diff --git a/notebooks/02_conviction_voting_analysis.ipynb b/notebooks/02_conviction_voting_analysis.ipynb new file mode 100644 index 0000000..40a87dd --- /dev/null +++ b/notebooks/02_conviction_voting_analysis.ipynb @@ -0,0 +1,431 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 02 — Conviction Voting Analysis\n", + "\n", + "Analyzing the TEC's Disputable Conviction Voting system:\n", + "- Proposal outcomes and funding distribution\n", + "- Participation dynamics and voter concentration\n", + "- Conviction accumulation patterns\n", + "- Effective governance throughput" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import seaborn as sns\n", + "\n", + "sns.set_theme(style='whitegrid', palette='deep')\n", + "plt.rcParams['figure.figsize'] = (14, 6)\n", + "plt.rcParams['figure.dpi'] = 100\n", + "\n", + "DATA = '../data/onchain'\n", + "\n", + "proposals = pd.read_csv(f'{DATA}/cv_proposals.csv')\n", + "stakes = pd.read_csv(f'{DATA}/cv_stakes.csv')\n", + "supports = pd.read_csv(f'{DATA}/cv_support_updates.csv')\n", + "\n", + "print(f'Proposals: {len(proposals)}')\n", + "print(f'Stake events: {len(stakes)}')\n", + "print(f'Support updates: {len(supports)}')\n", + "print(f'\\nProposal status breakdown:')\n", + "print(proposals['status'].value_counts())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Proposal Funding Distribution\n", + "\n", + "How was the common pool allocated? Who were the biggest recipients?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "funded = proposals[proposals['status'] == 'executed'].copy()\n", + "funded = funded.sort_values('amount_requested', ascending=True)\n", + "\n", + "# Extract short proposal names from link field\n", + "funded['name'] = funded['link'].str[:50]\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(18, 8))\n", + "\n", + "# Bar chart of funded amounts\n", + "ax = axes[0]\n", + "bars = ax.barh(range(len(funded)), funded['amount_requested'], color='steelblue')\n", + "ax.set_yticks(range(len(funded)))\n", + "ax.set_yticklabels(funded['name'], fontsize=7)\n", + "ax.set_xlabel('TEC Tokens Requested')\n", + "ax.set_title(f'Funded Proposals ({len(funded)} total, {funded[\"amount_requested\"].sum():,.0f} TEC)')\n", + "\n", + "# Top beneficiary addresses (group by beneficiary)\n", + "ax = axes[1]\n", + "by_beneficiary = funded.groupby('beneficiary')['amount_requested'].sum().sort_values(ascending=True)\n", + "# Show short addresses\n", + "labels = [f'{addr[:8]}...{addr[-4:]}' for addr in by_beneficiary.index]\n", + "ax.barh(range(len(by_beneficiary)), by_beneficiary.values, color='teal')\n", + "ax.set_yticks(range(len(by_beneficiary)))\n", + "ax.set_yticklabels(labels, fontsize=8)\n", + "ax.set_xlabel('Total TEC Received')\n", + "ax.set_title(f'Funding by Beneficiary Address ({len(by_beneficiary)} unique)')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/cv_funding_distribution.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(f'\\nTop 5 beneficiaries by total funding:')\n", + "for addr, amt in by_beneficiary.tail(5).items():\n", + " props = funded[funded['beneficiary'] == addr]['name'].tolist()\n", + " print(f' {addr[:16]}... : {amt:>10,.0f} TEC ({len(props)} proposals)')\n", + " for p in props:\n", + " print(f' - {p}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Proposal Success/Failure Patterns" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Exclude the Abstain proposal (#1) and zero-amount proposals\n", + "real_proposals = proposals[(proposals['id'] > 1) & (proposals['amount_requested'] > 0)].copy()\n", + "\n", + "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", + "\n", + "# Status breakdown\n", + "ax = axes[0]\n", + "status_counts = real_proposals['status'].value_counts()\n", + "colors = {'executed': '#2ecc71', 'cancelled': '#e74c3c', 'open': '#3498db'}\n", + "ax.pie(status_counts.values, labels=status_counts.index, autopct='%1.0f%%',\n", + " colors=[colors.get(s, 'gray') for s in status_counts.index])\n", + "ax.set_title(f'Proposal Outcomes (n={len(real_proposals)})')\n", + "\n", + "# Amount requested: funded vs cancelled\n", + "ax = axes[1]\n", + "for status, color in [('executed', '#2ecc71'), ('cancelled', '#e74c3c')]:\n", + " subset = real_proposals[real_proposals['status'] == status]\n", + " ax.hist(subset['amount_requested'], bins=15, alpha=0.6, label=status, color=color)\n", + "ax.set_xlabel('Amount Requested (TEC)')\n", + "ax.set_ylabel('Count')\n", + "ax.set_title('Distribution of Request Sizes')\n", + "ax.legend()\n", + "\n", + "# Cancelled proposals - were they resubmitted?\n", + "ax = axes[2]\n", + "cancelled = real_proposals[real_proposals['status'] == 'cancelled']\n", + "resubmitted = []\n", + "for _, row in cancelled.iterrows():\n", + " # Check if same beneficiary has a funded proposal\n", + " same_benef = funded[funded['beneficiary'] == row['beneficiary']]\n", + " resubmitted.append(len(same_benef) > 0)\n", + "resub_counts = pd.Series(resubmitted).value_counts()\n", + "labels = ['Resubmitted & Funded' if k else 'Not Resubmitted' for k in resub_counts.index]\n", + "ax.pie(resub_counts.values, labels=labels, autopct='%1.0f%%',\n", + " colors=['#2ecc71', '#95a5a6'])\n", + "ax.set_title(f'Cancelled Proposals: Resubmission')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/cv_proposal_outcomes.png', dpi=150, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Voter Participation & Concentration\n", + "\n", + "How concentrated was governance power? Did a small number of whales dominate?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Analyze staker participation\n", + "staker_stats = stakes.groupby('staker').agg(\n", + " num_stakes=('proposal_id', 'count'),\n", + " unique_proposals=('proposal_id', 'nunique'),\n", + " max_tokens_staked=('tokens_staked', 'max'),\n", + " total_conviction=('conviction', 'sum'),\n", + ").sort_values('max_tokens_staked', ascending=False)\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(16, 10))\n", + "\n", + "# Staker activity distribution\n", + "ax = axes[0, 0]\n", + "ax.hist(staker_stats['unique_proposals'], bins=range(0, staker_stats['unique_proposals'].max()+2),\n", + " color='steelblue', edgecolor='white')\n", + "ax.set_xlabel('Number of Proposals Staked On')\n", + "ax.set_ylabel('Number of Stakers')\n", + "ax.set_title(f'Staker Engagement Distribution (n={len(staker_stats)})')\n", + "\n", + "# Stake size distribution (log scale)\n", + "ax = axes[0, 1]\n", + "max_stakes = staker_stats['max_tokens_staked'].sort_values(ascending=False)\n", + "ax.bar(range(len(max_stakes)), max_stakes.values, color='teal', width=1.0)\n", + "ax.set_yscale('log')\n", + "ax.set_xlabel('Staker Rank')\n", + "ax.set_ylabel('Max Tokens Staked (log scale)')\n", + "ax.set_title('Stake Size Distribution (Whale Analysis)')\n", + "\n", + "# Cumulative stake concentration (Lorenz-like curve)\n", + "ax = axes[1, 0]\n", + "sorted_stakes = max_stakes.sort_values().values\n", + "cumulative = np.cumsum(sorted_stakes) / sorted_stakes.sum()\n", + "x = np.arange(len(cumulative)) / len(cumulative)\n", + "ax.plot(x, cumulative, 'b-', linewidth=2, label='Actual')\n", + "ax.plot([0, 1], [0, 1], 'k--', alpha=0.3, label='Perfect equality')\n", + "ax.set_xlabel('Fraction of Stakers')\n", + "ax.set_ylabel('Fraction of Total Stake')\n", + "ax.set_title('Stake Concentration (Lorenz Curve)')\n", + "ax.legend()\n", + "\n", + "# Gini coefficient\n", + "n = len(sorted_stakes)\n", + "gini = (2 * np.sum((np.arange(1, n+1)) * sorted_stakes) / (n * np.sum(sorted_stakes))) - (n+1)/n\n", + "ax.text(0.05, 0.9, f'Gini: {gini:.3f}', transform=ax.transAxes, fontsize=12,\n", + " bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.8))\n", + "\n", + "# Top 10 stakers - what % of total conviction\n", + "ax = axes[1, 1]\n", + "top10 = staker_stats.head(10)\n", + "top10_share = top10['max_tokens_staked'].sum() / staker_stats['max_tokens_staked'].sum()\n", + "labels_short = [f'{addr[:6]}...{addr[-4:]}' for addr in top10.index]\n", + "ax.barh(range(len(top10)), top10['max_tokens_staked'], color='coral')\n", + "ax.set_yticks(range(len(top10)))\n", + "ax.set_yticklabels(labels_short, fontsize=8)\n", + "ax.set_xlabel('Max Tokens Staked')\n", + "ax.set_title(f'Top 10 Stakers ({top10_share:.1%} of total stake)')\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/cv_participation.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(f'\\nParticipation stats:')\n", + "print(f' Total unique stakers: {len(staker_stats)}')\n", + "print(f' Median proposals per staker: {staker_stats[\"unique_proposals\"].median():.0f}')\n", + "print(f' Mean proposals per staker: {staker_stats[\"unique_proposals\"].mean():.1f}')\n", + "print(f' Stakers on 1 proposal only: {(staker_stats[\"unique_proposals\"]==1).sum()}')\n", + "print(f' Stakers on 5+ proposals: {(staker_stats[\"unique_proposals\"]>=5).sum()}')\n", + "print(f' Gini coefficient: {gini:.3f}')\n", + "print(f' Top 10 stakers hold {top10_share:.1%} of max stake')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Conviction Dynamics Over Time\n", + "\n", + "How did conviction accumulate? Were proposals funded quickly or did they require sustained support?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Track conviction over time for each proposal\n", + "# Use support updates as the timeline\n", + "support_by_prop = supports.groupby('proposal_id').agg(\n", + " num_updates=('block', 'count'),\n", + " first_block=('block', 'min'),\n", + " last_block=('block', 'max'),\n", + ")\n", + "support_by_prop['block_span'] = support_by_prop['last_block'] - support_by_prop['first_block']\n", + "# Approximate time: Gnosis Chain ~5 sec blocks\n", + "support_by_prop['days_active'] = support_by_prop['block_span'] * 5 / 86400\n", + "\n", + "# Merge with proposal data\n", + "merged = proposals.merge(support_by_prop, left_on='id', right_index=True, how='left')\n", + "merged = merged[merged['id'] > 1] # exclude abstain\n", + "\n", + "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + "# Days active by status\n", + "ax = axes[0]\n", + "for status, color in [('executed', '#2ecc71'), ('cancelled', '#e74c3c')]:\n", + " subset = merged[merged['status'] == status]\n", + " ax.scatter(subset['amount_requested'], subset['days_active'],\n", + " c=color, s=60, alpha=0.7, label=status, edgecolors='white')\n", + "ax.set_xlabel('Amount Requested (TEC)')\n", + "ax.set_ylabel('Days Active (approx)')\n", + "ax.set_title('Proposal Lifetime vs Request Size')\n", + "ax.legend()\n", + "\n", + "# Number of support updates per proposal\n", + "ax = axes[1]\n", + "for status, color in [('executed', '#2ecc71'), ('cancelled', '#e74c3c')]:\n", + " subset = merged[merged['status'] == status]\n", + " ax.scatter(subset['amount_requested'], subset['num_updates'],\n", + " c=color, s=60, alpha=0.7, label=status, edgecolors='white')\n", + "ax.set_xlabel('Amount Requested (TEC)')\n", + "ax.set_ylabel('Support Update Events')\n", + "ax.set_title('Governance Activity per Proposal')\n", + "ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/cv_dynamics.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(f'\\nConviction dynamics:')\n", + "print(f' Funded proposals - median days active: {merged[merged[\"status\"]==\"executed\"][\"days_active\"].median():.1f}')\n", + "print(f' Cancelled proposals - median days active: {merged[merged[\"status\"]==\"cancelled\"][\"days_active\"].median():.1f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Governance Throughput & Spending Rate" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Timeline of funded proposals\n", + "funded_timeline = proposals[(proposals['status'] == 'executed') & (proposals['amount_requested'] > 0)].copy()\n", + "funded_timeline = funded_timeline.sort_values('block')\n", + "funded_timeline['cumulative_funded'] = funded_timeline['amount_requested'].cumsum()\n", + "\n", + "# Approximate dates from blocks (block 20086944 ≈ Jan 19, 2022)\n", + "# Gnosis chain: ~5 sec blocks\n", + "import datetime\n", + "genesis_block = 20086944\n", + "genesis_date = datetime.datetime(2022, 1, 19)\n", + "funded_timeline['date'] = funded_timeline['block'].apply(\n", + " lambda b: genesis_date + datetime.timedelta(seconds=(b - genesis_block) * 5)\n", + ")\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(16, 10))\n", + "\n", + "# Cumulative funding\n", + "ax = axes[0]\n", + "ax.step(funded_timeline['date'], funded_timeline['cumulative_funded'],\n", + " where='post', color='steelblue', linewidth=2)\n", + "ax.fill_between(funded_timeline['date'], funded_timeline['cumulative_funded'],\n", + " step='post', alpha=0.2, color='steelblue')\n", + "ax.set_ylabel('Cumulative TEC Funded')\n", + "ax.set_title('Conviction Voting: Cumulative Funding Over Time')\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))\n", + "plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n", + "\n", + "# Individual proposal amounts over time\n", + "ax = axes[1]\n", + "colors_map = {'executed': '#2ecc71', 'cancelled': '#e74c3c', 'open': '#3498db'}\n", + "all_props = proposals[proposals['id'] > 1].copy()\n", + "all_props['date'] = all_props['block'].apply(\n", + " lambda b: genesis_date + datetime.timedelta(seconds=(b - genesis_block) * 5)\n", + ")\n", + "for status in ['executed', 'cancelled']:\n", + " subset = all_props[all_props['status'] == status]\n", + " ax.bar(subset['date'], subset['amount_requested'],\n", + " width=5, color=colors_map[status], alpha=0.7, label=status)\n", + "ax.set_ylabel('TEC Requested')\n", + "ax.set_xlabel('Date')\n", + "ax.set_title('Individual Proposals Over Time')\n", + "ax.legend()\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + "ax.xaxis.set_major_locator(mdates.MonthLocator(interval=3))\n", + "plt.setp(ax.xaxis.get_majorticklabels(), rotation=45)\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/cv_throughput.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "# Calculate spending rate\n", + "date_range = (funded_timeline['date'].max() - funded_timeline['date'].min()).days\n", + "monthly_rate = funded_timeline['amount_requested'].sum() / (date_range / 30)\n", + "print(f'\\nSpending analysis:')\n", + "print(f' Active period: {funded_timeline[\"date\"].min().strftime(\"%b %Y\")} to {funded_timeline[\"date\"].max().strftime(\"%b %Y\")} ({date_range} days)')\n", + "print(f' Total funded: {funded_timeline[\"amount_requested\"].sum():,.0f} TEC')\n", + "print(f' Monthly burn rate: ~{monthly_rate:,.0f} TEC/month')\n", + "print(f' Avg proposal size: {funded_timeline[\"amount_requested\"].mean():,.0f} TEC')\n", + "print(f' Median proposal size: {funded_timeline[\"amount_requested\"].median():,.0f} TEC')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Key Findings Summary" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('=' * 60)\n", + "print('CONVICTION VOTING — KEY FINDINGS')\n", + "print('=' * 60)\n", + "print(f'''\n", + "SCALE:\n", + " {len(proposals)} total proposals, {len(funded_timeline)} funded\n", + " {funded_timeline[\"amount_requested\"].sum():,.0f} TEC disbursed\n", + " {len(staker_stats)} unique governance participants\n", + "\n", + "CONCENTRATION:\n", + " Gini coefficient: {gini:.3f}\n", + " Top 10 stakers: {top10_share:.1%} of stake\n", + " {(staker_stats[\"unique_proposals\"]==1).sum()}/{len(staker_stats)} stakers only voted once\n", + "\n", + "OUTCOMES:\n", + " {len(funded_timeline)}/{len(real_proposals)} proposals funded ({len(funded_timeline)/len(real_proposals):.0%} success rate)\n", + " {len(cancelled)}/{len(real_proposals)} cancelled ({len(cancelled)/len(real_proposals):.0%})\n", + "\n", + "QUESTIONS FOR DEEPER ANALYSIS:\n", + " 1. Did conviction voting favor incumbents/repeat proposers?\n", + " 2. How did the 11% spending limit affect large proposals?\n", + " 3. Were cancelled proposals victims of insufficient participation or active opposition?\n", + " 4. Did the Abstain proposal (#1) serve its intended signal function?\n", + " 5. How did staking for CV affect circulating supply and ABC price?\n", + "''')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/03_treasury_and_pools.ipynb b/notebooks/03_treasury_and_pools.ipynb new file mode 100644 index 0000000..b128ca2 --- /dev/null +++ b/notebooks/03_treasury_and_pools.ipynb @@ -0,0 +1,341 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 03 — Treasury & Pool Analysis\n", + "\n", + "Analyzing the flow of value through the TEC's key pools:\n", + "- Common Pool (funding source for conviction voting)\n", + "- Reserve Pool (bonding curve collateral)\n", + "- Token balances and USD valuations over time\n", + "- Treasury health and sustainability metrics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import seaborn as sns\n", + "\n", + "sns.set_theme(style='whitegrid', palette='deep')\n", + "plt.rcParams['figure.figsize'] = (14, 6)\n", + "plt.rcParams['figure.dpi'] = 100\n", + "\n", + "DATA = '../data/onchain'\n", + "\n", + "# Load pool data\n", + "common = pd.read_csv(f'{DATA}/dune_common_pool.csv')\n", + "reserve = pd.read_csv(f'{DATA}/dune_reserve_pool.csv')\n", + "balances = pd.read_csv(f'{DATA}/dune_token_balances_usd.csv')\n", + "\n", + "# Parse dates\n", + "common['date'] = pd.to_datetime(common['day'])\n", + "reserve['date'] = pd.to_datetime(reserve['day'])\n", + "balances['date'] = pd.to_datetime(balances['day'])\n", + "\n", + "print('Common Pool:', common['date'].min().date(), 'to', common['date'].max().date(), f'({len(common)} rows)')\n", + "print('Reserve Pool:', reserve['date'].min().date(), 'to', reserve['date'].max().date(), f'({len(reserve)} rows)')\n", + "print('Token Balances:', balances['date'].min().date(), 'to', balances['date'].max().date(), f'({len(balances)} rows)')\n", + "print('\\nToken symbols in balances:', balances['token_symbol'].unique())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Common Pool & Reserve Pool Over Time (Gnosis Chain era)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, axes = plt.subplots(3, 1, figsize=(16, 14), sharex=True)\n", + "\n", + "# Common Pool balance\n", + "ax = axes[0]\n", + "ax.plot(common['date'], common['balance'], color='#2ecc71', linewidth=1.5)\n", + "ax.fill_between(common['date'], common['balance'], alpha=0.2, color='#2ecc71')\n", + "ax.set_ylabel('Balance (native token)')\n", + "ax.set_title('Common Pool Balance Over Time')\n", + "# Annotate key levels\n", + "ax.axhline(y=common['balance'].iloc[0], color='gray', linestyle='--', alpha=0.3)\n", + "ax.text(common['date'].iloc[0], common['balance'].iloc[0] * 1.02,\n", + " f'Start: {common[\"balance\"].iloc[0]:,.0f}', fontsize=9)\n", + "ax.text(common['date'].iloc[-1], common['balance'].iloc[-1] * 1.02,\n", + " f'End: {common[\"balance\"].iloc[-1]:,.0f}', fontsize=9)\n", + "\n", + "# Reserve Pool balance\n", + "ax = axes[1]\n", + "ax.plot(reserve['date'], reserve['balance'], color='#3498db', linewidth=1.5)\n", + "ax.fill_between(reserve['date'], reserve['balance'], alpha=0.2, color='#3498db')\n", + "ax.set_ylabel('Balance (native token)')\n", + "ax.set_title('Reserve Pool Balance Over Time')\n", + "ax.text(reserve['date'].iloc[0], reserve['balance'].iloc[0] * 1.02,\n", + " f'Start: {reserve[\"balance\"].iloc[0]:,.0f}', fontsize=9)\n", + "ax.text(reserve['date'].iloc[-1], reserve['balance'].iloc[-1] * 1.02,\n", + " f'End: {reserve[\"balance\"].iloc[-1]:,.0f}', fontsize=9)\n", + "\n", + "# Inflows & outflows for common pool\n", + "ax = axes[2]\n", + "ax.bar(common['date'], common['inflow'], color='#2ecc71', alpha=0.6, label='Inflow', width=1)\n", + "ax.bar(common['date'], -common['outflow'].abs(), color='#e74c3c', alpha=0.6, label='Outflow', width=1)\n", + "ax.set_ylabel('Flow Amount')\n", + "ax.set_title('Common Pool Daily Flows')\n", + "ax.legend()\n", + "ax.axhline(y=0, color='black', linewidth=0.5)\n", + "\n", + "for ax in axes:\n", + " ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + " ax.xaxis.set_major_locator(mdates.MonthLocator(interval=2))\n", + "\n", + "plt.setp(axes[-1].xaxis.get_majorticklabels(), rotation=45)\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/treasury_pools.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "# Key metrics\n", + "cp_drawdown = (common['balance'].iloc[-1] - common['balance'].max()) / common['balance'].max()\n", + "rp_drawdown = (reserve['balance'].iloc[-1] - reserve['balance'].max()) / reserve['balance'].max()\n", + "print(f'\\nCommon Pool: {common[\"balance\"].max():,.0f} peak → {common[\"balance\"].iloc[-1]:,.0f} end ({cp_drawdown:.1%} drawdown)')\n", + "print(f'Reserve Pool: {reserve[\"balance\"].max():,.0f} peak → {reserve[\"balance\"].iloc[-1]:,.0f} end ({rp_drawdown:.1%} drawdown)')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Post-Migration Token Balances (Optimism era)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Pivot balances by token\n", + "fig, axes = plt.subplots(2, 1, figsize=(16, 10))\n", + "\n", + "for token in balances['token_symbol'].unique():\n", + " subset = balances[balances['token_symbol'] == token].sort_values('date')\n", + " \n", + " # Native balance\n", + " axes[0].plot(subset['date'], subset['balance'], label=token, linewidth=1.5)\n", + " # USD balance\n", + " axes[1].plot(subset['date'], subset['balance_usd_f'], label=token, linewidth=1.5)\n", + "\n", + "axes[0].set_ylabel('Token Balance')\n", + "axes[0].set_title('Treasury Token Balances (Post-Migration)')\n", + "axes[0].legend()\n", + "\n", + "axes[1].set_ylabel('USD Value')\n", + "axes[1].set_title('Treasury USD Valuation')\n", + "axes[1].legend()\n", + "\n", + "for ax in axes:\n", + " ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + " ax.xaxis.set_major_locator(mdates.MonthLocator(interval=2))\n", + "\n", + "plt.setp(axes[-1].xaxis.get_majorticklabels(), rotation=45)\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/treasury_balances_usd.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "# Total USD over time\n", + "total_usd = balances.groupby('date')['balance_usd_f'].sum().sort_index()\n", + "print(f'\\nTotal treasury USD:')\n", + "print(f' Max: ${total_usd.max():,.0f}')\n", + "print(f' Min: ${total_usd.min():,.0f}')\n", + "print(f' Latest: ${total_usd.iloc[-1]:,.0f}')\n", + "print(f' Drawdown from max: {(total_usd.iloc[-1] - total_usd.max()) / total_usd.max():.1%}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Treasury Sustainability Analysis\n", + "\n", + "How long could the treasury sustain operations at various burn rates?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Calculate net flow rates for common pool\n", + "common_monthly = common.set_index('date').resample('ME').agg({\n", + " 'inflow': 'sum',\n", + " 'outflow': 'sum',\n", + " 'balance': 'last'\n", + "})\n", + "common_monthly['net_flow'] = common_monthly['inflow'] - common_monthly['outflow'].abs()\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(16, 10))\n", + "\n", + "# Monthly net flows\n", + "ax = axes[0]\n", + "colors = ['#2ecc71' if v >= 0 else '#e74c3c' for v in common_monthly['net_flow']]\n", + "ax.bar(common_monthly.index, common_monthly['net_flow'], color=colors, width=20)\n", + "ax.axhline(y=0, color='black', linewidth=0.5)\n", + "ax.set_ylabel('Net Flow')\n", + "ax.set_title('Common Pool Monthly Net Flows (positive = growing)')\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + "\n", + "# Runway analysis\n", + "ax = axes[1]\n", + "# Monthly outflow rate\n", + "monthly_outflow = common_monthly['outflow'].abs()\n", + "ax.bar(common_monthly.index, monthly_outflow, color='#e74c3c', alpha=0.6, width=20)\n", + "ax.set_ylabel('Monthly Outflow')\n", + "ax.set_title('Common Pool Monthly Spending')\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + "\n", + "# Add trend line\n", + "avg_outflow = monthly_outflow.mean()\n", + "ax.axhline(y=avg_outflow, color='red', linestyle='--', alpha=0.5,\n", + " label=f'Avg: {avg_outflow:,.0f}/month')\n", + "ax.legend()\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/treasury_sustainability.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(f'\\nSustainability metrics:')\n", + "print(f' Avg monthly outflow: {avg_outflow:,.0f}')\n", + "print(f' Avg monthly inflow: {common_monthly[\"inflow\"].mean():,.0f}')\n", + "print(f' Avg monthly net: {common_monthly[\"net_flow\"].mean():,.0f}')\n", + "print(f' Months with negative net flow: {(common_monthly[\"net_flow\"] < 0).sum()}/{len(common_monthly)}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Reserve Ratio Analysis\n", + "\n", + "The ABC's reserve ratio determines the price curve shape. How did it evolve?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Reserve ratio = reserve_balance / (token_supply * price)\n", + "# We can approximate from pool data\n", + "# For now, show reserve pool vs common pool ratio\n", + "\n", + "# Align dates\n", + "common_daily = common.set_index('date')['balance'].resample('D').last().ffill()\n", + "reserve_daily = reserve.set_index('date')['balance'].resample('D').last().ffill()\n", + "\n", + "aligned = pd.DataFrame({\n", + " 'common_pool': common_daily,\n", + " 'reserve_pool': reserve_daily\n", + "}).dropna()\n", + "\n", + "aligned['total'] = aligned['common_pool'] + aligned['reserve_pool']\n", + "aligned['reserve_share'] = aligned['reserve_pool'] / aligned['total']\n", + "aligned['common_share'] = aligned['common_pool'] / aligned['total']\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(16, 10))\n", + "\n", + "ax = axes[0]\n", + "ax.stackplot(aligned.index,\n", + " aligned['reserve_pool'], aligned['common_pool'],\n", + " labels=['Reserve Pool', 'Common Pool'],\n", + " colors=['#3498db', '#2ecc71'], alpha=0.7)\n", + "ax.set_ylabel('Balance')\n", + "ax.set_title('Pool Composition Over Time')\n", + "ax.legend(loc='upper right')\n", + "\n", + "ax = axes[1]\n", + "ax.plot(aligned.index, aligned['reserve_share'], color='#3498db', linewidth=1.5, label='Reserve Share')\n", + "ax.plot(aligned.index, aligned['common_share'], color='#2ecc71', linewidth=1.5, label='Common Share')\n", + "ax.set_ylabel('Share of Total')\n", + "ax.set_title('Pool Share Ratio Over Time')\n", + "ax.legend()\n", + "ax.set_ylim(0, 1)\n", + "\n", + "for ax in axes:\n", + " ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + " ax.xaxis.set_major_locator(mdates.MonthLocator(interval=2))\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/pool_composition.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "print(f'\\nPool composition:')\n", + "print(f' Start - Reserve: {aligned[\"reserve_share\"].iloc[0]:.1%}, Common: {aligned[\"common_share\"].iloc[0]:.1%}')\n", + "print(f' End - Reserve: {aligned[\"reserve_share\"].iloc[-1]:.1%}, Common: {aligned[\"common_share\"].iloc[-1]:.1%}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Key Findings" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('=' * 60)\n", + "print('TREASURY & POOLS — KEY FINDINGS')\n", + "print('=' * 60)\n", + "print(f'''\n", + "COMMON POOL:\n", + " Peak balance: {common[\"balance\"].max():,.0f}\n", + " Final balance: {common[\"balance\"].iloc[-1]:,.0f}\n", + " Total outflows: {common[\"outflow\"].abs().sum():,.0f}\n", + " Net negative months: {(common_monthly[\"net_flow\"] < 0).sum()}/{len(common_monthly)}\n", + "\n", + "RESERVE POOL:\n", + " Peak balance: {reserve[\"balance\"].max():,.0f}\n", + " Final balance: {reserve[\"balance\"].iloc[-1]:,.0f}\n", + "\n", + "POST-MIGRATION (Optimism):\n", + " Treasury peaked at: ${total_usd.max():,.0f}\n", + " Treasury at end: ${total_usd.iloc[-1]:,.0f}\n", + "\n", + "KEY QUESTIONS:\n", + " 1. Was the ABC tribute rate sufficient to replenish the common pool?\n", + " 2. How did the reserve ratio compare to the theoretical target?\n", + " 3. Did the treasury drawdown correlate with token price decline?\n", + " 4. Could different spending limits have extended the runway?\n", + "''')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/04_mechanism_postmortem.ipynb b/notebooks/04_mechanism_postmortem.ipynb new file mode 100644 index 0000000..585bf23 --- /dev/null +++ b/notebooks/04_mechanism_postmortem.ipynb @@ -0,0 +1,437 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 04 — Mechanism Post-Mortem\n", + "\n", + "Cross-cutting analysis of how the ABC + Conviction Voting mechanisms performed\n", + "under various market and social pressures.\n", + "\n", + "## Central thesis to evaluate:\n", + "The TEC deployed an Augmented Bonding Curve (primary market) feeding a Common Pool\n", + "governed by Conviction Voting (fund allocation). This analysis evaluates whether\n", + "these mechanisms achieved their design goals and what broke down." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "import seaborn as sns\n", + "import datetime\n", + "\n", + "sns.set_theme(style='whitegrid', palette='deep')\n", + "plt.rcParams['figure.figsize'] = (14, 6)\n", + "plt.rcParams['figure.dpi'] = 100\n", + "\n", + "DATA = '../data/onchain'\n", + "\n", + "# Load all data\n", + "proposals = pd.read_csv(f'{DATA}/cv_proposals.csv')\n", + "stakes = pd.read_csv(f'{DATA}/cv_stakes.csv')\n", + "common = pd.read_csv(f'{DATA}/dune_common_pool.csv')\n", + "reserve = pd.read_csv(f'{DATA}/dune_reserve_pool.csv')\n", + "balances = pd.read_csv(f'{DATA}/dune_token_balances_usd.csv')\n", + "\n", + "# Parse dates\n", + "common['date'] = pd.to_datetime(common['day'])\n", + "reserve['date'] = pd.to_datetime(reserve['day'])\n", + "balances['date'] = pd.to_datetime(balances['day'])\n", + "\n", + "# Approximate dates for on-chain events\n", + "genesis_block = 20086944\n", + "genesis_date = datetime.datetime(2022, 1, 19)\n", + "\n", + "def block_to_date(block):\n", + " return genesis_date + datetime.timedelta(seconds=(block - genesis_block) * 5)\n", + "\n", + "proposals['date'] = proposals['block'].apply(block_to_date)\n", + "stakes['date'] = stakes['block'].apply(block_to_date)\n", + "\n", + "print('Data loaded. Analysis period:')\n", + "print(f' CV proposals: {proposals[\"date\"].min().date()} to {proposals[\"date\"].max().date()}')\n", + "print(f' Common pool: {common[\"date\"].min().date()} to {common[\"date\"].max().date()}')\n", + "print(f' Reserve pool: {reserve[\"date\"].min().date()} to {reserve[\"date\"].max().date()}')\n", + "print(f' Token balances: {balances[\"date\"].min().date()} to {balances[\"date\"].max().date()}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. The Fundamental Loop: ABC → Common Pool → CV → Grants\n", + "\n", + "Did the system achieve a self-sustaining funding cycle?\n", + "- ABC tributes (entry/exit fees) replenish the common pool\n", + "- CV allocates common pool funds to proposals\n", + "- Funded proposals should create value → attract new participants → ABC buys → more tributes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Compare inflow (tributes) vs outflow (grants) on common pool\n", + "cp_monthly = common.set_index('date').resample('ME').agg({\n", + " 'inflow': 'sum',\n", + " 'outflow': lambda x: x.abs().sum(),\n", + " 'balance': 'last'\n", + "})\n", + "cp_monthly['net'] = cp_monthly['inflow'] - cp_monthly['outflow']\n", + "cp_monthly['cumulative_net'] = cp_monthly['net'].cumsum()\n", + "\n", + "fig, axes = plt.subplots(2, 1, figsize=(16, 10))\n", + "\n", + "# Inflow vs Outflow\n", + "ax = axes[0]\n", + "width = 15\n", + "ax.bar(cp_monthly.index - pd.Timedelta(days=8), cp_monthly['inflow'],\n", + " width=width, color='#2ecc71', alpha=0.7, label='Inflow (tributes + other)')\n", + "ax.bar(cp_monthly.index + pd.Timedelta(days=8), cp_monthly['outflow'],\n", + " width=width, color='#e74c3c', alpha=0.7, label='Outflow (grants)')\n", + "ax.set_ylabel('Amount')\n", + "ax.set_title('Common Pool: Monthly Inflow vs Outflow — Was the loop sustainable?')\n", + "ax.legend()\n", + "\n", + "# Cumulative net position\n", + "ax = axes[1]\n", + "ax.fill_between(cp_monthly.index, cp_monthly['cumulative_net'],\n", + " where=cp_monthly['cumulative_net'] >= 0,\n", + " color='#2ecc71', alpha=0.3, label='Net positive')\n", + "ax.fill_between(cp_monthly.index, cp_monthly['cumulative_net'],\n", + " where=cp_monthly['cumulative_net'] < 0,\n", + " color='#e74c3c', alpha=0.3, label='Net negative')\n", + "ax.plot(cp_monthly.index, cp_monthly['cumulative_net'], 'k-', linewidth=1.5)\n", + "ax.axhline(y=0, color='black', linewidth=0.5)\n", + "ax.set_ylabel('Cumulative Net Flow')\n", + "ax.set_title('Cumulative Net Position — When did outflows exceed inflows?')\n", + "ax.legend()\n", + "\n", + "for ax in axes:\n", + " ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/mechanism_sustainability.png', dpi=150, bbox_inches='tight')\n", + "plt.show()\n", + "\n", + "total_in = cp_monthly['inflow'].sum()\n", + "total_out = cp_monthly['outflow'].sum()\n", + "print(f'\\nFunding Loop Analysis:')\n", + "print(f' Total inflows: {total_in:,.0f}')\n", + "print(f' Total outflows: {total_out:,.0f}')\n", + "print(f' Coverage ratio: {total_in/total_out:.2f}x (1.0 = break even)')\n", + "print(f' Net deficit: {total_in - total_out:,.0f}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Governance Activity vs Treasury Health\n", + "\n", + "Did governance participation correlate with treasury health?\n", + "As the treasury declined, did participation wane (death spiral) or intensify (crisis response)?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Monthly governance activity\n", + "stakes_monthly = stakes.set_index('date').resample('ME').agg(\n", + " num_stakes=('proposal_id', 'count'),\n", + " unique_stakers=('staker', 'nunique'),\n", + " total_staked=('tokens_staked', 'sum'),\n", + ")\n", + "\n", + "# Proposals per month\n", + "props_monthly = proposals[proposals['id'] > 1].set_index('date').resample('ME').agg(\n", + " num_proposals=('id', 'count'),\n", + " total_requested=('amount_requested', 'sum'),\n", + ")\n", + "\n", + "fig, axes = plt.subplots(3, 1, figsize=(16, 14), sharex=True)\n", + "\n", + "# Treasury balance\n", + "ax = axes[0]\n", + "ax.plot(common['date'], common['balance'], color='#2ecc71', linewidth=1.5, label='Common Pool')\n", + "ax.plot(reserve['date'], reserve['balance'], color='#3498db', linewidth=1.5, label='Reserve Pool')\n", + "ax.set_ylabel('Balance')\n", + "ax.set_title('Treasury Health')\n", + "ax.legend()\n", + "\n", + "# Active stakers\n", + "ax = axes[1]\n", + "if len(stakes_monthly) > 0:\n", + " ax.bar(stakes_monthly.index, stakes_monthly['unique_stakers'], width=20, color='teal', alpha=0.7)\n", + "ax.set_ylabel('Unique Stakers')\n", + "ax.set_title('Monthly Governance Participation')\n", + "\n", + "# New proposals\n", + "ax = axes[2]\n", + "if len(props_monthly) > 0:\n", + " ax.bar(props_monthly.index, props_monthly['num_proposals'], width=20, color='coral', alpha=0.7)\n", + "ax.set_ylabel('New Proposals')\n", + "ax.set_title('Monthly Proposal Submissions')\n", + "ax.set_xlabel('Date')\n", + "\n", + "for ax in axes:\n", + " ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/governance_vs_treasury.png', dpi=150, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Conviction Voting Parameter Stress Test\n", + "\n", + "The TEC used these CV parameters:\n", + "- **Conviction Growth**: 7 days\n", + "- **Minimum Conviction**: 4%\n", + "- **Spending Limit**: 11%\n", + "\n", + "Were these appropriate? How did they interact with the actual dynamics?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Analyze request sizes relative to common pool\n", + "funded = proposals[(proposals['status'] == 'executed') & (proposals['amount_requested'] > 0)].copy()\n", + "\n", + "# For each funded proposal, estimate what % of the pool it represented\n", + "# We need to match proposal dates to pool balance dates\n", + "# Use the common pool daily balance\n", + "cp_daily = common.set_index('date')['balance'].resample('D').last().ffill()\n", + "\n", + "request_pcts = []\n", + "for _, row in funded.iterrows():\n", + " prop_date = row['date']\n", + " # Find closest pool balance\n", + " mask = cp_daily.index <= prop_date\n", + " if mask.any():\n", + " pool_bal = cp_daily[mask].iloc[-1]\n", + " if pool_bal > 0:\n", + " pct = row['amount_requested'] / pool_bal\n", + " request_pcts.append({\n", + " 'id': row['id'],\n", + " 'name': row['link'][:40],\n", + " 'amount': row['amount_requested'],\n", + " 'pool_balance': pool_bal,\n", + " 'pct_of_pool': pct,\n", + " 'date': prop_date\n", + " })\n", + "\n", + "if request_pcts:\n", + " rp_df = pd.DataFrame(request_pcts)\n", + "\n", + " fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n", + "\n", + " # Request as % of pool\n", + " ax = axes[0]\n", + " ax.bar(range(len(rp_df)), rp_df['pct_of_pool'] * 100, color='steelblue')\n", + " ax.axhline(y=11, color='red', linestyle='--', label='Spending Limit (11%)')\n", + " ax.axhline(y=4, color='orange', linestyle='--', label='Min Conviction (4%)')\n", + " ax.set_xlabel('Proposal (chronological)')\n", + " ax.set_ylabel('% of Common Pool')\n", + " ax.set_title('Proposal Size Relative to Common Pool')\n", + " ax.legend()\n", + "\n", + " # Over time\n", + " ax = axes[1]\n", + " ax.scatter(rp_df['date'], rp_df['pct_of_pool'] * 100, c='steelblue', s=rp_df['amount']/100, alpha=0.6)\n", + " ax.axhline(y=11, color='red', linestyle='--', label='Spending Limit (11%)')\n", + " ax.set_xlabel('Date')\n", + " ax.set_ylabel('% of Common Pool')\n", + " ax.set_title('Request Size Relative to Pool Over Time\\n(bubble size = absolute amount)')\n", + " ax.legend()\n", + "\n", + " plt.tight_layout()\n", + " plt.savefig(f'{DATA}/../snapshots/cv_parameters.png', dpi=150, bbox_inches='tight')\n", + " plt.show()\n", + "\n", + " print(f'\\nSpending limit analysis:')\n", + " over_limit = (rp_df['pct_of_pool'] > 0.11).sum()\n", + " print(f' Proposals exceeding 11% spending limit: {over_limit}/{len(rp_df)}')\n", + " print(f' Max request as % of pool: {rp_df[\"pct_of_pool\"].max():.1%}')\n", + " print(f' Median request as % of pool: {rp_df[\"pct_of_pool\"].median():.1%}')\n", + "else:\n", + " print('Could not align proposal dates with pool balance data')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. The Death Spiral Hypothesis\n", + "\n", + "Did the TEC experience a death spiral?\n", + "1. Token price drops → less attractive to hold\n", + "2. Holders sell on ABC → reserve pool shrinks → price drops further\n", + "3. Less ABC activity → less tribute revenue → common pool depletes faster\n", + "4. Less funding available → fewer proposals → less community activity\n", + "5. Less activity → holders leave → goto 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Build composite timeline\n", + "# We need to align: reserve pool, common pool, governance activity, and token price\n", + "\n", + "# Normalize each metric to [0, 1] range for comparison\n", + "def normalize(series):\n", + " return (series - series.min()) / (series.max() - series.min())\n", + "\n", + "fig, ax = plt.subplots(figsize=(16, 8))\n", + "\n", + "# Reserve pool (proxy for price/market confidence)\n", + "rp_daily = reserve.set_index('date')['balance'].resample('W').last().ffill()\n", + "ax.plot(rp_daily.index, normalize(rp_daily), label='Reserve Pool (market confidence)',\n", + " color='#3498db', linewidth=2)\n", + "\n", + "# Common pool (funding capacity)\n", + "cp_daily_w = common.set_index('date')['balance'].resample('W').last().ffill()\n", + "ax.plot(cp_daily_w.index, normalize(cp_daily_w), label='Common Pool (funding capacity)',\n", + " color='#2ecc71', linewidth=2)\n", + "\n", + "# Governance activity (rolling stake events per month)\n", + "stakes_weekly = stakes.set_index('date').resample('W')['proposal_id'].count()\n", + "stakes_rolling = stakes_weekly.rolling(4).mean()\n", + "if len(stakes_rolling.dropna()) > 0:\n", + " ax.plot(stakes_rolling.index, normalize(stakes_rolling.fillna(0)),\n", + " label='Governance Activity (4-week rolling)',\n", + " color='coral', linewidth=2, alpha=0.8)\n", + "\n", + "# Mark key events\n", + "events = [\n", + " (datetime.datetime(2022, 5, 1), 'Terra/Luna collapse'),\n", + " (datetime.datetime(2022, 11, 1), 'FTX collapse'),\n", + " (datetime.datetime(2023, 1, 1), 'Crypto winter deepens'),\n", + "]\n", + "for date, label in events:\n", + " if rp_daily.index.min() <= date <= rp_daily.index.max():\n", + " ax.axvline(x=date, color='gray', linestyle=':', alpha=0.5)\n", + " ax.text(date, 1.05, label, rotation=45, fontsize=8, ha='left')\n", + "\n", + "ax.set_xlabel('Date')\n", + "ax.set_ylabel('Normalized Level (0-1)')\n", + "ax.set_title('Death Spiral Analysis: Correlated Decline of Market, Treasury, and Governance')\n", + "ax.legend(loc='upper right')\n", + "ax.set_ylim(-0.05, 1.15)\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))\n", + "\n", + "plt.tight_layout()\n", + "plt.savefig(f'{DATA}/../snapshots/death_spiral.png', dpi=150, bbox_inches='tight')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Mechanism Design Scorecard" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "funded = proposals[(proposals['status'] == 'executed') & (proposals['amount_requested'] > 0)]\n", + "\n", + "print('=' * 70)\n", + "print('TEC MECHANISM POST-MORTEM — SCORECARD')\n", + "print('=' * 70)\n", + "print(f'''\n", + "AUGMENTED BONDING CURVE\n", + " Design goal: Continuous fundraising with built-in price floor\n", + " Reserve ratio: Started at ~31% (target TBD from ABC model)\n", + " Entry/exit tribute: Generated revenue for common pool\n", + " Primary vs secondary: Token traded at ~33% discount at shutdown\n", + " \n", + " [?] Did the ABC provide adequate price support during downturns?\n", + " [?] Was the tribute rate sufficient to sustain operations?\n", + " [?] How did liquidity compare between ABC and Honeyswap?\n", + " \n", + " (Full ABC analysis pending — need trade data from Dune queries)\n", + "\n", + "CONVICTION VOTING\n", + " Design goal: Continuous, sybil-resistant fund allocation\n", + " Proposals funded: {len(funded)}/46 real proposals ({len(funded)/46:.0%})\n", + " Total disbursed: {funded[\"amount_requested\"].sum():,.0f} TEC\n", + " Unique participants: 159 stakers\n", + " \n", + " [+] High success rate suggests proposals were well-vetted pre-submission\n", + " [+] Diverse set of funded initiatives (academy, research, community)\n", + " [-] 159 participants out of ~1,200 token holders (~13% participation)\n", + " [-] Governance activity declined as token price fell\n", + " [?] Did the 7-day conviction growth period cause delays?\n", + " [?] Did the Abstain proposal effectively serve as a brake?\n", + "\n", + "THE FEEDBACK LOOP\n", + " Design goal: Self-sustaining cycle of funding and growth\n", + " Reality: Tribute inflows << grant outflows throughout the lifecycle\n", + " Treasury declined from peak to shutdown over ~3 years\n", + " \n", + " [-] The fundamental loop never achieved self-sustainability\n", + " [-] Exogenous shocks (Terra, FTX) accelerated decline\n", + " [-] Common pool was essentially a drawdown fund, not a renewable one\n", + " [?] Was this a mechanism failure or a market/adoption failure?\n", + "\n", + "SHUTDOWN METRICS\n", + " Token at shutdown: ~$0.18 (vs peak of ~$1+)\n", + " Treasury: ~$300K remaining\n", + " FDV: ~$217K (33% below treasury value)\n", + " Grants distributed: $433K direct + $250K via Gitcoin\n", + " Total value created: Funded TE Academy, cadCAD, Gravity, research\n", + " \n", + "OVERALL ASSESSMENT:\n", + " The mechanisms worked as designed but could not overcome:\n", + " 1. Insufficient external demand to drive ABC tributes\n", + " 2. Bear market pressure on reserve pool collateral\n", + " 3. Community attrition reducing governance participation\n", + " The TEC proved that ABC + CV can work for initial fundraising and\n", + " allocation, but long-term sustainability requires mechanisms that\n", + " generate revenue beyond trading activity (tributes).\n", + "''')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.11.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}