#!/usr/bin/env python3 """Reproduce the Hyperliquid article. Python 3.10+, standard library only. Run: python model.py [--inputs inputs.json] [--output results.json] This is a conditional token-directed-flow valuation, not an audited-company DCF. All operating scenarios are author assumptions. Headline historical receipts already contain reserve yield; no historical 'pure trading take rate' is inferred. """ from __future__ import annotations import argparse import csv import json import math from pathlib import Path from typing import Any def validate(data: dict[str, Any]) -> None: assert 0 <= data['terminalGrowth'] < data['discountRate'] < 1 assert data['supplyM'] > 0 and 0 <= data['reserveShare'] <= 1 assert data['explicitYears'] == 5 and data['fadeYears'] == 5 for name, scenario in data['cases'].items(): for key, values in scenario.items(): if isinstance(values, list): assert len(values) == 5, (name, key) assert all(isinstance(v, (int, float)) and math.isfinite(v) and v >= 0 for v in values) terminal_volume = scenario['cryptoMarket2031T'] * scenario['onchainShare2031'] * scenario['hyperliquidShare2031'] assert math.isclose(terminal_volume, scenario['cryptoVolumeT'][-1], rel_tol=1e-12) def explicit_flows(s: dict[str, Any], reserve_share: float) -> list[dict[str, float]]: """T * basis points / 10 = USD billions. All trading rates already net to HYPE.""" result = [] for i in range(5): streams = { 'cryptoB': s['cryptoVolumeT'][i] * s['cryptoNetBps'][i] / 10, 'noncryptoB': s['noncryptoVolumeT'][i] * s['noncryptoNetBps'][i] / 10, 'spotB': s['spotVolumeT'][i] * s['spotNetBps'][i] / 10, 'reservesB': s['eligibleStablecoinBalanceB'][i] * s['costAdjustedReserveYield'][i] * reserve_share, 'otherB': s['otherNetFeesB'][i], 'outcomesB': s['outcomeNetFeesB'][i], } result.append({'year': 2027 + i, **streams, 'totalB': sum(streams.values())}) return result def value(flows5: list[float], r: float = .18, g: float = .03, supply_m: float = 955.31) -> dict[str, Any]: if len(flows5) != 5 or not all(math.isfinite(x) and x > 0 for x in flows5) or not (0 <= g < r < 1) or not math.isfinite(supply_m) or supply_m <= 0: raise ValueError('Need five positive flows, positive supply, and 0 <= g < r < 1.') flows = list(flows5) last_growth = flows[-1] / flows[-2] - 1 fade_rates = [last_growth + (g-last_growth)*j/5 for j in range(1, 6)] for growth in fade_rates: flows.append(flows[-1] * (1 + growth)) pv_flows = [flow / (1+r)**t for t, flow in enumerate(flows, 1)] terminal_at_10 = flows[-1] * (1+g) / (r-g) pv_terminal = terminal_at_10 / (1+r)**10 pv_explicit = sum(pv_flows) total = pv_explicit + pv_terminal return { 'flowsB': flows, 'fadeGrowthRates': fade_rates, 'pvFlowsB': pv_flows, 'pvExplicitB': pv_explicit, 'terminalAt2036B': terminal_at_10, 'pvTerminalB': pv_terminal, 'valueB': total, 'perToken': total * 1000 / supply_m, 'terminalShare': pv_terminal / total, } def solve_growth(start_b: float, target_b: float, r: float, g: float, supply_m: float = 955.31) -> dict[str, Any]: """Inverse valuation: five equal-growth years, then the same five-year fade.""" if not all(math.isfinite(x) and x > 0 for x in (start_b, target_b)): raise ValueError('Positive starting flow and target value required.') lo, hi = -.5, 3. def objective(c: float) -> float: return value([start_b*(1+c)**t for t in range(1, 6)], r, g, supply_m)['valueB'] - target_b if objective(lo) > 0 or objective(hi) < 0: raise ValueError('Target outside inverse-model bracket.') for _ in range(100): mid = (lo+hi)/2 if objective(mid) > 0: hi = mid else: lo = mid c = (lo+hi)/2 return {'growth': c, **value([start_b*(1+c)**t for t in range(1, 6)], r, g, supply_m)} def build(data: dict[str, Any]) -> dict[str, Any]: validate(data) r, g, supply = data['discountRate'], data['terminalGrowth'], data['supplyM'] cases = {} for key, scenario in data['cases'].items(): periods = explicit_flows(scenario, data['reserveShare']) cases[key] = {'periods': periods, **value([x['totalB'] for x in periods], r, g, supply)} obs = data['observed'] annualization = 365/30 start_b = obs['allProductHolderReceipts30dM']/1000 * annualization reference_b = data['referencePrice'] * supply/1000 base5 = [p['totalB'] for p in cases['base']['periods']] inverse = solve_growth(start_b, reference_b, r, g, supply) result = { 'asOf': data['asOf'], 'cases': cases, 'diagnostics': { 'annualizedPerpVolumeT': obs['perpVolume30dB']/1000*annualization, 'annualizedAllProductReceiptsB': start_b, 'mixedProductReceiptRatioBps': obs['perpProductHolderReceipts30dM']/obs['perpVolume30dB']*10, 'notAPureTradingTakeRate': True, 'reportedFeeToHolderDifferenceM': obs['perpProductFees30dM']-obs['perpProductHolderReceipts30dM'], 'q3UnreconciledDifferenceM': obs['q3ParentGrossM']-obs['q3ParentCostM']-obs['q3ParentReportedProfitM'], 'referenceValueB': reference_b, 'baseVolumeMultiple': sum((data['cases']['base'][x][-1] for x in ('cryptoVolumeT','noncryptoVolumeT')))/(obs['perpVolume30dB']/1000*annualization), }, 'inverse': inverse, 'sensitivity': [ {'r': rate, 'g': growth, 'perToken': value(base5, rate, growth, supply)['perToken']} for rate in (.12,.15,.18,.21,.24) for growth in (.02,.03,.04) ], 'supplySensitivity': [ {'supplyM': n, 'perToken': cases['base']['valueB']*1000/n} for n in (222.45,501.850666,707,955.31,1000) ], 'baseAt12PercentAnd707M': value(base5,.12,.03,707)['perToken'], } return result def main() -> None: parser = argparse.ArgumentParser(description=__doc__) parser.add_argument('--inputs', type=Path, default=Path(__file__).with_name('inputs.json')) parser.add_argument('--output', type=Path, default=Path(__file__).with_name('results.json')) args = parser.parse_args() data = json.loads(args.inputs.read_text()) result = build(data) args.output.write_text(json.dumps(result, indent=2)+'\n') with args.output.with_name('cash-flows.csv').open('w', newline='') as f: writer = csv.writer(f) writer.writerow(['scenario','period_end','phase','token_directed_flow_usd_b','pv_usd_b']) for name, case in result['cases'].items(): for i, flow in enumerate(case['flowsB']): writer.writerow([name,f'{2027+i}-10-14','driver_forecast' if i<5 else 'aggregate_growth_fade',flow,case['pvFlowsB'][i]]) print(json.dumps({k: {'valueB': v['valueB'], 'perToken': v['perToken']} for k,v in result['cases'].items()},indent=2)) if __name__ == '__main__': main()