""" Broker fee simulation v3 — TWEE SCENARIO'S Scenario A: "De passieve DCA-belegger" - €300/maand passief naar IWDA - Geen lump sums, geen losse trades - Cash buffer €2.000 (modest) - 30 jaar -> Hier wint TR/SC FREE op DCA, BUX verliest dramatisch Scenario B: "De actieve langetermijn belegger" - €250/maand IWDA + €50/maand EIMI (DCA) - €2.000/jaar lump sum in IWDA - 6 losse trades per jaar (rebalancing, individuele aandelen) van €500 elk - Cash buffer €10.000 (voor opportunities en crypto-allocatie) - 30 jaar -> Hier kantelt het verhaal sterk in het voordeel van SC PRIME+: - Losse trades >€250 zijn FREE bij PRIME+ vs €0,99 bij FREE - 2,5% rente op €10k buffer = €250/jaar credit - DEGIRO wordt duurder: 6×€3 voor non-core trades + per-ETF handling - BUX Basic verliest catastrofaal door servicefee compounding op grotere portefeuille """ import json from dataclasses import dataclass from typing import Callable YEARS = 30 ANNUAL_RETURN = 0.07 SPREAD_GETTEX = 0.0010 SPREAD_TRADEGATE = 0.0010 SPREAD_BUX_ZERO = 0.0015 @dataclass class BrokerCosts: transaction_costs: float = 0.0 spread_costs: float = 0.0 service_fees: float = 0.0 cash_interest: float = 0.0 @dataclass class Scenario: name: str description: str monthly_dca_total: float # Total monthly DCA (across all ETFs) num_etfs_in_plan: int # How many separate ETFs (matters for DEGIRO) yearly_lump: float # Yearly lump sum extra_trades_per_year: int # Active trades (each €500) extra_trade_size: float cash_buffer: float @property def annual_contribution(self) -> float: # All money flowing into the portfolio return self.monthly_dca_total * 12 + self.yearly_lump + self.extra_trades_per_year * self.extra_trade_size SCENARIO_A = Scenario( name="A. Passieve DCA-belegger", description="€300/maand IWDA, geen extra's, €2.000 cash buffer", monthly_dca_total=300.0, num_etfs_in_plan=1, yearly_lump=0.0, extra_trades_per_year=0, extra_trade_size=0.0, cash_buffer=2000.0, ) SCENARIO_B = Scenario( name="B. Actieve langetermijn-belegger", description="€250 IWDA + €50 EIMI maandelijks, €2.000 lump sum/jaar, 6 losse trades/jaar (€500 elk), €10.000 buffer", monthly_dca_total=300.0, num_etfs_in_plan=2, yearly_lump=2000.0, extra_trades_per_year=6, extra_trade_size=500.0, cash_buffer=10000.0, ) def make_scalable_free(scn: Scenario): def fn(year_idx, avg_portfolio): # Savings plan orders: FREE # Lump sum: €0.99 if exists # Extra trades (each <€250 trigger €0.99, ≥€250 also €0.99 on FREE plan) tc = 0.0 if scn.yearly_lump > 0: tc += 0.99 tc += scn.extra_trades_per_year * 0.99 # FREE plan: €0.99 per order spread = scn.annual_contribution * SPREAD_GETTEX return BrokerCosts(transaction_costs=tc, spread_costs=spread, cash_interest=0.0) return fn def make_scalable_prime_plus(scn: Scenario): def fn(year_idx, avg_portfolio): service = 4.99 * 12 # PRIME+: orders ≥€250 are FREE on Gettex # Lump sum (€2.000) ≥€250: FREE # Extra trades (€500 each): FREE # Lump sum <€250 would still cost €0.99 — none here tc = 0.0 spread = scn.annual_contribution * SPREAD_GETTEX cash_int = scn.cash_buffer * 0.025 return BrokerCosts( transaction_costs=tc, spread_costs=spread, service_fees=service, cash_interest=cash_int, ) return fn def make_trade_republic(scn: Scenario): def fn(year_idx, avg_portfolio): # Savings plan: free # Lump sum + extra trades: €1 each tc = 0.0 if scn.yearly_lump > 0: tc += 1.0 tc += scn.extra_trades_per_year * 1.0 spread = scn.annual_contribution * 0.0010 cash_int = scn.cash_buffer * 0.02 return BrokerCosts(transaction_costs=tc, spread_costs=spread, cash_interest=cash_int) return fn def make_degiro(scn: Scenario): def fn(year_idx, avg_portfolio): # No automatic plan - manual orders monthly per ETF # 12 months × N_ETFs × €1 handling tc = 12 * scn.num_etfs_in_plan * 1.0 # Yearly lump sum: same direction, >€1000, same month -> FREE if same ETF as DCA # Conservative: assume FREE # Extra trades: assume non-core ETFs / aandelen, €3 + €1 handling = €4 per trade # (Half core ETFs at €1, half non-core at €4 — average €2.50) # For realism: extra trades are diverse (rebalancing/individual) -> €4 average tc += scn.extra_trades_per_year * 4.0 spread = scn.annual_contribution * SPREAD_TRADEGATE return BrokerCosts(transaction_costs=tc, spread_costs=spread, cash_interest=0.0) return fn def make_bux_basic(scn: Scenario): def fn(year_idx, avg_portfolio): # Plan = free, zero orders for lump = free # Extra trades: market/limit orders €3.99 on Basic (worst tier) tc = 0.0 tc += scn.extra_trades_per_year * 3.99 spread = scn.annual_contribution * SPREAD_BUX_ZERO service = avg_portfolio * 0.0020 return BrokerCosts(transaction_costs=tc, spread_costs=spread, service_fees=service) return fn def simulate(name: str, cost_fn: Callable, scn: Scenario, years: int = YEARS) -> dict: portfolio = 0.0 cum_fees = 0.0 cum_int = 0.0 yearly = [] for year in range(years): avg_portfolio = portfolio + scn.annual_contribution / 2 c = cost_fn(year, avg_portfolio) gross_fees = c.transaction_costs + c.spread_costs + c.service_fees net_drag = gross_fees - c.cash_interest effective = scn.annual_contribution - net_drag portfolio = portfolio * (1 + ANNUAL_RETURN) + effective * (1 + ANNUAL_RETURN / 2) cum_fees += gross_fees cum_int += c.cash_interest yearly.append({ "year": year + 1, "portfolio": round(portfolio, 2), "transaction_costs": round(c.transaction_costs, 2), "spread_costs": round(c.spread_costs, 2), "service_fees": round(c.service_fees, 2), "cash_interest": round(c.cash_interest, 2), }) return { "broker": name, "scenario": scn.name, "final_portfolio": round(portfolio, 2), "total_gross_fees": round(cum_fees, 2), "total_cash_interest": round(cum_int, 2), "net_total_cost": round(cum_fees - cum_int, 2), "total_invested": round(scn.annual_contribution * years, 2), "yearly": yearly, } def run_scenario(scn: Scenario): brokers = [ ("Scalable Capital (FREE)", make_scalable_free(scn)), ("Scalable Capital (PRIME+)", make_scalable_prime_plus(scn)), ("Trade Republic", make_trade_republic(scn)), ("DEGIRO", make_degiro(scn)), ("BUX (Basic)", make_bux_basic(scn)), ] results = [simulate(n, fn, scn) for n, fn in brokers] results.sort(key=lambda r: r["final_portfolio"], reverse=True) print("=" * 95) print(f"SCENARIO: {scn.name}") print(f" {scn.description}") print(f" Totaal ingelegd: €{scn.annual_contribution * YEARS:,.0f} over {YEARS} jaar") print("=" * 95) print(f"{'Broker':<30} {'Eindwaarde':>14} {'Fees':>10} {'Rente':>10} {'Netto':>10}") print("-" * 95) for r in results: print( f"{r['broker']:<30} " f"€{r['final_portfolio']:>11,.0f} " f"€{r['total_gross_fees']:>7,.0f} " f"€{r['total_cash_interest']:>7,.0f} " f"€{r['net_total_cost']:>7,.0f}" ) print() winner = results[0] print(f"Winnaar: {winner['broker']} (€{winner['final_portfolio']:,.0f})") print() print("Verschillen tov winnaar:") for r in results[1:]: diff = winner["final_portfolio"] - r["final_portfolio"] pct = diff / winner["final_portfolio"] * 100 print(f" {r['broker']:<30} −€{diff:>10,.0f} ({pct:>5.2f}%)") print() return results if __name__ == "__main__": print() res_a = run_scenario(SCENARIO_A) print() res_b = run_scenario(SCENARIO_B) with open("/home/claude/simulation_v3.json", "w") as f: json.dump({"scenario_a": res_a, "scenario_b": res_b}, f, indent=2)