How to determine the best ConstantModelType for a ti-engine function

This guide shows how to programmatically determine the best ConstantModelType for your indicator using the JavaScript package ti-engine.

The rating model is overly simplified and should be refined to suit your needs before usage.

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🎯 Goal

• Determine the best ConstantModelType for the RSI using an in-memory dataset

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📦 Requirements

Install ti-engine:

# npm
npm install ti-engine

# yarn
yarn add ti-engine

# pnpm
pnpm add ti-engine

Initialize (recommended, required in browsers):

import init from "ti-engine";

await init(); // In Node it's a no-op; in browsers it loads WASM

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💻 Step-by-Step

1. Use an in-memory dataset

For this guide, we’ll use a constant array of prices:

const data = [
  6037.59, 5970.84, 5906.94, 5881.63, 5868.55, 5942.47, 5975.38, 5909.03,
  5918.25, 5827.04, 5836.22, 5842.91, 5949.91, 5937.34, 5996.66, 6049.24,
  6086.37, 6118.71, 6101.24, 6012.28, 6067.70, 6039.31, 6071.17, 6040.53,
  5994.57, 6037.88, 6061.48, 6083.57, 6025.99, 6066.44, 6068.50, 6051.97,
  6115.07, 6114.63, 6129.58, 6144.15, 6117.52, 6013.13, 5983.25, 5955.25,
  5956.06, 5861.57, 5954.50, 5849.72, 5778.15, 5842.63, 5738.52, 5770.20,
  5614.56, 5572.07, 5599.30, 5521.52, 5638.94
];

2. Calculate the RSI for multiple constant models

The default model for the RSI is a smoothed moving average.

We’ll test several models and compute the RSI series for each using the bulk function.

import init, { momentumIndicators, ConstantModelType } from "ti-engine";

await init();

const period = 14;
const models = [
  ConstantModelType.SimpleMovingAverage,
  ConstantModelType.SmoothedMovingAverage,
  ConstantModelType.ExponentialMovingAverage,
  ConstantModelType.SimpleMovingMedian,
  ConstantModelType.SimpleMovingMode,
];

// Example:
// const rsiSeries = momentumIndicators.bulk.relativeStrengthIndex(data, model, period);

3. Rate each RSI to find the best

The logic is overly simple for the purpose of the guide.

• If RSI > 70 (overbought) and the next price < current price, the model gets +1
• If RSI < 30 (oversold) and the next price > current price, the model gets +1

We normalize the score by the number of "attempts" (how many times we evaluated a signal).

Alignment note for bulk outputs:

• momentumIndicators.bulk.relativeStrengthIndex(prices, model, period) returns an array of length prices.length - period + 1
• rsiSeries[0] corresponds to the window anchoring at prices[period - 1]

import { momentumIndicators, ConstantModelType } from "ti-engine";

/**
 * Returns { model: ConstantModelType, rating: number }
 */
function chooseBestRsiModel(prices, period = 14) {
  const models = [
    ConstantModelType.SimpleMovingAverage,
    ConstantModelType.SmoothedMovingAverage,
    ConstantModelType.ExponentialMovingAverage,
    ConstantModelType.SimpleMovingMedian,
    ConstantModelType.SimpleMovingMode,
  ];

  let bestRating = -1;
  let bestModel = models[0];

  for (const m of models) {
    const rsi = momentumIndicators.bulk.relativeStrengthIndex(prices, m, period);

    let currentRating = 0;
    let attempts = 0;

    // rsi[0] anchors at prices[period - 1]
    for (let i = period - 1; i < prices.length - 1; i++) {
      const rsiVal = rsi[i - (period - 1)];

      // Overbought condition: expect price drop next step
      if (rsiVal > 70) {
        attempts += 1;
        if (prices[i + 1] < prices[i]) currentRating += 1;
      }

      // Oversold condition: expect price rise next step
      if (rsiVal < 30) {
        attempts += 1;
        if (prices[i + 1] > prices[i]) currentRating += 1;
      }
    }

    const averageRating = attempts > 0 ? currentRating / attempts : 0;
    if (averageRating > bestRating) {
      bestRating = averageRating;
      bestModel = m;
    }
  }

  return { model: bestModel, rating: bestRating };
}

4. Full example

import init, { momentumIndicators, ConstantModelType } from "ti-engine";

await init();

const data = [
  6037.59, 5970.84, 5906.94, 5881.63, 5868.55, 5942.47, 5975.38, 5909.03,
  5918.25, 5827.04, 5836.22, 5842.91, 5949.91, 5937.34, 5996.66, 6049.24,
  6086.37, 6118.71, 6101.24, 6012.28, 6067.70, 6039.31, 6071.17, 6040.53,
  5994.57, 6037.88, 6061.48, 6083.57, 6025.99, 6066.44, 6068.50, 6051.97,
  6115.07, 6114.63, 6129.58, 6144.15, 6117.52, 6013.13, 5983.25, 5955.25,
  5956.06, 5861.57, 5954.50, 5849.72, 5778.15, 5842.63, 5738.52, 5770.20,
  5614.56, 5572.07, 5599.30, 5521.52, 5638.94
];

function chooseBestRsiModel(prices, period = 14) {
  const models = [
    ConstantModelType.SimpleMovingAverage,
    ConstantModelType.SmoothedMovingAverage,
    ConstantModelType.ExponentialMovingAverage,
    ConstantModelType.SimpleMovingMedian,
    ConstantModelType.SimpleMovingMode,
  ];

  let bestRating = -1;
  let bestModel = models[0];

  for (const m of models) {
    const rsi = momentumIndicators.bulk.relativeStrengthIndex(prices, m, period);

    let currentRating = 0;
    let attempts = 0;

    for (let i = period - 1; i < prices.length - 1; i++) {
      const rsiVal = rsi[i - (period - 1)];

      if (rsiVal > 70) {
        attempts += 1;
        if (prices[i + 1] < prices[i]) currentRating += 1;
      }
      if (rsiVal < 30) {
        attempts += 1;
        if (prices[i + 1] > prices[i]) currentRating += 1;
      }
    }

    const averageRating = attempts > 0 ? currentRating / attempts : 0;
    if (averageRating > bestRating) {
      bestRating = averageRating;
      bestModel = m;
    }
  }

  return { model: bestModel, rating: bestRating };
}

const { model, rating } = chooseBestRsiModel(data, 14);

// Optional: pretty-print the enum
const modelName =
  Object.entries(ConstantModelType).find(([, v]) => v === model)?.[0] ??
  String(model);

console.log(`Best model for RSI is ${modelName} with a rating of ${rating}`);

Notes:

• Valid ConstantModelType values:
  • ConstantModelType.SimpleMovingAverage
  • ConstantModelType.SmoothedMovingAverage
  • ConstantModelType.ExponentialMovingAverage
  • ConstantModelType.SimpleMovingMedian
  • ConstantModelType.SimpleMovingMode
  • ConstantModelType.PersonalisedMovingAverage (advanced; not used here)

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🧪 Example Output

Best model for RSI is SmoothedMovingAverage with a rating of 0.57

(Output will vary with data and scoring rules.)

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✅ Next Steps

• Programmatically choose a period (e.g., grid search over common RSI periods like 7, 14, 21)
• Combine period selection and constant model selection
• Introduce punishment to the rating system (e.g., penalize false signals or whipsaws)