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"# Constructing Continuous Futures Contracts\n",
"\n",
"**Docker image**: `ml4t`\n",
"\n",
"**Purpose**: Walk through the construction of a continuous futures price\n",
"series from individual expiring contracts: detect rolls, compare\n",
"adjustment methods (raw, Panama / additive back-adjustment, ratio /\n",
"multiplicative back-adjustment), and validate against the vendor-built\n",
"continuous series.\n",
"\n",
"**Learning objectives**:\n",
"\n",
"- Detect roll dates using volume-based front-month identification (with a\n",
" no-rollback constraint to avoid spurious switches).\n",
"- Apply Panama (additive) back-adjustment to preserve dollar P&L across\n",
" rolls.\n",
"- Apply ratio (multiplicative) back-adjustment to preserve percentage\n",
" returns across rolls.\n",
"- Cross-check constructed continuous prices against Databento's pre-built\n",
" continuous series and quantify the disagreement.\n",
"\n",
"**Book reference**: §2.2 (\"The Asset-Class Market Data Landscape\" —\n",
"Futures); the methodology comparison underpins the engineering decision\n",
"in §2.2 to store raw contract histories alongside one or more continuous\n",
"variants.\n",
"\n",
"**Prerequisites**: `data` package on `PYTHONPATH`; individual ES contract\n",
"parquet at `ML4T_DATA_PATH/futures/market/individual/ES/data.parquet` and\n",
"the contract-definitions parquet at\n",
"`ML4T_DATA_PATH/futures/market/contract_definitions.parquet`."
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"\"\"\"Continuous Futures Construction.\"\"\"\n",
"\n",
"import re\n",
"from datetime import UTC, date, datetime\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_cme_futures\n",
"from utils import ML4T_DATA_PATH"
]
},
{
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"# Production defaults — Papermill injects overrides for CI"
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"source": [
"## 1. Understanding the Data\n",
"\n",
"### 1.1 Load Individual Contracts"
]
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"text": [
"Individual contracts: (19361, 10)\n",
"Unique contracts (by instrument_id): 194\n",
"Date range: 2016-01-03 00:00:00+00:00 to 2025-12-30 00:00:00+00:00\n",
"Sample:\n"
]
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"</style>\n",
"<small>shape: (5, 10)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>rtype</th><th>publisher_id</th><th>instrument_id</th><th>open</th><th>high</th><th>low</th><th>close</th><th>volume</th><th>product</th></tr><tr><td>datetime[ns, UTC]</td><td>u8</td><td>u16</td><td>u32</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>u64</td><td>str</td></tr></thead><tbody><tr><td>2016-01-03 00:00:00 UTC</td><td>35</td><td>1</td><td>49705</td><td>2037.75</td><td>2043.5</td><td>2037.5</td><td>2040.25</td><td>10915</td><td>&quot;ES&quot;</td></tr><tr><td>2016-01-03 00:00:00 UTC</td><td>35</td><td>1</td><td>10088</td><td>-13.2</td><td>-13.2</td><td>-13.3</td><td>-13.3</td><td>3</td><td>&quot;ES&quot;</td></tr><tr><td>2016-01-03 00:00:00 UTC</td><td>35</td><td>1</td><td>6505</td><td>2032.5</td><td>2036.0</td><td>2032.5</td><td>2032.5</td><td>16</td><td>&quot;ES&quot;</td></tr><tr><td>2016-01-03 00:00:00 UTC</td><td>35</td><td>1</td><td>2615</td><td>2024.5</td><td>2029.5</td><td>2024.5</td><td>2027.75</td><td>201</td><td>&quot;ES&quot;</td></tr><tr><td>2016-01-04 00:00:00 UTC</td><td>35</td><td>1</td><td>49705</td><td>2040.5</td><td>2043.0</td><td>1980.25</td><td>2012.5</td><td>2208553</td><td>&quot;ES&quot;</td></tr></tbody></table></div>"
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"shape: (5, 10)\n",
"┌───────────────┬───────┬──────────────┬───────────────┬───┬─────────┬─────────┬─────────┬─────────┐\n",
"│ timestamp ┆ rtype ┆ publisher_id ┆ instrument_id ┆ … ┆ low ┆ close ┆ volume ┆ product │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ datetime[ns, ┆ u8 ┆ u16 ┆ u32 ┆ ┆ f64 ┆ f64 ┆ u64 ┆ str │\n",
"│ UTC] ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"╞═══════════════╪═══════╪══════════════╪═══════════════╪═══╪═════════╪═════════╪═════════╪═════════╡\n",
"│ 2016-01-03 ┆ 35 ┆ 1 ┆ 49705 ┆ … ┆ 2037.5 ┆ 2040.25 ┆ 10915 ┆ ES │\n",
"│ 00:00:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"│ 2016-01-03 ┆ 35 ┆ 1 ┆ 10088 ┆ … ┆ -13.3 ┆ -13.3 ┆ 3 ┆ ES │\n",
"│ 00:00:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"│ 2016-01-03 ┆ 35 ┆ 1 ┆ 6505 ┆ … ┆ 2032.5 ┆ 2032.5 ┆ 16 ┆ ES │\n",
"│ 00:00:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"│ 2016-01-03 ┆ 35 ┆ 1 ┆ 2615 ┆ … ┆ 2024.5 ┆ 2027.75 ┆ 201 ┆ ES │\n",
"│ 00:00:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"│ 2016-01-04 ┆ 35 ┆ 1 ┆ 49705 ┆ … ┆ 1980.25 ┆ 2012.5 ┆ 2208553 ┆ ES │\n",
"│ 00:00:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ │\n",
"└───────────────┴───────┴──────────────┴───────────────┴───┴─────────┴─────────┴─────────┴─────────┘"
]
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"source": [
"es_individual = load_cme_futures(products=[\"ES\"], frequency=\"hourly\", continuous=False)\n",
"\n",
"print(f\"Individual contracts: {es_individual.shape}\")\n",
"print(f\"Unique contracts (by instrument_id): {es_individual['instrument_id'].n_unique()}\")\n",
"print(f\"Date range: {es_individual['timestamp'].min()} to {es_individual['timestamp'].max()}\")\n",
"print(\"Sample:\")\n",
"es_individual.head()"
]
},
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"text": [
"Contracts: 194 (sorted by first trade)\n"
]
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"</style>\n",
"<small>shape: (10, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>instrument_id</th><th>first_trade</th><th>last_trade</th><th>total_volume</th><th>trading_days</th></tr><tr><td>u32</td><td>datetime[ns, UTC]</td><td>datetime[ns, UTC]</td><td>u64</td><td>u32</td></tr></thead><tbody><tr><td>10088</td><td>2016-01-03 00:00:00 UTC</td><td>2016-03-18 00:00:00 UTC</td><td>10604</td><td>53</td></tr><tr><td>2615</td><td>2016-01-03 00:00:00 UTC</td><td>2016-09-16 00:00:00 UTC</td><td>105455488</td><td>219</td></tr><tr><td>49705</td><td>2016-01-03 00:00:00 UTC</td><td>2016-03-18 00:00:00 UTC</td><td>106005112</td><td>66</td></tr><tr><td>6505</td><td>2016-01-03 00:00:00 UTC</td><td>2016-06-17 00:00:00 UTC</td><td>99217351</td><td>143</td></tr><tr><td>10123</td><td>2016-01-04 00:00:00 UTC</td><td>2016-06-17 00:00:00 UTC</td><td>3518027</td><td>104</td></tr><tr><td>5681</td><td>2016-01-04 00:00:00 UTC</td><td>2016-03-18 00:00:00 UTC</td><td>3577875</td><td>61</td></tr><tr><td>8036</td><td>2016-01-04 00:00:00 UTC</td><td>2016-09-16 00:00:00 UTC</td><td>3730125</td><td>131</td></tr><tr><td>2928</td><td>2016-01-04 00:00:00 UTC</td><td>2016-12-16 00:00:00 UTC</td><td>113635968</td><td>288</td></tr><tr><td>7978</td><td>2016-01-04 00:00:00 UTC</td><td>2016-06-17 00:00:00 UTC</td><td>10617</td><td>77</td></tr><tr><td>8012</td><td>2016-01-12 00:00:00 UTC</td><td>2016-03-11 00:00:00 UTC</td><td>941</td><td>24</td></tr></tbody></table></div>"
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"shape: (10, 5)\n",
"┌───────────────┬─────────────────────────┬─────────────────────────┬──────────────┬──────────────┐\n",
"│ instrument_id ┆ first_trade ┆ last_trade ┆ total_volume ┆ trading_days │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ u32 ┆ datetime[ns, UTC] ┆ datetime[ns, UTC] ┆ u64 ┆ u32 │\n",
"╞═══════════════╪═════════════════════════╪═════════════════════════╪══════════════╪══════════════╡\n",
"│ 10088 ┆ 2016-01-03 00:00:00 UTC ┆ 2016-03-18 00:00:00 UTC ┆ 10604 ┆ 53 │\n",
"│ 2615 ┆ 2016-01-03 00:00:00 UTC ┆ 2016-09-16 00:00:00 UTC ┆ 105455488 ┆ 219 │\n",
"│ 49705 ┆ 2016-01-03 00:00:00 UTC ┆ 2016-03-18 00:00:00 UTC ┆ 106005112 ┆ 66 │\n",
"│ 6505 ┆ 2016-01-03 00:00:00 UTC ┆ 2016-06-17 00:00:00 UTC ┆ 99217351 ┆ 143 │\n",
"│ 10123 ┆ 2016-01-04 00:00:00 UTC ┆ 2016-06-17 00:00:00 UTC ┆ 3518027 ┆ 104 │\n",
"│ 5681 ┆ 2016-01-04 00:00:00 UTC ┆ 2016-03-18 00:00:00 UTC ┆ 3577875 ┆ 61 │\n",
"│ 8036 ┆ 2016-01-04 00:00:00 UTC ┆ 2016-09-16 00:00:00 UTC ┆ 3730125 ┆ 131 │\n",
"│ 2928 ┆ 2016-01-04 00:00:00 UTC ┆ 2016-12-16 00:00:00 UTC ┆ 113635968 ┆ 288 │\n",
"│ 7978 ┆ 2016-01-04 00:00:00 UTC ┆ 2016-06-17 00:00:00 UTC ┆ 10617 ┆ 77 │\n",
"│ 8012 ┆ 2016-01-12 00:00:00 UTC ┆ 2016-03-11 00:00:00 UTC ┆ 941 ┆ 24 │\n",
"└───────────────┴─────────────────────────┴─────────────────────────┴──────────────┴──────────────┘"
]
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"source": [
"contract_stats = (\n",
" es_individual.group_by(\"instrument_id\")\n",
" .agg(\n",
" pl.col(\"timestamp\").min().alias(\"first_trade\"),\n",
" pl.col(\"timestamp\").max().alias(\"last_trade\"),\n",
" pl.col(\"volume\").sum().alias(\"total_volume\"),\n",
" pl.len().alias(\"trading_days\"),\n",
" )\n",
" .sort(\"first_trade\")\n",
")\n",
"\n",
"print(f\"Contracts: {len(contract_stats)} (sorted by first trade)\")\n",
"contract_stats.head(10)"
]
},
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"### 1.2 Understanding Contract Naming\n",
"\n",
"ES contract symbols follow the pattern: ES + Month Code + Year\n",
"\n",
"Month codes:\n",
"- H = March, M = June, U = September, Z = December (standard quarterly)\n",
"- F = January, G = February, J = April, K = May, N = July, Q = August, V = October, X = November"
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"_MONTH_CODES = {\n",
" \"F\": 1,\n",
" \"G\": 2,\n",
" \"H\": 3,\n",
" \"J\": 4,\n",
" \"K\": 5,\n",
" \"M\": 6,\n",
" \"N\": 7,\n",
" \"Q\": 8,\n",
" \"U\": 9,\n",
" \"V\": 10,\n",
" \"X\": 11,\n",
" \"Z\": 12,\n",
"}\n",
"_SYMBOL_RE = re.compile(r\"^([A-Z]+)([FGHJKMNQUVXZ])(\\d+)$\")\n",
"\n",
"\n",
"def parse_contract_symbol(symbol: str) -> dict:\n",
" \"\"\"Parse a futures contract symbol like ESH24 or RTYM25 into product / month / year.\"\"\"\n",
" match = _SYMBOL_RE.match(symbol)\n",
" if not match:\n",
" raise ValueError(f\"Cannot parse symbol: {symbol}\")\n",
" product, month_code, year_str = match.groups()\n",
" year = int(year_str)\n",
" year = year + 2000 if year < 50 else year + 1900\n",
" return {\n",
" \"product\": product,\n",
" \"month_code\": month_code,\n",
" \"month\": _MONTH_CODES[month_code],\n",
" \"year\": year,\n",
" }"
]
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"text": [
"ES contract definitions: 40 contracts\n"
]
},
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"</style>\n",
"<small>shape: (10, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>symbol</th><th>month_code</th><th>month</th><th>year</th><th>expiration</th></tr><tr><td>str</td><td>str</td><td>i64</td><td>i64</td><td>datetime[ns, UTC]</td></tr></thead><tbody><tr><td>&quot;ESH0&quot;</td><td>&quot;H&quot;</td><td>3</td><td>2000</td><td>2030-03-15 13:30:00 UTC</td></tr><tr><td>&quot;ESM0&quot;</td><td>&quot;M&quot;</td><td>6</td><td>2000</td><td>2030-06-21 13:30:00 UTC</td></tr><tr><td>&quot;ESU0&quot;</td><td>&quot;U&quot;</td><td>9</td><td>2000</td><td>2030-09-20 13:30:00 UTC</td></tr><tr><td>&quot;ESZ0&quot;</td><td>&quot;Z&quot;</td><td>12</td><td>2000</td><td>2030-12-20 14:30:00 UTC</td></tr><tr><td>&quot;ESH1&quot;</td><td>&quot;H&quot;</td><td>3</td><td>2001</td><td>2031-03-21 13:30:00 UTC</td></tr><tr><td>&quot;ESM1&quot;</td><td>&quot;M&quot;</td><td>6</td><td>2001</td><td>2021-06-18 13:30:00 UTC</td></tr><tr><td>&quot;ESU1&quot;</td><td>&quot;U&quot;</td><td>9</td><td>2001</td><td>2021-09-17 13:30:00 UTC</td></tr><tr><td>&quot;ESZ1&quot;</td><td>&quot;Z&quot;</td><td>12</td><td>2001</td><td>2021-12-17 14:30:00 UTC</td></tr><tr><td>&quot;ESH2&quot;</td><td>&quot;H&quot;</td><td>3</td><td>2002</td><td>2022-03-18 13:30:00 UTC</td></tr><tr><td>&quot;ESM2&quot;</td><td>&quot;M&quot;</td><td>6</td><td>2002</td><td>2022-06-17 13:30:00 UTC</td></tr></tbody></table></div>"
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"shape: (10, 5)\n",
"┌────────┬────────────┬───────┬──────┬─────────────────────────┐\n",
"│ symbol ┆ month_code ┆ month ┆ year ┆ expiration │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ str ┆ str ┆ i64 ┆ i64 ┆ datetime[ns, UTC] │\n",
"╞════════╪════════════╪═══════╪══════╪═════════════════════════╡\n",
"│ ESH0 ┆ H ┆ 3 ┆ 2000 ┆ 2030-03-15 13:30:00 UTC │\n",
"│ ESM0 ┆ M ┆ 6 ┆ 2000 ┆ 2030-06-21 13:30:00 UTC │\n",
"│ ESU0 ┆ U ┆ 9 ┆ 2000 ┆ 2030-09-20 13:30:00 UTC │\n",
"│ ESZ0 ┆ Z ┆ 12 ┆ 2000 ┆ 2030-12-20 14:30:00 UTC │\n",
"│ ESH1 ┆ H ┆ 3 ┆ 2001 ┆ 2031-03-21 13:30:00 UTC │\n",
"│ ESM1 ┆ M ┆ 6 ┆ 2001 ┆ 2021-06-18 13:30:00 UTC │\n",
"│ ESU1 ┆ U ┆ 9 ┆ 2001 ┆ 2021-09-17 13:30:00 UTC │\n",
"│ ESZ1 ┆ Z ┆ 12 ┆ 2001 ┆ 2021-12-17 14:30:00 UTC │\n",
"│ ESH2 ┆ H ┆ 3 ┆ 2002 ┆ 2022-03-18 13:30:00 UTC │\n",
"│ ESM2 ┆ M ┆ 6 ┆ 2002 ┆ 2022-06-17 13:30:00 UTC │\n",
"└────────┴────────────┴───────┴──────┴─────────────────────────┘"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"defn_path = ML4T_DATA_PATH / \"futures\" / \"market\" / \"contract_definitions.parquet\"\n",
"contract_defs = pl.read_parquet(defn_path).filter(pl.col(\"product\") == \"ES\")\n",
"contract_df = (\n",
" pl.DataFrame(\n",
" [\n",
" {**parse_contract_symbol(r[\"symbol\"]), \"symbol\": r[\"symbol\"]}\n",
" for r in contract_defs.iter_rows(named=True)\n",
" ]\n",
" )\n",
" .join(contract_defs.select(\"symbol\", \"expiration\"), on=\"symbol\")\n",
" .sort(\"year\", \"month\")\n",
")\n",
"print(f\"ES contract definitions: {contract_df.height} contracts\")\n",
"contract_df.select(\"symbol\", \"month_code\", \"month\", \"year\", \"expiration\").head(10)"
]
},
{
"cell_type": "markdown",
"id": "60048fe5",
"metadata": {
"papermill": {
"duration": 0.0018,
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"exception": false,
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"status": "completed"
}
},
"source": [
"### 1.3 Contract Expiration from Symbols\n",
"\n",
"Without a separate definitions file, we can derive expiration information\n",
"from contract symbols. For ES contracts, the pattern is ESH24 (March 2024),\n",
"ESM24 (June 2024), etc."
]
},
{
"cell_type": "markdown",
"id": "8e40a820",
"metadata": {
"papermill": {
"duration": 0.001837,
"end_time": "2026-05-15T03:46:37.613778+00:00",
"exception": false,
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"status": "completed"
}
},
"source": [
"`parse_contract_symbol` and the contract-definitions parquet give us actual\n",
"expiration dates. For products where we only see the symbol (no definitions\n",
"file), the expiration can be approximated as the 15th of the contract month\n",
"— close enough for roll detection but not for delivery scheduling."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ff3bcbc7",
"metadata": {
"execution": {
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"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ES definition rows: 40\n"
]
},
{
"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: (10, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>symbol</th><th>year</th><th>month</th><th>expiration</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>date</td></tr></thead><tbody><tr><td>&quot;ESH0&quot;</td><td>2000</td><td>3</td><td>2000-03-15</td></tr><tr><td>&quot;ESM0&quot;</td><td>2000</td><td>6</td><td>2000-06-15</td></tr><tr><td>&quot;ESU0&quot;</td><td>2000</td><td>9</td><td>2000-09-15</td></tr><tr><td>&quot;ESZ0&quot;</td><td>2000</td><td>12</td><td>2000-12-15</td></tr><tr><td>&quot;ESH1&quot;</td><td>2001</td><td>3</td><td>2001-03-15</td></tr><tr><td>&quot;ESM1&quot;</td><td>2001</td><td>6</td><td>2001-06-15</td></tr><tr><td>&quot;ESU1&quot;</td><td>2001</td><td>9</td><td>2001-09-15</td></tr><tr><td>&quot;ESZ1&quot;</td><td>2001</td><td>12</td><td>2001-12-15</td></tr><tr><td>&quot;ESH2&quot;</td><td>2002</td><td>3</td><td>2002-03-15</td></tr><tr><td>&quot;ESM2&quot;</td><td>2002</td><td>6</td><td>2002-06-15</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (10, 4)\n",
"┌────────┬──────┬───────┬────────────┐\n",
"│ symbol ┆ year ┆ month ┆ expiration │\n",
"│ --- ┆ --- ┆ --- ┆ --- │\n",
"│ str ┆ i64 ┆ i64 ┆ date │\n",
"╞════════╪══════╪═══════╪════════════╡\n",
"│ ESH0 ┆ 2000 ┆ 3 ┆ 2000-03-15 │\n",
"│ ESM0 ┆ 2000 ┆ 6 ┆ 2000-06-15 │\n",
"│ ESU0 ┆ 2000 ┆ 9 ┆ 2000-09-15 │\n",
"│ ESZ0 ┆ 2000 ┆ 12 ┆ 2000-12-15 │\n",
"│ ESH1 ┆ 2001 ┆ 3 ┆ 2001-03-15 │\n",
"│ ESM1 ┆ 2001 ┆ 6 ┆ 2001-06-15 │\n",
"│ ESU1 ┆ 2001 ┆ 9 ┆ 2001-09-15 │\n",
"│ ESZ1 ┆ 2001 ┆ 12 ┆ 2001-12-15 │\n",
"│ ESH2 ┆ 2002 ┆ 3 ┆ 2002-03-15 │\n",
"│ ESM2 ┆ 2002 ┆ 6 ┆ 2002-06-15 │\n",
"└────────┴──────┴───────┴────────────┘"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es_definition = contract_df.select(\n",
" \"symbol\",\n",
" \"year\",\n",
" \"month\",\n",
" pl.struct(\"year\", \"month\")\n",
" .map_elements(lambda x: date(x[\"year\"], x[\"month\"], 15), return_dtype=pl.Date)\n",
" .alias(\"expiration\"),\n",
")\n",
"print(f\"ES definition rows: {es_definition.height}\")\n",
"es_definition.head(10)"
]
},
{
"cell_type": "markdown",
"id": "97da4f79",
"metadata": {
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}
},
"source": [
"## 2. Roll Detection\n",
"\n",
"The \"roll\" is when we switch from the near-month contract to the next contract.\n",
"There are several approaches:\n",
"\n",
"1. **Volume-based**: Roll when the next contract has higher daily volume\n",
"2. **Open Interest-based**: Roll when next contract has higher open interest\n",
"3. **Fixed Schedule**: Roll N days before expiration (e.g., first Thursday of expiry month)\n",
"\n",
"We'll implement volume-based rolling."
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a66a18e7",
"metadata": {
"execution": {
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},
"outputs": [],
"source": [
"def identify_front_month(\n",
" individual_df: pl.DataFrame, min_outright_price: float = 500.0\n",
") -> pl.DataFrame:\n",
" \"\"\"Volume-based front-month detection with no-rollback constraint.\"\"\"\n",
" # Filter to outright contracts only (exclude calendar spreads at ~$50-100)\n",
" outrights = individual_df.filter(pl.col(\"close\") >= min_outright_price)\n",
"\n",
" # Aggregate to daily volume per contract\n",
" daily_volume = (\n",
" outrights.with_columns(pl.col(\"timestamp\").dt.date().alias(\"date\"))\n",
" .group_by([\"date\", \"instrument_id\"])\n",
" .agg(pl.col(\"volume\").sum().alias(\"daily_volume\"))\n",
" )\n",
"\n",
" daily_leader = (\n",
" daily_volume.group_by(\"date\")\n",
" .agg(pl.col(\"instrument_id\").sort_by(\"daily_volume\").last().alias(\"leader\"))\n",
" .sort(\"date\")\n",
" )\n",
"\n",
" # No-rollback constraint — switch to new leaders, never go back\n",
" leader_ids = daily_leader[\"leader\"].to_list()\n",
" dates = daily_leader[\"date\"].to_list()\n",
" used_contracts = {leader_ids[0]}\n",
" current_front = leader_ids[0]\n",
" front = [current_front]\n",
"\n",
" for i in range(1, len(leader_ids)):\n",
" if leader_ids[i] != current_front and leader_ids[i] not in used_contracts:\n",
" current_front = leader_ids[i]\n",
" used_contracts.add(current_front)\n",
" front.append(current_front)\n",
"\n",
" daily_front = pl.DataFrame({\"date\": dates, \"front_symbol\": front})\n",
"\n",
" # Expand back to hourly bars\n",
" hourly = individual_df.select(\"timestamp\").unique().sort(\"timestamp\")\n",
" hourly = hourly.with_columns(pl.col(\"timestamp\").dt.date().alias(\"date\"))\n",
" front_month = hourly.join(daily_front, on=\"date\", how=\"left\").drop(\"date\")\n",
"\n",
" front_month = front_month.with_columns(\n",
" pl.col(\"front_symbol\").shift(1).alias(\"prev_front\"),\n",
" ).with_columns(\n",
" (pl.col(\"front_symbol\") != pl.col(\"prev_front\")).alias(\"is_roll\"),\n",
" )\n",
" return front_month"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "da136227",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.641146Z",
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"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Front month identification (2024 sample):\n"
]
},
{
"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: (20, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>front_symbol</th><th>prev_front</th><th>is_roll</th></tr><tr><td>datetime[ns, UTC]</td><td>i64</td><td>i64</td><td>bool</td></tr></thead><tbody><tr><td>2024-01-01 00:00:00 UTC</td><td>17077</td><td>17077</td><td>false</td></tr><tr><td>2024-01-02 00:00:00 UTC</td><td>17077</td><td>17077</td><td>false</td></tr><tr><td>2024-01-03 00:00:00 UTC</td><td>17077</td><td>17077</td><td>false</td></tr><tr><td>2024-01-04 00:00:00 UTC</td><td>17077</td><td>17077</td><td>false</td></tr><tr><td>2024-01-05 00:00:00 UTC</td><td>17077</td><td>17077</td><td>false</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>2024-01-18 00:00:00 UTC</td><td>17077</td><td>17077</td><td>false</td></tr><tr><td>2024-01-19 00:00:00 UTC</td><td>17077</td><td>17077</td><td>false</td></tr><tr><td>2024-01-21 00:00:00 UTC</td><td>17077</td><td>17077</td><td>false</td></tr><tr><td>2024-01-22 00:00:00 UTC</td><td>17077</td><td>17077</td><td>false</td></tr><tr><td>2024-01-23 00:00:00 UTC</td><td>17077</td><td>17077</td><td>false</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (20, 4)\n",
"┌─────────────────────────┬──────────────┬────────────┬─────────┐\n",
"│ timestamp ┆ front_symbol ┆ prev_front ┆ is_roll │\n",
"│ --- ┆ --- ┆ --- ┆ --- │\n",
"│ datetime[ns, UTC] ┆ i64 ┆ i64 ┆ bool │\n",
"╞═════════════════════════╪══════════════╪════════════╪═════════╡\n",
"│ 2024-01-01 00:00:00 UTC ┆ 17077 ┆ 17077 ┆ false │\n",
"│ 2024-01-02 00:00:00 UTC ┆ 17077 ┆ 17077 ┆ false │\n",
"│ 2024-01-03 00:00:00 UTC ┆ 17077 ┆ 17077 ┆ false │\n",
"│ 2024-01-04 00:00:00 UTC ┆ 17077 ┆ 17077 ┆ false │\n",
"│ 2024-01-05 00:00:00 UTC ┆ 17077 ┆ 17077 ┆ false │\n",
"│ … ┆ … ┆ … ┆ … │\n",
"│ 2024-01-18 00:00:00 UTC ┆ 17077 ┆ 17077 ┆ false │\n",
"│ 2024-01-19 00:00:00 UTC ┆ 17077 ┆ 17077 ┆ false │\n",
"│ 2024-01-21 00:00:00 UTC ┆ 17077 ┆ 17077 ┆ false │\n",
"│ 2024-01-22 00:00:00 UTC ┆ 17077 ┆ 17077 ┆ false │\n",
"│ 2024-01-23 00:00:00 UTC ┆ 17077 ┆ 17077 ┆ false │\n",
"└─────────────────────────┴──────────────┴────────────┴─────────┘"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"front_months = identify_front_month(es_individual)\n",
"print(\"Front month identification (2024 sample):\")\n",
"front_months.filter(pl.col(\"timestamp\") >= datetime(2024, 1, 1, tzinfo=UTC)).head(20)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "7c6f2cfc",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.659274Z",
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"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total roll events: 40\n",
"Most recent 10 roll dates:\n"
]
},
{
"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: (10, 3)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>prev_front</th><th>front_symbol</th></tr><tr><td>datetime[ns, UTC]</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>2023-09-10 00:00:00 UTC</td><td>3445</td><td>314863</td></tr><tr><td>2023-12-11 00:00:00 UTC</td><td>314863</td><td>17077</td></tr><tr><td>2024-03-11 00:00:00 UTC</td><td>17077</td><td>5602</td></tr><tr><td>2024-06-16 00:00:00 UTC</td><td>5602</td><td>118</td></tr><tr><td>2024-09-16 00:00:00 UTC</td><td>118</td><td>183748</td></tr><tr><td>2024-12-16 00:00:00 UTC</td><td>183748</td><td>5002</td></tr><tr><td>2025-03-17 00:00:00 UTC</td><td>5002</td><td>4916</td></tr><tr><td>2025-06-16 00:00:00 UTC</td><td>4916</td><td>14160</td></tr><tr><td>2025-09-15 00:00:00 UTC</td><td>14160</td><td>294973</td></tr><tr><td>2025-12-15 00:00:00 UTC</td><td>294973</td><td>42140878</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (10, 3)\n",
"┌─────────────────────────┬────────────┬──────────────┐\n",
"│ timestamp ┆ prev_front ┆ front_symbol │\n",
"│ --- ┆ --- ┆ --- │\n",
"│ datetime[ns, UTC] ┆ i64 ┆ i64 │\n",
"╞═════════════════════════╪════════════╪══════════════╡\n",
"│ 2023-09-10 00:00:00 UTC ┆ 3445 ┆ 314863 │\n",
"│ 2023-12-11 00:00:00 UTC ┆ 314863 ┆ 17077 │\n",
"│ 2024-03-11 00:00:00 UTC ┆ 17077 ┆ 5602 │\n",
"│ 2024-06-16 00:00:00 UTC ┆ 5602 ┆ 118 │\n",
"│ 2024-09-16 00:00:00 UTC ┆ 118 ┆ 183748 │\n",
"│ 2024-12-16 00:00:00 UTC ┆ 183748 ┆ 5002 │\n",
"│ 2025-03-17 00:00:00 UTC ┆ 5002 ┆ 4916 │\n",
"│ 2025-06-16 00:00:00 UTC ┆ 4916 ┆ 14160 │\n",
"│ 2025-09-15 00:00:00 UTC ┆ 14160 ┆ 294973 │\n",
"│ 2025-12-15 00:00:00 UTC ┆ 294973 ┆ 42140878 │\n",
"└─────────────────────────┴────────────┴──────────────┘"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"roll_dates = front_months.filter(pl.col(\"is_roll\"))\n",
"print(f\"Total roll events: {len(roll_dates)}\")\n",
"print(\"Most recent 10 roll dates:\")\n",
"roll_dates.tail(10).select(\"timestamp\", \"prev_front\", \"front_symbol\")"
]
},
{
"cell_type": "markdown",
"id": "7cdd83cb",
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}
},
"source": [
"### 2.2 Calendar-Based Roll (Alternative)\n",
"\n",
"An alternative to volume-based rolling is **calendar-based**: roll a fixed number\n",
"of days before contract expiration. This is simpler and more predictable, but may\n",
"not track liquidity as well as volume-based methods.\n",
"\n",
"Common calendar roll schedules:\n",
"- 5 business days before expiry (conservative)\n",
"- First notice day (for physical delivery commodities)\n",
"- 2 weeks before expiry (popular for equity index futures)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "6bee75ab",
"metadata": {
"execution": {
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"status": "completed"
}
},
"outputs": [],
"source": [
"def identify_front_month_calendar(\n",
" individual_df: pl.DataFrame,\n",
" definition_df: pl.DataFrame,\n",
" roll_days_before: int = 5,\n",
") -> pl.DataFrame:\n",
" \"\"\"Identify front month using calendar-based roll (fixed days before expiry).\"\"\"\n",
" # Get expiration dates from definitions\n",
" # NOTE: Requires individual data to have a \"symbol\" column with contract names\n",
" expirations = definition_df.select([\"symbol\", \"expiration\"]).with_columns(\n",
" pl.col(\"expiration\").cast(pl.Date).alias(\"expiry_date\")\n",
" )\n",
"\n",
" # Join with individual data (requires symbol column)\n",
" with_expiry = individual_df.join(expirations, on=\"symbol\", how=\"left\").with_columns(\n",
" pl.col(\"timestamp\").cast(pl.Date).alias(\"trade_date\")\n",
" )\n",
"\n",
" # Calculate days to expiry\n",
" with_expiry = with_expiry.with_columns(\n",
" (pl.col(\"expiry_date\") - pl.col(\"trade_date\")).dt.total_days().alias(\"days_to_expiry\")\n",
" )\n",
"\n",
" # Filter to contracts with more than roll_days_before to expiry\n",
" # Then select the nearest such contract for each day\n",
" front_month = (\n",
" with_expiry.filter(pl.col(\"days_to_expiry\") > roll_days_before)\n",
" .sort([\"timestamp\", \"days_to_expiry\"])\n",
" .group_by(\"timestamp\")\n",
" .first()\n",
" .select([\"timestamp\", pl.col(\"symbol\").alias(\"front_symbol\")])\n",
" .sort(\"timestamp\")\n",
" )\n",
"\n",
" # Add roll indicators\n",
" front_month = front_month.with_columns(\n",
" pl.col(\"front_symbol\").shift(1).alias(\"prev_front\"),\n",
" ).with_columns(\n",
" (pl.col(\"front_symbol\") != pl.col(\"prev_front\")).alias(\"is_roll\"),\n",
" )\n",
"\n",
" return front_month"
]
},
{
"cell_type": "markdown",
"id": "9e45a997",
"metadata": {
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}
},
"source": [
"Calendar-based roll detection requires the individual data to carry a symbol\n",
"column that joins to the contract-definitions table. The Databento individual\n",
"parquet uses numeric `instrument_id` rather than ESH24-style symbols, so we\n",
"present the calendar logic above as a teaching reference and use volume-based\n",
"detection for the rest of the notebook."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "f0be101b",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.683379Z",
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"status": "completed"
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},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Volume-based rolls (ES, 2016-2025): 40\n"
]
}
],
"source": [
"volume_rolls = front_months.filter(pl.col(\"is_roll\"))\n",
"print(f\"Volume-based rolls (ES, 2016-2025): {len(volume_rolls)}\")"
]
},
{
"cell_type": "markdown",
"id": "c4842138",
"metadata": {
"papermill": {
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"end_time": "2026-05-15T03:46:37.689746+00:00",
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"status": "completed"
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},
"source": [
"**Volume vs Calendar Trade-offs**:\n",
"- **Volume-based**: Follows liquidity naturally, but roll timing varies\n",
"- **Calendar-based**: Predictable timing, easier to automate, but may roll into less liquid contract\n",
"\n",
"For this notebook, we use **volume-based** roll detection as our primary method since it\n",
"better reflects actual market liquidity transitions."
]
},
{
"cell_type": "markdown",
"id": "8cb739a6",
"metadata": {
"lines_to_next_cell": 2,
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"status": "completed"
}
},
"source": [
"## 3. Adjustment Methods\n",
"\n",
"When we roll from contract A to contract B, there's usually a price gap.\n",
"If we don't adjust, our time series will have artificial jumps.\n",
"\n",
"### 3.1 No Adjustment (Raw)\n",
"\n",
"Simply use prices as-is. Returns calculated on roll dates are invalid."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "4876b3bc",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.698374Z",
"iopub.status.busy": "2026-05-15T03:46:37.698280Z",
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"end_time": "2026-05-15T03:46:37.700865+00:00",
"exception": false,
"start_time": "2026-05-15T03:46:37.695735+00:00",
"status": "completed"
}
},
"outputs": [],
"source": [
"def create_continuous_raw(individual_df: pl.DataFrame, front_months: pl.DataFrame) -> pl.DataFrame:\n",
" \"\"\"Create continuous series with no adjustment (raw prices).\"\"\"\n",
" # Join individual prices with front month info\n",
" continuous = (\n",
" individual_df.join(\n",
" front_months.select([\"timestamp\", \"front_symbol\"]), on=\"timestamp\", how=\"inner\"\n",
" )\n",
" .filter(pl.col(\"instrument_id\") == pl.col(\"front_symbol\"))\n",
" .select([\"timestamp\", \"open\", \"high\", \"low\", \"close\", \"volume\", \"instrument_id\"])\n",
" .sort(\"timestamp\")\n",
" )\n",
"\n",
" return continuous"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "8a365130",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.705369Z",
"iopub.status.busy": "2026-05-15T03:46:37.705263Z",
"iopub.status.idle": "2026-05-15T03:46:37.712075Z",
"shell.execute_reply": "2026-05-15T03:46:37.711700Z"
},
"papermill": {
"duration": 0.009714,
"end_time": "2026-05-15T03:46:37.712513+00:00",
"exception": false,
"start_time": "2026-05-15T03:46:37.702799+00:00",
"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Raw continuous series: 3110 hourly bars\n"
]
},
{
"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: (10, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>open</th><th>high</th><th>low</th><th>close</th><th>volume</th><th>instrument_id</th></tr><tr><td>datetime[ns, UTC]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>u64</td><td>u32</td></tr></thead><tbody><tr><td>2016-01-03 00:00:00 UTC</td><td>2037.75</td><td>2043.5</td><td>2037.5</td><td>2040.25</td><td>10915</td><td>49705</td></tr><tr><td>2016-01-04 00:00:00 UTC</td><td>2040.5</td><td>2043.0</td><td>1980.25</td><td>2012.5</td><td>2208553</td><td>49705</td></tr><tr><td>2016-01-05 00:00:00 UTC</td><td>2012.25</td><td>2017.0</td><td>1992.25</td><td>2010.5</td><td>1680047</td><td>49705</td></tr><tr><td>2016-01-06 00:00:00 UTC</td><td>2010.75</td><td>2013.25</td><td>1970.5</td><td>1989.25</td><td>2215021</td><td>49705</td></tr><tr><td>2016-01-07 00:00:00 UTC</td><td>1989.25</td><td>1991.0</td><td>1928.5</td><td>1936.0</td><td>2962323</td><td>49705</td></tr><tr><td>2016-01-08 00:00:00 UTC</td><td>1936.0</td><td>1964.75</td><td>1910.0</td><td>1913.25</td><td>2666590</td><td>49705</td></tr><tr><td>2016-01-10 00:00:00 UTC</td><td>1909.25</td><td>1912.25</td><td>1893.5</td><td>1897.0</td><td>55198</td><td>49705</td></tr><tr><td>2016-01-11 00:00:00 UTC</td><td>1897.25</td><td>1929.0</td><td>1892.5</td><td>1919.0</td><td>2438670</td><td>49705</td></tr><tr><td>2016-01-12 00:00:00 UTC</td><td>1918.75</td><td>1940.25</td><td>1899.0</td><td>1927.0</td><td>2419158</td><td>49705</td></tr><tr><td>2016-01-13 00:00:00 UTC</td><td>1927.25</td><td>1946.5</td><td>1878.0</td><td>1882.5</td><td>2889603</td><td>49705</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (10, 7)\n",
"┌─────────────────────────┬─────────┬─────────┬─────────┬─────────┬─────────┬───────────────┐\n",
"│ timestamp ┆ open ┆ high ┆ low ┆ close ┆ volume ┆ instrument_id │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ datetime[ns, UTC] ┆ f64 ┆ f64 ┆ f64 ┆ f64 ┆ u64 ┆ u32 │\n",
"╞═════════════════════════╪═════════╪═════════╪═════════╪═════════╪═════════╪═══════════════╡\n",
"│ 2016-01-03 00:00:00 UTC ┆ 2037.75 ┆ 2043.5 ┆ 2037.5 ┆ 2040.25 ┆ 10915 ┆ 49705 │\n",
"│ 2016-01-04 00:00:00 UTC ┆ 2040.5 ┆ 2043.0 ┆ 1980.25 ┆ 2012.5 ┆ 2208553 ┆ 49705 │\n",
"│ 2016-01-05 00:00:00 UTC ┆ 2012.25 ┆ 2017.0 ┆ 1992.25 ┆ 2010.5 ┆ 1680047 ┆ 49705 │\n",
"│ 2016-01-06 00:00:00 UTC ┆ 2010.75 ┆ 2013.25 ┆ 1970.5 ┆ 1989.25 ┆ 2215021 ┆ 49705 │\n",
"│ 2016-01-07 00:00:00 UTC ┆ 1989.25 ┆ 1991.0 ┆ 1928.5 ┆ 1936.0 ┆ 2962323 ┆ 49705 │\n",
"│ 2016-01-08 00:00:00 UTC ┆ 1936.0 ┆ 1964.75 ┆ 1910.0 ┆ 1913.25 ┆ 2666590 ┆ 49705 │\n",
"│ 2016-01-10 00:00:00 UTC ┆ 1909.25 ┆ 1912.25 ┆ 1893.5 ┆ 1897.0 ┆ 55198 ┆ 49705 │\n",
"│ 2016-01-11 00:00:00 UTC ┆ 1897.25 ┆ 1929.0 ┆ 1892.5 ┆ 1919.0 ┆ 2438670 ┆ 49705 │\n",
"│ 2016-01-12 00:00:00 UTC ┆ 1918.75 ┆ 1940.25 ┆ 1899.0 ┆ 1927.0 ┆ 2419158 ┆ 49705 │\n",
"│ 2016-01-13 00:00:00 UTC ┆ 1927.25 ┆ 1946.5 ┆ 1878.0 ┆ 1882.5 ┆ 2889603 ┆ 49705 │\n",
"└─────────────────────────┴─────────┴─────────┴─────────┴─────────┴─────────┴───────────────┘"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es_continuous_raw = create_continuous_raw(es_individual, front_months)\n",
"print(f\"Raw continuous series: {len(es_continuous_raw)} hourly bars\")\n",
"es_continuous_raw.head(10)"
]
},
{
"cell_type": "markdown",
"id": "ccc60bf3",
"metadata": {
"lines_to_next_cell": 2,
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"status": "completed"
}
},
"source": [
"### 3.2 Panama (Back-Adjustment)\n",
"\n",
"Add the price gap to all historical prices. This preserves dollar P&L\n",
"but distorts percentage returns for old data.\n",
"\n",
"Gap = Close_new_contract - Close_old_contract\n",
"Adjusted_price = Price + cumulative_gap\n",
"\n",
"Note: We add (not subtract) because we're bringing old prices UP to the\n",
"current contract's level, eliminating the discontinuity at roll dates."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "cd8ebfea",
"metadata": {
"execution": {
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"status": "completed"
}
},
"outputs": [],
"source": [
"def _compute_roll_gaps(individual_df: pl.DataFrame, front_months: pl.DataFrame) -> pl.DataFrame:\n",
" \"\"\"Compute price gaps at each roll date (new - old contract close).\"\"\"\n",
" roll_info = front_months.filter(pl.col(\"is_roll\"))\n",
" prices_lookup = individual_df.select([\"timestamp\", \"instrument_id\", \"close\"])\n",
"\n",
" old_prices = (\n",
" roll_info.select([\"timestamp\", pl.col(\"prev_front\").alias(\"instrument_id\")])\n",
" .join(prices_lookup, on=[\"timestamp\", \"instrument_id\"], how=\"left\")\n",
" .rename({\"close\": \"old_close\"})\n",
" )\n",
"\n",
" new_prices = (\n",
" roll_info.select([\"timestamp\", pl.col(\"front_symbol\").alias(\"instrument_id\")])\n",
" .join(prices_lookup, on=[\"timestamp\", \"instrument_id\"], how=\"left\")\n",
" .rename({\"close\": \"new_close\"})\n",
" )\n",
"\n",
" return (\n",
" old_prices.select([\"timestamp\", \"old_close\"])\n",
" .join(new_prices.select([\"timestamp\", \"new_close\"]), on=\"timestamp\", how=\"inner\")\n",
" .with_columns((pl.col(\"new_close\") - pl.col(\"old_close\")).alias(\"gap\"))\n",
" .select([\"timestamp\", \"gap\"])\n",
" .drop_nulls()\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "a971222c",
"metadata": {
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"status": "completed"
}
},
"source": [
"### Panama Adjustment\n",
"\n",
"Apply the computed gaps cumulatively backwards through the raw series."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "0a9b4e76",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.734576Z",
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"status": "completed"
}
},
"outputs": [],
"source": [
"def create_continuous_panama(\n",
" individual_df: pl.DataFrame, front_months: pl.DataFrame\n",
") -> pl.DataFrame:\n",
" \"\"\"Create continuous series with Panama (back) adjustment.\n",
"\n",
" Uses vectorized Polars joins instead of row-by-row iteration for O(n) complexity.\n",
" \"\"\"\n",
" raw = create_continuous_raw(individual_df, front_months)\n",
" roll_info = front_months.filter(pl.col(\"is_roll\"))\n",
"\n",
" if len(roll_info) == 0:\n",
" return raw.with_columns(pl.lit(0.0).alias(\"cumulative_adjustment\"))\n",
"\n",
" gaps_df = _compute_roll_gaps(individual_df, front_months)\n",
"\n",
" if len(gaps_df) == 0:\n",
" return raw.with_columns(pl.lit(0.0).alias(\"cumulative_adjustment\"))\n",
"\n",
" # Adjustment applies to dates STRICTLY BEFORE each roll date\n",
" raw_with_gaps = raw.join(gaps_df, on=\"timestamp\", how=\"left\").with_columns(\n",
" pl.col(\"gap\").fill_null(0.0)\n",
" )\n",
"\n",
" # Cumulative sum in reverse, shift by 1 to exclude roll date from adjustment\n",
" raw_with_gaps = raw_with_gaps.with_columns(\n",
" pl.col(\"gap\")\n",
" .reverse()\n",
" .cum_sum()\n",
" .shift(1)\n",
" .fill_null(0.0)\n",
" .reverse()\n",
" .alias(\"cumulative_adjustment\")\n",
" )\n",
"\n",
" adjusted = raw_with_gaps.with_columns(\n",
" [\n",
" (pl.col(\"open\") + pl.col(\"cumulative_adjustment\")).alias(\"adj_open\"),\n",
" (pl.col(\"high\") + pl.col(\"cumulative_adjustment\")).alias(\"adj_high\"),\n",
" (pl.col(\"low\") + pl.col(\"cumulative_adjustment\")).alias(\"adj_low\"),\n",
" (pl.col(\"close\") + pl.col(\"cumulative_adjustment\")).alias(\"adj_close\"),\n",
" ]\n",
" )\n",
"\n",
" return adjusted"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "52057509",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.742869Z",
"iopub.status.busy": "2026-05-15T03:46:37.742761Z",
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"shell.execute_reply": "2026-05-15T03:46:37.751689Z"
},
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"duration": 0.01244,
"end_time": "2026-05-15T03:46:37.752563+00:00",
"exception": false,
"start_time": "2026-05-15T03:46:37.740123+00:00",
"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Panama-adjusted: cumulative_adjustment at the start of the series = +625.50 (adjusts every historical price up by this amount so the most recent contract is unchanged)\n"
]
},
{
"data": {
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"<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: (10, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>close</th><th>adj_close</th><th>cumulative_adjustment</th><th>instrument_id</th></tr><tr><td>datetime[ns, UTC]</td><td>f64</td><td>f64</td><td>f64</td><td>u32</td></tr></thead><tbody><tr><td>2016-01-03 00:00:00 UTC</td><td>2040.25</td><td>2665.75</td><td>625.5</td><td>49705</td></tr><tr><td>2016-01-04 00:00:00 UTC</td><td>2012.5</td><td>2638.0</td><td>625.5</td><td>49705</td></tr><tr><td>2016-01-05 00:00:00 UTC</td><td>2010.5</td><td>2636.0</td><td>625.5</td><td>49705</td></tr><tr><td>2016-01-06 00:00:00 UTC</td><td>1989.25</td><td>2614.75</td><td>625.5</td><td>49705</td></tr><tr><td>2016-01-07 00:00:00 UTC</td><td>1936.0</td><td>2561.5</td><td>625.5</td><td>49705</td></tr><tr><td>2016-01-08 00:00:00 UTC</td><td>1913.25</td><td>2538.75</td><td>625.5</td><td>49705</td></tr><tr><td>2016-01-10 00:00:00 UTC</td><td>1897.0</td><td>2522.5</td><td>625.5</td><td>49705</td></tr><tr><td>2016-01-11 00:00:00 UTC</td><td>1919.0</td><td>2544.5</td><td>625.5</td><td>49705</td></tr><tr><td>2016-01-12 00:00:00 UTC</td><td>1927.0</td><td>2552.5</td><td>625.5</td><td>49705</td></tr><tr><td>2016-01-13 00:00:00 UTC</td><td>1882.5</td><td>2508.0</td><td>625.5</td><td>49705</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (10, 5)\n",
"┌─────────────────────────┬─────────┬───────────┬───────────────────────┬───────────────┐\n",
"│ timestamp ┆ close ┆ adj_close ┆ cumulative_adjustment ┆ instrument_id │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ datetime[ns, UTC] ┆ f64 ┆ f64 ┆ f64 ┆ u32 │\n",
"╞═════════════════════════╪═════════╪═══════════╪═══════════════════════╪═══════════════╡\n",
"│ 2016-01-03 00:00:00 UTC ┆ 2040.25 ┆ 2665.75 ┆ 625.5 ┆ 49705 │\n",
"│ 2016-01-04 00:00:00 UTC ┆ 2012.5 ┆ 2638.0 ┆ 625.5 ┆ 49705 │\n",
"│ 2016-01-05 00:00:00 UTC ┆ 2010.5 ┆ 2636.0 ┆ 625.5 ┆ 49705 │\n",
"│ 2016-01-06 00:00:00 UTC ┆ 1989.25 ┆ 2614.75 ┆ 625.5 ┆ 49705 │\n",
"│ 2016-01-07 00:00:00 UTC ┆ 1936.0 ┆ 2561.5 ┆ 625.5 ┆ 49705 │\n",
"│ 2016-01-08 00:00:00 UTC ┆ 1913.25 ┆ 2538.75 ┆ 625.5 ┆ 49705 │\n",
"│ 2016-01-10 00:00:00 UTC ┆ 1897.0 ┆ 2522.5 ┆ 625.5 ┆ 49705 │\n",
"│ 2016-01-11 00:00:00 UTC ┆ 1919.0 ┆ 2544.5 ┆ 625.5 ┆ 49705 │\n",
"│ 2016-01-12 00:00:00 UTC ┆ 1927.0 ┆ 2552.5 ┆ 625.5 ┆ 49705 │\n",
"│ 2016-01-13 00:00:00 UTC ┆ 1882.5 ┆ 2508.0 ┆ 625.5 ┆ 49705 │\n",
"└─────────────────────────┴─────────┴───────────┴───────────────────────┴───────────────┘"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es_continuous_panama = create_continuous_panama(es_individual, front_months)\n",
"panama_first = es_continuous_panama[\"cumulative_adjustment\"][0]\n",
"print(\n",
" f\"Panama-adjusted: cumulative_adjustment at the start of the series = {panama_first:+.2f} \"\n",
" f\"(adjusts every historical price up by this amount so the most recent contract is unchanged)\"\n",
")\n",
"es_continuous_panama.select(\n",
" \"timestamp\", \"close\", \"adj_close\", \"cumulative_adjustment\", \"instrument_id\"\n",
").head(10)"
]
},
{
"cell_type": "markdown",
"id": "31049a10",
"metadata": {
"lines_to_next_cell": 2,
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"status": "completed"
}
},
"source": [
"### 3.3 Ratio Adjustment\n",
"\n",
"Multiply historical prices by the ratio of new/old contract prices.\n",
"This preserves percentage returns but distorts dollar amounts.\n",
"\n",
"Ratio = Close_new_contract / Close_old_contract\n",
"Adjusted_price = Price * cumulative_ratio"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "44cc7612",
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"status": "completed"
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"outputs": [],
"source": [
"def _compute_roll_ratios(individual_df: pl.DataFrame, front_months: pl.DataFrame) -> pl.DataFrame:\n",
" \"\"\"Compute price ratios (new/old) at each roll date.\"\"\"\n",
" roll_info = front_months.filter(pl.col(\"is_roll\"))\n",
" prices_lookup = individual_df.select([\"timestamp\", \"instrument_id\", \"close\"])\n",
"\n",
" old_prices = (\n",
" roll_info.select([\"timestamp\", pl.col(\"prev_front\").alias(\"instrument_id\")])\n",
" .join(prices_lookup, on=[\"timestamp\", \"instrument_id\"], how=\"left\")\n",
" .rename({\"close\": \"old_close\"})\n",
" )\n",
"\n",
" new_prices = (\n",
" roll_info.select([\"timestamp\", pl.col(\"front_symbol\").alias(\"instrument_id\")])\n",
" .join(prices_lookup, on=[\"timestamp\", \"instrument_id\"], how=\"left\")\n",
" .rename({\"close\": \"new_close\"})\n",
" )\n",
"\n",
" return (\n",
" old_prices.select([\"timestamp\", \"old_close\"])\n",
" .join(new_prices.select([\"timestamp\", \"new_close\"]), on=\"timestamp\", how=\"inner\")\n",
" .filter(pl.col(\"old_close\") != 0)\n",
" .with_columns((pl.col(\"new_close\") / pl.col(\"old_close\")).alias(\"ratio\"))\n",
" .select([\"timestamp\", \"ratio\"])\n",
" .drop_nulls()\n",
" )"
]
},
{
"cell_type": "markdown",
"id": "67a61d28",
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"status": "completed"
}
},
"source": [
"### Ratio Adjustment\n",
"\n",
"Apply the computed ratios cumulatively backwards through the raw series."
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "16764689",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.776657Z",
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"status": "completed"
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},
"outputs": [],
"source": [
"def create_continuous_ratio(\n",
" individual_df: pl.DataFrame, front_months: pl.DataFrame\n",
") -> pl.DataFrame:\n",
" \"\"\"Create continuous series with ratio adjustment.\n",
"\n",
" Uses vectorized Polars joins instead of row-by-row iteration for O(n) complexity.\n",
" \"\"\"\n",
" raw = create_continuous_raw(individual_df, front_months)\n",
" roll_info = front_months.filter(pl.col(\"is_roll\"))\n",
"\n",
" if len(roll_info) == 0:\n",
" return raw.with_columns(pl.lit(1.0).alias(\"cumulative_ratio\"))\n",
"\n",
" ratios_df = _compute_roll_ratios(individual_df, front_months)\n",
"\n",
" if len(ratios_df) == 0:\n",
" return raw.with_columns(pl.lit(1.0).alias(\"cumulative_ratio\"))\n",
"\n",
" # Adjustment applies to dates STRICTLY BEFORE each roll date\n",
" raw_with_ratios = raw.join(ratios_df, on=\"timestamp\", how=\"left\").with_columns(\n",
" pl.col(\"ratio\").fill_null(1.0)\n",
" )\n",
"\n",
" # Cumulative product in reverse, shift by 1 to exclude roll date\n",
" raw_with_ratios = raw_with_ratios.with_columns(\n",
" pl.col(\"ratio\")\n",
" .reverse()\n",
" .cum_prod()\n",
" .shift(1)\n",
" .fill_null(1.0)\n",
" .reverse()\n",
" .alias(\"cumulative_ratio\")\n",
" )\n",
"\n",
" adjusted = raw_with_ratios.with_columns(\n",
" [\n",
" (pl.col(\"open\") * pl.col(\"cumulative_ratio\")).alias(\"adj_open\"),\n",
" (pl.col(\"high\") * pl.col(\"cumulative_ratio\")).alias(\"adj_high\"),\n",
" (pl.col(\"low\") * pl.col(\"cumulative_ratio\")).alias(\"adj_low\"),\n",
" (pl.col(\"close\") * pl.col(\"cumulative_ratio\")).alias(\"adj_close\"),\n",
" ]\n",
" )\n",
"\n",
" return adjusted"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "ab406e6a",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.785363Z",
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"duration": 0.013632,
"end_time": "2026-05-15T03:46:37.796033+00:00",
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"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Ratio-adjusted: cumulative_ratio at the start of the series = 1.1130 (historical prices are scaled up by this factor)\n"
]
},
{
"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: (10, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>close</th><th>adj_close</th><th>cumulative_ratio</th><th>instrument_id</th></tr><tr><td>datetime[ns, UTC]</td><td>f64</td><td>f64</td><td>f64</td><td>u32</td></tr></thead><tbody><tr><td>2016-01-03 00:00:00 UTC</td><td>2040.25</td><td>2270.877911</td><td>1.113039</td><td>49705</td></tr><tr><td>2016-01-04 00:00:00 UTC</td><td>2012.5</td><td>2239.991078</td><td>1.113039</td><td>49705</td></tr><tr><td>2016-01-05 00:00:00 UTC</td><td>2010.5</td><td>2237.765</td><td>1.113039</td><td>49705</td></tr><tr><td>2016-01-06 00:00:00 UTC</td><td>1989.25</td><td>2214.11292</td><td>1.113039</td><td>49705</td></tr><tr><td>2016-01-07 00:00:00 UTC</td><td>1936.0</td><td>2154.843591</td><td>1.113039</td><td>49705</td></tr><tr><td>2016-01-08 00:00:00 UTC</td><td>1913.25</td><td>2129.521953</td><td>1.113039</td><td>49705</td></tr><tr><td>2016-01-10 00:00:00 UTC</td><td>1897.0</td><td>2111.435068</td><td>1.113039</td><td>49705</td></tr><tr><td>2016-01-11 00:00:00 UTC</td><td>1919.0</td><td>2135.921927</td><td>1.113039</td><td>49705</td></tr><tr><td>2016-01-12 00:00:00 UTC</td><td>1927.0</td><td>2144.82624</td><td>1.113039</td><td>49705</td></tr><tr><td>2016-01-13 00:00:00 UTC</td><td>1882.5</td><td>2095.296002</td><td>1.113039</td><td>49705</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (10, 5)\n",
"┌─────────────────────────┬─────────┬─────────────┬──────────────────┬───────────────┐\n",
"│ timestamp ┆ close ┆ adj_close ┆ cumulative_ratio ┆ instrument_id │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ datetime[ns, UTC] ┆ f64 ┆ f64 ┆ f64 ┆ u32 │\n",
"╞═════════════════════════╪═════════╪═════════════╪══════════════════╪═══════════════╡\n",
"│ 2016-01-03 00:00:00 UTC ┆ 2040.25 ┆ 2270.877911 ┆ 1.113039 ┆ 49705 │\n",
"│ 2016-01-04 00:00:00 UTC ┆ 2012.5 ┆ 2239.991078 ┆ 1.113039 ┆ 49705 │\n",
"│ 2016-01-05 00:00:00 UTC ┆ 2010.5 ┆ 2237.765 ┆ 1.113039 ┆ 49705 │\n",
"│ 2016-01-06 00:00:00 UTC ┆ 1989.25 ┆ 2214.11292 ┆ 1.113039 ┆ 49705 │\n",
"│ 2016-01-07 00:00:00 UTC ┆ 1936.0 ┆ 2154.843591 ┆ 1.113039 ┆ 49705 │\n",
"│ 2016-01-08 00:00:00 UTC ┆ 1913.25 ┆ 2129.521953 ┆ 1.113039 ┆ 49705 │\n",
"│ 2016-01-10 00:00:00 UTC ┆ 1897.0 ┆ 2111.435068 ┆ 1.113039 ┆ 49705 │\n",
"│ 2016-01-11 00:00:00 UTC ┆ 1919.0 ┆ 2135.921927 ┆ 1.113039 ┆ 49705 │\n",
"│ 2016-01-12 00:00:00 UTC ┆ 1927.0 ┆ 2144.82624 ┆ 1.113039 ┆ 49705 │\n",
"│ 2016-01-13 00:00:00 UTC ┆ 1882.5 ┆ 2095.296002 ┆ 1.113039 ┆ 49705 │\n",
"└─────────────────────────┴─────────┴─────────────┴──────────────────┴───────────────┘"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es_continuous_ratio = create_continuous_ratio(es_individual, front_months)\n",
"ratio_first = es_continuous_ratio[\"cumulative_ratio\"][0]\n",
"print(\n",
" f\"Ratio-adjusted: cumulative_ratio at the start of the series = {ratio_first:.4f} \"\n",
" f\"(historical prices are scaled up by this factor)\"\n",
")\n",
"es_continuous_ratio.select(\n",
" \"timestamp\", \"close\", \"adj_close\", \"cumulative_ratio\", \"instrument_id\"\n",
").head(10)"
]
},
{
"cell_type": "markdown",
"id": "27440054",
"metadata": {
"papermill": {
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"end_time": "2026-05-15T03:46:37.800642+00:00",
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"status": "completed"
}
},
"source": [
"## 4. Validation\n",
"\n",
"Let's compare our construction to DataBento's pre-built continuous series.\n",
"\n",
"We compare our construction against DataBento's production continuous series.\n",
"Both use volume-based roll detection, but the implementations differ in detail\n",
"(daily aggregation window, exact crossover threshold, etc.), so some divergence\n",
"on roll timing is expected — typically by a day or two around the roll date."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "b3f57ebc",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.805492Z",
"iopub.status.busy": "2026-05-15T03:46:37.805400Z",
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"shell.execute_reply": "2026-05-15T03:46:37.813716Z"
},
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"end_time": "2026-05-15T03:46:37.814352+00:00",
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"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"DataBento continuous: (89031, 13)\n"
]
},
{
"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: (5, 13)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>open</th><th>high</th><th>low</th><th>close</th><th>volume</th><th>rtype</th><th>publisher_id</th><th>instrument_id</th><th>tenor</th><th>product</th><th>year</th><th>ts_event</th></tr><tr><td>datetime[ns, UTC]</td><td>f64</td><td>f64</td><td>f64</td><td>f64</td><td>u64</td><td>u8</td><td>u16</td><td>u32</td><td>i8</td><td>str</td><td>i64</td><td>datetime[ns, UTC]</td></tr></thead><tbody><tr><td>2011-01-02 23:00:00 UTC</td><td>1256.0</td><td>1257.75</td><td>1255.25</td><td>1256.75</td><td>7479</td><td>34</td><td>1</td><td>70248</td><td>0</td><td>&quot;ES&quot;</td><td>2011</td><td>2011-01-02 23:00:00 UTC</td></tr><tr><td>2011-01-03 00:00:00 UTC</td><td>1256.75</td><td>1257.5</td><td>1255.75</td><td>1257.0</td><td>2715</td><td>34</td><td>1</td><td>70248</td><td>0</td><td>&quot;ES&quot;</td><td>2011</td><td>2011-01-03 00:00:00 UTC</td></tr><tr><td>2011-01-03 01:00:00 UTC</td><td>1257.0</td><td>1257.25</td><td>1256.75</td><td>1257.0</td><td>2095</td><td>34</td><td>1</td><td>70248</td><td>0</td><td>&quot;ES&quot;</td><td>2011</td><td>2011-01-03 01:00:00 UTC</td></tr><tr><td>2011-01-03 02:00:00 UTC</td><td>1257.0</td><td>1257.5</td><td>1256.75</td><td>1257.25</td><td>1556</td><td>34</td><td>1</td><td>70248</td><td>0</td><td>&quot;ES&quot;</td><td>2011</td><td>2011-01-03 02:00:00 UTC</td></tr><tr><td>2011-01-03 03:00:00 UTC</td><td>1257.25</td><td>1261.0</td><td>1257.25</td><td>1260.25</td><td>6798</td><td>34</td><td>1</td><td>70248</td><td>0</td><td>&quot;ES&quot;</td><td>2011</td><td>2011-01-03 03:00:00 UTC</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (5, 13)\n",
"┌───────────────────┬─────────┬─────────┬─────────┬───┬───────┬─────────┬──────┬───────────────────┐\n",
"│ timestamp ┆ open ┆ high ┆ low ┆ … ┆ tenor ┆ product ┆ year ┆ ts_event │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ datetime[ns, UTC] ┆ f64 ┆ f64 ┆ f64 ┆ ┆ i8 ┆ str ┆ i64 ┆ datetime[ns, UTC] │\n",
"╞═══════════════════╪═════════╪═════════╪═════════╪═══╪═══════╪═════════╪══════╪═══════════════════╡\n",
"│ 2011-01-02 ┆ 1256.0 ┆ 1257.75 ┆ 1255.25 ┆ … ┆ 0 ┆ ES ┆ 2011 ┆ 2011-01-02 │\n",
"│ 23:00:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 23:00:00 UTC │\n",
"│ 2011-01-03 ┆ 1256.75 ┆ 1257.5 ┆ 1255.75 ┆ … ┆ 0 ┆ ES ┆ 2011 ┆ 2011-01-03 │\n",
"│ 00:00:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 00:00:00 UTC │\n",
"│ 2011-01-03 ┆ 1257.0 ┆ 1257.25 ┆ 1256.75 ┆ … ┆ 0 ┆ ES ┆ 2011 ┆ 2011-01-03 │\n",
"│ 01:00:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 01:00:00 UTC │\n",
"│ 2011-01-03 ┆ 1257.0 ┆ 1257.5 ┆ 1256.75 ┆ … ┆ 0 ┆ ES ┆ 2011 ┆ 2011-01-03 │\n",
"│ 02:00:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 02:00:00 UTC │\n",
"│ 2011-01-03 ┆ 1257.25 ┆ 1261.0 ┆ 1257.25 ┆ … ┆ 0 ┆ ES ┆ 2011 ┆ 2011-01-03 │\n",
"│ 03:00:00 UTC ┆ ┆ ┆ ┆ ┆ ┆ ┆ ┆ 03:00:00 UTC │\n",
"└───────────────────┴─────────┴─────────┴─────────┴───┴───────┴─────────┴──────┴───────────────────┘"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"es_databento = load_cme_futures(products=[\"ES\"], tenors=[0], frequency=\"hourly\", continuous=True)\n",
"print(f\"DataBento continuous: {es_databento.shape}\")\n",
"es_databento.head()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "12986317",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:37.819737Z",
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"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Hours compared: 2581\n",
"Mean absolute difference: $27.19\n",
"Median signed difference: $+2.50\n",
"Max absolute difference: $583.00\n",
"Hourly bars with >$1 difference: 2444 (94.7%)\n"
]
},
{
"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: (9, 5)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>statistic</th><th>timestamp</th><th>our_close</th><th>databento_close</th><th>diff</th></tr><tr><td>str</td><td>str</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;count&quot;</td><td>&quot;2581&quot;</td><td>2581.0</td><td>2581.0</td><td>2581.0</td></tr><tr><td>&quot;null_count&quot;</td><td>&quot;0&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td></tr><tr><td>&quot;mean&quot;</td><td>&quot;2020-12-31 13:05:33.359163+00:…</td><td>3779.25988</td><td>3776.152557</td><td>3.107323</td></tr><tr><td>&quot;std&quot;</td><td>null</td><td>1303.918646</td><td>1301.680135</td><td>41.951684</td></tr><tr><td>&quot;min&quot;</td><td>&quot;2016-01-04 00:00:00+00:00&quot;</td><td>1829.75</td><td>1830.25</td><td>-326.75</td></tr><tr><td>&quot;25%&quot;</td><td>&quot;2018-07-04 00:00:00+00:00&quot;</td><td>2713.0</td><td>2714.0</td><td>-12.25</td></tr><tr><td>&quot;50%&quot;</td><td>&quot;2021-01-04 00:00:00+00:00&quot;</td><td>3683.75</td><td>3683.5</td><td>2.5</td></tr><tr><td>&quot;75%&quot;</td><td>&quot;2023-06-30 00:00:00+00:00&quot;</td><td>4523.75</td><td>4521.25</td><td>20.0</td></tr><tr><td>&quot;max&quot;</td><td>&quot;2025-12-30 00:00:00+00:00&quot;</td><td>6980.0</td><td>6987.75</td><td>583.0</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (9, 5)\n",
"┌────────────┬─────────────────────────────────┬─────────────┬─────────────────┬───────────┐\n",
"│ statistic ┆ timestamp ┆ our_close ┆ databento_close ┆ diff │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ str ┆ str ┆ f64 ┆ f64 ┆ f64 │\n",
"╞════════════╪═════════════════════════════════╪═════════════╪═════════════════╪═══════════╡\n",
"│ count ┆ 2581 ┆ 2581.0 ┆ 2581.0 ┆ 2581.0 │\n",
"│ null_count ┆ 0 ┆ 0.0 ┆ 0.0 ┆ 0.0 │\n",
"│ mean ┆ 2020-12-31 13:05:33.359163+00:… ┆ 3779.25988 ┆ 3776.152557 ┆ 3.107323 │\n",
"│ std ┆ null ┆ 1303.918646 ┆ 1301.680135 ┆ 41.951684 │\n",
"│ min ┆ 2016-01-04 00:00:00+00:00 ┆ 1829.75 ┆ 1830.25 ┆ -326.75 │\n",
"│ 25% ┆ 2018-07-04 00:00:00+00:00 ┆ 2713.0 ┆ 2714.0 ┆ -12.25 │\n",
"│ 50% ┆ 2021-01-04 00:00:00+00:00 ┆ 3683.75 ┆ 3683.5 ┆ 2.5 │\n",
"│ 75% ┆ 2023-06-30 00:00:00+00:00 ┆ 4523.75 ┆ 4521.25 ┆ 20.0 │\n",
"│ max ┆ 2025-12-30 00:00:00+00:00 ┆ 6980.0 ┆ 6987.75 ┆ 583.0 │\n",
"└────────────┴─────────────────────────────────┴─────────────┴─────────────────┴───────────┘"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"comparison = (\n",
" es_continuous_raw.select(\"timestamp\", pl.col(\"close\").alias(\"our_close\"))\n",
" .join(\n",
" es_databento.select(\"timestamp\", pl.col(\"close\").alias(\"databento_close\")),\n",
" on=\"timestamp\",\n",
" how=\"inner\",\n",
" )\n",
" .with_columns(\n",
" (pl.col(\"our_close\") - pl.col(\"databento_close\")).alias(\"diff\"),\n",
" )\n",
")\n",
"\n",
"mean_abs = comparison[\"diff\"].abs().mean()\n",
"median_diff = comparison[\"diff\"].median()\n",
"max_abs = comparison[\"diff\"].abs().max()\n",
"large_diffs = comparison.filter(pl.col(\"diff\").abs() > 1)\n",
"\n",
"print(f\"Hours compared: {len(comparison)}\")\n",
"print(f\"Mean absolute difference: ${mean_abs:.2f}\")\n",
"print(f\"Median signed difference: ${median_diff:+.2f}\")\n",
"print(f\"Max absolute difference: ${max_abs:.2f}\")\n",
"print(\n",
" f\"Hourly bars with >$1 difference: {len(large_diffs)} ({100 * len(large_diffs) / len(comparison):.1f}%)\"\n",
")\n",
"comparison.describe()"
]
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"Sample of bars with large differences:\n"
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"<small>shape: (10, 4)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>timestamp</th><th>our_close</th><th>databento_close</th><th>diff</th></tr><tr><td>datetime[ns, UTC]</td><td>f64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>2016-06-10 00:00:00 UTC</td><td>2087.0</td><td>2113.75</td><td>-26.75</td></tr><tr><td>2016-10-18 00:00:00 UTC</td><td>2132.25</td><td>2127.0</td><td>5.25</td></tr><tr><td>2016-12-07 00:00:00 UTC</td><td>2236.75</td><td>2211.25</td><td>25.5</td></tr><tr><td>2016-06-20 00:00:00 UTC</td><td>2077.75</td><td>2079.5</td><td>-1.75</td></tr><tr><td>2016-04-29 00:00:00 UTC</td><td>2060.25</td><td>2071.75</td><td>-11.5</td></tr><tr><td>2016-09-28 00:00:00 UTC</td><td>2164.0</td><td>2150.75</td><td>13.25</td></tr><tr><td>2016-02-05 00:00:00 UTC</td><td>1873.75</td><td>1903.5</td><td>-29.75</td></tr><tr><td>2016-03-16 00:00:00 UTC</td><td>2020.0</td><td>2010.75</td><td>9.25</td></tr><tr><td>2016-03-02 00:00:00 UTC</td><td>1979.5</td><td>1975.75</td><td>3.75</td></tr><tr><td>2016-06-14 00:00:00 UTC</td><td>2062.5</td><td>2068.25</td><td>-5.75</td></tr></tbody></table></div>"
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"shape: (10, 4)\n",
"┌─────────────────────────┬───────────┬─────────────────┬────────┐\n",
"│ timestamp ┆ our_close ┆ databento_close ┆ diff │\n",
"│ --- ┆ --- ┆ --- ┆ --- │\n",
"│ datetime[ns, UTC] ┆ f64 ┆ f64 ┆ f64 │\n",
"╞═════════════════════════╪═══════════╪═════════════════╪════════╡\n",
"│ 2016-06-10 00:00:00 UTC ┆ 2087.0 ┆ 2113.75 ┆ -26.75 │\n",
"│ 2016-10-18 00:00:00 UTC ┆ 2132.25 ┆ 2127.0 ┆ 5.25 │\n",
"│ 2016-12-07 00:00:00 UTC ┆ 2236.75 ┆ 2211.25 ┆ 25.5 │\n",
"│ 2016-06-20 00:00:00 UTC ┆ 2077.75 ┆ 2079.5 ┆ -1.75 │\n",
"│ 2016-04-29 00:00:00 UTC ┆ 2060.25 ┆ 2071.75 ┆ -11.5 │\n",
"│ 2016-09-28 00:00:00 UTC ┆ 2164.0 ┆ 2150.75 ┆ 13.25 │\n",
"│ 2016-02-05 00:00:00 UTC ┆ 1873.75 ┆ 1903.5 ┆ -29.75 │\n",
"│ 2016-03-16 00:00:00 UTC ┆ 2020.0 ┆ 2010.75 ┆ 9.25 │\n",
"│ 2016-03-02 00:00:00 UTC ┆ 1979.5 ┆ 1975.75 ┆ 3.75 │\n",
"│ 2016-06-14 00:00:00 UTC ┆ 2062.5 ┆ 2068.25 ┆ -5.75 │\n",
"└─────────────────────────┴───────────┴─────────────────┴────────┘"
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"source": [
"print(\"Sample of bars with large differences:\")\n",
"large_diffs.head(10)"
]
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"source": [
"Most differences come from roll-timing disagreements — when our volume-based\n",
"detector rolls a day earlier or later than Databento's, the two series report\n",
"the price of a different contract for those hours, and the\n",
"contango/backwardation spread between expiries produces a gap. The median\n",
"signed difference is essentially zero, but mean absolute difference is on the\n",
"order of tens of dollars, with occasional larger gaps around roll dates where\n",
"the two algorithms disagree by more than a day."
]
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},
"source": [
"### 4.1 Visualize the Difference"
]
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"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot both series\n",
"fig = make_subplots(\n",
" rows=2, cols=1, shared_xaxes=True, subplot_titles=(\"Price Comparison\", \"Difference\")\n",
")\n",
"\n",
"comp_pd = comparison.to_pandas()\n",
"\n",
"fig.add_trace(\n",
" go.Scatter(x=comp_pd[\"timestamp\"], y=comp_pd[\"our_close\"], name=\"Our Construction\"),\n",
" row=1,\n",
" col=1,\n",
")\n",
"fig.add_trace(\n",
" go.Scatter(x=comp_pd[\"timestamp\"], y=comp_pd[\"databento_close\"], name=\"DataBento\"), row=1, col=1\n",
")\n",
"fig.add_trace(\n",
" go.Scatter(x=comp_pd[\"timestamp\"], y=comp_pd[\"diff\"], name=\"Difference\"), row=2, col=1\n",
")\n",
"\n",
"fig.update_layout(height=600, title=\"ES Continuous: Our Construction vs DataBento\")\n",
"fig.show()"
]
},
{
"cell_type": "markdown",
"id": "cd43ac54",
"metadata": {
"lines_to_next_cell": 2,
"papermill": {
"duration": 0.004578,
"end_time": "2026-05-15T03:46:38.899202+00:00",
"exception": false,
"start_time": "2026-05-15T03:46:38.894624+00:00",
"status": "completed"
}
},
"source": [
"## 5. Construct + Validate Helper\n",
"\n",
"The construction logic is generic across products. The Databento subscription\n",
"bundled with the book ships individual contract data for ES only; the other\n",
"29 products are delivered exclusively as pre-built continuous series. We\n",
"therefore wrap the construction-plus-validation in a single function and\n",
"apply it to ES — the only product where individual data is currently\n",
"available on disk."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "42595d2c",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:38.909838Z",
"iopub.status.busy": "2026-05-15T03:46:38.909578Z",
"iopub.status.idle": "2026-05-15T03:46:38.912382Z",
"shell.execute_reply": "2026-05-15T03:46:38.912009Z"
},
"papermill": {
"duration": 0.008256,
"end_time": "2026-05-15T03:46:38.912641+00:00",
"exception": false,
"start_time": "2026-05-15T03:46:38.904385+00:00",
"status": "completed"
}
},
"outputs": [],
"source": [
"def construct_and_validate(product: str) -> dict:\n",
" \"\"\"Construct a raw continuous series and validate it against the vendor continuous.\"\"\"\n",
" individual = load_cme_futures(products=[product], frequency=\"hourly\", continuous=False)\n",
" fronts = identify_front_month(individual)\n",
" continuous_raw = create_continuous_raw(individual, fronts)\n",
" databento = load_cme_futures(\n",
" products=[product], tenors=[0], frequency=\"hourly\", continuous=True\n",
" )\n",
" cmp = continuous_raw.select(\"timestamp\", pl.col(\"close\").alias(\"our_close\")).join(\n",
" databento.select(\"timestamp\", pl.col(\"close\").alias(\"db_close\")),\n",
" on=\"timestamp\",\n",
" how=\"inner\",\n",
" )\n",
" diff = (cmp[\"our_close\"] - cmp[\"db_close\"]).abs()\n",
" return {\n",
" \"product\": product,\n",
" \"rows\": len(continuous_raw),\n",
" \"contracts_used\": continuous_raw[\"instrument_id\"].n_unique(),\n",
" \"validation_rows\": len(cmp),\n",
" \"mean_abs_diff\": float(diff.mean()),\n",
" \"max_abs_diff\": float(diff.max()),\n",
" }"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "e4884e21",
"metadata": {
"execution": {
"iopub.execute_input": "2026-05-15T03:46:38.922990Z",
"iopub.status.busy": "2026-05-15T03:46:38.922894Z",
"iopub.status.idle": "2026-05-15T03:46:38.941311Z",
"shell.execute_reply": "2026-05-15T03:46:38.940877Z"
},
"papermill": {
"duration": 0.024618,
"end_time": "2026-05-15T03:46:38.941858+00:00",
"exception": false,
"start_time": "2026-05-15T03:46:38.917240+00:00",
"status": "completed"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Construction-vs-vendor validation (ES):\n"
]
},
{
"data": {
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"</style>\n",
"<small>shape: (1, 6)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>product</th><th>rows</th><th>contracts_used</th><th>validation_rows</th><th>mean_abs_diff</th><th>max_abs_diff</th></tr><tr><td>str</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td></tr></thead><tbody><tr><td>&quot;ES&quot;</td><td>3110</td><td>41</td><td>2581</td><td>27.19043</td><td>583.0</td></tr></tbody></table></div>"
],
"text/plain": [
"shape: (1, 6)\n",
"┌─────────┬──────┬────────────────┬─────────────────┬───────────────┬──────────────┐\n",
"│ product ┆ rows ┆ contracts_used ┆ validation_rows ┆ mean_abs_diff ┆ max_abs_diff │\n",
"│ --- ┆ --- ┆ --- ┆ --- ┆ --- ┆ --- │\n",
"│ str ┆ i64 ┆ i64 ┆ i64 ┆ f64 ┆ f64 │\n",
"╞═════════╪══════╪════════════════╪═════════════════╪═══════════════╪══════════════╡\n",
"│ ES ┆ 3110 ┆ 41 ┆ 2581 ┆ 27.19043 ┆ 583.0 │\n",
"└─────────┴──────┴────────────────┴─────────────────┴───────────────┴──────────────┘"
]
},
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],
"source": [
"validation_summary = pl.DataFrame([construct_and_validate(\"ES\")])\n",
"print(\"Construction-vs-vendor validation (ES):\")\n",
"validation_summary"
]
},
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"source": [
"## 6. Production Pipeline\n",
"\n",
"The teaching examples above demonstrate roll detection and adjustment methods on a single product.\n",
"For production use, the pipeline is:\n",
"\n",
"1. **Download**: Databento provides pre-rolled continuous contracts (hourly OHLCV) for\n",
" front, second, and third month tenors → `data/futures/market/continuous/hourly/`\n",
"2. **Session aggregation**: [`05_futures_session_aggregation`](05_futures_session_aggregation.ipynb) assigns CME session dates\n",
" and aggregates hourly bars into daily OHLCV → `data/futures/market/continuous/daily/continuous_daily.parquet`\n",
"3. **Loading**: `load_cme_futures()` reads the daily parquet for downstream analysis"
]
},
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"source": [
"---\n",
"\n",
"## Key Takeaways\n",
"\n",
"1. **Roll detection (volume-based, ES, 2016-2025)** finds **40 rolls** —\n",
" matching the four quarterly rolls per year × 10 years. The\n",
" no-rollback constraint is necessary because raw daily-volume leadership\n",
" can flicker between contracts during the roll window.\n",
"2. **Calendar spreads contaminate raw individual data**: CME ships outright\n",
" contracts alongside calendar spreads that trade at the inter-month\n",
" price difference (~$50–100) rather than the index level (~$5,000). The\n",
" `min_outright_price` filter in `identify_front_month` is what prevents\n",
" a high-volume spread from being selected as \"front month\".\n",
"3. **Panama (additive) adjustment** for the ES series adds about\n",
" **$625 to the earliest 2016 prices** (so the start-of-history close is\n",
" ~30% above the original quote). This preserves dollar P&L across rolls\n",
" but distorts percentage returns the further back you go.\n",
"4. **Ratio (multiplicative) adjustment** for the same window has a\n",
" **cumulative ratio of ~1.11 at the start of the series** (about\n",
" +11% scaling). Returns stay correct in percentage terms across the\n",
" whole window — the right choice for IC, momentum features, and any\n",
" statistical analysis.\n",
"5. **Validation against Databento's continuous** (2,581 daily-aligned bars):\n",
" **mean absolute difference ~$27**, **median signed difference ~$2.50**\n",
" (essentially zero relative to ~$3,800 average price). 2,444 of those\n",
" bars differ by more than $1 (most by a few dollars; **maximum absolute\n",
" gap ~$583**). Differences come almost entirely from roll-timing\n",
" disagreements — when our detector rolls a day earlier or later than the\n",
" vendor's algorithm, the two series report the price of a different\n",
" contract for those hours. The signed median near zero means the\n",
" disagreements wash out: neither algorithm is systematically high or\n",
" low.\n",
"\n",
"### Adjustment Method Selection\n",
"| Use Case | Recommended Method | Reason |\n",
"|----------|-------------------|--------|\n",
"| Backtesting P&L | Panama (additive) | Preserves dollar gains/losses across rolls |\n",
"| Statistical analysis | Ratio | Preserves percentage returns accurately |\n",
"| Live trading | Raw + position management | Handle rolls in execution layer |\n",
"\n",
"### Next Steps\n",
"\n",
"- **Chapter 8**: Carry and momentum features built on continuous series.\n",
"- **Chapter 16**: Backtesting with adjusted P&L."
]
}
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