Fama-FrenchとAQRファクターによるリスク調整とベンチマーク
ノートブック Machine Learning for Trading
サマリー
この文書では、ベンチマーク、リスク要因の分析、ファクター調査の入力データとして、公開されているFama-FrenchとAQRのファクターリターンデータセットを紹介します。Fama-Frenchライブラリの市場、規模、バリュー、収益性、投資、モメンタムのファクターに加え、AQRの系列が扱うクオリティ、低ベータ、代替的なバリュー概念も説明します。データは月次で利用でき、ローダーを使った日次Fama-Frenchデータの読み込みと期間指定にも対応しています。
ワークフローでは、データセットのダウンロードとキャッシュ、読み込みと概要把握、月次リターンからの年率換算平均、ボラティリティ、シャープレシオの計算を行います。説明されているFama-Frenchデータは1926年に始まりますが、AQRのデータ期間はファクターごとに異なります。各データ提供元が独自の系列と形式を定義しています。この資料はデータの取得と調査のガイドであり、いずれかのファクターが持続的なリターンを生む証拠ではありません。リスク調整や運用成績の評価にファクターを使う際は、提供元の定義、更新時期、要約統計の限界を考慮する必要があります。
主なアイデア
- Fama-Frenchデータセットには、研究ベンチマーク向けの市場、規模、バリュー、収益性、投資、モメンタムのリターンが含まれます。
- AQRのデータセットには、クオリティ、低ベータ、代替的なバリューのファクター系列が含まれ、利用期間はファクターごとに異なります。
- 月次と日次のデータを読み込め、分析対象の期間も指定できます。
- 年率換算平均、ボラティリティ、シャープレシオは基本的な要約指標ですが、将来のファクター運用成績を示すものではありません。
- ファクターの定義とデータ形式は提供元が定めるため、結果は選択した系列と対象期間に左右されます。
タグ
全文
# Academic Factor Data Dataset
# Academic Factor Data Dataset
Fama-French and AQR factor returns for benchmarking and risk adjustment.
| Property | Value |
|----------|-------|
| **Provider** | Ken French Library, AQR |
| **Asset Class** | Factor Returns |
| **Frequency** | Monthly (daily available) |
| **Factors** | FF3, FF5, Momentum, QMJ, BAB |
| **Coverage** | 1926-present (FF), varies (AQR) |
| **Size** | ~5 MB |
| **API Key** | None (free) |
| **Loader** | `load_ff_factors()`, `load_aqr_factors()` |
```python
"""Academic Factor Data - download, explore, and update workflow."""
from pathlib import Path
import polars as pl
```
## 1. Configuration
Academic factor data is **provider-defined** (no local config file). Each provider
maintains their own factor definitions and data format.
```python
print("=== Academic Factor Configuration ===")
print("\nFama-French (Ken French Library):")
print(" - FF3: Mkt-RF, SMB, HML")
print(" - FF5: FF3 + RMW, CMA")
print(" - Momentum: MOM")
print(" - Coverage: 1926-present")
print("\nAQR Research:")
print(" - QMJ: Quality Minus Junk")
print(" - BAB: Betting Against Beta")
print(" - VME: Value Minus Everything")
print(" - HML Devil: Industry-adjusted value")
print(" - Coverage: varies by factor")
```
## 2. API Key Setup
**No API key required.** Both Ken French Library and AQR provide free public access.
```python
print("Ken French Library: Free, no API key required")
print(" URL: https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html")
print("\nAQR Research: Free, no API key required")
print(" URL: https://www.aqr.com/Insights/Datasets")
```
## 3. Download Data
The `ml4t-data` library handles downloading and caching factor data.
```python
def download_ff_factors(
datasets: list[str] | None = None, frequency: str = "monthly", dry_run: bool = False
):
"""Download Fama-French factor data.
Args:
datasets: Specific datasets to download (default: core factors)
frequency: "monthly" or "daily"
dry_run: If True, show what would be downloaded
"""
from ml4t.data.providers.fama_french import FamaFrenchProvider
from utils import ML4T_DATA_PATH
output_dir = ML4T_DATA_PATH / "factors" / "fama-french"
# Default core datasets
if datasets is None:
datasets = ["ff3", "ff5", "mom"]
print("=== Fama-French Download ===")
print(f"Datasets: {datasets}")
print(f"Frequency: {frequency}")
print(f"Output: {output_dir}")
if dry_run:
print("\n[DRY RUN] Would download:")
for ds in datasets:
print(f" - {ds}")
return
output_dir.mkdir(parents=True, exist_ok=True)
provider = FamaFrenchProvider(cache_path=output_dir, use_cache=True)
print(f"\nDownloading {len(datasets)} datasets...")
for dataset in datasets:
print(f" {dataset}...", end=" ", flush=True)
try:
df = provider.fetch(dataset, frequency=frequency)
print(f"OK ({len(df):,} rows)")
except Exception as e:
print(f"ERROR: {e}")
print("\n=== Complete ===")
print(f"Data saved to: {output_dir}")
def download_aqr_factors(datasets: list[str] | None = None, dry_run: bool = False):
"""Download AQR factor data.
Args:
datasets: Specific datasets to download (default: core factors)
dry_run: If True, show what would be downloaded
"""
from ml4t.data.providers.aqr import AQRProvider
from utils import ML4T_DATA_PATH
output_dir = ML4T_DATA_PATH / "factors" / "aqr"
# Default core datasets
if datasets is None:
datasets = ["qmj", "bab"]
print("=== AQR Download ===")
print(f"Datasets: {datasets}")
print(f"Output: {output_dir}")
if dry_run:
print("\n[DRY RUN] Would download:")
for ds in datasets:
print(f" - {ds}")
return
output_dir.mkdir(parents=True, exist_ok=True)
provider = AQRProvider(cache_path=output_dir)
print(f"\nDownloading {len(datasets)} datasets...")
for dataset in datasets:
print(f" {dataset}...", end=" ", flush=True)
try:
df = provider.fetch(dataset)
print(f"OK ({len(df):,} rows)")
except Exception as e:
print(f"ERROR: {e}")
print("\n=== Complete ===")
print(f"Data saved to: {output_dir}")
```
### Download Fama-French Factors
```python
# Uncomment to download
# download_ff_factors()
```
### Download AQR Factors
```python
# Uncomment to download
# download_aqr_factors()
```
### Dry Run (Preview)
```python
download_ff_factors(dry_run=True)
```
## 4. Load and Explore
Once downloaded, use the loaders throughout the book:
```python
from data import load_aqr_factors, load_ff_factors
```
### Fama-French Factors
```python
# Load Fama-French factors
ff = load_ff_factors()
print(f"Shape: {ff.shape}")
print(f"Columns: {ff.columns}")
print(f"Date range: {ff['timestamp'].min()} to {ff['timestamp'].max()}")
print(f"Memory: {ff.estimated_size('mb'):.1f} MB")
```
```python
# Preview
ff.tail(10)
```
```python
# Factor statistics (annualized)
factor_cols = [c for c in ff.columns if c not in ["timestamp", "date"]]
print("Factor Annualized Statistics (%):")
for col in factor_cols[:6]:
series = ff[col].drop_nulls()
mean_annual = series.mean() * 12 # Monthly to annual
vol_annual = series.std() * (12**0.5)
sharpe = mean_annual / vol_annual if vol_annual > 0 else 0
print(f" {col:8s}: mean={mean_annual:6.2f}, vol={vol_annual:6.2f}, SR={sharpe:.2f}")
```
### AQR Factors
```python
# Load AQR factors
aqr = load_aqr_factors()
print(f"Shape: {aqr.shape}")
print(f"Columns: {aqr.columns}")
print(f"Date range: {aqr['timestamp'].min()} to {aqr['timestamp'].max()}")
```
```python
# Preview
aqr.tail(10)
```
## 5. Data Profile
```python
from ml4t.data.storage.data_profile import get_profile_path, load_profile
from utils import ML4T_DATA_PATH
for provider, subdir in [("Fama-French", "fama-french"), ("AQR", "aqr")]:
profile_path = get_profile_path(ML4T_DATA_PATH / "factors" / subdir)
profile = load_profile(profile_path)
if profile is None:
print(f"No {provider} profile at {profile_path}")
else:
print(f"=== {provider} Profile ===")
print(f"Written by {profile.source}")
print(profile.summary())
print(
"\nff_download.py and aqr_download.py unzip the providers' own CSV releases and do\n"
"not go through ml4t.data.storage.data_profile, so neither carries a profile today.\n"
"Nothing in this notebook writes one either."
)
```
## 6. Loader Options
The loaders support filtering by frequency and date range:
```python
# Daily frequency
ff_daily = load_ff_factors(frequency="daily")
print(f"FF daily: {ff_daily.shape}")
```
```python
# Date range
recent_ff = load_ff_factors(start_date="2020-01-01")
print(f"FF 2020+: {recent_ff.shape}")
```
## 7. Documentation
### Fama-French Factors
From Ken French's Data Library:
| Factor | Description |
|--------|-------------|
| Mkt-RF | Market excess return |
| SMB | Small Minus Big (size) |
| HML | High Minus Low (value) |
| RMW | Robust Minus Weak (profitability) |
| CMA | Conservative Minus Aggressive (investment) |
| Mom | Momentum (12-1 month return) |
[Ken French Data Library](https://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html)
### AQR Factors
Alternative factors from AQR Capital:
| Factor | Description |
|--------|-------------|
| QMJ | Quality Minus Junk (profitability, growth, safety) |
| BAB | Betting Against Beta (low-beta premium) |
| VME | Value Minus Everything (alternative value) |
| HML Devil | Value with industry adjustment |
[AQR Datasets](https://www.aqr.com/Insights/Datasets)
## 8. Updating Data
To update with the latest data:
```python
# Update Fama-French factors
download_ff_factors()
# Update AQR factors
download_aqr_factors()
```
Factor data is typically updated monthly.
## Summary
| Item | Value |
|------|-------|
| Providers | Ken French, AQR |
| Frequencies | Monthly, Daily |
| Coverage | 1926-present (FF), varies (AQR) |
| API Key | None (free) |
| Loaders | `load_ff_factors()`, `load_aqr_factors()` |
**Primary use**: Risk attribution, alpha measurement, factor investing research.出典を明記したうえで、ライセンスに従って全文を掲載しています。 ライセンス: MIT
この要約は原文をもとにStratmillのリサーチエージェントが作成したもので、出典の複製ではありません。