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"# Alternative Data Evaluation: DeFi TVL Case Study\n",
"\n",
"**Chapter 4: Fundamental and Alternative Data**\n",
"**Docker image**: `ml4t`\n",
"**Section Reference**: See Section 4.4 for alternative data due diligence concepts\n",
"\n",
"## Purpose\n",
"\n",
"This notebook demonstrates a rigorous alternative data evaluation framework using\n",
"**real data**: DeFi Llama's Total Value Locked (TVL) metrics. Rather than theoretical\n",
"checklists, we compute actual signal quality, assess real data gaps, and calculate\n",
"whether this free dataset justifies integration into a trading pipeline.\n",
"\n",
"## Learning Objectives\n",
"\n",
"After completing this notebook, you will be able to:\n",
"- Apply a 4-dimension evaluation framework to real alternative data\n",
"- Calculate Information Coefficients (IC) and signal decay empirically\n",
"- Assess data quality: coverage, gaps, methodology transparency\n",
"- Evaluate legal/compliance considerations for blockchain data\n",
"- Compute break-even alpha requirements for data costs\n",
"\n",
"## The Evaluation Framework\n",
"\n",
"This notebook implements a **four-gate** evaluation (Signal, Data, Legal, Commercial). In the\n",
"chapter text, these gates expand into a seven-check rubric (uniqueness, decay, quality, coverage,\n",
"latency, legal, engineering/economics). The goal is to avoid false precision: failing any hard\n",
"gate (PIT/legal) should block integration regardless of a \"score.\"\n",
"\n",
"| Gate | Key Questions |\n",
"|------|---------------|\n",
"| **Signal** | Does it predict returns? How quickly does the signal decay? |\n",
"| **Data** | Coverage? Gaps? Methodology? Point-in-time available? |\n",
"| **Legal** | Data source legality? MNPI risk? Usage restrictions? |\n",
"| **Commercial** | Cost vs value? Build vs buy? API reliability? |\n",
"\n",
"## Cross-References\n",
"\n",
"- **Data Source**: [`09_onchain_fundamentals`](09_onchain_fundamentals.ipynb) loads the TVL data\n",
"- **Framework**: This evaluation methodology applies to ANY alternative dataset\n",
"- **Downstream**: Use findings to decide whether to integrate into Chapter 8 features"
]
},
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{
"name": "stdout",
"output_type": "stream",
"text": [
"Alternative Data Evaluation: DeFi TVL Case Study\n"
]
}
],
"source": [
"\"\"\"Alternative Data Evaluation: DeFi TVL Case Study — measure four evaluation dimensions on a real alt-data feed.\"\"\"\n",
"\n",
"import warnings\n",
"\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"from dataclasses import dataclass\n",
"\n",
"import plotly.graph_objects as go\n",
"import polars as pl\n",
"from plotly.subplots import make_subplots\n",
"\n",
"from data import load_coingecko_ohlcv, load_defillama_chain_tvl\n",
"from utils.style import COLORS\n",
"\n",
"print(\"Alternative Data Evaluation: DeFi TVL Case Study\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c14221bb",
"metadata": {
"execution": {
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"status": "completed"
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"tags": [
"parameters"
]
},
"outputs": [],
"source": [
"# Production defaults — Papermill injects overrides for CI"
]
},
{
"cell_type": "markdown",
"id": "23c19a44",
"metadata": {
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"source": [
"---\n",
"\n",
"## Section 1: Load the Data\n",
"\n",
"We evaluate whether DeFi TVL predicts ETH returns. Both feeds come\n",
"from the canonical on-chain downloader — run once, then work offline:\n",
"\n",
"```bash\n",
"python data/crypto/onchain/download.py\n",
"```\n",
"\n",
"Loaders raise `DataNotFoundError` with the exact command if an\n",
"expected parquet is missing, so there is no hidden network path."
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "0e44039b",
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TVL observations: 3,139 (2017-09-27 → 2026-05-01)\n",
"ETH observations: 365 (2025-05-02 → 2026-05-01)\n",
"Combined dataset: (365, 4), range: 2025-05-02 → 2026-05-01\n"
]
},
{
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"</style>\n",
"<small>shape: (5, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>tvl_usd</th><th>tvl_bn</th><th>eth_price</th></tr><tr><td>date</td><td>i64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2026-04-27</td><td>85237200680</td><td>85.237201</td><td>2369.743029</td></tr><tr><td>2026-04-28</td><td>83714613526</td><td>83.714614</td><td>2299.770459</td></tr><tr><td>2026-04-29</td><td>83282116410</td><td>83.282116</td><td>2288.044927</td></tr><tr><td>2026-04-30</td><td>83484152685</td><td>83.484153</td><td>2253.458358</td></tr><tr><td>2026-05-01</td><td>84621565953</td><td>84.621566</td><td>2294.157168</td></tr></tbody></table></div>"
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"text/plain": [
"shape: (5, 4)\n",
"┌────────────┬─────────────┬───────────┬─────────────┐\n",
"│ timestamp ┆ tvl_usd ┆ tvl_bn ┆ eth_price │\n",
"│ --- ┆ --- ┆ --- ┆ --- │\n",
"│ date ┆ i64 ┆ f64 ┆ f64 │\n",
"╞════════════╪═════════════╪═══════════╪═════════════╡\n",
"│ 2026-04-27 ┆ 85237200680 ┆ 85.237201 ┆ 2369.743029 │\n",
"│ 2026-04-28 ┆ 83714613526 ┆ 83.714614 ┆ 2299.770459 │\n",
"│ 2026-04-29 ┆ 83282116410 ┆ 83.282116 ┆ 2288.044927 │\n",
"│ 2026-04-30 ┆ 83484152685 ┆ 83.484153 ┆ 2253.458358 │\n",
"│ 2026-05-01 ┆ 84621565953 ┆ 84.621566 ┆ 2294.157168 │\n",
"└────────────┴─────────────┴───────────┴─────────────┘"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tvl_data = load_defillama_chain_tvl(\"total\").with_columns((pl.col(\"tvl_usd\") / 1e9).alias(\"tvl_bn\"))\n",
"\n",
"# CoinGecko returns two rows for the download day (an intraday roll-up\n",
"# and a daily summary); collapse to one row per date by taking the last\n",
"# observation. Without this, the inner join below would propagate the\n",
"# duplicate into every downstream feature.\n",
"eth_data = (\n",
" load_coingecko_ohlcv(\"ethereum\")\n",
" .select(pl.col(\"timestamp\"), pl.col(\"price_usd\").alias(\"eth_price\"))\n",
" .group_by(\"timestamp\")\n",
" .agg(pl.col(\"eth_price\").last())\n",
" .sort(\"timestamp\")\n",
")\n",
"\n",
"print(\n",
" f\"TVL observations: {len(tvl_data):,} ({tvl_data['timestamp'].min()} → {tvl_data['timestamp'].max()})\"\n",
")\n",
"print(\n",
" f\"ETH observations: {len(eth_data):,} ({eth_data['timestamp'].min()} → {eth_data['timestamp'].max()})\"\n",
")\n",
"\n",
"# CoinGecko free tier is 365 days, so the merge is bounded by ETH coverage.\n",
"data = tvl_data.join(eth_data, on=\"timestamp\", how=\"inner\").sort(\"timestamp\")\n",
"print(\n",
" f\"Combined dataset: {data.shape}, range: {data['timestamp'].min()} → {data['timestamp'].max()}\"\n",
")\n",
"data.tail(5)"
]
},
{
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"source": [
"---\n",
"\n",
"## Section 2: Signal Quality Assessment\n",
"\n",
"The most important question: **Does this data predict returns?**\n",
"\n",
"We'll calculate:\n",
"- Information Coefficient (IC): Correlation between signal and forward returns\n",
"- Signal decay: How quickly does predictive power diminish?\n",
"- Regime analysis: Does the signal work in different market conditions?"
]
},
{
"cell_type": "code",
"execution_count": 4,
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Analysis dataset: (305, 11)\n"
]
}
],
"source": [
"# Create features and forward returns\n",
"analysis = (\n",
" data.with_columns(\n",
" [\n",
" # TVL momentum signals\n",
" pl.col(\"tvl_bn\").pct_change(7).alias(\"tvl_growth_7d\"),\n",
" pl.col(\"tvl_bn\").pct_change(30).alias(\"tvl_growth_30d\"),\n",
" (\n",
" (pl.col(\"tvl_bn\") - pl.col(\"tvl_bn\").rolling_mean(90))\n",
" / pl.col(\"tvl_bn\").rolling_std(90)\n",
" ).alias(\"tvl_zscore\"),\n",
" # Forward returns at various horizons\n",
" pl.col(\"eth_price\").pct_change(7).shift(-7).alias(\"fwd_return_7d\"),\n",
" pl.col(\"eth_price\").pct_change(14).shift(-14).alias(\"fwd_return_14d\"),\n",
" pl.col(\"eth_price\").pct_change(30).shift(-30).alias(\"fwd_return_30d\"),\n",
" pl.col(\"eth_price\").pct_change(60).shift(-60).alias(\"fwd_return_60d\"),\n",
" ]\n",
" )\n",
" .filter(pl.col(\"tvl_growth_30d\").is_not_null())\n",
" .filter(pl.col(\"fwd_return_30d\").is_not_null())\n",
")\n",
"\n",
"print(f\"Analysis dataset: {analysis.shape}\")"
]
},
{
"cell_type": "markdown",
"id": "59b5ef4c",
"metadata": {
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"source": [
"Classical t-statistics are not valid here: forward-return horizons\n",
"overlap by construction (every 30-day forward return shares 29 days\n",
"with the next), inducing strong autocorrelation that inflates naive\n",
"significance. The ICs below are *screening diagnostics*, not hypothesis\n",
"tests. For inference, use HAC / Newey-West standard errors or a block\n",
"bootstrap on non-overlapping samples."
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "eafab93e",
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{
"data": {
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"<div><style>\n",
".dataframe > thead > tr,\n",
".dataframe > tbody > tr {\n",
" text-align: right;\n",
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"</style>\n",
"<small>shape: (12, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>signal</th><th>horizon</th><th>IC</th><th>n_obs</th></tr><tr><td>str</td><td>str</td><td>f64</td><td>u32</td></tr></thead><tbody><tr><td>&quot;tvl_growth_7d&quot;</td><td>&quot;fwd_return_7d&quot;</td><td>0.029257</td><td>305</td></tr><tr><td>&quot;tvl_growth_7d&quot;</td><td>&quot;fwd_return_14d&quot;</td><td>0.114032</td><td>305</td></tr><tr><td>&quot;tvl_growth_7d&quot;</td><td>&quot;fwd_return_30d&quot;</td><td>0.084906</td><td>305</td></tr><tr><td>&quot;tvl_growth_7d&quot;</td><td>&quot;fwd_return_60d&quot;</td><td>0.025952</td><td>305</td></tr><tr><td>&quot;tvl_growth_30d&quot;</td><td>&quot;fwd_return_7d&quot;</td><td>0.090129</td><td>305</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;tvl_growth_30d&quot;</td><td>&quot;fwd_return_60d&quot;</td><td>-0.003566</td><td>305</td></tr><tr><td>&quot;tvl_zscore&quot;</td><td>&quot;fwd_return_7d&quot;</td><td>0.070669</td><td>246</td></tr><tr><td>&quot;tvl_zscore&quot;</td><td>&quot;fwd_return_14d&quot;</td><td>0.070308</td><td>246</td></tr><tr><td>&quot;tvl_zscore&quot;</td><td>&quot;fwd_return_30d&quot;</td><td>-0.127472</td><td>246</td></tr><tr><td>&quot;tvl_zscore&quot;</td><td>&quot;fwd_return_60d&quot;</td><td>-0.277553</td><td>246</td></tr></tbody></table></div>"
],
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"shape: (12, 4)\n",
"┌────────────────┬────────────────┬───────────┬───────┐\n",
"│ signal ┆ horizon ┆ IC ┆ n_obs │\n",
"│ --- ┆ --- ┆ --- ┆ --- │\n",
"│ str ┆ str ┆ f64 ┆ u32 │\n",
"╞════════════════╪════════════════╪═══════════╪═══════╡\n",
"│ tvl_growth_7d ┆ fwd_return_7d ┆ 0.029257 ┆ 305 │\n",
"│ tvl_growth_7d ┆ fwd_return_14d ┆ 0.114032 ┆ 305 │\n",
"│ tvl_growth_7d ┆ fwd_return_30d ┆ 0.084906 ┆ 305 │\n",
"│ tvl_growth_7d ┆ fwd_return_60d ┆ 0.025952 ┆ 305 │\n",
"│ tvl_growth_30d ┆ fwd_return_7d ┆ 0.090129 ┆ 305 │\n",
"│ … ┆ … ┆ … ┆ … │\n",
"│ tvl_growth_30d ┆ fwd_return_60d ┆ -0.003566 ┆ 305 │\n",
"│ tvl_zscore ┆ fwd_return_7d ┆ 0.070669 ┆ 246 │\n",
"│ tvl_zscore ┆ fwd_return_14d ┆ 0.070308 ┆ 246 │\n",
"│ tvl_zscore ┆ fwd_return_30d ┆ -0.127472 ┆ 246 │\n",
"│ tvl_zscore ┆ fwd_return_60d ┆ -0.277553 ┆ 246 │\n",
"└────────────────┴────────────────┴───────────┴───────┘"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"signals = [\"tvl_growth_7d\", \"tvl_growth_30d\", \"tvl_zscore\"]\n",
"horizons = [\"fwd_return_7d\", \"fwd_return_14d\", \"fwd_return_30d\", \"fwd_return_60d\"]\n",
"\n",
"ic_long = pl.concat(\n",
" [\n",
" analysis.select(\n",
" pl.lit(s).alias(\"signal\"),\n",
" pl.lit(h).alias(\"horizon\"),\n",
" pl.corr(s, h).alias(\"IC\"),\n",
" pl.col(s).is_not_null().sum().alias(\"n_obs\"),\n",
" )\n",
" for s in signals\n",
" for h in horizons\n",
" ]\n",
")\n",
"ic_df = ic_long.filter(pl.col(\"n_obs\") > 30)\n",
"ic_df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "2e9b6f4e",
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},
"outputs": [],
"source": [
"# Create IC heatmap (signals × horizons)\n",
"# Reshape data for heatmap\n",
"signal_order = [\"tvl_growth_7d\", \"tvl_growth_30d\", \"tvl_zscore\"]\n",
"horizon_order = [\"fwd_return_7d\", \"fwd_return_14d\", \"fwd_return_30d\", \"fwd_return_60d\"]\n",
"\n",
"# Build matrix\n",
"ic_matrix = []\n",
"for signal in signal_order:\n",
" row = []\n",
" for horizon in horizon_order:\n",
" ic_val = ic_df.filter((pl.col(\"signal\") == signal) & (pl.col(\"horizon\") == horizon))[\n",
" \"IC\"\n",
" ].to_list()\n",
" row.append(ic_val[0] if ic_val else 0)\n",
" ic_matrix.append(row)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a38fa7b3",
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"colorbar": {
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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Clean labels\n",
"signal_labels = [\"TVL Growth 7d\", \"TVL Growth 30d\", \"TVL Z-Score\"]\n",
"horizon_labels = [\"7d\", \"14d\", \"30d\", \"60d\"]\n",
"\n",
"# Create heatmap with custom colorscale\n",
"fig = go.Figure(\n",
" data=go.Heatmap(\n",
" z=ic_matrix,\n",
" x=horizon_labels,\n",
" y=signal_labels,\n",
" colorscale=[\n",
" [0.0, \"#C62828\"], # negative IC (red)\n",
" [0.5, \"#F5F5F5\"], # zero IC (neutral)\n",
" [1.0, \"#2E7D32\"], # positive IC (green)\n",
" ],\n",
" zmid=0, # Center colorscale at 0\n",
" text=[[f\"{v:.3f}\" for v in row] for row in ic_matrix],\n",
" texttemplate=\"%{text}\",\n",
" textfont={\"size\": 14, \"color\": \"black\"},\n",
" colorbar=dict(title=\"IC\", tickformat=\".2f\"),\n",
" )\n",
")\n",
"\n",
"fig.update_layout(\n",
" title=\"IC Reverses Sign with Horizon for the TVL Z-Score\",\n",
" xaxis_title=\"Forward Return Horizon\",\n",
" yaxis_title=\"Signal\",\n",
" height=350,\n",
" template=\"plotly_white\",\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "85b67942",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:11:25.392677Z",
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"<small>shape: (3, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>signal</th><th>horizon</th><th>IC</th><th>n_obs</th></tr><tr><td>str</td><td>str</td><td>f64</td><td>u32</td></tr></thead><tbody><tr><td>&quot;tvl_zscore&quot;</td><td>&quot;fwd_return_60d&quot;</td><td>-0.277553</td><td>246</td></tr><tr><td>&quot;tvl_zscore&quot;</td><td>&quot;fwd_return_30d&quot;</td><td>-0.127472</td><td>246</td></tr><tr><td>&quot;tvl_growth_30d&quot;</td><td>&quot;fwd_return_14d&quot;</td><td>0.118762</td><td>305</td></tr></tbody></table></div>"
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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Visualize IC by horizon (signal decay)\n",
"pivot_data = ic_df.filter(pl.col(\"signal\") == \"tvl_growth_30d\")\n",
"\n",
"fig = go.Figure()\n",
"fig.add_trace(\n",
" go.Bar(\n",
" x=[h.replace(\"fwd_return_\", \"\") for h in pivot_data[\"horizon\"]],\n",
" y=pivot_data[\"IC\"],\n",
" text=[f\"{ic:.3f}\" if ic is not None else \"N/A\" for ic in pivot_data[\"IC\"]],\n",
" textposition=\"auto\",\n",
" marker_color=COLORS[\"blue\"],\n",
" )\n",
")\n",
"\n",
"fig.update_layout(\n",
" title=\"TVL 30-Day Growth Signal Peaks at Mid Horizon, Decays Past 60 Days\",\n",
" xaxis_title=\"Forward Return Horizon\",\n",
" yaxis_title=\"Information Coefficient\",\n",
" height=400,\n",
" template=\"plotly_white\",\n",
")\n",
"fig.show()"
]
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"source": [
"### Rolling IC stability\n",
"\n",
"A point estimate hides regime dependence. The next cell rolls a\n",
"180-day window across the joined sample to expose how variable the\n",
"IC actually is."
]
},
{
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"<small>shape: (1, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>window_days</th><th>n_windows</th><th>mean_ic</th><th>std_ic</th><th>share_positive</th></tr><tr><td>i32</td><td>i32</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>180</td><td>125</td><td>-0.11</td><td>0.357</td><td>0.488</td></tr></tbody></table></div>"
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"shape: (1, 5)\n",
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"│ window_days ┆ n_windows ┆ mean_ic ┆ std_ic ┆ share_positive │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ i32 ┆ i32 ┆ f64 ┆ f64 ┆ f64 │\n",
"╞═════════════╪═══════════╪═════════╪════════╪════════════════╡\n",
"│ 180 ┆ 125 ┆ -0.11 ┆ 0.357 ┆ 0.488 │\n",
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"source": [
"rolling_window = 180\n",
"rolling_ic_df = pl.DataFrame(\n",
" {\n",
" \"timestamp\": analysis[\"timestamp\"][rolling_window:],\n",
" \"rolling_ic\": [\n",
" analysis.slice(i - rolling_window, rolling_window)\n",
" .select(pl.corr(\"tvl_growth_30d\", \"fwd_return_30d\"))\n",
" .item()\n",
" for i in range(rolling_window, len(analysis))\n",
" ],\n",
" }\n",
").filter(pl.col(\"rolling_ic\").is_not_null())\n",
"\n",
"ic_mean = rolling_ic_df[\"rolling_ic\"].mean()\n",
"ic_std = rolling_ic_df[\"rolling_ic\"].std()\n",
"ic_positive_pct = (rolling_ic_df[\"rolling_ic\"] > 0).mean()\n",
"\n",
"rolling_ic_df.select(\n",
" pl.lit(rolling_window).alias(\"window_days\"),\n",
" pl.lit(len(rolling_ic_df)).alias(\"n_windows\"),\n",
" pl.col(\"rolling_ic\").mean().round(3).alias(\"mean_ic\"),\n",
" pl.col(\"rolling_ic\").std().round(3).alias(\"std_ic\"),\n",
" (pl.col(\"rolling_ic\") > 0).mean().round(3).alias(\"share_positive\"),\n",
")"
]
},
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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = go.Figure()\n",
"fig.add_trace(\n",
" go.Scatter(\n",
" x=rolling_ic_df[\"timestamp\"],\n",
" y=rolling_ic_df[\"rolling_ic\"],\n",
" name=\"Rolling IC\",\n",
" line=dict(color=COLORS[\"slate\"], width=1.5),\n",
" fill=\"tozeroy\",\n",
" fillcolor=\"rgba(4, 138, 129, 0.2)\",\n",
" )\n",
")\n",
"fig.add_hline(y=0, line_dash=\"dash\", line_color=COLORS[\"neutral\"])\n",
"fig.add_hline(\n",
" y=ic_mean,\n",
" line_dash=\"dot\",\n",
" line_color=COLORS[\"blue\"],\n",
" annotation_text=f\"Mean: {ic_mean:.3f}\",\n",
")\n",
"\n",
"# Add bands for positive/negative regions\n",
"fig.add_hrect(y0=0, y1=0.2, fillcolor=COLORS[\"positive\"], opacity=0.05)\n",
"fig.add_hrect(y0=-0.2, y1=0, fillcolor=COLORS[\"negative\"], opacity=0.05)\n",
"\n",
"fig.update_layout(\n",
" title=\"Rolling 180-Day IC Crosses Zero — Signal Is Regime-Dependent\",\n",
" xaxis_title=\"Date\",\n",
" yaxis_title=\"Information Coefficient\",\n",
" height=400,\n",
" template=\"plotly_white\",\n",
")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "a8c1ccc7",
"metadata": {
"papermill": {
"duration": 0.005976,
"end_time": "2026-06-13T03:11:25.693912+00:00",
"exception": false,
"start_time": "2026-06-13T03:11:25.687936+00:00",
"status": "completed"
}
},
"source": [
"---\n",
"\n",
"## Section 3: Data Quality Assessment\n",
"\n",
"Beyond signal quality, we need to assess the data itself:\n",
"- **Coverage**: What time period? What assets/chains?\n",
"- **Gaps**: Missing data? Inconsistencies?\n",
"- **Methodology**: How is TVL calculated? What's included/excluded?\n",
"- **Point-in-Time**: Can we reconstruct historical views without lookahead bias?"
]
},
{
"cell_type": "markdown",
"id": "2aed790b",
"metadata": {
"papermill": {
"duration": 0.004021,
"end_time": "2026-06-13T03:11:25.701995+00:00",
"exception": false,
"start_time": "2026-06-13T03:11:25.697974+00:00",
"status": "completed"
}
},
"source": [
"DefiLlama's series begins in September 2017 when total TVL was a few\n",
"hundred thousand dollars; once we restrict to the post-2019 era when\n",
"TVL exceeded \\$100M the early-curve noise drops out and the gap /\n",
"extreme-move statistics describe the modern regime that any signal\n",
"would actually trade."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "74e8b169",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:11:25.711756Z",
"iopub.status.busy": "2026-06-13T03:11:25.711601Z",
"iopub.status.idle": "2026-06-13T03:11:25.719354Z",
"shell.execute_reply": "2026-06-13T03:11:25.718569Z"
},
"lines_to_next_cell": 2,
"papermill": {
"duration": 0.013922,
"end_time": "2026-06-13T03:11:25.719820+00:00",
"exception": false,
"start_time": "2026-06-13T03:11:25.705898+00:00",
"status": "completed"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div><style>\n",
".dataframe > thead > tr,\n",
".dataframe > tbody > tr {\n",
" text-align: right;\n",
" white-space: pre-wrap;\n",
"}\n",
"</style>\n",
"<small>shape: (1, 9)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>start</th><th>end</th><th>n_obs</th><th>min_bn</th><th>max_bn</th><th>mean_bn</th><th>nulls</th><th>date_gaps</th><th>daily_moves_gt_20pct</th></tr><tr><td>date</td><td>date</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>i32</td><td>i32</td><td>i32</td></tr></thead><tbody><tr><td>2019-01-04</td><td>2026-05-01</td><td>2675</td><td>0.11</td><td>177.5</td><td>64.9</td><td>0</td><td>0</td><td>7</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (1, 9)\n",
"┌────────────┬────────────┬───────┬────────┬───┬─────────┬───────┬───────────┬─────────────────────┐\n",
"│ start ┆ end ┆ n_obs ┆ min_bn ┆ … ┆ mean_bn ┆ nulls ┆ date_gaps ┆ daily_moves_gt_20pc │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ t │\n",
"│ date ┆ date ┆ u32 ┆ f64 ┆ ┆ f64 ┆ i32 ┆ i32 ┆ --- │\n",
"│ ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ i32 │\n",
"╞════════════╪════════════╪═══════╪════════╪═══╪═════════╪═══════╪═══════════╪═════════════════════╡\n",
"│ 2019-01-04 ┆ 2026-05-01 ┆ 2675 ┆ 0.11 ┆ … ┆ 64.9 ┆ 0 ┆ 0 ┆ 7 │\n",
"└────────────┴────────────┴───────┴────────┴───┴─────────┴───────┴───────────┴─────────────────────┘"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tvl_modern = tvl_data.filter(pl.col(\"tvl_bn\") >= 0.1).sort(\"timestamp\")\n",
"date_gaps = tvl_modern.with_columns(\n",
" (pl.col(\"timestamp\") - pl.col(\"timestamp\").shift(1)).dt.total_days().alias(\"days_gap\")\n",
")\n",
"gaps = date_gaps.filter(pl.col(\"days_gap\") > 1)\n",
"extreme_changes = tvl_modern.with_columns(\n",
" pl.col(\"tvl_bn\").pct_change().alias(\"daily_change\")\n",
").filter(pl.col(\"daily_change\").abs() > 0.20)\n",
"\n",
"tvl_modern.select(\n",
" pl.col(\"timestamp\").min().alias(\"start\"),\n",
" pl.col(\"timestamp\").max().alias(\"end\"),\n",
" pl.len().alias(\"n_obs\"),\n",
" pl.col(\"tvl_bn\").min().round(2).alias(\"min_bn\"),\n",
" pl.col(\"tvl_bn\").max().round(1).alias(\"max_bn\"),\n",
" pl.col(\"tvl_bn\").mean().round(1).alias(\"mean_bn\"),\n",
" pl.lit(tvl_modern[\"tvl_usd\"].null_count()).alias(\"nulls\"),\n",
" pl.lit(len(gaps)).alias(\"date_gaps\"),\n",
" pl.lit(len(extreme_changes)).alias(\"daily_moves_gt_20pct\"),\n",
")"
]
},
{
"cell_type": "markdown",
"id": "211d4812",
"metadata": {
"lines_to_next_cell": 2,
"papermill": {
"duration": 0.010774,
"end_time": "2026-06-13T03:11:25.738287+00:00",
"exception": false,
"start_time": "2026-06-13T03:11:25.727513+00:00",
"status": "completed"
}
},
"source": [
"A weighted composite is a teaching scaffold, not a calibrated metric:\n",
"the weights below come from the chapter rubric, and the absolute\n",
"number is meaningful only relative to other datasets scored the same\n",
"way. Coverage is bounded at 25 because longer-than-five-year history\n",
"adds little marginal evidence for a strategy with a one-year holdout.\n",
"The PIT term gets 15 points because backfill risk is the single most\n",
"common path to inflated alt-data backtests."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6386c1cf",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:11:25.754672Z",
"iopub.status.busy": "2026-06-13T03:11:25.754509Z",
"iopub.status.idle": "2026-06-13T03:11:25.760790Z",
"shell.execute_reply": "2026-06-13T03:11:25.759733Z"
},
"papermill": {
"duration": 0.015539,
"end_time": "2026-06-13T03:11:25.761407+00:00",
"exception": false,
"start_time": "2026-06-13T03:11:25.745868+00:00",
"status": "completed"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div><style>\n",
".dataframe > thead > tr,\n",
".dataframe > tbody > tr {\n",
" text-align: right;\n",
" white-space: pre-wrap;\n",
"}\n",
"</style>\n",
"<small>shape: (7, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>dimension</th><th>value</th></tr><tr><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;coverage_years&quot;</td><td>&quot;7.3&quot;</td></tr><tr><td>&quot;missing_pct&quot;</td><td>&quot;0.00%&quot;</td></tr><tr><td>&quot;max_gap_days&quot;</td><td>&quot;0&quot;</td></tr><tr><td>&quot;extreme_moves_pct&quot;</td><td>&quot;0.26%&quot;</td></tr><tr><td>&quot;methodology_transparent&quot;</td><td>&quot;True&quot;</td></tr><tr><td>&quot;pit_available&quot;</td><td>&quot;False&quot;</td></tr><tr><td>&quot;composite_score&quot;</td><td>&quot;85/100&quot;</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (7, 2)\n",
"┌─────────────────────────┬────────┐\n",
"│ dimension ┆ value │\n",
"│ --- ┆ --- │\n",
"│ str ┆ str │\n",
"╞═════════════════════════╪════════╡\n",
"│ coverage_years ┆ 7.3 │\n",
"│ missing_pct ┆ 0.00% │\n",
"│ max_gap_days ┆ 0 │\n",
"│ extreme_moves_pct ┆ 0.26% │\n",
"│ methodology_transparent ┆ True │\n",
"│ pit_available ┆ False │\n",
"│ composite_score ┆ 85/100 │\n",
"└─────────────────────────┴────────┘"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"@dataclass\n",
"class DataQualityMetrics:\n",
" coverage_years: float\n",
" missing_pct: float\n",
" gap_days: int\n",
" extreme_moves_pct: float\n",
" methodology_transparent: bool\n",
" pit_available: bool\n",
"\n",
" @property\n",
" def score(self) -> float:\n",
" return min(\n",
" 100.0,\n",
" min(25.0, self.coverage_years * 5)\n",
" + max(0.0, 25.0 - self.missing_pct * 100)\n",
" + max(0.0, 20.0 - self.extreme_moves_pct * 100)\n",
" + (15 if self.methodology_transparent else 0)\n",
" + (15 if self.pit_available else 0),\n",
" )\n",
"\n",
"\n",
"coverage_years = (tvl_modern[\"timestamp\"].max() - tvl_modern[\"timestamp\"].min()).days / 365\n",
"missing_pct = tvl_modern[\"tvl_usd\"].null_count() / len(tvl_modern)\n",
"extreme_pct = len(extreme_changes) / len(tvl_modern)\n",
"\n",
"quality = DataQualityMetrics(\n",
" coverage_years=coverage_years,\n",
" missing_pct=missing_pct,\n",
" gap_days=int(gaps[\"days_gap\"].max()) if len(gaps) > 0 else 0,\n",
" extreme_moves_pct=extreme_pct,\n",
" methodology_transparent=True, # docs.llama.fi documents TVL aggregation\n",
" pit_available=False, # no vintage endpoint; historical revisions possible\n",
")\n",
"\n",
"pl.DataFrame(\n",
" {\n",
" \"dimension\": [\n",
" \"coverage_years\",\n",
" \"missing_pct\",\n",
" \"max_gap_days\",\n",
" \"extreme_moves_pct\",\n",
" \"methodology_transparent\",\n",
" \"pit_available\",\n",
" \"composite_score\",\n",
" ],\n",
" \"value\": [\n",
" f\"{quality.coverage_years:.1f}\",\n",
" f\"{quality.missing_pct:.2%}\",\n",
" str(quality.gap_days),\n",
" f\"{quality.extreme_moves_pct:.2%}\",\n",
" str(quality.methodology_transparent),\n",
" str(quality.pit_available),\n",
" f\"{quality.score:.0f}/100\",\n",
" ],\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "a88f256b",
"metadata": {
"papermill": {
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"status": "completed"
}
},
"source": [
"---\n",
"\n",
"## Section 4: Legal and Compliance Assessment\n",
"\n",
"Alternative data carries legal risks. Key considerations:\n",
"- **Source legality**: Is the data legally obtained?\n",
"- **MNPI risk**: Could this constitute material non-public information?\n",
"- **Usage rights**: Any restrictions on use?"
]
},
{
"cell_type": "markdown",
"id": "52866ebd",
"metadata": {
"papermill": {
"duration": 0.005757,
"end_time": "2026-06-13T03:11:25.798963+00:00",
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"status": "completed"
}
},
"source": [
"Legal review is qualitative and deal-killer logic dominates: any\n",
"CRITICAL flag (e.g. unlicensed scraping, MNPI exposure) outweighs\n",
"every quantitative score elsewhere. The rubric below records the\n",
"four standard inputs (source legitimacy, MNPI exposure, usage\n",
"restrictions, jurisdictional notes) for the DefiLlama feed."
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "4dece25a",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:11:25.814287Z",
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"shell.execute_reply": "2026-06-13T03:11:25.816850Z"
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"status": "completed"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div><style>\n",
".dataframe > thead > tr,\n",
".dataframe > tbody > tr {\n",
" text-align: right;\n",
" white-space: pre-wrap;\n",
"}\n",
"</style>\n",
"<small>shape: (6, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>dimension</th><th>value</th></tr><tr><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;data_source&quot;</td><td>&quot;DefiLlama (defillama.com)&quot;</td></tr><tr><td>&quot;source_type&quot;</td><td>&quot;Public&quot;</td></tr><tr><td>&quot;mnpi_risk&quot;</td><td>&quot;Low&quot;</td></tr><tr><td>&quot;mnpi_reasoning&quot;</td><td>&quot;TVL is computed from on-chain …</td></tr><tr><td>&quot;usage_restrictions&quot;</td><td>&quot;Attribution requested; API rat…</td></tr><tr><td>&quot;jurisdictional_issues&quot;</td><td>&quot;Crypto regulation varies by ju…</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (6, 2)\n",
"┌───────────────────────┬─────────────────────────────────┐\n",
"│ dimension ┆ value │\n",
"│ --- ┆ --- │\n",
"│ str ┆ str │\n",
"╞═══════════════════════╪═════════════════════════════════╡\n",
"│ data_source ┆ DefiLlama (defillama.com) │\n",
"│ source_type ┆ Public │\n",
"│ mnpi_risk ┆ Low │\n",
"│ mnpi_reasoning ┆ TVL is computed from on-chain … │\n",
"│ usage_restrictions ┆ Attribution requested; API rat… │\n",
"│ jurisdictional_issues ┆ Crypto regulation varies by ju… │\n",
"└───────────────────────┴─────────────────────────────────┘"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"legal = {\n",
" \"data_source\": \"DefiLlama (defillama.com)\",\n",
" \"source_type\": \"Public\",\n",
" \"mnpi_risk\": \"Low\",\n",
" \"mnpi_reasoning\": (\n",
" \"TVL is computed from on-chain transactions visible to anyone with an \"\n",
" \"RPC endpoint; DefiLlama aggregates publicly verifiable state without \"\n",
" \"insider access.\"\n",
" ),\n",
" \"usage_restrictions\": \"Attribution requested; API rate limits apply\",\n",
" \"jurisdictional_issues\": (\n",
" \"Crypto regulation varies by jurisdiction; institutional use typically \"\n",
" \"requires a compliance sign-off on the underlying smart-contract exposure.\"\n",
" ),\n",
"}\n",
"\n",
"pl.DataFrame(\n",
" {\n",
" \"dimension\": list(legal.keys()),\n",
" \"value\": list(legal.values()),\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "10f9dc8e",
"metadata": {
"papermill": {
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"start_time": "2026-06-13T03:11:25.825966+00:00",
"status": "completed"
}
},
"source": [
"---\n",
"\n",
"## Section 5: Commercial Viability Assessment\n",
"\n",
"Even free data has costs: integration time, maintenance, opportunity cost.\n",
"We calculate whether the signal justifies the investment."
]
},
{
"cell_type": "markdown",
"id": "94441eab",
"metadata": {
"lines_to_next_cell": 2,
"papermill": {
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"status": "completed"
}
},
"source": [
"Even free data has costs: integration hours, ongoing maintenance,\n",
"engineer attention diverted from other research. The break-even alpha\n",
"is the gross trading return required to justify the *total* annual\n",
"cost, scaled to the fraction of AUM that actually deploys this\n",
"signal. The 3× ROI target below is conservative — research budgets\n",
"typically demand a multiple of return-over-cost before continuing\n",
"investment."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "16fdce2f",
"metadata": {
"execution": {
"iopub.execute_input": "2026-06-13T03:11:25.850276Z",
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"status": "completed"
}
},
"outputs": [
{
"data": {
"text/html": [
"<div><style>\n",
".dataframe > thead > tr,\n",
".dataframe > tbody > tr {\n",
" text-align: right;\n",
" white-space: pre-wrap;\n",
"}\n",
"</style>\n",
"<small>shape: (3, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>fund_size</th><th>aum_usd</th><th>total_annual_cost</th><th>allocated_capital</th><th>required_alpha_bps</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;Small ($10M)&quot;</td><td>10000000</td><td>9000</td><td>1000000</td><td>270.0</td></tr><tr><td>&quot;Mid ($50M)&quot;</td><td>50000000</td><td>9000</td><td>5000000</td><td>54.0</td></tr><tr><td>&quot;Large ($500M)&quot;</td><td>500000000</td><td>9000</td><td>50000000</td><td>5.4</td></tr></tbody></table></div>"
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"shape: (3, 5)\n",
"┌───────────────┬───────────┬───────────────────┬───────────────────┬────────────────────┐\n",
"│ fund_size ┆ aum_usd ┆ total_annual_cost ┆ allocated_capital ┆ required_alpha_bps │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ str ┆ i64 ┆ i64 ┆ i64 ┆ f64 │\n",
"╞═══════════════╪═══════════╪═══════════════════╪═══════════════════╪════════════════════╡\n",
"│ Small ($10M) ┆ 10000000 ┆ 9000 ┆ 1000000 ┆ 270.0 │\n",
"│ Mid ($50M) ┆ 50000000 ┆ 9000 ┆ 5000000 ┆ 54.0 │\n",
"│ Large ($500M) ┆ 500000000 ┆ 9000 ┆ 50000000 ┆ 5.4 │\n",
"└───────────────┴───────────┴───────────────────┴───────────────────┴────────────────────┘"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def calculate_required_alpha(\n",
" data_cost: float,\n",
" annual_hours: float,\n",
" hourly_rate: float,\n",
" aum: float,\n",
" allocation_pct: float = 0.10,\n",
" target_roi: float = 3.0,\n",
") -> dict:\n",
" total_cost = data_cost + annual_hours * hourly_rate\n",
" allocated_capital = aum * allocation_pct\n",
" required_alpha_bps = (total_cost * target_roi / allocated_capital) * 10_000\n",
" return {\n",
" \"total_annual_cost\": total_cost,\n",
" \"allocated_capital\": allocated_capital,\n",
" \"required_alpha_bps\": required_alpha_bps,\n",
" }\n",
"\n",
"\n",
"integration_hours = 40\n",
"maintenance_hours = 20\n",
"hourly_rate = 150\n",
"annual_hours = integration_hours + maintenance_hours\n",
"\n",
"cost_table = pl.DataFrame(\n",
" [\n",
" {\n",
" \"fund_size\": label,\n",
" \"aum_usd\": aum,\n",
" **calculate_required_alpha(0, annual_hours, hourly_rate, aum),\n",
" }\n",
" for label, aum in [\n",
" (\"Small ($10M)\", 10_000_000),\n",
" (\"Mid ($50M)\", 50_000_000),\n",
" (\"Large ($500M)\", 500_000_000),\n",
" ]\n",
" ]\n",
").with_columns(\n",
" pl.col(\"required_alpha_bps\").round(1),\n",
" pl.col(\"total_annual_cost\").cast(pl.Int64),\n",
" pl.col(\"allocated_capital\").cast(pl.Int64),\n",
")\n",
"cost_table"
]
},
{
"cell_type": "markdown",
"id": "cbfc7ce7",
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}
},
"source": [
"An IC magnitude does not translate directly to basis points of alpha\n",
"without a full strategy design — portfolio construction, turnover,\n",
"transaction costs, risk model, and capacity constraints all enter\n",
"between IC and realised PnL. The numbers below are screening\n",
"diagnostics that establish whether deeper prototyping is warranted,\n",
"not deployable performance estimates."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e9a8fb6b",
"metadata": {
"execution": {
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},
"outputs": [
{
"data": {
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".dataframe > thead > tr,\n",
".dataframe > tbody > tr {\n",
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"</style>\n",
"<small>shape: (5, 2)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>metric</th><th>value</th></tr><tr><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;data_cost_usd_per_year&quot;</td><td>&quot;0&quot;</td></tr><tr><td>&quot;integration_cost_usd_per_year&quot;</td><td>&quot;9,000&quot;</td></tr><tr><td>&quot;best_abs_ic&quot;</td><td>&quot;0.278&quot;</td></tr><tr><td>&quot;average_abs_ic&quot;</td><td>&quot;0.090&quot;</td></tr><tr><td>&quot;rolling_ic_share_positive&quot;</td><td>&quot;48.8%&quot;</td></tr></tbody></table></div>"
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"text/plain": [
"shape: (5, 2)\n",
"┌───────────────────────────────┬───────┐\n",
"│ metric ┆ value │\n",
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"╞═══════════════════════════════╪═══════╡\n",
"│ data_cost_usd_per_year ┆ 0 │\n",
"│ integration_cost_usd_per_year ┆ 9,000 │\n",
"│ best_abs_ic ┆ 0.278 │\n",
"│ average_abs_ic ┆ 0.090 │\n",
"│ rolling_ic_share_positive ┆ 48.8% │\n",
"└───────────────────────────────┴───────┘"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"best_ic = ic_df[\"IC\"].abs().max()\n",
"avg_ic = ic_df[\"IC\"].abs().mean()\n",
"\n",
"pl.DataFrame(\n",
" {\n",
" \"metric\": [\n",
" \"data_cost_usd_per_year\",\n",
" \"integration_cost_usd_per_year\",\n",
" \"best_abs_ic\",\n",
" \"average_abs_ic\",\n",
" \"rolling_ic_share_positive\",\n",
" ],\n",
" \"value\": [\n",
" \"0\",\n",
" f\"{annual_hours * hourly_rate:,}\",\n",
" f\"{best_ic:.3f}\",\n",
" f\"{avg_ic:.3f}\",\n",
" f\"{ic_positive_pct:.1%}\",\n",
" ],\n",
" }\n",
")"
]
},
{
"cell_type": "markdown",
"id": "76fdd789",
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"status": "completed"
}
},
"source": [
"---\n",
"\n",
"## Section 6: Final Evaluation Summary\n",
"\n",
"With all the components in place, we can synthesize an informed decision."
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "217499f8",
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"outputs": [
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"mode": "gauge+number",
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}
}
},
"title": {
"text": "Four-Dimension Evaluation Dashboard — DefiLlama TVL"
},
"xaxis": {
"anchor": "y",
"domain": [
0.0,
0.44
],
"range": [
"2025-11-28",
"2026-04-01"
],
"type": "date"
},
"xaxis2": {
"anchor": "y2",
"domain": [
0.56,
1.0
]
},
"yaxis": {
"anchor": "x",
"domain": [
0.0,
0.36
],
"title": {
"text": "Rolling IC"
}
},
"yaxis2": {
"anchor": "x2",
"domain": [
0.0,
0.36
],
"range": [
0,
110
],
"title": {
"text": "Dimension Score (0–100)"
}
}
}
},
"image/png": 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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Build the four-panel dashboard in a SINGLE cell so the inline backend\n",
"# does not capture progressive-render intermediates with only the first\n",
"# (or first two) gauges populated (feedback_split_cell_figure_bug).\n",
"fig = make_subplots(\n",
" rows=2,\n",
" cols=2,\n",
" subplot_titles=(\n",
" \"Signal Quality (IC)\",\n",
" \"Data Quality Score\",\n",
" \"IC Stability Over Time\",\n",
" \"Cost-Benefit Analysis\",\n",
" ),\n",
" specs=[\n",
" [{\"type\": \"indicator\"}, {\"type\": \"indicator\"}],\n",
" [{\"type\": \"scatter\"}, {\"type\": \"bar\"}],\n",
" ],\n",
" vertical_spacing=0.28,\n",
" horizontal_spacing=0.12,\n",
")\n",
"\n",
"# Signal quality gauge\n",
"fig.add_trace(\n",
" go.Indicator(\n",
" mode=\"gauge+number\",\n",
" value=avg_ic * 100, # Convert to percentage for readability\n",
" title={\"text\": \"Average |IC| × 100\"},\n",
" gauge={\n",
" \"axis\": {\"range\": [0, 10]},\n",
" \"bar\": {\"color\": COLORS[\"blue\"]},\n",
" \"steps\": [\n",
" {\"range\": [0, 2], \"color\": COLORS[\"negative\"]},\n",
" {\"range\": [2, 5], \"color\": \"#FFC107\"},\n",
" {\"range\": [5, 10], \"color\": COLORS[\"positive\"]},\n",
" ],\n",
" \"threshold\": {\n",
" \"line\": {\"color\": \"black\", \"width\": 2},\n",
" \"thickness\": 0.75,\n",
" \"value\": avg_ic * 100,\n",
" },\n",
" },\n",
" ),\n",
" row=1,\n",
" col=1,\n",
")\n",
"\n",
"# Data quality gauge\n",
"fig.add_trace(\n",
" go.Indicator(\n",
" mode=\"gauge+number\",\n",
" value=quality.score,\n",
" title={\"text\": \"Data Quality\"},\n",
" gauge={\n",
" \"axis\": {\"range\": [0, 100]},\n",
" \"bar\": {\"color\": COLORS[\"slate\"]},\n",
" \"steps\": [\n",
" {\"range\": [0, 50], \"color\": COLORS[\"negative\"]},\n",
" {\"range\": [50, 75], \"color\": \"#FFC107\"},\n",
" {\"range\": [75, 100], \"color\": COLORS[\"positive\"]},\n",
" ],\n",
" },\n",
" ),\n",
" row=1,\n",
" col=2,\n",
")\n",
"\n",
"# Rolling IC and cost-benefit panels\n",
"fig.add_trace(\n",
" go.Scatter(\n",
" x=rolling_ic_df[\"timestamp\"],\n",
" y=rolling_ic_df[\"rolling_ic\"],\n",
" mode=\"lines\",\n",
" name=\"Rolling IC\",\n",
" line=dict(color=COLORS[\"slate\"], width=2),\n",
" fill=\"tozeroy\",\n",
" fillcolor=\"rgba(4, 138, 129, 0.2)\",\n",
" showlegend=False,\n",
" ),\n",
" row=2,\n",
" col=1,\n",
")\n",
"# Add zero line on the rolling-IC subplot. Mixed-spec make_subplots places the\n",
"# row=2,col=1 scatter on the primary x/y axes (indicator subplots don't claim\n",
"# numbered axes), so reference the first scatter axes directly via \"x domain\".\n",
"fig.add_shape(\n",
" type=\"line\",\n",
" x0=0,\n",
" x1=1,\n",
" y0=0,\n",
" y1=0,\n",
" xref=\"x domain\",\n",
" yref=\"y\",\n",
" line=dict(dash=\"dash\", color=COLORS[\"neutral\"]),\n",
")\n",
"\n",
"risk_to_score = {\"Low\": 100, \"Medium\": 50, \"High\": 20, \"Critical\": 0}\n",
"dimension_names = [\"Signal\", \"Data\", \"Legal\", \"Commercial\"]\n",
"dimension_scores = [\n",
" min(100, avg_ic * 1000),\n",
" quality.score,\n",
" risk_to_score[legal[\"mnpi_risk\"]],\n",
" 85, # free data + ~$9k/yr engineering at the median fund size\n",
"]\n",
"dimension_colors = [\n",
" COLORS[\"positive\"] if s >= 60 else (COLORS[\"amber\"] if s >= 40 else COLORS[\"negative\"])\n",
" for s in dimension_scores\n",
"]\n",
"\n",
"fig.add_trace(\n",
" go.Bar(\n",
" x=dimension_names,\n",
" y=dimension_scores,\n",
" marker_color=dimension_colors,\n",
" text=[f\"{s:.0f}\" for s in dimension_scores],\n",
" textposition=\"outside\",\n",
" showlegend=False,\n",
" ),\n",
" row=2,\n",
" col=2,\n",
")\n",
"\n",
"fig.update_layout(\n",
" height=750,\n",
" title=\"Four-Dimension Evaluation Dashboard — DefiLlama TVL\",\n",
" template=\"plotly_white\",\n",
" margin=dict(t=90, b=60, l=60, r=60),\n",
")\n",
"# Force the rolling-IC subplot (row=2, col=1) x-axis to render as datetime.\n",
"# In mixed-spec make_subplots (indicator + scatter + bar) the subplot xaxis can\n",
"# default to numeric epoch ticks when traces are added before the layout pass;\n",
"# the explicit type+range below pins it to the actual data window.\n",
"_ts_min = rolling_ic_df[\"timestamp\"].min()\n",
"_ts_max = rolling_ic_df[\"timestamp\"].max()\n",
"fig.update_xaxes(type=\"date\", range=[_ts_min, _ts_max], row=2, col=1)\n",
"fig.update_yaxes(title_text=\"Rolling IC\", row=2, col=1)\n",
"fig.update_yaxes(title_text=\"Dimension Score (0–100)\", range=[0, 110], row=2, col=2)\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "9cd3e1e4",
"metadata": {
"papermill": {
"duration": 0.007446,
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"source": [
"### Evidence summary\n",
"\n",
"The framework records measurements; it does not declare a single answer.\n",
"The table below restates the four-dimension findings together with\n",
"the most important caveats so a research lead can decide whether to\n",
"fund a TVL prototype or close the file."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "62884064",
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"execution": {
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"outputs": [
{
"data": {
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"<div><style>\n",
".dataframe > thead > tr,\n",
".dataframe > tbody > tr {\n",
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" white-space: pre-wrap;\n",
"}\n",
"</style>\n",
"<small>shape: (4, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>dimension</th><th>headline_metric</th><th>binding_caveat</th></tr><tr><td>str</td><td>str</td><td>str</td></tr></thead><tbody><tr><td>&quot;Signal&quot;</td><td>&quot;avg |IC| 0.090, best |IC| 0.27…</td><td>&quot;Rolling IC mean -0.110, positi…</td></tr><tr><td>&quot;Data&quot;</td><td>&quot;85/100 (coverage 7.3y, missing…</td><td>&quot;No point-in-time vintages — hi…</td></tr><tr><td>&quot;Legal&quot;</td><td>&quot;MNPI risk Low (public on-chain…</td><td>&quot;Crypto regulation varies by ju…</td></tr><tr><td>&quot;Commercial&quot;</td><td>&quot;~$9,000/yr engineering, $0 dat…</td><td>&quot;Required alpha at small fund (…</td></tr></tbody></table></div>"
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"text/plain": [
"shape: (4, 3)\n",
"┌────────────┬─────────────────────────────────┬─────────────────────────────────┐\n",
"│ dimension ┆ headline_metric ┆ binding_caveat │\n",
"│ --- ┆ --- ┆ --- │\n",
"│ str ┆ str ┆ str │\n",
"╞════════════╪═════════════════════════════════╪═════════════════════════════════╡\n",
"│ Signal ┆ avg |IC| 0.090, best |IC| 0.27… ┆ Rolling IC mean -0.110, positi… │\n",
"│ Data ┆ 85/100 (coverage 7.3y, missing… ┆ No point-in-time vintages — hi… │\n",
"│ Legal ┆ MNPI risk Low (public on-chain… ┆ Crypto regulation varies by ju… │\n",
"│ Commercial ┆ ~$9,000/yr engineering, $0 dat… ┆ Required alpha at small fund (… │\n",
"└────────────┴─────────────────────────────────┴─────────────────────────────────┘"
]
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"execution_count": 18,
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],
"source": [
"evidence_summary = pl.DataFrame(\n",
" {\n",
" \"dimension\": [\"Signal\", \"Data\", \"Legal\", \"Commercial\"],\n",
" \"headline_metric\": [\n",
" f\"avg |IC| {avg_ic:.3f}, best |IC| {best_ic:.3f}\",\n",
" f\"{quality.score:.0f}/100 (coverage {quality.coverage_years:.1f}y, missing {quality.missing_pct:.1%})\",\n",
" f\"MNPI risk {legal['mnpi_risk']} (public on-chain data)\",\n",
" f\"~${annual_hours * hourly_rate:,}/yr engineering, $0 data fees\",\n",
" ],\n",
" \"binding_caveat\": [\n",
" f\"Rolling IC mean {ic_mean:+.3f}, positive in {ic_positive_pct:.0%} of windows; sign reverses with horizon — likely contrarian rather than directional\",\n",
" \"No point-in-time vintages — historical revisions can backfill, inflating any backtest\",\n",
" \"Crypto regulation varies by jurisdiction; institutional deployment requires compliance review of underlying smart-contract exposure\",\n",
" f\"Required alpha at small fund (10% allocation, 3× ROI) is {cost_table.filter(pl.col('fund_size') == 'Small ($10M)')['required_alpha_bps'].item():.1f} bps — small funds carry the cost-recovery burden\",\n",
" ],\n",
" }\n",
")\n",
"evidence_summary"
]
},
{
"cell_type": "markdown",
"id": "b51d6c79",
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"source": [
"Reading this evidence:\n",
"\n",
"- The **signal** dimension passes the screening bar in magnitude\n",
" (`avg |IC|` exceeds 0.05) but the rolling-window analysis shows it\n",
" is regime-dependent and the sign flips with horizon, which is\n",
" characteristic of mean-reverting rather than directional alpha. A\n",
" prototype should test a *reversal* specification before a momentum\n",
" one.\n",
"- The **data** dimension scores well on coverage and completeness but\n",
" fails on point-in-time availability — the binding limitation for\n",
" any production backtest. Either build a snapshot archive going\n",
" forward or accept that historical performance may be optimistic.\n",
"- The **legal** dimension is clean for the DefiLlama feed itself; the\n",
" institutional friction is downstream compliance review of the\n",
" underlying smart-contract exposure, not the data acquisition.\n",
"- The **commercial** dimension is permissive at scale but punishing\n",
" at small AUM; the break-even calculation should drive whether the\n",
" project even starts.\n",
"\n",
"None of these dimensions is decisive in isolation. Combine them with\n",
"the strategy hypothesis and the fund's research-budget constraints\n",
"before committing to a prototype."
]
},
{
"cell_type": "markdown",
"id": "9f5a14db",
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"source": [
"---\n",
"\n",
"## Key Takeaways\n",
"\n",
"1. **The IC matrix shows horizon-dependent sign flips.** The TVL\n",
" z-score reaches IC = -0.28 at the 60-day horizon over the joined\n",
" window — strong in magnitude but consistent with mean reversion,\n",
" not directional alpha. The 30-day TVL growth signal peaks around\n",
" the 14-day horizon and decays past 60 days.\n",
"2. **Rolling IC stability is the discriminator.** A point estimate\n",
" near 0.10 is uninformative when the rolling 180-day mean sits\n",
" close to zero (and turns negative in segments) while the share of\n",
" positive windows hovers around 50% — record point IC, rolling\n",
" mean, and the share-positive together, never just the headline.\n",
"3. **Composite scores are scaffolds.** The data-quality 0-100 score is\n",
" only meaningful relative to other datasets scored with the same\n",
" rubric; the binding constraint here is the absence of point-in-time\n",
" vintages, not the headline number.\n",
"4. **Legal clearance is necessary but not sufficient.** Public on-chain\n",
" data clears MNPI; institutional deployment still requires a\n",
" compliance review of the underlying smart-contract exposure.\n",
"5. **Cost recovery scales with AUM.** Free data still costs roughly\n",
" \\$9,000/year in engineering; small funds need three-digit basis\n",
" points of alpha to justify it, large funds need single digits.\n",
"\n",
"## Applying This Framework\n",
"\n",
"The same workflow applies to any alt-data candidate:\n",
"\n",
"1. Load real history; never theorise about availability.\n",
"2. Compute signal magnitude *and* stability (point IC + rolling IC + sign behaviour vs horizon).\n",
"3. Score data quality on the same axes, treating the composite as relative.\n",
"4. Document the legal posture, including downstream compliance\n",
" constraints, not just data acquisition.\n",
"5. Compute break-even alpha at the relevant fund size.\n",
"6. Surface the four-dimension evidence to the research lead — let\n",
" them combine it with the strategy hypothesis to make the call."
]
}
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