{
“cells”: [
{

“cell_type”: “code”, “execution_count”: 1, “id”: “b2ffd8c4”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:30.766550Z”, “iopub.status.busy”: “2026-03-30T19:38:30.766339Z”, “iopub.status.idle”: “2026-03-30T19:38:31.475352Z”, “shell.execute_reply”: “2026-03-30T19:38:31.475085Z”

}

}, “outputs”: [

{

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

“Loaded Index([‘DBC’, ‘GLD’, ‘SPY’, ‘TLT’], dtype=’object’)n”

]

}

], “source”: [

“import pandas as pdn”, “import numpy as npn”, “import matplotlib.pyplot as pltn”, “from pathlib import Pathn”, “from pyvallocation.views import FlexibleViewsProcessorn”, “from pyvallocation.portfolioapi import AssetsDistribution, PortfolioWrappern”, “from pyvallocation.utils.projection import convert_scenarios_compound_to_simple, project_scenariosn”, “from pyvallocation import probabilitiesn”, “import seaborn as snsn”, “n”, “# load daily close data for some ETFsn”, “_candidates = [n”, “ Path("examples/ETF_prices.csv"),n”, “ Path("../examples/ETF_prices.csv"),n”, “ Path("../../examples/ETF_prices.csv"),n”, “ Path("../../../examples/ETF_prices.csv"),n”, “]n”, “_csv = next((p for p in _candidates if p.exists()), None)n”, “if _csv is None:n”, “ raise FileNotFoundError("ETF_prices.csv not found")n”, “df = pd.read_csv(_csv, index_col=0, parse_dates=True)n”, “print(‘Loaded ‘, df.columns)n”, “n”, “# resample to weekly frequencyn”, “weekly_prices = df.resample(‘W’).ffill()n”, “n”, “# compute compounded returnsn”, “weekly_returns = np.log(weekly_prices).diff().dropna()n”, “n”, “# store the returns shapen”, “T, N = weekly_returns.shapen”, “n”, “# inputs/parametersn”, “ANNUALIZATION_FACTOR = 52n”, “HALF_LIFE = ANNUALIZATION_FACTOR * 10 # 10 yearsn”, “TARGET_RETURN = 0.05/ANNUALIZATION_FACTORn”, “MAX_CVAR = 0.1/ANNUALIZATION_FACTOR**.5n”, “n”, “INVESTMENT_HORIZON = 52 # weeks aheadn”, “TARGET_RETURN_HORIZON = TARGET_RETURN*INVESTMENT_HORIZONn”, “MAX_CVAR_HORIZON = MAX_CVAR*(INVESTMENT_HORIZON**.5)”

]

}, {

“cell_type”: “markdown”, “id”: “9ea2a883”, “metadata”: {}, “source”: [

“## Probability vector”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “c9996f20”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:31.476554Z”, “iopub.status.busy”: “2026-03-30T19:38:31.476484Z”, “iopub.status.idle”: “2026-03-30T19:38:31.478191Z”, “shell.execute_reply”: “2026-03-30T19:38:31.477990Z”

}

}, “outputs”: [], “source”: [

“p_exp = probabilities.generate_exp_decay_probabilities(T,HALF_LIFE)n”, “n”, “# you can use also uniform probabilitiesn”, “# p_uniform = probabilities.generate_uniform_probabilities(T)”

]

}, {

“cell_type”: “markdown”, “id”: “deb6fa5f”, “metadata”: {}, “source”: [

“## Horizon projection”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “34b2c606”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:31.479279Z”, “iopub.status.busy”: “2026-03-30T19:38:31.479219Z”, “iopub.status.idle”: “2026-03-30T19:38:31.484306Z”, “shell.execute_reply”: “2026-03-30T19:38:31.484046Z”

}

}, “outputs”: [], “source”: [

“hor_scenarios = project_scenarios(weekly_returns,investment_horizon=INVESTMENT_HORIZON,p=p_exp)n”, “hor_scenarios_simple = convert_scenarios_compound_to_simple(hor_scenarios)”

]

}, {

“cell_type”: “markdown”, “id”: “78074a4f”, “metadata”: {}, “source”: [

“## Simple optimization”

]

}, {

“cell_type”: “code”, “execution_count”: 4, “id”: “d8de6c09”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:31.485390Z”, “iopub.status.busy”: “2026-03-30T19:38:31.485328Z”, “iopub.status.idle”: “2026-03-30T19:38:31.487237Z”, “shell.execute_reply”: “2026-03-30T19:38:31.487053Z”

}

}, “outputs”: [], “source”: [

“def print_portfolio(name: str, weights: pd.Series, ret: float, risk: float, risk_measure: str):n”, “ """Prints formatted portfolio details."""n”, “ print(f"\n— {name} —")n”, “ print(f" > Expected Return: {ret:.4f}")n”, “ print(f" > {risk_measure}: {risk:.4f}")n”, “ print(" > Weights:")n”, “ print(weights.round(3).to_string())n”, “ print("-" * 25)”

]

}, {

“cell_type”: “code”, “execution_count”: 5, “id”: “ab091502”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:31.488138Z”, “iopub.status.busy”: “2026-03-30T19:38:31.488077Z”, “iopub.status.idle”: “2026-03-30T19:38:32.020017Z”, “shell.execute_reply”: “2026-03-30T19:38:32.019780Z”

}

}, “outputs”: [

{

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

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

]

}, {

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

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

]

}, {

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

“n”, “— Minimum CVaR Portfolio —n”, “ > Expected Return: 0.1022n”, “ > CVaR (alpha=0.01): 0.1889n”, “ > Weights:n”, “DBC -0.000n”, “GLD 0.291n”, “SPY 0.459n”, “TLT 0.250n”, “————————-n”, “No portfolio with CVaR <= 0.1000 exists on the frontier; using min-risk instead.n”, “n”, “— CVaR Portfolio with Return >= 0.0500 —n”, “ > Expected Return: 0.1022n”, “ > CVaR (alpha=0.01): 0.1889n”, “ > Weights:n”, “DBC -0.000n”, “GLD 0.291n”, “SPY 0.459n”, “TLT 0.250n”, “————————-n”

]

}

], “source”: [

“from pyvallocation.optimization import InfeasibleOptimizationErrorn”, “n”, “cvar_dist = AssetsDistribution(scenarios=hor_scenarios_simple)n”, “cvar_wrapper = PortfolioWrapper.from_scenarios(hor_scenarios_simple)n”, “n”, “# Compute the frontiern”, “cvar_frontier = cvar_wrapper.cvar_frontier(num_portfolios=20, alpha=0.01)n”, “n”, “w_min_cvar, r_min_cvar, cvar_min_cvar = cvar_frontier.min_risk()n”, “print_portfolio("Minimum CVaR Portfolio", w_min_cvar, r_min_cvar, cvar_min_cvar, cvar_frontier.risk_measure)n”, “n”, “try:n”, “ w_cvar_risk_target, r_cvar_risk_target, cvar_risk_target = cvar_frontier.at_risk(max_risk=MAX_CVAR_HORIZON)n”, “ print_portfolio(f"CVaR Portfolio with Risk <= {MAX_CVAR_HORIZON:.4f}", n”, “ w_cvar_risk_target, r_cvar_risk_target, cvar_risk_target, cvar_frontier.risk_measure)n”, “except InfeasibleOptimizationError:n”, “ print(f"No portfolio with CVaR <= {MAX_CVAR_HORIZON:.4f} exists on the frontier; using min-risk instead.")n”, “ w_cvar_risk_target, r_cvar_risk_target, cvar_risk_target = w_min_cvar, r_min_cvar, cvar_min_cvarn”, “n”, “try:n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target = cvar_frontier.at_return(min_return=TARGET_RETURN_HORIZON)n”, “ print_portfolio(f"CVaR Portfolio with Return >= {TARGET_RETURN_HORIZON:.4f}", n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target, cvar_frontier.risk_measure)n”, “except InfeasibleOptimizationError:n”, “ print(f"No portfolio with return >= {TARGET_RETURN_HORIZON:.4f} exists on the frontier; using min-risk instead.")n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target = w_min_cvar, r_min_cvar, cvar_min_cvar”

]

}, {

“cell_type”: “markdown”, “id”: “6a8db208”, “metadata”: {}, “source”: [

“## Imposing views”

]

}, {

“cell_type”: “code”, “execution_count”: 6, “id”: “85abd0ac”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:32.021250Z”, “iopub.status.busy”: “2026-03-30T19:38:32.021149Z”, “iopub.status.idle”: “2026-03-30T19:38:32.062432Z”, “shell.execute_reply”: “2026-03-30T19:38:32.062214Z”

}

}, “outputs”: [

{

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

“Prior Effective number of scenarios: 936.3855678136669n”, “Posterior Effective number of scenarios: 934.5468032923278n”

]

}, {

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”, “text/plain”: [

“<Figure size 640x480 with 1 Axes>”

]

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

}

], “source”: [

“# Suppose we think S&P 500 will deliver 2% and GLD 5% annualized returnn”, “mean_views_1 = {"SPY": 0.02/ANNUALIZATION_FACTOR, ‘GLD’:0.05/ANNUALIZATION_FACTOR}n”, “n”, “fv = FlexibleViewsProcessor(n”, “ prior_risk_drivers=weekly_returns,n”, “ prior_probabilities=p_exp,n”, “ mean_views=mean_views_1n”, “)n”, “n”, “q = fv.get_posterior_probabilities()n”, “print(‘Prior Effective number of scenarios:’,probabilities.compute_effective_number_scenarios(p_exp))n”, “print(‘Posterior Effective number of scenarios:’,probabilities.compute_effective_number_scenarios(q))n”, “plt.plot(q); plt.plot(p_exp); plt.show()”

]

}, {

“cell_type”: “code”, “execution_count”: 7, “id”: “086b55d9”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:32.063463Z”, “iopub.status.busy”: “2026-03-30T19:38:32.063393Z”, “iopub.status.idle”: “2026-03-30T19:38:32.585825Z”, “shell.execute_reply”: “2026-03-30T19:38:32.585574Z”

}

}, “outputs”: [

{

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

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

]

}, {

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

“No constraints set; using default long-only, fully-invested.n”

]

}, {

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

“n”, “— CVaR Portfolio with Return >= 0.0500 —n”, “ > Expected Return: 0.0503n”, “ > CVaR (alpha=0.01): 0.2020n”, “ > Weights:n”, “DBC -0.000n”, “GLD 0.445n”, “SPY 0.252n”, “TLT 0.303n”, “————————-n”

]

}

], “source”: [

“projected_scenarios_compound = project_scenarios(R=weekly_returns,investment_horizon=INVESTMENT_HORIZON,p=q.flatten()/q.sum())n”, “projected_scenarios_simple = convert_scenarios_compound_to_simple(projected_scenarios_compound)n”, “n”, “cvar_wrapper = PortfolioWrapper(AssetsDistribution(scenarios=projected_scenarios_simple))n”, “n”, “cvar_frontier = cvar_wrapper.cvar_frontier(num_portfolios=20, alpha=0.01)n”, “n”, “try:n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target = cvar_frontier.at_return(min_return=TARGET_RETURN_HORIZON)n”, “ print_portfolio(f"CVaR Portfolio with Return >= {TARGET_RETURN_HORIZON:.4f}", n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target, cvar_frontier.risk_measure)n”, “except InfeasibleOptimizationError:n”, “ print(f"Target return {TARGET_RETURN_HORIZON:.4f} infeasible after views; falling back to min-risk portfolio.")n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target = cvar_frontier.min_risk()n”, “ print_portfolio("Min-Risk Portfolio (fallback)", n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target, cvar_frontier.risk_measure)”

]

}, {

“cell_type”: “code”, “execution_count”: 8, “id”: “4e6165a5”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:32.586944Z”, “iopub.status.busy”: “2026-03-30T19:38:32.586871Z”, “iopub.status.idle”: “2026-03-30T19:38:33.387105Z”, “shell.execute_reply”: “2026-03-30T19:38:33.386885Z”

}

}, “outputs”: [

{

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

“Prior Effective number of scenarios: 936.3855678136669n”, “Posterior Effective number of scenarios: 907.9092294685015n”

]

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 640x480 with 1 Axes>”

]

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

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 640x480 with 1 Axes>”

]

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

}, {

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

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

]

}, {

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

“No constraints set; using default long-only, fully-invested.n”

]

}, {

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

“n”, “— CVaR Portfolio with Return >= 0.0500 —n”, “ > Expected Return: 0.0500n”, “ > CVaR (alpha=0.01): 0.2861n”, “ > Weights:n”, “DBC 0.000n”, “GLD 0.554n”, “SPY 0.199n”, “TLT 0.247n”, “————————-n”

]

}

], “source”: [

“# Suppose we think SPY will have a >50% correlation with GLD and SPY will have a negative returnn”, “mean_views_2 = {‘SPY’:(‘<’,0)}n”, “corr_views = {(‘SPY’,’GLD’):(‘>’,.5)}n”, “n”, “q = FlexibleViewsProcessor(n”, “ prior_risk_drivers=weekly_returns,n”, “ prior_probabilities=p_exp,n”, “ mean_views=mean_views_2,n”, “ corr_views=corr_viewsn”, “).get_posterior_probabilities()n”, “print(‘Prior Effective number of scenarios:’,probabilities.compute_effective_number_scenarios(p_exp))n”, “print(‘Posterior Effective number of scenarios:’,probabilities.compute_effective_number_scenarios(q))n”, “plt.plot(q); plt.plot(p_exp); plt.show()n”, “n”, “sns.kdeplot(x=weekly_returns[‘SPY’], y=weekly_returns[‘GLD’], weights=q.flatten(), fill=True, alpha=0.5, color=’black’)n”, “n”, “sns.kdeplot(x=weekly_returns[‘SPY’], y=weekly_returns[‘GLD’], weights=p_exp,n”, “ fill=True, alpha=0.5, color=’red’)n”, “n”, “plt.xlabel("SPY"); plt.ylabel("GLD"); plt.title("Joint KDE"); plt.show()n”, “n”, “projected_scenarios_compound = project_scenarios(R=weekly_returns,investment_horizon=INVESTMENT_HORIZON,p=q.flatten())n”, “projected_scenarios_simple = convert_scenarios_compound_to_simple(projected_scenarios_compound)n”, “n”, “cvar_wrapper = PortfolioWrapper(AssetsDistribution(scenarios=projected_scenarios_simple))n”, “n”, “cvar_frontier = cvar_wrapper.cvar_frontier(num_portfolios=20, alpha=0.01)n”, “n”, “try:n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target = cvar_frontier.at_return(min_return=TARGET_RETURN_HORIZON)n”, “ print_portfolio(f"CVaR Portfolio with Return >= {TARGET_RETURN_HORIZON:.4f}", n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target, cvar_frontier.risk_measure)n”, “except InfeasibleOptimizationError:n”, “ print(f"Target return {TARGET_RETURN_HORIZON:.4f} infeasible under bearish views; falling back to min-risk portfolio.")n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target = cvar_frontier.min_risk()n”, “ print_portfolio("Min-Risk Portfolio (fallback)", n”, “ w_cvar_return_target, r_cvar_return_target, cvar_return_target, cvar_frontier.risk_measure)”

]

}, {

“cell_type”: “markdown”, “id”: “f93131eb”, “metadata”: {}, “source”: [

“## Opinion pooling”

]

}, {

“cell_type”: “code”, “execution_count”: 9, “id”: “849e29fc”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:33.388214Z”, “iopub.status.busy”: “2026-03-30T19:38:33.388140Z”, “iopub.status.idle”: “2026-03-30T19:38:33.423752Z”, “shell.execute_reply”: “2026-03-30T19:38:33.423531Z”

}

}, “outputs”: [

{
“data”: {
“text/plain”: [

“[<matplotlib.lines.Line2D at 0x16c082540>]”

]

}, “execution_count”: 9, “metadata”: {}, “output_type”: “execute_result”

}, {

“data”: {

“image/png”: 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”, “text/plain”: [

“<Figure size 640x480 with 1 Axes>”

]

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

}

], “source”: [

“# If we have multiple conflicting views we can leverage the simple opinion poolingn”, “n”, “# first view –> SPY returns will be negativen”, “q1 = FlexibleViewsProcessor(n”, “ prior_risk_drivers=weekly_returns,n”, “ prior_probabilities=p_exp,n”, “ mean_views={‘SPY’:(‘<’,0)},n”, “).get_posterior_probabilities()n”, “n”, “# second view –> SPY returns will be positive, SPY annualized vol will be >20%n”, “q2 = FlexibleViewsProcessor(n”, “ prior_risk_drivers=weekly_returns,n”, “ prior_probabilities=p_exp,n”, “ mean_views={‘SPY’:(‘>’,0),’TLT’:-0.05/ANNUALIZATION_FACTOR},n”, “ vol_views={‘SPY’:(‘>’,0.2/(ANNUALIZATION_FACTOR**.5))}n”, “).get_posterior_probabilities()n”, “n”, “# final probability vector, assigning 75% confidence to the first and 25% to the second viewn”, “q_ = q1*.75 + q2*.25n”, “n”, “plt.plot(q1);plt.plot(q2);plt.plot(q_)”

]

}

], “metadata”: {

“kernelspec”: {

“display_name”: “.venv”, “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”: 5

}