raymondjacobson/Audius_Artist_Coins_Bonding_Curve.ipynb Secret

## Audius_Artist_Coins_Bonding_Curve.ipynb
{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0a77a3bf",
      "metadata": {
        "id": "0a77a3bf"
      },
      "outputs": [],
      "source": [
        "# --- Parameters (edit these) ---\n",
        "\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from decimal import Decimal, ROUND_HALF_UP, getcontext\n",
        "\n",
        "# Token decimals & reference price\n",
        "quote_d = 8          # $AUDIO decimals\n",
        "base_d  = 9          # artist coin decimals\n",
        "audio_price = 0.065  # $AUDIO in USD (for display)\n",
        "\n",
        "# Market caps in AUDIO\n",
        "initial_market_cap   = 100_000\n",
        "graduation_market_cap = 1_000_000\n",
        "token_supply         = 1_000_000_000\n",
        "\n",
        "# Segment count & anchors (human price = AUDIO/base)\n",
        "n = 17\n",
        "a_start_h = initial_market_cap / token_supply\n",
        "a_end_h   = graduation_market_cap / token_supply\n",
        "anchors_h = np.linspace(a_start_h, a_end_h, n)\n",
        "\n",
        "# Liquidity shape per segment (there are n-1 segments; we provide n values for convenience)\n",
        "L_start = 1e32\n",
        "L_end   = 1e35\n",
        "power   = 0.879617604321\n",
        "L_values = L_start * np.power(L_end / L_start, np.linspace(0, 1, n) ** power)\n",
        "\n",
        "# Target total base to sell by graduation (in **tokens**, not atoms)\n",
        "target_base_tokens = 250_000_000\n",
        "\n",
        "# Plotting knobs\n",
        "max_quote_tokens = 10_000\n",
        "samples_per_seg  = 400\n",
        "\n",
        "# Set big precision for integer conversions\n",
        "getcontext().prec = 60\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "97aa0253",
      "metadata": {
        "id": "97aa0253"
      },
      "outputs": [],
      "source": [
        "\n",
        "# --- Helper functions ---\n",
        "\n",
        "def human_to_raw_price(p_human, quote_d=8, base_d=9):\n",
        "    # P_human = P_raw * 10^(quote_d - base_d)\n",
        "    return p_human * (10 ** (quote_d - base_d))\n",
        "\n",
        "def price_from_sqrtprice_raw(sqrt_price_q64_64):\n",
        "    # Returns RAW price (atoms ratio)\n",
        "    return (sqrt_price_q64_64 / 2**64) ** 2\n",
        "\n",
        "def sqrtprice_from_price_human(p_human, quote_d=quote_d, base_d=base_d):\n",
        "    # Convert HUMAN price -> RAW, then to Q64.64 sqrtPrice\n",
        "    p_raw = human_to_raw_price(p_human, quote_d, base_d)\n",
        "    return (p_raw ** 0.5) * 2**64\n",
        "\n",
        "# Closed forms per segment S0->S1 (Q64.64), constant L\n",
        "def seg_quote_atoms(Li, S0, S1):\n",
        "    # integer atoms (mirrors SDK behavior)\n",
        "    return int((Li * (S1 - S0)) // (2**128))\n",
        "\n",
        "def seg_base_atoms(Li, S0, S1, use_extra_pow2=False):\n",
        "    # Two conventions observed in the wild; choose the one whose average price looks sane:\n",
        "    # A) dB_atoms = L*(S1-S0)/(S0*S1)\n",
        "    # B) dB_atoms = L*(S1-S0)*2^64/(S0*S1)\n",
        "    num = Li * (S1 - S0)\n",
        "    den = S0 * S1\n",
        "    if use_extra_pow2:\n",
        "        num = num * (2**64)\n",
        "    return num / den  # keep precision; don't floor base here\n",
        "\n",
        "def totals_for_Ls(L_vals, sqrt_anchors, use_extra_pow2=False):\n",
        "    total_q = 0\n",
        "    total_b = 0.0\n",
        "    for i in range(len(sqrt_anchors) - 1):\n",
        "        Li       = L_vals[i]\n",
        "        S0, S1   = sqrt_anchors[i], sqrt_anchors[i+1]\n",
        "        total_q += seg_quote_atoms(Li, S0, S1)\n",
        "        total_b += seg_base_atoms(Li, S0, S1, use_extra_pow2=use_extra_pow2)\n",
        "    return total_q, total_b\n",
        "\n",
        "def human_avg_price(Q_atoms, B_atoms, quote_d=quote_d, base_d=base_d):\n",
        "    Q_tokens = Q_atoms / (10**quote_d)\n",
        "    B_tokens = B_atoms / (10**base_d)\n",
        "    return Q_tokens / B_tokens\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c9295b5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9c9295b5",
        "outputId": "3d79df76-e739-4608-d4ad-0ff93cdb66cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Avg price check: 0.00080000 AUDIO/base — in range? True\n",
            "Convention: A (no extra 2^64)\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# --- Build sqrt anchors & pick base convention ---\n",
        "\n",
        "sqrt_anchors = [sqrtprice_from_price_human(a) for a in anchors_h]\n",
        "\n",
        "# Compare the two base conventions; keep the one whose avg price lies within [start, end]\n",
        "Q_atoms_A, B_atoms_A = totals_for_Ls(L_values, sqrt_anchors, use_extra_pow2=False)\n",
        "Q_atoms_B, B_atoms_B = totals_for_Ls(L_values, sqrt_anchors, use_extra_pow2=True)\n",
        "\n",
        "p_start_h, p_end_h = anchors_h[0], anchors_h[-1]\n",
        "avg_A = human_avg_price(Q_atoms_A, B_atoms_A)\n",
        "avg_B = human_avg_price(Q_atoms_B, B_atoms_B)\n",
        "\n",
        "use_extra = not (p_start_h <= avg_A <= p_end_h)\n",
        "Q_atoms, B_atoms = (Q_atoms_B, B_atoms_B) if use_extra else (Q_atoms_A, B_atoms_A)\n",
        "avg_human_price = human_avg_price(Q_atoms, B_atoms)\n",
        "\n",
        "print(f\"Avg price check: {avg_human_price:.8f} AUDIO/base — in range? {p_start_h <= avg_human_price <= p_end_h}\")\n",
        "print(\"Convention:\", \"B (×2^64)\" if use_extra else \"A (no extra 2^64)\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "256c4102",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "256c4102",
        "outputId": "c4724355-9c77-4377-c99d-3b36aae5607f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Target base (tokens): 250,000,000\n",
            "Achieved base (tokens): 250,000,000\n",
            "Total spend (AUDIO): 200,000.000000\n",
            "Average paid price (AUDIO/base): 0.00080000\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# --- Scale liquidity to target total base out at graduation ---\n",
        "\n",
        "target_base_atoms = target_base_tokens * (10**base_d)\n",
        "\n",
        "# Initial scale\n",
        "scale_liq = target_base_atoms / B_atoms\n",
        "L_values_scaled = L_values * scale_liq\n",
        "\n",
        "# Refine to nail it\n",
        "for _ in range(2):\n",
        "    Qs, Bs = totals_for_Ls(L_values_scaled, sqrt_anchors, use_extra_pow2=use_extra)\n",
        "    L_values_scaled = L_values_scaled * (target_base_atoms / Bs)\n",
        "\n",
        "Q_atoms_s, B_atoms_s = totals_for_Ls(L_values_scaled, sqrt_anchors, use_extra_pow2=use_extra)\n",
        "avg_human_price_s = human_avg_price(Q_atoms_s, B_atoms_s)\n",
        "\n",
        "print(f\"Target base (tokens): {target_base_tokens:,}\")\n",
        "print(f\"Achieved base (tokens): {B_atoms_s/10**base_d:,.0f}\")\n",
        "print(f\"Total spend (AUDIO): {Q_atoms_s/10**quote_d:,.6f}\")\n",
        "print(f\"Average paid price (AUDIO/base): {avg_human_price_s:.8f}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "12885fa7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "12885fa7",
        "outputId": "f81dfe62-b096-4704-d7eb-09eeb1f06d59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graduates after spend: 200000.000000 AUDIO (≈ $13000.00)\n",
            "Spot price at grad: 0.001000 AUDIO/base\n",
            "FDV at grad: 1,000,000 AUDIO\n",
            "Circulating MC at grad: 250,000 AUDIO\n"
          ]
        }
      ],
      "source": [
        "\n",
        "  # --- Graduation spend & quick economics ---\n",
        "\n",
        "  # Segment offsets using **scaled** L\n",
        "  segment_offsets_atoms_scaled = [0]\n",
        "  for i in range(n - 1):\n",
        "      Li = L_values_scaled[i]\n",
        "      S0, S1 = sqrt_anchors[i], sqrt_anchors[i + 1]\n",
        "      dq_atoms = seg_quote_atoms(Li, S0, S1)\n",
        "      segment_offsets_atoms_scaled.append(segment_offsets_atoms_scaled[-1] + dq_atoms)\n",
        "\n",
        "  segment_offsets_tokens_scaled = [q / (10**quote_d) for q in segment_offsets_atoms_scaled]\n",
        "  grad_offset_audio = segment_offsets_tokens_scaled[-1]\n",
        "\n",
        "  # Economics at graduation\n",
        "  spot_price = a_end_h                       # human price at last anchor\n",
        "  fdv_audio = token_supply * spot_price\n",
        "  circ_mc_audio = (B_atoms_s / 10**base_d) * spot_price\n",
        "\n",
        "  print(f\"Graduates after spend: {grad_offset_audio:.6f} AUDIO (≈ ${grad_offset_audio * audio_price:.2f})\")\n",
        "  print(f\"Spot price at grad: {spot_price:.6f} AUDIO/base\")\n",
        "  print(f\"FDV at grad: {fdv_audio:,.0f} AUDIO\")\n",
        "  print(f\"Circulating MC at grad: {circ_mc_audio:,.0f} AUDIO\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "570505d5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "570505d5",
        "outputId": "e4a9dd7c-2341-4b20-8e82-da10a312313f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "# --- Plot price vs cumulative quote invested ---\n",
        "\n",
        "plt.figure(figsize=(10,6))\n",
        "\n",
        "for idx, (anchor_price_h, Li, x0) in enumerate(zip(anchors_h, L_values_scaled, segment_offsets_tokens_scaled)):\n",
        "    if idx >= len(segment_offsets_tokens_scaled) - 1:\n",
        "        # Last anchor: extend to max_quote_tokens if any headroom\n",
        "        span_tokens = max(0.0, max_quote_tokens - x0)\n",
        "    else:\n",
        "        span_tokens = max(0.0, segment_offsets_tokens_scaled[idx+1] - x0)\n",
        "    if span_tokens <= 0:\n",
        "        continue\n",
        "\n",
        "    q_local_tokens = np.linspace(0, span_tokens, samples_per_seg)\n",
        "    q_local_atoms  = q_local_tokens * (10**quote_d)\n",
        "\n",
        "    # Compute sqrt start and advance by local quote to read off prices\n",
        "    sqrt_start = sqrtprice_from_price_human(anchor_price_h)\n",
        "    sqrt_vals  = sqrt_start + (q_local_atoms * 2**128) / Li\n",
        "    p_raw      = (sqrt_vals / 2**64) ** 2\n",
        "    prices_h   = p_raw * (10 ** (quote_d - base_d))\n",
        "\n",
        "    plt.plot(x0 + q_local_tokens, prices_h)\n",
        "\n",
        "# Vertical lines at segment boundaries\n",
        "for x0 in segment_offsets_tokens_scaled:\n",
        "    plt.axvline(x=x0, linestyle=\":\", alpha=0.3)\n",
        "\n",
        "# Graduation marker\n",
        "plt.axvline(x=grad_offset_audio, linestyle=\"--\", linewidth=1)\n",
        "plt.text(grad_offset_audio, plt.ylim()[1]*0.9, \"Graduation\", rotation=90, ha=\"right\", va=\"top\")\n",
        "\n",
        "plt.xlabel(\"Cumulative quote invested (AUDIO tokens)\")\n",
        "plt.ylabel(\"Price in quote (AUDIO/base)\")\n",
        "plt.title(\"Price vs Cumulative Quote Invested\")\n",
        "plt.grid(True, alpha=0.3)\n",
        "plt.tight_layout()\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ec8bd0ca",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ec8bd0ca",
        "outputId": "a92e9d18-156b-4273-f9fa-42dab7dd001b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "// --- Copy‑pastable TS config ---\n",
            "migrationQuoteThreshold: new BN('19999999999979'),\n",
            "\n",
            "finalPrice: 0.001\n",
            "migrationBaseAmt: 199999999.99979\n",
            "sqrtStartPrice: new BN('58333726687135160'),\n",
            "\n",
            "curve: [\n",
            "  { sqrtPrice: new BN('72917158358918944'), liquidity: new BN('571036328485977016116623156707328') },\n",
            "  { sqrtPrice: new BN('85035288539934656'), liquidity: new BN('1043411423495750709858184007254016') },\n",
            "  { sqrtPrice: new BN('95629956661080960'), liquidity: new BN('1731136466785421588791727929425920') },\n",
            "  { sqrtPrice: new BN('105162621329683952'), liquidity: new BN('2784492297092260868761762427568128') },\n",
            "  { sqrtPrice: new BN('113900242488156176'), liquidity: new BN('4394180611354686489557890324496384') },\n",
            "  { sqrtPrice: new BN('122013743294725040'), liquidity: new BN('6840220463838088156143347719733248') },\n",
            "  { sqrtPrice: new BN('129620375970452032'), liquidity: new BN('10535563568720958689076110235271168') },\n",
            "  { sqrtPrice: new BN('136804715491285680'), liquidity: new BN('16087957909342315540840878738243584') },\n",
            "  { sqrtPrice: new BN('143630144797608192'), liquidity: new BN('24389233889298894343757645175848960') },\n",
            "  { sqrtPrice: new BN('150145618679041632'), liquidity: new BN('36744235180880011090284105357590528') },\n",
            "  { sqrtPrice: new BN('156389881777698304'), liquidity: new BN('55056701589137894898672439807442944') },\n",
            "  { sqrtPrice: new BN('162394222299848896'), liquidity: new BN('82096750510584693432534016978845696') },\n",
            "  { sqrtPrice: new BN('168184338599951136'), liquidity: new BN('121885042984704084499599386380075008') },\n",
            "  { sqrtPrice: new BN('173781644785153568'), liquidity: new BN('180243519129256358159775850305683456') },\n",
            "  { sqrtPrice: new BN('179204208595009184'), liquidity: new BN('265583529998693286685541757007953920') },\n",
            "  { sqrtPrice: new BN('184467440737095520'), liquidity: new BN('390031788037694723880191805379051520') },\n",
            "]\n"
          ]
        }
      ],
      "source": [
        "\n",
        "# --- Export: copy-pastable TypeScript config ---\n",
        "\n",
        "# Integer Q64.64 sqrt prices for each anchor (0..n-1). Index 0 is sqrtStartPrice.\n",
        "sqrt_anchors_int = [str(Decimal(s).to_integral_value(rounding=ROUND_HALF_UP)) for s in sqrt_anchors]\n",
        "\n",
        "# Integer liquidities for each segment (there are n-1 segments; segment i uses L_values_scaled[i])\n",
        "liqs_int = [str(Decimal(L_values_scaled[i]).to_integral_value(rounding=ROUND_HALF_UP)) for i in range(n - 1)]\n",
        "\n",
        "# Spend to last anchor (atoms) from the final L_values_scaled\n",
        "Q_atoms_final, B_atoms_final = totals_for_Ls(L_values_scaled, sqrt_anchors, use_extra_pow2=use_extra)\n",
        "final_price = graduation_market_cap / token_supply\n",
        "\n",
        "print(\"\\n// --- Copy‑pastable TS config ---\")\n",
        "print(f\"migrationQuoteThreshold: new BN('{Q_atoms_final}'),\\n\")\n",
        "print(f\"finalPrice: {final_price}\")\n",
        "print(f\"migrationBaseAmt: {Q_atoms_final / final_price / 1e8}\")  # AUDIO tokens spent converted to base via price\n",
        "print(f\"sqrtStartPrice: new BN('{sqrt_anchors_int[0]}'),\\n\")\n",
        "print(\"curve: [\")\n",
        "for i in range(n - 1):\n",
        "    print(f\"  {{ sqrtPrice: new BN('{sqrt_anchors_int[i+1]}'), liquidity: new BN('{liqs_int[i]}') }},\")\n",
        "print(\"]\")\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "language_info": {
      "name": "python"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}
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