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Model MarkowitzBigDemo
All quantitative model results · Pinned source · Javadoc
Harry Markowitz, Portfolio Selection (1952) explains diversification as a portfolio problem: an asset's risk cannot be judged independently of how its returns move with the other holdings. Expected portfolio return is a weighted mean; portfolio variance depends on the full covariance matrix.
The efficient set contains portfolios for which expected return cannot be improved without accepting more variance, or variance reduced without giving up expected return. An investor selects a tradeoff according to preferences and constraints. Holding many positively correlated assets need not provide much diversification.
The framework assumes the relevant return means and covariances are supplied. In practice, estimates can be unstable and constraints can materially change the answer. A solved optimization is therefore a set of weights under assumptions, not evidence of future outperformance. The original article was reviewed. Rolling calibration, rebalance schedules, costs and the dataset used here belong to the demo, not to the static 1952 formulation.
Benchmarks the optimizer on generated high-dimensional inputs. Its asset count exercises numerical scaling; no historical strategy is simulated, and runtime or feasibility says nothing about out-of-sample returns.
This demo shows the performance/speed of running our implementation of Markowitz algorithm on a large pool of assets, e.g., N = 10000.
Using this example. Benchmarks Markowitz optimization on a large generated asset universe. Increase the asset count only after checking memory and solver runtime; this demonstrates numerical scaling, not historical investment performance.
Model and publication. Related theory and the scope of this example are distinguished below.
Implementation in this tutorial. Benchmarks the optimizer on generated high-dimensional inputs. Its asset count exercises numerical scaling; no historical strategy is simulated, and runtime or feasibility says nothing about out-of-sample returns.
| Item | Value |
|---|---|
| Status | completed |
| Run | 39114360151 |
| UTC publication | 2026-10-18 09:18 UTC |
| Source commit | 355aa9242fc2a247b256aecc8406cbb409aa5ff3 |
| Data mode | original main defaults |
| Exit code | 0 |
| Elapsed seconds | 39.14 |
| Time limit seconds | 180 |
| Maximum Java heap | 1200m |
The original main returned successfully. Read the output and model 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.portfoliooptimization.MarkowitzBigDemoThe 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.
09:13:30.460 [main] INFO t.n.a.d.m.p.MarkowitzBigDemo,91 - elapsed = 29314ms; Markowitz model weights: [0.002053, 0.001897, 0.000499, 0.002768, 0.000000, 0.000063, 0.000000, 0.000010, 0.000000, 0.001796, 0.001775, 0.002254, 0.000360, 0.000964, 0.000536, 0.000000, 0.002950, 0.000040, 0.000414, 0.000000, 0.000280, 0.002675, 0.002040, 0.000030, 0.000000, 0.001555, 0.000000, 0.000723, 0.002574, 0.000000, 0.000511, 0.000047, 0.000402, 0.000642, 0.001237, 0.000922, 0.000000, 0.000000, 0.000000, 0.001072, 0.002744, 0.000000, 0.000908, 0.000000, 0.000746, 0.000000, 0.003683, 0.000000, 0.000092, 0.002107, 0.000000, 0.000468, 0.002050, 0.000392, 0.001366, 0.000000, 0.002414, 0.001718, 0.000000, 0.000021, 0.000000, 0.000000, 0.001972, 0.000000, 0.001206, 0.001506, 0.000000, 0.000000, 0.001122, 0.000000, 0.000000, 0.002058, 0.000644, 0.000027, 0.000000, 0.000840, 0.001128, 0.001340, 0.000678, 0.000000, 0.000875, 0.001594, 0.000000, 0.000502, 0.000847, 0.000806, 0.000000, 0.000994, 0.001857, 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Download MarkowitzBigDemo.zip contains stdout, stderr, the execution record, checksums and any exported CSV/chart files.
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.