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SPY Long Put 45 DTE Options Backtest: Optimal Hedging

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SPY-45-DTE-Long-Put-vs-AGG-vs-3mo-T-Bill-1
6 min read

In this post we’ll take a look at the backtest results of opening SPY long put 45 DTE positions from January 3 2007 through November 8 2019 and see if there are any discernible trends. We’ll also explore various bond and cash asset allocations to see which approach offers the best hedge.

There are 20 backtests in this study evaluating over 64,500 SPY long put 45 DTE trades.

This study will not explore a leveraged implementation of SPY long put 45 DTE positions.

Let’s dive in!

Prior Research

Basics

How to Trade Options Efficiently Mini-Series

Backtesting Concepts

Building a Research Framework

AAPL – Apple Inc.

AMZN – Amazon.com, Inc.

BTC – Bitcoin

C – Citigroup Inc.

DIA – SPDR Dow Jones Industrial Average

  • DIA Short Put 7 DTE Cash-Secured (coming soon)
  • DIA Short Put 7 DTE Leveraged (coming soon)
  • DIA Short Put 45 DTE Cash-Secured (coming soon)
  • DIA Short Put 45 DTE Leveraged (coming soon)

DIS – Walt Disney Co

EEM – MSCI Emerging Markets Index

GE – General Electric Company

GLD – SPDR Gold Trust

IWM – Russel 2000 Index

MU – Micron Technology, Inc.

QQQ – Nasdaq 100 Index

SLV – iShares Silver Trust

SPY – S&P 500 Index

T – AT&T Inc.

TLT – Barclays 20+ Yr Treasury Bond

TSLA – Tesla, Inc.

USO – United States Oil Fund

VXX – S&P 500 VIX Short-Term Futures

VZ – Verizon Communications Inc.

Other

Methodology

Core Strategy

  • Symbol SPY
  • Strategy Long Put
  • Start Date 2007-01-03
  • End Date 2019-11-08
  • Positions opened 1
  • Entry Days every trading day in which entry criteria is satisfied
  • Timing 3:46pm ET
  • Strike Selection
    • 5 delta +/- 4.5 delta, closest to 5
    • 10 delta +/- 5 delta, closest to 10
    • 16 delta +/- 6 delta, closest to 16
    • 30 delta +/- 8 delta, closest to 30
    • 50 delta +/- 8 delta, closest to 50
  • Trade Entry
    • 5D long put
    • 10D long put
    • 16D long put
    • 30D long put
    • 50D long put
  • Trade Exit
    • 10% max profit or expiration, whichever occurs first
    • 25% max profit or expiration, whichever occurs first
    • 50% max profit or expiration, whichever occurs first
    • Hold till expiration

Days Till Expiration

Some studies look at ultra-short-duration option strategies while others explore longer durations. The nuances and range for each approach are summarized below.

0 DTE Strategies

Between 0 and 3, closest to 0.

The range is up to 3 days from expiration for two reasons: to allow opening positions on Friday that have a Monday expiration and to allow more opportunities for occurrences of strategies focused in the 10-40 delta range. As expiration nears, it becomes increasingly difficult to open positions in this range.

This visual from Options Playbook does a great job illustrating the concept. Notice how at 1 DTE delta jumps from .50 to .10 with a single dollar change in the underlying. Compare this to the 60 DTE scenario where the change in delta for a $1 change in underlying is much smaller. By allowing positions to be opened as far out as 3 DTE, delta sensitivity to $1 differences in strikes becomes muted.

Delta sensitivity at different expiration horizons.

7 DTE Strategies

Between 3 and 11, closest to 7.

The range is 4 days either side of 7 to ensure a position can be opened each trading day while remaining true to the duration target. For example, opening a position Wednesday will have either a 2 DTE horizon (next Friday) or a 9-DTE horizon (the Friday after next). In this scenario the 9-DTE position would be selected.

45 DTE Strategies

Between 28 and 62, closest to 45.

The range is 17 days either side of 45 to account for quadruple witching. As the end of each calendar quarter approaches, namely during the last 7-10 days of Mar, Jun, Sep and Dec, the expiration dates of option contracts widen significantly.

730 DTE Strategies (LEAPS)

Between 550 and 910, closest to 730.

The range is 180 days either side of 730 to account for underlying that have LEAPS expirations in 6-month increments.

Calculating Returns

Returns are calculated daily using notional returns. The change in daily values of the option is divided by the stock price at the time of order entry.

The formula for daily return is:

option profit / opening stock price.

For example, suppose we opened a XYZ short put at $1.10 on 1/3/2007 with a stock price of $50:

  • On 1/4/2007, our option increased to $1.50. The notional daily return calculation would be ( $1.10 – $1.50 ) / $50 = -.008 which is -.8% daily return on 1/4/2007
  • On 1/5/2007 our option decreased to $0.80. The notional daily return calculation would be ( $1.10 – $0.80 ) / $50 = .006 which is .6% daily return on 1/5/2007

By using notional returns on daily stock values when calculating returns we isolate the performance of the option strategy from the effects of leverage. This allows us to identify strategy performance in a non-margin context such as in a US-based retirement account.

By measuring strategy performance as a daily percentage change we abstract the strategy performance from absolute dollar gain/loss to a relative percentage value. This is a fancy way of saying the strategy becomes capital agnostic. In other words, think of an ETF that executes the respective option strategy. We can allocate $100 to the “option ETF” and $100 to the underlying and have an apples-to-apples, dollar-for-dollar comparison.

Monthly and Annual Returns

To identify the monthly and/or annual returns for an option strategy, the respective daily returns are summed.

Graphing Underlying and Option Curves

The underlying position derives its monthly performance values from Portfolio Visualizer. Portfolio returns are calculated in a compound fashion using this monthly data.

Option strategies derive monthly performance values from the backtesting tool by summing the respective daily returns. Portfolio returns are calculated using the following formula:

( backtest starting capital * monthly return ) + portfolio balance

Margin

Margin requirements and margin calls are assumed to always be satisfied and never occur, respectively.

In practice the option strategy may experience varied performance, particularly during high-volatility periods, than what’s depicted. Margin requirements may prevent the portfolio from sustaining the number of concurrent open positions the strategy demands.

Moneyness

Positions that become ITM during the life of the trade are assumed to never experience early assignment.

In practice early assignment may impact performance positively (assigned then position experiences greater losses) or negatively (assigned then position recovers).

Commission

The following commission structure is used throughout the backtest:

  • 1 USD, all in, per contract:
    • to open
    • to close early
    • expired ITM
  • 0 USD, all in, per contract expired OTM / worthless

While these costs are competitive at the time of writing, trade commissions were significantly more expensive in the late 2000s and early 2010s.

In practice strategy performance may be lower than what’s depicted due to elevated trading fees in the earlier years of the backtest.

Slippage

Slippage is factored into all trade execution prices accordingly:

  • Buy: Bid + (Ask – Bid) * slippage%
  • Sell: Ask – (Ask – Bid) * slippage%

The following table outlines the slippage values used and example calculations:

  • A slippage % of .50 = midpoint
  • A slippage % of 1.00 = market maker’s price

Inflation

All values depicted are in nominal dollars. In other words, values shown are not adjusted for inflation.

In practice this may influence calculations that are anchored to a particular value in time such as the last “peak” when calculating drawdown days.

Calculating Strategy Statistics

Automated backtesters are generally great tools for generating trade logs but dismal tools to generate statistics. Therefore, I build all strategy performance statistics directly from the trade logs. Below is a breakdown on how I calculate each stat and the associated formula behind the calculation.

Win Rate

Trades that were closed at management targets (profit, DTE) as winners but became unprofitable due to commissions are considered non-winning trades. This phenomenon is typically observed when managing 2.5D and 5D trades early.

( count of trades with P/L > 0 ) / count of all trades

Annual Volatility

The standard deviation of all the monthly returns are calculated then multiplied the by the square root of 12.

STDEV.S(monthly return values) * SQRT(12)

Worst Monthly Return

Identify the smallest value among the monthly returns:

MIN(monthly return values)

Average P/L per Day

This measures changes in capital efficiency due to early management.

( average P/L per trade ) / average trade duration

Average Trade Duration

This measures the average number of days each position remains open, rounded to the nearest whole day.

ROUND ( AVERAGE ( trade duration values ) , 0 )

Compound Annual Growth Rate

This measures the compounded annual rate of return, sometimes referred to as the geometric return. The following formula is used:

Sharpe Ratio

Total P/L alone is not enough to determine whether a strategy outperforms. To get the complete picture, volatility must be taken into account. By dividing the compound annual growth rate by the volatility we identify the risk-adjusted return, known as the Sharpe ratio.

strategy CAGR / strategy volatility

Profit Spent on Commission

The following formula is used to calculate the percent of profits spent on commissions:

If a strategy is depicted as having percent greater than 100, this means the strategy is unprofitable due to commissions but would have been profitable if trades were commission free throughout the duration of the backtest.

If a strategy is depicted as “unprofitable” this means the strategy lost money even if trades were commission free throughout the duration of the backtest.

Total P/L

How much money is in the portfolio after the study? This stat answers that question and depicts it as a %

( portfolio end value / portfolio start value ) - 1

Scope

This study seeks to measure the performance of opening long put 45 DTE positions and will interpret the results from the lens of hedging relative to comparable bond and cash asset allocations.

Results

Win Rate

SPY Long Put 45 DTE win rate

Managing trades early improved the win rate.

The higher the delta the higher the win rate.

Annual Volatility

SPY Long Put 45 DTE annual volatility

Of the option strategies, holding till expiration yielded the lowest portfolio volatility.

Of the buy-and-hold strategies, cash yielded the lowest portfolio volatility.

Buy and hold strategies outperformed option strategies with regard to volatility up to 84/16, equities / fixed income. Beyond 84/16, using options held till expiration yielded the lowest portfolio volatility.

Worst Monthly Return

SPY Long Put 45 DTE worst monthly return

Options outperformed a buy-and-hold asset allocation when it comes to softening the worst month.

Of the buy-and-hold strategies, cash outperformed bonds when it came to dampening the worst month.

Average P/L Per Day

SPY Long Put 45 DTE average profit and loss per day

Looking strictly at the cost to implement the SPY long put 45 DTE option strategy, holding till expiration had the lowest hedge cost across all strategies except 5D. Managing at 50% or expiration had the lowest hedge cost at 5D.

Average Trade Duration

SPY Long Put 45 DTE average trade duration

Managing trades at 10% profit on premium paid caused the strategy to cycle through capital roughly 2x faster than holding till expiration.

Compound Annual Growth Rate

SPY Long Put 45 DTE compound annual growth rate

Buy-and-hold strategies outperformed options with regard to compound annual returns.

Of the options strategies, managing long positions early outperformed holding long positions till expiration.

Sharpe Ratio

SPY Long Put 45 DTE Sharpe ratio

Buy-and-hold strategies outperformed options with regard to risk-adjusted returns.

Of the options strategies, risk-adjusted performance was roughly the same at each level of hedge whether managing early or holding till expiration.

The greater the hedge the greater buy-and-hold asset allocations outperformed option strategies.

Profit Spent on Commission

SPY Long Put 45 DTE profit spent on commission

The buy and hold strategies were rebalanced monthly in order to keep the asset allocation as close to the target as possible.

Since larger hedges yield a lower total return due the cost of the hedge, the commission represents a greater percentage of remaining profits.

Total P/L

SPY Long Put 45 DTE total profit and loss

A portfolio of various buy-and-hold asset allocations outperforms a comparably hedged portfolio using options.

Managing option hedges early yielded greater total returns than holding long puts till expiration.

Overall

SPY Long Put 45 DTE overall performance

All long put 45 DTE strategies were profitable when married with a long SPY position.

Discussion

The idea of this study is to quantify the performance of different portfolio hedging tools. In particular, cash (defined as 3mo t-bills), bonds (defined as AGG ETF) and options (defined as SPY long put 45 DTE strategies) are the instruments evaluated.

As the size of the hedge relative to the size of the portfolio increases, a basic asset allocation increasingly outperforms on both a risk adjusted and total return perspective relative to an option strategy. Said another way, a 5% hedge has roughly the same “cost” (opportunity or explicit) no matter the tool selected whereas a 50% hedge “costs” much more when options are used as the instrument of implementation.

Recall that options offered the softest worst month among bonds and cash. That is, they provided the most effective hedge during a market drop. This only paints half the picture though.

Let’s take a look at the max draw down of each strategy, measured from the most recent end-of-month high (Oct 2007) to the lowest end-of month before the next high (Feb 2009):

SPY Long Put 45 DTE max drawdown pct table

While it is true options provided the most effective hedge within a given month, monthly-rebalanced cash or bonds outperformed with regard to max draw down %. This is due to the higher costs of the option hedge being incurred when the more effective and less expensive cash or bonds is successfully hedging the less extreme drops.

If you can time the market, an option hedge is optimal. If you can’t, a buy-and-hold asset allocation is optimal.

“But what about a collar option strategy?”, you might ask. It seems intuitive to sell an out-of-the money call to help pay for the long put hedge at the risk of capping potential gains.

The SPY short call 45 DTE strategy explores the short call half of the collar strategy. It is systematically unprofitable across all scenarios except the 5D hold-till-expiration strategy where a 0.2% CAGR is realized over 12 years. Not worth the time or risk.

Not depicted in the study is the free lunch retail traders have over institutional market participants: interest rate arbitrage. Consider online high-yield savings accounts. They are FDIC insured to not lose value (up to 250k per account) so they experience no interest rate risk or reinvestment risk and tend to have interest rates comparable the 10-year treasury note. Sometimes they even beat the 30-year treasury bond during seasons of flat yield or inverted yield curves. Parking money in such accounts can potentially outperform bonds – no price volatility and subsequently lower portfolio volatility, softer worst months, and CAGR may potentially be higher. Win win win.

Summary

Systematically opening long put 45 DTE positions on SPY cost money no matter which strategy or management target was chosen.

Options provide a more effective hedge when markets drop. However, timing is everything.

If implementing a “continuous hedge,” an asset allocation of bonds or cash outperforms long put options on both a risk-adjusted and total return basis.

Retail traders have a free lunch available in the form of high-yield online savings accounts, potentially tipping the scale in favor of cash over bonds.

Thanks for reading 🙂

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1 Comment

  1. Erick
    December 7, 2019 @ 8:32 am

    This post is knocking on a good door. One of the bigger challenges in business and investing is deciding how much cushion to have to absorb the bad times.

    I’d be curious to see more combinations that include bonds, commodities short-premium and buy n hold strategies.

    Reply

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