feat(report): item-level agricultural filter, FIFO/Average COGS column
Agricultural goods stock report now filters by the per-item agricultural flag (only agro items inside agro documents count) and grosses up rows to include VAT. Adds a single "Sold in period (purchase price)" column whose costing method follows the company's Default Stock Valuation Method (Moving Average -> weighted average, otherwise FIFO). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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# Copyright (c) 2026, Company and contributors
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# For license information, please see license.txt
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from collections import defaultdict, deque
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import frappe
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from frappe import _
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from frappe.utils import flt, getdate
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def execute(filters=None):
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@ -43,6 +46,15 @@ def get_columns(filters=None):
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"fieldtype": "Currency",
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"width": 200
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},
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{
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# Dövr ərzində satılmış malların (alış qiyməti ilə) məbləği.
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# Hesablama metodu şirkətin Default Stock Valuation Method-una görə
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# seçilir (Moving Average -> orta, əks halda FIFO).
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"label": _("Sold in period (purchase price)"),
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"fieldname": "sold_purchase_price",
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"fieldtype": "Currency",
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"width": 220
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},
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{
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# Dövrün sonuna malların (alış qiyməti ilə) qalıq məbləği
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"label": _("Closing balance (purchase price)"),
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@ -53,31 +65,150 @@ def get_columns(filters=None):
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]
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def _sum(doctype, company, date_field_from=None, date_field_to=None, before=None):
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"""Sum grand_total of submitted agricultural-goods documents of a doctype."""
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conditions = "docstatus = 1 AND agricultural_goods = 1"
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def _fetch_rows(parent_dt, child_dt, company, to_date):
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"""Fetch agricultural item rows of submitted agricultural documents.
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Only rows whose Item is itself flagged as agricultural goods are returned.
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Each row is grossed up to include VAT proportionally (grand_total/net_total).
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"""
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conditions = "p.docstatus = 1 AND p.agricultural_goods = 1 AND it.agricultural_goods = 1"
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params = {}
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if company:
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conditions += " AND company = %(company)s"
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conditions += " AND p.company = %(company)s"
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params["company"] = company
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if before:
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conditions += " AND posting_date < %(before)s"
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params["before"] = before
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else:
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if date_field_from:
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conditions += " AND posting_date >= %(from_date)s"
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params["from_date"] = date_field_from
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if date_field_to:
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conditions += " AND posting_date <= %(to_date)s"
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params["to_date"] = date_field_to
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if to_date:
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conditions += " AND p.posting_date <= %(to_date)s"
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params["to_date"] = to_date
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result = frappe.db.sql(
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f"SELECT COALESCE(SUM(grand_total), 0) FROM `tab{doctype}` WHERE {conditions}",
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params
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)
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return float(result[0][0]) if result else 0.0
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rows = frappe.db.sql(f"""
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SELECT
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p.name AS parent,
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p.posting_date AS posting_date,
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c.idx AS idx,
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c.item_code AS item_code,
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c.qty AS qty,
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c.net_amount AS net_amount,
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p.grand_total AS grand_total,
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p.net_total AS net_total
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FROM `tab{child_dt}` c
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JOIN `tab{parent_dt}` p ON p.name = c.parent
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JOIN `tabItem` it ON it.name = c.item_code
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WHERE {conditions}
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ORDER BY p.posting_date, p.name, c.idx
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""", params, as_dict=1)
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for r in rows:
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# Gross up the row to include its share of document-level VAT/charges.
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factor = (flt(r.grand_total) / flt(r.net_total)) if flt(r.net_total) else 1.0
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r["gross"] = flt(r.net_amount) * factor
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r["unit"] = (r["gross"] / flt(r.qty)) if flt(r.qty) else 0.0
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return rows
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def _sum_gross(rows, start=None, end=None):
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"""Sum grossed-up amount of rows whose posting_date is within [start, end]."""
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total = 0.0
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for r in rows:
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d = getdate(r.posting_date)
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if start and d < start:
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continue
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if end and d > end:
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continue
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total += r["gross"]
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return total
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def _fifo_sold_purchase_price(purchases, sales, start, end):
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"""FIFO cost of goods sold within [start, end], valued against purchases.
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Per item, purchases form FIFO layers consumed by sales in chronological
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order. Sales before the period still consume layers (to advance the FIFO
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position) but do not contribute to the reported cost.
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"""
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layers = defaultdict(deque) # item_code -> deque([qty, unit_cost])
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events = []
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for r in purchases:
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events.append((getdate(r.posting_date), 0, r)) # purchases first on a day
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for r in sales:
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events.append((getdate(r.posting_date), 1, r))
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events.sort(key=lambda e: (e[0], e[1]))
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cogs = 0.0
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for d, kind, r in events:
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item = r["item_code"]
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if kind == 0:
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qty = flt(r.qty)
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if qty:
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layers[item].append([qty, r["unit"]])
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continue
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# sale: consume layers
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remaining = flt(r.qty)
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consumed_cost = 0.0
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dq = layers[item]
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while remaining > 1e-9 and dq:
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lqty, lunit = dq[0]
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take = min(lqty, remaining)
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consumed_cost += take * lunit
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lqty -= take
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remaining -= take
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if lqty <= 1e-9:
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dq.popleft()
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else:
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dq[0][0] = lqty
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in_period = (not start or d >= start) and (not end or d <= end)
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if in_period:
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cogs += consumed_cost
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return cogs
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def _avg_sold_purchase_price(purchases, sales, start, end):
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"""Moving weighted-average cost of goods sold within [start, end].
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Per item, all stock is blended into a single pool (quantity + value); the
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average unit cost is recomputed on every purchase. Sales are valued at the
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current average. Like FIFO, sales before the period still consume stock
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(to keep the average correct) but do not contribute to the reported cost.
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"""
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# item_code -> [stock_qty, stock_value]
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pools = defaultdict(lambda: [0.0, 0.0])
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events = []
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for r in purchases:
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events.append((getdate(r.posting_date), 0, r)) # purchases first on a day
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for r in sales:
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events.append((getdate(r.posting_date), 1, r))
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events.sort(key=lambda e: (e[0], e[1]))
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cogs = 0.0
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for d, kind, r in events:
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item = r["item_code"]
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pool = pools[item]
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if kind == 0:
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qty = flt(r.qty)
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pool[0] += qty
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pool[1] += qty * r["unit"]
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continue
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# sale: value at current average, capped at available stock
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qty = flt(r.qty)
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avg = (pool[1] / pool[0]) if pool[0] > 1e-9 else 0.0
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take = min(qty, pool[0]) if pool[0] > 0 else 0.0
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consumed_cost = take * avg
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pool[0] -= take
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pool[1] -= consumed_cost
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in_period = (not start or d >= start) and (not end or d <= end)
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if in_period:
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cogs += consumed_cost
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return cogs
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def get_data(filters=None):
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@ -85,25 +216,32 @@ def get_data(filters=None):
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filters = filters or {}
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company = filters.get("company")
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from_date = filters.get("from_date")
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to_date = filters.get("to_date")
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from_date = getdate(filters["from_date"]) if filters.get("from_date") else None
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to_date = getdate(filters["to_date"]) if filters.get("to_date") else None
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# Opening balance = cumulative (purchases - sales) of agricultural goods
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# before the start of the period, at purchase/sale price incl. VAT.
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purchases = _fetch_rows("Purchase Invoice", "Purchase Invoice Item", company, to_date)
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sales = _fetch_rows("Sales Invoice", "Sales Invoice Item", company, to_date)
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# Opening balance = cumulative (purchases - sales) before the period start.
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if from_date:
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opening_purchases = _sum("Purchase Invoice", company, before=from_date)
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opening_sales = _sum("Sales Invoice", company, before=from_date)
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opening_purchases = _sum_gross(purchases, end=_day_before(from_date))
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opening_sales = _sum_gross(sales, end=_day_before(from_date))
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opening_balance = opening_purchases - opening_sales
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else:
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opening_balance = 0.0
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# Purchased during the period (purchase price, incl. VAT)
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purchased = _sum("Purchase Invoice", company, from_date, to_date)
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purchased = _sum_gross(purchases, start=from_date, end=to_date)
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sold = _sum_gross(sales, start=from_date, end=to_date)
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# Sold during the period (sale price, incl. VAT)
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sold = _sum("Sales Invoice", company, from_date, to_date)
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# Pick the cost-of-goods-sold method from the company's Default Stock
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# Valuation Method. Moving Average -> weighted average, otherwise FIFO.
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valuation_method = _get_valuation_method(company)
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if valuation_method == "Moving Average":
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sold_purchase_price = _avg_sold_purchase_price(purchases, sales, from_date, to_date)
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else:
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sold_purchase_price = _fifo_sold_purchase_price(purchases, sales, from_date, to_date)
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# Simplified closing balance: opening + purchased - sold
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# Closing balance keeps the simplified formula (sale price for sold).
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closing_balance = opening_balance + purchased - sold
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if from_date and to_date:
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@ -118,5 +256,21 @@ def get_data(filters=None):
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"opening_balance": opening_balance,
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"purchased": purchased,
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"sold": sold,
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"sold_purchase_price": sold_purchase_price,
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"closing_balance": closing_balance
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}]
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def _get_valuation_method(company):
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"""Resolve the Default Stock Valuation Method (per company, else global)."""
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method = None
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if company:
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method = frappe.db.get_value("Company", company, "valuation_method")
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if not method:
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method = frappe.db.get_single_value("Stock Settings", "valuation_method")
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return method or "FIFO"
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def _day_before(d):
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from datetime import timedelta
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return d - timedelta(days=1)
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