# How the Betting Against Beta Factor Strategy Works: A Complete Python Implementation > Learn how the betting against beta factor strategy works. Build a market-neutral portfolio long low-beta and short high-beta assets to capture risk-adjusted premiums with Python. Optimize your trading today. - Repository: [Papers With Backtest/awesome-systematic-trading](https://github.com/paperswithbacktest/awesome-systematic-trading) - Tags: deep-dive - Published: 2026-08-02 --- **The betting against beta factor strategy constructs a market-neutral portfolio by going long low-beta securities and short high-beta securities, scaling each leg to achieve a target beta of 1.0 to capture the risk-adjusted premium where low-beta assets outperform.** The betting against beta factor strategy is a systematic approach implemented in the **awesome-systematic-trading** repository that exploits the empirical inverse relationship between market beta and expected returns. This strategy creates a zero-cost, zero-beta portfolio by leveraging the spread between low-beta and high-beta assets across US equities and global country ETFs. ## Core Mechanics of the Betting Against Beta Strategy The strategy follows a rigorous quantitative pipeline from data collection through execution. According to the source code in the `paperswithbacktest/awesome-systematic-trading` repository, the implementation relies on rolling window calculations and precise beta estimation to maintain market neutrality. ### Data Collection and Beta Calculation Each implementation maintains a **rolling window** of daily closing prices spanning approximately 252 trading days (`self.period = 12 * 21`). In [`betting-against-beta-factor-in-stocks.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/betting-against-beta-factor-in-stocks.py), this data feeds into QuantConnect's coarse and fine selection pipeline, while the country ETF version processes incoming price ticks directly. Beta estimation follows the standard financial formula implemented using NumPy: ```python cov = np.cov(asset_returns, market_returns)[0][1] market_variance = np.var(market_returns) beta = cov / market_variance ``` In the stock implementation (lines 100-106), this calculation uses a one-year rolling window of returns against the SPY benchmark. The country ETF version (lines 84-89) applies the same formula after refreshing monthly price windows. ### Portfolio Construction and Decile Ranking Assets are ranked by their estimated beta to form long and short legs. The US equities implementation in [`betting-against-beta-factor-in-stocks.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/betting-against-beta-factor-in-stocks.py) uses a decile-based approach (lines 112-116): - **Long leg**: Lowest-beta decile (bottom 10%) - **Short leg**: Highest-beta decile (top 10%) For the country ETF implementation in [`betting-against-beta-factor-in-country-equity-indexes.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/betting-against-beta-factor-in-country-equity-indexes.py), the strategy uses a median split, placing assets below the median beta in the long portfolio and those above in the short portfolio (lines 95-97). ### Leverage Normalization for Market Neutrality To achieve true market neutrality, each leg is **beta-scaled** to target a portfolio beta of 1.0: ```python long_lvg = 1 / long_mean_beta short_lvg = 1 / short_mean_beta ``` This scaling ensures the long and short positions offset market exposure. The implementation caps leverage via `self.leverage_cap` to prevent excessive margin requirements (lines 28-35 for stocks, lines 46-52 for ETFs). ## Implementation Details for US Equities The [`betting-against-beta-factor-in-stocks.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/betting-against-beta-factor-in-stocks.py) file implements the strategy for a CRSP-like universe of US equities. The algorithm uses QuantConnect's `RollingWindow[float]` objects to maintain price history and calculates betas against the SPY benchmark. Rebalancing occurs at the **start of each month** using the `Selection` scheduler (line 41). Positions are entered via `SetHoldings` (lines 55-58) with a custom fee model (`CustomFeeModel`) set to 0.5 basis points to simulate realistic transaction costs. The implementation enforces a leverage limit of `self.leverage_cap * 3` to control margin risk. ## Implementation Details for Country ETFs The [`betting-against-beta-factor-in-country-equity-indexes.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/betting-against-beta-factor-in-country-equity-indexes.py) file adapts the strategy for global equity-style ETFs. This version processes price data directly from incoming ticks and triggers rebalancing when the month changes (lines 72-74). Rather than deciles, this implementation uses a **median-beta split** to create balanced long and short exposure. The leverage cap is set to 15x for this universe, reflecting the different volatility characteristics of diversified country indexes compared to individual stocks. ## Risk Management and Execution Both implementations employ strict risk controls through the `CustomFeeModel` class and leverage limits. The stock version applies fees of 0.5 basis points per trade, while position sizing respects the calculated leverage ratios to maintain the zero-beta target. The `SetHoldings` method adjusts portfolio weights automatically based on the scaled betas, ensuring the strategy remains market-neutral after each rebalance. This execution framework allows the strategy to capture the betting against beta premium while isolating idiosyncratic risk from systematic market movements. ## Summary - The betting against beta factor strategy exploits the inverse relationship between beta and risk-adjusted returns by going long low-beta assets and short high-beta assets. - Beta calculation uses 252-day rolling windows of daily returns against a market benchmark (SPY), implemented via `np.cov` and `np.var` in lines 100-106 of the stock file. - Portfolio construction uses decile ranking for US equities and median splits for country ETFs to form long and short legs. - Leverage normalization scales each leg to achieve a target beta of 1.0, creating a zero-cost, market-neutral portfolio. - Monthly rebalancing and custom fee models ensure realistic backtesting conditions in QuantConnect. ## Frequently Asked Questions ### What is the betting against beta anomaly? The betting against beta anomaly refers to the empirical finding that low-beta securities generate higher risk-adjusted returns than predicted by the Capital Asset Pricing Model (CAPM), while high-beta securities underperform. This violates the standard CAPM assumption that higher beta should correlate linearly with higher expected returns, creating an exploitable spread for market-neutral strategies. ### How is beta calculated in the betting against beta strategy? Beta is calculated as the covariance between asset returns and market returns divided by the variance of market returns: `beta = cov(asset_returns, market_returns) / var(market_returns)`. The awesome-systematic-trading implementation uses NumPy's `np.cov` and `np.var` functions on 252-day rolling windows to estimate this relationship daily for each security in the universe. ### What is the difference between the stock and ETF implementations? The stock implementation in [`betting-against-beta-factor-in-stocks.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/betting-against-beta-factor-in-stocks.py) uses decile-based ranking (bottom 10% long, top 10% short) and leverages QuantConnect's coarse/fine universe selection pipeline. The country ETF implementation in [`betting-against-beta-factor-in-country-equity-indexes.py`](https://github.com/paperswithbacktest/awesome-systematic-trading/blob/main/betting-against-beta-factor-in-country-equity-indexes.py) uses a simpler median split and processes price data directly from ticks, with a higher leverage cap (15x vs. variable stock cap) reflecting the diversification benefits of country indexes. ### Why is leverage scaling important in BAB strategies? Leverage scaling ensures the portfolio achieves **market neutrality** by adjusting position sizes inversely to the average beta of each leg. By applying `1 / mean_beta` scaling to both long and short positions, the strategy creates a zero-beta portfolio that isolates the alpha from the beta-return relationship while eliminating exposure to broad market movements.