Model SMA2DynamicDemo

SMA2DynamicDemo — AlgoQuant usage tutorial

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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.

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Strategy introduction

This is a technical-indicator tutorial. No single original research paper has been established as the source of this exact demo policy, so the introduction explains its mechanics rather than attributing it to an unverified publication.

An indicator transforms observed price or volume history into a statistic. A strategy adds decisions about when the statistic is available, what constitutes an entry or exit, how large a position to hold and how orders are executed. Those extra choices determine account performance; an indicator series alone is not a backtest.

The recorded run uses the documented synthetic quick-mode inputs, so it teaches the AlgoQuant API rather than measuring a historical market opportunity. Read the specific mechanism below together with the signal, order, simulation and report code. Distinguish periods measured in observations from calendar time and avoid treating parameter tuning on one sample as independent validation.

How this demo connects to the idea

The signal compares a fast and a slow arithmetic mean of prices. A fast mean above or below the slow mean produces a directional target after the required history is available. Lag parameters count observations. SMA2DynamicDemo additionally recalibrates parameters on trailing windows; that procedure is separate from the crossover formula.

The dynamic example evaluates alternative moving-average parameters. Quick mode bounds that search. Choosing the best configuration on the evaluated sample is parameter fitting, not an independent forecast test.

What this example teaches

Runs a dynamically calibrated SMA crossover over rebalance/data-window combinations. Each combination has an independent simulator and performance measures. The strategy periodically recalibrates using a trailing data window. --quick reduces both the date range and parameter grid for a smoke run. Defaults to synthetic data; --live enables Yahoo downloads. Performance, execution and trade CSV files are written by SimulationResultsExporter.

Using this example. Separates trailing-window calibration from subsequent strategy execution and exports results for multiple parameter combinations. Use --quick for the smaller example configuration. Repeated calibration requires fresh strategy state; report rows belong to their own parameter set and should not be concatenated into one account.

Model and publication. No single original research publication is claimed for this library/example policy.

Implementation in this tutorial. The signal compares a fast and a slow arithmetic mean of prices. A fast mean above or below the slow mean produces a directional target after the required history is available. Lag parameters count observations. SMA2DynamicDemo additionally recalibrates parameters on trailing windows; that procedure is separate from the crossover formula.

Verified execution record

Item	Value
Status	completed
Run	39114360151
UTC publication	2026-10-18 11:43 UTC
Source commit	355aa9242fc2a247b256aecc8406cbb409aa5ff3
Data mode	synthetic --quick
Exit code	0
Elapsed seconds	4.65
Time limit seconds	180
Maximum Java heap	1200m

The original main returned successfully. Read the output and model assumptions before interpreting it.

Reproduce the calculation

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.technical.SMA2DynamicDemo -Dexec.args="--quick"

The 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.

Observed output

Data: deterministic SYNTHETIC prices; charts: false; quick: true
09:15:32.120 [main] INFO  t.n.a.d.m.technical.SMA2DynamicDemo,174 - running strategy SMA2Dynamic: rebalance period = P1M; data window = P3M; trading 000001.SS @ SS...
09:15:32.724 [main] INFO  t.n.a.m.s.t.c.sma2.SMA2Dynamic,125 - will run a newly calibrated strategy @ 2016-01-04T15:00: SMA2product=000001.SS @ SS, fastlag=7, slowlag=20
09:15:32.936 [main] INFO  t.n.a.m.s.t.c.sma2.SMA2Dynamic,125 - will run a newly calibrated strategy @ 2016-02-04T15:00: SMA2product=000001.SS @ SS, fastlag=9, slowlag=17
09:15:33.092 [main] INFO  t.n.a.m.s.t.c.sma2.SMA2Dynamic,125 - will run a newly calibrated strategy @ 2016-03-04T15:00: SMA2product=000001.SS @ SS, fastlag=3, slowlag=13
Simulation checked: 65 NAV observations; 1 executions.
09:15:33.127 [main] INFO  t.n.a.e.r.SimulationResultsExporter,766 - Saving to /home/runner/work/algoquant-examples/algoquant-examples/target/model-results/SMA2DynamicDemo/work/log/SMA2DynamicDemo.2026.10.18,09.15.33.measures.csv...
09:15:33.153 [main] INFO  t.n.a.e.r.SimulationResultsExporter,766 - Saving to /home/runner/work/algoquant-examples/algoquant-examples/target/model-results/SMA2DynamicDemo/work/log/SMA2DynamicDemo.2026.10.18,09.15.33.executions.csv...
09:15:33.158 [main] INFO  t.n.a.e.r.SimulationResultsExporter,766 - Saving to /home/runner/work/algoquant-examples/algoquant-examples/target/model-results/SMA2DynamicDemo/work/log/SMA2DynamicDemo.2026.10.18,09.15.33.trades.csv...
09:15:33.159 [main] INFO  t.n.a.d.m.technical.SMA2DynamicDemo,150 - time elapsed: 3623 milliseconds

Charts from this run

Recorded executions/trades

Observations are recorded events in file order, not a continuous market-price series; quantities are not signed exposure.

Derived from files/log/SMA2DynamicDemo.2026.10.18,09.15.33.executions.csv using the precision available in that file. Download plotted data. Undefined or malformed cells are omitted, never replaced by zero.

CSV performance statistics (panel set 1)

One axis per measure; raw units and report definitions retained. Empty Product selects aggregate account rows.

Derived from files/log/SMA2DynamicDemo.2026.10.18,09.15.33.measures.csv using the precision available in that file. Download plotted data. Undefined or malformed cells are omitted, never replaced by zero.

Downloadable evidence

Download SMA2DynamicDemo.zip contains stdout, stderr, the execution record, checksums and any exported CSV/chart files.

Export	Bytes
files/log/SMA2DynamicDemo.2026.10.18,09.15.33.executions.csv	126
files/log/SMA2DynamicDemo.2026.10.18,09.15.33.measures.csv	428
files/log/SMA2DynamicDemo.2026.10.18,09.15.33.trades.csv	102
files/derived-charts/charts.json	1132
files/derived-charts/derived-01.csv	128
files/derived-charts/derived-01.png	58002
files/derived-charts/derived-02.csv	205
files/derived-charts/derived-02.png	90557

Interpreting the result

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.