Model SMA2Demo010

SMA2Demo010 — 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 fast and slow simple moving averages summarize different lookback windows. Their relative ordering supplies the directional signal; the strategy and broker determine how that signal becomes holdings.

What this example teaches

Runs a fixed two-simple-moving-average crossover strategy using the simulator builder. The fast and slow lags count price observations (daily bars in this example). Prices are synthetic by default; pass --live for Yahoo history and --charts for a desktop chart. The report is written to the console.

Using this example. Read this first for explicit simulator assembly: define the product and lags, load its order-book cache, construct SMA2, register measures and call simulator.run. The built-in strategy targets fixed long or short unit positions; withInitialCash does not turn it into full-equity sizing.

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	0.78
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.SMA2Demo010 -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
09:13:30.976 [main] INFO  t.n.a.d.model.technical.SMA2Demo010,97 - simulating strategy SMA2product=^HSCE @ HKEX, fastlag=1, slowlag=2...
Simulation checked: 1580 NAV observations; 65 executions.
09:13:31.203 [main] INFO  t.n.a.d.model.technical.SMA2Demo010,127 - Performance Report:
Product	Profit Loss	Information Ratio For Trades	Omega For Trades
^HSCE	1015.9419250515057	4.671747367451512	Infinity
Total	1015.9419250515057	4.671747367451512	Infinity

Charts from this run

Reported performance statistics

Products and aggregate shown exactly as reported; P/L is monetary, not percent. Currency conventions belong to the demo.

Derived from stdout.log using the precision available in that file. Download plotted data. Undefined or malformed cells are omitted, never replaced by zero.

Downloadable evidence

Download SMA2Demo010.zip contains stdout, stderr, the execution record, checksums and any exported CSV/chart files.

Export	Bytes
files/derived-charts/charts.json	521
files/derived-charts/derived-01.csv	210
files/derived-charts/derived-01.png	62645

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.