{
“cells”: [
{

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

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:28.645408Z”, “iopub.status.busy”: “2026-03-30T19:38:28.645210Z”, “iopub.status.idle”: “2026-03-30T19:38:28.981960Z”, “shell.execute_reply”: “2026-03-30T19:38:28.981700Z”

}

}, “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”, “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 = (weekly_prices).pct_change().dropna()n”, “n”, “# compute the vol of SPY to create a risk driver series (toy example)n”, “spy_vol = weekly_returns.SPY.ewm(span=52).std().ffill().fillna(0)n”, “n”, “# store the returns shapen”, “T, N = weekly_returns.shapen”, “n”, “# inputs/parametersn”, “ANNUALIZATION_FACTOR = 52n”, “HALF_LIFE = ANNUALIZATION_FACTOR*10 # 10 years”

]

}, {

“cell_type”: “code”, “execution_count”: 2, “id”: “799b6ccf”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:28.983184Z”, “iopub.status.busy”: “2026-03-30T19:38:28.983111Z”, “iopub.status.idle”: “2026-03-30T19:38:29.391924Z”, “shell.execute_reply”: “2026-03-30T19:38:29.391701Z”

}

}, “outputs”: [

{

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

“Effective number of scenarios of probabilities with uniform dist: 1005.9999999999983n”, “Effective number of scenarios of probabilities via exp decay: 936.3855678136669n”, “Effective number of scenarios of probabilities via gaussian kernel: 257.5267385712329n”

]

}, {

“data”: {

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”, “text/plain”: [

“<Figure size 640x480 with 1 Axes>”

]

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

}

], “source”: [

“from pyvallocation import probabilitiesn”, “n”, “# generate uniform probabilitiesn”, “p_uniform = probabilities.generate_uniform_probabilities(T)n”, “print(‘Effective number of scenarios of probabilities with uniform dist:’, probabilities.compute_effective_number_scenarios(p_uniform))n”, “n”, “# generate exponential decay probabilitiesn”, “p_exp = probabilities.generate_exp_decay_probabilities(T,HALF_LIFE)n”, “print(‘Effective number of scenarios of probabilities via exp decay:’, probabilities.compute_effective_number_scenarios(p_exp))n”, “n”, “# generate state conditioning probabilities via gaussian kerneln”, “p_gk = probabilities.generate_gaussian_kernel_probabilities(spy_vol)n”, “print(‘Effective number of scenarios of probabilities via gaussian kernel:’, probabilities.compute_effective_number_scenarios(p_gk))n”, “n”, “# visualize themn”, “plt.plot(pd.Series(p_uniform,index=weekly_returns.index), label=’Uniform’)n”, “plt.plot(pd.Series(p_exp,index=weekly_returns.index), label=’Exponential’)n”, “plt.plot(pd.Series(p_gk,index=weekly_returns.index), label=’GK’)n”, “plt.legend();plt.grid();plt.show()”

]

}, {

“cell_type”: “code”, “execution_count”: 3, “id”: “e03dfb97”, “metadata”: {

“execution”: {

“iopub.execute_input”: “2026-03-30T19:38:29.393024Z”, “iopub.status.busy”: “2026-03-30T19:38:29.392931Z”, “iopub.status.idle”: “2026-03-30T19:38:29.397877Z”, “shell.execute_reply”: “2026-03-30T19:38:29.397671Z”

}

}, “outputs”: [

{

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

“Original mean from uniform prob:n”, “ DBC 0.024524n”, “GLD 0.104242n”, “SPY 0.113997n”, “TLT 0.037689n”, “Name: mu, dtype: float64n”, “Jorion-shirnked mean from uniform prob:n”, “ DBC 0.039253n”, “GLD 0.086396n”, “SPY 0.092165n”, “TLT 0.047038n”, “Name: mu, dtype: float64 n”, “—n”, “Mean from exponential probabilities:n”, “ DBC 0.030864n”, “GLD 0.109510n”, “SPY 0.126099n”, “TLT 0.017656n”, “Name: mu, dtype: float64n”, “Jorion-shirnked mean from exponential prob:n”, “ DBC 0.039221n”, “GLD 0.091669n”, “SPY 0.102732n”, “TLT 0.030414n”, “Name: mu, dtype: float64 n”, “—n”, “Mean from state conditioning:n”, “ DBC -0.013594n”, “GLD 0.109575n”, “SPY 0.083216n”, “TLT 0.054685n”, “Name: mu, dtype: float64n”, “Jorion-shirnked mean from state conditioning:n”, “ DBC 0.013998n”, “GLD 0.090472n”, “SPY 0.074106n”, “TLT 0.056392n”, “Name: mu, dtype: float64 n”, “—n”

]

}

], “source”: [

“from pyvallocation import momentsn”, “n”, “mu_uniform, cov_uniform = moments.estimate_sample_moments(weekly_returns,p_uniform)n”, “mu_exp, cov_exp = moments.estimate_sample_moments(weekly_returns,p_exp)n”, “mu_gk, cov_gk = moments.estimate_sample_moments(weekly_returns, p_gk)n”, “n”, “mu_uniform_jorion = moments.shrink_mean_jorion(mu_uniform,cov_uniform,T)n”, “print(‘Original mean from uniform prob:\n’, mu_uniform*ANNUALIZATION_FACTOR)n”, “print(‘Jorion-shirnked mean from uniform prob:\n’, mu_uniform_jorion*ANNUALIZATION_FACTOR,’\n—‘)n”, “n”, “print(‘Mean from exponential probabilities:\n’,mu_exp*ANNUALIZATION_FACTOR)n”, “print(‘Jorion-shirnked mean from exponential prob:\n’, moments.shrink_mean_jorion(mu_exp,cov_exp,T)*ANNUALIZATION_FACTOR,’\n—‘)n”, “n”, “print(‘Mean from state conditioning:\n’,mu_gk*ANNUALIZATION_FACTOR)n”, “print(‘Jorion-shirnked mean from state conditioning:\n’, moments.shrink_mean_jorion(mu_gk,cov_gk,T)*ANNUALIZATION_FACTOR,’\n—‘)n”, “n”, “cov_uniform_lw_cc = moments.shrink_covariance_ledoit_wolf(weekly_returns,cov_uniform,target=’constant_correlation’)n”, “cov_uniform_lw_id = moments.shrink_covariance_ledoit_wolf(weekly_returns,cov_uniform,target=’identity’)”

]

}

], “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

}