13 posts tagged with "Strategy Library"

In our next post in the series of options trading strategies on the public domain, we are presenting the Weekend Effect.

This trade (and others) has been discussed in detail in Euan Sinclair’s Positional Option Trading (Wiley, 2020) book. We highly recommend reading Euan’s books as he provides practical guidance on Options Trading in great detail. 

Overview​

The Weekend Effect in options refers to the tendency of options prices to decrease more over the weekend than in their respective weekday period.

Traders aware of the weekend effect can potentially profit by selling short-term options on Friday and buying them back on Monday at a lower price.

However, this strategy also comes with risks, such as unexpected market events that could cause a significant change in option prices over the weekend. Therefore, it is strongly suggested (mandatory) to pair this trade with a hedge. We will cover hedging strategies in a later article.

The validity of this hypothesis can be evaluated using MesoSim through a series of straightforward tests, which we are undertaking in the subsequent section of this article.

Trade Rules​

The original trading rule is defined in Euan Sinclair’s book:

"On a Friday, sell the options that expire the next Monday."

Based on this guidance, our backtest trading rules are as follows:

• Find next Monday's or Tuesday's expiration
• Enter trades using the chosen expiration on Friday, 15 minutes before close
• Exit trades on Monday or Tuesday, 15 minutes after the open

For demonstrational purposes, we will be using short puts and strangles to showcase this trade. It should be noted though, that the strategy should work with more complex structures, such as butterflies and iron condors.

Short Put version​

Job Definition​

We set the strategy to enter a position on a Friday and exit on Monday by using the following conditions in the Job Definition.

In the Expirations section, we specify that expirations will be selected between three and four DTEs, targeting 3. This enables trades entered on Friday and kept until Monday or Tuesday (in case of a long weekend).

"Expirations": [
  {
    "Name": "exp",
    "DTE": "3",
    "Min": “3”,
    "Max": “4”,
    "Roots": null
  }
]

The Entry schedule is set to enter the trade each Friday 15 minutes before market close, and the Exit set to exit the trade the next trading day, 15 minutes after the market opens.

"Entry": {
  "Schedule": {
    "BeforeMarketCloseMinutes": "15",
    "Every": "fri"
  },
  ...
},
"Exit": {
  "Schedule": {
    "AfterMarketOpenMinutes": "15",
    "Every": "day"
  },
  "MaxDaysInTrade": "1",
  ...
},

The Strike Selection is done based on Delta, we simply pick a 10 delta put to avoid selling tail risk.

"Legs": [
    {
      "Name": "short_put",
      "Qty": "-1",
      "ExpirationName": "exp",
      "StrikeSelector": {
        "Min": "5",
        "Max": "15",
        "Delta": "10"
      },
      "OptionType": "Put"
    }
]

Backtest results: 2016 - 2022​

Short Put - Friday to Monday​

The full run with all the statistics is available at the following link: https://portal.deltaray.io/backtests/755c4562-b166-490b-9295-74c36d56dc21

Short Put -  Weekday version​

To validate the existence of weekend premium, we modify the backtest’s entry and exit schedule to run to cover the opposite timeframe: Mondays to Fridays (4 DTE short put). 

The strategy multiple times has drawdowns above 50%, including (but not limited to) the Covid crash period. 

Backtest URL: https://portal.deltaray.io/backtests/7f95f66e-f1be-4e8a-a524-d30f92229e71

Short strangle​

While the Short Put setup meets the Weekend Effect's instructions (sell options on Friday) and shows the effect in its purest form, it is possible to improve the strategy by selling more options: 

If we sell Calls as well as Puts we end up having a Short Strangle structure. The collected premium is increased, therefore we expect larger returns. Since there is more risk on the downside, selling Calls likely won't increase our risk exposure.

Backtest URL: https://portal.deltaray.io/backtests/e1b98b9c-cbb2-43f0-8abb-78ad1384e71f

Backtest results: 2023​

Finally, we run the Short Put and Short Strangle trades for the Weekend Effect in the most recent period: 2023. The results speak for themselves:

Conclusion and further work​

Our analysis confirms the significance of the Weekend Effect when the strategy is backtested on S&P Index Options (SPX). Not surprisingly, the Strategy suffers its largest drawdowns during crash situations, such as COVID. Therefore, this (and any other option selling strategy) should be traded when adequate hedging is in place.

We would like to thank Euan Sinclair for spreading his knowledge via his excellent books and courses! 

The Short Strangle version of the Weekend Effect is available in MesoSim as a built-in template, named: [WeekendEffect]

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.

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 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!

Overview​

NetZero (aka 60-40-20) is an At The Money Broken Wing Butterfly trade on SPX that Andrew Falde has devised. This trade has been explained in great detail by Mark Mosley in the Raleigh Durham Open Systematic Options Trading Strategies recording.

The options structure utilized in this trade involves a delta-neutral, positive theta, and negative vega income structure. The Broken Wing Butterfly's legs are strategically placed at deltas 60, 40, and 20 on the put side.

Trade Rules​

The original trade rules are described as:

• Enter trade 60-80 days to expiration
• Select strikes having the deltas closest to 60, 40, and 20 deltas
• Exit trades when
  • Middle leg’s delta changes by 30%
  • Upper leg’s delta changes by 30%
  • 30 days to expiration is reached

Mark Mosley has incorporated an additional exit rule into the trading strategy inspired by the methods of John Locke, whereby an early exit is taken when the delta to theta ratio (delta divided by theta) reaches a threshold of 60%.

We will validate the two variants by simulating their historical performance using MesoSim. In all simulation runs, we will use Multiple Positions in Flight to avoid relying on one execution path.

Simulating the original trade​

As the structure definition is trivial, we will only spend time detailing the trade's exit rules.

To enable exit criteria based on delta comparison of the Upper Long and Middle Short Leg with their initial states, we will be recording the delta values of each leg at initiation and storing them in the designated variables:

"Entry": {
  ...,
  "VarDefines": {
    "initial_leg_shorts_delta": "leg_shorts_delta",
    "initial_leg_upper_long_delta": "leg_upper_long_delta"
  },
}

Then using the Exit.Conditions to describe the three criteria:

"Exit": {
  ...,
  "MaxDaysInTrade": 999,
  "ProfitTarget": null,
  "StopLoss": null,
  "Conditions": [
    "(leg_shorts_delta - initial_leg_shorts_delta)/initial_leg_shorts_delta > 0.3",
    "(leg_upper_long_delta - initial_leg_upper_long_delta)/initial_leg_upper_long_delta > 0.3",
    "leg_shorts_dte <= 30"
  ]
}

Backtest results​

The full run with further statistics (full tearsheet) is available in the following link: https://portal.deltaray.io/backtests/148c6282-e485-4807-8418-385c96815bb2

Upon reviewing the Log Return graph, we can observe that the trading strategy exhibits strong performance characteristics in markets that trade sideways, which is reflected by its outstanding performance during the periods of 2015 and 2022.

The trading strategy encounters its most challenging period during the interval between two black swan events, namely the Volmageddon in February 2018 and the Covid Crash in 2020.

Simulating Mark Mosley’s variant​

During the presentation, Mark Mosley introduced an additional exit rule that employs the delta-to-theta ratio as a benchmark for exiting trades early. We suspect that this additional trading rule comes from John Locke’s methodology on trading Broken Wing Butterflies.

Additional trading rule​

To integrate the additional trading rule, we have made the following modifications to the job definition:

• Entry.AbortCondition guarantees that we only establish positions that conform to the delta to theta ratio requirement
• Exit.Conditions are responsible for exiting the trade when the ratio becomes unfavorable

The respective rule in Job Definition:

"pos_theta ~= 0 and abs(pos_delta / pos_theta) > 0.6"

Backtest Results​

Backtest Run’s URL: https://portal.deltaray.io/backtests/3c9e63c6-d946-4d65-b736-b7d0dba4dd4f

It is apparent that the performance of the strategy is greatly improved:

• Sharpe increased from 0.74 to 1.16
• CAGR reaches 19.85%
• Max Drawdown is reduced from 50% to 33%

Our variant​

This time, instead of conducting a comprehensive study on IV Rank, Underlying State, or Theta, as we did for the Boxcar trade, we will make two changes to the strategy to improve its performance.

Days in trade vs. Date till Expiry​

The original rules for this trade specify the time-based exit using the "Date until Expiry" method, whereas we prefer to set the time barrier based on Days In Trade. We believe that Days In Trade is more predictable in terms of expected performance than the Date until Expiry, which can vary trade by trade. To make this change, we can set the MaxDaysInTrade variable to 30.

Truly Net Zero​

According to the original trade rules, legs should be chosen closest to Delta 60, 40, and 20. However, this approach may not always result in delta=0 at the initiation, which adds slight directionality to the trade. To address this issue, we will dynamically select the Lower Long leg such that the overall structure ends up at 0 delta. To accomplish this modification, we will set the target delta for the leg to pos_delta:

{
  "Name": "lower_long",
  "Qty": "1",
  "ExpirationName": "exp1",
  "StrikeSelector": {
    "Delta": "pos_delta",
  },
  "OptionType": "Put"
}

For further information on how delta hedging is performed, please refer to the documentation.

Furthermore, we will incorporate an extra exit criterion that will terminate the trade if it becomes overly directional during its lifecycle. Our selected thresholds to exit the position are Delta -10 and 10:

"abs(pos_delta) > 10"

Backtest Results​

Backtest Run's URL: https://portal.deltaray.io/backtests/0fed4a1f-3b66-4adb-aa36-0a49b59936f0

Setting the initial delta of the structure to 0 and maintaining it at around that level resulted in noticeable improvements in Sharpe ratio, CAGR, and Max Drawdown compared to both variants:

• Sharpe: 1.32
• CAGR: 20.1%
• Max Drawdown: -29.94%

Although the trade experienced difficulties in the post-volmageddon period, we consider it a promising subject for further research as it demonstrated remarkable performance in sideways markets.

Future work​

Hedging​

Although the recovery rate is quick, a Max Drawdown of -29.94% is still a significant loss of investment. As this drawdown happened during a black swan event (Covid crash), it is recommended to include a hedge while trading this strategy. Such a hedge can be as straightforward as buying long puts (teenies), implementing a Black Swan Hedge according to Ron Bertino's PMTT course, incorporating Brent Pedersen's findings on hedging power, or using David Sun's Bomb Shelter or Vibranium Shield.

Volmageddon - COVID period​

We attempted to pinpoint the root cause of the structure's difficult period by examining its Risk Profile and analyzing various metrics, including:

• Position Theta, Gamma, and Vega throughout the trade
• Relative (to the underlying) and Absolute Prices of the structure and the individual legs

Unfortunately none of these metrics yielded significant insights into the reasons for the trade's underperformance during the mentioned period. Since we acknowledge that there is no silver bullet solution to trading, we accept the structure in its current state.

If you have any suggestions for improving the trade, please feel free to leave a comment. We welcome and value your input.

Conclusion​

We have performed a simulation of a public SPX trade using MesoSim, which has shown exceptional performance in 2022. Therefore, we consider it a promising candidate for future research. We believe this trade would complement the Boxcar-NG trade well, resulting in a well-rounded portfolio.