{
“cells”: [
{

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“# Multi-Asset Repricing: The Prayer Workflown”, “n”, “This notebook demonstrates the full Prayer (Meucci 2011) chain:n”, “Invariants → Estimate → Project → Reprice → Optimisen”, “for stocks, bonds, and options in a single portfolio.”

]

}, {

“cell_type”: “code”, “execution_count”: 1, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:41.538137Z”, “iopub.status.busy”: “2026-03-30T19:38:41.537637Z”, “iopub.status.idle”: “2026-03-30T19:38:42.199481Z”, “shell.execute_reply”: “2026-03-30T19:38:42.199166Z”

}

}, “outputs”: [], “source”: [

“import numpy as npn”, “import pandas as pdn”, “from pyvallocation import (n”, “ project_scenarios, reprice_exp, reprice_taylor, make_repricing_fn,n”, “ PortfolioWrapper,n”, “)n”, “from pyvallocation.plotting import plot_frontiersn”, “import matplotlib.pyplot as pltn”, “n”, “rng = np.random.default_rng(42)”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## P1: Invariantsn”, “n”, “| Instrument | Risk Driver | Invariant |\n", "||---||\n", "| Stock/ETF | log price | log return |\n", "| Bond | yield | yield change |\n", "| Option | log price + log implied vol | joint changes |

]

}, {

“cell_type”: “code”, “execution_count”: 2, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:42.200738Z”, “iopub.status.busy”: “2026-03-30T19:38:42.200636Z”, “iopub.status.idle”: “2026-03-30T19:38:42.202973Z”, “shell.execute_reply”: “2026-03-30T19:38:42.202786Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Stock invariants: (500, 1)n”, “Yield invariants: (500, 1)n”, “Option invariants: (500, 2)n”

]

}

], “source”: [

“# Simulate daily invariants (in practice: extract from market data)n”, “T = 500n”, “stock_inv = rng.normal(0.0005, 0.015, (T, 1)) # daily log returnsn”, “yield_inv = rng.normal(0.0, 0.003, (T, 1)) # daily yield changesn”, “option_inv = rng.multivariate_normal( # (log-return, vol-change)n”, “ [0.0005, 0.0], [[0.015**2, 0], [0, 0.005**2]], Tn”, “)n”, “print(f"Stock invariants: {stock_inv.shape}")n”, “print(f"Yield invariants: {yield_inv.shape}")n”, “print(f"Option invariants: {option_inv.shape}")”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## P3+P4: Project to 1-month horizon and reprice”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:42.217415Z”, “iopub.status.busy”: “2026-03-30T19:38:42.217311Z”, “iopub.status.idle”: “2026-03-30T19:38:42.229830Z”, “shell.execute_reply”: “2026-03-30T19:38:42.229617Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“Stock mean=+0.0101 std=0.0689n”, “Bond mean=+2.7506 std=10.1273n”, “Option mean=-0.0100 std=0.0277n”, “n”, “After normalizing to percentage returns:n”, “Stock mean=+0.010051 std=0.068859n”, “Bond (pct) mean=+0.041034 std=0.151082n”, “Option (pct) mean=-0.003993 std=0.007507n”

]

}

], “source”: [

“horizon = 21 # trading daysn”, “n_sim = 3000n”, “n”, “# Stock: exact repricing via exp(Δy) - 1n”, “stock_scen = project_scenarios(stock_inv, horizon, n_simulations=n_sim, reprice=reprice_exp)n”, “n”, “# Bond: full repricing via user pricing functionn”, “maturity = 10n”, “bond_price = lambda Y: 100 * np.exp(-Y * maturity)n”, “bond_scen = project_scenarios(n”, “ yield_inv, horizon, n_simulations=n_sim,n”, “ reprice=make_repricing_fn(bond_price, np.array([0.04])),n”, “)n”, “n”, “# Normalize bond P&L to percentage return (divide by initial bond price)n”, “bond_scen_pct = bond_scen / bond_price(np.array([0.04]))n”, “n”, “# Option: delta-gamma-theta Taylor approximationn”, “delta, gamma, vega, theta = 0.55, 0.03, 0.15, -0.02n”, “option_scen = project_scenarios(n”, “ option_inv, horizon, n_simulations=n_sim,n”, “ reprice=lambda dy: reprice_taylor(n”, “ dy, delta=np.array([delta, vega]),n”, “ gamma=np.array([gamma, 0.0]),n”, “ theta=theta, tau=horizon/252,n”, “ ),n”, “)n”, “n”, “# Normalize option P&L to percentage return (divide by notional option price)n”, “option_price_0 = 5.0 # assumed initial option price in dollarsn”, “option_scen_pct = option_scen / option_price_0n”, “# Sum across risk drivers to get single-instrument P&Ln”, “option_scen_pct = option_scen_pct.sum(axis=1, keepdims=True)n”, “n”, “for name, s in [("Stock", stock_scen), ("Bond", bond_scen), ("Option", option_scen)]:n”, “ print(f"{name:8s} mean={np.mean(s):+.4f} std={np.std(s):.4f}")n”, “n”, “print("\nAfter normalizing to percentage returns:")n”, “for name, s in [("Stock", stock_scen), ("Bond (pct)", bond_scen_pct), ("Option (pct)", option_scen_pct)]:n”, “ print(f"{name:12s} mean={np.mean(s):+.6f} std={np.std(s):.6f}")”

]

}, {

“cell_type”: “markdown”, “metadata”: {}, “source”: [

“## P5-P8: Combine and optimise”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:42.230815Z”, “iopub.status.busy”: “2026-03-30T19:38:42.230756Z”, “iopub.status.idle”: “2026-03-30T19:38:45.308093Z”, “shell.execute_reply”: “2026-03-30T19:38:45.307883Z”

}

}, “outputs”: [

{

“name”: “stderr”, “output_type”: “stream”, “text”: [

“Estimating mu and cov from scenarios (no explicit values provided).n”

]

}, {

“data”: {

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“text/plain”: [

“<Figure size 1200x500 with 2 Axes>”

]

}, “metadata”: {}, “output_type”: “display_data”

}

], “source”: [

“all_scen = np.column_stack([stock_scen, bond_scen_pct, option_scen_pct])n”, “wrapper = PortfolioWrapper.from_scenarios(all_scen)n”, “n”, “mv = wrapper.variance_frontier(num_portfolios=12)n”, “cvar = wrapper.cvar_frontier(num_portfolios=12, alpha=0.05, seed=42)n”, “n”, “fig, axes = plt.subplots(1, 2, figsize=(12, 5))n”, “plot_frontiers(mv, labels=["Mean-Variance"], ax=axes[0])n”, “axes[0].set_title("MV Frontier (Stock + Bond + Option)")n”, “plot_frontiers(cvar, labels=["Mean-CVaR"], ax=axes[1])n”, “axes[1].set_title("CVaR Frontier (Stock + Bond + Option)")n”, “fig.tight_layout()n”, “plt.show()”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:45.309247Z”, “iopub.status.busy”: “2026-03-30T19:38:45.309171Z”, “iopub.status.idle”: “2026-03-30T19:38:45.312498Z”, “shell.execute_reply”: “2026-03-30T19:38:45.312245Z”

}

}, “outputs”: [

{

“name”: “stdout”, “output_type”: “stream”, “text”: [

“ Min-Variance Min-CVaRn”, “Stock 0.009 0.022n”, “Bond 0.002 0.011n”, “Option 0.988 0.967n”, “n”, “MV: return=-0.0038, vol=0.0075n”, “CVaR: return=-0.0032, CVaR=0.0186n”

]

}

], “source”: [

“w_mv, ret_mv, risk_mv = mv.min_risk()n”, “w_cvar, ret_cvar, risk_cvar = cvar.min_risk()n”, “n”, “result = pd.DataFrame({n”, “ "Min-Variance": w_mv,n”, “ "Min-CVaR": w_cvar,n”, “})n”, “result.index = ["Stock", "Bond", "Option"]n”, “print(result.round(3))n”, “print(f"\nMV: return={ret_mv:.4f}, vol={risk_mv:.4f}")n”, “print(f"CVaR: return={ret_cvar:.4f}, CVaR={risk_cvar:.4f}")n”

]

}

], “metadata”: {

“kernelspec”: {

“display_name”: “Python 3”, “language”: “python”, “name”: “python3”

}, “language_info”: {

“codemirror_mode”: {

“name”: “ipython”, “version”: 3

}, “file_extension”: “.py”, “mimetype”: “text/x-python”, “name”: “python”, “nbconvert_exporter”: “python”, “pygments_lexer”: “ipython3”, “version”: “3.12.9”

}

}, “nbformat”: 4, “nbformat_minor”: 4

}