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ferro-ta/wasm/README.md
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Pratik Bhadane 53566b9d82 feat: expand rust parity, wasm exports, and api conformance
Move several hot Python analysis paths to Rust-backed helpers. This adds Rust implementations for backtest strategy signal generation and the core portfolio loop, options and futures payoff aggregation, Greeks aggregation, ratio calculation, trade extraction, chunked close-only indicator runs, and forward-fill helpers. Wire the Python analysis and data modules to prefer these paths, and add coverage for the new batch fast path.

Expand the WASM package to export WMA, ADX, and MFI from ferro_ta_core, refresh the Node examples, benchmarks, and README, and add a Node-vs-Python conformance test so the browser and node surface stays aligned with the main Python package.

Introduce a generated cross-surface API manifest in docs/, along with scripts to rebuild and verify it from source exports. Enforce manifest freshness in the Python and WASM CI workflows so release candidates catch surface drift before push.
2026-03-24 14:28:51 +05:30

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ferro-ta WASM

WebAssembly bindings for the ferro-ta technical analysis library.

Install from npm

Once published, install the Node.js build from npm:

npm install ferro-ta-wasm
const { sma, ema, wma, rsi, adx, mfi, bbands, atr, obv, macd } = require('ferro-ta-wasm');

const close = new Float64Array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10]);
const smaOut = sma(close, 3);
console.log('SMA:', Array.from(smaOut));

Decision: We chose WebAssembly (wasm-bindgen / wasm-pack) as the second binding because it runs in browsers and Node.js without any native addons, and shares zero unsafe FFI surface with the Python build. Node.js users get a pure-JS entry point; browser users get the same .wasm file.

Available Indicators

Category Function Parameters Returns
Overlap sma close: Float64Array, timeperiod: number Float64Array
Overlap ema close: Float64Array, timeperiod: number Float64Array
Overlap wma close: Float64Array, timeperiod: number Float64Array
Overlap bbands close, timeperiod, nbdevup, nbdevdn Array[upper, middle, lower]
Momentum rsi close: Float64Array, timeperiod: number Float64Array
Momentum adx high, low, close: Float64Array, timeperiod Float64Array
Momentum macd close, fastperiod, slowperiod, signalperiod Array[macd, signal, hist]
Momentum mom close: Float64Array, timeperiod: number Float64Array
Momentum stochf high, low, close, fastk_period, fastd_period Array[fastk, fastd]
Volatility atr high, low, close: Float64Array, timeperiod Float64Array
Volume obv close: Float64Array, volume: Float64Array Float64Array
Volume mfi high, low, close, volume: Float64Array, timeperiod Float64Array

Adding more indicators

WASM exports live in src/lib.rs and can either implement logic directly or delegate to ferro_ta_core. To add a new indicator:

  1. Add a #[wasm_bindgen] export in src/lib.rs (prefer delegating to ferro_ta_core where possible).
  2. Add at least two #[wasm_bindgen_test] tests covering output length and a known value.
  3. Update this README table.
  4. Run wasm-pack test --node to verify.

Prerequisites

# Install Rust (if not already present)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Install wasm-pack
curl https://rustwasm.github.io/wasm-pack/installer/init.sh -sSf | sh
# OR via cargo:
cargo install wasm-pack

Build

cd wasm/
wasm-pack build --target nodejs --out-dir pkg

This produces a pkg/ directory containing:

  • ferro_ta_wasm.js — JavaScript glue code
  • ferro_ta_wasm_bg.wasm — compiled WebAssembly binary
  • ferro_ta_wasm.d.ts — TypeScript declarations

For a browser build:

wasm-pack build --target web --out-dir pkg-web

Usage (Node.js)

const { sma, ema, wma, rsi, adx, mfi, bbands, atr, obv, macd } = require('./pkg/ferro_ta_wasm.js');

const close = new Float64Array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10]);

// Simple Moving Average (period 3)
const smaOut = sma(close, 3);
console.log('SMA:', Array.from(smaOut));  // [ NaN, NaN, 44.193, ... ]

// RSI (period 5)
const rsiOut = rsi(close, 5);
console.log('RSI:', Array.from(rsiOut));

// WMA (period 5)
const wmaOut = wma(close, 5);
console.log('WMA:', Array.from(wmaOut));

// Bollinger Bands (period 5, ±2σ) — returns [upper, middle, lower]
const [upper, middle, lower] = bbands(close, 5, 2.0, 2.0);
console.log('BBANDS upper:', Array.from(upper));

// MACD (fast=3, slow=5, signal=2) — returns [macd_line, signal_line, histogram]
const [macdLine, signalLine, histogram] = macd(close, 3, 5, 2);
console.log('MACD:', Array.from(macdLine));
console.log('Signal:', Array.from(signalLine));
console.log('Histogram:', Array.from(histogram));

// ATR (period 3)
const high   = new Float64Array([45.0, 46.0, 47.0, 46.0, 45.0, 44.0, 45.0]);
const low    = new Float64Array([43.0, 44.0, 45.0, 44.0, 43.0, 42.0, 43.0]);
const atrOut = atr(high, low, close, 3);
console.log('ATR:', Array.from(atrOut));

// ADX (period 3)
const adxOut = adx(high, low, close, 3);
console.log('ADX:', Array.from(adxOut));

// OBV
const volume  = new Float64Array([1000, 1200, 900, 1500, 800, 600, 700]);
const obvOut  = obv(close, volume);
console.log('OBV:', Array.from(obvOut));

// MFI (period 3)
const mfiOut = mfi(high, low, close, volume, 3);
console.log('MFI:', Array.from(mfiOut));

Usage (Browser)

<script type="module">
  import init, { sma, macd } from './pkg-web/ferro_ta_wasm.js';
  await init();  // loads the .wasm binary

  const close = new Float64Array([44.34, 44.09, 44.15, 43.61, 44.33]);
  const smaOut = sma(close, 3);
  console.log('SMA:', Array.from(smaOut));

  // MACD
  const [macdLine, signal, hist] = macd(close, 3, 5, 2);
  console.log('MACD line:', Array.from(macdLine));
</script>

Run Tests

cd wasm/
wasm-pack test --node

CI Artifact

Every CI run on main builds the WASM package and uploads it as a GitHub Actions artifact named wasm-pkg. To download the latest pre-built package without building from source:

  1. Go to the Actions tab.
  2. Open the latest successful CI run.
  3. Download the wasm-pkg artifact from the Artifacts section.
  4. Unzip and use pkg/ferro_ta_wasm.js directly in your project.

Limitations

  • Only 12 indicators are currently exposed (SMA, EMA, WMA, BBANDS, RSI, ADX, MACD, MOM, STOCHF, ATR, OBV, MFI). Additional indicators will be added following the same pattern in src/lib.rs.
  • Large arrays (> 10M bars) may be slow due to JS↔WASM memory copies. For high-throughput use cases prefer the Python (PyO3) binding.
  • WASM does not support multi-threading natively in browsers (SharedArrayBuffer requires COOP/COEP headers).