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groww

Groww mapping adapter.

Groww mixes stock and index instruments in one FNO segment with no dedicated discriminator column, contaminates its plain equity series with INF-prefixed exchange traded funds, and needs a crossref allowlist to tell BSE exchange traded funds from similarly-tagged rows. None of that is expressible as equality rules, so this module carries the classification overrides:

  • nse_fixed_income is matched by a negative-series check: an NSE cash row whose series is not one of the known equity, fund, and trust series and whose ISIN is not INF-prefixed is fixed income.
  • bse_exchange_traded_funds requires the row's trading symbol to be in the cross-broker allowlist from known_bse_etf_symbols.
  • nse_exchange_traded_funds is the redirect target for NSE equity rows with an INF-prefixed ISIN and series EQ.
  • NSE futures and options rows whose underlying is an equity index are redirected to the index segments; the same redirect handles the MCX index underlyings, because the canonical segment order lists the general commodity segments before the index ones.
  • bse_equities series B rows with an INF-prefixed ISIN, bse_fixed_income rows with a missing or INF-prefixed ISIN, and the blank-symbol BSE index row are dropped into the uncategorised buckets.
  • Index symbols are normalized and aliased, and the fund, trust, and mutual fund symbols carry broker-specific suffixes that are stripped in to_identity.

Classes:

Name Description
GrowwMappingAdapter

Mapping adapter for Groww, covering 23 real segments plus the uncategorised buckets.

Functions:

Name Description
normalize_index_name

Reduce an index name to uppercase alphanumerics so that differently-spelled broker names collide.
isin_invalid

Check whether a raw row's ISIN is missing or is a fund-family ISIN.

GrowwMappingAdapter 

GrowwMappingAdapter()

Bases: BrokerMappingAdapter

Mapping adapter for Groww, covering 23 real segments plus the uncategorised buckets.

Example programs

examples/stock_brokers/instruments/mapping/groww/GrowwMappingAdapter/example_1_redirects_and_suffixes.py
"""Classifies Groww rows that need redirects, exclusions and symbol clean-up on top of the rules.

Groww puts stock and index derivatives in one FNO segment with nothing to tell them apart, lists exchange traded funds under its plain equity series, and has no series or tag of its own for NSE bonds. `GrowwMappingAdapter.classify` therefore files an NSE cash row as fixed income when its series is none of the known equity, fund or trust series and it has a non-fund ISIN, moves NSE equity rows with a fund ISIN to exchange traded funds, moves derivatives on NIFTY or on the MCX commodity indices to the index segments, and drops BSE series B rows that carry a fund ISIN.

`to_identity` brings index names to one spelling (Groww's `NIFTYJR` is stored as NIFTYNXT50) and strips Groww's `-MF`, `-IV` and `-EQ` suffixes from fund and trust symbols. `to_broker_fields` falls back to the trading symbol when a BSE bond has no name. `run` normally gathers the BSE exchange traded fund list and the index name lookup from the database; this program sets both to empty by hand, so no database is needed. `uncategorised_exchange` shows where a dropped row is filed.

Run it from the project root:

    python examples/stock_brokers/instruments/mapping/groww/GrowwMappingAdapter/example_1_redirects_and_suffixes.py
"""

from stock_brokers.instruments.mapping.groww import (
    GrowwMappingAdapter,
)


class RedirectsAndSuffixesExample:
    """Classifies eight hand-written Groww rows and prints each one's segment, identity and broker fields.

    Attributes:
        adapter (GrowwMappingAdapter): The adapter being shown.
        raw_rows (list): The raw rows, as dictionaries of text columns.
    """

    def __init__(self):
        """Builds the adapter, sets the lists `run` would gather, and writes the rows.

        Returns:
            None: This method returns nothing.
        """
        self.adapter = GrowwMappingAdapter()
        self.adapter.bse_etf_symbols = set()
        self.adapter.nse_index_master_lookup = {}
        self.raw_rows = [
            self.raw_row('NSE', 'CASH', 'EQ', 'EQ', '10576', 'NIFTYBEES', 'Nippon India ETF Nifty 50 BeES', 'INF204KB14I2', None, None),
            self.raw_row('NSE', 'CASH', 'EQ', 'GS', '15542', '762GS2036', None, 'IN0020160019', None, None),
            self.raw_row('NSE', 'CASH', 'EQ', 'MF', '20105', 'HDFCNIFTY-MF', 'HDFC Nifty 50 Index Fund', 'INF179KB1HP9', None, None),
            self.raw_row('NSE', 'CASH', 'IDX', None, 'NIFTY JR', 'NIFTYJR', 'NIFTY NEXT 50', None, None, None),
            self.raw_row('NSE', 'FNO', 'FUT', None, '52168', 'NIFTY26OCTFUT', None, None, 'NIFTY', '2026-10-27'),
            self.raw_row('MCX', 'COMMODITY', 'FUT', None, '466583', 'MCXBULLDEX26OCTFUT', None, None, 'MCXBULLDEX', '2026-10-28'),
            self.raw_row('BSE', 'CASH', 'EQ', 'F', '973412', '800IIFL28', None, 'INE530B07393', None, None),
            self.raw_row('BSE', 'CASH', 'EQ', 'B', '590154', 'LICMFGOLD', 'LIC MF Gold ETF', 'INF767K01RW0', None, None),
        ]

    def raw_row(self, exchange, segment, instrument_type, series, token, trading_symbol, name, isin, underlying_symbol, expiry):
        """Builds one raw row in the shape of `groww.instruments`.

        Args:
            exchange (str): The exchange, as NSE, BSE or MCX.
            segment (str): Groww's segment, as CASH, FNO or COMMODITY.
            instrument_type (str): Groww's instrument type.
            series (str | None): The exchange series, for cash rows.
            token (str): The `exchange_token` column.
            trading_symbol (str): The `trading_symbol` column.
            name (str | None): The readable name, which Groww leaves blank on some rows.
            isin (str | None): The ISIN, if Groww published one.
            underlying_symbol (str | None): The underlying, for derivatives.
            expiry (str | None): The expiry date as text, for derivatives.

        Returns:
            dict: The raw row, every column as text as the raw table stores it.
        """
        return {
            'exchange': exchange,
            'exchange_token': token,
            'trading_symbol': trading_symbol,
            'name': name,
            'instrument_type': instrument_type,
            'segment': segment,
            'series': series,
            'isin': isin,
            'underlying_symbol': underlying_symbol,
            'expiry_date': expiry,
            'strike_price': None,
            'lot_size': '1',
            'tick_size': '0.01',
        }

    def run(self):
        """Prints each row's segment, identity and broker symbol, or where an unmatched row is filed.

        Returns:
            None: This method returns nothing.
        """
        for raw_row in self.raw_rows:
            label = f'{raw_row["exchange"]} {raw_row["trading_symbol"]} ({raw_row["instrument_type"]}, series {raw_row["series"]})'
            segment_configuration = self.adapter.classify(raw_row)
            if segment_configuration is None:
                exchange = self.adapter.uncategorised_exchange(raw_row)
                print(f'{label} -> no segment, filed under {exchange}_uncategorised')
                continue
            identity = self.adapter.to_identity(raw_row, segment_configuration)
            broker_fields = self.adapter.to_broker_fields(raw_row, segment_configuration)
            print(f'{label} -> {segment_configuration["segment"]}')
            print(f'    identity {identity}, broker symbol {broker_fields["broker_symbol"]!r}')


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

It prints:

NSE NIFTYBEES (EQ, series EQ) -> nse_exchange_traded_funds
    identity {'symbol': 'NIFTYBEES'}, broker symbol 'Nippon India ETF Nifty 50 BeES'
NSE 762GS2036 (EQ, series GS) -> nse_fixed_income
    identity {'symbol': 'IN0020160019'}, broker symbol None
NSE HDFCNIFTY-MF (EQ, series MF) -> nse_mutual_funds
    identity {'symbol': 'HDFCNIFTY'}, broker symbol 'HDFC Nifty 50 Index Fund'
NSE NIFTYJR (IDX, series None) -> nse_equity_indices
    identity {'symbol': 'NIFTYNXT50'}, broker symbol 'NIFTY NEXT 50'
NSE NIFTY26OCTFUT (FUT, series None) -> nse_equity_index_futures
    identity {'underlying_symbol': 'NIFTY', 'expiry_date': datetime.date(2026, 10, 27)}, broker symbol 'NIFTY26OCTFUT'
MCX MCXBULLDEX26OCTFUT (FUT, series None) -> mcx_commodity_index_futures
    identity {'underlying_symbol': 'MCXBULLDEX', 'expiry_date': datetime.date(2026, 10, 28)}, broker symbol 'MCXBULLDEX26OCTFUT'
BSE 800IIFL28 (EQ, series F) -> bse_fixed_income
    identity {'symbol': 'INE530B07393'}, broker symbol '800IIFL28'
BSE LICMFGOLD (EQ, series B) -> no segment, filed under bse_uncategorised
examples/stock_brokers/instruments/mapping/groww/GrowwMappingAdapter/example_2_mapping_one_day.py
"""Maps one day of Groww rows, taking the BSE exchange traded fund list and index spellings from what is already stored.

`GrowwMappingAdapter.run` first asks the other brokers' tables which BSE symbols are real exchange traded funds, and reads the NSE index names already stored in `unified.instruments`, so that its own index names can be stored under the same spelling. It then hands over to the shared mapping run, which reads Groww's raw rows, classifies them, and writes `unified.instruments` and `unified.broker_mappings` in one transaction.

The adapter normally talks to PostgreSQL, so this program replaces its `engine` with a stand-in. The stand-in connection answers queries by matching a piece of their text: the raw-row query gets three hand-written Groww rows, Shoonya's BSE fund-group query names SETFGOLD, and the index name query returns "Nifty IT", as if another broker had already mapped it that day. Every other cross-broker query gets no rows, and every write is recorded instead of run. pandas only reads through something it recognises as an SQLAlchemy connectable, and it recognises one by the `ConnectionEventsTarget` base class, so the stand-in connection inherits from that class.

Notice that SETFGOLD, an ordinary-looking BSE equity row in Groww's file, is stored as an exchange traded fund because another broker confirms it, and that Groww's `NIFTYIT` is stored under the existing spelling "Nifty IT".

Run it from the project root:

    python examples/stock_brokers/instruments/mapping/groww/GrowwMappingAdapter/example_2_mapping_one_day.py
"""

import collections
import datetime

from sqlalchemy.engine import interfaces

from stock_brokers.instruments.mapping.groww import (
    GrowwMappingAdapter,
)


class StandInResult:
    """A stand-in for an SQLAlchemy result holding a fixed set of rows.

    Attributes:
        columns (list): The column names.
        rows (list): The rows, as dictionaries.
    """

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

        Args:
            rows (list): The rows, as dictionaries that all share the same keys.

        Returns:
            None: This method returns nothing.
        """
        self.rows = rows
        self.columns = []
        if rows:
            self.columns = list(rows[0])

    def keys(self):
        """The column names, as pandas asks for them.

        Returns:
            list: The column names.
        """
        return self.columns

    def fetchall(self):
        """Every row as a plain tuple, as pandas asks for them.

        Returns:
            list: The rows as tuples in column order.
        """
        tuples = []
        for row in self.rows:
            tuples.append(tuple(row.values()))
        return tuples

    def all(self):
        """Every row as a named tuple, so a column can be read as an attribute.

        Returns:
            list: The rows as named tuples.
        """
        row_type = collections.namedtuple('StandInRow', self.columns)
        named_rows = []
        for row in self.rows:
            named_rows.append(row_type(**row))
        return named_rows


class StandInConnection(interfaces.ConnectionEventsTarget):
    """A stand-in for a PostgreSQL connection that answers queries from prepared rows and records writes.

    Attributes:
        answers (list): Pairs of (a piece of query text, the rows to answer with).
        unanswered_queries (int): How many queries matched no prepared answer and got no rows.
        writes (list): Each write statement's first words and its parameters.
    """

    def __init__(self, answers):
        """Holds the prepared answers and starts with nothing recorded.

        Args:
            answers (list): Pairs of (a piece of query text, the rows to answer with).

        Returns:
            None: This method returns nothing.
        """
        self.answers = answers
        self.unanswered_queries = 0
        self.writes = []

    def __enter__(self):
        """Opens the connection for a `with` block.

        Returns:
            StandInConnection: This connection.
        """
        return self

    def __exit__(self, exception_type, exception, traceback):
        """Closes the connection at the end of a `with` block.

        Args:
            exception_type (type | None): The type of any exception raised in the block.
            exception (BaseException | None): Any exception raised in the block.
            traceback (object | None): The traceback of any exception raised in the block.

        Returns:
            bool: False, so any exception carries on.
        """
        return False

    def execute(self, statement, parameters=None):
        """Answers a query from the prepared rows, or records a write.

        Args:
            statement (sqlalchemy.sql.elements.TextClause): The statement to run.
            parameters (dict | list | None): Its bound parameters.

        Returns:
            StandInResult: The prepared rows whose text matches, otherwise an empty result.
        """
        sql = ' '.join(str(statement).split())
        if not sql.startswith('SELECT'):
            self.writes.append((' '.join(sql.split()[:3]), parameters))
            return StandInResult([])
        for query_text, rows in self.answers:
            if query_text in sql:
                return StandInResult(rows)
        self.unanswered_queries += 1
        return StandInResult([])


class StandInEngine:
    """A stand-in for an SQLAlchemy engine that always hands out the same stand-in connection.

    Attributes:
        connection (StandInConnection): The connection handed out.
    """

    def __init__(self, connection):
        """Holds the connection to hand out.

        Args:
            connection (StandInConnection): The connection handed out.

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

    def connect(self):
        """Hands out the connection for reading.

        Returns:
            StandInConnection: The connection.
        """
        return self.connection

    def begin(self):
        """Hands out the connection for one transaction.

        Returns:
            StandInConnection: The connection.
        """
        return self.connection


class MappingOneDayExample:
    """Maps three Groww rows for one date and prints what would be written.

    Attributes:
        mapping_date (datetime.date): The snapshot date being mapped.
        connection (StandInConnection): The stand-in connection.
        adapter (GrowwMappingAdapter): The adapter being shown, using the stand-in engine.
    """

    def __init__(self):
        """Builds the adapter and swaps its engine for the stand-in.

        Returns:
            None: This method returns nothing.
        """
        self.mapping_date = datetime.date(2026, 9, 28)
        raw_rows = [
            self.raw_row('BSE', 'EQ', 'A', '590098', 'SETFGOLD', 'SBI Gold ETF', 'INF200KA16D8'),
            self.raw_row('NSE', 'EQ', 'EQ', '11536', 'TCS', 'Tata Consultancy Services', 'INE467B01029'),
            self.raw_row('NSE', 'IDX', None, 'NIFTY IT', 'NIFTYIT', 'NIFTY IT', None),
        ]
        shoonya_fund_group = [
            {
                'symbol': 'SETFGOLD',
            },
        ]
        stored_index_names = [
            {
                'symbol': 'Nifty IT',
            },
        ]
        answers = [
            (
                'SELECT * FROM groww.instruments',
                raw_rows,
            ),
            (
                'FROM shoonya.instruments WHERE download_date = :d AND exchange = \'BSE\' AND instrument = \'E\'',
                shoonya_fund_group,
            ),
            (
                'FROM unified.instruments WHERE segment = \'nse_equity_indices\'',
                stored_index_names,
            ),
        ]
        self.connection = StandInConnection(answers)
        self.adapter = GrowwMappingAdapter()
        self.adapter.engine = StandInEngine(self.connection)

    def raw_row(self, exchange, instrument_type, series, token, trading_symbol, name, isin):
        """Builds one cash-market raw row in the shape of `groww.instruments`.

        Args:
            exchange (str): The exchange, as NSE or BSE.
            instrument_type (str): Groww's instrument type.
            series (str | None): The exchange series.
            token (str): The `exchange_token` column.
            trading_symbol (str): The `trading_symbol` column.
            name (str): The readable name.
            isin (str | None): The ISIN, if Groww published one.

        Returns:
            dict: The raw row, every column as text as the raw table stores it.
        """
        return {
            'exchange': exchange,
            'exchange_token': token,
            'trading_symbol': trading_symbol,
            'name': name,
            'instrument_type': instrument_type,
            'segment': 'CASH',
            'series': series,
            'isin': isin,
            'underlying_symbol': None,
            'expiry_date': None,
            'strike_price': None,
            'lot_size': '1',
            'tick_size': '0.01',
            'download_date': self.mapping_date,
        }

    def run(self):
        """Maps the day and prints the summary and the rows written.

        Returns:
            None: This method returns nothing.
        """
        summary = self.adapter.run(self.mapping_date)
        print(f'BSE exchange traded funds from other brokers: {sorted(self.adapter.bse_etf_symbols)}')
        print(f'Index name lookup: {self.adapter.nse_index_master_lookup}')
        print(f'Cross-broker queries that found nothing: {self.connection.unanswered_queries}')
        print(f'Summary: matched {summary["matched"]}, uncategorised {summary["uncategorised"]}, errors {summary["errors"]}')
        for statement, parameters in self.connection.writes:
            print(statement)
            if not isinstance(parameters, list):
                continue
            for row in parameters:
                if 'segment' in row:
                    print(f'  {row["segment"]}: {row["symbol"]}')
                else:
                    print(f'  token {row["broker_token"]}: order symbol {row["order_symbol"]!r}, attributes {row["attributes"]}')


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

It prints:

groww: 3/3 raw row(s) classified, 0 uncategorised, 3 segment membership(s), 3 distinct instrument(s), 0 error(s).
BSE exchange traded funds from other brokers: ['SETFGOLD']
Index name lookup: {'NIFTYIT': 'Nifty IT'}
Cross-broker queries that found nothing: 10
Summary: matched 3, uncategorised 0, errors []
DELETE FROM unified.broker_mappings
INSERT INTO unified.instruments
  nse_equities: TCS
  bse_exchange_traded_funds: SETFGOLD
  nse_equity_indices: Nifty IT
INSERT INTO unified.broker_mappings
  token 11536: order symbol 'TCS', attributes {"isin": "INE467B01029", "display_name": "Tata Consultancy Services", "instrument_type": "EQ", "series": "EQ"}
  token 590098: order symbol 'SETFGOLD', attributes {"isin": "INF200KA16D8", "display_name": "SBI Gold ETF", "instrument_type": "EQ", "series": "A"}
  token NIFTY IT: order symbol 'NIFTYIT', attributes {"display_name": "NIFTY IT", "instrument_type": "IDX"}

Load and validate the broker's rules file and create the database engine.

Raises:

Type Description
NotImplementedError

If the subclass did not set BROKER_NAME.
ValueError

If the rules file fails validation.
FileNotFoundError

If the broker has no rules file.

Example programs

examples/stock_brokers/instruments/mapping/base/BrokerMappingAdapter/example_1_classifying_rows_by_rules.py
"""Classifies three raw instrument rows using nothing but a broker's rules file.

`BrokerMappingAdapter` is the base class every broker's mapping adapter subclasses. A subclass only has to name its broker in `BROKER_NAME`, and the base class loads the matching rules file from `stock_brokers/instruments/mapping/utilities/rules/`, checks it against the canonical segment vocabulary, and classifies rows by plain equality rules. This program's subclass sets `BROKER_NAME` to Dhan and overrides nothing, so every answer below comes from the base class and Dhan's rules file alone, without the extra filters the real `DhanMappingAdapter` adds.

The rows are written out by hand in the shape of `dhan.instruments`, where every column is text. The adapter builds a database engine when it is constructed, but an engine does not connect until it is used, and this program never asks it to, so nothing touches PostgreSQL.

For each row the program prints the segment the rules chose, any extra segments (the base class never adds any), the identity fields that go into the instrument id, the broker's own fields, and the deterministic instrument id. The third row matches no rule. The base class cannot tell which exchange it belongs to, so it falls into the unprefixed `uncategorised` segment, and the run would store its token as its symbol.

Run it from the project root:

    python examples/stock_brokers/instruments/mapping/base/BrokerMappingAdapter/example_1_classifying_rows_by_rules.py
"""

from stock_brokers.instruments.mapping import base
from stock_brokers.instruments.mapping.base import (
    BrokerMappingAdapter,
)


class DhanRulesOnlyAdapter(BrokerMappingAdapter):
    """A mapping adapter that uses Dhan's rules file and nothing but the base class's behaviour."""

    BROKER_NAME = 'dhan'


class ClassifyingRowsByRulesExample:
    """Runs three hand-written Dhan rows through the base class's rules engine and prints the result.

    Attributes:
        adapter (DhanRulesOnlyAdapter): The adapter being shown.
        raw_rows (list): The raw rows to classify, as dictionaries of text columns.
    """

    def __init__(self):
        """Builds the adapter and the three raw rows.

        Returns:
            None: This method returns nothing.
        """
        self.adapter = DhanRulesOnlyAdapter()
        self.raw_rows = [
            {
                'exch_id': 'NSE',
                'segment': 'E',
                'security_id': '1594',
                'isin': 'INE009A01021',
                'instrument': 'EQUITY',
                'underlying_symbol': 'INFY',
                'symbol_name': 'INFOSYS LIMITED',
                'display_name': 'Infosys',
                'instrument_type': 'ES',
                'series': 'EQ',
                'lot_size': '1.0',
                'sm_expiry_date': None,
                'strike_price': None,
                'option_type': None,
                'tick_size': '10.0',
            },
            {
                'exch_id': 'NSE',
                'segment': 'D',
                'security_id': '40215',
                'isin': None,
                'instrument': 'OPTIDX',
                'underlying_symbol': 'NIFTY',
                'symbol_name': 'NIFTY-Oct2026-25000-CE',
                'display_name': 'NIFTY 27 OCT 25000 CALL',
                'instrument_type': 'OP',
                'series': None,
                'lot_size': '75.0',
                'sm_expiry_date': '2026-10-27',
                'strike_price': '25000.00000',
                'option_type': 'CE',
                'tick_size': '5.0',
            },
            {
                'exch_id': 'NSE',
                'segment': 'X',
                'security_id': '990001',
                'isin': None,
                'instrument': 'SPREAD',
                'underlying_symbol': '',
                'symbol_name': 'TEST SPREAD',
                'display_name': 'Test spread',
                'instrument_type': 'SP',
                'series': None,
                'lot_size': '1.0',
                'sm_expiry_date': None,
                'strike_price': None,
                'option_type': None,
                'tick_size': '0',
            },
        ]

    def run(self):
        """Prints how the rules file classifies each row.

        Returns:
            None: This method returns nothing.
        """
        equities = self.adapter.segment_config('nse_equities')
        print(f'Rules file: {self.adapter.config["raw_table"]}, {len(self.adapter.config["segments"])} segments')
        print(f'nse_equities is a {equities["shape"]} on {equities["exchange"]} with {len(equities["rules"])} rules')
        print(f'Unconfigured segment: {self.adapter.segment_config("nse_bonds")}')
        for raw_row in self.raw_rows:
            print()
            print(f'Row {raw_row["security_id"]} ({raw_row["symbol_name"]})')
            segment_configuration = self.adapter.classify(raw_row)
            if segment_configuration is None:
                exchange = self.adapter.uncategorised_exchange(raw_row)
                print(f'  No rule matched, and the base class finds no exchange: {exchange}')
                segment_configuration = self.adapter.segment_config('uncategorised')
            extra_segments = self.adapter.classify_extra(raw_row)
            identity = self.adapter.to_identity(raw_row, segment_configuration)
            broker_fields = self.adapter.to_broker_fields(raw_row, segment_configuration)
            identifier = base.instrument_id(
                segment_configuration['exchange'],
                segment_configuration['segment'],
                segment_configuration['shape'],
                identity,
            )
            print(f'  Segment: {segment_configuration["segment"]} ({segment_configuration["shape"]})')
            print(f'  Extra segments: {extra_segments}')
            print(f'  Identity: {identity}')
            print(f'  Broker fields: {broker_fields}')
            print(f'  Instrument id: {identifier}')


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

It prints:

Rules file: dhan.instruments, 32 segments
nse_equities is a security on nse with 2 rules
Unconfigured segment: None

Row 1594 (INFOSYS LIMITED)
  Segment: nse_equities (security)
  Extra segments: []
  Identity: {'symbol': 'INFY'}
  Broker fields: {'broker_token': '1594', 'broker_symbol': 'Infosys', 'order_symbol': None, 'lot_size': 1.0, 'tick_size': 0.1}
  Instrument id: f310e1b0-e082-5b16-808a-25f741abb9de

Row 40215 (NIFTY-Oct2026-25000-CE)
  Segment: nse_equity_index_options (option)
  Extra segments: []
  Identity: {'underlying_symbol': 'NIFTY', 'expiry_date': datetime.date(2026, 10, 27), 'strike_price': 25000.0, 'option_type': 'CE'}
  Broker fields: {'broker_token': '40215', 'broker_symbol': 'NIFTY-Oct2026-25000-CE', 'order_symbol': None, 'lot_size': 75.0, 'tick_size': 0.05}
  Instrument id: fe85c704-ce49-5190-b068-d4d3646ed162

Row 990001 (TEST SPREAD)
  No rule matched, and the base class finds no exchange: None
  Segment: uncategorised (security)
  Extra segments: []
  Identity: {'symbol': ''}
  Broker fields: {'broker_token': '990001', 'broker_symbol': 'Test spread', 'order_symbol': None, 'lot_size': 1.0, 'tick_size': None}
  Instrument id: 55a8952d-9071-5670-a4fe-a5384d3876bd
examples/stock_brokers/instruments/mapping/base/BrokerMappingAdapter/example_2_mapping_one_day.py
"""Maps one day's raw rows into the two unified tables, against a stand-in database.

`run` is what the daily mapping job calls for each broker. It reads the broker's raw rows for one date with `read_raw_rows`, classifies each row, and then, inside one transaction, deletes that broker's mappings for the date and upserts one row per instrument into `unified.instruments` and one per broker mapping into `unified.broker_mappings`. It returns a summary and prints a one-line report.

The adapter normally reads and writes PostgreSQL, so this program replaces its `engine` with a small stand-in. The stand-in connection answers the raw-row query with four hand-written rows in the shape of `dhan.instruments`, and records every other statement it is sent instead of running it. pandas only reads through something it recognises as an SQLAlchemy connectable, and it recognises one by the `ConnectionEventsTarget` base class, so the stand-in connection inherits from that class and nothing else.

The subclass uses Dhan's rules file and overrides nothing. The fourth row has a strike price that is not a number, so building its identity fails; `run` records that as an error and carries on with the other rows rather than stopping. The unmatched row is stored under its token as its symbol, and its tick size of 5.0 is kept as written, because Dhan's rules divide the tick size by 100 only in the real segments and not in the uncategorised one. The last lines show that a subclass which forgets to set `BROKER_NAME` cannot be built.

Run it from the project root:

    python examples/stock_brokers/instruments/mapping/base/BrokerMappingAdapter/example_2_mapping_one_day.py
"""

import datetime

from sqlalchemy.engine import interfaces

from stock_brokers.instruments.mapping.base import (
    BrokerMappingAdapter,
)


class DhanRulesOnlyAdapter(BrokerMappingAdapter):
    """A mapping adapter that uses Dhan's rules file and nothing but the base class's behaviour."""

    BROKER_NAME = 'dhan'


class UnnamedAdapter(BrokerMappingAdapter):
    """A mapping adapter that forgets to name its broker."""


class StandInResult:
    """A stand-in for an SQLAlchemy result holding a fixed set of rows.

    Attributes:
        columns (list): The column names.
        rows (list): The rows, as tuples in column order.
    """

    def __init__(self, columns, rows):
        """Holds the columns and rows.

        Args:
            columns (list): The column names.
            rows (list): The rows, as tuples in column order.

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

    def keys(self):
        """The column names, as pandas asks for them.

        Returns:
            list: The column names.
        """
        return self.columns

    def fetchall(self):
        """Every row, as pandas asks for them.

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

    def all(self):
        """Every row, as the adapter's own queries ask for them.

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


class StandInConnection(interfaces.ConnectionEventsTarget):
    """A stand-in for a PostgreSQL connection that serves one raw table and records every other statement.

    Attributes:
        raw_table (str): The raw table whose rows this connection serves.
        raw_rows (list): The raw rows, as dictionaries of column values.
        statements (list): Each recorded statement's first line and its parameters.
    """

    def __init__(self, raw_table, raw_rows):
        """Holds the raw rows and starts with no statements recorded.

        Args:
            raw_table (str): The raw table whose rows this connection serves.
            raw_rows (list): The raw rows, as dictionaries of column values.

        Returns:
            None: This method returns nothing.
        """
        self.raw_table = raw_table
        self.raw_rows = raw_rows
        self.statements = []

    def __enter__(self):
        """Opens the connection for a `with` block.

        Returns:
            StandInConnection: This connection.
        """
        return self

    def __exit__(self, exception_type, exception, traceback):
        """Closes the connection at the end of a `with` block.

        Args:
            exception_type (type | None): The type of any exception raised in the block.
            exception (BaseException | None): Any exception raised in the block.
            traceback (object | None): The traceback of any exception raised in the block.

        Returns:
            bool: False, so any exception carries on.
        """
        return False

    def execute(self, statement, parameters=None):
        """Answers the raw-row query from the held rows and records anything else.

        Args:
            statement (sqlalchemy.sql.elements.TextClause): The statement to run.
            parameters (dict | list | None): Its bound parameters.

        Returns:
            StandInResult: The raw rows for the raw-row query, otherwise an empty result.
        """
        sql = ' '.join(str(statement).split())
        if sql.startswith(f'SELECT * FROM {self.raw_table} '):
            columns = list(self.raw_rows[0])
            rows = []
            for raw_row in self.raw_rows:
                values = []
                for column in columns:
                    values.append(raw_row[column])
                rows.append(tuple(values))
            return StandInResult(columns, rows)
        self.statements.append((sql, parameters))
        return StandInResult([], [])


class StandInEngine:
    """A stand-in for an SQLAlchemy engine that always hands out the same stand-in connection.

    Attributes:
        connection (StandInConnection): The connection handed out.
    """

    def __init__(self, connection):
        """Holds the connection to hand out.

        Args:
            connection (StandInConnection): The connection handed out.

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

    def connect(self):
        """Hands out the connection for reading.

        Returns:
            StandInConnection: The connection.
        """
        return self.connection

    def begin(self):
        """Hands out the connection for one transaction.

        Returns:
            StandInConnection: The connection.
        """
        return self.connection


class MappingOneDayExample:
    """Maps four raw rows for one date and prints what would be written.

    Attributes:
        mapping_date (datetime.date): The snapshot date being mapped.
        connection (StandInConnection): The stand-in connection holding the raw rows.
        adapter (DhanRulesOnlyAdapter): The adapter being shown, using the stand-in engine.
    """

    def __init__(self):
        """Builds the adapter and swaps its engine for the stand-in.

        Returns:
            None: This method returns nothing.
        """
        self.mapping_date = datetime.date(2026, 9, 28)
        raw_rows = [
            self.raw_row('1594', 'NSE', 'E', 'EQUITY', 'ES', 'INFY', 'INFOSYS LIMITED', None, None, None),
            self.raw_row('40215', 'NSE', 'D', 'OPTIDX', 'OP', 'NIFTY', 'NIFTY-Oct2026-25000-CE', '2026-10-27', '25000.00000', 'CE'),
            self.raw_row('990001', 'NSE', 'X', 'SPREAD', 'SP', '', 'TEST SPREAD', None, None, None),
            self.raw_row('40216', 'NSE', 'D', 'OPTIDX', 'OP', 'NIFTY', 'NIFTY-Oct2026-XX-PE', '2026-10-27', 'not a number', 'PE'),
        ]
        self.connection = StandInConnection('dhan.instruments', raw_rows)
        self.adapter = DhanRulesOnlyAdapter()
        self.adapter.engine = StandInEngine(self.connection)

    def raw_row(self, security_id, exchange, segment, instrument, instrument_type, underlying_symbol, symbol_name, expiry, strike_price, option_type):
        """Builds one raw row in the shape of `dhan.instruments`.

        Args:
            security_id (str): Dhan's token for the row.
            exchange (str): The `exch_id` column.
            segment (str): Dhan's one-letter segment code.
            instrument (str): The `instrument` column.
            instrument_type (str): The `instrument_type` column.
            underlying_symbol (str): The `underlying_symbol` column.
            symbol_name (str): The `symbol_name` column.
            expiry (str | None): The `sm_expiry_date` column.
            strike_price (str | None): The `strike_price` column.
            option_type (str | None): The `option_type` column.

        Returns:
            dict: The raw row, every column as text as the raw table stores it.
        """
        return {
            'exch_id': exchange,
            'segment': segment,
            'security_id': security_id,
            'isin': None,
            'instrument': instrument,
            'underlying_security_id': None,
            'underlying_symbol': underlying_symbol,
            'symbol_name': symbol_name,
            'display_name': symbol_name,
            'instrument_type': instrument_type,
            'series': None,
            'lot_size': '1.0',
            'sm_expiry_date': expiry,
            'strike_price': strike_price,
            'option_type': option_type,
            'tick_size': '5.0',
            'download_date': self.mapping_date,
        }

    def short_row(self, row):
        """Shortens one written row to its filled-in columns, leaving out the dates the example already names.

        Args:
            row (dict): One row the adapter asked to insert.

        Returns:
            dict: The row's columns that hold a value, with the instrument id cut to its first eight characters.
        """
        shortened = {}
        for column, value in row.items():
            if value is None or isinstance(value, datetime.date):
                continue
            if column == 'instrument_id':
                value = value[:8]
            shortened[column] = value
        return shortened

    def run(self):
        """Reads the raw rows, maps them, and prints the summary and the statements sent.

        Returns:
            None: This method returns nothing.
        """
        raw = self.adapter.read_raw_rows(self.connection, self.mapping_date)
        print(f'read_raw_rows returned {len(raw)} rows with {len(raw.columns)} columns')
        print()
        summary = self.adapter.run(self.mapping_date)
        print()
        for key in summary:
            print(f'{key}: {summary[key]}')
        print()
        print('Statements sent inside the write transaction:')
        for sql, parameters in self.connection.statements:
            print(f'  {sql[:60]}...')
            if isinstance(parameters, list):
                for row in parameters:
                    print(f'    {self.short_row(row)}')
            else:
                print(f'    {parameters}')
        print()
        try:
            UnnamedAdapter()
        except NotImplementedError as error:
            print(f'NotImplementedError: {error}')


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

It prints:

read_raw_rows returned 4 rows with 17 columns

dhan: 3/4 raw row(s) classified, 1 uncategorised, 3 segment membership(s), 3 distinct instrument(s), 1 error(s).

broker: dhan
mapping_date: 2026-09-28
raw_rows: 4
matched: 3
uncategorised: 1
memberships: 3
instruments_upserted: 3
errors: [('nse_equity_index_options', "could not convert string to float: 'not a number'")]

Statements sent inside the write transaction:
  DELETE FROM unified.broker_mappings WHERE broker = :b AND ma...
    {'b': 'dhan', 'd': datetime.date(2026, 9, 28)}
  INSERT INTO unified.instruments AS master (instrument_id, ex...
    {'instrument_id': '8fdcd38e', 'exchange': 'unknown', 'segment': 'uncategorised', 'shape': 'security', 'symbol': '990001'}
    {'instrument_id': 'f310e1b0', 'exchange': 'nse', 'segment': 'nse_equities', 'shape': 'security', 'symbol': 'INFY'}
    {'instrument_id': 'fe85c704', 'exchange': 'nse', 'segment': 'nse_equity_index_options', 'shape': 'option', 'underlying_symbol': 'NIFTY', 'strike_price': 25000.0, 'option_type': 'CE'}
  INSERT INTO unified.broker_mappings (instrument_id, broker, ...
    {'instrument_id': '8fdcd38e', 'broker': 'dhan', 'broker_token': '990001', 'broker_symbol': 'TEST SPREAD', 'lot_size': 1.0, 'tick_size': 5.0, 'attributes': '{"display_name": "TEST SPREAD", "instrument_type": "SP"}'}
    {'instrument_id': 'f310e1b0', 'broker': 'dhan', 'broker_token': '1594', 'broker_symbol': 'INFOSYS LIMITED', 'lot_size': 1.0, 'tick_size': 0.05, 'attributes': '{"display_name": "INFOSYS LIMITED", "instrument_type": "ES"}'}
    {'instrument_id': 'fe85c704', 'broker': 'dhan', 'broker_token': '40215', 'broker_symbol': 'NIFTY-Oct2026-25000-CE', 'lot_size': 1.0, 'tick_size': 0.05, 'attributes': '{"display_name": "NIFTY-Oct2026-25000-CE", "instrument_type": "OP"}'}

NotImplementedError: UnnamedAdapter must set BROKER_NAME

Methods:

Name Description
run

Map Groww's raw rows for one date, precomputing the BSE exchange traded fund allowlist and the NSE index name lookup first.
classify

Classify a raw row, applying Groww's pre-checks, redirects, and exclusions on top of the rules.
to_identity

Build the unified identity fields, applying Groww's index aliases, index normalization, and symbol suffix stripping.
to_broker_fields

Build the per-broker mapping fields, with Groww's fallback of the trading symbol when a BSE fixed income row has no name.
uncategorised_exchange

Determine the canonical exchange for an unmatched Groww row from its exchange column.
segment_config

Look up one segment configuration by its segment value.
classify_extra

Return additional segments this raw row also belongs to beyond the primary match.
read_raw_rows

Read the broker's raw rows for one date.
Source code in stock_brokers/instruments/mapping/base.py 
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def __init__(self):
    """
    Load and validate the broker's rules file and create the database engine.

    Raises:
        NotImplementedError: If the subclass did not set BROKER_NAME.
        ValueError: If the rules file fails validation.
        FileNotFoundError: If the broker has no rules file.
    """
    if not self.BROKER_NAME:
        raise NotImplementedError(f"{type(self).__name__} must set BROKER_NAME")
    with open(RULES_DIRECTORY / f"{self.BROKER_NAME}.yaml") as rules_file:
        self.config = yaml.safe_load(rules_file)
    self._validate_config()
    self.engine = get_postgres_engine()
    self.raw_attributes = RawAttributes()

run 

run(mapping_date)

Map Groww's raw rows for one date, precomputing the BSE exchange traded fund allowlist and the NSE index name lookup first.

Parameters:

Name Type Description Default
mapping_date date

The raw snapshot date to map.

 required

Returns:

Type Description
dict

The run summary, as described in BrokerMappingAdapter.run.

Example usage

From example_2_mapping_one_day.py
summary = self.adapter.run(self.mapping_date)
Source code in stock_brokers/instruments/mapping/groww.py 
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def run(self, mapping_date):
    """
    Map Groww's raw rows for one date, precomputing the BSE exchange traded fund allowlist and the NSE index name lookup first.

    Args:
        mapping_date (datetime.date): The raw snapshot date to map.

    Returns:
        dict: The run summary, as described in BrokerMappingAdapter.run.
    """
    with self.engine.connect() as connection:
        self.bse_etf_symbols = known_bse_etf_symbols(connection, mapping_date)
        self.nse_index_master_lookup = equity_index_lookup(connection, mapping_date)
    return super().run(mapping_date)

classify 

classify(raw_row)

Classify a raw row, applying Groww's pre-checks, redirects, and exclusions on top of the rules.

Parameters:

Name Type Description Default
raw_row dict

One raw row from groww.instruments.

 required

Returns:

Type Description
dict | None

The matched segment configuration, or None when no segment matches.

Example usage

From example_1_redirects_and_suffixes.py
segment_configuration = self.adapter.classify(raw_row)
Source code in stock_brokers/instruments/mapping/groww.py 
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def classify(self, raw_row):
    """
    Classify a raw row, applying Groww's pre-checks, redirects, and exclusions on top of the rules.

    Args:
        raw_row (dict): One raw row from groww.instruments.

    Returns:
        dict | None: The matched segment configuration, or None when no segment matches.
    """
    series = raw_row.get("series")
    if (
        raw_row.get("exchange") == "NSE"
        and raw_row.get("segment") == "CASH"
        and series
        and not pd.isna(series)
        and series not in NON_BOND_SERIES
        and not isin_invalid(raw_row)
    ):
        return self.segment_config("nse_fixed_income")
    if raw_row.get("exchange") == "BSE" and raw_row.get("trading_symbol") in self.bse_etf_symbols:
        return self.segment_config("bse_exchange_traded_funds")

    segment_configuration = super().classify(raw_row)
    if segment_configuration is None:
        return None
    segment = segment_configuration["segment"]

    if segment == "nse_equities":
        isin = raw_row.get("isin")
        if isin and str(isin).startswith("INF"):
            if raw_row.get("series") == "EQ":
                return self.segment_config("nse_exchange_traded_funds")
            return None
    if segment == "nse_equity_futures" and raw_row.get("underlying_symbol") in NSE_INDEX_UNDERLYINGS:
        return self.segment_config("nse_equity_index_futures")
    if segment == "nse_equity_options" and raw_row.get("underlying_symbol") in NSE_INDEX_UNDERLYINGS:
        return self.segment_config("nse_equity_index_options")
    if segment == "mcx_commodity_futures" and raw_row.get("underlying_symbol") in MCX_INDEX_UNDERLYINGS:
        return self.segment_config("mcx_commodity_index_futures")
    if segment == "mcx_commodity_options" and raw_row.get("underlying_symbol") in MCX_INDEX_UNDERLYINGS:
        return self.segment_config("mcx_commodity_index_options")
    if segment == "bse_equities" and raw_row.get("series") == "B" and isin_invalid(raw_row):
        return None
    if segment == "bse_equity_indices" and not raw_row.get("trading_symbol"):
        return None
    if segment == "bse_fixed_income" and isin_invalid(raw_row):
        return None
    return segment_configuration

to_identity 

to_identity(raw_row, segment_configuration)

Build the unified identity fields, applying Groww's index aliases, index normalization, and symbol suffix stripping.

Parameters:

Name Type Description Default
raw_row dict

One raw row from groww.instruments.

 required
segment_configuration dict

The segment configuration whose identity mapping applies.

 required

Returns:

Type Description
dict

Identity fields, as described in BrokerMappingAdapter.to_identity.

Raises:

Type Description
ValueError

If an expiry value is numeric with no explicit transform.

Example usage

From example_1_redirects_and_suffixes.py
identity = self.adapter.to_identity(raw_row, segment_configuration)
Source code in stock_brokers/instruments/mapping/groww.py 
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def to_identity(self, raw_row, segment_configuration):
    """
    Build the unified identity fields, applying Groww's index aliases, index normalization, and symbol suffix stripping.

    Args:
        raw_row (dict): One raw row from groww.instruments.
        segment_configuration (dict): The segment configuration whose identity mapping applies.

    Returns:
        dict: Identity fields, as described in BrokerMappingAdapter.to_identity.

    Raises:
        ValueError: If an expiry value is numeric with no explicit transform.
    """
    identity = super().to_identity(raw_row, segment_configuration)
    segment = segment_configuration["segment"]
    if segment == "bse_equity_indices":
        identity["symbol"] = BSE_INDEX_ALIASES.get(identity["symbol"], identity["symbol"])
    elif segment == "nse_equity_indices":
        normalized = normalize_index_name(identity["symbol"])
        if normalized in NSE_INDEX_ALIASES:
            identity["symbol"] = NSE_INDEX_ALIASES[normalized]
        else:
            identity["symbol"] = self.nse_index_master_lookup.get(normalized, identity["symbol"])
    elif segment == "nse_exchange_traded_funds":
        identity["symbol"] = re.sub(r"-EQ$", "", identity["symbol"])
    elif segment == "nse_investment_trusts":
        identity["symbol"] = re.sub(r"-IV$", "", identity["symbol"])
    elif segment == "nse_mutual_funds":
        identity["symbol"] = re.sub(r"-MF$", "", identity["symbol"])
    return identity

to_broker_fields 

to_broker_fields(raw_row, segment_configuration)

Build the per-broker mapping fields, with Groww's fallback of the trading symbol when a BSE fixed income row has no name.

Parameters:

Name Type Description Default
raw_row dict

One raw row from groww.instruments.

 required
segment_configuration dict

The segment configuration whose broker field mapping applies.

 required

Returns:

Type Description
dict

Keys "broker_token" (str), "broker_symbol", "lot_size" (float | None), and "tick_size" (float | None).

Example usage

From example_1_redirects_and_suffixes.py
broker_fields = self.adapter.to_broker_fields(raw_row, segment_configuration)
Source code in stock_brokers/instruments/mapping/groww.py 
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def to_broker_fields(self, raw_row, segment_configuration):
    """
    Build the per-broker mapping fields, with Groww's fallback of the trading symbol when a BSE fixed income row has no name.

    Args:
        raw_row (dict): One raw row from groww.instruments.
        segment_configuration (dict): The segment configuration whose broker field mapping applies.

    Returns:
        dict: Keys "broker_token" (str), "broker_symbol", "lot_size" (float | None), and "tick_size" (float | None).
    """
    broker_fields = super().to_broker_fields(raw_row, segment_configuration)
    broker_symbol = broker_fields["broker_symbol"]
    if segment_configuration["segment"] == "bse_fixed_income" and (
        not broker_symbol or pd.isna(broker_symbol)
    ):
        broker_fields["broker_symbol"] = raw_row.get("trading_symbol")
    return broker_fields

uncategorised_exchange 

uncategorised_exchange(raw_row)

Determine the canonical exchange for an unmatched Groww row from its exchange column.

Parameters:

Name Type Description Default
raw_row dict

One raw row from groww.instruments.

 required

Returns:

Type Description
str | None

"nse", "bse", or "mcx", or None for any other value.

Example usage

From example_1_redirects_and_suffixes.py
exchange = self.adapter.uncategorised_exchange(raw_row)
Source code in stock_brokers/instruments/mapping/groww.py 
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def uncategorised_exchange(self, raw_row):
    """
    Determine the canonical exchange for an unmatched Groww row from its exchange column.

    Args:
        raw_row (dict): One raw row from groww.instruments.

    Returns:
        str | None: "nse", "bse", or "mcx", or None for any other value.
    """
    exchange_identifier = raw_row.get("exchange")
    if exchange_identifier == "NSE":
        return "nse"
    if exchange_identifier == "BSE":
        return "bse"
    if exchange_identifier == "MCX":
        return "mcx"
    return None

_validate_config 

_validate_config()

Check the loaded rules file against the canonical vocabulary.

Every configured segment must be a real canonical segment for its exchange with the matching shape, and the segments must appear in the canonical asset-class order. The file must end with the unprefixed uncategorised entry on exchange unknown, which is the guaranteed fallback for rows whose exchange cannot be determined; unknown is allowed for that one entry only, since it is the value stored in the exchange column of the mapped table for those rows.

Raises:

Type Description
ValueError

On any invalid exchange, segment, shape, ordering, or missing or misplaced uncategorised entry.
Source code in stock_brokers/instruments/mapping/base.py 
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def _validate_config(self):
    """
    Check the loaded rules file against the canonical vocabulary.

    Every configured segment must be a real canonical segment for its exchange with the matching shape, and the segments must appear in the canonical asset-class order. The file must end with the unprefixed ``uncategorised`` entry on exchange ``unknown``, which is the guaranteed fallback for rows whose exchange cannot be determined; ``unknown`` is allowed for that one entry only, since it is the value stored in the exchange column of the mapped table for those rows.

    Raises:
        ValueError: On any invalid exchange, segment, shape, ordering, or missing or misplaced uncategorised entry.
    """
    previous_rank = None
    has_plain_uncategorised = False
    for segment_configuration in self.config["segments"]:
        exchange = segment_configuration["exchange"]
        segment = segment_configuration["segment"]
        shape = segment_configuration["shape"]
        is_plain_uncategorised = segment == "uncategorised" and exchange == "unknown"
        if is_plain_uncategorised:
            has_plain_uncategorised = True
            previous_rank = (1000000000, 1000000000)
            continue
        if exchange not in CANONICAL_EXCHANGES:
            raise ValueError(
                f"{self.BROKER_NAME}: {exchange!r} is not a canonical exchange, "
                f"expected one of {CANONICAL_EXCHANGES} or 'unknown' on the plain "
                f"'uncategorised' entry"
            )
        bare_segment = segment
        if segment.startswith(f"{exchange}_"):
            bare_segment = segment[len(exchange) + 1:]
        try:
            declared_shape = segment_shape(bare_segment)
        except ValueError:
            raise ValueError(
                f"{self.BROKER_NAME}: {segment!r} is not a canonical segment for exchange "
                f"{exchange!r}; use the canonical segment value instead"
            )
        if declared_shape != shape:
            raise ValueError(
                f"{self.BROKER_NAME}: segment {segment!r} has shape {declared_shape!r} "
                f"in the canonical vocabulary, not {shape!r} as configured here"
            )
        rank = segment_rank(exchange, bare_segment)
        if previous_rank is not None and rank < previous_rank:
            raise ValueError(
                f"{self.BROKER_NAME}: segment {segment!r} appears out of canonical order; "
                f"segments must be listed fixed income, equities, currencies, commodities, "
                f"then the catch-alls, then the uncategorised buckets, and within a class "
                f"simple instruments, futures, options, indices, index futures, index options"
            )
        previous_rank = rank
    if not has_plain_uncategorised:
        raise ValueError(
            f"{self.BROKER_NAME}: the rules file must end with the unprefixed "
            f"'uncategorised' entry on exchange 'unknown'"
        )

segment_config 

segment_config(segment)

Look up one segment configuration by its segment value.

Parameters:

Name Type Description Default
segment str

The exchange-prefixed segment value, for example "nse_equities".

 required

Returns:

Type Description
dict | None

The matching segment configuration, or None when the rules file does not configure that segment.

Example usage

From example_1_classifying_rows_by_rules.py
equities = self.adapter.segment_config('nse_equities')
From example_1_classifying_rows_by_rules.py
segment_configuration = self.adapter.segment_config('uncategorised')
From example_1_classifying_rows_by_rules.py
print(f'Unconfigured segment: {self.adapter.segment_config("nse_bonds")}')
Source code in stock_brokers/instruments/mapping/base.py 
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def segment_config(self, segment):
    """
    Look up one segment configuration by its segment value.

    Args:
        segment (str): The exchange-prefixed segment value, for example "nse_equities".

    Returns:
        dict | None: The matching segment configuration, or None when the rules file does not configure that segment.
    """
    for segment_configuration in self.config["segments"]:
        if segment_configuration["segment"] == segment:
            return segment_configuration
    return None

_field 

_field(raw_row, specification)

Read one raw column value, applying a named transform when the specification asks for one.

Parameters:

Name Type Description Default
raw_row dict

One raw row from the broker's instrument table.

 required
specification str | dict

Either a bare column name, or a dict with "column" and optionally "transform" keys.

 required

Returns:

Type Description
Any

The column value, transformed if requested.
Source code in stock_brokers/instruments/mapping/base.py 
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def _field(self, raw_row, specification):
    """
    Read one raw column value, applying a named transform when the specification asks for one.

    Args:
        raw_row (dict): One raw row from the broker's instrument table.
        specification (str | dict): Either a bare column name, or a dict with "column" and optionally "transform" keys.

    Returns:
        Any: The column value, transformed if requested.
    """
    if isinstance(specification, dict):
        value = raw_row.get(specification["column"])
        transform = specification.get("transform")
        if transform:
            return TRANSFORMS[transform](value)
        return value
    return raw_row.get(specification)

_row_matches 

_row_matches(raw_row, match)

Check one equality rule against a raw row.

Parameters:

Name Type Description Default
raw_row dict

One raw row from the broker's instrument table.

 required
match dict

Mapping of raw column name to expected value, or to a list of acceptable values.

 required

Returns:

Type Description
bool

True when every column in the rule matches.
Source code in stock_brokers/instruments/mapping/base.py 
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def _row_matches(self, raw_row, match):
    """
    Check one equality rule against a raw row.

    Args:
        raw_row (dict): One raw row from the broker's instrument table.
        match (dict): Mapping of raw column name to expected value, or to a list of acceptable values.

    Returns:
        bool: True when every column in the rule matches.
    """
    for column_name, expected in match.items():
        actual = raw_row.get(column_name)
        if isinstance(expected, list):
            if actual not in expected:
                return False
        elif actual != expected:
            return False
    return True

classify_extra 

classify_extra(raw_row)

Return additional segments this raw row also belongs to beyond the primary match.

A raw row can genuinely satisfy more than one segment's criteria at once — a BSE row can be both a real equity and a crossref-confirmed exchange traded fund. The base implementation returns an empty list; the brokers where dual membership was confirmed override it.

Parameters:

Name Type Description Default
raw_row dict

One raw row from the broker's instrument table.

 required

Returns:

Type Description
list[dict]

Additional segment configurations, empty by default.

Example usage

From example_1_classifying_rows_by_rules.py
extra_segments = self.adapter.classify_extra(raw_row)
Source code in stock_brokers/instruments/mapping/base.py 
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def classify_extra(self, raw_row):
    """
    Return additional segments this raw row also belongs to beyond the primary match.

    A raw row can genuinely satisfy more than one segment's criteria at once — a BSE row can be both a real equity and a crossref-confirmed exchange traded fund. The base implementation returns an empty list; the brokers where dual membership was confirmed override it.

    Args:
        raw_row (dict): One raw row from the broker's instrument table.

    Returns:
        list[dict]: Additional segment configurations, empty by default.
    """
    return []

_uncategorised_config 

_uncategorised_config(raw_row)

Pick the uncategorised segment configuration a row falls back into.

The per-exchange bucket is used when uncategorised_exchange resolves an exchange and the rules file configures that bucket; otherwise the unprefixed uncategorised entry is used.

Parameters:

Name Type Description Default
raw_row dict

One raw row from the broker's instrument table.

 required

Returns:

Type Description
dict

The uncategorised segment configuration.

Raises:

Type Description
ValueError

If no uncategorised configuration exists, which validation should have made impossible.
Source code in stock_brokers/instruments/mapping/base.py 
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def _uncategorised_config(self, raw_row):
    """
    Pick the uncategorised segment configuration a row falls back into.

    The per-exchange bucket is used when ``uncategorised_exchange`` resolves an exchange and the rules file configures that bucket; otherwise the unprefixed ``uncategorised`` entry is used.

    Args:
        raw_row (dict): One raw row from the broker's instrument table.

    Returns:
        dict: The uncategorised segment configuration.

    Raises:
        ValueError: If no uncategorised configuration exists, which validation should have made impossible.
    """
    exchange = self.uncategorised_exchange(raw_row)
    if exchange is not None:
        preferred = self.segment_config(segment_value(exchange, "uncategorised"))
        if preferred is not None:
            return preferred
    fallback = self.segment_config("uncategorised")
    if fallback is None:
        raise ValueError(
            f"{self.BROKER_NAME}: no uncategorised segment configuration found, "
            f"which validation should have made impossible"
        )
    return fallback

read_raw_rows 

read_raw_rows(connection, mapping_date)

Read the broker's raw rows for one date.

Adapters override this where the fetch itself is part of the mapping: to impose a row order the classification depends on, or to bring in rows the plain query would miss.

Parameters:

Name Type Description Default
connection Connection

An open SQLAlchemy connection.

 required
mapping_date date

The raw snapshot date to read.

 required

Returns:

Type Description
DataFrame

The raw rows for that date.

Example usage

From example_2_mapping_one_day.py
raw = self.adapter.read_raw_rows(self.connection, self.mapping_date)
Source code in stock_brokers/instruments/mapping/base.py 
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def read_raw_rows(self, connection, mapping_date):
    """
    Read the broker's raw rows for one date.

    Adapters override this where the fetch itself is part of the mapping: to impose a row order the classification depends on, or to bring in rows the plain query would miss.

    Args:
        connection (sqlalchemy.engine.Connection): An open SQLAlchemy connection.
        mapping_date (datetime.date): The raw snapshot date to read.

    Returns:
        pandas.DataFrame: The raw rows for that date.
    """
    raw_table = self.config["raw_table"]
    return pd.read_sql(
        text(f"SELECT * FROM {raw_table} WHERE download_date = :d"),
        connection,
        params={
            "d": mapping_date,
        },
    )

_write_results 

_write_results(instrument_rows, broker_rows, mapping_date)

Write one run's rows into the mapped tables inside a single transaction.

Both row lists are sorted by instrument id before the insert so concurrent writers acquire locks in the same order, which is the standard fix for the multi-writer deadlock this design was observed hitting when adapters ran in parallel.

The seen dates widen rather than overwrite, so they do not depend on the order dates happen to be mapped in. Assigning the run's date to the last seen date directly looks equivalent while a backfill walks forward, and is wrong the moment any older date is mapped afterwards: it drags the last seen date backwards, and leaves the first seen date frozen at whichever date was written first rather than the earliest the instrument was actually seen.

Parameters:

Name Type Description Default
instrument_rows dict

Mapping of instrument id to the unified.instruments row.

 required
broker_rows dict

Mapping of instrument id to the unified.broker_mappings row.

 required
mapping_date date

The mapping date the run covers.

 required
Source code in stock_brokers/instruments/mapping/base.py 
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def _write_results(self, instrument_rows, broker_rows, mapping_date):
    """
    Write one run's rows into the mapped tables inside a single transaction.

    Both row lists are sorted by instrument id before the insert so concurrent writers acquire locks in the same order, which is the standard fix for the multi-writer deadlock this design was observed hitting when adapters ran in parallel.

    The seen dates widen rather than overwrite, so they do not depend on the order dates happen to be mapped in. Assigning the run's date to the last seen date directly looks equivalent while a backfill walks forward, and is wrong the moment any older date is mapped afterwards: it drags the last seen date backwards, and leaves the first seen date frozen at whichever date was written first rather than the earliest the instrument was actually seen.

    Args:
        instrument_rows (dict): Mapping of instrument id to the unified.instruments row.
        broker_rows (dict): Mapping of instrument id to the unified.broker_mappings row.
        mapping_date (datetime.date): The mapping date the run covers.
    """
    sorted_instruments = sorted(instrument_rows.values(), key=lambda row: row["instrument_id"])
    sorted_brokers = sorted(broker_rows.values(), key=lambda row: row["instrument_id"])

    with self.engine.begin() as connection:
        connection.execute(
            text(
                f"DELETE FROM {tables.BROKER_MAPPINGS} "
                "WHERE broker = :b AND mapping_date = :d"
            ),
            {
                "b": self.BROKER_NAME,
                "d": mapping_date,
            },
        )
        if sorted_instruments:
            connection.execute(
                text(
                    f"""
                    INSERT INTO {tables.MASTER} AS master
                        (instrument_id, exchange, segment, shape, symbol, underlying_symbol,
                         expiry_date, strike_price, option_type, first_seen_date, last_seen_date)
                    VALUES
                        (:instrument_id, :exchange, :segment, :shape, :symbol, :underlying_symbol,
                         :expiry_date, :strike_price, :option_type, :first_seen_date, :last_seen_date)
                    ON CONFLICT (instrument_id) DO UPDATE SET
                        first_seen_date = LEAST(master.first_seen_date, EXCLUDED.first_seen_date),
                        last_seen_date = GREATEST(master.last_seen_date, EXCLUDED.last_seen_date)
                    """
                ),
                sorted_instruments,
            )
        if sorted_brokers:
            connection.execute(
                text(
                    f"""
                    INSERT INTO {tables.BROKER_MAPPINGS}
                        (instrument_id, broker, broker_token, broker_symbol, order_symbol, lot_size, tick_size, mapping_date, attributes)
                    VALUES
                        (:instrument_id, :broker, :broker_token, :broker_symbol, :order_symbol, :lot_size, :tick_size, :mapping_date, CAST(:attributes AS jsonb))
                    ON CONFLICT (instrument_id, broker, mapping_date) DO UPDATE SET
                        broker_token = EXCLUDED.broker_token, broker_symbol = EXCLUDED.broker_symbol,
                        order_symbol = EXCLUDED.order_symbol,
                        lot_size = EXCLUDED.lot_size, tick_size = EXCLUDED.tick_size,
                        attributes = EXCLUDED.attributes
                    """
                ),
                sorted_brokers,
            )

normalize_index_name 

normalize_index_name(name)

Reduce an index name to uppercase alphanumerics so that differently-spelled broker names collide.

Parameters:

Name Type Description Default
name str

The raw index name from a broker table.

 required

Returns:

Type Description
str

The name with every non-alphanumeric character removed and uppercased.
Source code in stock_brokers/instruments/mapping/groww.py 
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def normalize_index_name(name):
    """
    Reduce an index name to uppercase alphanumerics so that differently-spelled broker names collide.

    Args:
        name (str): The raw index name from a broker table.

    Returns:
        str: The name with every non-alphanumeric character removed and uppercased.
    """
    return re.sub(r"[^A-Z0-9]", "", str(name).upper())

isin_invalid 

isin_invalid(raw_row)

Check whether a raw row's ISIN is missing or is a fund-family ISIN.

Parameters:

Name Type Description Default
raw_row dict

One raw row from groww.instruments.

 required

Returns:

Type Description
bool

True when the ISIN is absent or starts with "INF", meaning the row cannot be identified as fixed income by its ISIN.
Source code in stock_brokers/instruments/mapping/groww.py 
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def isin_invalid(raw_row):
    """
    Check whether a raw row's ISIN is missing or is a fund-family ISIN.

    Args:
        raw_row (dict): One raw row from groww.instruments.

    Returns:
        bool: True when the ISIN is absent or starts with "INF", meaning the row cannot be identified as fixed income by its ISIN.
    """
    isin = raw_row.get("isin")
    return not isin or str(isin).startswith("INF")