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.
This commit is contained in:
kingchenc
@@ -13,6 +13,7 @@ The Rust examples live in the `wickra-examples` workspace member crate.
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| --- | --- | --- |
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| `streaming.rs` | Feed a synthetic price series through SMA / EMA / RSI / MACD tick by tick. | `cargo run -p wickra-examples --bin streaming` |
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| `backtest.rs` | Compute a basket of indicators over an OHLCV CSV and print a summary. | `cargo run -p wickra-examples --bin backtest -- <ohlcv.csv>` |
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| `multi_timeframe.rs` | Resample a 1-minute CSV via wickra-data and print indicators per timeframe. | `cargo run -p wickra-examples --bin multi_timeframe` |
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| `fetch_btcusdt.rs` | Download real BTCUSDT klines from the Binance REST API into `examples/data/`. | `cargo run -p wickra-examples --bin fetch_btcusdt` |
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| `live_binance.rs` | Stream live Binance klines through an indicator over a resilient WebSocket. | `cargo run -p wickra-examples --bin live_binance` |
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@@ -41,6 +42,7 @@ cd ../../examples/node && npm install # links wickra + installs `ws`
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| --- | --- | --- |
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| `streaming.js` | Feed a synthetic price series through several indicators tick by tick. | `node streaming.js` |
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| `backtest.js` | Basket of indicators over an OHLCV CSV; defaults to the bundled BTCUSDT daily dataset. | `node backtest.js [ohlcv.csv]` |
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| `multi_timeframe.js` | Roll a 1-minute CSV up to 5m / 15m / 1h / 4h / 1d and print indicators per timeframe. | `node multi_timeframe.js [path/to/1m.csv]` |
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| `live_trading.js` | Live Binance feed → RSI / MACD / Bollinger → signals. | `node live_trading.js --symbol BTCUSDT --interval 1m` |
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## WebAssembly — `examples/wasm/`
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@@ -0,0 +1,154 @@
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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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@@ -0,0 +1,85 @@
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//! Multi-timeframe indicators with the Wickra Rust stack.
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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), resamples it via [`wickra_data::resample::resample_all`] to 5m /
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//! 15m / 1h / 4h / 1d, and prints the last RSI(14), MACD(12,26,9) histogram
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//! and ADX(14) at each timeframe. The Rust counterpart of
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//! `examples/python/multi_timeframe.py`.
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//!
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//! Run with:
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//! ```text
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//! cargo run --release -p wickra-examples --bin multi_timeframe
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//! ```
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use std::env;
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use std::path::PathBuf;
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use wickra::{Adx, Candle, Indicator, MacdIndicator, Rsi};
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use wickra_data::aggregator::Timeframe;
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use wickra_data::csv::CandleReader;
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use wickra_data::resample::resample_all;
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const ONE_MINUTE_MS: i64 = 60_000;
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fn default_csv() -> PathBuf {
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PathBuf::from(env!("CARGO_MANIFEST_DIR"))
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.join("..")
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.join("data")
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.join("btcusdt-1m.csv")
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}
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fn summarize(label: &str, candles: &[Candle]) {
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if candles.is_empty() {
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println!(" {label:<5} (empty)");
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return;
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}
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let mut rsi = Rsi::new(14).expect("RSI(14) constructor cannot fail");
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let mut macd = MacdIndicator::new(12, 26, 9).expect("MACD(12,26,9) constructor cannot fail");
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let mut adx = Adx::new(14).expect("ADX(14) constructor cannot fail");
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let mut last_rsi: Option<f64> = None;
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let mut last_hist: Option<f64> = None;
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let mut last_adx: Option<f64> = None;
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for c in candles {
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if let Some(v) = rsi.update(c.close) {
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last_rsi = Some(v);
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}
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if let Some(m) = macd.update(c.close) {
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last_hist = Some(m.histogram);
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}
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if let Some(a) = adx.update(*c) {
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last_adx = Some(a.adx);
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}
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}
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let last_close = candles.last().expect("non-empty").close;
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println!(
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" {label:<5} bars={:>5} last_close={last_close:>10.2} rsi={} macd_hist={} adx={}",
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candles.len(),
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last_rsi.map_or_else(|| " --".to_string(), |v| format!("{v:>6.2}")),
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last_hist.map_or_else(|| " -- ".to_string(), |v| format!("{v:+6.2}")),
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last_adx.map_or_else(|| " --".to_string(), |v| format!("{v:>6.2}")),
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);
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}
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fn main() -> Result<(), Box<dyn std::error::Error>> {
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let path = env::args().nth(1).map_or_else(default_csv, PathBuf::from);
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println!("Multi-timeframe view of {}", path.display());
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let mut reader = CandleReader::open(&path)?;
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let ones: Vec<Candle> = reader.read_all()?;
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summarize("1m", &ones);
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for &(label, minutes) in &[
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("5m", 5_i64),
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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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let tf = Timeframe::millis(minutes * ONE_MINUTE_MS)?;
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let resampled = resample_all(tf, ones.iter().copied().map(Ok))?;
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summarize(label, &resampled);
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}
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Ok(())
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}
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