"""R632：证券主动权重贡献、费用与精确时间链接；不吞掉复合交互。"""
from decimal import Decimal as D
from math import prod


def weights(values):
    out={k:D(str(v)) for k,v in values.items()}
    if any(not v.is_finite() or v<0 for v in out.values()) or abs(sum(out.values())-1)>D('1e-20'):
        raise ValueError('invalid weights')
    return out


P=[weights({'A':.6,'B':.4}),weights({'A':.3,'B':.7})]
B=[weights({'A':.5,'B':.5})]*2
RET=[{'A':D('.10'),'B':D('0')},{'A':D('-.05'),'B':D('.04')}]
COST=[D('.001'),D('.002')]
portfolio=[];benchmark=[];effects=[]
for p,b,returns,cost in zip(P,B,RET,COST):
    if not cost.is_finite() or cost<0:raise ValueError('invalid modeled cost')
    if any(not r.is_finite() or r<-1 for r in returns.values()):raise ValueError('invalid return')
    rp=sum(p[s]*returns[s] for s in p)-cost;rb=sum(b[s]*returns[s] for s in b)
    if rp<=-1 or rb<=-1:raise ValueError('nonpositive growth base')
    row={s:(p[s]-b[s])*returns[s] for s in p};row['fees']=-cost
    assert sum(row.values())==rp-rb
    portfolio.append(rp);benchmark.append(rb);effects.append(row)
rp=prod(1+r for r in portfolio)-1;rb=prod(1+r for r in benchmark)-1
multipliers=[prod(1+r for r in portfolio[:t])*prod(1+r for r in benchmark[t+1:]) for t in range(2)]
linked={s:sum(effects[t][s]*multipliers[t] for t in range(2)) for s in effects[0]}
raw=sum(sum(row.values()) for row in effects);interaction=(rp-rb)-raw
assert rp==D('.070649') and rb==D('.04475')
assert linked=={'A':D('.020540'),'B':D('.008472'),'fees':D('-.003113')}
assert sum(linked.values())==rp-rb==D('.025899')
assert interaction==D('.000899')
def group_effects(values,mapping):
    grouped={}
    for security,value in values.items():
        if not value.is_finite():raise ValueError('nonfinite effect')
        group=mapping.get(security,'Unknown')
        grouped[group]=grouped.get(group,D(0))+value
    return grouped

def check_flows(values):
    if any(not D(str(x)).is_finite() or D(str(x))!=0 for x in values):
        raise ValueError('external flow outside attribution contract')

classified=group_effects({'A':D('.02'),'U':D('.003')},{'A':'Known'})
assert sum(classified.values())==D('.023')
assert classified['Unknown']==D('.003')
check_flows([0,0])
try:check_flows([0,100])
except ValueError:pass
else:raise AssertionError('contribution mixed into return attribution')
for bad in [{'A':float('nan'),'B':1},{'A':float('inf'),'B':0}]:
    try:weights(bad)
    except ValueError:pass
    else:raise AssertionError('nonfinite weights accepted')
print('portfolio=7.064900% benchmark=4.475000% arithmetic_excess=2.589900pp')
print('period_active=0.900000pp,1.600000pp raw_sum=2.500000pp temporal_interaction=0.089900pp')
print('linked_A=2.054000pp linked_B=0.847200pp linked_fees=-0.311300pp residual=0')
print('unknown_classification=retained NaN_weights=rejected')