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Model KagiMRBasketSimulationDemo
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This quantitative model reference is provided for study, further research and application within the AlgoQuant framework. Read the research idea and implementation choices separately from the execution record.
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Alexandre d’Aspremont, Identifying Small Mean Reverting Portfolios (2008 author preprint; published 2011) asks how to build a mean-reverting basket using only a few assets. A dense basket may look attractive statistically but be expensive and difficult to trade.
The paper uses a vector autoregressive representation and a Box–Tiao predictability ratio as a proxy for mean reversion. Minimizing this ratio seeks combinations whose current variation is less explained by lagged variation. A cardinality restriction limits the number of nonzero holdings. Greedy search and semidefinite relaxations address the resulting sparse optimization problem; regularized estimation can help stabilize its inputs.
Its central tradeoff is between sparsity and the strength of the mean-reversion proxy. Finding a basket does not specify a complete trading strategy or guarantee a profit. Kagi signals, entry and exit policies, sizing and the S&P 500 universe in these demos are separate choices. Sections 2–3 of the author preprint were reviewed.
SimpleMRBasketProvider uses the configured price matrix, covariance penalty, clustering and basket-selection windows. The demo selects the first basket and passes it to KagiMRBasketSimulation for a trading interval. The Kagi simulation and S&P 500 settings are implementation choices; the example does not demonstrate every greedy/semidefinite method in the paper.
Using this example. Creates mean-reverting baskets with SimpleMRBasketProvider and evaluates a basket through KagiMRBasketSimulation. MRSimParamSP500 supplies the data and interval conventions; the plotted P/L belongs to the selected basket and trading period.
Model and publication. The paper searches for sparse portfolios with low autoregressive predictability, using generalized eigenvalue formulations and sparse approximations. Basket selection and profitable execution are separate questions.
Implementation in this tutorial. SimpleMRBasketProvider uses the configured price matrix, covariance penalty, clustering and basket-selection windows. The demo selects the first basket and passes it to KagiMRBasketSimulation for a trading interval. The Kagi simulation and S&P 500 settings are implementation choices; the example does not demonstrate every greedy/semidefinite method in the paper.
| Item | Value |
|---|---|
| Status | completed |
| Run | 39122217560 |
| UTC publication | 2026-10-18 11:43 UTC |
| Source commit | f497645628296f441e1e254127a7547d15106bbc |
| Data mode | original main defaults |
| Exit code | 0 |
| Elapsed seconds | 162.04 |
| Time limit seconds | 1800 |
| Maximum Java heap | 6g |
| AlgoQuant source commit | aba178deb0e78dc72184546a1e0934c3abce74a1 |
| NM Dev source commit | 04c12d1640fb700f52a2f7dc5cadf560040490e6 |
| Combined runtime SHA-256 | c1199a1c204cc4ffdf937b756bceacaf30bb64f35a844770490fea268742379b |
The original main returned successfully without a detected calculation error. Read its assumptions before interpreting it.
Follow the repository setup for JDK 8, Maven credentials and an external NM license. From the repository root:
mvn -U -Dmaven.test.skip=true clean package
mvn exec:java -Dexec.mainClass=tech.nmfin.algoquant.demo.model.daspremont2008.KagiMRBasketSimulationDemoThe automated run uses scripts/ModelDemoRunner.java to initialize the external license before the unchanged main, save JFreeChart windows as PNGs, and exit after main returns. It uses an isolated working directory with the repository datasets/configuration linked in. Running directly may leave desktop windows open.
11:14:33.854 [main] INFO t.n.a.m.m.d.strategy.SimpleMRBasketProvider - computing baskets at time 2013-01-01T00:00 (covPenalty=1.5E-4; inSamplePeriodForClustering=P10Y; inSamplePeriodForBasketSelection=P5Y; nBaskets=1; basketSize=5)
11:14:33.877 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - looking up configuration file: config.default.xml
11:14:33.878 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - looking up configuration file: config/config.default.xml
11:14:33.878 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - loading config file: file:/home/runner/work/algoquant/algoquant/target/model-results/KagiMRBasketSimulationDemo/work/config/config.default.xml
11:14:34.272 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - current directory: /home/runner/work/algoquant/algoquant/target/model-results/KagiMRBasketSimulationDemo/work
11:14:34.272 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - current config:
11:14:34.284 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - nm.license: /home/runner/work/algoquant/algoquant/license/nm.lic
11:14:34.284 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - logback.configurationFile: /home/runner/work/algoquant/algoquant/config/model-demo-logback.xml
11:14:34.284 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - DATA_FOLDER: ./test-data
11:14:34.284 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - DATA_GAINCAPITAL: ${DATA_FOLDER}/processed/gaincapital/extracted-from-raw-arrange-by-dates
11:14:34.284 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - DATA_BLOOMBERG: ${DATA_FOLDER}/processed/bloomberg
11:14:34.285 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - DATA_YAHOO: ${DATA_FOLDER}/yahoo
11:14:34.287 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - DATA_SGX: ${DATA_FOLDER}/raw/sgx
11:14:34.287 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - DATA_HOLIDAYS: ./log/holidays
11:14:34.287 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - DATA_COMPUSTAT_HK: ${DATA_FOLDER}/compustat/hk
11:14:34.287 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - PDF_OUTPUT_FOLDER: ./log/
11:14:34.287 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - PDF_PLOT_WIDTH: 800
11:14:34.287 [main] INFO tech.nmfin.algoquant.util.config.AQConfig - PDF_PLOT_HEIGHT: 600
11:14:35.472 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - failed to load from file, reason: no data read from file; load from web
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11:14:35.729 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - writing csv.zip file ./log/yahoo/pricematrix/MMM.csv.zip
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11:14:35.951 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - writing csv.zip file ./log/yahoo/pricematrix/AOS.csv.zip
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11:14:35.983 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - failed to load from file, reason: no data read from file; load from web
11:14:35.983 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - reading Yahoo! data for ABT from the Internet from 2003-01-01T00:00 to 2015-01-01T16:00
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11:16:07.407 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - finished writing ./log/yahoo/pricematrix/WTW.csv.zip
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11:16:07.407 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - reading Yahoo! data for WDAY from the Internet from 2003-01-01T00:00 to 2015-01-01T16:00
11:16:07.514 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - writing csv.zip file ./log/yahoo/pricematrix/WDAY.csv.zip
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11:16:07.518 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - reading Yahoo! data for WYNN from the Internet from 2003-01-01T00:00 to 2015-01-01T16:00
11:16:07.745 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - writing csv.zip file ./log/yahoo/pricematrix/WYNN.csv.zip
11:16:07.762 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - finished writing ./log/yahoo/pricematrix/WYNN.csv.zip
11:16:07.762 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - failed to load from file, reason: no data read from file; load from web
11:16:07.762 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - reading Yahoo! data for XEL from the Internet from 2003-01-01T00:00 to 2015-01-01T16:00
11:16:07.897 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - writing csv.zip file ./log/yahoo/pricematrix/XEL.csv.zip
11:16:07.912 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - finished writing ./log/yahoo/pricematrix/XEL.csv.zip
11:16:07.912 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - failed to load from file, reason: no data read from file; load from web
11:16:07.912 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - reading Yahoo! data for XYL from the Internet from 2003-01-01T00:00 to 2015-01-01T16:00
11:16:08.028 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - writing csv.zip file ./log/yahoo/pricematrix/XYL.csv.zip
11:16:08.032 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - finished writing ./log/yahoo/pricematrix/XYL.csv.zip
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11:16:08.032 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - reading Yahoo! data for YUM from the Internet from 2003-01-01T00:00 to 2015-01-01T16:00
11:16:08.168 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - writing csv.zip file ./log/yahoo/pricematrix/YUM.csv.zip
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11:16:08.190 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - reading Yahoo! data for ZBRA from the Internet from 2003-01-01T00:00 to 2015-01-01T16:00
11:16:08.310 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - writing csv.zip file ./log/yahoo/pricematrix/ZBRA.csv.zip
11:16:08.325 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - finished writing ./log/yahoo/pricematrix/ZBRA.csv.zip
11:16:08.326 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - failed to load from file, reason: no data read from file; load from web
11:16:08.326 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - reading Yahoo! data for ZBH from the Internet from 2003-01-01T00:00 to 2015-01-01T16:00
11:16:08.432 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - writing csv.zip file ./log/yahoo/pricematrix/ZBH.csv.zip
11:16:08.454 [main] INFO t.n.a.d.h.yahoo.YahooEODCsvZipFileWriter - finished writing ./log/yahoo/pricematrix/ZBH.csv.zip
11:16:08.454 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - failed to load from file, reason: no data read from file; load from web
11:16:08.454 [main] INFO t.n.a.d.h.yahoo.YahooEODCacheFactory - reading Yahoo! data for ZTS from the Internet from 2003-01-01T00:00 to 2015-01-01T16:00
11:16:08.572 [main] INFO t.n.a.d.h.yaho
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2.246 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=2.095057894510777
11:17:02.329 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=2.304563683961855
11:17:02.403 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=2.5140694734129325
11:17:02.483 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=2.7235752628640104
11:17:02.540 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=2.933081052315088
11:17:02.595 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=3.1425868417661658
11:17:02.649 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=3.3520926312172437
11:17:02.699 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=3.5615984206683207
11:17:02.747 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=3.771104210119399
11:17:02.796 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=3.980609999570477
11:17:02.845 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=4.190115789021554
11:17:02.865 [main] INFO t.n.a.m.m.d.strategy.MRModelKagiFactory - sorted simulated kagi results: [H=2.514069->performance=0.105783, H=2.304564->performance=0.102711, H=2.095058->performance=0.094449, H=1.885552->performance=0.078597, H=0.838023->performance=0.074872, H=1.676046->performance=0.062855, H=2.933081->performance=0.060499, H=1.466541->performance=0.056198, H=2.723575->performance=0.055807, H=1.257035->performance=0.053412, H=1.047529->performance=0.052526, H=3.352093->performance=0.040372, H=3.142587->performance=0.039404, H=0.628517->performance=0.037559, H=3.980610->performance=0.021106, H=3.771104->performance=0.019092, H=3.561598->performance=0.017422, H=4.190116->performance=0.015421, H=0.419012->performance=-0.011607, H=0.209506->performance=-0.080328]
11:17:02.865 [main] INFO t.n.a.m.m.daspremont2008.strategy.KagiMRStrategy - trading basket (inSampleInterval = [2008-01-01T00:00,2013-01-01T00:00)) (size = 5) (mu = -2.431347) (sample stdev = 0.676884) (theta = 0.091037) (sigma = 0.286152) (ou ratio = 0.318143) (predictability = 0.840409) (obj. func. value = 0.014706) (components = portfolio = { AES @ NEW_YORK=0.758522, BG @ NEW_YORK=-0.163497, XOM @ NEW_YORK=-0.159291, MGM @ NEW_YORK=-0.145123, VLO @ NEW_YORK=0.592857, }): initialCapital=1000000.0, scale=43935.235104225016, H=2.5140694734129325
11:17:02.868 [main] INFO t.n.a.d.m.d.KagiMRBasketSimulationDemo - pnl series = [-3105.847186079025, -15313.89449309349, -7473.091302862167, -23734.83353518486, -21290.749947538374, -25364.156049718855, -17284.61836241722, -23253.480422964094, -18143.809679021833, -16371.33331202507, -4954.70236205101, -8497.724979391098, -9030.3772859478, -6219.122140874862, 6156.0126276111605, -2753.4507808589933, -1718.2723426723478, 2741.87837887764, 65910.65986439705, 54935.73628136635, 59930.94042107582, 41516.48876473427, 36453.31038090706, 29432.882120103837, 29411.84644505501, -10402.578248052596, -7168.0971632289875, -19049.297417669295, -11662.634103803633, -19765.57584097862, -29653.023266820906, -28209.149517087935, -36262.771163015364, -1304.9713926601398, -9452.748859434127, -18436.779898672103, 4049.2353744220745, -3503.3362970638264, -21619.258523015975, -14055.906971006392, -41546.24571803093, -72836.70062735557, -64565.51416781425, -54705.634137182235, -41181.49963191032, -24199.72997191429, -2860.6715002346027, -29436.90797141075, 2271.359191865922, 7280.502224893571, 2315.1434030246746, -6023.4439182567585, -7552.534333257674, -18376.303694753646, -13821.40069582939, -10403.365885763167, -11367.504345922469, -18807.71754649162, -19088.57141783714, -27600.863753347396, -19576.276995687484, 10547.298062295915, 24095.369445772172, 9588.700653047563, -6535.798933057784, -10990.216747312545, -22837.473943738936, -33181.9415588665, -9256.711378126143, 619.8985595417034, 13152.51079937935, -3094.746027021407, 20516.569011659623, 17861.81794354439, 13992.476070375444, 5939.238590211869, 1121.2469939899456, -20924.27286341667, 6126.4868392658245, 5969.830709428788, -2300.123323469161, 13958.13026043892, 23400.88565823555, 4875.172481508256, 4982.27288052559, -3836.308252363204, -15668.948104887008, 6954.859916658403, 22386.95916554451, 11479.868356676103, 24511.401332826616, 15951.792709321977, 3951.757665605546, 4668.475410432817, -16013.701633481978, -30610.697048215865, -15362.294988660811, -6907.656589536666, -9181.34091761589, -4207.011853246688, -4447.797784833907, 1309.3569173526776, 1034.8373975467694, -12191.096815137862, -2373.8730392742145, 13056.558122606279, 11468.745696992875, 322.36397358894465, 637.3236598682415, 18310.16452214241, 20953.820942850114, 31090.711007089616, 29308.037301988603, 31603.416104288102, 34068.554101419446, 28224.62140507698, 18707.245089006425, 241.2542976856239, -3677.4432073116295, -18108.871685552596, 221.66988034248425, 819.8388647556312, -10695.308835554122, -15362.927164602279, -32368.98707537651]
Parameter axes are separate. Record order is preserved; estimated parameters are not investment performance.
Derived from stdout.log using the precision available in that file. Download plotted data. Undefined or malformed cells are omitted, never replaced by zero.
Download KagiMRBasketSimulationDemo.zip contains stdout, stderr, the execution record, checksums and any exported CSV/chart files.
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files/charts/chart-01.png |
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files/derived-charts/charts.json |
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files/derived-charts/derived-01.csv |
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files/derived-charts/derived-01.png |
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Read the reported units: optimizer weights, model-fit coefficients, forecast correlations and simulated P/L are different outputs. Synthetic runs demonstrate the API rather than historical market returns. Inspect undefined values and open positions instead of replacing them with zero. Chart exports visualize exactly this run.
For model assumptions, original publications and adaptation limits, use the literature guide.