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A history endpoint is useful when you can explain exactly which fund, which dates and which observations you collected. A long list with an unnoticed missing page is harder to trust than a short, correctly bounded dataset.
This guide builds a reusable Python collector for the ILovePSX Pakistan mutual fund NAV API. It follows every cursor within your selected range, preserves the complete NAV rows and creates an explicit analysis export. That file also feeds the Google Sheets, Excel and pandas workflows in this journal.
The useful part, upfront.
- Keep the same slug, dates and ordering throughout pagination.
- A null next_cursor completes the selected range, not all possible fund history.
- Save source observations before adding charting or return calculations.
Start with a canonical identity and an honest date range#
Resolve the fund using the registry and copy its returned slug. Similar names, plans and parent funds can describe different identities. Use funds:read for discovery and nav:read for history; do not create the slug by lowercasing the name. The fictional example-research-fund in this article is only a teaching identity and will not resolve to a real fund.
Choose from and to dates before the first request. The current code defaults to a 366-day range when dates are omitted and permits up to 3,653 calendar days per request range. These bounds constrain a query; they do not promise that the dataset contains that much history. An explicit range is easier to reproduce and makes a scheduled refresh independent of shifting default dates.
References: ILovePSX public API reference · ILovePSX NAV API guide
Keep history cursors separate from registry pages#
GET /v1/mutual-funds/funds/{fund_slug}/nav accepts from, to, order, limit and cursor. limit controls observations per page, with a current upper bound of 1,000. order is asc or desc. Registry lists use page and per_page instead; those parameters do not replace the NAV history cursor.
History observations live under data.navs. data.fund supplies their shared identity and data.from, data.to and data.order repeat the request context. Pagination continues through meta.next_cursor. Treat that value as opaque and send it back unchanged. Keep dates, ordering and identity constant; a cursor is not a portable bookmark for another selection.
| Field | Meaning | Application behavior |
|---|---|---|
| data.navs | Observations on this page | Append validated rows |
| meta.next_cursor | Continuation of this traversal | Repeat unchanged; stop on null |
| meta.data_as_of | Date context for this page | Retain each page's metadata |
| meta.generated_at | Response generation time | Keep separate from NAV dates |
References: ILovePSX public API reference
Collect all pages and publish the file only after success#
Save the following as collect_nav.py. Set ILOVEPSX_API_KEY, FUND_SLUG, NAV_FROM and NAV_TO in your private local environment. Use a real returned slug and ISO dates. Run python collect_nav.py. The standard-library script refuses redirects, applies a request timeout and reports failures without printing the secret or headers.
The collect function accepts a page-fetching function, so it can be tested without credentials or a network. It validates page identity, date range and increasing observation dates before appending records. A repeated cursor, empty continuation or exhausted safety cap fails the collection. The old export remains intact when fetching fails; the final file is replaced only after the complete selection has been assembled.
import json
import os
from datetime import date, datetime, timezone
from pathlib import Path
from urllib.error import HTTPError, URLError
from urllib.parse import quote, urlencode
from urllib.request import HTTPRedirectHandler, Request, build_opener
BASE = "https://funds.api.ilovepsx.com/v1/mutual-funds"
class NoRedirect(HTTPRedirectHandler):
def redirect_request(self, request, fp, code, message, headers, newurl):
return None
def collect(get_page, slug, start, end, page_size=500, max_pages=50):
date.fromisoformat(start)
date.fromisoformat(end)
if start > end:
raise ValueError("The start date must precede the end date.")
params = {"from": start, "to": end, "order": "asc", "limit": page_size}
path = "/funds/" + quote(slug, safe="") + "/nav"
rows, pages, seen, cursors = [], [], set(), set()
last_date = None
for _ in range(max_pages):
payload = get_page(path, dict(params))
if payload.get("success") is not True:
raise ValueError("Expected a successful NAV response.")
data, meta = payload["data"], payload["meta"]
if (data["fund"]["slug"], data["from"], data["to"], data["order"]) != (
slug, start, end, "asc"
):
raise ValueError("Page identity or selection changed.")
for row in data["navs"]:
observed = row["nav_date"]
date.fromisoformat(observed)
if row["fund_slug"] != slug or not start <= observed <= end:
raise ValueError("Row is outside the requested identity/range.")
if last_date is not None and observed <= last_date:
raise ValueError("History contains duplicate or unordered dates.")
key = (slug, observed)
if key in seen:
raise ValueError("Duplicate observation.")
seen.add(key)
rows.append(row)
last_date = observed
pages.append(meta)
cursor = meta.get("next_cursor")
if cursor is None:
return {"selection": {"fund_slug": slug, "from": start, "to": end},
"retrieved_at": datetime.now(timezone.utc).isoformat(),
"pages": pages, "rows": rows}
if not data["navs"] or cursor in cursors:
raise ValueError("Pagination did not make progress.")
cursors.add(cursor)
params["cursor"] = cursor
raise ValueError("Page safety limit reached; export was not completed.")
def main():
key = os.environ["ILOVEPSX_API_KEY"]
opener = build_opener(NoRedirect)
def get_page(path, params):
request = Request(BASE + path + "?" + urlencode(params),
headers={"X-API-Key": key})
try:
with opener.open(request, timeout=25) as response:
return json.load(response)
except HTTPError as error:
raise RuntimeError("API request failed: HTTP " + str(error.code)) from None
except URLError:
raise RuntimeError("API network request failed.") from None
result = collect(get_page, os.environ["FUND_SLUG"],
os.environ["NAV_FROM"], os.environ["NAV_TO"])
target = Path("nav-export.json")
temporary = target.with_suffix(".json.tmp")
temporary.write_text(json.dumps(result, indent=2), encoding="utf-8")
temporary.replace(target)
print("Export completed:", len(result["rows"]), "observations.")
if __name__ == "__main__":
main()References: ILovePSX public API reference · ILovePSX authentication, scopes and limits
Inspect a complete synthetic NAV history response#
Every identifier and financial value below is fictional. This complete one-row response includes the optional price, load and change fields rather than removing them. Null demonstrates an absent value. It is not a zero offer price, a zero load or evidence that a distribution did not occur.
The response ends after one observation even though the requested range spans three calendar days. That is valid. The endpoint returns accepted observations available within the range; it does not generate rows for each requested day. History change fields currently remain null in this path, while the latest-NAV endpoint can calculate a change from its preceding accepted observation. Do not assume identical populated fields across operations.
{
"success": true,
"data": {
"fund": {
"id": "00000000-0000-4000-8000-000000000201",
"slug": "example-research-fund",
"name": "Example Research Fund",
"mufap_fund_id": 999999
},
"currency": "PKR",
"from": "2026-09-28",
"to": "2026-09-30",
"order": "asc",
"navs": [
{
"fund_id": "00000000-0000-4000-8000-000000000201",
"fund_slug": "example-research-fund",
"fund_name": "Example Research Fund",
"mufap_fund_id": 999999,
"nav_date": "2026-09-28",
"nav": "100.125000",
"offer_price": null,
"repurchase_price": null,
"market_price": null,
"front_end_load_pct": null,
"back_end_load_pct": null,
"contingent_load_pct": null,
"nav_change_abs": null,
"nav_change_pct": null,
"currency": "PKR"
}
]
},
"meta": {
"request_id": "synthetic-history-page",
"generated_at": "2026-10-08T08:00:00Z",
"data_as_of": "2026-09-28",
"data_freshness_days": 10,
"next_cursor": null
}
}The synthetic fund, ID, MUFAP identifier, dates and NAV are invented. The field names and nesting follow the public repository contract.
References: ILovePSX public API reference
Make an empty range a useful result#
For a known fund with no observations in the chosen range, data.navs can be an empty array and meta.next_cursor can be null. data_as_of and data_freshness_days are then null. The collector produces rows: [] with the original selection and page metadata. Your downstream workflow can display No observations for this selection, rather than an apparently successful table of zero values.
A 404 for an unknown fund and a 422 for invalid input require a different action. Refine the registry identity or date selection before trying again. A network failure does not establish that data is absent. Retain the last successful export and tell users when it was retrieved; do not mark it as refreshed just because a job started.
References: ILovePSX public API reference
Preserve exact values and explain calendar gaps#
Save NAV strings exactly as received. A value such as 100.125000 contains a defined decimal representation; converting it to float during collection introduces an unnecessary approximation. Later financial arithmetic can use Decimal constructed from the string. Charts may use an explicitly labelled approximate numeric column without replacing the exact source column.
A two-day or three-day gap is a reason to inspect the observation schedule, not automatic proof of an ingestion defect. Weekend dates, source publication patterns and coverage differences all matter. Do not forward-fill the collected file. If your research needs a calendar-aligned series, produce a separately named transformation and document which values were carried forward.
References: ILovePSX NAV API guide
Reconcile corrections before extending an analysis#
A later retrieval can legitimately change an observation for the same fund and date. Keep raw export versions or a controlled history of imports, and compare the exact NAV string and other observed fields. Upsert by fund identity and observation date in your working dataset, with retrieval metadata attached. Never count the replacement as a second day's observation.
The updates endpoint can help identify published changes when the events cover your dataset. It should complement periodic bounded reconciliation, not become an unsupported promise that every older correction will always be present in a retained feed. For reports, retain report_date and data_version as well: monthly holdings, allocations and credit quality belong to a different observation cadence from daily NAV.
References: ILovePSX public API reference
Check the export before sharing or charting it#
Confirm the selection, row count, first and last returned dates, duplicate policy and final cursor. Keep page metadata in the export so a partial-download problem can be investigated. Ensure a successful empty selection is handled deliberately and that an API failure never overwrites the last successful output.
Use the private file in the companion Sheets, Excel or pandas guide. If you distribute it, check your applicable data-use terms and share only the intended dataset. API keys never belong in the exported file. A well-described ten-row dataset gives another developer enough context to reproduce the query and understand its limits.
References: ILovePSX authentication, scopes and limits
Try a complete example.
Use these fictional records to prototype a full fund view: profile, reported returns, holdings, asset and sector allocations, credit quality, fees and ratings. Every name, identifier and number is invented. This is educational mock data, with no connection to a real fund, source document or agency opinion.
The JSON mirrors the public response structure. The complete FMR example includes all supported expansions; empty arrays and nulls demonstrate valid missing-data states. Your live integration must still request the sections it needs and check the returned coverage.
Inspect the complete FMR model
{
"success": true,
"data": {
"id": "00000000-0000-4000-8000-000000000101",
"report_ids": [
"00000000-0000-4000-8000-000000000101"
],
"fund": {
"id": "00000000-0000-4000-8000-000000000201",
"slug": "example-research-fund",
"name": "Example Research Fund",
"mufap_fund_id": null,
"amc": {
"id": "00000000-0000-4000-8000-000000000301",
"slug": "example-asset-management",
"name": "Example Asset Management"
}
},
"report_date": "2026-09-30",
"currency": "PKR",
"data_version": 1,
"identity": {
"fund_name_as_reported": "Example Research Fund",
"fund_short_name_as_reported": "ERF",
"parent_fund_name_as_reported": null,
"plan_name_as_reported": null,
"fund_type_as_reported": "Open-end",
"category_as_reported": "Illustrative mixed-asset category",
"risk_profile_as_reported": "Illustrative higher-risk profile",
"is_shariah_compliant": false,
"is_plan": false,
"is_pension_scheme": false,
"is_etf": false
},
"fund_size": {
"nav_per_unit": "100.500000",
"nav_date": "2026-09-30",
"offer_price": "100.500000",
"redemption_price": "100.500000",
"repurchase_price": "100.500000",
"market_price": null,
"net_assets_pkr": "1005000000.0000",
"fund_size_pkr": "1005000000.0000",
"fund_size_including_fof_pkr": "1005000000.0000",
"fund_size_excluding_fof_pkr": "1005000000.0000",
"amount_invested_by_fof_pkr": "0.0000",
"units_outstanding": "10000000.000000"
},
"profile": {
"launch_date": "2025-01-01",
"inception_date": "2025-01-01",
"maturity_date": null,
"benchmark": "Example composite benchmark",
"old_benchmark": null,
"new_benchmark": null,
"benchmark_effective_date": null,
"risk_of_principal_erosion": "Principal may fluctuate; synthetic disclosure.",
"pricing_mechanism": "Forward pricing — illustrative",
"dealing_days": "Monday to Friday — illustrative",
"dealing_time": "09:00 to 14:00 Pakistan time — illustrative",
"cutoff_time": "14:00 Pakistan time — illustrative",
"settlement_period": "T+2 — illustrative, not an actual dealing policy",
"minimum_initial_investment_pkr": "5000.0000",
"minimum_subsequent_investment_pkr": "1000.0000",
"minimum_redemption_pkr": "1000.0000",
"unit_classes": [
"Illustrative standard units"
],
"leverage": "None in this synthetic example"
},
"report_fragments": [
{
"id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"schema_version": "fmr_page_v3",
"data_version": 1,
"promoted_at": "2026-10-08T08:00:00Z",
"report_date": "2026-09-30",
"report_month": "September 2026",
"page_type": "FUND_REPORT",
"extraction_mode": "PROFILE_PERFORMANCE_PORTFOLIO",
"amc_name_raw": "Example Asset Management",
"fund_name_raw": "Example Research Fund",
"fund_short_name_raw": "ERF",
"parent_fund_name_raw": null,
"plan_name_raw": null,
"fund_type_raw": "Open-end",
"category_raw": "Illustrative mixed-asset category",
"risk_profile_raw": "Illustrative higher-risk profile",
"risk_of_principal_erosion_raw": "Principal may fluctuate; synthetic disclosure.",
"is_shariah_compliant": false,
"is_plan": false,
"is_pension_scheme": false,
"is_etf": false,
"launch_date": "2025-01-01",
"inception_date": "2025-01-01",
"maturity_date": null,
"benchmark_raw": "Example composite benchmark",
"old_benchmark_raw": null,
"new_benchmark_raw": null,
"benchmark_effective_date": null,
"par_value": "100.000000",
"listing_raw": null,
"leverage_raw": "None in this synthetic example",
"nav_per_unit": "100.500000",
"nav_date": "2026-09-30",
"offer_price": "100.500000",
"redemption_price": "100.500000",
"repurchase_price": "100.500000",
"market_price": null,
"net_assets": "1005000000.0000",
"fund_size": "1005000000.0000",
"fund_size_including_fof": "1005000000.0000",
"fund_size_excluding_fof": "1005000000.0000",
"amount_invested_by_fof": "0.0000",
"units_outstanding": "10000000.000000",
"currency": "PKR",
"pricing_mechanism_raw": "Forward pricing — illustrative",
"dealing_days_raw": "Monday to Friday — illustrative",
"dealing_time_raw": "09:00 to 14:00 Pakistan time — illustrative",
"cutoff_time_raw": "14:00 Pakistan time — illustrative",
"settlement_period_raw": "T+2 — illustrative, not an actual dealing policy",
"minimum_initial_investment": "5000.0000",
"minimum_subsequent_investment": "1000.0000",
"minimum_redemption": "1000.0000",
"unit_classes": [
"Illustrative standard units"
]
}
],
"sub_funds": [],
"reported_returns": [
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 1,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"period_label_raw": "1 year",
"period_key": "1Y",
"fund_return_percent": "12.00000000",
"benchmark_return_percent": "10.00000000",
"peer_return_percent": null,
"return_type": "ABSOLUTE",
"is_annualized": false,
"is_since_inception": false,
"calculation_basis_raw": "Synthetic cumulative return for the stated period"
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 2,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"period_label_raw": "Month to date",
"period_key": "MTD",
"fund_return_percent": "0.50000000",
"benchmark_return_percent": "0.40000000",
"peer_return_percent": null,
"return_type": "ABSOLUTE",
"is_annualized": false,
"is_since_inception": false,
"calculation_basis_raw": "Synthetic cumulative September return"
}
],
"allocations": [
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 1,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"allocation_type": "ASSET_ALLOCATION",
"label_raw": "Equities",
"label_normalized": null,
"percent_of_total_assets": "60.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"amount": "603000000.0000",
"current_period": null,
"previous_period": null,
"change_percent": null,
"bucket_start_days": null,
"bucket_end_days": null,
"bucket_start_years": null,
"bucket_end_years": null
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 2,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"allocation_type": "ASSET_ALLOCATION",
"label_raw": "Cash",
"label_normalized": null,
"percent_of_total_assets": "20.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"amount": "201000000.0000",
"current_period": null,
"previous_period": null,
"change_percent": null,
"bucket_start_days": null,
"bucket_end_days": null,
"bucket_start_years": null,
"bucket_end_years": null
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 3,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"allocation_type": "ASSET_ALLOCATION",
"label_raw": "Government securities",
"label_normalized": null,
"percent_of_total_assets": "15.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"amount": "150750000.0000",
"current_period": null,
"previous_period": null,
"change_percent": null,
"bucket_start_days": null,
"bucket_end_days": null,
"bucket_start_years": null,
"bucket_end_years": null
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 4,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"allocation_type": "ASSET_ALLOCATION",
"label_raw": "Other assets",
"label_normalized": null,
"percent_of_total_assets": "5.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"amount": "50250000.0000",
"current_period": null,
"previous_period": null,
"change_percent": null,
"bucket_start_days": null,
"bucket_end_days": null,
"bucket_start_years": null,
"bucket_end_years": null
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 5,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"allocation_type": "SECTOR_ALLOCATION",
"label_raw": "Technology",
"label_normalized": null,
"percent_of_total_assets": "25.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"amount": "251250000.0000",
"current_period": null,
"previous_period": null,
"change_percent": null,
"bucket_start_days": null,
"bucket_end_days": null,
"bucket_start_years": null,
"bucket_end_years": null
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 6,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"allocation_type": "SECTOR_ALLOCATION",
"label_raw": "Fertilizer",
"label_normalized": null,
"percent_of_total_assets": "20.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"amount": "201000000.0000",
"current_period": null,
"previous_period": null,
"change_percent": null,
"bucket_start_days": null,
"bucket_end_days": null,
"bucket_start_years": null,
"bucket_end_years": null
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 7,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"allocation_type": "SECTOR_ALLOCATION",
"label_raw": "Banks",
"label_normalized": null,
"percent_of_total_assets": "15.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"amount": "150750000.0000",
"current_period": null,
"previous_period": null,
"change_percent": null,
"bucket_start_days": null,
"bucket_end_days": null,
"bucket_start_years": null,
"bucket_end_years": null
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 8,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"allocation_type": "CREDIT_QUALITY",
"label_raw": "Government securities — illustrative bucket",
"label_normalized": null,
"percent_of_total_assets": "15.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"amount": null,
"current_period": null,
"previous_period": null,
"change_percent": null,
"bucket_start_days": null,
"bucket_end_days": null,
"bucket_start_years": null,
"bucket_end_years": null
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 9,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"allocation_type": "CREDIT_QUALITY",
"label_raw": "Unrated / no rating assigned in this example",
"label_normalized": null,
"percent_of_total_assets": "85.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"amount": null,
"current_period": null,
"previous_period": null,
"change_percent": null,
"bucket_start_days": null,
"bucket_end_days": null,
"bucket_start_years": null,
"bucket_end_years": null
}
],
"holdings": [
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 1,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"holding_group": "TOP_HOLDINGS",
"rank": 1,
"name_raw": "Example Technology Company",
"holding_type": "EQUITY",
"asset_class": "Equities",
"issuer_name_raw": "Example Technology Company",
"security_name_raw": "Example Technology Company ordinary shares",
"symbol_raw": "EXTECH",
"sector_raw": "Technology",
"percent_of_total_assets": "18.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"percent_held": null,
"market_value": "180900000.0000",
"value_before_provisioning": null,
"provisioning_amount": null,
"value_after_provisioning": null,
"quantity": "1000000.000000",
"units": "shares",
"credit_rating": null,
"rating_agency": null,
"instrument_type_raw": "Ordinary shares",
"coupon_rate": null,
"yield_percent": null,
"ytm_percent": null,
"issue_date": null,
"maturity_date": null,
"listed_or_unlisted": "Listed — illustrative",
"secured_or_unsecured": null,
"is_shariah_compliant": null,
"is_top_holding": true,
"is_non_compliant": null
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 2,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"holding_group": "TOP_HOLDINGS",
"rank": 2,
"name_raw": "Example Fertilizer Company",
"holding_type": "EQUITY",
"asset_class": "Equities",
"issuer_name_raw": "Example Fertilizer Company",
"security_name_raw": "Example Fertilizer Company ordinary shares",
"symbol_raw": "EXFERT",
"sector_raw": "Fertilizer",
"percent_of_total_assets": "12.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"percent_held": null,
"market_value": "120600000.0000",
"value_before_provisioning": null,
"provisioning_amount": null,
"value_after_provisioning": null,
"quantity": "800000.000000",
"units": "shares",
"credit_rating": null,
"rating_agency": null,
"instrument_type_raw": "Ordinary shares",
"coupon_rate": null,
"yield_percent": null,
"ytm_percent": null,
"issue_date": null,
"maturity_date": null,
"listed_or_unlisted": "Listed — illustrative",
"secured_or_unsecured": null,
"is_shariah_compliant": null,
"is_top_holding": true,
"is_non_compliant": null
},
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 3,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"holding_group": "TOP_HOLDINGS",
"rank": 3,
"name_raw": "Example Banking Company",
"holding_type": "EQUITY",
"asset_class": "Equities",
"issuer_name_raw": "Example Banking Company",
"security_name_raw": "Example Banking Company ordinary shares",
"symbol_raw": "EXBANK",
"sector_raw": "Banks",
"percent_of_total_assets": "8.000000",
"percent_of_net_assets": null,
"percent_of_gross_assets": null,
"percent_held": null,
"market_value": "80400000.0000",
"value_before_provisioning": null,
"provisioning_amount": null,
"value_after_provisioning": null,
"quantity": "500000.000000",
"units": "shares",
"credit_rating": null,
"rating_agency": null,
"instrument_type_raw": "Ordinary shares",
"coupon_rate": null,
"yield_percent": null,
"ytm_percent": null,
"issue_date": null,
"maturity_date": null,
"listed_or_unlisted": "Listed — illustrative",
"secured_or_unsecured": null,
"is_shariah_compliant": null,
"is_top_holding": true,
"is_non_compliant": null
}
],
"metrics": [
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 1,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"metric_key": "NUMBER_OF_HOLDINGS",
"label_raw": "Illustrative number of equity holdings",
"value": "12.00000000",
"unit": "count",
"period": "September 2026",
"benchmark_value": null
}
],
"fees": [
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 1,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"fee_type": "TOTAL_EXPENSE_RATIO",
"label_raw": "Illustrative annualized total expense ratio",
"value_percent": "2.000000",
"value_amount": null,
"currency": "PKR",
"period": "September 2026 — annualized illustration",
"with_government_levies": true,
"government_levy_percent": null,
"secp_fee_percent": null,
"is_actual_charged": true,
"is_maximum_allowed": false
}
],
"ratings": [
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 1,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"rating_type": "ILLUSTRATIVE",
"rating_raw": "Illustrative rating — not an agency opinion",
"rating_agency": "Example agency (fictional)",
"rating_date": null,
"outlook_raw": null
}
],
"parties": [
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 1,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"role": "FUND_MANAGER",
"name_raw": "Example Research Team",
"designation_raw": "Illustrative fund-management team",
"organization_raw": "Example Asset Management"
}
],
"special_terms": [
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 1,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"term_type": "INVESTMENT_OBJECTIVE",
"label_raw": "Illustrative investment objective",
"value_raw": "A fictional mixed-asset portfolio used only to demonstrate the response model.",
"value_numeric": null,
"value_percent": null,
"value_amount": null,
"value_date": null,
"currency": "PKR"
}
],
"compliance": [
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 1,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"compliance_type": "ILLUSTRATIVE_DISCLOSURE",
"name_raw": "Synthetic compliance disclosure",
"limit_type_raw": null,
"regulatory_limit_percent": null,
"actual_exposure_percent": null,
"excess_percent": null,
"gross_assets_percent": null,
"net_assets_percent": null,
"status_raw": "Illustrative only — no regulatory assessment has been performed"
}
],
"notes": [
{
"sub_fund_key": null,
"sub_fund_name_raw": null,
"row_order": 1,
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"note_kind": "NOTE",
"section_type": "PORTFOLIO",
"note_type": "ILLUSTRATIVE",
"text": "All figures and entities are synthetic. The three reported top holdings are a partial list. Sector percentages use total assets. Credit-quality labels and rating text are not agency opinions."
}
],
"source": [
{
"report_id": "00000000-0000-4000-8000-000000000101",
"page_number": 1,
"document": null,
"promoted_at": "2026-10-08T08:00:00Z",
"data_version": 1
}
],
"sections": {
"sub_funds": {
"status": "NOT_REPORTED",
"row_count": 0
},
"reported_returns": {
"status": "AVAILABLE",
"row_count": 2
},
"allocations": {
"status": "AVAILABLE",
"row_count": 9
},
"holdings": {
"status": "AVAILABLE",
"row_count": 3
},
"metrics": {
"status": "AVAILABLE",
"row_count": 1
},
"fees": {
"status": "AVAILABLE",
"row_count": 1
},
"ratings": {
"status": "AVAILABLE",
"row_count": 1
},
"parties": {
"status": "AVAILABLE",
"row_count": 1
},
"special_terms": {
"status": "AVAILABLE",
"row_count": 1
},
"compliance": {
"status": "AVAILABLE",
"row_count": 1
},
"notes": {
"status": "AVAILABLE",
"row_count": 1
},
"source": {
"status": "AVAILABLE",
"row_count": 1
}
},
"available_expansions": [
"allocations",
"compliance",
"fees",
"holdings",
"metrics",
"notes",
"parties",
"ratings",
"reported_returns",
"source",
"special_terms",
"sub_funds"
]
},
"meta": {
"request_id": "00000000000040008000000000000401",
"generated_at": "2026-10-08T08:00:00Z",
"data_as_of": "2026-09-30",
"data_freshness_days": 8
}
}Inspect the latest NAV model
{
"success": true,
"data": [
{
"fund_id": "00000000-0000-4000-8000-000000000201",
"fund_slug": "example-research-fund",
"fund_name": "Example Research Fund",
"mufap_fund_id": null,
"nav_date": "2026-09-30",
"nav": "100.500000",
"offer_price": "100.500000",
"repurchase_price": "100.500000",
"market_price": null,
"front_end_load_pct": "0.000000",
"back_end_load_pct": "0.000000",
"contingent_load_pct": null,
"nav_change_abs": null,
"nav_change_pct": null,
"currency": "PKR"
}
],
"meta": {
"request_id": "00000000000040008000000000000402",
"generated_at": "2026-10-08T08:00:00Z",
"data_as_of": "2026-09-30",
"data_freshness_days": 8
}
}Sources & further reading
Check the published contract for current fields and limits. Source documents describe reported facts; API availability still depends on promoted production coverage.
Put the guide to work.
Start with the documented contract, keep your key server-side, and make dates and missing data visible in your product.
Review NAV history parameters