Dynamic Coretime Pricing

gistfile1.txt

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        "id": "WpIbCa7V-Vim"
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      "source": [
        "pip install matplotlib numpy\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n"
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      "source": [
        "# Constants\n",
        "MIN_PRICE = 100 # the minimum price for a unit\n",
        "MAX_PRICE_INCREASE_FACTOR = 2 # the factor to which the price may increase maximally\n",
        "\n",
        "BULK_TARGET = 30 # if cores_sold is BULK_TARGET, the price should not change. cores_sold below BULK_TARGET shall lead to a price decrease. cores_sold above BULK_TARGET shall lead to a price increase\n",
        "BULK_LIMIT = 45 # if cores_sold reaches BULK_LIMIT, the price increases to MAX_PRICE_INCREASE_FACTOR\n",
        "\n",
        "# Scaling parameters\n",
        "SCALE_DOWN = 2\n",
        "SCALE_UP = 2\n",
        "\n",
        "old_price = 1000\n",
        "\n",
        "\n",
        "def plot_with_function(f, title):\n",
        "  # Generate a list of units sold from 0 to BULK_LIMIT\n",
        "  cores_sold = np.linspace(0, BULK_LIMIT, BULK_LIMIT+1)\n",
        "\n",
        "  # Calculate prices\n",
        "  prices = np.array([f(old_price, x) for x in cores_sold])\n",
        "\n",
        "  # Plot the results\n",
        "  plt.figure(figsize=(10, 6))\n",
        "  plt.plot(cores_sold, prices)\n",
        "  plt.axvline(BULK_TARGET, color='r', linestyle='--')\n",
        "  plt.title(title)\n",
        "  plt.xlabel('Units Sold')\n",
        "  plt.ylabel('Price')\n",
        "  plt.grid(True)\n",
        "  plt.show()\n",
        "\n",
        "  return cores_sold, prices\n"
      ],
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      "source": [
        "# linear\n",
        "def adjust_price_based_on_demand_linear (old_price, cores_sold):\n",
        "    if cores_sold < BULK_TARGET:\n",
        "        return old_price / 2 + old_price / 2 * (cores_sold / BULK_TARGET)\n",
        "    else:\n",
        "        return old_price + old_price / 2 * ((cores_sold - BULK_TARGET) / (BULK_LIMIT - BULK_TARGET))\n",
        "\n",
        "\n",
        "cores_sold, prices = plot_with_function(adjust_price_based_on_demand_linear, \"Linear function\")"
      ],
      "metadata": {
        "id": "szrS0TAALks9"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "cores_sold, prices"
      ],
      "metadata": {
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    },
    {
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      "source": [
        "BULK_TARGET = 30\n",
        "BULK_LIMIT = 45\n",
        "MIN_PRICE = 1\n",
        "MAX_PRICE_INCREASE_FACTOR = 2\n",
        "SCALE_DOWN = 2\n",
        "SCALE_UP = 2\n",
        "old_price = 1000\n",
        "\n",
        "# power function\n",
        "def adjust_price_based_on_demand_power(old_price, cores_sold):\n",
        "    if cores_sold <= BULK_TARGET:\n",
        "        return (old_price - MIN_PRICE) * (1 - (abs(cores_sold - BULK_TARGET)**SCALE_DOWN / BULK_TARGET**SCALE_DOWN)) + MIN_PRICE\n",
        "    else:\n",
        "        return ((MAX_PRICE_INCREASE_FACTOR - 1) * old_price * ((cores_sold - BULK_TARGET)**SCALE_UP / (BULK_LIMIT - BULK_TARGET)**SCALE_UP)) + old_price\n",
        "\n",
        "\n",
        "\n",
        "plot_with_function(adjust_price_based_on_demand_power, \"Power function with elongated platou\")"
      ],
      "metadata": {
        "id": "W5MnstzmKGvQ"
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      "execution_count": null,
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    {
      "cell_type": "code",
      "source": [
        "cores_sold, prices"
      ],
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        "id": "BPcuGdhcqNLR"
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      "execution_count": null,
      "outputs": []
    }
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