"""Bounded educational2x staking models, independent win probability.485. Integer units, no fees/deposits, hypothetical cap, no real platform limits. Blocked progression retains residual capital; it is not automatically zero. All strategies share each generated path of opportunities. Pattern waits for three observed losses, bets once on the next opportunity, then resets its watch. Labouchere resets[1,2,3,4] after completing a list. Paroli resets after3wins. Usage: python staking_models.py [--initial 100 --cap 64 --strategy all] """ import argparse import json import logging import random from statistics import median STRATEGIES=('flat','martingale','fibonacci','dalembert','labouchere','pattern','paroli') def run_path(outcomes, initial=1000, base=1, cap=500, strategy='martingale', milestones=(), ledger=False): if strategy not in STRATEGIES or any(not isinstance(x,int) or x<1 for x in (initial,base,cap)): raise ValueError('Known strategy and positive integer parameters required') bank=initial;bet=base*5 if strategy=='labouchere' else base;executed=turnover=gross=0 level=win_run=observed_losses=0 fibonacci=[1,1];cancel=[1,2,3,4] blocked=None;snapshots={};rows=[] def reason(): insufficient=bet>bank;over_cap=bet>cap return 'funds_and_cap' if insufficient and over_cap else 'funds' if insufficient else 'cap' if over_cap else None def state():return dict(balance=bank,executed=executed,turnover=turnover,gross=gross, nextStake=bet,blocked=blocked,zeroBalance=bank==0) blocked=reason() for opportunity,won in enumerate(outcomes,1): if blocked:break place=strategy!='pattern' or observed_losses>=3 if place: before=bank;paid=2*bet if won else 0 bank+=paid-bet;executed+=1;turnover+=bet;gross+=paid if ledger:rows.append(dict(opportunity=opportunity,balanceBefore=before,stake=bet,won=won,gross=paid,balanceAfter=bank)) if strategy=='martingale':bet=base if won else bet*2 elif strategy=='fibonacci': level=max(0,level-2) if won else level+1 while level>=len(fibonacci):fibonacci.append(fibonacci[-1]+fibonacci[-2]) bet=base*fibonacci[level] elif strategy=='dalembert': level=max(0,level-1) if won else level+1;bet=base*(level+1) elif strategy=='labouchere': if won:cancel=cancel[1:-1] if len(cancel)>1 else [] else:cancel.append(bet//base) if not cancel:cancel=[1,2,3,4] bet=base*(cancel[0]+cancel[-1] if len(cancel)>1 else cancel[0]) elif strategy=='paroli': win_run=win_run+1 if won else 0 if win_run==3:win_run=0 bet=base*2**win_run elif strategy=='pattern':observed_losses=0 blocked=reason() elif strategy=='pattern':observed_losses=0 if won else observed_losses+1 if opportunity in milestones:snapshots[opportunity]=state() assert bank==initial+gross-turnover final=state() for point in milestones: if point<=len(outcomes) and point not in snapshots:snapshots[point]=dict(final) return dict(final=final,milestones=snapshots,ledger=rows) def benchmark(sessions=5000, initial=1000, base=1, cap=500, horizon=500, seed=20261023, strategies=('martingale',)): if any(not isinstance(x,int) or x<1 for x in (sessions,horizon)) or any(s not in STRATEGIES for s in strategies): raise ValueError('Positive sessions/horizon and known strategies required') points=tuple(n for n in (50,100,250,500) if n<=horizon) if horizon not in points:points+= (horizon,) samples={s:{n:[] for n in points} for s in strategies};rng=random.Random(seed) for _ in range(sessions): outcomes=[rng.random()<.485 for _ in range(horizon)] for strategy in strategies: for point,state in run_path(outcomes,initial,base,cap,strategy,points)['milestones'].items(): samples[strategy][point].append(state) results=[] for strategy in strategies: for point in points: states=samples[strategy][point];active=[s['balance'] for s in states if not s['blocked']] total_turnover=sum(s['turnover'] for s in states);total_gross=sum(s['gross'] for s in states) total_final=sum(s['balance'] for s in states) assert total_final==sessions*initial+total_gross-total_turnover results.append(dict(strategy=strategy,opportunities=point,sessions=sessions, blocked=sum(s['blocked'] is not None for s in states),active=len(active), zeroBalances=sum(s['zeroBalance'] for s in states), medianFinal=median(s['balance'] for s in states), medianActive=median(active) if active else None, meanNet=(total_gross-total_turnover)/sessions, meanTurnover=total_turnover/sessions,totalGross=total_gross, totalTurnover=total_turnover,totalFinal=total_final, blockReasons={reason:sum(s['blocked']==reason for s in states) for reason in ('funds','cap','funds_and_cap')})) return dict(scope='Synthetic independent2x model; no casino transactions or measured limits', seed=seed,pWin=.485,initial=initial,base=base,cap=cap,horizon=horizon,results=results) if __name__=='__main__': parser=argparse.ArgumentParser(description=__doc__) parser.add_argument('--sessions',type=int,default=5000) parser.add_argument('--initial',type=int,default=1000) parser.add_argument('--base',type=int,default=1) parser.add_argument('--cap',type=int,default=500) parser.add_argument('--horizon',type=int,default=500) parser.add_argument('--seed',type=int,default=20261023) parser.add_argument('--strategy',choices=(*STRATEGIES,'all'),default='martingale') args=parser.parse_args() result=benchmark(args.sessions,args.initial,args.base,args.cap,args.horizon,args.seed, STRATEGIES if args.strategy=='all' else (args.strategy,)) logging.basicConfig(level=logging.INFO,format='%(message)s') logging.info(json.dumps(result,indent=2))