diff --git a/bindings/node/examples/backtest.js b/bindings/node/examples/backtest.js
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+// Offline backtest example for the Wickra Node binding.
+//
+// Reads an OHLCV CSV, streams every candle through a basket of indicators
+// (SMA, EMA, RSI, MACD, Bollinger Bands, ATR, ADX, OBV) with the O(1)
+// `update` call, and prints a summary of each resulting series. It is the
+// Node counterpart of `examples/python/backtest.py` and the Rust
+// `crates/wickra/examples/backtest.rs`.
+//
+// Run it from the repository after building the native module:
+//
+//   cd bindings/node
+//   npm install
+//   npx napi build --platform --release
+//   node examples/backtest.js                  # uses the bundled BTCUSDT 1d data
+//   node examples/backtest.js path/to/ohlcv.csv # or any OHLCV CSV
+//
+// In your own project, install the package and use `require('wickra')`
+// instead of the relative `require('..')` below.
+
+const fs = require('node:fs');
+const path = require('node:path');
+
+const wickra = require('..');
+
+// The OHLCV columns the default layout requires; the CSV header must name
+// every one of them (any extra columns are ignored).
+const REQUIRED_COLUMNS = ['timestamp', 'open', 'high', 'low', 'close', 'volume'];
+
+// Default dataset: the checked-in BTCUSDT daily candles under
+// crates/wickra/examples/data/, resolved relative to this file.
+const DEFAULT_CSV = path.join(
+  __dirname,
+  '..',
+  '..',
+  '..',
+  'crates',
+  'wickra',
+  'examples',
+  'data',
+  'btcusdt-1d.csv',
+);
+
+// Parse an OHLCV CSV into column arrays of numbers.
+//
+// The Wickra CSV layout is plain numeric — no quoted fields, no embedded
+// commas — so splitting on `,` is a complete and correct parse for it.
+function readHistory(csvPath) {
+  const text = fs.readFileSync(csvPath, 'utf8');
+  const lines = text.split(/\r?\n/).filter((line) => line.length > 0);
+  if (lines.length === 0) {
+    throw new Error(`${csvPath}: file is empty`);
+  }
+
+  const header = lines[0].split(',').map((cell) => cell.trim());
+  const missing = REQUIRED_COLUMNS.filter((col) => !header.includes(col));
+  if (missing.length > 0) {
+    throw new Error(
+      `${csvPath}: CSV header is missing required column(s): ${missing.join(', ')}; ` +
+        `found: ${header.join(', ')}`,
+    );
+  }
+  if (lines.length === 1) {
+    throw new Error(`${csvPath}: CSV has a header but no data rows`);
+  }
+
+  const columnIndex = {};
+  for (const col of REQUIRED_COLUMNS) {
+    columnIndex[col] = header.indexOf(col);
+  }
+
+  const cols = { timestamp: [], open: [], high: [], low: [], close: [], volume: [] };
+  for (let row = 1; row < lines.length; row++) {
+    const cells = lines[row].split(',');
+    for (const col of REQUIRED_COLUMNS) {
+      const raw = cells[columnIndex[col]];
+      const value = raw === undefined ? NaN : Number(raw.trim());
+      if (raw === undefined || raw.trim() === '' || !Number.isFinite(value)) {
+        throw new Error(
+          `${csvPath}: row ${row + 1} column '${col}' is not numeric: ${JSON.stringify(raw)}`,
+        );
+      }
+      cols[col].push(value);
+    }
+  }
+  return cols;
+}
+
+// Running min / max / mean / last of one indicator output series. Null and
+// non-finite values (the warmup phase) are skipped.
+class Series {
+  constructor(name) {
+    this.name = name;
+    this.count = 0;
+    this.sum = 0;
+    this.min = Infinity;
+    this.max = -Infinity;
+    this.last = NaN;
+  }
+
+  add(value) {
+    if (value === null || value === undefined || !Number.isFinite(value)) {
+      return;
+    }
+    this.count += 1;
+    this.sum += value;
+    if (value < this.min) this.min = value;
+    if (value > this.max) this.max = value;
+    this.last = value;
+  }
+
+  print() {
+    if (this.count === 0) {
+      console.log(`  ${this.name.padEnd(12)} (no valid samples — series too short)`);
+      return;
+    }
+    const cell = (v) => v.toFixed(4).padStart(14);
+    console.log(
+      `  ${this.name.padEnd(12)} mean=${cell(this.sum / this.count)}  ` +
+        `min=${cell(this.min)}  max=${cell(this.max)}  last=${cell(this.last)}`,
+    );
+  }
+}
+
+function main() {
+  const csvPath = process.argv[2] || DEFAULT_CSV;
+
+  let history;
+  try {
+    history = readHistory(csvPath);
+  } catch (err) {
+    console.error(`error: ${err.message}`);
+    process.exit(1);
+  }
+  const bars = history.close.length;
+
+  const sma = new wickra.SMA(20);
+  const ema = new wickra.EMA(20);
+  const rsi = new wickra.RSI(14);
+  const macd = new wickra.MACD(12, 26, 9);
+  const bb = new wickra.BollingerBands(20, 2.0);
+  const atr = new wickra.ATR(14);
+  const adx = new wickra.ADX(14);
+  const obv = new wickra.OBV();
+
+  const series = {
+    sma: new Series('SMA(20)'),
+    ema: new Series('EMA(20)'),
+    rsi: new Series('RSI(14)'),
+    macdLine: new Series('MACD line'),
+    macdHist: new Series('MACD hist'),
+    bbUpper: new Series('BB upper'),
+    bbLower: new Series('BB lower'),
+    atr: new Series('ATR(14)'),
+    adx: new Series('ADX(14)'),
+    obv: new Series('OBV'),
+  };
+
+  for (let i = 0; i < bars; i++) {
+    const close = history.close[i];
+    series.sma.add(sma.update(close));
+    series.ema.add(ema.update(close));
+    series.rsi.add(rsi.update(close));
+
+    const m = macd.update(close); // { macd, signal, histogram } or null
+    if (m) {
+      series.macdLine.add(m.macd);
+      series.macdHist.add(m.histogram);
+    }
+
+    const b = bb.update(close); // { upper, middle, lower, stddev } or null
+    if (b) {
+      series.bbUpper.add(b.upper);
+      series.bbLower.add(b.lower);
+    }
+
+    series.atr.add(atr.update(history.high[i], history.low[i], close));
+
+    const a = adx.update(history.high[i], history.low[i], close); // { plusDi, minusDi, adx } or null
+    if (a) {
+      series.adx.add(a.adx);
+    }
+
+    series.obv.add(obv.update(close, history.volume[i]));
+  }
+
+  console.log(`Backtest summary for ${csvPath} (${bars} bars)`);
+  for (const s of Object.values(series)) {
+    s.print();
+  }
+}
+
+main();