indica

Fast technical analysis indicators for stock markets. Built in Rust.

Why

JavaScript/Python TA libraries are slow when screening thousands of stocks. indica computes all indicators for 2,000 stocks in 6ms using Rust + Rayon parallelism.

Indicators

• SMA — Simple Moving Average
• EMA — Exponential Moving Average
• RSI — Relative Strength Index (Wilder's smoothing)
• MACD — Moving Average Convergence Divergence with crossover detection
• Bollinger Bands — Upper, middle, lower bands + %B
• ATR — Average True Range (Wilder's smoothing)
• Pivot Points — Classic (R3/R2/R1/Pivot/S1/S2/S3)
• Volume Trend — Surging / increasing / stable / declining / drying up
• Relative Strength — Stock vs benchmark comparison

Installation

Rust

[dependencies]
indica = "0.1"

Node.js (via NAPI-RS)

npm install indica

Usage (Rust)

use indica::{sma, rsi, macd, bollinger_bands, atr, pivot_points};

fn main() {
    let closes = vec![44.34, 44.09, 44.15, 43.61, 44.33, 44.83, 45.10,
                      45.42, 45.84, 46.08, 45.89, 46.03, 45.61, 46.28,
                      46.28, 46.00, 46.03, 46.41, 46.22, 46.21];

    // Simple Moving Average
    let sma_20 = sma(&closes, 20);
    println!("SMA(20): {:?}", sma_20); // Some(45.52)

    // RSI
    let rsi_14 = rsi(&closes, 14);
    println!("RSI(14): {:?}", rsi_14); // Some(55.37)

    // MACD
    if let Some(result) = macd(&closes, 12, 26, 9) {
        println!("MACD: {}, Signal: {}, Crossover: {:?}",
            result.value, result.signal, result.crossover);
    }

    // Bollinger Bands
    if let Some(bb) = bollinger_bands(&closes, 20, 2.0) {
        println!("BB Upper: {}, Lower: {}, %B: {}", bb.upper, bb.lower, bb.percent_b);
    }
}

Batch Processing (screen thousands of stocks)

use indica::batch::{StockData, batch_compute_parallel};

let stocks = vec![
    StockData {
        symbol: "RELIANCE".to_string(),
        closes: vec![/* 250 daily closes */],
        highs: vec![/* ... */],
        lows: vec![/* ... */],
        volumes: vec![/* ... */],
    },
    // ... 2000 more stocks
];

// Computes ALL indicators for ALL stocks using all CPU cores
let results = batch_compute_parallel(&stocks);

for snap in &results {
    println!("{}: RSI={:?}, SMA20={:?}", snap.symbol, snap.rsi_14, snap.sma_20);
}

Usage (Node.js)

const { calcRsi, calcMacd, batchComputeIndicators } = require('indica');

// Single indicator
const rsi = calcRsi([44.34, 44.09, /* ... */], 14);
console.log('RSI:', rsi); // 55.37

// MACD with crossover detection
const macd = calcMacd(closes, 12, 26, 9);
console.log(macd); // { value: 0.34, signal: 0.28, histogram: 0.06, crossover: 'bullish' }

// Batch: screen 2000 stocks at once
const results = batchComputeIndicators([
  { symbol: 'RELIANCE', closes: [...], highs: [...], lows: [...], volumes: [...] },
  { symbol: 'TCS', closes: [...], highs: [...], lows: [...], volumes: [...] },
  // ... thousands more
]);
// Returns in ~6ms using all CPU cores

API Reference

Single Indicators

Batch

All functions return Option (Rust's null) when there isn't enough data for the calculation. No panics, no NaN.

Building from Source

# Rust library
cargo build --release
cargo test

# Node.js native addon
npm install
npm run build

License

MIT