Dhan mapping adapter.
Dhan carries ISIN and explicit instrument tags, so its classification is almost entirely rules driven. Four segments still need a classify post-filter, for exclusions and allowlists no equality rule can express:
bse_equity_indicesdrops security id 846, a duplicate and unconfirmed CAPINS index.nse_equity_indicesdrops the seven "Nifty GS ..." government securities bond indices.bse_fixed_incomeandnse_fixed_incomedrop rows with no ISIN or an INF-prefixed one, a defensive fund-contamination exclusion.bse_exchange_traded_fundsneeds a positive cross-broker allowlist, because Dhan's own MF and ETF instrument type bucket has no reliable internal signal to tell real exchange traded funds from BSE STAR mutual fund scheme codes.
One redirect handles the ordering interaction with the canonical segment order: nse_mutual_funds is listed before nse_exchange_traded_funds, so a mutual fund or ETF instrument type row with series EQ is redirected to the exchange traded funds segment rather than relying on the YAML listing order.
Classes:
| Name | Description |
|---|---|
DhanMappingAdapter |
Mapping adapter for Dhan, covering 27 real segments plus the uncategorised buckets. |
Functions:
| Name | Description |
|---|---|
isin_invalid |
Check whether a raw row's ISIN is missing or is a fund-family ISIN. |
Bases: BrokerMappingAdapter
Mapping adapter for Dhan, covering 27 real segments plus the uncategorised buckets.
Example programs
examples/stock_brokers/instruments/mapping/dhan/DhanMappingAdapter/example_1_filters_and_redirects.py
"""Classifies Dhan rows that the plain rules would get wrong, and shows the filters that correct them.
Dhan's instrument file carries ISINs and explicit instrument tags, so almost every row is classified by the equality rules in `dhan.yaml`. `DhanMappingAdapter.classify` adds a few corrections on top: an NSE fund row with series EQ is moved from mutual funds to exchange traded funds, the seven "Nifty GS" bond indices and BSE index 846 are dropped from the equity indices, fixed income rows without a usable ISIN are dropped, and a BSE fund row counts as an exchange traded fund only when other brokers confirm the symbol.
That confirmation is a set of symbols that `run` normally gathers from the other brokers' tables before it classifies anything. This program sets it by hand to one symbol, GOLDBEES, so no database is needed. A row the corrections drop is not lost: `uncategorised_exchange` reads Dhan's `exch_id` column so the row lands in the uncategorised bucket of its own exchange. The MCX spread at the end matches no rule at all and is filed the same way.
Run it from the project root:
python examples/stock_brokers/instruments/mapping/dhan/DhanMappingAdapter/example_1_filters_and_redirects.py
"""
from stock_brokers.instruments.mapping.dhan import (
DhanMappingAdapter,
)
class FiltersAndRedirectsExample:
"""Classifies seven hand-written Dhan rows and prints where each one lands.
Attributes:
adapter (DhanMappingAdapter): The adapter being shown.
raw_rows (list): The raw rows, as dictionaries of text columns.
"""
def __init__(self):
"""Builds the adapter, sets its BSE exchange traded fund allowlist, and writes the rows.
Returns:
None: This method returns nothing.
"""
self.adapter = DhanMappingAdapter()
self.adapter.bse_etf_symbols = {
'GOLDBEES',
}
self.raw_rows = [
self.raw_row('NSE', 'E', '1594', 'INE009A01021', 'EQUITY', 'INFY', 'ES', 'EQ'),
self.raw_row('NSE', 'E', '10576', 'INF204KB17I5', 'EQUITY', 'NIFTYBEES', 'ETF', 'EQ'),
self.raw_row('NSE', 'I', '41', None, 'INDEX', 'Nifty GS 10Yr', 'INDEX', None),
self.raw_row('NSE', 'E', '15542', None, 'EQUITY', '762GS2036', 'GB', 'GB'),
self.raw_row('BSE', 'E', '590095', 'INF204KB17J3', 'EQUITY', 'GOLDBEES', 'ETF', 'X'),
self.raw_row('BSE', 'E', '176234', 'INF179K01XZ4', 'EQUITY', 'HDFCSTARMF', 'MF', 'X'),
self.raw_row('MCX', 'M', '455521', None, 'SPREAD', 'GOLDM', 'SP', None),
]
def raw_row(self, exchange, segment, security_id, isin, instrument, underlying_symbol, instrument_type, series):
"""Builds one raw row in the shape of `dhan.instruments`.
Args:
exchange (str): The `exch_id` column.
segment (str): Dhan's one-letter segment code.
security_id (str): Dhan's token for the row.
isin (str | None): The ISIN, if Dhan published one.
instrument (str): The `instrument` column.
underlying_symbol (str): The trading symbol.
instrument_type (str): The `instrument_type` column.
series (str | None): The exchange series.
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': isin,
'instrument': instrument,
'underlying_symbol': underlying_symbol,
'symbol_name': underlying_symbol,
'display_name': underlying_symbol,
'instrument_type': instrument_type,
'series': series,
'lot_size': '1.0',
'tick_size': '1.0',
}
def run(self):
"""Prints the segment each row lands in and why.
Returns:
None: This method returns nothing.
"""
for raw_row in self.raw_rows:
segment_configuration = self.adapter.classify(raw_row)
label = f'{raw_row["exch_id"]} {raw_row["underlying_symbol"]} ({raw_row["instrument_type"]}, series {raw_row["series"]})'
if segment_configuration is not None:
identity = self.adapter.to_identity(raw_row, segment_configuration)
print(f'{label} -> {segment_configuration["segment"]} as {identity}')
continue
exchange = self.adapter.uncategorised_exchange(raw_row)
print(f'{label} -> no segment after the rules and filters, filed under {exchange}_uncategorised')
if __name__ == '__main__':
FiltersAndRedirectsExample().run()
It prints:
NSE INFY (ES, series EQ) -> nse_equities as {'symbol': 'INFY'}
NSE NIFTYBEES (ETF, series EQ) -> nse_exchange_traded_funds as {'symbol': 'NIFTYBEES'}
NSE Nifty GS 10Yr (INDEX, series None) -> no segment after the rules and filters, filed under nse_uncategorised
NSE 762GS2036 (GB, series GB) -> no segment after the rules and filters, filed under nse_uncategorised
BSE GOLDBEES (ETF, series X) -> bse_exchange_traded_funds as {'symbol': 'GOLDBEES'}
BSE HDFCSTARMF (MF, series X) -> no segment after the rules and filters, filed under bse_uncategorised
MCX GOLDM (SP, series None) -> no segment after the rules and filters, filed under mcx_uncategorised
examples/stock_brokers/instruments/mapping/dhan/DhanMappingAdapter/example_2_mapping_one_day.py
"""Maps one day of Dhan rows, gathering the BSE exchange traded fund allowlist from other brokers first.
`DhanMappingAdapter.run` first asks the other brokers' tables which BSE symbols are real exchange traded funds, then hands over to the shared mapping run, which reads Dhan'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 Dhan rows, and Flattrade's BSE fund-group query gets one row naming GOLDBEES. 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 GOLDBEES becomes a BSE exchange traded fund because Flattrade lists it, while the BSE STAR mutual fund scheme code, which no other broker confirms, is filed under `bse_uncategorised`.
Run it from the project root:
python examples/stock_brokers/instruments/mapping/dhan/DhanMappingAdapter/example_2_mapping_one_day.py
"""
import collections
import datetime
from sqlalchemy.engine import interfaces
from stock_brokers.instruments.mapping.dhan import (
DhanMappingAdapter,
)
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 Dhan 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 (DhanMappingAdapter): 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('2885', 'NSE', 'INE002A01018', 'RELIANCE', 'ES'),
self.raw_row('590095', 'BSE', 'INF204KB17J3', 'GOLDBEES', 'ETF'),
self.raw_row('176234', 'BSE', 'INF179K01XZ4', 'HDFCSTARMF', 'MF'),
]
flattrade_fund_group = [
{
'symbol': 'GOLDBEES',
},
]
answers = [
(
'SELECT * FROM dhan.instruments',
raw_rows,
),
(
'FROM flattrade.instruments WHERE download_date = :d AND exchange = \'BSE\' AND instrument = \'E\'',
flattrade_fund_group,
),
]
self.connection = StandInConnection(answers)
self.adapter = DhanMappingAdapter()
self.adapter.engine = StandInEngine(self.connection)
def raw_row(self, security_id, exchange, isin, symbol, instrument_type):
"""Builds one cash-market raw row in the shape of `dhan.instruments`.
Args:
security_id (str): Dhan's token for the row.
exchange (str): The `exch_id` column.
isin (str): The ISIN.
symbol (str): The trading symbol.
instrument_type (str): The `instrument_type` column.
Returns:
dict: The raw row, every column as text as the raw table stores it.
"""
series = 'A'
if exchange == 'NSE':
series = 'EQ'
return {
'exch_id': exchange,
'segment': 'E',
'security_id': security_id,
'isin': isin,
'instrument': 'EQUITY',
'underlying_symbol': symbol,
'symbol_name': symbol,
'display_name': symbol,
'instrument_type': instrument_type,
'series': series,
'lot_size': '1.0',
'tick_size': '5.0',
'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 confirmed by other brokers: {sorted(self.adapter.bse_etf_symbols)}')
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"]}: tick size {row["tick_size"]}, attributes {row["attributes"]}')
if __name__ == '__main__':
MappingOneDayExample().run()
It prints:
dhan: 2/3 raw row(s) classified, 1 uncategorised, 3 segment membership(s), 3 distinct instrument(s), 0 error(s).
BSE exchange traded funds confirmed by other brokers: ['GOLDBEES']
Cross-broker queries that found nothing: 10
Summary: matched 2, uncategorised 1, errors []
DELETE FROM unified.broker_mappings
INSERT INTO unified.instruments
bse_uncategorised: HDFCSTARMF
nse_equities: RELIANCE
bse_exchange_traded_funds: GOLDBEES
INSERT INTO unified.broker_mappings
token 176234: tick size 5.0, attributes {"isin": "INF179K01XZ4", "display_name": "HDFCSTARMF", "instrument_type": "MF", "series": "A"}
token 2885: tick size 0.05, attributes {"isin": "INE002A01018", "display_name": "RELIANCE", "instrument_type": "ES", "series": "EQ"}
token 590095: tick size 0.05, attributes {"isin": "INF204KB17J3", "display_name": "GOLDBEES", "instrument_type": "ETF", "series": "A"}
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 Dhan's raw rows for one date, precomputing the BSE exchange traded fund allowlist first. |
classify |
Classify a raw row, applying Dhan's post-filters and the mutual fund redirect on top of the rules. |
uncategorised_exchange |
Determine the canonical exchange for an unmatched Dhan row from its exch_id 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. |
to_identity |
Build the unified identity fields for a raw row under one segment. |
to_broker_fields |
Build the per-broker mapping fields for a raw row under one segment. |
read_raw_rows |
Read the broker's raw rows for one date. |
Source code in stock_brokers/instruments/mapping/base.py
Map Dhan's raw rows for one date, precomputing the BSE exchange traded fund allowlist 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
Source code in stock_brokers/instruments/mapping/dhan.py
Classify a raw row, applying Dhan's post-filters and the mutual fund redirect on top of the rules.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from dhan.instruments. |
required |
Returns:
| Type | Description |
|---|---|
dict | None
|
The matched segment configuration, or None when no segment matches. |
Example usage
Source code in stock_brokers/instruments/mapping/dhan.py
Determine the canonical exchange for an unmatched Dhan row from its exch_id column.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from dhan.instruments. |
required |
Returns:
| Type | Description |
|---|---|
str | None
|
"nse", "bse", or "mcx", or None for any other value. |
Example usage
Source code in stock_brokers/instruments/mapping/dhan.py
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
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
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
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
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
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
Source code in stock_brokers/instruments/mapping/base.py
Build the unified identity fields for a raw row under one segment.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from the broker's instrument table. |
required |
segment_configuration
|
dict
|
The segment configuration whose identity mapping applies. |
required |
Returns:
| Type | Description |
|---|---|
dict
|
Identity fields, with "symbol" for securities and "underlying_symbol", "expiry_date", "strike_price", "option_type" for derivatives. |
Raises:
| Type | Description |
|---|---|
ValueError
|
If an expiry value is numeric with no explicit transform, since the epoch unit cannot be guessed safely. |
Example usage
From example_1_classifying_rows_by_rules.py
identity = self.adapter.to_identity(raw_row, segment_configuration)
Source code in stock_brokers/instruments/mapping/base.py
Build the per-broker mapping fields for a raw row under one segment.
A tick size of zero or below is stored as None, because no instrument moves in steps of nothing, and brokers send 0 or -1 where they have no tick for a row.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from the broker's instrument table. |
required |
segment_configuration
|
dict
|
The segment configuration whose broker field mapping applies. |
required |
Returns:
| Type | Description |
|---|---|
dict
|
Keys "broker_token" (str), "broker_symbol", "order_symbol", "lot_size" (float | None), and "tick_size" (float | None). |
Example usage
From example_1_classifying_rows_by_rules.py
broker_fields = self.adapter.to_broker_fields(raw_row, segment_configuration)
Source code in stock_brokers/instruments/mapping/base.py
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
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
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
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 dhan.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. |