SPX Short Vertical Put 7 DTE s1 signal Options Backtest
In this post we’ll take a look at the backtest results of opening one SPX short vertical put 7 DTE position each trading day from Jan 3 2007 through Sep 30 2021 and see if there are any discernible trends.
We will also explore the performance of the s1 signal. The s1 signal is a boolean (TRUE / FALSE) daily indicator that attempts to identify the days in which short put and short vertical put positions on the S&P 500 are most likely to be profitable at expiration. s1 is based on data from CBOE and S&P Global.
The thesis is that if we limit order entry to only the days in which s1 is TRUE, we will be “swinging at only the best pitches.” On the days when s1 is FALSE, take no action. Existing positions will remain untouched and no new positions will be opened.
To test this thesis we will:
- limit order entry to only the days in which s1 = TRUE to see if the strategy outperforms daily entry in a statistically significant way
- limit order entry to only the days in which s1 = FALSE to see if the strategy underperforms daily entry in a statistically significant way
Performance of the s1 signal is explored in different contexts in other, non-paywalled s1 signal studies.
All this talk about signals is fine and dandy but a signal-based strategy in isolation isn’t helpful. Let’s compare it against the following benchmark and see how it performs:
- SPY buy/hold (total return) | 100% allocation, no leverage
There are 15 backtests in this study evaluating over 33,000 SPX short vertical put 7 DTE trades.
Let’s dive in!
Contents
Summary
The s1 signal yielded a 86% improvement in capital efficiency over “market-agnostic” 7 DTE daily short vertical put strats. The s1 signal was able to:
- realize more total return
- with about half as many trades
- capturing up to 84% more premium per trade
- boosting sharpe ratio by up to 2.6x
The s1 = TRUE signals’s 10D-2.5D short vertical put 7 DTE strat outperformed buy/hold SPY with regard to:
- total return
- risk-adjusted return
- annualized volatility
- max drawdown
- max drawdown duration
The s1 = TRUE signal outperformed a comparable “market agnostic” daily short put strategy with regard to:
- total return (all strats)
- risk-adjusted return (all strats)
- annualized volatility (3 out of 5 strats)
- max drawdown (the 5D-1D strat)
- win rate (all strats)
- premium capture (all strats)
- profit spent on commission (all strats)
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Methodology
Strategy Details
- Symbol: SPX
- Strategy: Short Put
- Days Till Expiration: 7 DTE +/- 4, closest to 7
- Start Date: 2007-01-03
- End Date: 2021-09-30
- Positions opened per trade: 1
- Entry Days: each trading day in which s1 signal = TRUE
- Entry Signal: s1 signal
- Timing 3:46pm ET
- Strike Selection
- 5 delta +/- 4.5, closest to 5
- 10 delta +/- 5, closest to 10
- 16 delta +/- 6, closest to 16
- 30 delta +/- 8, closest to 30
- 50 delta +/- 8, closest to 50
- Trade Entry
- 5D short put
- 10D short put
- 16D short put
- 30D short put
- 50D short put
- Trade Exit
- Hold till expiration
- Max Margin Utilization Target (short option strats only): 100% | 5x leverage
- Max Drawdown Target: 99% | account value shall not go negative
Assumptions
- Margin requirement for short CALL and PUT positions is 20% of notional
- Margin requirement for short STRADDLE and STRANGLE positions is 20% of the larger strike
- Margin requirement for short VERTICAL SPREAD positions is the difference between the strikes
- Margin requirement for short CALENDAR SPREAD positions, where the short option expires after the long option, is 20% of the short option
- Margin requirement for long CALENDAR SPREAD positions, where the short option expires before the long option, is the net cost of the spread
- Margin requirement for short IRON CONDOR positions is the difference between the call-side strikes if both sides are the same width, otherwise margin requirement is the width of the wider side
- Early assignment never occurs
- There is ample liquidity at all times
- Margin calls never occur (starting capital is set using hindsight bias so that max margin utilization never exceeds 100%)
- Apply a 20% discount to displayed results. For example, if a strat depicts a CAGR of 10%, assume that it’ll yield 8% in practice.
Mechanics
- Prices are in USD
- Prices are nominal (not adjusted for inflation)
- All statistics are pre-tax, where applicable
- Margin collateral is invested in 3mo US treasuries and earns interest daily
- Option positions are opened at 3:46pm ET
- Option positions are closed at 3:46pm ET (4:00pm if closed on the date of expiration)
- Commission to open, close early, or expire ITM is 1.00 USD per non-index underlying (eg: SPY, IWM, AAPL, etc.) contract
- Commission to open, close early, or expire ITM is 1.32 USD per index underlying (eg: SPX, RUT, etc.) contract
- Commission to expire worthless is 0.00 USD per contract (non-index and index)
- Commission to open or close non-option positions, if applicable, is 0.00 USD
- Slippage is calculated according to the slippage table
- Starting capital for short option backtests is adjusted in $1000 increments such that max margin utilization is between 80-100%, closest to 100%, of max margin utilization target
- Starting capital for long option backtests is adjusted in $1000 increments such that max drawdown is between 80-100%, closest to 100%, of max drawdown target
- Positions that have an exit date beyond the backtest end date are excluded
- For comprehensive details, visit the methodology page
Results
Starting Capital
Starting capital requirements of the s1 = TRUE strategies was roughly half of the daily-entry strategies.
Margin Utilization
s1 = TRUE signal yielded a higher average margin utilization vs daily entry. 50D-5D was an exception
Hindsight bias was used to maximize Reg-T margin utilization for each strategy. This allows a “best case” scenario for the option strategy to outperform the benchmark.
Also displayed is the date in which each strategy experienced maximum margin utilization.
Premium Capture
s1 = TRUE signal had higher rates of premium capture vs daily entry.
The higher the delta the lower the premium capture. 30D-16D was an exception.
Win Rate
s1 = TRUE signal outperformed daily entry with regard to win rate.
The higher the delta the lower the win rate.
Monthly Returns
s1 = TRUE signal outperformed daily entry with regard to average monthly return. 50D-5D was an exception.
The higher the delta the greater the average monthly return.
Also displayed is the best and worst monthly returns for each strategy.
Max Drawdown
s1 = TRUE signal underperformed daily entry with regard to max drawdown. 5D-1D was an exception.
The higher the delta the greater the max drawdown.
Max Drawdown Duration
Max drawdown duration performance was mixed between s1 = TRUE signal and daily entry.
The higher the delta the greater the max drawdown. 30D-16D was an exception.
Average Trade Duration
The average trade duration for all strategies was 7 days.
Compound Annual Growth Rate
s1 = TRUE signal outperformed daily entry with regard to compound annual growth rate.
The higher the delta the greater the CAGR.
Annual Volatility
s1 = TRUE signal outperformed daily entry with regard to annual volatility. 16D-5D and 30D-16D were exceptions.
The higher the delta the greater the annual volatility. 30D-16D was an exception.
Sharpe Ratio
s1 = TRUE signal outperformed daily entry with regard to sharpe ratio.
The higher the delta the lower the risk-adjusted return. 30D-16D was an exception.
The 5D-1D s1 = TRUE signal strategy had the greatest risk-adjusted return among the option strategies.
Profit Spent on Commission
9.98% – the average percent of profits spent on commission across profitable option strategies.
Total P/L
s1 = TRUE signal outperformed daily entry with regard to total P/L.
Higher delta strategies yielded greater total P/L than lower delta strategies. 30D-16D was an exception.
Overall
All option strategies were eventually profitable.
Discussion
The results of opening positions only on the days in which the s1 signal = TRUE is compelling. Let’s take a look under the hood and see what’s happening in a bit more detail. In the comparison we’ll look at buy/hold SPY, opening a 10D-2.5D short vertical put daily, opening a 10D-2.5D short vertical put only on days when s1 = TRUE, and opening a 10D-2.5D short vertical put only on days when s1 = FALSE.
Consistent with the methodology, since these positions are comprised of two option positions (short leg + long leg), commissions are $2.64.
At one or more points during the backtest, the strategy risked the entire account per the max margin use being near 100%.
The s1 signal = TRUE strat has 53% ( 1765 / 3332 ) of the number of occurrences vs daily entry.
Looking at strictly the strategy income/expense, 91% ( 186,121 / 204,531 ) of the profits were captured when s1 = TRUE vs daily entry. Meanwhile, when s1 = FALSE the strategy returned less than the interest earned on 3mo treasuries.
Capital efficiency is calculated as ( ( strat return / benchmark return ) / ( strat occurrences / benchmark occurrences ) -1 ) * 100.
When we plug in the numbers we get:
- ( ( 186,121 / 204,531 ) / ( 1765 / 3332 ) -1 ) * 100
- 72% increase in capital efficiency
Risk adjusted return when s1 = TRUE is 54% greater than daily entry.
Max drawdown when s1 = TRUE underperforms daily entry. The bulk of this was experiencing a poor sequence of returns shortly out of the gate in 2008 whereas daily entry experienced it’s max drawdown in March 2020.
Limiting order entry to days in which s1 signal = TRUE yields greater total return and lower annualized volatility vs both buy/hold and daily-entry while opening positions roughly 53% of the time.
Think about it for a moment. An investor trades roughly half as often and generates greater total return and lower volatility. There’s no need to swing at every pitch; open a position only when it’s worthwhile.
Distribution of s1 signals
The s1 = TRUE signal, while suggesting order entry on roughly half the trading days since 2007, is not evenly distributed throughout time. Here’s a breakdown of participation rate by year:
Up-to-date s1 signal historical boolean values and s1 signal historical raw + boolean values are available for download in the trade log store.
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Additional Resources
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Lenn
November 7, 2021 @ 10:06 am
Hello,
You say that
“Performance of the s1 signal is explored in different contexts in other, non-paywalled s1 signal studies. In particular, different durations (0-3 DTE, 7 DTE, 45 DTE and LEAPS), different strategies (uncovered and verticals)…”
However I could only find the two posts comparing put spreads to short puts, not any Posts comparing different DTEs.
Is there any way to see a comparison between the different DTEs? I’d be really interested.
Best
spintwig.com
November 7, 2021 @ 12:08 pm
Yes! The 45 DTE version the short put study is being released this Friday. I’m also pulling together the data for the other DTEs and approaches as well. Stay tuned!
Steve Highland
November 12, 2021 @ 11:23 am
11-12-2021
Hi, This may be obvious but I’m new here. I’m trying to understand what the ‘s1 signal’ is all about. I have read here: “The s1 signal is a boolean (TRUE / FALSE) daily indicator that attempts to identify the days in which short put and short vertical put positions on the S&P 500 are most likely to be profitable at expiration. s1 is based on data from CBOE and S&P Global.”
I take it from this that it is a quant strategy based on TECHNICAL indicators, right? I realize it must be proprietary but can you please give more information on what it is and why it is likely to be reliable in order to help me understand what it is and why I should explore it further?
Thanks!
Steve
spintwig.com
November 20, 2021 @ 5:44 pm
Welcome Steve — correct, the s1 signal is quantitive in nature and uses historic values to suggest when to open a position and when to sit out.
It is reliable for several reasons:
1) s1 uses market data as inputs – simply stated: relevant, empirical data is used. There are no “moon phases” or other nonsensical inputs.
2) s1 data inputs are from official sources – using CBOE and S&P Global as the data providers ensures input data is as clean as possible and free from influence or error of 3rd parties
3) s1 avoids overfitting – it’s easy to look back in time and identify an “optimal” methodology that avoids all the drawdowns. The problem with this approach is that it tunes the algo to both the signal as well as the noise in the market. The result is typically a “perfect” backtest and a poor forward-looking result. The s1 signal paints with “broad strokes” and doesn’t look for a “perfect” backtest. The result is typically a “great” backtest and a great forward-looking result.
Hope this helps!
Dom
November 12, 2021 @ 12:27 pm
I’m not too sure I see the point in this, for the effort involved and time spent! I could have a 98% cash and 2% BTC portfolio that would outperform the standard buy and hold and any of these active strategies.
You’re doing all this and not even really generating any Alpha and with a 6%+ drag from inflation right now you’re doing all this for a poultry <4% a year… kinda pathetic really.
I think all you've proved here is that taking on the extra risk just isn't worth the returns involved.
spintwig.com
November 20, 2021 @ 5:50 pm
I’m not too sure I see the point in this, for the effort involved and time spent! I could have a 100% BTC and 0% cash portfolio that would outperform your proposed strategy as well as buy and hold and any of these active strategies.
Anyone can look back and say what would have made more/less money. I’m not sure I see your point.
M
December 21, 2021 @ 11:16 am
I appreciate the need to be vague on what kind of data the S1 signal uses but is there any details you can give? Is it some sort of price mean reversion thing?
spintwig.com
December 23, 2021 @ 12:48 pm
I can share that it uses official data sources from CBOE and S&P Global as inputs. However, I suspect that’s less than helpful for what you’re looking for.
As for the mechanics, there’s not much I can say without risking someone reverse engineering the model. There are a lot of smart folks out there! I can say that it’s a systematic and repeatable calculation; there are no chances that a historical signal value would ever change from true to false or vice versa.
Adam
January 23, 2022 @ 2:36 am
Hi
Nice work! I’m just trying to understanding this … The tail end of your research discussed 10D-2.5D put credit spreads, even though the 50D-5D produced the highest profit. Does this mean the 10D-2.5D put credit spread (S1 signal) is the wisest choice to trade, with all other factors considered?
spintwig.com
January 26, 2022 @ 1:39 am
Thanks Adam!
My selection of the 10D-2.5D strategy in the discussion section was a random choice among the available strats in the study. There is nothing inherently special about it over another configuration.
As for which is the wisest strategy to trade, that would be up to you. I’d suggest identifying one or more specific performance goals (eg: “must have a CAGR of no less than 6%”, or “must have a Sharpe ratio of no less than 1.00” or “must have a max drawdown no garter than ‘x’ percent”) that you’d like to achieve, then implement the strat that performs closest to those goals. Your goals may be similar or different vs others’ goals.
Mario
March 17, 2022 @ 6:35 am
Hi
I hope this is not a stupid question, how was starting capital calculated?
Looking at all the info in the backtest everything else looks pretty similar (nr of concurrent positions, notional exposure, margin utilization, etc). Why is the starting capital different for Daily entry and signal entry?
spintwig.com
March 17, 2022 @ 10:21 am
Hey Mario – not a stupid question at all!
The starting capital is different because of some of the backtests perform better than others.
Suppose an arbitrary, fixed amount of capital is assigned at the beginning of each backtest. The better-performing strategies may not need all the capital. They may be able to execute the strategy with, say, 20% less money. Similarly, underperforming strategies may get blown out with margin calls and the strategy can’t run for the entire duration of the backtest. More capital may be needed.
In order to solve for this and accurately represent the performance across strategies of differing performance, I normalize everything to a maximum margin utilization of 100%. In other words, I find the minimum amount of capital needed to successfully execute the strategies over the entire duration of the backtest and set that as the starting capital. It allows the purest representation of strategy performance – no cash drag for the outperforming strategies and no margin calls for the underperforming strategies.
Despite the differing stating amounts, CAGR (geometric return) is calculated based on starting and ending portfolio values and dates – the standard way to calculate CAGR. This ensures that despite the different amounts of starting capital, strategy performance is accurately calculated across strategies.
Mario
March 17, 2022 @ 10:39 am
Hi, thanks for the fast response.
I guess that makes sense but at the same time, I don’t understand the difference. With a similar number of winning trades, similar concurrent positions, etc, why is the strategy without S1 needing double the capital?
spintwig.com
March 18, 2022 @ 1:25 am
It mostly has to do with the loss frequency and severity, and a bit of sequence of returns risk.
Consider the “win rate statistics” table in the discussion section. The loss rate for the “all” strategy is 4.80% while the loss rate for the s1 = true strat is 4.19%. The s1 = true strat experienced 12.7% fewer losing trades.
It’s not uncommon for a trader to experience hundreds of profitable trades only to experience a single losing trade that wipes out months (or years) of profit. By reducing the occurrences of losing trades by just 12.7%, the strategies experience a significant outperformance.
As for sequence risk, if a strategy experiences a material loss right out of the gate, this can disproportionally hurt the strategy vs experiencing a material loss later in the backtest. This wasn’t a major factor in this scenario though, as demonstrated by the max margin utilization dates.