total-fees-calculation

name

Total_Fees_Calculation

description

Skill for computing total payment processing fees for a merchant over a specific day, date range, or month in the dabstep dataset. Use this skill whenever the question asks for "total fees", "fees paid", or "fees charged" for a merchant over some time period. The computation requires matching each transaction to a fee rule from fees.json using multiple merchant and transaction properties, then summing fixed_amount + rate * eur_amount / 10000 for each matched transaction.

Total Fees Calculation — dabstep Dataset

Problem Summary

Compute total payment processing fees (in euros) for a given merchant over a specified period (a specific day of year, a month, or a full year). Round the final answer to 2 decimal places.

Data Files Required

• payments.csv — transactions (filter by merchant, year, day_of_year)
• fees.json — 1000 fee rules with conditions and fee parameters
• merchant_data.json — per-merchant properties: account_type, capture_delay, merchant_category_code
• manual.md — read for domain definitions

Fee Formula

fee = fixed_amount + rate * eur_amount / 10000

Sum fees across all transactions that match a fee rule. Transactions with no matching rule contribute 0.

---

Step-by-Step Algorithm

Step 1: Load Merchant Properties

From merchant_data.json, for the target merchant extract:

• account_type: one of R, D, H, F, S, O
• capture_delay: raw value (numeric like "1" or "7", or categorical like "immediate", "manual")
• merchant_category_code (MCC): integer

Map numeric capture_delay to fee rule category:

def map_capture_delay(cd):
    try:
        n = int(cd)
        return '<3' if n < 3 else ('3-5' if n <= 5 else '>5')
    except (ValueError, TypeError):
        return cd  # already a string category

Step 2: Determine the Query Period and Month Boundaries

2023 is NOT a leap year. Day-of-year to month mapping:

Month	day_of_year range
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

• "Nth day of the year" → day_of_year == N; its month is determined by the table above.
• "Month X 2023" → use the full month range.
• "Full year 2023" → process each month separately (different monthly stats per month).

Step 3: Compute Monthly Statistics (Full Natural Month)

Monthly stats must be computed from ALL transactions in the full natural month (not just the queried day). This is critical for fee rule matching.

MONTH_RANGES = {
    1:(1,31), 2:(32,59), 3:(60,90), 4:(91,120),
    5:(121,151), 6:(152,181), 7:(182,212), 8:(213,243),
    9:(244,273), 10:(274,304), 11:(305,334), 12:(335,365)
}

month_txns = payments[
    (payments['merchant'] == MERCHANT) &
    (payments['year'] == YEAR) &
    (payments['day_of_year'].between(MONTH_START, MONTH_END))
]
monthly_vol = month_txns['eur_amount'].sum()
fraud_vol = month_txns[month_txns['has_fraudulent_dispute'] == True]['eur_amount'].sum()
fraud_rate = fraud_vol / monthly_vol if monthly_vol > 0 else 0

Categorize into fee rule buckets:

def categorize_volume(v):
    if v < 100_000:    return '<100k'
    elif v < 1_000_000: return '100k-1m'
    elif v < 5_000_000: return '1m-5m'
    else:               return '>5m'

def categorize_fraud(f):
    if f < 0.072:   return '<7.2%'
    elif f < 0.077: return '7.2%-7.7%'
    elif f < 0.083: return '7.7%-8.3%'
    else:           return '>8.3%'

Step 4: Match Each Transaction to a Fee Rule

The single most important rule: empty list [] in any fee rule field means "applies to all" (same semantics as null).

In fees.json: account_type: [], merchant_category_code: [], aci: [] all mean "no restriction on this field".

def rule_specificity(rule):
    """Higher = more specific. Used as tiebreaker when multiple rules match."""
    return sum([
        bool(rule['account_type']),
        rule['capture_delay'] is not None,
        rule['monthly_fraud_level'] is not None,
        rule['monthly_volume'] is not None,
        bool(rule['merchant_category_code']),
        rule['is_credit'] is not None,
        bool(rule['aci']),
        rule['intracountry'] is not None,
    ])

def find_best_rule(txn, acct_type, cap_delay, mcc, fraud_level, vol_category, fees):
    """Find the best matching fee rule for a transaction."""
    # intracountry: use columns from payments.csv directly
    intracountry = (txn['issuing_country'] == txn['acquirer_country'])
    matches = []
    for rule in fees:
        # card_scheme: exact match required
        if rule['card_scheme'] != txn['card_scheme']:
            continue
        # account_type: [] or null means all
         rule[]  acct_type   rule[]:
            
        
         rule[]     rule[] != cap_delay:
            
        
         rule[]     rule[] != fraud_level:
            
        
         rule[]     rule[] != vol_category:
            
        
         rule[]  mcc   rule[]:
            
        
         rule[]     rule[] != (txn[]):
            
        
         rule[]  txn[]   rule[]:
            
        
         rule[]     (rule[]) != intracountry:
            
        matches.append(rule)
      matches:
           
    
     (matches, key= r: (-rule_specificity(r), r[]))[]

Step 5: Sum All Transaction Fees

total_fees = 0.0
for _, txn in target_transactions.iterrows():
    rule = find_best_rule(txn, acct_type, cap_delay, mcc, fraud_level, vol_category, fees)
    if rule:
        total_fees += rule['fixed_amount'] + rule['rate'] * txn['eur_amount'] / 10000

print(round(total_fees, 2))

---

Complete Python Template (Single Day or Month)

import json
import pandas as pd

# Load data (update paths as provided in the problem)
payments = pd.read_csv('<path>/payments.csv')
with open('<path>/fees.json') as f:
    fees = json.load(f)
with open('<path>/merchant_data.json') as f:
    merchant_data = json.load(f)

MERCHANT = '<merchant_name>'
YEAR = 2023

# Date range for the QUERY (e.g., single day or full month):
TARGET_DAYS = [<day_of_year>]              # e.g., [10] for day 10; list(range(60,91)) for March
# Date range of the MONTH containing the target (for monthly stats):
MONTH_START, MONTH_END = <start>, <end>   # e.g., 1, 31 for January

MONTH_RANGES = {
    1:(1,31), 2:(32,59), 3:(60,90), 4:(91,120),
    5:(121,151), 6:(152,181), 7:(182,212), 8:(213,243),
    9:(244,273), 10:(274,304), 11:(305,), :(,)
}

 ():
    :
        n = (cd)
           n <   (  n <=   )
     (ValueError, TypeError):
         cd

 ():
     v < :     
     v < :  
     v < :  
    :                

 ():
     f < :    
     f < :  
     f < :  
    :            

 ():
     ([(rule[]),
                rule[]   ,
                rule[]   ,
                rule[]   ,
                (rule[]),
                rule[]   ,
                (rule[]),
                rule[]   ])

 ():
    intracountry = (txn[] == txn[])
    matches = []
     rule  fees:
         rule[] != txn[]: 
         rule[]  acct_type   rule[]: 
         rule[]     rule[] != cap_delay: 
         rule[]     rule[] != fraud_level: 
         rule[]     rule[] != vol_category: 
         rule[]  mcc   rule[]: 
         rule[]     rule[] != (txn[]): 
         rule[]  txn[]   rule[]: 
         rule[]     (rule[]) != intracountry: 
        matches.append(rule)
      matches:  
     (matches, key= r: (-rule_specificity(r), r[]))[]


merchant = (m  m  merchant_data  m[] == MERCHANT)
acct_type = merchant[]
cap_delay = map_capture_delay(merchant[])
mcc = merchant[]


month_txns = payments[
    (payments[] == MERCHANT) & (payments[] == YEAR) &
    (payments[].between(MONTH_START, MONTH_END))
]
monthly_vol = month_txns[].()
fraud_vol = month_txns[month_txns[] == ][].()
fraud_rate = fraud_vol / monthly_vol  monthly_vol >   
fraud_level = categorize_fraud(fraud_rate)
vol_category = categorize_volume(monthly_vol)


target_txns = payments[
    (payments[] == MERCHANT) & (payments[] == YEAR) &
    (payments[].isin(TARGET_DAYS))
]
total_fees = 
 _, txn  target_txns.iterrows():
    rule = find_best_rule(txn, acct_type, cap_delay, mcc, fraud_level, vol_category)
     rule:
        total_fees += rule[] + rule[] * txn[] / 

((total_fees, ))

Full Year Variation

For a full year query, monthly stats differ per month. Precompute them and apply per-transaction:

# Precompute monthly stats for all 12 months
monthly_stats = {}
for month, (ms, me) in MONTH_RANGES.items():
    mt = payments[(payments['merchant']==MERCHANT) & (payments['year']==YEAR) &
                  (payments['day_of_year'].between(ms, me))]
    mv = mt['eur_amount'].sum()
    fv = mt[mt['has_fraudulent_dispute']==True]['eur_amount'].sum()
    fr = fv/mv if mv > 0 else 0
    monthly_stats[month] = (categorize_fraud(fr), categorize_volume(mv))

def get_month(day):
    for m, (s, e) in MONTH_RANGES.items():
        if s <= day <= e: return m
    return None

all_txns = payments[(payments['merchant']==MERCHANT) & (payments['year']==YEAR)]
total_fees = 0.0
for _, txn in all_txns.iterrows():
    m = get_month(txn['day_of_year'])
    fl, vc = monthly_stats[m]
    rule = find_best_rule(txn, acct_type, cap_delay, mcc, fl, vc)
    if rule:
        total_fees += rule['fixed_amount'] + rule['rate'] * txn['eur_amount'] / 10000
print(round(total_fees, 2))

---

Key Facts About the Data

• Merchants: Crossfit_Hanna, Belles_cookbook_store, Golfclub_Baron_Friso, Martinis_Fine_Steakhouse, Rafa_AI
• Years in data: 2023 only
• Card schemes: TransactPlus, GlobalCard, NexPay, SwiftCharge
• Empty list [] = "all values" — true for account_type, merchant_category_code, aci in fee rules
• MCC coverage: Many merchant MCCs (5942, 7372, 7993, 7997, etc.) are NOT in any explicit MCC list in fees.json; only rules with merchant_category_code: [] match these merchants
• intracountry: Always compute from payments.csv using issuing_country == acquirer_country — ignore merchant_data's acquirer list for this
• Fraud: has_fraudulent_dispute == True → has_fraudulent_dispute is a boolean column; fraud rate = sum(fraudulent eur_amount) / sum(total eur_amount) for the month
• Transactions with no matching rule: These have fee = 0 (excluded from total)

Common Pitfalls

1. Omitting monthly_fraud_level/monthly_volume from rule matching — these fields significantly narrow the candidates
2. Treating [] as "no match" — an empty list in a fee rule means "applies to all values"
3. Computing monthly stats from only the queried day — must use the entire natural month
4. Using merchant_data's acquirer to determine intracountry — use acquirer_country from payments.csv directly
5. Missing the numeric-to-category mapping for capture_delay — "1" → "<3", "7" → ">5", "4" → "3-5"