ferro-ta WASM

WebAssembly bindings for the ferro-ta technical analysis library. Full feature parity with the Python and Rust core packages.

Install from npm

npm install ferro-ta-wasm

const ferro = require('ferro-ta-wasm');

const close = new Float64Array([44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10]);
console.log('SMA:', Array.from(ferro.sma(close, 3)));
console.log('RSI:', Array.from(ferro.rsi(close, 14)));

Available Indicators (200+ exports)

| Category | Functions | Examples | |----------|-----------|----------| | Overlap Studies (20) | Moving averages, bands, SAR | sma, ema, wma, dema, tema, trima, kama, t3, bbands, macd, macdfix, macdext, sar, sarext, mama, midpoint, midprice, ma, mavp, hull_ma | | Momentum (26) | Oscillators, directional movement | rsi, mom, stoch, stochf, adx, adxr, dx, plus_di, minus_di, roc, willr, aroon, aroonosc, cci, bop, stochrsi, apo, ppo, cmo, trix_indicator, ultosc | | Candlestick Patterns (61) | All TA-Lib patterns | cdlhammer, cdlengulfing, cdldoji, cdlmorningstar, cdlshootingstar, ... (all 61) | | Volatility (3) | True range, ATR | atr, natr, trange | | Volume (6) | On-balance volume, accumulation | obv, mfi, vwap, vwma, ad, adosc | | Price Transforms (4) | Synthetic prices | avgprice, medprice, typprice, wclprice | | Cycle / Hilbert (6) | Hilbert Transform suite | ht_trendline, ht_dcperiod, ht_dcphase, ht_phasor, ht_sine, ht_trendmode | | Statistics (10) | Regression, correlation | stddev, var, linearreg, linearreg_slope, linearreg_intercept, linearreg_angle, tsf, beta_rolling, correl | | Math (19) | Operators and transforms | math_add, math_sub, math_mult, math_div, transform_sin, transform_cos, transform_exp, transform_sqrt, ... | | Extended (10) | Supertrend, channels, Ichimoku | supertrend, donchian, keltner_channels, ichimoku, pivot_points, chandelier_exit, choppiness_index | | Streaming API (9 classes) | Bar-by-bar stateful | WasmStreamingSMA, WasmStreamingEMA, WasmStreamingRSI, WasmStreamingATR, WasmStreamingBBands, WasmStreamingMACD, WasmStreamingStoch, WasmStreamingVWAP, WasmStreamingSupertrend | | Options (14) | Pricing, Greeks, IV | black_scholes_price, black_76_price, black_scholes_greeks, implied_volatility, iv_rank, smile_metrics, ... | | Futures (12) | Basis, roll, curve | futures_basis, annualized_basis, roll_yield, weighted_continuous, calendar_spreads, curve_summary, ... | | Backtesting (9) | Signal generation, engines | backtest_core, backtest_ohlcv, rsi_threshold_signals, macd_crossover_signals, walk_forward_indices, monte_carlo_bootstrap, ... | | Alerts & Regime (7) | Signals and regime detection | check_threshold, check_cross, regime_adx, regime_combined, detect_breaks_cusum | | Batch & Portfolio (9) | Multi-asset analytics | batch_sma, batch_ema, batch_rsi, correlation_matrix, portfolio_volatility, drawdown_series | | Aggregation (8) | Tick/volume/time bars | aggregate_tick_bars, aggregate_volume_bars_ticks, volume_bars, ohlcv_agg |

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

Build

cd wasm/

# Build both Node.js and web targets
npm run build

# Or build individually:
npm run build:node   # → node/
npm run build:web    # → web/

This produces two directories:

• node/ -- CommonJS glue for Node.js (require())
• web/ -- ESM glue for browsers and web workers (import)

Both contain ferro_ta_wasm.js, ferro_ta_wasm_bg.wasm, and ferro_ta_wasm.d.ts.

Usage (Node.js)

const {
  sma, ema, rsi, bbands, macd, atr, adx, obv, mfi,
  cdlhammer, cdlengulfing,
  WasmStreamingSMA,
} = require('ferro-ta-wasm');

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

// Indicators
console.log('SMA:', Array.from(sma(close, 3)));
console.log('RSI:', Array.from(rsi(close, 5)));

// Multi-output
const [upper, middle, lower] = bbands(close, 5, 2.0, 2.0);
const [macdLine, signal, hist] = macd(close, 3, 5, 2);

// Streaming (bar-by-bar)
const stream = new WasmStreamingSMA(3);
for (const price of close) {
  console.log('streaming SMA:', stream.update(price));
}

Usage (Browser)

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

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

Run Tests

cd wasm/
wasm-pack test --node

Limitations

• 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).
• The npm package ships both Node.js (require) and browser/web worker (import) builds. Conditional exports in package.json select the right one automatically.

License

MIT