Overview​

Writing options is a common and straightforward method for generating income from derivatives, often referred to as Volatility Risk Premium (VRP) harvesting.

Selling put options is akin to operating an insurance business:

the option seller is rewarded with the premium (option price) in exchange for taking the risk of assignment if the market moves below the strike price at expiration.

This simple strategy can be highly profitable in bull markets, but it can result in significant losses during bear markets and sudden down moves.

Simulating Option Writing on SPX​

MesoSim was created to study options trading strategies. It offers numerous built-in templates demonstrating key features as well as ready-to-use strategies. 

For simulating Option Writing, we will utilize the [SPX-ShortPut] built-in template.

The above screenshots capture the overview of two runs for different time periods:

• 2021. 01. 01. - 2021. 12. 31.: Bull Market Simulation
• 2022. 01. 01. - 2022. 12. 31.: Bear Market Simulation

Trailing Stops​

In general​

Trailing Stops are well known to equity, futures and options traders alike. The idea is simple: As the profit increases we store the highest profit achieved (high watermark) and create a stop order relative to the high watermark. This way, our stop loss will not be fixed, but it will continuously move higher as the strategy gains.

Programatically it can be described as:

1. At entry:
   Set pnl_high_watermark to 0.

2. When current_pnl > pnl_high_watermark:
   Set pnl_high_watermark to current_pnl.

3. When current_pnl < pnl_high_watermark * x%:
   Exit the position.

Implementing in MesoSim​

To explore how a trailing stop could enhance a trading strategy - such as option writing-  in different market conditions, we will implement it in MesoSim. The above logic can be translated into a Job Definition as follows:

1. At entry initialize pnl_high_watermark variable to 0:

"Entry": {
  ...
  "VarDefines": {
      "pnl_high_watermark": "0"
  }
},

2. Increase pnl_high_watermark when we reach a new high of pnl:

"Adjustment": {
  ...
  "ConditionalAdjustments": {
    "pos_pnl > pnl_high_watermark -- record new high watermark": {
      "UpdateVarsAdjustment": {
        "VarDefines": {
          "pnl_high_watermark": "pos_pnl"
        }
      }
    }
  }
},

3. Exit when the current pnl falls below the 75% of the high watermark:

"Exit": {
  ...
  "Conditions": [
    "pos_pnl < (pnl_high_watermark * 0.75) -- Exit if pnl drops 25pct from high water mark"
  ]
},

Trailing Stop with SPX Short Put results​

Incorporating the trailing stop snippets into the Short Put template and re-running the tests produces the following results:

The above runs can be accessed in the following URLs:

• 2021. 01. 01. - 2021. 12. 31.: Bull Market Simulation
• 2022. 01. 01. - 2022. 12. 31.: Bear Market Simulation

Summarizing the results​

When comparing the original Short Put runs with the Trailing Stop version, we can conclude that the trailing stop signifficantly improved the strategy performance in Bear market. In contrast, the results in bull market became more modest, although it remains fairly good (with a sharpe ratio around 2).

Conclusion​

Trailing stop is a dynamic approach to increase strategy performance. It has proven to be beneficial for the simple SPX Put Writing strategy by locking in gains without sacrificing much upside potential. 

Someone interested in this approach should try different relative ranges from the high watermark to see how it affects her strategy performance.

While we continually add and release features to the MesoSim backtesting service, it's beneficial to mark milestones. We firmly believe that MesoSim v2.10 represents an important advancement for the options trading community, offering unparalleled flexibility in the low-code backtesting universe.

Key changes​

Full control over simulation dynamics​

By adding VarDefines to Entry, Exit, and all Adjustment types you are now empowered to capture or change state throughout the execution steps. Additionally, we have introduced a new Adjustment Type (called UpdateVarsAdjustment) built to alter variable states.

Lastly, we have converted all the numeric fields to Statements, enabling you to utilize Lua for calculating simulation parameters during execution. 

Backtest Analytics with DataVoyager​

Data Voyager (and Vega) is a data exploration tool that has been integrated into our Portal.Click on the Analytics tab to study how Greeks and other variables are impact your Strategy's Performance. You can also visualize the variables you created during backtest.

You can find a generic demonstration video of DataVoyager here.

Mark legs and enter later​

We allow now specifying Legs with Qty=0.

The concept here is to identify a contract at a specific moment and wait until favorable conditions arise. Once your conditions are met you can use AddLegsAdjustment to create a real Leg at the pre-defined contract. 

Concurrency in intraday runs​

The simulation performance has been significantly enhanced in this release!

The intraday strategies now run 1.5 to 2 times faster than before.The daily strategies' performance has also improved, with a margin of 15-30%.

These performance improvements now enable us to introduce concurrency in intraday runs. You can now simulate Multiple Entry Iron Condors [MEIC] (and others), where positions are taken multiple times per day.

The maximum concurrent positions in flight have also been increased from 10 to 12, allowing intraday strategies to enter positions every 30 minutes.

And there is more...​

The number of changes between version 2.9 and 2.10 is substantial:

We have implemented over 50 changes, including enhancements, fixes, and performance improvements, to deliver the current release.

You can find the full list of changes on the Service Status page.

Steps Towards Live Trading​

While the Simulator has become more flexible, we have also made progress in the areas of Live Trading and Forward Testing. Internal testing of a Position Monitor has commenced, and active work is underway for Order Creation (IBKR).

Once we are satisfied with the quality of the live offering, we will open up for a private beta. If you would like to participate, please send us an email at: [email protected]

John Locke (@Locke4Success) is a reputable source when it comes to Options Trading Strategies. You can find many of his trading strategies on YouTube, for free.

His strategies and their variants are often simulated with MesoSim.We are now adding his Super Bull strategy as a featured trade to our strategy library.

The rules were constructed as discussed in his youtube videos.

Structure​

SuperBull is a Bullish Call Vertical Spread that is initiated precisely at 65 DTE.

Vertical spreads are characterized by defined risk and limited upside potential.

Rules​

The rules are summarized in the two screenshots and explained in the video in the Guidelines section.

Entry​

Timing and expirations​

The trade is entered exactly at 65 Days To Expiration, utilizing the generic monthly expiration cycles:

"Expirations": [ {
    	"Name": "exp1",
    	"DTE": "65",
    	"Min": 65,
    	"Max": 65,
    	"Roots": {
          	"Include": [ "SPX” ]
    	}
  }
]

Strike Selection​

The strikes are selected in a manner where the long legs are positioned 20 points above the current price of the underlying asset and the shorts are chosen to achieve a Risk/Reward ratio that is close to 1.0

To construct the spread we are using MesoSim’s Complex StrikeSelector feature.

Long Call​

The strike closest to 20 points above the underlying price can be determined by setting the Complex StrikeSelector’s Target to underlying_price + 20 and specifying the constraint to ensure that the chosen leg’s strike will be always above the underlying price. This constraint is necessary because the Selector selects the nearest contract to our target, which - in some cases - may be below the underlying price.

The Statement field of the selector is evaluated for each contract and represents the strike. According to the SuperBull rules, our target will be 20 points above the current underlying price.

{
    "Name": "long",
    "Qty": "1",
    "ExpirationName": "exp1",
    "StrikeSelector": {
      	"Complex": {
        	"Statement": "leg_long_strike",
        	"Target": "underlying_price + 20",
        	"Constraint": "leg_long_strike > underlying_price"
      	}
    },
    "OptionType": "Call" 
}

Short Call​

Our objective is to choose the short leg in a way that the Risk/Reward ratio of the spread is close to 1.Risk/Reward ratio can be calculated:

risk_reward=Entry Debit / Max Profit

Where

Entry Debit (maximum loss) = Long Leg’s Price - Short Leg’s Price Max Profit = Short Leg’s Strike - Long Leg’s Strike - Entry Debit

This calculation can be defined as:

"Complex": {
	"Statement": "(leg_long_price * leg_long_qty + leg_short_price * leg_short_qty) / ((leg_short_strike - leg_long_strike) * leg_long_qty - (leg_long_price * leg_long_qty + leg_short_price * leg_short_qty))",
	"Target": "1",
	"Constraint": "leg_short_strike > leg_long_strike"
}

About the Risk/Reward ratio​

While John suggests aiming for a 1:1 Risk/Reward ratio, he shows multiple examples where the trade is initiated with a ratio lower than that. He explains that this is due to the unusual volatility skew observed on those particular days. In such trades, the risk/reward ratio was around 1.5 at the initiation.

Sizing​

John suggests that the trade should be sized so that it risks a maximum of 10% of the account size. This rule was implemented in our run using Entry.AbortConditions: We abort the entry if the Entry Debit would be greater than 10% of our NAV.

In our initial attempts, we used one contract for each leg. This turned out to be inadequate: Most of the time we under-sized the position (Maximum Risk was around 1% of NAV). To alleviate this problem we introduced Entry.QtyMultiplier to MesoSim, which is capable of scaling all the legs, once they are identified. We leverage this feature to target a maximum loss of 10% (in terms of account size) for our trades.

Exit ​

The exit rules for the strategy can be summarized as follows:

When the next monthly expiration becomes 65 days away, we enter a second trade. From that point onwards, we monitor the progress of the first trade. If the first generates a positive return, we exit the trade. However, if the first trade is at a loss we wait until it becomes profitable or reaches expiration.

These rules in MesoSim can be expressed as:

"Exit": {
	"Schedule": {
      	"BeforeMarketCloseMinutes": 30,
  	    "Every": "day"
	},
	"Conditions": [
  	     "(pos_in_flight > 1) and (days_in_trade > 28) and (pos_pnl > 0)"
	]
  }

OptionNet Explorer accuracy​

OptionNet Explorer is frequently utilized by options traders for modeling and manually simulating trades. It is essential to comprehend the accuracy and limitations of its simulation and real trading accuracy before engaging in trading activities. This section provides concise insights into these limitations, allowing the reader to better understand the comparison between MesoSim and OptionNet simulated results.

Backtest result resolution​

ONE displays the strategy’s summary PnL chart using only the realized dollar amounts.

This represents the lowest resolution possible for backtesting as it lacks information about drawdowns experienced during the trades.

Furthermore, common financial metrics, like CAGR (Compound Annual Growth Rate), Max Drawdown, and Sharpe Ratio are absent from ONE’s report.

Live Trading accuracy​

OptionNet Explorer can be utilized for initiating and monitoring live trades. However, there is one potential accuracy issue to be aware of. When entering complex (multi-legged) trades using the compounded Option Price, the individual leg prices in OptionNet often differ from the actual fills. Therefore, it is necessary to manually adjust the prices of the individual legs after entering the trade. This adjustment can be performed using the Trade Log window.

Lack of benchmark​

OptionNet Explorer does not have the feature of comparing strategies to other strategies or benchmarks, such as the S&P-500 (^SPX).

Without reference points, it becomes challenging to determine whether the strategy “beat the market” or not.

Compared to MesoSim​

In contrast, MesoSim’s performance reporting tracks daily NAV values and computes quantitative performance metrics for both the strategy and the benchmark, such as Sharpe, Sortino, Omega, and more.

This information provides a more precise representation of the strategy’s performance, allowing the user to better understand its expected performance. 

SuperBull Results​

We’ll be using the 2013 - 2023 time period to assess the strategy performance under various market conditions.

 Overview​

You can access the associated MesoSim run here: https://portal.deltaray.io/backtests/45d26d23-6e7f-4655-9893-be15f29ea140

Analysis​

Our win rate of 85.2% (81/95) confirms that we are close to what John achieves (86%) with this trade.

Both the Log Return and yearly breakdown charts show that the trade performs well during Bull Markets, such as 2013-2014, 2019, and 2021. However, it exhibits higher volatility during sideways markets. 

If we compare SuperBull with the S&P-500 Buy and Hold strategy we can conclude that it performs better in the validation period:

• Higher CAGR: 16.7% vs 10.11%
• Slightly lower max drawdown: -34.06% vs -34.83%
• Improved Sharpe ratio 0.89 vs 0.67
• It has low margin requirements

Surprisingly, this strategy didn't exhibit a large drawdown during COVID as the Maximum Loss based Entry Filter prevented it from entering during market turmoil. 

The Relaxed SuperBull​

Rafael Munhoz, a key contributor to our community has thoroughly analyzed this trade and developed his own variant. Based on his experience, it proved challenging to find optimal trades that offered a 1:1 risk/reward ratio. He made adjustments to the trade to address these challenges and generously shared his results with us.

Entry Differences​

The original trade targets verticals using the 1:1 risk/reward ratio exactly at 65DTE.Rafael made a decision to relax these rules:

• Consider trades between 65 to 75 DTE, targeting 60
• Allow for an expanded range of risk/reward values using the inverted metric: reward/risk >= 0.7
• Define the Call Debit spread using ATM Delta=48 and Debit price of $500
• Enable up to 10 positions in flight
• Trade -2x2 contracts in each position

Exit Improvements​

The original exit rules are substituted with the following, more conventional, triple barrier type of exit strategy:

• Profit Target: 50% of max profit (entry debit)
• Stop Loss: 200% of max profit
• Max Days In Trade: 60

Results​

You can access the MesoSim run here: https://portal.deltaray.io/backtests/5cc071d8-d9c1-43c0-8107-644de848dcdc

His results are improving the original trade by having higher CAGR, reduced Drawdown, (and therefore) improved Sharpe:

• CAGR: 19.4% vs 16.7%
• Max Drawdown: 30.55% vs 34.06%
• Sharpe: 1.06 vs 0.89

Similar to the original trade, the Relaxed SuperBull also excels in bull markets andit has outstanding performance in the 2013-2014 period.

Due to its more dynamic exit criteria and re-entry rules, it responds fairly well to crashes, such as COVID.

However, unlike the other trade, this variant demands more attention from the trader as the entries and exits are more frequent and dynamic compared to the original strategy.

Thanks and final note​

Both Rafael and ourselves believe that John's trade is worth studying. None of us is affiliated with John in any way. 

We would like to thank John Locke and Rafael Munhoz for sharing the SuperBull and the SuperBull-Relaxed trades with the options trading community!

We released MesoSim v2.9 today, which further extends the simulator's flexibility with a new strike selector. Additionally, we are expanding our built-in strategy library to showcase the new feature.

List of improvements​

Complex StrikeSelector​

To enhance the simulation capabilities we have added the Complex StrikeSelector. This feature allows users to find a leg based on complex criteria, such as the delta-to-theta ratio. The Complex StrikeSelect is particularly useful for building spreads and optimizing multi-legged structures. Similar to other Strike Selectors, it can be used during Entry, MoveLeg and AddLeg Adjustments.Please check out the documentation or take a look at the two built-in templates that utilize this feature:

• RUT-ComplexStrikeSelector-DeltaToTheta

• SuperBull

"Complex": {
    "Statement": "pos_delta / pos_theta",
    "Target": "0.5",
    "Constraints": [
         "leg_long_strike < leg_short_strike"
    ]
}

Official Settlement prices​

Initially, we used closing and opening prices to account for settlements (PM and AM, respectively). While this approach provides a close approximation for PM settlements, AM-settled options may exhibit larger differences (more details here). 

As part of the VIX stabilization effort, we have begun utilizing the official settlement prices for all the instruments we support.

Reg-T margin calculation​

We have addressed a remaining bug and included the missing structures in the Reg-T margin calculation. This makes Reg-T complete and fully supported under the Advanced plan.

New variables​

To better support intraday strategies we are adding two variables for tracking the time of the day: minutes_before_open and minutes_after_close These variables be used in intraday entry, exit or adjustment conditions.

VIX progress update​

To ensure an accurate simulation of VIX Options' Settlement we have switched to using the official settlement prices. This is crucial because all VIX options are AM settled and approximation using open didn't was not sufficiently accurate.

We are currently awaiting the resolution of the data issues we reported to CBOE. Once that is resolved we will promote VIX to Stable. Please stay tuned for further updates. 

New ThetaEngine Variant​

Claudio Valerio graciously shared his work on ThetaEngine with us.His variant, the Volatility Hedged Theta Engine is featured in this blog-post.

Upcoming sale​

We're planning a sale starting on the 4th of July.

We have previously covered David Sun’s (@thetradebusters) Theta Engine in one of our blog posts. The power of Theta Engine comes from David’s unique method for determining the size of short put positions. For more details on his approach, please refer to our blog post and his website.

About the variant​

Claudio Valerio, a valued member of our community has created and shared a variant of the Theta Engine strategy. His modification includes a dynamically added long put hedge during periods of elevated Implied Volatility (IVRank > 50). He selects the contract closest to Delta=4 with the same expiration as the short put. The number of purchased contracts is three times the number of shorts sold. Additionally, Claudio has adjusted how the parallel positions are spread out and increased the planned capital of the trade.

Volatility based Hedging​

IV Rank is used to define two regimes for the market.

Low volatility regime​

When the ATM Implied Volatility for SPX Options is below the mid point of its 52 week high-low range (IVRank < 50), we go with naked shorts as the original trade did.

Elevated volatility regime​

When IVRank reaches (or goes beyond) 50, then 3x as many longs are added as shorts, creating a 3:1 Put Ratio Backspread. With the added hedge he is substantially reducing the downside risk and capping the upside profit potential.

Note on hedging​

While ThetaEngine originally does not contain a hedging component, David has shared his hedging strategies (Vibranium Shield and Bomb Shelter) in great detail. Hedging is a must when it comes to selling options, therefore we suggest that you study David’s original hedges or Claudio’s variant with the built-in hedge. It shall be noted that while reactive hedging (as presented here) is a cost-efficient way, it will likely not provide good coverage for scenarios where the market is closed and crashing, such as during the 9/11 events.

Differences​

The following section shows the two trades side-by-side.

Job Definition​

Risk Graph​

To study the characteristics of the trade we'll be using OptionNet Explorer. The following two screenshots are showing the Risk Graph when the position is naked short and when the hedge is added.

Thanks​

We would like to thank Claudio for sharing the Volatility Hedged Theta Engine and David for creating the original trade plan!