deriv_technical_analysis

deriv_technical_analysis

A comprehensive Dart package for Technical Analysis, providing over 50 technical indicators for financial market analysis.

Installation

Add this to your package's pubspec.yaml file:

dependencies:
  deriv_technical_analysis: ^1.0.0

Requirements:

• Dart SDK: >=3.0.0 <4.0.0
• Flutter: >=3.10.1

Core Concepts

The package is built around three core interfaces.

1. IndicatorResult

Represents the output of an indicator calculation:

abstract class IndicatorResult {
  double get quote; // The calculated indicator value
}

2. IndicatorOHLC

Represents price data with Open, High, Low, and Close values:

abstract class IndicatorOHLC {
  double get open; // Opening price
  double get high; // Highest price
  double get low; // Lowest price
  double get close; // Closing price
}

3. IndicatorDataInput

Manages the input data and result creation:

abstract class IndicatorDataInput {
  List<IndicatorOHLC> get entries; // Input data points
  IndicatorResult createResult(int index, double value); // Creates result objects
}

Available Indicators

The package includes the following technical indicators:

Moving Averages

• Simple Moving Average (SMA)
• Exponential Moving Average (EMA)
• Double Exponential Moving Average (DEMA)
• Triple Exponential Moving Average (TEMA)
• Triangular Moving Average (TRIMA)
• Weighted Moving Average (WMA)
• Modified Moving Average (MMA)
• Least Squares Moving Average (LSMA)
• Hull Moving Average (HMA)
• Variable Moving Average (VMA)
• Welles Wilder Smoothing Moving Average (WWSMA)
• Zero-Lag Exponential Moving Average (ZELMA)

Oscillators

• Relative Strength Index (RSI)
• Stochastic
  • Fast Stochastic
  • Slow Stochastic
  • Smoothed Stochastic
  • Stochastic Momentum Index (SMI)
• Moving Average Convergence Divergence (MACD)
  • MACD Line
  • Signal Line
  • MACD Histogram
• Awesome Oscillator
• Williams %R
• Rate of Change (ROC)
• Chande Momentum Oscillator (CMO)
• Gator Oscillator
  • Top Oscillator
  • Bottom Oscillator

Trend Indicators

• Average Directional Index (ADX)
  • ADX Line
  • Positive Directional Index (+DI)
  • Negative Directional Index (-DI)
  • ADX Histogram
• Parabolic SAR
• Ichimoku Cloud
  • Conversion Line (Tenkan-sen)
  • Base Line (Kijun-sen)
  • Leading Span A (Senkou Span A)
  • Leading Span B (Senkou Span B)
  • Lagging Span (Chikou Span)

Volatility Indicators

• Bollinger Bands
  • Upper Band
  • Lower Band
  • Bandwidth
  • %B (Percent B)
• Average True Range (ATR)
• Standard Deviation
• Variance

Channel Indicators

• Donchian Channel
• Moving Average Envelope
  • Upper Envelope
  • Lower Envelope

Other Indicators

• Aroon
  • Aroon Up
  • Aroon Down
  • Aroon Oscillator
• Commodity Channel Index (CCI)
• Detrended Price Oscillator (DPO)
• ZigZag
• Fixed Channel Bands (FCB)
  • FCB High
  • FCB Low
• Bullish/Bearish Pattern Recognition

Helper Indicators

• Price Values (Open, High, Low, Close)
• Various Price Averages (HL2, HLC3, HLCC4, OHLC4)
• True Range
• Directional Movement
• Gain/Loss
• Mean and Difference calculations

Implementation Guide

1. Create Your Result Class

The output of indicators is a generic type, which means you can decide what kind of model class should be created when an indicator calculates the result of an item. This makes it easy to create a list of result items that fits your specific needs. You can define what type of data the list should contain.

With IndicatorDataInput.createResult, you can choose the type of output you want. Each result from the indicator represents a value (like a quote) for a specific item, and your custom model class can include extra properties if needed.

For example, if you want to process or visualize a list of items with a certain data type, you can set it up like below:

class SampleResult implements IndicatorResult {
  SampleResult(this.quote, this.anotherProperty);

  @override
  final double quote; // Required by IndicatorResult

  final OterPropertyType otherProperty; // Your custom properties
}

To start, create a class that implements the IndicatorResult interface:

class SampleResult implements IndicatorResult {
  SampleResult(this.quote, this.anotherProperty);

  @override
  final double quote; // Required by IndicatorResult

  final DateTime time; // Your custom properties
}

2. Create Your Input Class

Next, You can implement IndicatorDataInput and implemenet createResult method:

class SampleInput implements IndicatorDataInput {
  SampleInput(this.entries);

  @override
  final List<IndicatorOHLC> entries;

  @override
  IndicatorResult createResult(int index, double value) =>
      SampleResult(value, getDateTimeFromIndex(index));
}

If the package only gave you a list of numbers (e.g., List<double>), you would need to convert it into your own type, such as List<SampleResult>. By using a generic type, the package lets you directly define the output type, so you don’t have to do extra conversions. This way, each result is automatically created as the type you want.

3. Implement OHLC Interface

You can implement IndicatorOHLC for both single-value and OHLC data:

// Single value implementation
class SampleTick implements IndicatorOHLC {
  const SampleTick(this.quote);

  final double quote;

  @override
  double get close => quote;

  @override
  double get high => quote;

  @override
  double get low => quote;

  @override
  double get open => quote;
}

// Full OHLC implementation
class SampleOHLC implements IndicatorOHLC {
  const SampleOHLC(this.open, this.close, this.high, this.low);

  @override
  final double close;
  @override
  final double high;
  @override
  final double low;
  @override
  final double open;
}

Using Indicators

Basic Usage

// Prepare your data
final List<SampleOHLC> data = [
  SampleOHLC(0, 75.1, 74.06, 75.11),
  SampleOHLC(0, 75.9, 76.03, 74.64),
  // ... more data points
];

// Create input
final input = SampleInput(data);

// Create and use indicator
final rsi = RSIIndicator<SampleResult>(input);
final allValues = rsi.calculateValues();
final specificValue = rsi.getValue(5);

Chaining Indicators

Indicators can be used as input for other indicators:

// Create first indicator
final macd = MACDIndicator<SampleResult>(input);

// Use MACD as input for SMA
final sma = SMAIndicator<SampleResult>(macd, 3);

Performance Optimization

Most indicators extend CachedIndicator which provides performance optimizations:

// Calculate all values (cached for future use)
final values = indicator.calculateValues();

// Get specific value (uses cache if available)
final value = indicator.getValue(5);

// Invalidate cached value if needed
indicator.invalidate(3);

// Refresh specific value
indicator.refreshValueFor(5);

Architecture

The package follows a hierarchical structure:

1. Indicator<T>: Base abstract class defining core indicator functionality

   • Generic type T extends IndicatorResult
   • Handles basic indicator operations

2. CachedIndicator<T>: Extends Indicator<T> adding caching capabilities

   • Optimizes performance through value caching
   • Provides invalidation and refresh mechanisms
   • Handles batch calculations efficiently

3. Specific Indicators: Extend CachedIndicator<T> with specific calculations

   • Implement technical analysis algorithms
   • Can be composed with other indicators
   • Support both single-value and OHLC inputs