Model AccumulationDistributionLineDemo

AccumulationDistributionLineDemo — 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

Accumulation/distribution aggregates volume signed by the close’s location within its high-low range. The demo combines that indicator with smoothed price changes. Its actual target rule, documented above, is an example policy and is not evidence that a divergence signal has positive expected return.

The accumulation/distribution line weights volume by where the close lies within the high–low range and accumulates that signed flow. It is a price/volume indicator, not a direct observation of investor buying and selling.

What this example teaches

Demonstrates an ADL/price divergence strategy driven by daily OHLCV bars. The implemented rule targets +1 unit when smoothed price changes are positive and smoothed money-flow volume is negative, and -1 unit for the reverse signs. It targets zero otherwise. This documents the actual rule, rather than assuming the more usual bullish-divergence interpretation.

Prices are synthetic by default; --live downloads Yahoo history. Pass --charts to plot the ADL; performance measures appear in the console.

Using this example. Adapts OHLCV bars to an indicator-driven strategy and sends only the change from current position to the target. Order quantities are signed product units; this fixed-unit example does not invest all account cash. DemoSupport controls licensing, synthetic data and optional live downloads.

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

Implementation in this tutorial. Accumulation/distribution aggregates volume signed by the close’s location within its high-low range. The demo combines that indicator with smoothed price changes. Its actual target rule, documented above, is an example policy and is not evidence that a divergence signal has positive expected return.

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	0.93
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.AccumulationDistributionLineDemo -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
Simulation checked: 1304 NAV observations; 103 executions.
09:12:36.414 [main] INFO  t.n.a.d.m.t.AccumulationDistributionLineDemo,138 - Performance Report:
Product	Profit Loss	Information Ratio For Trades	Omega For Trades
AAPL	320.3584623213392	1.0189458623959644	20.224689078550735
Total	320.3584623213392	1.0189458623959644	20.224689078550735

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 AccumulationDistributionLineDemo.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	293
files/derived-charts/derived-01.png	84015

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.