56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
"""
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Sheet 8: Consolidated macro dashboard.
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Aggregate time series, key ratios, stress flags from workbook and Monte Carlo.
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"""
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from typing import Any, Optional
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import pandas as pd
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from fqbm.state import FQBMState
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from fqbm.sheets.monte_carlo import run_n_simulations, ShockSpec
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def dashboard_aggregate(
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state: FQBMState,
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mc_runs: int = 0,
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shock_spec: Optional[ShockSpec] = None,
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) -> dict[str, Any]:
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"""
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Build dashboard: state snapshot, key ratios, optional MC summary.
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"""
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rwa = state.Loans
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cr = (state.E_b / rwa) if rwa > 0 else 0
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ratios = {
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"capital_ratio": cr,
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"reserves_to_deposits": (state.R / state.Deposits) if state.Deposits else 0,
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"loans_to_deposits": (state.Loans / state.Deposits) if state.Deposits else 0,
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}
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snapshot = {
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"B": state.B, "R": state.R, "C": state.C,
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"Loans": state.Loans, "Deposits": state.Deposits,
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"E_cb": state.E_cb, "E_b": state.E_b,
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"S": state.S, "Spread": state.Spread, "O": state.O,
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"L_cb": getattr(state, "L_cb", 0),
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}
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out = {"state": snapshot, "ratios": ratios}
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# Part VII: shadow banking leverage ratio (bank)
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try:
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from fqbm.sheets.shadow_banking import leverage_ratio
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total_assets_bank = state.Loans
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out["ratios"]["leverage_ratio_bank"] = leverage_ratio(total_assets_bank, state.E_b)
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except Exception:
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pass
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if mc_runs > 0:
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df = run_n_simulations(mc_runs, state, shock_spec)
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out["mc_summary"] = {
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"p_insolvency": df["insolvent"].mean(),
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"p_reserve_breach": df["reserve_breach"].mean(),
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"inflation_mean": df["inflation"].mean(),
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"inflation_std": df["inflation"].std(),
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"p_debt_unsustainable": 1 - df["debt_sustainable"].mean(),
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}
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return out
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