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- aggressive momentum strategy
- re-balanced weekly
- trades individual S&P 100 stocks
- uses U.S. Treasury bonds as risk-off asset
Stocks on the Loose is a proprietary premium strategy by TuringTrader.com. It was introduced in early 2020, based on concepts reaching back to 2015.
Stocks on the Loose aims to beat the S&P 500 at lower volatility and with lower maximum drawdowns. To achieve its objective, the strategy uses a stock-trading momentum strategy, but gradually rotates into U.S. Treasuries when stock market conditions are unfavorable.
With its weekly rebalancing schedule, Stocks on the Loose has moderate maintenance requirements.
The chart above shows the portfolio’s historical performance and drawdowns, compared to their benchmark, throughout the simulation. The chart below shows the portfolio’s annual returns:
This table shows the performance metrics for TuringTrader's Stocks on the Loose:
The portfolio last required rebalancing after the exchange's close on n/a. Due to fluctuations in asset prices and portfolio values, the exact allocations vary daily. The current asset allocation is as follows:
The operation of Stocks on the Loose can be summarized as follows:
- trade all S&P 100 stocks, plus U.S. treasuries as a risk-off asset
- rebalance once per week (on Wednesdays)
- disqualify stocks trading below their 100-day moving average
- disqualify stocks that made any single-day moves exceeding 15% in the past 90 days
- rank stocks by their volatility-adjusted momentum, calculated as the product of slope and R2 of a 90-day logarithmic regression
- only open new positions, if the S&P 500 is trading above its 200-day moving average
- use fixed-fractional position sizing, based on the 20-day average trading range
- ensure healthy position sizes by limiting total portfolio risk, capping the maximum allocation to a single stock, and by scaling back exposure with increasing market volatility
- invest any unused capital in U.S. treasuries
Most of these rules are taken verbatim from Clenow's Stocks on the Move strategy. We therefore recommend reading Clenow's book to better understand the strategy's rationale, and checking the C# source code in the TuringTrader.org open-source repository.
Expanding upon Clenow's work, we made additions to the money-management to prevent the following issues: concentration in too few assets, taking excessive total portfolio risk, and holding idle cash. These proprietary changes lead to an overall more even-keeled behavior.
Stocks on the Loose typically allocates 100% of its capital towards a set of 3 to 6 stocks. In more turbulent times, stocks are gradually replaced with an ETF representing U.S. Treasury bonds.
With this allocation, the portfolio bears significant concentration risk, in addition to the market risk. Nonetheless, the portfolio's beta is below 0.5, thanks to the strategy's timely active management.
Returns & Volatility
Over a full economic cycle, Stocks on the Loose outperforms the S&P 500 by a wide margin. Further, the portfolio beats the S&P 500 in many years, even outside of recessions.
This behavior results in more predictable returns and lower drawdowns. The Monte-Carlo simulation confirms these claims.
Account & Tax Considerations
Stocks on the Loose trades frequently and regularly triggers taxable events. The chart above shows that investors should expect almost all capital gains to be short term. Therefore, the strategy works best in tax-deferred accounts.
Because the strategy holds up to 6 high-flying and potentially expensive stocks simultaneously, it requires no less than $20,000 to function as intended.