examples: add multi-timeframe demos for Rust and Node
Python's examples/python/multi_timeframe.py had no Rust or Node sibling. Add both — the Rust version uses wickra-data's `Resampler` / `resample_all` (the canonical path; no manual roll-up), the Node version mirrors the Python one's inline aggregation because wickra-data's resampler is currently Rust-only. * examples/rust/src/bin/multi_timeframe.rs — reads the bundled 1m CSV via `CandleReader`, resamples to 5m / 15m / 1h / 4h / 1d via `resample_all`, prints last RSI(14), MACD(12,26,9) histogram and ADX(14) per timeframe. * examples/node/multi_timeframe.js — same outputs from a hand-rolled bucket aggregator; reuses the new examples/data/ default path. * examples/README.md gains the new rows. Run side by side: the Rust and Node summaries are bit-identical at every timeframe (50000 / 10000 / 3334 / 834 / 209 / 35 bars; same RSI, MACD histogram and ADX to two decimals) — confirming both the Rust resampler and the inline Node aggregator produce the same OHLC buckets.
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kingchenc
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// Multi-timeframe indicators with the Wickra Node binding.
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//
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// Reads the bundled 1-minute BTCUSDT CSV (or a path passed on the command
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// line), rolls it up to coarser timeframes (5m / 15m / 1h / 4h / 1d) and
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// prints the last RSI(14), MACD(12,26,9) histogram and ADX(14) on each.
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// Mirrors examples/python/multi_timeframe.py — wickra-data's Resampler is
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// only exposed in Rust today, so the roll-up is computed inline here.
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//
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// Run with:
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// node examples/node/multi_timeframe.js [path/to/1m.csv]
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const fs = require('node:fs');
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const path = require('node:path');
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const wickra = require('wickra');
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const REQUIRED_COLUMNS = ['timestamp', 'open', 'high', 'low', 'close', 'volume'];
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const DEFAULT_CSV = path.join(__dirname, '..', 'data', 'btcusdt-1m.csv');
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const ONE_MINUTE_MS = 60_000;
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function readCsv(csvPath) {
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const text = fs.readFileSync(csvPath, 'utf8');
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const lines = text.split(/\r?\n/).filter((line) => line.length > 0);
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if (lines.length === 0) {
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throw new Error(`${csvPath}: file is empty`);
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}
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const header = lines[0].split(',').map((cell) => cell.trim());
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const missing = REQUIRED_COLUMNS.filter((col) => !header.includes(col));
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if (missing.length > 0) {
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throw new Error(
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`${csvPath}: missing required column(s): ${missing.join(', ')}; found: ${header.join(', ')}`,
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);
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}
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if (lines.length === 1) {
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throw new Error(`${csvPath}: CSV has a header but no data rows`);
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}
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const idx = {};
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for (const col of REQUIRED_COLUMNS) {
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idx[col] = header.indexOf(col);
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}
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const cols = { timestamp: [], open: [], high: [], low: [], close: [], volume: [] };
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for (let i = 1; i < lines.length; i++) {
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const cells = lines[i].split(',');
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for (const col of REQUIRED_COLUMNS) {
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const value = Number(cells[idx[col]]);
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if (!Number.isFinite(value)) {
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throw new Error(
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`${csvPath}: row ${i + 1} column '${col}' is not numeric: ${JSON.stringify(cells[idx[col]])}`,
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);
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}
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cols[col].push(value);
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}
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}
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return cols;
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}
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// Roll an OHLCV series up to `bucketMs`-sized buckets keyed on each bar's
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// `floor(timestamp / bucketMs)`. Input timestamps must be monotonic
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// non-decreasing (the bundled BTCUSDT-1m dataset is contiguous, so this
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// holds by construction).
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function resample(cols, bucketMs) {
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if (cols.timestamp.length === 0) {
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throw new Error('resample: empty input series');
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}
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const out = { timestamp: [], open: [], high: [], low: [], close: [], volume: [] };
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let bucketStart = Math.floor(cols.timestamp[0] / bucketMs) * bucketMs;
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let [o, h, l, c, v] = [cols.open[0], cols.high[0], cols.low[0], cols.close[0], cols.volume[0]];
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for (let i = 1; i < cols.timestamp.length; i++) {
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const start = Math.floor(cols.timestamp[i] / bucketMs) * bucketMs;
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if (start === bucketStart) {
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if (cols.high[i] > h) h = cols.high[i];
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if (cols.low[i] < l) l = cols.low[i];
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c = cols.close[i];
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v += cols.volume[i];
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} else {
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out.timestamp.push(bucketStart);
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out.open.push(o);
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out.high.push(h);
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out.low.push(l);
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out.close.push(c);
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out.volume.push(v);
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bucketStart = start;
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o = cols.open[i];
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h = cols.high[i];
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l = cols.low[i];
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c = cols.close[i];
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v = cols.volume[i];
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}
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}
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// Flush the final open bucket.
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out.timestamp.push(bucketStart);
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out.open.push(o);
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out.high.push(h);
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out.low.push(l);
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out.close.push(c);
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out.volume.push(v);
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return out;
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}
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function summarize(label, cols) {
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if (cols.close.length === 0) {
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console.log(` ${label.padEnd(5)} (empty)`);
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return;
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}
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const rsi = new wickra.RSI(14);
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const macd = new wickra.MACD(12, 26, 9);
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const adx = new wickra.ADX(14);
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let lastRsi = null;
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let lastHist = null;
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let lastAdx = null;
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for (let i = 0; i < cols.close.length; i++) {
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const r = rsi.update(cols.close[i]);
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if (r !== null) lastRsi = r;
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const m = macd.update(cols.close[i]);
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if (m) lastHist = m.histogram;
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const a = adx.update(cols.high[i], cols.low[i], cols.close[i]);
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if (a) lastAdx = a.adx;
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}
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const lastClose = cols.close[cols.close.length - 1];
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const fmtR = lastRsi === null ? ' --' : lastRsi.toFixed(2).padStart(6);
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const fmtH = lastHist === null ? ' -- ' : `${lastHist >= 0 ? '+' : ''}${lastHist.toFixed(2)}`.padStart(6);
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const fmtA = lastAdx === null ? ' --' : lastAdx.toFixed(2).padStart(6);
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console.log(
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` ${label.padEnd(5)} bars=${String(cols.close.length).padStart(5)} ` +
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`last_close=${lastClose.toFixed(2).padStart(10)} ` +
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`rsi=${fmtR} macd_hist=${fmtH} adx=${fmtA}`,
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);
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}
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function main() {
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const csvPath = process.argv[2] || DEFAULT_CSV;
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let cols;
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try {
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cols = readCsv(csvPath);
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} catch (err) {
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console.error(`error: ${err.message}`);
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process.exit(1);
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}
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console.log(`Multi-timeframe view of ${csvPath}`);
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summarize('1m', cols);
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for (const [label, mins] of [
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['5m', 5],
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['15m', 15],
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['1h', 60],
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['4h', 240],
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['1d', 1440],
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]) {
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summarize(label, resample(cols, mins * ONE_MINUTE_MS));
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}
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}
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main();
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