Applicable Fee Ids

Applicable Fee IDs — Solution Guide

CRITICAL: Turn Efficiency

DO NOT read manual.md or payments-readme.md — all domain knowledge needed is already in this skill. Reading them wastes 2–4 turns and risks hitting the turn limit without producing an answer.

Execute all logic in a single Python code block. Splitting into multiple steps wastes turns. Aim to complete the full analysis in 1–2 code blocks and output the answer immediately.

Dataset Files

acquirer_countries.csv is NOT needed. Do NOT use it.

Fee Rule Matching Logic

A fee applies when every non-null/non-empty condition in the fee record matches the transaction or merchant characteristic.

Null/Empty = Applies to All

• List fields (account_type, aci, merchant_category_code): [] or None → matches all values
• Scalar fields (capture_delay, is_credit, intracountry, monthly_volume, monthly_fraud_level): None → matches all values

intracountry

• In fees.json: 0.0 = international, 1.0 = domestic, None = both
• Compute directly: intracountry = (issuing_country == acquirer_country) from payments.csv
• Never use acquirer_countries.csv — per-transaction acquirer_country column in payments.csv is the correct source

capture_delay Mapping

def map_capture_delay(raw):
    if raw in ('immediate', 'manual'): return raw
    try:
        n = int(raw)
        if n < 3:    return '<3'
        elif n <= 5: return '3-5'
        else:        return '>5'
    except: return raw

Monthly Volume and Fraud Brackets

def get_monthly_bracket(month_txs):
    total_vol = month_txs['eur_amount'].sum()
    fraud_vol = month_txs[month_txs['has_fraudulent_dispute'] == True]['eur_amount'].sum()
    fraud_pct = (fraud_vol / total_vol * 100) if total_vol > 0 else 0
    if total_vol < 100_000:       vol_bracket = '<100k'
    elif total_vol < 1_000_000:   vol_bracket = '100k-1m'
    elif total_vol < 5_000_000:   vol_bracket = '1m-5m'
    else:                          vol_bracket = '>5m'
    if fraud_pct < 7.2:            fraud_bracket = '<7.2%'
    elif fraud_pct < 7.7:          fraud_bracket = '7.2%-7.7%'
    elif fraud_pct < 8.3:          fraud_bracket = '7.7%-8.3%'
    else:                           fraud_bracket = '>8.3%'
    return vol_bracket, fraud_bracket

Natural calendar months (2023, non-leap year):

Jan: 1–31    Feb: 32–59   Mar: 60–90   Apr: 91–120
May: 121–151 Jun: 152–181 Jul: 182–212 Aug: 213–243
Sep: 244–273 Oct: 274–304 Nov: 305–334 Dec: 335–365

Complete Single-Run Templates

Type 1: Merchant + Day Query

"What fee IDs apply to Merchant_X on day N of 2023?"

Run this entire block at once:

import pandas as pd, json

# ── CONFIG ──────────────────────────────────────
MERCHANT = 'Belles_cookbook_store'
DAY      = 200
MONTH_START, MONTH_END = 182, 212  # July; look up in day-range table above
# ─────────────────────────────────────────────────

with open('/path/to/fees.json') as f:          fees = json.load(f)
with open('/path/to/merchant_data.json') as f: merchants = json.load(f)
payments = pd.read_csv('/path/to/payments.csv')

merchant  = next(m for m in merchants if m['merchant'] == MERCHANT)
acct_type = merchant['account_type']
mcc       = merchant['merchant_category_code']

def map_capture_delay(raw):
    if raw in ('immediate', 'manual'): return raw
    try:
        n = int(raw)
        return '<3' if n < 3 else ('3-5' if n <= 5 else '>5')
    except: return raw

cap_delay = map_capture_delay(merchant['capture_delay'])

period_tx = payments[(payments['merchant'] == MERCHANT) &
                     (payments['year'] == 2023) &
                     (payments['day_of_year'] == DAY)].copy()
period_tx['intracountry'] = period_tx['issuing_country'] == period_tx['acquirer_country']
combos = period_tx[['card_scheme','is_credit','aci','intracountry']].drop_duplicates()

month_tx = payments[(payments['merchant'] == MERCHANT) &
                    (payments['year'] == 2023) &
                    (payments['day_of_year'] >= MONTH_START) &
                    (payments['day_of_year'] <= MONTH_END)]
total_vol = month_tx['eur_amount'].sum()
fraud_vol = month_tx[month_tx['has_fraudulent_dispute'] == True]['eur_amount'].sum()
fraud_pct = (fraud_vol / total_vol * 100) if total_vol > 0 else 0
vol_bracket   = '<100k' if total_vol < 100_000 else ('100k-1m' if total_vol < 1_000_000 else ('1m-5m' if total_vol < 5_000_000 else '>5m'))
fraud_bracket = '<7.2%' if fraud_pct < 7.2 else ('7.2%-7.7%' if fraud_pct < 7.7 else ('7.7%-8.3%' if fraud_pct < 8.3 else '>8.3%'))

def fee_applies(fee, cs, cr, ac, ic):
    if fee['card_scheme'] is not None and fee['card_scheme'] != cs:                    return False
    if fee['account_type'] and acct_type not in fee['account_type']:                   return False
    if fee['capture_delay'] is not None and fee['capture_delay'] != cap_delay:         return False
    if fee['merchant_category_code'] and mcc not in fee['merchant_category_code']:     return False
    if fee['is_credit'] is not None and fee['is_credit'] != cr:                        return False
    if fee['aci'] and ac not in fee['aci']:                                             return False
    if fee['intracountry'] is not None and bool(fee['intracountry']) != ic:            return False
    if fee['monthly_volume'] is not None and fee['monthly_volume'] != vol_bracket:     return False
    if fee['monthly_fraud_level'] is not None and fee['monthly_fraud_level'] != fraud_bracket: return False
    return True

applicable = set()
for _, row in combos.iterrows():
    for fee in fees:
        if fee_applies(fee, row['card_scheme'], row['is_credit'], row['aci'], row['intracountry']):
            applicable.add(fee['ID'])

print(', '.join(str(x) for x in sorted(applicable)))

Type 2: Merchant + Month Query

"What fee IDs apply to Merchant_X in [month] 2023?"

Same as Type 1 but:

• DAY → use month range for day_of_year filter: period_tx = payments[... & (day_of_year >= MONTH_START) & (day_of_year <= MONTH_END)]
• month_tx = same as period_tx
• Apply monthly constraints normally

Type 3: Merchant + Full-Year Query

"What fee IDs apply to Merchant_X in 2023?"

Same as Type 1 but:

• period_tx = payments[(payments['merchant'] == MERCHANT) & (payments['year'] == 2023)]
• Skip monthly_volume and monthly_fraud_level checks (no single month represents the full year)

def fee_applies_yearly(fee, cs, cr, ac, ic):
    if fee['card_scheme'] is not None and fee['card_scheme'] != cs:                return False
    if fee['account_type'] and acct_type not in fee['account_type']:               return False
    if fee['capture_delay'] is not None and fee['capture_delay'] != cap_delay:     return False
    if fee['merchant_category_code'] and mcc not in fee['merchant_category_code']: return False
    if fee['is_credit'] is not None and fee['is_credit'] != cr:                    return False
    if fee['aci'] and ac not in fee['aci']:                                         return False
    if fee['intracountry'] is not None and bool(fee['intracountry']) != ic:        return False
    # monthly_volume and monthly_fraud_level intentionally skipped
    return True

Type 4: Simple Attribute Filter (no payments data needed)

"What fee IDs apply to account_type=F and aci=A?"

import json
with open('/path/to/fees.json') as f: fees = json.load(f)
matching = [fee['ID'] for fee in fees
            if (not fee['account_type'] or 'F' in fee['account_type'])
            and (not fee['aci'] or 'A' in fee['aci'])]
print(', '.join(str(x) for x in sorted(matching)))

Type 5: Reverse Lookup — Fee → Merchants

"Which merchants were affected by fee ID 709 in 2023?"

import pandas as pd, json
with open('/path/to/fees.json') as f: fees = json.load(f)
with open('/path/to/merchant_data.json') as f: merchants = json.load(f)
payments = pd.read_csv('/path/to/payments.csv')

target_fee = next(f for f in fees if f['ID'] == 709)

# Build merchant lookup for attribute-based conditions
mdata = {m['merchant']: m for m in merchants}

def map_capture_delay(raw):
    if raw in ('immediate', 'manual'): return raw
    try:
        n = int(raw)
        return '<3' if n < 3 else ('3-5' if n <= 5 else '>5')
    except: return raw

mask = (payments['year'] == 2023)
if target_fee['card_scheme']:      mask &= (payments['card_scheme'] == target_fee['card_scheme'])
if target_fee['is_credit'] is not None: mask &= (payments['is_credit'] == target_fee['is_credit'])
if target_fee['aci']:              mask &= (payments['aci'].isin(target_fee['aci']))
if target_fee['intracountry'] is not None:
    mask &= ((payments['issuing_country'] == payments['acquirer_country']) == bool(target_fee['intracountry']))

candidate_tx = payments[mask]

# Filter by merchant-level conditions
affected = []
for merchant_name in candidate_tx['merchant'].unique():
    m = mdata.get(merchant_name)
    if m is None: continue
    if target_fee['account_type'] and m['account_type'] not in target_fee['account_type']: continue
    if target_fee['capture_delay'] is not None and map_capture_delay(m['capture_delay']) != target_fee['capture_delay']: continue
    if target_fee['merchant_category_code'] and m['merchant_category_code'] not in target_fee['merchant_category_code']: continue
    affected.append(merchant_name)

print(', '.join(sorted(affected)))

Critical Pitfalls

1. Output immediately after fee matching — do NOT run post-computation verification. The computed result is the answer. Every extra turn risks hitting the turn limit.

2. intracountry from payments.csv only — use issuing_country == acquirer_country per transaction. Never use acquirer_countries.csv.

3. Monthly constraints are decisive — a fee with monthly_volume='1m-5m' does NOT apply to a merchant with <100k monthly volume. Always compute actual monthly stats from transaction data.

4. capture_delay mapping is required — '1' or '2' → '<3'; '7' → '>5'. A fee with capture_delay='<3' won't match a merchant with capture_delay='manual'.

5. Empty list [] = applies to all — same as None. Never treat [] as "no match".

6. Use actual transaction combos from payments.csv for the specific period. A fee only applies if a matching transaction actually occurred.

7. Format: comma-separated integers sorted ascending. Empty string if no fees apply.