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Tutorials
AlgoQuant is a framework for studying, simulating, researching and applying quantitative strategies. Start with the tutorials to learn its APIs; tutorial numerical results are incidental to their teaching purpose.
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Release and downloads; Maven:
tech.nmfin:algoquant-dist:3.3.2. - Yahoo, Sina and Eastmoney stock/index history and CSV ZIP caching.
- Updated CSI300 data-loading tutorial.
- Complete compatibility survey and known limitations: 84 main sources compile, 31 tests pass; 49 demos completed cleanly, four returned with errors/warnings, one failed, one needed credentials, one aggregate timed out, and the web app passed dedicated checks. Release promotion reused validation without rerunning test suites.
These observations concern execution, not financial validity. CSI300 completed both default baselines but skipped some constituent histories and reported NaN correlation measures. The 990-optimizer grid was not run.
These examples are tutorials that teach how to use AlgoQuant. Their numerical results are incidental and irrelevant to their teaching purpose. Use them to learn API calls, simulation setup, data flow and reporting. The technical examples use synthetic quick-mode data. Read a result here as an illustration of a library call, not as investment evidence.
| Example | What to study |
|---|---|
| SMA2Demo010 | Build a first SMA crossover simulation and read its report. |
| SMA2Demo011 | Explore another SMA strategy configuration. |
| SMA2Demo020 | Configure signals, execution and performance measures. |
| SMA2DynamicDemo | Run parameter/rebalance experiments and export reports. |
| AccumulationDistributionLineDemo | Use a price/volume indicator in a simulation. |
| ChaikinMoneyFlowDemo | Configure a rolling volume-flow indicator. |
| Example | What to study |
|---|---|
| TutorialStrategyDemo | Follow market events, orders, fills and reporting. |
| BootstrapSimulationDemo | Use resampling and repeated simulations; a framework lesson. |
| GMA12Demo | Call return-model and analytical-rule APIs. |
| CoinBacktestDemo | Apply backtesting infrastructure to a crypto dataset. |
| CSI300SimulationDemo | Configure a constituent universe and historical-data simulation. |
| Example | What to study |
|---|---|
| PortfolioOptimizationAlgorithmDemo | Call an optimizer and map weights to products. |
| PortfolioOptimizationBacktestDemo | Connect rolling weight estimation with simulation. |
| PortfolioOptimComparisons1Demo | Compare allocation algorithms on common generated inputs. |
| PortfolioOptimComparisons2Demo | Compare allocation workflows, reports and rebalancing. |
| SOCPMarkowitzComparisonDemo | Compare solver formulations and numerical outputs. |
| CovarianceComparisonDemo | Compare covariance estimators and inspect numerical diagnostics. |
| MarkowitzBigDemo | Exercise optimizer scaling on generated high-dimensional inputs. |
| PerformanceOptimizedYahooEODPortfolioDemo | Assemble a search objective from performance measures. |
DemoSupport explains shared setup. Methodology and reproduction instructions describe the earlier technical run. Full numerical records remain on each example page and in Execution records.