{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.7.12"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Bootstrap Simulation with Portfolio Optimizer: Usage for Financial Planning\n\n> This notebook illustrates the usage of bootstrapping in Python, using the [Portfolio Optimizer](https://portfoliooptimizer.io/) Web API.\n\n> Idea is to simulate how a portfolio of U.S. stocks is likely to behave over the next 30 years using data from [Robert Shiller's website](http://www.econ.yale.edu/~shiller/).","metadata":{"tags":[]},"id":"c4974798"},{"cell_type":"markdown","source":"> Let's import the nominal total returns for U.S. stocks found in Shiller's Excel file, over the period January 1871 - December 1989. ","metadata":{},"id":"5b46ad2b-e5d2-436f-abec-328b167214e1"},{"cell_type":"code","source":"import pandas as pd\n\n# Import total return prices \nshiller_tr_prices_in = pd.DataFrame([4.44, 4.52, 4.65, 4.81, 4.95, 4.93, 4.86, 4.95, 5.02, 4.78, 4.86, 4.99, 5.13, 5.18, 5.37, 5.55, 5.57, 5.54, 5.54, 5.50, 5.42, 5.47, 5.48, 5.64, 5.71, 5.78, 5.77, 5.72, 5.76, 5.71, 5.73, 5.76, 5.35, 4.92, 4.77, 5.25, 5.57, 5.77, 5.72, 5.60, 5.48, 5.49, 5.53, 5.57, 5.69, 5.72, 5.80, 5.80, 5.83, 5.86, 5.97, 6.08, 5.88, 5.80, 5.84, 5.90, 5.88, 5.82, 5.95, 5.99, 6.15, 6.26, 6.28, 6.08, 5.89, 5.89, 5.85, 5.64, 5.33, 5.34, 5.28, 5.28, 5.27, 5.00, 4.78, 4.46, 4.50, 4.21, 4.42, 4.76, 5.09, 5.22, 5.17, 5.18, 5.20, 5.12, 5.24, 5.41, 5.45, 5.59, 5.73, 5.71, 5.85, 5.81, 5.81, 5.81, 6.05, 6.30, 6.22, 6.45, 6.77, 6.83, 7.00, 7.08, 7.36, 8.20, 8.66, 8.67, 9.04, 9.23, 9.44, 9.26, 8.56, 8.63, 9.06, 9.42, 9.44, 9.75, 10.30, 10.77, 11.45, 11.46, 11.63, 11.64, 12.20, 12.40, 12.01, 11.78, 11.92, 11.78, 11.90, 11.61, 11.49, 11.29, 11.32, 11.37, 11.29, 11.28, 11.97, 12.38, 12.55, 12.27, 11.79, 11.91, 11.90, 11.69, 11.89, 12.19, 12.04, 12.20, 12.07, 11.58, 11.77, 11.50, 11.73, 11.54, 11.25, 11.61, 11.63, 11.16, 10.32, 9.95, 10.01, 10.70, 10.42, 10.14, 9.99, 10.03, 9.86, 10.22, 10.30, 10.33, 10.27, 10.27, 10.71, 11.36, 11.27, 11.98, 12.80, 12.76, 12.80, 13.10, 12.88, 12.75, 12.55, 13.17, 13.42, 13.57, 13.97, 14.37, 14.77, 14.44, 14.33, 14.28, 14.66, 15.05, 15.36, 14.97, 14.65, 14.34, 14.21, 13.78, 14.10, 14.08, 14.24, 14.22, 13.73, 13.84, 14.09, 13.71, 14.12, 14.47, 14.88, 14.85, 14.60, 14.38, 14.71, 14.93, 14.68, 14.70, 15.15, 15.46, 15.20, 15.46, 15.88, 15.65, 15.56, 15.52, 15.75, 15.63, 15.56, 15.94, 16.68, 16.61, 16.55, 16.22, 16.00, 15.33, 14.27, 13.99, 14.78, 15.02, 14.80, 15.35, 15.34, 15.09, 14.90, 15.46, 16.77, 16.82, 16.63, 17.19, 17.57, 17.66, 17.91, 17.94, 18.00, 17.97, 18.03, 18.36, 17.96, 18.39, 18.39, 18.26, 18.65, 18.39, 17.79, 17.86, 16.34, 15.64, 14.25, 13.98, 15.04, 15.56, 15.87, 15.39, 15.15, 15.43, 15.96, 16.24, 15.70, 15.56, 15.30, 15.95, 16.27, 15.82, 15.89, 15.81, 15.69, 15.53, 15.59, 16.32, 17.28, 17.68, 17.82, 18.15, 18.32, 18.12, 17.57, 16.59, 16.46, 17.22, 17.01, 17.22, 17.21, 16.95, 15.91, 15.07, 15.92, 16.34, 17.51, 16.93, 16.99, 16.89, 16.99, 16.53, 16.67, 17.51, 18.35, 19.60, 20.61, 20.01, 19.37, 19.85, 20.45, 20.48, 19.62, 19.34, 20.68, 21.56, 21.71, 22.59, 22.62, 22.21, 23.02, 24.52, 26.46, 27.53, 28.00, 28.42, 27.31, 26.77, 27.77, 28.56, 28.32, 28.27, 28.88, 26.99, 27.43, 28.01, 28.32, 28.77, 27.51, 26.78, 26.88, 27.35, 26.82, 27.90, 30.19, 32.12, 33.18, 34.14, 35.49, 38.58, 36.76, 40.55, 37.95, 38.60, 38.54, 38.23, 39.18, 38.68, 39.64, 40.11, 40.29, 41.80, 41.83, 41.72, 42.80, 44.08, 44.31, 43.05, 41.53, 40.71, 42.92, 42.81, 41.27, 39.73, 39.10, 37.09, 35.53, 34.54, 33.85, 32.91, 33.16, 34.85, 35.59, 34.78, 34.82, 35.84, 35.23, 35.43, 37.05, 38.46, 40.30, 42.81, 45.28, 45.87, 47.01, 49.22, 50.76, 50.30, 47.97, 48.68, 50.36, 52.39, 52.72, 53.61, 53.48, 54.96, 57.03, 56.79, 55.56, 54.98, 53.70, 54.58, 53.35, 57.48, 59.44, 57.85, 59.24, 58.90, 57.43, 55.83, 50.55, 51.00, 49.45, 48.07, 50.13, 46.59, 46.32, 41.51, 39.30, 41.54, 43.54, 42.18, 44.13, 46.74, 49.48, 49.78, 51.83, 54.28, 53.91, 54.79, 58.73, 60.28, 60.70, 59.19, 60.22, 63.16, 65.49, 66.89, 68.08, 69.97, 70.29, 70.81, 70.72, 71.81, 70.53, 68.27, 70.22, 68.79, 67.92, 64.93, 61.92, 63.69, 64.40, 67.65, 67.86, 66.25, 68.15, 69.61, 69.09, 69.08, 70.86, 72.57, 72.57, 69.40, 65.91, 66.59, 69.56, 70.17, 70.54, 70.23, 72.56, 75.13, 75.36, 75.67, 76.06, 78.12, 78.84, 78.99, 78.43, 75.93, 75.61, 73.25, 72.19, 72.44, 70.79, 67.56, 68.81, 70.98, 71.99, 70.05, 68.61, 68.86, 72.03, 73.31, 72.26, 70.86, 71.63, 71.60, 67.97, 68.29, 68.61, 68.93, 69.25, 66.59, 68.09, 67.50, 69.56, 75.12, 73.69, 74.85, 74.90, 78.42, 81.66, 86.53, 89.89, 90.42, 89.34, 88.46, 88.54, 87.96, 90.29, 91.57, 90.71, 91.82, 96.00, 99.42, 102.17, 98.53, 96.70, 91.73, 95.08, 94.16, 91.51, 93.90, 91.86, 89.70, 85.97, 81.90, 75.68, 73.72, 78.77, 81.79, 80.74, 80.57, 83.73, 84.44, 85.70, 87.08, 87.20, 91.46, 94.35, 93.04, 93.00, 93.92, 97.33, 101.13, 108.67, 112.13, 116.34, 109.07, 111.34, 117.58, 114.65, 111.84, 111.26, 102.61, 110.39, 110.05, 103.70, 102.46, 102.89, 99.41, 103.51, 104.20, 99.48, 91.13, 95.71, 95.60, 93.72, 94.69, 98.13, 90.83, 91.11, 90.55, 93.34, 95.17, 100.83, 104.95, 105.36, 108.23, 112.86, 120.30, 125.57, 124.99, 126.48, 131.85, 135.90, 139.53, 133.23, 133.57, 136.05, 142.51, 145.47, 141.05, 135.05, 130.59, 126.88, 128.20, 129.68, 128.47, 133.02, 138.23, 143.47, 144.84, 142.80, 140.25, 140.49, 143.89, 151.32, 157.27, 156.52, 155.26, 164.71, 174.38, 182.38, 184.73, 180.69, 179.60, 186.20, 190.37, 196.51, 200.03, 205.52, 213.19, 220.71, 225.21, 229.56, 230.86, 216.13, 211.05, 213.50, 224.65, 235.12, 245.46, 250.24, 245.66, 249.94, 256.72, 256.11, 262.20, 267.37, 275.07, 285.72, 290.60, 298.24, 315.32, 334.45, 330.57, 339.36, 348.59, 351.28, 348.38, 368.41, 392.97, 406.47, 387.93, 392.17, 406.27, 436.24, 446.54, 478.17, 481.51, 518.56, 522.79, 533.53, 531.94, 541.51, 553.45, 604.39, 640.41, 667.61, 598.70, 441.91, 461.25, 469.68, 500.85, 521.50, 556.38, 524.94, 473.65, 465.32, 461.15, 462.74, 400.86, 373.61, 350.49, 362.93, 392.45, 401.76, 365.26, 331.78, 322.86, 335.29, 326.93, 279.93, 244.21, 249.20, 204.07, 202.28, 202.14, 204.39, 156.86, 139.06, 121.77, 129.25, 195.56, 215.78, 187.20, 186.51, 181.53, 189.82, 168.42, 168.97, 187.96, 243.05, 285.77, 309.94, 295.54, 294.10, 266.51, 273.97, 280.32, 297.37, 320.42, 305.05, 311.21, 280.63, 285.41, 272.98, 263.39, 258.10, 261.22, 269.61, 272.47, 273.57, 266.41, 250.61, 270.49, 292.84, 305.05, 322.14, 345.02, 353.42, 364.00, 399.37, 400.57, 423.91, 449.51, 460.37, 462.33, 439.16, 459.29, 487.98, 499.24, 506.50, 534.71, 551.40, 543.77, 562.60, 581.21, 582.57, 549.89, 527.50, 509.97, 542.51, 550.24, 474.45, 407.59, 373.93, 370.15, 382.11, 375.20, 352.60, 340.42, 345.68, 355.78, 428.59, 433.05, 415.28, 463.38, 465.39, 453.37, 448.11, 446.07, 447.27, 392.53, 408.62, 417.50, 429.37, 424.82, 471.82, 478.41, 471.73, 462.49, 461.81, 460.77, 460.11, 466.66, 404.42, 371.71, 386.11, 396.37, 415.25, 421.33, 433.34, 417.79, 420.81, 396.73, 401.41, 391.20, 385.00, 400.82, 423.73, 424.06, 427.73, 413.05, 396.20, 372.91, 382.64, 373.13, 355.33, 343.03, 349.41, 369.46, 385.59, 385.72, 392.08, 423.29, 432.37, 436.90, 465.31, 495.25, 515.13, 534.64, 557.96, 570.13, 584.24, 557.73, 571.98, 569.13, 545.21, 554.87, 575.23, 573.84, 592.44, 584.71, 597.60, 628.34, 647.33, 640.51, 632.68, 650.92, 649.07, 665.95, 688.50, 714.22, 716.48, 737.27, 767.94, 784.74, 771.45, 776.92, 832.71, 870.30, 901.68, 919.94, 959.51, 965.16, 939.35, 1002.94, 1008.13, 1004.72, 979.14, 963.25, 824.34, 808.94, 808.87, 836.35, 844.06, 880.12, 847.81, 819.91, 808.80, 840.57, 896.89, 882.95, 863.87, 890.09, 883.68, 873.84, 866.30, 827.77, 843.67, 912.75, 961.40, 1005.50, 985.86, 961.36, 954.88, 985.42, 935.26, 933.88, 949.18, 917.69, 931.46, 935.38, 933.74, 887.94, 943.59, 982.98, 1001.40, 1033.05, 1053.34, 1087.67, 1116.33, 1144.55, 1160.34, 1199.69, 1246.71, 1273.75, 1188.36, 1267.48, 1319.80, 1382.39, 1387.86, 1390.83, 1502.37, 1567.20, 1549.87, 1579.80, 1589.81, 1571.69, 1608.80, 1688.60, 1741.47, 1741.74, 1702.26, 1763.54, 1831.17, 1806.80, 1820.37, 1824.13, 1832.58, 1892.11, 1955.81, 1973.03, 1951.16, 1919.64, 1989.96, 2079.60, 2100.16, 2083.92, 2103.86, 2009.78, 2029.96, 1966.89, 2004.53, 2022.55, 1939.49, 2007.76, 2062.21, 2100.16, 2163.71, 2221.66, 2279.08, 2380.47, 2485.70, 2516.05, 2628.25, 2691.22, 2765.01, 2840.13, 2962.49, 3109.40, 3176.88, 3294.62, 3280.29, 3405.24, 3402.58, 3611.70, 3887.91, 3876.40, 4063.23, 3871.30, 4144.90, 4196.23, 4096.27, 4135.39, 4433.62, 4499.54, 4372.01, 4360.73, 4611.53, 4598.51, 4456.57, 4413.81, 4382.10, 4461.10, 4377.98, 4203.02, 4271.11, 4384.03, 4566.42, 4655.66, 4763.85, 4515.96, 4347.17, 4090.92, 4017.35, 4030.21, 4124.00, 4152.89, 4253.29, 4291.31, 4443.88, 4565.31, 4705.50, 4896.28, 5040.41, 5260.18, 5435.20, 5552.79, 5789.11, 5715.93, 5876.39, 5990.24, 6096.05, 6059.15, 6315.36, 6295.31, 6062.24, 6073.01, 6113.70, 6325.49, 6231.83, 6007.24, 5942.80, 6036.98, 5999.31, 6238.60, 6101.59, 6192.55, 6024.07, 5923.23, 6132.96, 6297.98, 6639.74, 6930.13, 7165.52, 7374.68, 7467.85, 7387.19, 7385.18, 7668.76, 7627.28, 7729.91, 8098.97, 8193.36, 7907.71, 8058.77, 8086.31, 7848.24, 7284.40, 6453.11, 6628.53, 6828.98, 6788.53, 6594.79, 7069.83, 7396.89, 7703.68, 7826.66, 7818.25, 8207.62, 8394.07, 8412.42, 8309.66, 8561.57, 8809.34, 8853.61, 8826.71, 9038.20, 9339.36, 9477.74, 9674.20, 9838.16, 9958.40, 9923.65, 10316.93, 10190.69, 10391.19, 10596.12, 10695.61, 10536.42, 10833.80, 10939.61, 10976.50, 11147.68, 11341.04, 10830.07, 10841.39, 11071.25, 11469.56, 11756.15, 11882.86, 11857.93, 12092.99, 12041.15, 11576.30, 11960.92, 11362.15, 11298.76, 11301.06, 10649.27, 10306.07, 10247.82, 10792.53, 10869.71, 11318.77, 11741.07, 12050.42, 12290.52, 12543.42, 12419.01, 12666.52, 12901.14, 13114.59, 13127.36, 12749.07, 13145.78, 13143.60, 12584.19, 12388.09, 13337.39, 13678.67, 14081.08, 14088.12, 13815.82, 14300.60, 14689.32, 14951.80, 15144.13, 14540.74, 14506.17, 14228.67, 14552.38, 15063.80, 14315.05, 13713.14, 13674.32, 13760.35, 13945.66, 14084.81, 13376.74, 13297.98, 12873.01, 13132.09, 12771.30, 11341.08, 11310.51, 11369.66, 11739.87, 12482.03, 12792.56, 12818.80, 13736.20, 14300.72, 14894.23, 15315.88, 15878.52, 15702.57, 15451.93, 15380.35, 15146.97, 15523.54, 15234.15, 14568.10, 15611.61, 16302.04, 16642.26, 17078.22, 17293.22, 17159.04, 17247.60, 17160.74, 17810.09, 17594.55, 17668.31, 18596.95, 19027.14, 19215.47, 18576.63, 18326.80, 18027.69, 17564.65, 17215.38, 17423.95, 17139.23, 17481.43, 18222.33, 16974.09, 15819.46, 16088.74, 15691.17, 16409.26, 15619.17, 15196.84, 15266.61, 13534.76, 13025.63, 11721.73, 12000.39, 12449.67, 11691.30, 12700.93, 14073.92, 14774.25, 14994.15, 16000.84, 16464.21, 16535.33, 15378.48, 15247.35, 16005.18, 16331.81, 16139.00, 17679.57, 18418.29, 18566.14, 18769.88, 18698.29, 18867.05, 19370.38, 19262.25, 19732.31, 19120.03, 19050.97, 19773.38, 19667.88, 19202.76, 19193.10, 18964.90, 18978.04, 19149.70, 19396.03, 18993.62, 18771.13, 18359.49, 18540.55, 18526.62, 17899.21, 17725.95, 17773.76, 18632.85, 19659.07, 19792.10, 19780.39, 21230.55, 21316.03, 20725.12, 19598.44, 19975.57, 20812.36, 20593.14, 21076.02, 21589.17, 21181.31, 21694.22, 22003.47, 23107.81, 23464.79, 22678.97, 22606.69, 23603.13, 24385.89, 25458.77, 23224.97, 22955.94, 24112.90, 25768.57, 27049.94, 27998.77, 28793.58, 29751.47, 31124.98, 30738.11, 30741.95, 29798.89, 31034.32, 31436.55, 30928.85, 31194.81, 30566.70, 30812.87, 28255.44, 28744.21, 29619.84, 29969.90, 28530.71, 27985.27, 27217.82, 28707.06, 28871.21, 27350.13, 27417.10, 27635.07, 30978.19, 33729.63, 35247.57, 35725.50, 37128.28, 37919.40, 39385.50, 41038.88, 42854.66, 43606.34, 43915.57, 42858.88, 44279.59, 44567.15, 44059.11, 44003.33, 44697.46, 42413.12, 42601.41, 42818.46, 42711.30, 41922.85, 41542.53, 45367.70, 46006.55, 45817.97, 46408.31, 46081.10, 48246.81, 51040.04, 50796.92, 51318.07, 52722.58, 54047.06, 55262.41, 54243.45, 53221.76, 54018.21, 57486.94, 60531.08, 60987.08, 64462.64, 68449.20, 70326.34, 70672.88, 72887.89, 71574.13, 73207.71, 71410.63, 71346.87, 73867.94, 75130.71, 80144.97, 85324.34, 89058.99, 88297.80, 88451.93, 92432.42, 95319.45, 101472.57, 98398.72, 86735.93, 76065.74, 75051.79, 78240.11, 80845.59, 83459.79, 82722.76, 80915.01, 85770.92, 85509.69, 84042.25, 85663.92, 88922.93, 87128.88, 89158.40, 92291.93, 95339.67, 95188.61, 98583.97, 102643.90, 106131.03, 109104.34, 114225.54, 114750.91, 115081.23, 112997.67, 116093.87, 113528.99])\n\n# Compute associated returns\nshiller_tr_ret_in = shiller_tr_prices_in.pct_change().dropna()","metadata":{"trusted":true},"execution_count":1,"outputs":[],"id":"bac3c4b1"},{"cell_type":"markdown","source":"> Let's generate bootstrap samples from these returns, using the stationary block bootstrap method.","metadata":{},"id":"cae7450a-f3ed-470a-843e-192bd5f4a6f2"},{"cell_type":"code","source":"# Without an API key, the number of simulations is limited to 250 and the number of asset returns is limited to 500.\n# With an API key, the default number of simulations is limited to 1250 and the number of asset returns is limited to 2500, these limits being customizable on demand (below, 10,000 simulations).\nportfolio_optimizer_api_key = ''","metadata":{"trusted":true},"execution_count":34,"outputs":[],"id":"a098939d-6a67-4f83-966c-b5c2b188662e"},{"cell_type":"code","source":"import numpy as np\nimport requests\n\n# Calculate bootstrap samples of the Shiller's returns with Portfolio Optimizer\n# This is only for illustration purposes - there are Pyhon packages for bootstrapping time series\npo_request_body = { 'assets': [{'assetReturns': shiller_tr_ret_in[0].values.tolist() if portfolio_optimizer_api_key else shiller_tr_ret_in[0].values.tolist()[0:500]}], \n 'bootstrapMethod': 'stationaryBlock',\n 'bootstrapAverageBlockLength': 120, \n 'simulations': 10000 if portfolio_optimizer_api_key else 250,\n 'simulationsLength': 360 }\n\n#\nresponse = requests.post('https://api.portfoliooptimizer.io/v1/assets/returns/simulation/bootstrap', json=po_request_body, headers={'X-API-Key': portfolio_optimizer_api_key})\n\n#\nbootstrap_ret = response.json()","metadata":{"trusted":true},"execution_count":28,"outputs":[],"id":"0a694505-53c6-48c5-9014-bd7af38ba55a"},{"cell_type":"markdown","source":"> Let's compute the cumulative wealth at every month horizon.","metadata":{},"id":"61ab9a19-13d4-46fd-91e7-93ba7e855b82"},{"cell_type":"code","source":"#\ncum_prices_in = []\nfor simulation in bootstrap_ret['simulations']:\n monthly_returns = simulation['assets'][0]['assetReturns']\n monthly_returns = np.hstack([0, monthly_returns])\n monthly_prices = np.cumprod(1 + monthly_returns)\n cum_prices_in.append(monthly_prices)\n\n#\ncum_prices_in = np.array(cum_prices_in).T","metadata":{"trusted":true},"execution_count":29,"outputs":[],"id":"29c0bdbc-8096-4f32-b732-cdad4a0ea152"},{"cell_type":"markdown","source":"> Let's compute and plot statistics of interest (median, 95% percentile confidence interval of the cumulative wealth at every month horizon).","metadata":{},"id":"5063931a-ddfa-4533-bf7c-8bf99e3f50a2"},{"cell_type":"code","source":"# Adapted from: https://stackoverflow.com/questions/66146705/creating-a-fanchart-from-a-series-of-monte-carlo-projections-in-python\nimport matplotlib.pyplot as plt\n\n# This function display a nice fanchart from the cumulative wealth at every month horizon\ndef create_fanchart(arr):\n #\n fig, ax = plt.subplots()\n \n #\n med = np.median(arr, axis=1)\n ax.plot(med, color='black', lw=2)\n \n #\n alpha = 0.025\n low_ci = np.percentile(arr, alpha*100, axis=1)\n high_ci = np.percentile(arr, (1-alpha)*100, axis=1)\n x = np.arange(arr.shape[0])\n ax.fill_between(x, low_ci, high_ci, color='blue', alpha=0.5)\n\n #\n ax.legend(['Median projected cumulative wealth', '95% confidence interval projected cumulative wealth'], loc='upper center', bbox_to_anchor=(0.5, -0.1))\n \n #\n return fig, ax\n\n# Plot the bootstrap samples of the U.S. stocks\nplt.plot(cum_prices_in)\n\n# Plot the associated fanchart for median / 95% confidence interval\nfig, ax = create_fanchart(cum_prices_in)\n\n# \nplt.show()","metadata":{"trusted":true},"execution_count":30,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}],"id":"afff4c5a-1311-4b58-b087-7cfa23899d90"},{"cell_type":"markdown","source":"> Let's also plot the cumulative wealth curves associated to the statistics of interests computed at the terminal horizon only.","metadata":{},"id":"86fa851b-4383-4919-9c90-a30c541374f8"},{"cell_type":"code","source":"# Find the cumulative wealth curve corresponding to the median of the terminal wealth values\nmedian = np.percentile(cum_prices_in[-1, :], 50, interpolation='nearest')\nmedian_idx = np.argwhere(cum_prices_in[-1, :] == median)[0][0]\n\n# Find the cumulative wealth curves corresponding to the percentiles of the terminal wealth values\nalpha = 0.025\nlow_ci = np.percentile(cum_prices_in[-1, :], alpha*100, interpolation='nearest')\nlow_ci_idx = np.argwhere(cum_prices_in[-1, :] == low_ci)[0][0]\nhigh_ci = np.percentile(cum_prices_in[-1, :], (1-alpha)*100, interpolation='nearest')\nhigh_ci_idx = np.argwhere(cum_prices_in[-1, :] == high_ci)[0][0]\n\n# Plot the wealth curves\nfig, ax = plt.subplots()\nax.plot(cum_prices_in[:, median_idx], color='black', lw=2)\nax.plot(cum_prices_in[:, low_ci_idx], color='blue', alpha=0.5, lw=2)\nax.plot(cum_prices_in[:, high_ci_idx], color='blue', alpha=0.5, lw=2)\nax.legend(['Median projected terminal cumulative wealth', '95% confidence interval projected terminal cumulative wealth'], loc='upper center', bbox_to_anchor=(0.5, -0.1))\n\n#\nplt.show() ","metadata":{"trusted":true},"execution_count":31,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}],"id":"2448581b-7579-4cc7-9198-b98d9b8b9c61"},{"cell_type":"markdown","source":"> Let's now import the nominal total returns for U.S. stocks found in Shiller's Excel file, over the period January 1990 - December 2019...","metadata":{},"id":"70bffa52-6554-454a-a632-816fc345babe"},{"cell_type":"code","source":"# Import total return prices (out of sample)\nshiller_tr_prices_out = pd.DataFrame([113528.99, 110662.41, 113660.74, 113886.76, 118275.73, 122028.02, 122237.01, 112629.28, 107741.30, 105249.02, 108391.93, 113365.63, 112589.36, 125657.55, 129483.25, 132408.63, 132172.07, 132631.01, 133667.45, 137249.58, 136834.85, 137082.62, 137103.48, 138384.80, 148568.38, 147676.90, 146183.05, 146569.41, 149600.98, 147612.60, 150435.85, 151853.24, 152428.54, 150626.35, 154778.87, 159842.20, 160071.31, 162832.36, 166334.53, 164103.15, 165292.86, 166723.36, 166825.07, 169765.30, 172065.57, 174203.09, 174216.86, 175763.11, 178815.50, 178681.51, 176138.77, 170245.88, 172048.87, 173956.70, 173055.04, 178389.69, 179848.61, 179053.06, 178393.45, 176566.03, 180894.29, 187802.81, 192606.80, 198802.56, 205460.06, 211992.19, 219515.19, 220643.88, 228848.98, 230939.69, 236388.44, 244402.29, 244803.06, 259260.63, 258743.74, 259255.46, 265362.37, 268757.17, 259418.01, 267401.43, 272817.27, 284058.67, 298412.25, 301990.61, 311829.88, 325431.10, 323403.24, 312391.70, 341188.58, 359398.35, 380015.11, 381338.69, 385886.23, 392237.45, 387720.65, 397937.55, 398882.74, 424421.57, 446971.93, 462198.15, 461176.19, 461716.73, 482347.00, 448724.88, 426746.28, 432255.90, 479693.90, 499381.62, 524591.76, 524244.72, 539573.99, 562506.00, 561950.07, 558512.24, 583772.56, 561740.96, 558383.89, 551279.20, 590453.41, 607038.25, 606317.11, 591292.93, 614596.34, 623351.57, 605655.19, 624814.26, 630123.17, 636040.35, 629169.17, 596361.89, 591753.66, 572106.05, 574705.59, 562424.81, 511353.60, 513644.65, 548976.62, 535860.12, 521605.62, 510935.01, 453469.09, 467907.69, 491551.75, 498758.14, 497273.27, 480600.73, 504367.61, 486645.72, 472924.27, 444926.39, 397056.07, 401574.78, 382466.00, 377240.83, 402238.52, 398078.42, 397194.47, 371716.94, 376580.47, 396485.37, 417546.89, 441363.92, 443999.22, 443265.96, 457283.63, 466566.53, 472224.70, 486702.76, 510729.24, 516287.03, 508214.02, 513141.52, 499990.56, 514285.98, 502779.32, 495809.97, 509613.58, 510139.97, 534497.07, 549078.76, 541680.52, 550797.35, 549399.65, 536174.00, 543345.54, 555203.23, 565247.07, 567006.38, 568599.19, 553687.56, 575631.81, 587983.80, 596615.42, 596523.40, 605395.83, 610237.47, 605445.72, 589072.21, 593322.39, 606928.54, 622299.60, 644811.65, 657728.72, 671868.74, 676531.66, 687337.48, 670341.56, 698372.58, 722068.67, 724568.12, 728742.40, 698137.92, 719614.67, 741152.92, 705540.84, 714287.04, 666900.33, 656477.89, 639242.28, 666375.99, 683458.11, 654440.04, 614661.99, 627636.55, 597213.54, 476608.45, 435588.35, 434052.18, 429281.37, 400493.19, 377699.78, 424215.86, 452444.68, 465401.43, 471324.01, 509575.39, 528152.94, 540824.10, 552129.76, 564398.38, 572049.88, 555461.95, 588466.03, 612524.10, 576494.99, 556068.51, 555188.38, 559987.48, 578869.96, 605372.81, 620457.58, 643505.20, 665794.67, 686783.05, 679152.96, 694249.99, 698839.91, 673257.40, 694152.27, 621968.16, 617071.56, 635718.62, 646970.83, 657047.93, 688485.13, 717157.60, 737852.81, 737585.19, 714801.12, 706578.03, 727236.62, 751892.09, 774626.90, 772969.66, 751061.48, 767425.92, 800198.39, 818880.11, 841186.68, 853433.26, 892489.11, 882530.62, 911269.49, 913587.85, 924499.25, 944084.97, 980535.59, 995465.03, 1005118.19, 1003830.17, 1031176.16, 1033273.96, 1049121.79, 1082672.79, 1098884.79, 1094210.26, 1113682.89, 1084223.09, 1146099.85, 1153379.63, 1140597.96, 1172868.61, 1173539.38, 1183862.44, 1195465.92, 1190274.28, 1189338.59, 1160516.45, 1108223.03, 1156080.27, 1189995.94, 1176884.62, 1101340.88, 1095292.32, 1164990.61, 1197983.81, 1194346.86, 1207093.77, 1246906.12, 1261868.92, 1256342.67, 1249996.65, 1265021.97, 1314950.16, 1333865.39, 1368242.78, 1392187.95, 1390057.65, 1413596.36, 1438721.65, 1452950.15, 1456566.26, 1480662.79, 1521168.48, 1545361.06, 1589933.91, 1667252.62, 1619142.02, 1620205.43, 1593262.15, 1624532.43, 1658874.84, 1685119.96, 1726441.58, 1755464.10, 1687920.23, 1652900.91, 1560981.87, 1588094.94, 1680683.58, 1713443.83, 1777258.41, 1750053.90, 1774657.14, 1842598.86, 1784863.70, 1839950.36, 1840148.21, 1921752.22, 1969223.97, 2035145.12])\n\n# Compute associated returns\nshiller_tr_ret_out = shiller_tr_prices_out.pct_change().dropna()\n\n# Compute associated cumulative returns\nshiller_cum_prices_out = np.cumprod(1 + np.hstack([0, shiller_tr_ret_out[0].values]))","metadata":{"trusted":true},"execution_count":32,"outputs":[],"id":"89e2358a-5b78-4927-b00d-8d5014338be7"},{"cell_type":"markdown","source":"> ... and plot them together with the bootstrap simulations, to check whether these simulations can be trusted.","metadata":{},"id":"a9732d1e-6986-4902-bd5f-70c7d3caa0e5"},{"cell_type":"code","source":"# This function display a nice fanchart from the cumulative wealth at every month horizon, plus the out of sample U.S. returns\ndef create_fanchart_out(arr, arr_out):\n #\n fig, ax = plt.subplots()\n \n #\n med = np.median(arr, axis=1)\n ax.plot(med, color='black', lw=2)\n \n # 95% CI\n alpha = 0.025\n low_ci = np.percentile(arr, alpha*100, axis=1)\n high_ci = np.percentile(arr, (1-alpha)*100, axis=1)\n x = np.arange(arr.shape[0])\n ax.fill_between(x, low_ci, high_ci, color='blue', alpha=0.5)\n\n # 75% CI\n alpha = 0.125\n low_ci = np.percentile(arr, alpha*100, axis=1)\n high_ci = np.percentile(arr, (1-alpha)*100, axis=1)\n x = np.arange(arr.shape[0])\n ax.fill_between(x, low_ci, high_ci, color='blue', alpha=0.25)\n \n #\n ax.plot(arr_out, color='black', lw=2, linestyle='dashed')\n\n #\n ax.legend(['Median projected cumulative wealth', '75% confidence interval projected cumulative wealth', '95% confidence interval projected cumulative wealth', 'Out of sample realized cumulative wealth'], loc='upper center', bbox_to_anchor=(0.5, -0.1))\n \n #\n return fig, ax\n\n# Plot the fanchart for median / 95% confidence interval / out of sample U.S. returns\nfig, ax = create_fanchart_out(cum_prices_in, shiller_cum_prices_out)\n\n# \nplt.show()","metadata":{"trusted":true},"execution_count":33,"outputs":[{"output_type":"display_data","data":{"text/plain":"
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\n"},"metadata":{"needs_background":"light"}}],"id":"00fedc69-dfed-42f8-8aea-84dedb95c41b"},{"cell_type":"markdown","source":"> It seems (with 10,000 simulations) that the out of sample U.S. returns nicely always fit in the 95% confidence interval, and also most of the time fit in the 75% confidence interval! Maybe simulations can be trusted after all...","metadata":{},"id":"e1694a28-ea4d-426e-b8ca-ef4547a9fd1f"}]}