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feat: add VanguardDynamicSpending cash flow strategy
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‎main_notebook.ipynb‎

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": 2,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-29T11:26:06.735435Z",
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-29T11:26:09.894658Z",
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"'1.5.0'"
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]
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},
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"execution_count": 2,
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 146,
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"execution_count": 4,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-29T11:26:11.913869Z",
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},
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{
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"cell_type": "code",
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"execution_count": 221,
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"execution_count": 5,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-07-29T11:26:55.347301Z",
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"symbol portfolio_8286.PF\n",
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"symbol portfolio_6434.PF\n",
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"assets [RGBITR.INDX, RUCBTRNS.INDX, MCFTR.INDX, GC.COMM]\n",
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"weights [0.1, 0.1, 0.55, 0.25]\n",
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"rebalancing_period year\n",
@@ -750,7 +750,9 @@
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"output_type": "display_data"
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}
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],
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"source": "pf.dcf.wealth_index(discounting=\"fv\", include_negative_values=False).plot()"
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"source": [
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"pf.dcf.wealth_index(discounting=\"fv\", include_negative_values=False).plot()"
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]
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},
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{
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"cell_type": "markdown",
@@ -1471,10 +1473,10 @@
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"evalue": "'PortfolioDCF' object has no attribute 'cash_flow_parameters'",
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"output_type": "error",
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"traceback": [
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"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
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"\u001B[31mAttributeError\u001B[39m Traceback (most recent call last)",
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"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[20]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m \u001B[43mpf\u001B[49m\u001B[43m.\u001B[49m\u001B[43mdcf\u001B[49m\u001B[43m.\u001B[49m\u001B[43mcash_flow_parameters\u001B[49m\n",
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"\u001B[31mAttributeError\u001B[39m: 'PortfolioDCF' object has no attribute 'cash_flow_parameters'"
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"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
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"\u001b[31mAttributeError\u001b[39m Traceback (most recent call last)",
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"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[43mpf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdcf\u001b[49m\u001b[43m.\u001b[49m\u001b[43mcash_flow_parameters\u001b[49m\n",
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"\u001b[31mAttributeError\u001b[39m: 'PortfolioDCF' object has no attribute 'cash_flow_parameters'"
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]
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}
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],
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"output_type": "display_data"
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}
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],
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"source": "pf.dcf.wealth_index(discounting=\"fv\", include_negative_values=False).plot()"
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"source": [
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"pf.dcf.wealth_index(discounting=\"fv\", include_negative_values=False).plot()"
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]
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},
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{
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"cell_type": "code",
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"pf.dcf.plot_forecast_monte_carlo(backtest=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Vanguard Dynamic Spending"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Fixed Percentage strategy\n",
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"vds = ok.VanguardDynamicSpending(pf)\n",
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"vds.initial_investment = 10_000_000\n",
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"vds.frequency = \"year\"\n",
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"vds.percentage = -0.07\n",
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"vds.indexation = 0.09\n",
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"vds.maximum_annual_withdrawal = 10_000_000 / 10 # 10%\n",
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"vds.minimum_annual_withdrawal = 10_000_000 / 25 # 4%\n",
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"vds.ceiling = 0.10\n",
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"vds.floor = -0.10"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Portfolio symbol portfolio_6434.PF\n",
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"Cash flow initial investment 10000000\n",
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"Cash flow frequency year\n",
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"Cash flow strategy VDS\n",
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"Cash flow percentage -0.07000\n",
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"Minimum annual withdrawal 400,000.00000\n",
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"Maximum annual withdrawal 1,000,000.00000\n",
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"Ceiling 0.10000\n",
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"Floor -0.10000\n",
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"dtype: object"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"vds"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"vds.calculate_withdrawal_size(\n",
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" last_withdrawal=\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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" tolerance_rel=0.10,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Fixed Percentage"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-17T15:39:59.304002Z",
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"start_time": "2025-06-17T15:39:59.234001Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Portfolio symbol portfolio_6436.PF\n",
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"Cash flow initial investment 10000\n",
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"Cash flow frequency year\n",
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"Cash flow strategy fixed_percentage\n",
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"Cash flow percentage -0.12000\n",
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"dtype: object\n"
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]
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}
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],
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"source": [
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"# Fixed Percentage strategy\n",
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"pc = okama.portfolios.portfolio.cashflow_strategy.PercentageStrategy(pf)\n",
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"pc.initial_investment = 10_000\n",
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"pc.frequency = \"year\"\n",
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"pc.percentage = -0.12\n",
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"print(pc)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-17T15:40:00.794567Z",
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"start_time": "2025-06-17T15:40:00.733193Z"
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}
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},
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"outputs": [],
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"source": [
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"pf.dcf.cashflow_parameters = pc"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-17T15:40:02.295463Z",
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"start_time": "2025-06-17T15:40:02.232467Z"
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}
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},
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"outputs": [],
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"source": [
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"pf.dcf.mc.period = 50\n",
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"pf.dcf.mc.number = 100\n",
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"pf.dcf.mc.distribution = \"norm\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-17T15:40:16.348221Z",
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"start_time": "2025-06-17T15:40:04.809674Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"wealth_at_quantile=0.00, main_parameter=-1.000\n",
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"error_rel=1.000, gradient=0.000\n",
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"decreasing withdrawal\n",
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"wealth_at_quantile=0.00, main_parameter=-0.500\n",
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"error_rel=1.000, gradient=-0.000\n",
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"decreasing withdrawal\n",
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"wealth_at_quantile=0.06, main_parameter=-0.250\n",
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"error_rel=1.000, gradient=-0.000\n",
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"decreasing withdrawal\n",
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"wealth_at_quantile=60.09, main_parameter=-0.125\n",
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"error_rel=0.994, gradient=-0.006\n",
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"decreasing withdrawal\n",
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"wealth_at_quantile=1365.34, main_parameter=-0.062\n",
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"error_rel=0.863, gradient=-0.131\n",
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"decreasing withdrawal\n",
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"wealth_at_quantile=5971.99, main_parameter=-0.031\n",
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"error_rel=0.403, gradient=-0.461\n",
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"decreasing withdrawal\n",
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"wealth_at_quantile=11971.37, main_parameter=-0.016\n",
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"error_rel=0.197, gradient=-0.206\n",
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"increasing withdrawal\n",
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"wealth_at_quantile=6993.80, main_parameter=-0.023\n",
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"error_rel=0.301, gradient=0.103\n",
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"decreasing withdrawal\n",
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"wealth_at_quantile=9759.33, main_parameter=-0.020\n",
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"error_rel=0.024, gradient=-0.277\n",
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"solution found: -195.31 or 1.95% after 9 steps.\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"success True\n",
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"withdrawal_abs -195.31250\n",
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"withdrawal_rel 0.01953\n",
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"error_rel 0.02407\n",
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"attempts 9\n",
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"dtype: object"
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]
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},
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"execution_count": 18,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"sol = pf.dcf.find_the_largest_withdrawals_size(\n",
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" goal=\"maintain_balance_pv\", percentile=20, tolerance_rel=0.10, threshold=0.05\n",
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")\n",
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"sol"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-06-17T15:40:23.638200Z",
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"start_time": "2025-06-17T15:40:23.573691Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"success True\n",
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"withdrawal_abs -195.31250\n",
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"withdrawal_rel 0.01953\n",
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"error_rel 0.02407\n",
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"attempts 9\n",
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"dtype: object\n"
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]
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}
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],
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"source": [
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"print(sol)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"sol.solutions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎okama/__init__.py‎

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)
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from okama.portfolios.mc import MonteCarlo
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from okama.portfolios.dcf import PortfolioDCF
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from okama.portfolios.cashflow_strategies import CashFlow, IndexationStrategy, PercentageStrategy, TimeSeriesStrategy
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from okama.portfolios.cashflow_strategies import CashFlow, IndexationStrategy, PercentageStrategy, TimeSeriesStrategy, VanguardDynamicSpending
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from okama.macro import Inflation, Rate, Indicator
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from okama.frontier.multi_period import EfficientFrontierReb
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from okama.frontier.single_period import EfficientFrontier

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