Wisdom Capital mapping adapter.
Wisdom Capital reports instrument type as a numeric code — 1 for futures, 2 for options, 16 for underlying references — and lumps several segments into one code and exchange segment pair. The canonical segment order lists the general segment first in every one of those pairs, so listing order cannot separate them, and the separation is stated here instead, as an explicit table of redirects from the general segment to the specific one by underlying name.
The redirects cover twelve cases: index derivatives on all three exchanges, currency derivatives that share the rate derivative code, and the overnight rate index futures.
The rest of this adapter is the logic no rule can express:
- NSE cash equities carry fund contamination, separated by the ISIN's INF prefix, and the same symbol can appear under two series at once. The duplicate is resolved by reading the rows worst-series-first, so the best series is written last and wins.
- BSE cash rows have no native fund or trust marker at all, so both are cross-broker allowlists; bonds are the F and G series with a real ISIN; and equities are a clean series list, or the B series with a non-fund ISIN, after dropping the duplicate rows whose name ends in a hash.
- NSE fixed income merges two raw sources: cash bonds identified by ISIN, and the rate derivative underlying references identified by name, which carry no real lot or tick size.
- Option types are the numeric codes 3 and 4, remapped to CE and PE.
- The BSE rate futures segments carry placeholder rows that are not real contracts, dropped by matching the description against a single-leg contract pattern.
Classes:
| Name | Description |
|---|---|
WisdomCapitalMappingAdapter |
Mapping adapter for Wisdom Capital, covering 41 real segments plus the uncategorised buckets. |
Functions:
| Name | Description |
|---|---|
isin_is_fund |
Check whether a row's ISIN belongs to the fund family. |
isin_valid_bond |
Check whether a row carries an ISIN usable as a fixed income identity. |
Bases: BrokerMappingAdapter
Mapping adapter for Wisdom Capital, covering 41 real segments plus the uncategorised buckets.
Example programs
examples/stock_brokers/instruments/mapping/wisdom_capital/WisdomCapitalMappingAdapter/example_1_redirects_and_codes.py
"""Classifies Wisdom Capital rows, whose instrument types and option types are numeric codes shared by several segments.
Wisdom Capital writes futures as instrument type 1, options as 2 and underlying references as 16, and one code within one exchange segment covers several of this project's segments: NIFTY options and stock options, or currency futures and rate futures. `WisdomCapitalMappingAdapter.classify` lets the rules pick the general segment and then moves the row by its underlying name, using an explicit table of redirects. It also moves NSE equity rows with a fund ISIN to exchange traded funds, drops BSE rate futures whose description is not a single real contract, and classifies in code the NSE bonds, the NSE rate underlyings and every BSE cash row, none of which the rules cover.
`to_identity` turns option types 3 and 4 into CE and PE, and stores an NSE cash bond under its ISIN but a rate underlying under its name. `to_broker_fields` drops the sizes of rows that cannot be traded directly. `run` normally gathers the BSE exchange traded fund and investment trust lists from the database; this program sets them 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/wisdom_capital/WisdomCapitalMappingAdapter/example_1_redirects_and_codes.py
"""
from stock_brokers.instruments.mapping.wisdom_capital import (
WisdomCapitalMappingAdapter,
)
class RedirectsAndCodesExample:
"""Classifies eight hand-written Wisdom Capital rows and prints each one's segment, identity and sizes.
Attributes:
adapter (WisdomCapitalMappingAdapter): 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 = WisdomCapitalMappingAdapter()
self.adapter.bse_etf_symbols = set()
self.adapter.bse_trust_symbols = set()
self.raw_rows = [
self.raw_row('NSEFO', '40215', '2', 'NIFTY', 'NIFTY26OCT25000CE', None, None, '2026-10-27T14:30:00', '25000', '3'),
self.raw_row('NSECD', '8801', '1', 'USDINR', 'USDINR26OCTFUT', None, None, '2026-10-28T12:00:00', None, None),
self.raw_row('NSECM', '10576', '8', 'NIFTYBEES', 'NIFTYBEES-EQ', 'EQ', 'INF204KB14I2', None, None, None),
self.raw_row('NSECM', '15542', '8', '762GS2036', '762GS2036-GS', 'GS', 'IN0020160019', None, None, None),
self.raw_row('NSECD', '11001', '16', '726GS2033', '726GS2033-UNDIRC', None, None, None, None, None),
self.raw_row('BSECD', '870001', '1', '726GS2033', '726GS2033 CONTRACT PLACEHOLDER', None, None, '2026-10-29T12:00:00', None, None),
self.raw_row('BSECM', '999901', '8', 'FIN', 'FIN', 'SPOT', None, None, None, None),
self.raw_row('BSECM', '543210', '8', 'OLDNAME#', 'OLDNAME#', 'A', 'INE000Y01018', None, None, None),
]
def raw_row(self, exchange_segment, token, instrument_type, name, description, series, isin, expiry, strike, option_code):
"""Builds one raw row in the shape of `wisdom_capital.instruments`.
Args:
exchange_segment (str): The `exchangesegment` column, such as NSECM or BSECD.
token (str): The `exchangeinstrumentid` column, the exchange's token.
instrument_type (str): Wisdom Capital's numeric instrument type.
name (str): The `name` column, the symbol or underlying.
description (str): The `description` column.
series (str | None): The exchange series, for cash rows.
isin (str | None): The ISIN.
expiry (str | None): The `contractexpiration` column, for derivatives.
strike (str | None): The strike price, for options.
option_code (str | None): 3 for a call or 4 for a put.
Returns:
dict: The raw row, every column as text as the raw table stores it.
"""
return {
'exchangesegment': exchange_segment,
'exchangeinstrumentid': token,
'instrumenttype': instrument_type,
'name': name,
'description': description,
'series': series,
'ticksize': '0.05',
'lotsize': '1',
'displayname': description,
'isin': isin,
'contractexpiration': expiry,
'strikeprice': strike,
'optiontype': option_code,
}
def run(self):
"""Prints each row's segment, identity and sizes, or where an unmatched row is filed.
Returns:
None: This method returns nothing.
"""
for raw_row in self.raw_rows:
label = f'{raw_row["exchangesegment"]} {raw_row["description"]} (type {raw_row["instrumenttype"]})'
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}')
print(f' lot size {broker_fields["lot_size"]}, tick size {broker_fields["tick_size"]}')
if __name__ == '__main__':
RedirectsAndCodesExample().run()
It prints:
NSEFO NIFTY26OCT25000CE (type 2) -> nse_equity_index_options
identity {'underlying_symbol': 'NIFTY', 'expiry_date': datetime.date(2026, 10, 27), 'strike_price': 25000.0, 'option_type': 'CE'}
lot size 1.0, tick size 0.05
NSECD USDINR26OCTFUT (type 1) -> nse_currency_futures
identity {'underlying_symbol': 'USDINR', 'expiry_date': datetime.date(2026, 10, 28)}
lot size 1.0, tick size 0.05
NSECM NIFTYBEES-EQ (type 8) -> nse_exchange_traded_funds
identity {'symbol': 'NIFTYBEES'}
lot size 1.0, tick size 0.05
NSECM 762GS2036-GS (type 8) -> nse_fixed_income
identity {'symbol': 'IN0020160019'}
lot size 1.0, tick size 0.05
NSECD 726GS2033-UNDIRC (type 16) -> nse_fixed_income
identity {'symbol': '726GS2033'}
lot size None, tick size None
BSECD 726GS2033 CONTRACT PLACEHOLDER (type 1) -> no segment, filed under bse_uncategorised
BSECM FIN (type 8) -> bse_equity_indices
identity {'symbol': 'FINSER'}
lot size 1.0, tick size None
BSECM OLDNAME# (type 8) -> no segment, filed under bse_uncategorised
examples/stock_brokers/instruments/mapping/wisdom_capital/WisdomCapitalMappingAdapter/example_2_mapping_one_day.py
"""Maps one day of Wisdom Capital rows, where one symbol listed under two series keeps its best series.
A Wisdom Capital cash symbol can appear under two series on the same day, such as EQ and T0. Both rows get the same identity, and the shared mapping run keeps whichever it writes last. `WisdomCapitalMappingAdapter.read_raw_rows` therefore sorts the rows worst series first, so the preferred series, EQ, is written last and wins. `run` first gathers the BSE exchange traded fund and investment trust lists from the other brokers' tables, then hands over to the shared mapping run, which classifies the rows 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 four hand-written rows with the EQ row first, and Kotak's BSE investment trust query names one trust. 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 `read_raw_rows` puts the EQ row last, that the summary counts four memberships but three instruments, and that TCS is stored with the EQ row's token, 11536.
Run it from the project root:
python examples/stock_brokers/instruments/mapping/wisdom_capital/WisdomCapitalMappingAdapter/example_2_mapping_one_day.py
"""
import collections
import datetime
from sqlalchemy.engine import interfaces
from stock_brokers.instruments.mapping.wisdom_capital import (
WisdomCapitalMappingAdapter,
)
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 four Wisdom Capital 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 (WisdomCapitalMappingAdapter): 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('NSECM', '11536', 'TCS', 'EQ', 'INE467B01029'),
self.raw_row('NSECM', '1511536', 'TCS', 'T0', 'INE467B01029'),
self.raw_row('BSECM', '543245', 'INDIGRID', 'IF', 'INE219X23014'),
self.raw_row('BSECM', '500325', 'RELIANCE', 'A', 'INE002A01018'),
]
kotak_investment_trusts = [
{
'symbol': 'INDIGRID',
},
]
answers = [
(
'SELECT * FROM wisdom_capital.instruments',
raw_rows,
),
(
'pexchseg = \'bse_cm\' AND pgroup = \'IF\'',
kotak_investment_trusts,
),
]
self.connection = StandInConnection(answers)
self.adapter = WisdomCapitalMappingAdapter()
self.adapter.engine = StandInEngine(self.connection)
def raw_row(self, exchange_segment, token, name, series, isin):
"""Builds one cash-market raw row in the shape of `wisdom_capital.instruments`.
Args:
exchange_segment (str): The `exchangesegment` column, NSECM or BSECM.
token (str): The `exchangeinstrumentid` column.
name (str): The `name` column, the trading symbol.
series (str): The exchange series.
isin (str): The ISIN.
Returns:
dict: The raw row, every column as text as the raw table stores it.
"""
return {
'exchangesegment': exchange_segment,
'exchangeinstrumentid': token,
'instrumenttype': '8',
'name': name,
'description': f'{name}-{series}',
'series': series,
'ticksize': '0.1',
'lotsize': '1',
'displayname': name,
'isin': isin,
'contractexpiration': None,
'strikeprice': None,
'optiontype': None,
'download_date': self.mapping_date,
}
def run(self):
"""Reads the rows, maps the day, and prints the summary and the rows written.
Returns:
None: This method returns nothing.
"""
raw = self.adapter.read_raw_rows(self.connection, self.mapping_date)
print('Rows as read_raw_rows returns them:')
for raw_row in raw.to_dict('records'):
print(f' {raw_row["exchangesegment"]} {raw_row["name"]} series {raw_row["series"]}, token {raw_row["exchangeinstrumentid"]}')
summary = self.adapter.run(self.mapping_date)
print(f'BSE investment trusts from other brokers: {sorted(self.adapter.bse_trust_symbols)}')
print(f'Cross-broker queries that found nothing: {self.connection.unanswered_queries}')
print(f'Summary: matched {summary["matched"]}, memberships {summary["memberships"]}, instruments {summary["instruments_upserted"]}')
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"]}: {row["broker_symbol"]}')
if __name__ == '__main__':
MappingOneDayExample().run()
It prints:
Rows as read_raw_rows returns them:
BSECM INDIGRID series IF, token 543245
BSECM RELIANCE series A, token 500325
NSECM TCS series T0, token 1511536
NSECM TCS series EQ, token 11536
wisdom_capital: 4/4 raw row(s) classified, 0 uncategorised, 4 segment membership(s), 3 distinct instrument(s), 0 error(s).
BSE investment trusts from other brokers: ['INDIGRID']
Cross-broker queries that found nothing: 11
Summary: matched 4, memberships 4, instruments 3
DELETE FROM unified.broker_mappings
INSERT INTO unified.instruments
nse_equities: TCS
bse_equities: RELIANCE
bse_investment_trusts: INDIGRID
INSERT INTO unified.broker_mappings
token 11536: TCS-EQ
token 500325: RELIANCE
token 543245: INDIGRID-IF
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 Wisdom Capital's raw rows for one date, precomputing the two BSE allowlists first. |
read_raw_rows |
Read Wisdom Capital's rows worst-series-first, so the highest-priority series wins where one symbol is listed twice. |
classify |
Classify a raw row, applying the name redirects and then the code-driven checks for the segments with no rules. |
to_identity |
Build the unified identity fields, choosing the fixed income identity per source and remapping the numeric option type. |
to_broker_fields |
Build the per-broker mapping fields, forcing the sizes to nothing on the segments whose rows are not directly tradeable. |
uncategorised_exchange |
Determine the canonical exchange for an unmatched Wisdom Capital row from its exchange segment 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. |
Source code in stock_brokers/instruments/mapping/base.py
Map Wisdom Capital's raw rows for one date, precomputing the two BSE allowlists 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/wisdom_capital.py
Read Wisdom Capital's rows worst-series-first, so the highest-priority series wins where one symbol is listed twice.
A cash-market symbol can appear under two series at once, for example EQ and T0 together. The run collapses rows onto one identity keeping the last one written, so ordering the read by descending series priority makes the preferred series the survivor.
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, ordered by descending series priority. |
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/wisdom_capital.py
Classify a raw row, applying the name redirects and then the code-driven checks for the segments with no rules.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from wisdom_capital.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/wisdom_capital.py
Classify an NSE cash-market row no rule claimed, which is a bond when its series is not an equity series and its ISIN is real.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from wisdom_capital.instruments. |
required |
Returns:
| Type | Description |
|---|---|
dict | None
|
The fixed income segment configuration, or None. |
Source code in stock_brokers/instruments/mapping/wisdom_capital.py
Classify an NSE currency-segment underlying reference row, which is a rate derivative underlying rather than a currency or a rate index.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from wisdom_capital.instruments. |
required |
Returns:
| Type | Description |
|---|---|
dict | None
|
The fixed income segment configuration, or None. |
Source code in stock_brokers/instruments/mapping/wisdom_capital.py
Classify a BSE cash-market row, which carries no native fund, trust, or bond marker.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from wisdom_capital.instruments. |
required |
Returns:
| Type | Description |
|---|---|
dict | None
|
The matched segment configuration, or None. |
Source code in stock_brokers/instruments/mapping/wisdom_capital.py
Build the unified identity fields, choosing the fixed income identity per source and remapping the numeric option type.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from wisdom_capital.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_codes.py
identity = self.adapter.to_identity(raw_row, segment_configuration)
Source code in stock_brokers/instruments/mapping/wisdom_capital.py
Build the per-broker mapping fields, forcing the sizes to nothing on the segments whose rows are not directly tradeable.
The BSE equity indices carry a tick of 1 on every row whatever the index's real tick, so their tick is dropped and their lot size kept.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from wisdom_capital.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_codes.py
broker_fields = self.adapter.to_broker_fields(raw_row, segment_configuration)
Source code in stock_brokers/instruments/mapping/wisdom_capital.py
Determine the canonical exchange for an unmatched Wisdom Capital row from its exchange segment column.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from wisdom_capital.instruments. |
required |
Returns:
| Type | Description |
|---|---|
str | None
|
"nse", "bse", "mcx", or "ncdex", or None for any other value. |
Example usage
Source code in stock_brokers/instruments/mapping/wisdom_capital.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
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
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 row's ISIN belongs to the fund family.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from wisdom_capital.instruments. |
required |
Returns:
| Type | Description |
|---|---|
bool
|
True when the ISIN is present and starts with "INF". |
Source code in stock_brokers/instruments/mapping/wisdom_capital.py
Check whether a row carries an ISIN usable as a fixed income identity.
Parameters:
| Name | Type | Description | Default |
|---|---|---|---|
raw_row
|
dict
|
One raw row from wisdom_capital.instruments. |
required |
Returns:
| Type | Description |
|---|---|
bool
|
True when the ISIN is present and is not a fund ISIN. |