"""Original Litecoin.watch teaching datasets; not observed market/account data.
Python 3 standard library only. Writes CSV fixtures and computed JSON beside this file.
"""
from decimal import Decimal as D, ROUND_HALF_UP
from pathlib import Path
from datetime import date
import csv,json,hashlib,math
B=Path(__file__).parent

def csvwrite(name,rows):
 with (B/name).open('w',newline='',encoding='utf-8') as f:
  w=csv.DictWriter(f,fieldnames=list(rows[0]));w.writeheader();w.writerows(rows)
rows=[]
def row(id,day,account,type,amount,fee='0',transfer=''):
 rows.append(dict(row_id=id,date_utc=day+'T12:00:00Z',account=account,event=type,asset='LTC',amount=amount,fee=fee,transfer_id=transfer))
row('L01','2026-09-01','exchange','buy','10','.02')
row('L02','2026-09-02','exchange','transfer_out','-6','.001','T01')
row('L03','2026-09-02','hot','transfer_in','6','0','T01')
row('L04','2026-09-03','hot','transfer_out','-2','.0002','T02')
row('L05','2026-09-03','cold','transfer_in','2','0','T02')
row('L06','2026-09-04','exchange','sell','-1.5')
row('L07','2026-09-05','hot','reward','.25')
row('L08','2026-09-06','cold','transfer_out','-.75','.0001','T03')
row('L09','2026-09-06','exchange','transfer_in','.75','0','T03')
row('L10','2026-09-07','exchange','buy','2')
csvwrite('reconciliation-ledger.csv',rows)
def balance(rs):
 b={a:D(0) for a in ['exchange','hot','cold']}
 for r in rs:b[r['account']]+=D(r['amount'])-D(r['fee'])
 return b
b=balance(rows);assert b=={'exchange':D('5.229'),'hot':D('4.2498'),'cold':D('1.2499')}
assert sum(b.values())==D('10.7287')
mutations=[]
for kind in range(6):
 rr=[dict(r) for r in rows]
 if kind==0:rr[0]['fee']='0';name='Omit LTC purchase fee'
 if kind==1:
  for r in rr:
   if r['event']=='transfer_out':r['fee']='0'
  name='Omit all transfer fees'
 if kind==2:rr=[r for r in rr if r['row_id']!='L05'];name='Drop cold-wallet receipt'
 if kind==3:rr.append(dict(rr[-1]));name='Duplicate final buy'
 if kind==4:rr[1]['amount']='-6.001';name='Subtract withdrawal fee twice'
 if kind==5:rr=[r for r in rr if r['row_id']!='L07'];name='Omit reward at export boundary'
 result=balance(rr);mutations.append(dict(error=name,total_ltc=str(sum(result.values())),residual_ltc=str(sum(result.values())-sum(b.values()))))
csvwrite('reconciliation-errors.csv',mutations)
# Year length is 365 days; the intermediate cash flow occurs after 182 days.
dates=[date(2025,1,1),date(2025,7,2),date(2026,1,1)]
def npv(r,flows):return sum(v/(1+r)**((d-dates[0]).days/365) for d,v in flows)
def xirr(flows):
 lo=-.9999;hi=10
 assert npv(lo,flows)*npv(hi,flows)<0
 for _ in range(180):
  mid=(lo+hi)/2
  if npv(mid,flows)>0:lo=mid
  else:hi=mid
 return (lo+hi)/2
cases=[('Hold',0,1000),('Add at midpoint',-9000,8500),('Withdraw at midpoint',900,250)]
returns=[];flowscsv=[]
for label,mid,end in cases:
 flows=[(dates[0],-1000),(dates[1],mid),(dates[2],end)]
 rate=xirr(flows);assert abs(npv(rate,flows))<1e-7
 returns.append(dict(case=label,initial_contribution=1000,midpoint_investor_cashflow=mid,ending_value=end,profit=end+mid-1000,twr_pct=0,xirr_pct=rate*100))
 for day,v in flows:flowscsv.append(dict(case=label,date=day.isoformat(),investor_cashflow=v))
csvwrite('performance-cashflows.csv',flowscsv);csvwrite('performance-results.csv',returns)
# Toy Merkle tree. Explicit domain separation and fixed 8-decimal balances.
def H(b):return hashlib.sha256(b).digest()
def leaf(s):return H(b'\x00'+s.encode('ascii'))
def parent(a,b):return H(b'\x01'+a+b)
records=['A|2.00000000','B|3.00000000','C|4.00000000','D|1.00000000']
h=[leaf(s) for s in records];root=parent(parent(h[0],h[1]),parent(h[2],h[3]))
proof=[{'side':'left','hash':h[0].hex()},{'side':'right','hash':parent(h[2],h[3]).hex()}]
def verify(record,proof,expected):
 acc=leaf(record)
 for p in proof:
  s=bytes.fromhex(p['hash']);acc=parent(s,acc) if p['side']=='left' else parent(acc,s)
 return acc.hex()==expected
assert verify(records[1],proof,root.hex());assert not verify('B|3.10000000',proof,root.hex())
# An omitted fifth customer's balance does not change inclusion of B in the published four-leaf tree.
merkle={'scheme':'Teaching example only; not Kraken or OKX format','leaf_rule':'SHA256(0x00 || ASCII(id|amount_8dp))','parent_rule':'SHA256(0x01 || left_32_bytes || right_32_bytes)','records':records,'root':root.hex(),'B_proof':proof,'original_B_verifies':True,'altered_B_verifies':False,'published_liabilities_ltc':'10','reserves_ltc':'10.5','omitted_external_record':'E|5.00000000','reported_reserve_pct':'105','full_ledger_reserve_pct':'70','B_still_verifies_if_E_was_omitted':True}
(B/'merkle-example.json').write_text(json.dumps(merkle,indent=2))
# Refund sensitivity; an illustrative $120 invoice originally settled with 1.5 LTC at $80.
refund=[]
for price in [60,80,100]:
 for policy in ['Original LTC','Original USD']:
  ltc=D('1.5') if policy=='Original LTC' else D(120)/D(price)
  refund.append(dict(price_usd=price,policy=policy,refund_ltc=str(ltc),refund_value_usd=str(ltc*D(price))))
csvwrite('refund-sensitivity.csv',refund)
result={'dataset_type':'Synthetic teaching fixtures, computed 2026-09-22','reconciliation':{'rows':len(rows),'balances':{k:str(v) for k,v in b.items()},'total':'10.7287','errors':mutations},'performance':returns,'merkle':merkle,'refund':refund}
(B/'calculations.json').write_text(json.dumps(result,indent=2));print(json.dumps(result,indent=2))