Files
wickra/examples/node/multi_timeframe.js
T
kingchenc 962ced0712 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.
2026-05-23 00:16:08 +02:00

155 lines
5.0 KiB
JavaScript

// Multi-timeframe indicators with the Wickra Node binding.
//
// Reads the bundled 1-minute BTCUSDT CSV (or a path passed on the command
// line), rolls it up to coarser timeframes (5m / 15m / 1h / 4h / 1d) and
// prints the last RSI(14), MACD(12,26,9) histogram and ADX(14) on each.
// Mirrors examples/python/multi_timeframe.py — wickra-data's Resampler is
// only exposed in Rust today, so the roll-up is computed inline here.
//
// Run with:
// node examples/node/multi_timeframe.js [path/to/1m.csv]
const fs = require('node:fs');
const path = require('node:path');
const wickra = require('wickra');
const REQUIRED_COLUMNS = ['timestamp', 'open', 'high', 'low', 'close', 'volume'];
const DEFAULT_CSV = path.join(__dirname, '..', 'data', 'btcusdt-1m.csv');
const ONE_MINUTE_MS = 60_000;
function readCsv(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}: 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 idx = {};
for (const col of REQUIRED_COLUMNS) {
idx[col] = header.indexOf(col);
}
const cols = { timestamp: [], open: [], high: [], low: [], close: [], volume: [] };
for (let i = 1; i < lines.length; i++) {
const cells = lines[i].split(',');
for (const col of REQUIRED_COLUMNS) {
const value = Number(cells[idx[col]]);
if (!Number.isFinite(value)) {
throw new Error(
`${csvPath}: row ${i + 1} column '${col}' is not numeric: ${JSON.stringify(cells[idx[col]])}`,
);
}
cols[col].push(value);
}
}
return cols;
}
// Roll an OHLCV series up to `bucketMs`-sized buckets keyed on each bar's
// `floor(timestamp / bucketMs)`. Input timestamps must be monotonic
// non-decreasing (the bundled BTCUSDT-1m dataset is contiguous, so this
// holds by construction).
function resample(cols, bucketMs) {
if (cols.timestamp.length === 0) {
throw new Error('resample: empty input series');
}
const out = { timestamp: [], open: [], high: [], low: [], close: [], volume: [] };
let bucketStart = Math.floor(cols.timestamp[0] / bucketMs) * bucketMs;
let [o, h, l, c, v] = [cols.open[0], cols.high[0], cols.low[0], cols.close[0], cols.volume[0]];
for (let i = 1; i < cols.timestamp.length; i++) {
const start = Math.floor(cols.timestamp[i] / bucketMs) * bucketMs;
if (start === bucketStart) {
if (cols.high[i] > h) h = cols.high[i];
if (cols.low[i] < l) l = cols.low[i];
c = cols.close[i];
v += cols.volume[i];
} else {
out.timestamp.push(bucketStart);
out.open.push(o);
out.high.push(h);
out.low.push(l);
out.close.push(c);
out.volume.push(v);
bucketStart = start;
o = cols.open[i];
h = cols.high[i];
l = cols.low[i];
c = cols.close[i];
v = cols.volume[i];
}
}
// Flush the final open bucket.
out.timestamp.push(bucketStart);
out.open.push(o);
out.high.push(h);
out.low.push(l);
out.close.push(c);
out.volume.push(v);
return out;
}
function summarize(label, cols) {
if (cols.close.length === 0) {
console.log(` ${label.padEnd(5)} (empty)`);
return;
}
const rsi = new wickra.RSI(14);
const macd = new wickra.MACD(12, 26, 9);
const adx = new wickra.ADX(14);
let lastRsi = null;
let lastHist = null;
let lastAdx = null;
for (let i = 0; i < cols.close.length; i++) {
const r = rsi.update(cols.close[i]);
if (r !== null) lastRsi = r;
const m = macd.update(cols.close[i]);
if (m) lastHist = m.histogram;
const a = adx.update(cols.high[i], cols.low[i], cols.close[i]);
if (a) lastAdx = a.adx;
}
const lastClose = cols.close[cols.close.length - 1];
const fmtR = lastRsi === null ? ' --' : lastRsi.toFixed(2).padStart(6);
const fmtH = lastHist === null ? ' -- ' : `${lastHist >= 0 ? '+' : ''}${lastHist.toFixed(2)}`.padStart(6);
const fmtA = lastAdx === null ? ' --' : lastAdx.toFixed(2).padStart(6);
console.log(
` ${label.padEnd(5)} bars=${String(cols.close.length).padStart(5)} ` +
`last_close=${lastClose.toFixed(2).padStart(10)} ` +
`rsi=${fmtR} macd_hist=${fmtH} adx=${fmtA}`,
);
}
function main() {
const csvPath = process.argv[2] || DEFAULT_CSV;
let cols;
try {
cols = readCsv(csvPath);
} catch (err) {
console.error(`error: ${err.message}`);
process.exit(1);
}
console.log(`Multi-timeframe view of ${csvPath}`);
summarize('1m', cols);
for (const [label, mins] of [
['5m', 5],
['15m', 15],
['1h', 60],
['4h', 240],
['1d', 1440],
]) {
summarize(label, resample(cols, mins * ONE_MINUTE_MS));
}
}
main();