Use Cases

# User uploads CAS PDF
cas_data = casparser.parse(pdf_file, password)

# Import to your database
for folio in cas_data['mutual_funds']:
    for scheme in folio['schemes']:
        for txn in scheme['transactions']:
            db.insert_transaction({
                'user_id': user.id,
                'date': txn['date'],
                'scheme': scheme['scheme'],
                'amount': txn['amount'],
                'units': txn['units'],
                'nav': txn['nav']
            })

# Step 1: Request CAS generation
casparser.generate_cas(
    email=client.email,
    pan=client.pan,
    from_date="2020-01-01",
    to_date="2024-12-31"
)

# Step 2: Client receives CAS in email, uploads to portal
# Step 3: Parse and analyze
cas_data = casparser.parse(pdf_file, password)

# Generate asset allocation report
allocation = calculate_allocation(cas_data)
recommendations = generate_recommendations(allocation, client.risk_profile)

cas_data = casparser.parse(pdf_file, password)

# Extract equity transactions
equity_txns = []
for account in cas_data['demat_accounts']:
    for equity in account['equities']:
        for txn in equity.get('transactions', []):
            equity_txns.append({
                'isin': equity['isin'],
                'date': txn['date'],
                'type': txn['type'],
                'quantity': txn['quantity'],
                'price': txn['price']
            })

# Calculate capital gains
gains = calculate_capital_gains(equity_txns, method='FIFO')

# Step 1: Request OTP from CDSL
session = casparser.cdsl_fetch(pan=borrower.pan, dob=borrower.dob)

# Step 2: Borrower enters OTP
cas_urls = casparser.cdsl_verify(session_id, otp)

# Step 3: Parse and assess
cas_data = casparser.parse_from_url(cas_urls['detailed'])

# Calculate credit score factors
total_portfolio_value = cas_data['summary']['total_value']
equity_exposure = calculate_equity_percentage(cas_data)
loan_eligibility = assess_eligibility(total_portfolio_value, equity_exposure)

# Batch processing
results = []
for cas_file in cas_files:
    data = casparser.parse(cas_file, password)
    results.append({
        'total_value': data['summary']['total_value'],
        'equity_count': len(data['demat_accounts'][0]['equities']),
        'mf_count': sum(len(f['schemes']) for f in data['mutual_funds']),
        'age_group': classify_age(data['investor']['name'])
    })

# Aggregate insights
avg_portfolio_value = statistics.mean(r['total_value'] for r in results)
popular_stocks = Counter(e['isin'] for r in results for e in r['equities']).most_common(10)

cas_data = casparser.parse(pdf_file, password)

# Current allocation
current = {
    'equity': calculate_equity_value(cas_data),
    'debt': calculate_debt_value(cas_data),
    'gold': calculate_gold_value(cas_data)
}

# Target allocation (based on risk profile)
target = {'equity': 0.6, 'debt': 0.3, 'gold': 0.1}

# Rebalancing suggestions
suggestions = generate_rebalancing_plan(current, target)

cas_data = casparser.parse(pdf_file, password)

# Extract existing insurance
existing_coverage = sum(p['sum_assured'] for p in cas_data['insurance'])

# Calculate recommended coverage
portfolio_value = cas_data['summary']['total_value']
recommended_coverage = portfolio_value * 10  # 10x rule

# Gap analysis
coverage_gap = recommended_coverage - existing_coverage