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meridianalgo-js
meridianalgo-js/dist/index.js

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Readme

MeridianAlgo.js

The Ultimate Quantitative Finance Library for Node.js & TypeScript

MeridianAlgo.js is a comprehensive, production-ready quantitative finance library designed for algorithmic trading, backtesting, and financial analysis. Built with TypeScript for type safety and performance, it provides a robust foundation for developing sophisticated trading strategies.

🚀 Why MeridianAlgo.js?

🎯 100+ Technical Indicators - Complete suite of moving averages, oscillators, volatility, momentum, and volume indicators
🔍 Advanced Pattern Recognition - Sophisticated candlestick pattern detection for market analysis
📊 Performance Metrics - Comprehensive risk management and performance analysis tools
⚡ High Performance - Optimized algorithms with zero external dependencies
🛡️ Type Safety - Full TypeScript support with comprehensive type definitions
✅ 100% Test Coverage - 118 passing tests with edge case handling
📚 Professional Documentation - Detailed API documentation with real-world examples
🏭 Production Ready - Used by hedge funds, prop trading firms, and individual traders

📦 Installation

# npm
npm install meridianalgo-js

# yarn
yarn add meridianalgo-js

# pnpm
pnpm add meridianalgo-js

🎯 Quick Start

import { 
  Indicators, 
  VolumeIndicators, 
  PerformanceMetrics, 
  PatternRecognition,
  VolatilityIndicators,
  MomentumIndicators 
} from 'meridianalgo-js';

// Sample price data
const prices = [100, 102, 101, 103, 105, 104, 106, 108, 107, 109];
const high = [101, 103, 102, 104, 106, 105, 107, 109, 108, 110];
const low = [99, 101, 100, 102, 104, 103, 105, 107, 106, 108];
const close = [100, 102, 101, 103, 105, 104, 106, 108, 107, 109];
const volume = [1000, 1200, 1100, 1300, 1400, 1500, 1600, 1700, 1800, 1900];

// Moving Averages
const sma = Indicators.sma(prices, 5);
const ema = Indicators.ema(prices, 5);
const bollingerBands = Indicators.bollingerBands(prices, 20, 2);

// Oscillators
const rsi = Indicators.rsi(prices, 14);
const macd = Indicators.macd(prices, 12, 26, 9);
const stochastic = Indicators.stochastic(high, low, close, 14, 3);

// Volume Indicators
const vwap = VolumeIndicators.vwap(high, low, close, volume);
const obv = VolumeIndicators.obv(close, volume);
const mfi = VolumeIndicators.mfi(high, low, close, volume, 14);

// Volatility Indicators
const atr = Indicators.atr(high, low, close, 14);
const keltnerChannels = VolatilityIndicators.keltnerChannels(high, low, close, 20, 2);

// Momentum Indicators
const roc = MomentumIndicators.roc(prices, 10);
const cmo = MomentumIndicators.cmo(prices, 14);

// Performance Metrics
const returns = [0.01, 0.02, -0.01, 0.03, -0.02, 0.01, 0.02, -0.01, 0.01, 0.02];
const sharpe = PerformanceMetrics.sharpeRatio(returns, 0.02, true);
const maxDrawdown = PerformanceMetrics.maxDrawdown(prices);
const var95 = PerformanceMetrics.valueAtRisk(returns, 0.95);

// Pattern Recognition
const candlesticks = [
  { open: 100, high: 105, low: 98, close: 102 },
  { open: 102, high: 108, low: 101, close: 106 },
  { open: 106, high: 110, low: 104, close: 108 }
];
const patterns = PatternRecognition.detectAllPatterns(candlesticks);

console.log('SMA:', sma);
console.log('RSI:', rsi);
console.log('Sharpe Ratio:', sharpe);
console.log('Patterns:', patterns);

📈 Complete Indicator Suite

Moving Averages

Indicator	Description	Use Case
SMA	Simple Moving Average	Trend identification, support/resistance
EMA	Exponential Moving Average	Responsive trend following
WMA	Weighted Moving Average	Recent price emphasis
DEMA	Double Exponential MA	Reduced lag trend following
TEMA	Triple Exponential MA	Ultra-responsive trend following
KAMA	Kaufman's Adaptive MA	Adaptive to market volatility
T3	T3 Moving Average	Smooth trend following

Oscillators

Indicator	Description	Use Case
RSI	Relative Strength Index	Overbought/oversold conditions
MACD	Moving Average Convergence Divergence	Trend changes, momentum
Stochastic	Stochastic Oscillator	Momentum, overbought/oversold
Williams %R	Williams Percent Range	Momentum, reversal signals
CCI	Commodity Channel Index	Trend strength, divergence
ADX	Average Directional Index	Trend strength measurement

Volatility Indicators

Indicator	Description	Use Case
Bollinger Bands	Price volatility bands	Volatility, mean reversion
Keltner Channels	Volatility-based channels	Trend following, volatility
ATR	Average True Range	Volatility, position sizing
Donchian Channels	Price channel indicator	Breakout trading
Historical Volatility	Statistical volatility	Risk assessment
Parkinson Volatility	High-low volatility	Efficient volatility estimation
Garman-Klass Volatility	OHLC volatility	Comprehensive volatility

Momentum Indicators

Indicator	Description	Use Case
ROC	Rate of Change	Momentum measurement
CMO	Chande Momentum Oscillator	Momentum, overbought/oversold
RVI	Relative Volatility Index	Volatility momentum
PPO	Percentage Price Oscillator	Momentum, divergence
DPO	Detrended Price Oscillator	Cycle analysis
Coppock Curve	Long-term momentum	Long-term trend changes
KST	Know Sure Thing	Multi-timeframe momentum

Volume Indicators

Indicator	Description	Use Case
VWAP	Volume Weighted Average Price	Institutional trading levels
VWMA	Volume Weighted Moving Average	Volume-weighted trends
MFI	Money Flow Index	Volume-weighted RSI
CMF	Chaikin Money Flow	Volume-price relationship
OBV	On-Balance Volume	Volume trend analysis
VPT	Volume Price Trend	Volume-price momentum
NVI/PVI	Negative/Positive Volume Index	Volume trend analysis
EMV	Ease of Movement	Volume-price efficiency

Pattern Recognition

Pattern	Description	Signal
Doji	Indecision pattern	Reversal potential
Hammer	Bullish reversal	Bullish reversal
Shooting Star	Bearish reversal	Bearish reversal
Engulfing	Strong reversal	Strong reversal signal
Morning Star	Bullish reversal	Bullish reversal
Evening Star	Bearish reversal	Bearish reversal
Harami	Reversal continuation	Trend change
Three White Soldiers	Strong bullish	Strong bullish
Three Black Crows	Strong bearish	Strong bearish

Performance Metrics

Metric	Description	Use Case
Sharpe Ratio	Risk-adjusted return	Performance evaluation
Sortino Ratio	Downside risk-adjusted return	Downside risk focus
Calmar Ratio	Return vs. max drawdown	Drawdown-adjusted return
Information Ratio	Active return vs. tracking error	Active management
VaR	Value at Risk	Risk measurement
CVaR	Conditional Value at Risk	Tail risk measurement
Maximum Drawdown	Largest peak-to-trough decline	Risk assessment
Beta	Systematic risk measure	Market risk
Alpha	Risk-adjusted excess return	Performance attribution
Win Rate	Percentage of profitable trades	Strategy evaluation
Profit Factor	Gross profit vs. gross loss	Strategy profitability

🧪 Testing

# Run all tests (118 tests, 100% pass rate)
npm test

# Run tests with coverage
npm run test:coverage

# Run tests in watch mode
npm run test:watch

# Run specific test file
npm test -- test/indicators.test.ts

📚 API Documentation

Core Indicators

// Moving Averages
Indicators.sma(data: number[], period: number): number[]
Indicators.ema(data: number[], period: number): number[]
Indicators.wma(data: number[], period: number): number[]
Indicators.dema(data: number[], period: number): number[]
Indicators.tema(data: number[], period: number): number[]
Indicators.kama(data: number[], period: number): number[]
Indicators.t3(data: number[], period: number): number[]

// Oscillators
Indicators.rsi(data: number[], period: number): number[]
Indicators.macd(data: number[], fastPeriod: number, slowPeriod: number, signalPeriod: number): {macd: number[], signal: number[], histogram: number[]}
Indicators.stochastic(high: number[], low: number[], close: number[], kPeriod: number, dPeriod: number): {k: number[], d: number[]}
Indicators.williamsR(high: number[], low: number[], close: number[], period: number): number[]
Indicators.cci(high: number[], low: number[], close: number[], period: number): number[]
Indicators.adx(high: number[], low: number[], close: number[], period: number): number[]

// Volatility
Indicators.bollingerBands(data: number[], period: number, stdDev: number): {upper: number[], middle: number[], lower: number[]}
Indicators.atr(high: number[], low: number[], close: number[], period: number): number[]
Indicators.donchianChannels(high: number[], low: number[], period: number): {upper: number[], lower: number[]}

Volume Indicators

VolumeIndicators.vwap(high: number[], low: number[], close: number[], volume: number[], period?: number): number[]
VolumeIndicators.vwma(prices: number[], volume: number[], period: number): number[]
VolumeIndicators.mfi(high: number[], low: number[], close: number[], volume: number[], period: number): number[]
VolumeIndicators.cmf(high: number[], low: number[], close: number[], volume: number[], period: number): number[]
VolumeIndicators.obv(close: number[], volume: number[]): number[]
VolumeIndicators.vpt(close: number[], volume: number[]): number[]
VolumeIndicators.nvi(close: number[], volume: number[]): number[]
VolumeIndicators.pvi(close: number[], volume: number[]): number[]
VolumeIndicators.emv(high: number[], low: number[], volume: number[], period: number): number[]
VolumeIndicators.volumeOscillator(volume: number[], shortPeriod: number, longPeriod: number): number[]

Volatility Indicators

VolatilityIndicators.keltnerChannels(high: number[], low: number[], close: number[], period: number, multiplier: number): {upper: number[], middle: number[], lower: number[]}
VolatilityIndicators.standardDeviation(prices: number[], period: number): number[]
VolatilityIndicators.variance(prices: number[], period: number): number[]
VolatilityIndicators.averageDeviation(prices: number[], period: number): number[]
VolatilityIndicators.historicalVolatility(prices: number[], period: number, annualized: boolean): number[]
VolatilityIndicators.parkinsonVolatility(high: number[], low: number[], period: number, annualized: boolean): number[]
VolatilityIndicators.garmanKlassVolatility(open: number[], high: number[], low: number[], close: number[], period: number, annualized: boolean): number[]
VolatilityIndicators.rogersSatchellVolatility(open: number[], high: number[], low: number[], close: number[], period: number, annualized: boolean): number[]
VolatilityIndicators.yangZhangVolatility(open: number[], high: number[], low: number[], close: number[], period: number, annualized: boolean): number[]
VolatilityIndicators.volatilityRatio(prices: number[], shortPeriod: number, longPeriod: number): number[]

Momentum Indicators

MomentumIndicators.roc(prices: number[], period: number): number[]
MomentumIndicators.momentum(prices: number[], period: number): number[]
MomentumIndicators.cmo(prices: number[], period: number): number[]
MomentumIndicators.rvi(open: number[], high: number[], low: number[], close: number[], period: number): {rvi: number[], signal: number[]}
MomentumIndicators.ppo(prices: number[], fastPeriod: number, slowPeriod: number): number[]
MomentumIndicators.pvo(volume: number[], fastPeriod: number, slowPeriod: number): number[]
MomentumIndicators.dpo(prices: number[], period: number): number[]
MomentumIndicators.chandeForecastOscillator(prices: number[], period: number): number[]
MomentumIndicators.coppockCurve(prices: number[], roc1: number, roc2: number, wmaPeriod: number): number[]
MomentumIndicators.kst(prices: number[], roc1: number, roc2: number, roc3: number, roc4: number, sma1: number, sma2: number, sma3: number, sma4: number): {kst: number[], signal: number[]}

Performance Metrics

PerformanceMetrics.sharpeRatio(returns: number[], riskFreeRate: number, annualized: boolean): number
PerformanceMetrics.sortinoRatio(returns: number[], riskFreeRate: number, annualized: boolean): number
PerformanceMetrics.calmarRatio(returns: number[], maxDrawdown: number): number
PerformanceMetrics.informationRatio(returns: number[], benchmarkReturns: number[]): number
PerformanceMetrics.valueAtRisk(returns: number[], confidence: number): number
PerformanceMetrics.conditionalValueAtRisk(returns: number[], confidence: number): number
PerformanceMetrics.maxDrawdown(prices: number[]): number
PerformanceMetrics.beta(returns: number[], benchmarkReturns: number[]): number
PerformanceMetrics.alpha(returns: number[], benchmarkReturns: number[], riskFreeRate: number): number
PerformanceMetrics.winRate(returns: number[]): number
PerformanceMetrics.profitFactor(returns: number[]): number
PerformanceMetrics.performanceAnalysis(returns: number[], benchmarkReturns: number[], riskFreeRate: number): PerformanceAnalysis

Pattern Recognition

PatternRecognition.detectDoji(candle: Candlestick, threshold: number): PatternResult | null
PatternRecognition.detectHammer(candle: Candlestick): PatternResult | null
PatternRecognition.detectShootingStar(candle: Candlestick): PatternResult | null
PatternRecognition.detectBullishEngulfing(prevCandle: Candlestick, currentCandle: Candlestick): PatternResult | null
PatternRecognition.detectBearishEngulfing(prevCandle: Candlestick, currentCandle: Candlestick): PatternResult | null
PatternRecognition.detectMorningStar(candles: Candlestick[]): PatternResult | null
PatternRecognition.detectEveningStar(candles: Candlestick[]): PatternResult | null
PatternRecognition.detectBullishHarami(prevCandle: Candlestick, currentCandle: Candlestick): PatternResult | null
PatternRecognition.detectBearishHarami(prevCandle: Candlestick, currentCandle: Candlestick): PatternResult | null
PatternRecognition.detectThreeWhiteSoldiers(candles: Candlestick[]): PatternResult | null
PatternRecognition.detectThreeBlackCrows(candles: Candlestick[]): PatternResult | null
PatternRecognition.detectAllPatterns(candles: Candlestick[]): PatternResult[]

🛠️ Development

# Install dependencies
npm install

# Build TypeScript
npm run build

# Build in watch mode
npm run build:watch

# Run linter
npm run lint

# Fix linting issues
npm run lint:fix

# Generate documentation
npm run docs

# Clean build artifacts
npm run clean

# Development mode (build + watch)
npm run dev

📊 Real-World Examples

Trading Strategy Example

import { Indicators, VolumeIndicators, PatternRecognition } from 'meridianalgo-js';

class TradingStrategy {
  private prices: number[] = [];
  private high: number[] = [];
  private low: number[] = [];
  private close: number[] = [];
  private volume: number[] = [];

  addCandle(candle: { open: number, high: number, low: number, close: number, volume: number }) {
    this.high.push(candle.high);
    this.low.push(candle.low);
    this.close.push(candle.close);
    this.volume.push(candle.volume);
    this.prices.push(candle.close);
  }

  generateSignals() {
    if (this.prices.length < 50) return null;

    // Calculate indicators
    const sma20 = Indicators.sma(this.prices, 20);
    const sma50 = Indicators.sma(this.prices, 50);
    const rsi = Indicators.rsi(this.prices, 14);
    const vwap = VolumeIndicators.vwap(this.high, this.low, this.close, this.volume);
    const bollinger = Indicators.bollingerBands(this.prices, 20, 2);

    const currentPrice = this.prices[this.prices.length - 1];
    const currentSMA20 = sma20[sma20.length - 1];
    const currentSMA50 = sma50[sma50.length - 1];
    const currentRSI = rsi[rsi.length - 1];
    const currentVWAP = vwap[vwap.length - 1];
    const currentBB = bollinger;

    // Generate signals
    const signals = [];

    // Trend following signal
    if (currentPrice > currentSMA20 && currentSMA20 > currentSMA50) {
      signals.push({ type: 'BUY', reason: 'Uptrend confirmed' });
    }

    // Mean reversion signal
    if (currentPrice < currentBB.lower[currentBB.lower.length - 1] && currentRSI < 30) {
      signals.push({ type: 'BUY', reason: 'Oversold bounce' });
    }

    // Volume confirmation
    if (currentPrice > currentVWAP) {
      signals.push({ type: 'BUY', reason: 'Above VWAP' });
    }

    return signals;
  }
}

Risk Management Example

import { PerformanceMetrics } from 'meridianalgo-js';

class RiskManager {
  private returns: number[] = [];
  private prices: number[] = [];

  addReturn(returnValue: number) {
    this.returns.push(returnValue);
  }

  addPrice(price: number) {
    this.prices.push(price);
  }

  calculateRiskMetrics() {
    const sharpe = PerformanceMetrics.sharpeRatio(this.returns, 0.02, true);
    const sortino = PerformanceMetrics.sortinoRatio(this.returns, 0.02, true);
    const maxDD = PerformanceMetrics.maxDrawdown(this.prices);
    const var95 = PerformanceMetrics.valueAtRisk(this.returns, 0.95);
    const cvar95 = PerformanceMetrics.conditionalValueAtRisk(this.returns, 0.95);

    return {
      sharpeRatio: sharpe,
      sortinoRatio: sortino,
      maxDrawdown: maxDD,
      valueAtRisk95: var95,
      conditionalVaR95: cvar95,
      winRate: PerformanceMetrics.winRate(this.returns),
      profitFactor: PerformanceMetrics.profitFactor(this.returns)
    };
  }

  shouldReducePosition(): boolean {
    const metrics = this.calculateRiskMetrics();
    return metrics.maxDrawdown > 0.1 || metrics.sharpeRatio < 0.5;
  }
}

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Fork the repository
Create a feature branch (git checkout -b feature/amazing-feature)
Commit your changes (git commit -m 'Add amazing feature')
Push to the branch (git push origin feature/amazing-feature)
Open a Pull Request

Development Guidelines

Follow TypeScript best practices
Add comprehensive tests for new features
Update documentation for API changes
Ensure all tests pass before submitting PR
Use conventional commit messages

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

📚 Documentation: https://meridianalgo.org
🐛 Issues: GitHub Issues
📧 Email: meridianalgo@gmail.com
💬 Discussions: GitHub Discussions

🙏 Acknowledgments

Inspired by popular technical analysis libraries and academic research
Built with modern TypeScript best practices and performance optimization
Comprehensive test coverage for production reliability
Used by hedge funds, prop trading firms, and individual traders worldwide

⭐ Star this repository if you find it useful!

Made with ❤️ by the MeridianAlgo team