Facteurs Fama-French et données AQR

Code Machine Learning for Trading

Résumé

Ce guide présente les bases publiques de rendements factoriels de la Ken French Data Library et de AQR, notamment les facteurs de marché, de taille, de valeur, de rentabilité, d’investissement, de momentum, de qualité et de bêta faible. Il explique comment télécharger et charger des observations mensuelles ou quotidiennes, filtrer par date, examiner les profils des données et calculer les moyennes annualisées, la volatilité et les ratios de Sharpe à partir des rendements mensuels. Ces séries peuvent servir à la recherche sur l’investissement factoriel, à l’attribution du risque et à comparer les rendements d’une stratégie aux expositions établies.

Le document précise que la couverture factorielle est définie par les fournisseurs et que les sources ne nécessitent pas de clé API. Il ne présente aucun résultat factoriel empirique ni comparaison entre fournisseurs, et n’évalue pas si un facteur génère une prime persistante. La couverture varie selon les séries AQR, tandis que les données de Ken French remontent plus loin dans le temps. Les statistiques présentées sont de simples résumés ; elles ne suffisent pas à établir une causalité, la possibilité d’investir ou la performance après coûts de trading.

Idées clés

Les jeux de données Fama-French comprennent les rendements des facteurs de marché, de taille, de valeur, de rentabilité, d’investissement et de momentum.
AQR propose d’autres séries factorielles, notamment des facteurs de qualité et de bêta faible.
Les rendements factoriels peuvent servir à comparer des stratégies et à attribuer leurs expositions au risque.
Les données factorielles mensuelles peuvent être résumées par la moyenne annualisée, la volatilité et le ratio de Sharpe.
La couverture des données et la définition des facteurs dépendent du fournisseur.

Étiquettes

Investissement factoriel Statistiques Gestion des risques

Texte intégral

# dataset_card.py


```py
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# %% [markdown]
# # 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()` |

# %%
"""Academic Factor Data - download, explore, and update workflow."""

from pathlib import Path

import polars as pl

# %% [markdown]
# ## 1. Configuration
#
# Academic factor data is **provider-defined** (no local config file). Each provider
# maintains their own factor definitions and data format.

# %%
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")

# %% [markdown]
# ## 2. API Key Setup
#
# **No API key required.** Both Ken French Library and AQR provide free public access.

# %%
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")

# %% [markdown]
# ## 3. Download Data
#
# The `ml4t-data` library handles downloading and caching factor data.


# %%
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}")


# %% [markdown]
# ### Download Fama-French Factors

# %%
# Uncomment to download
# download_ff_factors()

# %% [markdown]
# ### Download AQR Factors

# %%
# Uncomment to download
# download_aqr_factors()

# %% [markdown]
# ### Dry Run (Preview)

# %%
download_ff_factors(dry_run=True)

# %% [markdown]
# ## 4. Load and Explore
#
# Once downloaded, use the loaders throughout the book:

# %%
from data import load_aqr_factors, load_ff_factors

# %% [markdown]
# ### Fama-French Factors

# %%
# 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")

# %%
# Preview
ff.tail(10)

# %%
# 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}")

# %% [markdown]
# ### AQR Factors

# %%
# 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()}")

# %%
# Preview
aqr.tail(10)

# %% [markdown]
# ## 5. Data Profile

# %%
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."
)

# %% [markdown]
# ## 6. Loader Options
#
# The loaders support filtering by frequency and date range:

# %%
# Daily frequency
ff_daily = load_ff_factors(frequency="daily")
print(f"FF daily: {ff_daily.shape}")

# %%
# Date range
recent_ff = load_ff_factors(start_date="2020-01-01")
print(f"FF 2020+: {recent_ff.shape}")

# %% [markdown]
# ## 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)

# %% [markdown]
# ## 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.

# %% [markdown]
# ## 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.

```

Reproduit dans son intégralité avec attribution, conformément à la licence de la source. Licence: MIT

Ce résumé a été rédigé par l’agent de recherche de Stratmill à partir de la source originale ; il n’en est pas une copie.

Facteurs Fama-French et AQR pour l’attribution du risque

Résumé

Idées clés

Étiquettes