;
    expired options come last because there are tens of thousands of them, each holding a few
    weeks of data, and most will be refused outright. Coarse intervals beat fine ones within
    each tier, since a day bar covers a year in one request and a minute bar covers two
    months.

    - `instrument_type` is the broker's own type string.
    - `expiry` is the contract expiry, or None for cash.
    - `interval` is the stored interval name.
    """
    kind = (instrument_type or "").upper()
    expired = False
    if expiry:
        try:
            expired = datetime.date.fromisoformat(str(expiry)[:10]) < datetime.date.today()
        except ValueError:
            expired = False

    if not expiry:
        base = 10                                   # cash: equities, indices
    elif kind in ("FUT", "FUTSTK", "FUTIDX", "FUTCOM", "FUTCUR"):
        base = 80 if expired else 30
    else:
        base = 90 if expired else 60                # options, live or dead

    if interval in ("day", "week", "month"):
        return base
    minutes = self._interval_minutes(interval)
    return base + (5 if minutes >= 15 else 15)

_interval_minutes staticmethod 

_interval_minutes(interval)

Minutes in an interval name like 15minute, or 0 when it is not a minute interval.

  • interval is the stored interval name.
Source code in stock_brokers/instruments/historical/base.py 
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@staticmethod
def _interval_minutes(interval):
    """
    Minutes in an interval name like `15minute`, or 0 when it is not a minute interval.

    - `interval` is the stored interval name.
    """
    if not interval.endswith("minute"):
        return 0
    try:
        return int(interval[:-len("minute")])
    except ValueError:
        return 0

seed 

seed()

Make sure every instrument and interval this broker serves has a progress row.

Safe to run again: existing rows are left exactly as they are, so re-seeding after a new instrument master picks up what is new without disturbing what is already downloaded. Returns how many rows were added.

Example usage

From example_1_one_window_of_daily_bars.py
print(f'Series added by the first seed: {self.downloader.seed()}')
From example_1_one_window_of_daily_bars.py
print(f'Series added by a second seed: {self.downloader.seed()}')
Source code in stock_brokers/instruments/historical/base.py 
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def seed(self):
    """
    Make sure every instrument and interval this broker serves has a progress row.

    Safe to run again: existing rows are left exactly as they are, so re-seeding after a new
    instrument master picks up what is new without disturbing what is already downloaded.
    Returns how many rows were added.
    """
    instruments = self.instruments()
    if not instruments:
        self._logger.warning(f"No instruments for {self.BROKER_NAME}; nothing to seed. "
                             f"Has the instrument master been downloaded?")
        return 0

    today = datetime.date.today()
    considered = 0
    batch = []

    with self._connection.cursor() as cursor:
        # Counted by measuring the table, not by summing cursor.rowcount: execute_values
        # splits a batch into pages of its own, and rowcount then reports only the last page,
        # which made a complete seed of 1,149,064 rows announce itself as 22,964.
        cursor.execute(f"select count(*) from {self._progress_table}")
        before = cursor.fetchone()[0]

        # Written a batch at a time as the instruments are walked rather than assembled in
        # full first. Wisdom Capital alone is 372,545 instruments across thirteen intervals,
        # and holding all 4.8 million rows in memory to write them costs a gigabyte for
        # nothing.
        for token, instrument_type, expiry in instruments:
            for interval in self.INTERVALS:
                batch.append((str(token), interval,
                              self.priority_for(instrument_type, expiry, interval), today))
            considered += len(self.INTERVALS)
            if len(batch) >= WRITE_BATCH_ROWS:
                self._insert_series(cursor, batch)
                batch = []
        if batch:
            self._insert_series(cursor, batch)

        cursor.execute(f"select count(*) from {self._progress_table}")
        added = cursor.fetchone()[0] - before
    self._connection.commit()
    self._logger.info(f"Seeded {added} new series ({considered} considered).")
    return added

_insert_series 

_insert_series(cursor, batch)

Register one batch of series, leaving any that already exist exactly as they are.

  • cursor is an open cursor on this broker's connection.
  • batch is a list of (token, interval, priority, seeded_date) tuples.
Source code in stock_brokers/instruments/historical/base.py 
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def _insert_series(self, cursor, batch):
    """
    Register one batch of series, leaving any that already exist exactly as they are.

    - `cursor` is an open cursor on this broker's connection.
    - `batch` is a list of `(token, interval, priority, seeded_date)` tuples.
    """
    execute_values(cursor, f"""
        insert into {self._progress_table}
            (instrument_token, "interval", priority, seeded_date)
        values %s
        on conflict (instrument_token, "interval") do nothing
    """, batch, page_size=WRITE_BATCH_ROWS)

claim 

claim()

The next series due for a window, or None when there is nothing to do.

Ordered by priority and then by how long a series has been waiting. Series that have reached the broker's limit, or that are backing off after failures, are skipped.

Example usage

From example_1_one_window_of_daily_bars.py
series = self.downloader.claim()
Source code in stock_brokers/instruments/historical/base.py 
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def claim(self):
    """
    The next series due for a window, or None when there is nothing to do.

    Ordered by priority and then by how long a series has been waiting. Series that have
    reached the broker's limit, or that are backing off after failures, are skipped.
    """
    with self._connection.cursor() as cursor:
        cursor.execute(f"""
            select instrument_token, "interval", earliest_bar_time, latest_bar_time,
                   oldest_requested_time, empty_window_streak, consecutive_failures
            from {self._progress_table}
            where reached_broker_limit = false
              and consecutive_failures < %s
              and (next_attempt_after is null or next_attempt_after <= now())
            order by priority, last_attempt_at nulls first
            limit 1
        """, (MAXIMUM_CONSECUTIVE_FAILURES,))
        row = cursor.fetchone()
    self._connection.commit()
    return row

next_window 

next_window(series)

The date range to ask for next, as a (start, end, direction) triple.

The backward walk comes first and runs to completion. A series starts at today and steps back a window at a time until it reaches the broker's earliest date or the broker stops returning anything, and only then does it start filling forward with whatever has happened since.

Doing it the other way round - preferring forward whenever the last stored bar is older than today - looks reasonable and does not work: outside market hours the last bar is always older than today, so the series asks for the same empty recent window forever and never walks back at all.

  • series is a row from claim.

Example usage

From example_1_one_window_of_daily_bars.py
start_date, end_date, direction = self.downloader.next_window(series)
Source code in stock_brokers/instruments/historical/base.py 
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def next_window(self, series):
    """
    The date range to ask for next, as a `(start, end, direction)` triple.

    The backward walk comes first and runs to completion. A series starts at today and steps
    back a window at a time until it reaches the broker's earliest date or the broker stops
    returning anything, and only then does it start filling forward with whatever has
    happened since.

    Doing it the other way round - preferring forward whenever the last stored bar is older
    than today - looks reasonable and does not work: outside market hours the last bar is
    always older than today, so the series asks for the same empty recent window forever and
    never walks back at all.

    - `series` is a row from `claim`.
    """
    _, interval, earliest, latest, oldest_requested, _, _ = series
    span = datetime.timedelta(days=self.MAXIMUM_WINDOW_DAYS.get(interval, 60) - 1)
    today = datetime.date.today()

    backward_done = (oldest_requested is not None
                     and oldest_requested.date() <= self.EARLIEST_AVAILABLE_DATE)

    if not backward_done:
        end = today if oldest_requested is None else (oldest_requested.date()
                                                      - datetime.timedelta(days=1))
        start = max(end - span, self.EARLIEST_AVAILABLE_DATE)
        if start > end:
            return None, None, "exhausted"
        return start, end, "backward"

    if latest is None:
        return None, None, "exhausted"
    start = latest.date()
    if start >= today:
        return None, None, "current"
    return start, min(start + span, today), "forward"

_fetch_bars 

_fetch_bars(token, interval, start_date, end_date)

Fetch and parse one window, logging in once first if the broker refuses the session.

A token expiring does not mean the download is over, and it does not mean this series is at fault. The rule is to log in first and then carry on: one login, then the same window again. Only if that login fails, or the fresh session is refused as well, does the error go back up - where visit and run stop the broker rather than carry on with a dead token.

Both alternatives are worse. Stopping outright leaves the download waiting for systemd to restart it into a login, and recording the refusal against whichever series it was on and moving to the next sends thousands of refused requests an hour - enough for Cloudflare to ban the machine from Fyers.

The login is re-armed by any window that comes back without a session error, so a backfill that runs for weeks gets its one login at each daily expiry.

  • token, interval, start_date and end_date are as for fetch_candles.
Source code in stock_brokers/instruments/historical/base.py 
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def _fetch_bars(self, token, interval, start_date, end_date):
    """
    Fetch and parse one window, logging in once first if the broker refuses the session.

    A token expiring does not mean the download is over, and it does not mean this series is at
    fault. The rule is to log in first and then carry on: one login, then the same window
    again. Only if that login fails, or the fresh session is refused as well, does the error go
    back up - where `visit` and `run` stop the broker rather than carry on with a dead token.

    Both alternatives are worse. Stopping outright leaves the download waiting for systemd to
    restart it into a login, and recording the refusal against whichever series it was on and
    moving to the next sends thousands of refused requests an hour - enough for Cloudflare to
    ban the machine from Fyers.

    The login is re-armed by any window that comes back without a session error, so a
    backfill that runs for weeks gets its one login at each daily expiry.

    - `token`, `interval`, `start_date` and `end_date` are as for `fetch_candles`.
    """
    try:
        bars = self.parse_response(self.fetch_candles(token, interval, start_date, end_date),
                                   interval)
    except CandleAuthenticationError as failure:
        if self._relogin_used:
            raise
        self._relogin_used = True
        self._logger.warning(f"{self.BROKER_NAME} refused the session ({failure}). Logging in "
                             f"once before continuing.")
        self.relogin()
        self._limiter.take()
        bars = self.parse_response(self.fetch_candles(token, interval, start_date, end_date),
                                   interval)
    self._relogin_used = False
    return bars

relogin 

relogin()

Establish a fresh session and rebuild everything built on the old one.

Through ensure_session, so it takes the same Redis lock and obeys the same floor on login attempts as any other process using it - which means it may wait, and may find another process has already logged in, in which case that session is simply used.

Raises:

Type Description
CandleAuthenticationError

If no working session could be established.

Example usage

From example_3_logging_in_again_after_a_refused_session.py
downloader.relogin()
Source code in stock_brokers/instruments/historical/base.py 
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def relogin(self):
    """
    Establish a fresh session and rebuild everything built on the old one.

    Through `ensure_session`, so it takes the same Redis lock and obeys the same floor on login
    attempts as any other process using it - which means it may wait, and may find another
    process has already logged in, in which case that session is simply used.

    Raises:
        CandleAuthenticationError: If no working session could be established.
    """
    from stock_brokers.api.utilities.session import ensure_session

    try:
        ensure_session(self.BROKER_NAME, logger=self._logger)
        self._api = self._build_api()
        self.after_relogin()
    except CandleAuthenticationError:
        raise
    except Exception as exception:
        raise CandleAuthenticationError(
            f"logging in again did not produce a working session: "
            f"{type(exception).__name__}: {str(exception)[:200]}") from exception
    self._logger.info(f"{self.BROKER_NAME} logged in again. Continuing.")

_build_api 

_build_api()

Construct this broker's authenticated API class, around whatever session is now stored.

The broker's own class from api_class_for, which is what every downloader builds. A downloader that builds its API differently overrides this.

Source code in stock_brokers/instruments/historical/base.py 
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def _build_api(self):
    """
    Construct this broker's authenticated API class, around whatever session is now stored.

    The broker's own class from `api_class_for`, which is what every downloader builds. A
    downloader that builds its API differently overrides this.
    """
    from stock_brokers.api.utilities.session import api_class_for

    return api_class_for(self.BROKER_NAME)()

after_relogin 

after_relogin()

Re-establish anything a downloader keeps on top of the broker session. Nothing by default.

For a broker whose historical endpoint takes a second credential minted from the session, such as a separate market data token, this is where that credential is renewed.

Example usage

From example_3_logging_in_again_after_a_refused_session.py
downloader.after_relogin()
Source code in stock_brokers/instruments/historical/base.py 
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def after_relogin(self):
    """
    Re-establish anything a downloader keeps on top of the broker session. Nothing by default.

    For a broker whose historical endpoint takes a second credential minted from the session,
    such as a separate market data token, this is where that credential is renewed.
    """

visit 

visit(series)

Fetch one window for one series and store it, advancing the watermark.

Returns the number of bars stored. The bars and the watermark are written in the same transaction, so an interruption costs at most the window in flight - and repeating that window is harmless, because bars upsert on their key.

  • series is a row from claim.

Example usage

From example_1_one_window_of_daily_bars.py
stored = self.downloader.visit(series)
From example_3_logging_in_again_after_a_refused_session.py
stored = downloader.visit(self.series('738561'))
Source code in stock_brokers/instruments/historical/base.py 
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def visit(self, series):
    """
    Fetch one window for one series and store it, advancing the watermark.

    Returns the number of bars stored. The bars and the watermark are written in the same
    transaction, so an interruption costs at most the window in flight - and repeating that
    window is harmless, because bars upsert on their key.

    - `series` is a row from `claim`.
    """
    token, interval, _, previous_latest, _, empty_streak, _ = series
    start_date, end_date, direction = self.next_window(series)

    if direction == "exhausted":
        if previous_latest is None:
            # Walked the whole range the broker serves and never saw a bar. A contract that
            # listed and never traded is the usual case, and there are tens of thousands of
            # them. Without this they would be claimed again the moment they were put down -
            # `next_window` answers "exhausted" every time, so nothing would move - and the
            # queue would spin through them at database speed making no requests at all.
            self._retire(token, interval,
                         "the broker served no bars anywhere in the range it offers")
            return 0
        # The backward walk is finished. Not retired: the series still gets new bars each
        # day, it simply has no more past to collect.
        self._finish_backfill(token, interval, "reached the broker's earliest date")
        return 0
    if direction == "current":
        self._defer_until_tomorrow(token, interval, "up to date")
        return 0

    self._limiter.take()
    try:
        bars = self._fetch_bars(token, interval, start_date, end_date)
    except CandleInstrumentUnknown as exception:
        self._retire(token, interval, str(exception))
        return 0
    except CandleThrottled as exception:
        # A throttle says something about the pace, not about this series, so it costs the
        # limiter five seconds and leaves the series claimable. Counting it as a failure
        # instead would push a perfectly good series an hour into the future - and a broker
        # that throttles does it to a run of series, not to one.
        self._limiter.back_off(5.0)
        self._record_throttle(token, interval, str(exception))
        return 0
    except (CandleAuthenticationError, CandleBlocked):
        # Neither says anything about this series, so neither is recorded against it. Before a
        # dead token was re-raised here, every refused request landed on whichever series was
        # claimed, and after five of those a good series stopped being claimed at all.
        raise
    except Exception as exception:
        self._record_failure(token, interval,
                             f"{type(exception).__name__}: {str(exception)[:200]}")
        return 0

    if not bars:
        streak = (empty_streak or 0) + 1
        if direction == "backward" and streak >= MAXIMUM_EMPTY_WINDOWS:
            # The broker has nothing older. Stop walking back, keep collecting forward.
            self._finish_backfill(token, interval,
                                  f"{streak} empty windows walking back from {end_date}")
        elif direction == "forward":
            self._defer_until_tomorrow(token, interval, "no new bars yet")
        else:
            self._record_empty(token, interval, streak, start_date, direction)
        return 0

    self._store(token, interval, bars, start_date, direction, previous_latest)
    return len(bars)

_store 

_store(token, interval, bars, requested_from, direction, previous_latest=None)

Write a window's bars and move the watermark, in one transaction.

  • token is the broker's instrument identifier.
  • interval is the stored interval name.
  • bars are the parsed (time, open, high, low, close, volume, oi) tuples.
  • requested_from is the start of the window that was asked for.
  • direction is whether the walk was forward or backward.
  • previous_latest is the newest bar already stored, used to tell a forward window that gained ground from one that returned only what was already known.
Source code in stock_brokers/instruments/historical/base.py 
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def _store(self, token, interval, bars, requested_from, direction, previous_latest=None):
    """
    Write a window's bars and move the watermark, in one transaction.

    - `token` is the broker's instrument identifier.
    - `interval` is the stored interval name.
    - `bars` are the parsed `(time, open, high, low, close, volume, oi)` tuples.
    - `requested_from` is the start of the window that was asked for.
    - `direction` is whether the walk was forward or backward.
    - `previous_latest` is the newest bar already stored, used to tell a forward window that
      gained ground from one that returned only what was already known.
    """
    # Collapsed on bar time before the write, last one winning. A broker can answer with the
    # same timestamp twice in one window - Dhan does - and Postgres refuses a batch that
    # touches the same row twice with "ON CONFLICT DO UPDATE command cannot affect row a
    # second time", which killed the whole process rather than the window.
    by_time = {}
    for bar in bars:
        volume = self._value_within_bigint(token, interval, bar[0], "volume", bar[5])
        open_interest = self._value_within_bigint(token, interval, bar[0], "oi", bar[6])
        by_time[bar[0]] = (bar[0], str(token), interval,
                           bar[1], bar[2], bar[3], bar[4], volume, open_interest)
    rows = list(by_time.values())
    times = list(by_time)

    try:
        with self._connection.cursor() as cursor:
            for start in range(0, len(rows), WRITE_BATCH_ROWS):
                execute_values(cursor, f"""
                    insert into {self._table}
                        ("time", instrument_token, "interval", open, high, low, close,
                         volume, oi)
                    values %s
                    on conflict (instrument_token, "interval", "time") do update set
                        open = excluded.open, high = excluded.high, low = excluded.low,
                        close = excluded.close, volume = excluded.volume, oi = excluded.oi,
                        downloaded_at = now()
                """, rows[start:start + WRITE_BATCH_ROWS])

            cursor.execute(f"""
                update {self._progress_table} set
                    earliest_bar_time = least(coalesce(earliest_bar_time, %s), %s),
                    latest_bar_time = greatest(coalesce(latest_bar_time, %s), %s),
                    oldest_requested_time = case when %s = 'backward'
                        then least(coalesce(oldest_requested_time, %s), %s)
                        else oldest_requested_time end,
                    bar_count = bar_count + %s,
                    empty_window_streak = 0,
                    consecutive_failures = 0,
                    next_attempt_after = null,
                    last_attempt_at = now(),
                    last_outcome = 'stored',
                    last_failure_reason = null
                where instrument_token = %s and "interval" = %s
            """, (min(times), min(times), max(times), max(times),
                  direction, requested_from, requested_from,
                  len(rows), str(token), interval))
        self._connection.commit()
    except Exception:
        self._connection.rollback()
        raise

    # A forward window that returned nothing newer than what was already stored means the
    # market has not produced more yet. Without this the series would be claimed again
    # immediately and spend the rate limit re-fetching the same bars all weekend.
    if direction == "forward" and previous_latest is not None and max(times) <= previous_latest:
        self._defer_until_tomorrow(token, interval, "forward window gained no new bars")

_value_within_bigint 

_value_within_bigint(token, interval, bar_time, column, value)

Returns a bar's count, or None when the count cannot fit in a BIGINT column.

Parameters:

Name Type Description Default
token str

The broker's instrument identifier.

 required
interval str

The stored interval name.

 required
bar_time datetime

The time of the bar the value belongs to.

 required
column str

The name of the column the value is written to, either "volume" or "oi".

 required
value int | float | None

The count exactly as the broker sent it.

 required

Returns:

Type Description
int | float | None

The value unchanged when it fits in a BIGINT or is not a number, and None when it does not fit.
Source code in stock_brokers/instruments/historical/base.py 
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def _value_within_bigint(self, token, interval, bar_time, column, value):
    """Returns a bar's count, or None when the count cannot fit in a BIGINT column.

    Args:
        token (str): The broker's instrument identifier.
        interval (str): The stored interval name.
        bar_time (datetime.datetime): The time of the bar the value belongs to.
        column (str): The name of the column the value is written to, either "volume" or "oi".
        value (int | float | None): The count exactly as the broker sent it.

    Returns:
        int | float | None: The value unchanged when it fits in a BIGINT or is not a number, and None when it does not fit.
    """
    if not isinstance(value, (int, float)):
        return value
    if BIGINT_MINIMUM <= value <= BIGINT_MAXIMUM:
        return value
    self._logger.warning(
        "%s %s %s: %s %s does not fit in BIGINT, storing NULL.",
        token,
        interval,
        bar_time.isoformat(),
        column,
        value,
    )
    return None

_record_empty 

_record_empty(token, interval, streak, requested_from, direction)

Note that a window came back with nothing, and step past it.

  • token, interval identify the series.
  • streak is how many empty windows in a row this makes.
  • requested_from is the start of the window that was asked for.
  • direction is whether the walk was forward or backward.
Source code in stock_brokers/instruments/historical/base.py 
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def _record_empty(self, token, interval, streak, requested_from, direction):
    """
    Note that a window came back with nothing, and step past it.

    - `token`, `interval` identify the series.
    - `streak` is how many empty windows in a row this makes.
    - `requested_from` is the start of the window that was asked for.
    - `direction` is whether the walk was forward or backward.
    """
    with self._connection.cursor() as cursor:
        cursor.execute(f"""
            update {self._progress_table} set
                empty_window_streak = %s,
                oldest_requested_time = case when %s = 'backward'
                    then least(coalesce(oldest_requested_time, %s), %s)
                    else oldest_requested_time end,
                last_attempt_at = now(),
                last_outcome = 'empty',
                consecutive_failures = 0,
                next_attempt_after = null
            where instrument_token = %s and "interval" = %s
        """, (streak, direction, requested_from, requested_from, str(token), interval))
    self._connection.commit()

_finish_backfill 

_finish_backfill(token, interval, reason)

Mark the backward walk complete, leaving the series collecting forward.

Distinct from _retire: the broker has no more past for this series, but it will keep producing new bars. Retiring it here would quietly stop a live instrument updating.

  • token, interval identify the series.
  • reason is why the walk stopped, kept for review.
Source code in stock_brokers/instruments/historical/base.py 
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def _finish_backfill(self, token, interval, reason):
    """
    Mark the backward walk complete, leaving the series collecting forward.

    Distinct from `_retire`: the broker has no more *past* for this series, but it will keep
    producing new bars. Retiring it here would quietly stop a live instrument updating.

    - `token`, `interval` identify the series.
    - `reason` is why the walk stopped, kept for review.
    """
    with self._connection.cursor() as cursor:
        cursor.execute(f"""
            update {self._progress_table} set
                oldest_requested_time = %s,
                empty_window_streak = 0,
                consecutive_failures = 0,
                next_attempt_after = null,
                last_attempt_at = now(),
                last_outcome = 'backfilled',
                limit_reason = %s
            where instrument_token = %s and "interval" = %s
        """, (self.EARLIEST_AVAILABLE_DATE, reason[:500], str(token), interval))
    self._connection.commit()
    self._logger.info(f"{token} {interval}: backfill complete ({reason}).")

_defer_until_tomorrow 

_defer_until_tomorrow(token, interval, reason)

Put a series aside until the next day.

Reached when a forward window brings nothing new, which outside market hours is every forward window. Without it the highest priority series would be re-fetched continuously and spend the whole rate limit re-reading bars already stored.

  • token, interval identify the series.
  • reason is recorded as the outcome.
Source code in stock_brokers/instruments/historical/base.py 
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def _defer_until_tomorrow(self, token, interval, reason):
    """
    Put a series aside until the next day.

    Reached when a forward window brings nothing new, which outside market hours is every
    forward window. Without it the highest priority series would be re-fetched continuously
    and spend the whole rate limit re-reading bars already stored.

    - `token`, `interval` identify the series.
    - `reason` is recorded as the outcome.
    """
    with self._connection.cursor() as cursor:
        cursor.execute(f"""
            update {self._progress_table} set
                next_attempt_after = date_trunc('day', now()) + interval '1 day',
                last_attempt_at = now(),
                last_outcome = %s,
                consecutive_failures = 0
            where instrument_token = %s and "interval" = %s
        """, (reason[:100], str(token), interval))
    self._connection.commit()

_record_throttle 

_record_throttle(token, interval, reason)

Note that the broker asked us to slow down, without holding it against the series.

The window is left exactly as it was, so the same series is claimed again once the limiter's back off has passed and the work is simply redone.

  • token, interval identify the series.
  • reason is what the broker said.
Source code in stock_brokers/instruments/historical/base.py 
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def _record_throttle(self, token, interval, reason):
    """
    Note that the broker asked us to slow down, without holding it against the series.

    The window is left exactly as it was, so the same series is claimed again once the
    limiter's back off has passed and the work is simply redone.

    - `token`, `interval` identify the series.
    - `reason` is what the broker said.
    """
    with self._connection.cursor() as cursor:
        cursor.execute(f"""
            update {self._progress_table} set
                last_attempt_at = now(),
                last_outcome = 'throttled',
                last_failure_reason = %s
            where instrument_token = %s and "interval" = %s
        """, (reason[:500], str(token), interval))
    self._connection.commit()
    self._logger.warning(f"{token} {interval}: throttled: {reason[:150]}")

_record_failure 

_record_failure(token, interval, reason)

Note a failure and push the next attempt out exponentially.

An hour, then two, four, eight and so on, so a series that is permanently broken stops crowding out the ones that are not.

  • token, interval identify the series.
  • reason is what went wrong, stored for a human to read.
Source code in stock_brokers/instruments/historical/base.py 
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def _record_failure(self, token, interval, reason):
    """
    Note a failure and push the next attempt out exponentially.

    An hour, then two, four, eight and so on, so a series that is permanently broken stops
    crowding out the ones that are not.

    - `token`, `interval` identify the series.
    - `reason` is what went wrong, stored for a human to read.
    """
    with self._connection.cursor() as cursor:
        cursor.execute(f"""
            update {self._progress_table} set
                consecutive_failures = consecutive_failures + 1,
                next_attempt_after = now() + interval '1 hour'
                                     * power(2, least(consecutive_failures, 6)),
                last_attempt_at = now(),
                last_outcome = 'failed',
                last_failure_reason = %s
            where instrument_token = %s and "interval" = %s
        """, (reason[:500], str(token), interval))
    self._connection.commit()
    self._logger.warning(f"{token} {interval}: {reason}")

_retire 

_retire(token, interval, reason)

Stop asking for a series the broker will not serve.

  • token, interval identify the series.
  • reason is why, stored so the decision can be reviewed.
Source code in stock_brokers/instruments/historical/base.py 
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def _retire(self, token, interval, reason):
    """
    Stop asking for a series the broker will not serve.

    - `token`, `interval` identify the series.
    - `reason` is why, stored so the decision can be reviewed.
    """
    with self._connection.cursor() as cursor:
        cursor.execute(f"""
            update {self._progress_table} set
                reached_broker_limit = true,
                limit_reason = %s,
                last_attempt_at = now(),
                last_outcome = 'retired'
            where instrument_token = %s and "interval" = %s
        """, (reason[:500], str(token), interval))
    self._connection.commit()

run 

run(stop_event=None, deadline_seconds=None)

Work the queue until stopped, out of work, or out of time.

Returns a (visits, bars) pair.

  • stop_event is a threading.Event that ends the loop when set.
  • deadline_seconds ends the loop after this long, for a job with a nightly window.

Example usage

From example_2_run_until_the_queue_is_drained.py
visits, bars = self.downloader.run()
From example_2_run_until_the_queue_is_drained.py
print(f'A run asked to stop before it starts: {self.downloader.run(stop_event=stop_event)}')
From example_3_logging_in_again_after_a_refused_session.py
visits, bars = downloader.run()
Source code in stock_brokers/instruments/historical/base.py 
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def run(self, stop_event=None, deadline_seconds=None):
    """
    Work the queue until stopped, out of work, or out of time.

    Returns a `(visits, bars)` pair.

    - `stop_event` is a `threading.Event` that ends the loop when set.
    - `deadline_seconds` ends the loop after this long, for a job with a nightly window.
    """
    started = time.monotonic()
    visits = 0
    bars = 0

    while True:
        if stop_event is not None and stop_event.is_set():
            self._logger.info("Stopping: asked to.")
            break
        if deadline_seconds is not None and time.monotonic() - started > deadline_seconds:
            self._logger.info(f"Stopping: reached the {deadline_seconds}s deadline.")
            break

        series = self.claim()
        if series is None:
            self._logger.info("Nothing due. The queue is drained or backing off.")
            break

        try:
            stored = self.visit(series)
        except (CandleAuthenticationError, CandleBlocked) as exception:
            self._logger.error(f"Stopping: {type(exception).__name__}: {exception}")
            break
        except CandleThrottled as exception:
            self._logger.warning(f"Stopping: {exception}")
            break
        except Exception as exception:
            # Anything `visit` did not expect - a response shaped in a way no probe produced,
            # a write the database refuses - costs this one series and not the run. Before
            # this, a single window that failed on the way into Postgres took the process
            # down, and since the same window would be claimed again on restart it took it
            # down every ten minutes thereafter.
            self._connection.rollback()
            self._record_failure(series[0], series[1],
                                 f"{type(exception).__name__}: {str(exception)[:200]}")
            self._logger.exception(f"{series[0]} {series[1]}: unexpected failure, "
                                   f"carrying on with the next series.")
            stored = 0

        visits += 1
        bars += stored
        if visits % 200 == 0:
            self._logger.info(f"{visits} windows, {bars} bars so far.")

    self._logger.info(f"Finished: {visits} windows, {bars} bars.")
    return visits, bars

summary 

summary()

How far the download has got, for a status display.

Example usage

From example_1_one_window_of_daily_bars.py
print(f'Summary: {self.downloader.summary()}')
From example_2_run_until_the_queue_is_drained.py
print(f'Summary: {self.downloader.summary()}')
Source code in stock_brokers/instruments/historical/base.py 
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def summary(self):
    """
    How far the download has got, for a status display.
    """
    with self._connection.cursor() as cursor:
        cursor.execute(f"""
            select count(*),
                   count(*) filter (where reached_broker_limit),
                   count(*) filter (where bar_count > 0),
                   coalesce(sum(bar_count), 0),
                   count(*) filter (where consecutive_failures >= %s)
            from {self._progress_table}
        """, (MAXIMUM_CONSECUTIVE_FAILURES,))
        total, retired, started, stored, stuck = cursor.fetchone()
    self._connection.commit()
    return {"series": total, "retired": retired, "with_bars": started,
            "bars": int(stored), "stuck": stuck}

close 

close()

Close the database connection.

Example usage

From example_1_one_window_of_daily_bars.py
self.downloader.close()
From example_2_run_until_the_queue_is_drained.py
self.downloader.close()
From example_3_logging_in_again_after_a_refused_session.py
downloader.close()
Source code in stock_brokers/instruments/historical/base.py 
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def close(self):
    """
    Close the database connection.
    """
    self._connection.close()