"Emre"

Who am I?

I am a young developer based in Berlin. Having worked with Flutter and Dart for the last 4 years, I've acquired a ton of experience in mobile app development. I know the best practices, helped found them myself and I am still learning till this day.

My Dream

My dream is it to build with that foundation that I've created the best applications possible. Always striving to improve from sprint to sprint. Thanks to the Ära (age) of AI everyday workflows are speed up, developers can start projects much faster but at the cost of quality and uniqueness. I am here to counteract that.

My Stack

I use

And those tools have enabled me to build full stack applications.

The most proud application yet is Paren, which enabled fast currency conversions in your vacation to help you exactly when you need it.

Code Example

As for coding example, I'll provide you the code of my little stock market prediction algorithm, which is pure regression based.

List<({double x, double y})> calculatePrediction(
    List<({double x, double y})> historicalData,
  ) {
    predictionDuration.value = localDuration.value ~/ 3;

    if (historicalData.length < 2) return [];

    // Calculate average daily change (drift) and volatility (stdDev)
    double totalChange = 0;
    double totalChangeSquared = 0;
    for (int i = 1; i < historicalData.length; i++) {
      double change = historicalData[i].y - historicalData[i - 1].y;
      totalChange += change;
      totalChangeSquared += change * change;
    }
    int n = historicalData.length - 1;
    double avgDailyChange = totalChange / n;
    double variance =
        (totalChangeSquared / n) - (avgDailyChange * avgDailyChange);
    variance = max(variance, 0);
    double stdDev = sqrt(variance).toDouble();

    var lastX = historicalData.last.x;
    var lastY = historicalData.last.y;
    var step = Duration.millisecondsPerDay.toDouble();

    // Generate predictions with random noise
    Random random =
        Random(); // Seed with a fixed value for consistency if needed
    List<({double x, double y})> prediction = [];
    double currentY = lastY;
    for (int i = 0; i < predictionDuration.value.inDays; i++) {
      double newX = lastX + (i + 1) * step;
      double noise = (random.nextDouble() * 2 - 1) *
          stdDev *
          0.5; // Noise scaled by 50% of volatility
      double newY = currentY + avgDailyChange + noise;
      prediction.add((x: newX, y: newY));
      currentY = newY;
    }
    // add the last historical point to the prediction
    prediction.insert(0, historicalData.last);
    return prediction;
  }

I hope you enjoyed reading this introduction. Thank you for being here.

Best regards,
Emre Yurtseven