The Iron Fly Agent (IFA) strategy is a MEAN REVERSION, intermediate to advanced, option selling strategy that aims on collecting near ATM extrinsic premium, while at the same time defining our risk.
IFA is NOT Proprietary or an Extremely Unique Strategy Approach:
This is a trading strategy I am willing to share with you because I consider it already a public strategy developed from TastyTrade, and it is NOT proprietary to myself. However, I will add more to the strategy by applying risk management and transparency so you can follow along as I forward test it. As the reader I hope you might learn more about equity or option strategy development that can help you in your own trading and investing. Fortunately most of the backtest work has been done through TastyTrade and their fantastic research team (links in the resources at the end) to infer the viability of the strategy. Now it is time to explain, implement, and forward test the strategy which I will call Iron Fly Agent (IFA).
Tastytrade and the Backtests:
Before I start, I want to say that TastyTrade, and its brokerage arm, Tastyworks are amazing companies with group of people that have spread the knowledge of options to everyday traders and investors. They have helped many people get a deeper understanding of options (a derivative instrument) and have done it with great sincerity and unselfishness. I compliment them because in the finance industry most traders and brokers have a conflict of interest.
Recently TastyTrade launched Tastyworks, since their founder Tom Sossnoff helped develop most of the software behind Think or Swim; I thought it was an excellent move to become their own broker and develop their own software and evolve it with their research.
For this case of options trading, and this strategy (IFA), I use Tastyworks, which has competitive commissions (and no exit fees), and in no way are they endorsing this strategy, or my trading, nor have I been paid to endorse their products or services. However, for obvious reasons that I have stated above I recommend their products, services, and research that you can find on their website to learn more (they also have done a lot more work on selling strangles which you should also check out!). I will also post additional resources at the end of this blog post (and future blog posts) that will be a treasure trove of information that act as cited support for the blog posts.
Iron Fly and Straddles and Short Strategy Risks:
An iron fly is similar to a straddle, but instead we put protective wings on it to define our risk. For more aggressive, risk-tolerate investors, they can remove the wings and sell the straddle outright. When selling options, it is similar to shorting stock. We have limited profit potential (stock goes to zero), but unlimited risk (stock goes up hundreds of percent, or ‘infinity’). However, since our reward is defined, we often have a higher probability of profit (POP), or higher inferred win percentage. And while having a higher POP is ultimately meaningless for statically purposes of an overall portfolio’s profitability it can assist with a trader’s psychology knowing that most of the time they will be a winner. The trade off with short strategies in general is that a losing trades or a series of losing trades can often cause long-lasting (deep) drawdowns. This is often the trade-off: long streaks of small gains, followed by a streak of large losses which causes a prolonged drawdown period.
For instance if we sell an option for $1 (our max gain), we could define our max-risk (where we would stop ourselves out) at 2x the option premium, or $3. Since our target would be 25% for straddles (or 50% for strangles) we would be risking $2 to make $0.25. So as you can see we can lose a lot of money ($2 on our $1 investment) compared to our expected target ($0.25). So this is ultimately the risk that needs to be managed when using a short strategy, or in this case shorting options. Higher win percentage always comes with a caveat, in this case, a potential for (inevitable) large losses, which can wipe out many previous successful trades.
Also to note in the above example we talked about how traders “force-stop” themselves out when our $1 investment reaches $3. However, if you don’t stop yourselves out, or put in a system or order in place, there is nothing to stop it from going to $5 or $10 or $100. And, yes, it can, and WILL happen. The statistics will bear out, thus it is important before initiating the strategy or any strategy to fully understand the risk and how to manage the risk before the strategy is implemented. As I will talk in future blog post, strategy identification is only HALF of the problem, the other half tied into the strategy is strategy money management, and ultimately portfolio money management.
So while straddles at a glance appear to be a more profitable strategy, even when selling them with an edge (high IVR), personally I want to always have DEFINED RISK strategies in place. In my opinion, a strategy should never have UNDEFINED RISK. A trade should always have a reasonable escape plan. Our escape plan is always in place when we put the trade on, because we turn our straddle into an IRON FLY.
We sell an ATM CALL and PUT to collect maximum extrinsic value, and we want to BUY 1 OTM PUT and CALL to always protect our position. Instead of selling a strangle, or iron condor, I want to use straddles which have the most extrinsic value (also more risk compared to strangles, since straddles are ATM), but I limit the risk by putting on the wings, as well as lowering buying power [reduction] (BPR), increasing return on capital (ROC), and in my opinion, increasing capital efficiency.
In short strategies one downfall that most traders will have is the TAIL RISK events that occur. Large up moves in the case of shorting stocks, or in this strategy a large move in either direction (typically down). So to avoid the “one-off” or “once in a decade” like moves we PROTECT every trade with wings. This ultimately will lower our profit of the strategy since we are taking LESS risk, but will also help against a huge strategy blow-up that often claims short strategies (even those strategies with protective stops that get hit by tail risk moves, or high sigma moves).
Basically I am trying to protect the strategy against huge follow-up moves after initiating the trade, for example the 1987 crash. I always assume the worst when the trade is put on (assume max loss will be hit), and plan accordingly when placing our wings.
Selling the ATM put and call is easy, but where do we want to sell the OTM PUT and CALL, which can lead to variance and trader discretion and placement, something we do not want since we are approaching this strategy as a quant would with rigid defined variables.
Simple, the research has been done (cited at the end of the blog) and we want to put those OTM wings between 10 to 16 delta. I never want to have a max loss exceed 1-2% of our total portfolio. So this means in the case where our $1 trade when to $3, and were stopped out, it would only cost us 1-2% of our total portfolio.
From the limited trading of the IFA strategy I have seen thus far wings are typically at 1.00-1.25x max loss as deltas do not always line up perfectly at 10 delta. This means that we risk 1-2% of our strategy’s capital for a 0.25% strategy capital gain. For this expectation win rates not only need to be high (POP), but also max loss should not be achieved frequently.
Tradable Symbols, Single Symbol vs ETFs:
One aspect of why trading strategies fall apart is lack of discipline from the trader. The trader losses the vision and begins arbitrarily trading outside of the strategy’s parameters. You strictly have to follow the trade strategy, especially on its basis on backtested data.
One temptation of this strategy is to trade individual stock names. However this will increase risk of the trade.
$AAPL for instance, which meets our liquidity requirements for trade entry, had a recent earnings warning that NO ONE could predict, that suddenly made the stock GAP DOWN (drop potentially beyond a stop order). Individual stocks are increasingly risky because while they tend to be subject to corporate events, products failures, earnings estimate adjustments, buyouts and takeovers, and the list goes on. There are a million reasons companies can suddenly drop or rise without warning. To reduce this risk we will avoid trading individual stock names, regardless of liquidity or IVR.
I will instead focus on ETFs only (and not triple leveraged ETNs or other dubious synthetic products [recognizing these comes with trading/investing experience]). After using some of proprietary software and filtering all tradable symbols we have come up with a list (that can grow, shrink, and change over time) [this list will be updated over time and you will be notified of any changed via email if you sign up] of ETFs that can meet our trade indicators and have the proper liquidity.
This in a way is a watchlist. It is a fixed list that we evaluate daily to determine of any of our indicators are met. Developed strategies have tradable universes, which they are typically limited too to prevent trades in either illiquid stocks, or stocks that do not and will not reach their criteria. While watchlists for some traders may vary from just a few to a dozen symbols, our list is fixed and contains all tradable liquid ETFs.
This strategy will never trade a symbol within its earning range and do NOT trade earnings events.Liquidity Criteria’s:
For me to enter and exit a trade we have to be able to ensure that we can GET OUT of a trade. Especially if volatility EXPANDS, spreads could widen as markets become more illiquid. So I prefer very tight (penny/nickel wide) markets, with deep bid ask volumes, so if markets do become more illiquid I can ensure that I am already the best positioned if markets become more illiquid. (Try thinking of the 1987 crash as a max risk point where the implied VIX exploded above 120, and how wide the spreads can become.)
I will only trade 3-4 star liquidity products (this is in the tastyworks platform and can be filtered). I ignore 2 star liquidity ETFs (and stocks).
Another liquidity criteria is looking at the Bid / Ask / Spread as a percentage (BAS%). This criteria is also noted in every trade that I will put on this website. When placing the trade I attempt to put the order at a midpoint, but the wider the spread the more often we will NOT be filled at the midpoint. If we try to “force” the trade by LOWERING the credit received (from $1 to $0.99 for instance), we lose SOME OF OUR EDGE. As also our TARGET will often be adjusted as we lower the credit received, and potentially skewing the forward-test compared to the backtest.
One trade example that was used is GDX at 2019-01-07 (trade #1 from our trades list). We sold the 18/21/25 Iron Fly for $1.58 credit. However, at the time the trade was placed I record the Bid Ask Spread and its percentage (Spread / Ask) (BAS%). It was $1.65 Ask - $1.55 Bid = $0.10 Spread. $0.10 spread / $1.65 Ask = 6% BAS%. If we take the midpoint we take 6% / 2 or 3%. 3% is what you can consider as “slippage” and in derivative products, like options it is often high (compared to stocks where it can be a penny). So 3% is the EDGE WE LOSE essentially when putting the trade on. If we LOWER our trade from the midpoint $1.60 ( [$1.65 + $1.55] / 2 = $1.60) we give up potential profit.
This is one issue with backtesting is often slippage is NOT taken into account, but in actual practice, or forward testing we can lose profit to the market because our trade will not get executed compared to the backtest. Also since we are adding often illiquid OTM 10 delta to 16 delta wings, this adds to the potential spread, and increases BAS%.
BAS% or slippage exists is almost every trade in almost any strategy, it is how market makers make money by bringing buyers and sellers together. The goal obviously would be to be on the ASK when selling and be on the BID when buying to reduce slippage to zero. But the main goal is to be in liquid stocks, and beyond just using the star system that tastyworks provides, but also to make sure BAS% is always BELOW 10%, so the MAX slippage I can assume to lose is ~5%. Regardless of the price of the underlying (since options are a derivative of the stock, and the stock is the underlying), I want a tight BAS% so my edge remains as high as possible, and ensures I can also get out of the trade.
Trade / Order Timing:
To systematize how I put on orders I will always try to place them near the close of the market (4 PM EST). I will try to place the order in around 3PM EST and work the order. I typically trade from the midpoint, but you start from the ASK and work your way DOWN to try to maximize the probability of a better fill until 3:45 PM EST, and which you should try to get filled on the order. Typically the BAS% should be tight enough regardless, and I personally like to just get filled, so the midpoint works for me. But if you want to try to squeeze more by walking down the order you can always try.
High Implied Volatility Rank (IVR):
Any ETF that we have to place a trade on has to have an IVR >= 50. IVR is a relative metric which uses the last 52 weeks of volatility, high to low as a percentage. IVR it is the inner volatility of the stock relative to itself over a 52 week period. Often volatility spikes and then normalizes so this number (or ratio) is mostly below 100. If it was at 100 then we would be at the maximum inner volatility of that symbol over the past 52 weeks. If it was at 0, then it would be at its low over a 52 week period. A majority of the time IVR is not at above 50. This is what backtests and data have shown, this is why this is a MEAN REVERSION strategy that relies on volatility MEAN REVERTING back to its normal (high to low, sell to buy).
One potential fail point of this strategy is when volatility accelerates while already above 50 IVR. Imagine if the market is crashing, and then keeps crashing (similar to the 1987 crash example), IVR would be at 100 and then be pinned at 100 as the market keeps crashing. Our indicators would trigger, but our positions would often go to max loss, the speed of the loss would depend on how fast the crash is occurring.
In other words, volatility keeps expanding, and does NOT mean revert (over the course of a month, since we buy 45 DTE options). Positions would blow past our wings and never return, thus a majority of our positions would be at max loss. This scenario, while highly unlikely, is NOT impossible, and this is why max risk of all position should be assessed. Another way to manage this is to make sure positions are uncorrelated to each other or to the market in general. Cross asset correlation (CAC) is the correct terminology, and having lower CAC is important to ensure if one asset move against the portfolio, it doesn’t weigh as much upon multiple positions.
Target % / Stop % / and Time Stop (Gamma Risk):
The backtested results show that straddles, as well as iron flies need to be managed early and often at 25% profit target of the credit received. Since we collect more premium than a strangle we have lower profit targets. This happens to be also the case with the iron fly. Iron flys could extend the duration more than 22 days in trade because we have reduced the total credit by a debit amount by adding wings.
The stop % for a straddle is 150% of the cost of the credit. In our earlier example of selling $1.00, and getting stopped out for 2x this would mean the cost of the iron fly has gone AGAINST us and is now HIGHER, at $3.00 (2 x $1.00 = $2.00; $2.00 + $1.00 = $3.00). However the optimal backtested stop is 150% of credit received or 1.5x or in the case of our example of a $1 credit, would be $2.50 price of the credit.
But instead of stopping ourselves out in options (often with high BAS% which can lead to large losses just from slippage), I use the Iron Fly and our wings automatically act as our stop.
The Iron Flies backtest well at 10 to 16 delta. Delta is the probability of the strike of the option being in the money (ITM) at expiration. This is a number easily listed in most option trading platforms.
For example when I buy the 10 delta call and put (debit) wings for protection for my straddle (credit), I lower the credit I receive but PROTECT my position by defining the MAX LOSS. Since I buy two 10 deltas worth of protection there is a 20% chance (10 delta + 10 delta) of the position being outside my wings by expiration (that the options market is pricing in, which is quite accurate).
I can also widen the wings to the 1.5x stop mark to simulate the straddle.
For this strategy, Iron Fly Agent (IFA), I will strictly have the wings at 10 delta and move up to a max of 16 delta, or make sure the BPR (or max loss) is within 1% of portfolio. Preferably I will have the wings at a lower delta to increase profit potential (and therefore increasing risk).
Time stop (and Gamma Risk):
The backtest shows profit potential is reached on average reached at 16 – 22 days. So on average I should look to take profits at the 22 day. Once a trade goes past 22 days it should be actively managed, as it is probably a loser to determine if it should be closed. Currently, this discretion is arbitrary for the trader.
At 10 – 12 DTE (days till expiration) the gamma risk of this strategy becomes very high. Since I have captured all the extrinsic value of the straddle, as we approach the expiration of the options, gamma risk explodes. In other words 10-12 days remaining often times there is no money left to be made in the trade and it is often too risky too hold, and it becomes more likely that our winner becomes a loser. The insurance contract we have sold to the counterparty has run its course. Often it is best to roll if IVR is still high. So with respect to gamma risk there is no reason to hold beyond 10 DTE and we will auto time stop out of the trade.
I don’t want to hold on to the trade and have it “come back” to a winner in the last 10 - 12 days. It also depends if the trade is at max loss or not which might give some wiggle room since this is not a traditional straddle, but all trades at a gain, break-even, or slight loss at 10 - 12 DTE will be closed realizing the gain/loss.
There is the temptation to let the wings ride as a “free trade” within the 10 day period and if the trade itself is in the profit zone on exiting. I will avoid doing this as this is more speculative than anything and while tastyworks does not charge an exit fee, if there is any bid on the wings the order will be filled, even though it might not have a material impact on BPR. In other words, I will exit the wings as well when closing the position if there is a market for them.
All exited trades are entered in with a GTC limit orders at the profit target and exited with a limit order at midpoint if time stopped, and worked as described above for entry (this time working against the ask when buying back the credit).
Interesting Facts on Iron Fly Agent (IFA):
70-90% of trades will be a loser before a winner. This makes sense as the stock (underlying) move immediately the straddle will move and theta decay will not take place as quickly. This is one reason the strategy works in my opinion. Most people do not want to be a loser, so holding onto a losing position is a horrible feeling. However, the turn-around for the strategy is 22 days on average and most losers become winners within a short and reasonable amount of time. However since most people do not want to be or hold losers, and based on the feeling on holding an initial loser, initiation this strategy could keep most people away from it, and therefore can be a competitive advantage.
50% of all trades never get to a 25% loss (or 1.25x of credit received). Almost a majority amount of trades never are “high risk.” I imagine this is typical, because it is essentially a short strategy.
4% of the time you will get to a 2x loser (NOT based on HIGH IVR).
7% of the time you will get to a 1.5x loser (NOT based on HIGH IVR).
The statistical inference is based on the data testing back to 2005 is that very few (less than 4%) of trades will reach a large loss. Since most wings are put at where we risk almost 1x to 1.25x in all trades in roughly 4% of trades. This does not account for the major edge of executing trades strictly at high IVR either where win rates in some stocks approach or are 100%.
Strategy Trade Sizing:
As I talked about above, I want to make sure our max loss per trade does not exceed 1-2% of total strategy capital. Depending on account size this could vary. In forward testing account sizes are typically small compared to larger portfolio of multi-strategies.
The reason max loss should not exceed 1-2% is has been proven many times that if max loss per trade, or trade risk exceeds 2% typically this leads to an inevitable, mathematical failure of a portfolio or account.
Since I roll positions if our time stop of 10 DTE is hit and IVR in the underlying is still high, there is NO reason to average down or double down in any trade. Tip: Never double down unless you are winning in a trade. Since this strategy is already complex with high BAS% doubling down isn’t practical either in my opinion.
Also from a practical standpoint if I am wrong 20 times in a row (which on a strategy that wins 70% of the time is (0.30 ^ 20 = 0.0000000000010%) almost ~0, I can one for say human nature in certain strategies can defy even the most minute odds. Anything is possible, but having a 20% drawdown is possible, but recovery from a 20% drawdown is not impossible. Time will tell. I have other strategies with 70% win rates lose 10 times in a row (0.00059%); so the highly improbably is STILL probable.
For others that are following along, and want to duplicate the trades at their own risk, smaller accounts $10k and under might have to break this 1% rule to 5%-10%. Keep in mind a max loss on even 1 or multiple trades could render the strategy useless to you if several trades reach max loss.
A $50k account would need max $1k max loss per trade position.
A $100k account could use portfolio margin, but I still would recommend $1k max loss per trade position. As account strategy size grows you would have to diversify into single name stocks to reduce max risk, again, CAC (beta) adjusted. This is an area where the strategy could evolve, but preferably not, into liquid (low BAS%) single stock symbols. Originally on strategy development for IFA strategy ETFs are double weighted (2-4% max risk adjusted). Again, the whole point of position sizing is to recognize how much a drawdown you WILL have, and plan accordingly.
Also making sure our positions are uncorrelated to each other further reduces market risk. Putting a position in both GDX (Gold Miners) and GDXJ (Junior Gold Miners) serves no purpose because they trade along the same category and sector. Only one position would need to be placed, typically in the more liquid of the two or the one with higher IVR. However, if they have different high IVRs (i.e. 50 and 80) multiple positions could be placed, but position size should be adjusted to perhaps 3% of total market risk. Again it would be up the discretion of the trader, but when in doubt trade small and assume the worst, because what can go wrong, WILL go wrong.
Max trade size is another issue for some strategies as account size grows with the strategy. Fortunately liquidity is often so deep that it is a non-issue with the symbols I have listed. These symbols have the deepest and most liquid underlyings in all the world’s markets.
The number of trades the strategy can hold would be 50 - 75 if risk adjusted correctly (beta weighted to approx. 0, every Friday through futures and options for larger accounts). Rarely will the number of symbols come close to 50 at any given time however, which again allow for single stock names depending on portfolio size.
I want to keep 10-25% in cash in case of safety margin if several trades simultaneously go bad, it will not spark a margin call.
Traders can avoid this fate by controlling their risks through stop losses. In Jack Schwager's famous book "Market Wizards" (1989), day trader and trend follower Larry Hite offers this practical advice: "Never risk more than 1% of total equity on any trade. By only risking 1%, I am indifferent to any individual trade." This is a very good approach. A trader can be wrong 20 times in a row and still have 80% of his or her equity left.
Selling options is like selling insurance. The underlying is a house or car, the piece of paper saying it will be protected if something bad happens is the insurance contract. Options are similar but retail and professional traders can participate in the market unlike most insurance contracts. The actuarial tables are the greeks and the options tables. In a sense I am an insurance agent, selling insurance to willing buyers who want to protect their stock.
But I give myself an edge by being patient, and selling insurance when prices are relatively high compared to where they have been in the past (IVR). So when the market crashes or has a pull back, IVR is typically high, option premiums are high, and that is usually when I will sell insurance. I am also patient in letting the volatility contract and letting 90% of all trades that start off as a loser, become a winner.
However, to remove any bias I have, I make the trade directionless, or delta neutral, by selling both a PUT and a CALL. I don’t know if the market will move up or down, or not at all (hint: no one knows). And I especially don’t know the timing of it. All I know is insurance premiums are high relative to where they have been, and stocks, the market, volatility, and ultimately human nature, are mean reverting. I rely on this mean reversion to make money by selling insurance. I act as an insurance agent.
The options market is efficiently priced, so there is no edge in that per se. But the edge comes from the panic where buyers are looking for protection and can overpay. I use the indicator IVR to tell me when prices are high and sell, and hope they mean-revert as they have in the past (backtest). Sometimes investors will pay through the nose for option premiums to protect their stock, but history (backtest) shows that this panic is often unwarranted (human nature), and this is where the strategy makes money.
Single Stock Names and IFA:
There has been limited testing, again, against highly liquid single stock symbols for straddles, which can allow for more market opportunity, but as described above can lead to higher risks in single stock symbols rather than ETFs. Also more research is needed to determine WHY option premiums might be high: is there a buyout looming? Earnings coming up? Upcoming major PR? Negative or positive news catalyst? There are a million reasons why premiums could make IVR high in a single stock name, and researching the reason why takes time and comprehension of events surrounding the underlying. Also discerning the risk can be more difficult to understand and murky. While single stock names might be evolved into eventually, they should be avoided if possible. If I had to trade QQQ or AAPL, I would trade QQQ if IVRs are relatively the same.
BPR, wings, and ROC:
While TastyTrade downplays ROC, wings are not only a great risk management tool, but also reduce BPR. In some cases a $5,000 BPR trade could be reduced to $700 based on the underlying stock price for a $5 credit IFA trade. It allows for selling premium higher stock prices because selling the naked option would be far too expensive.
Adding wings reduces the capital outlay, and adds a realistic max loss to the trade. $500 max gain / $5000 BPR = 10% Max ROC. While $500 max gain / $700 BPR = 71% Max ROC. Adding wings increases the Max ROC by 7x. Adding wings is a no-brainer and frees up capital for other CAC trades that could arise or exist. It allows you to trade smaller and more often, increasing the number of occurrences as well.
In margin accounts BPR is the max loss. In portfolio margin accounts this number can vary based on factors determined by a broker. Portfolio margin accounts ($100k+ accounts) need to take in consideration their max loss even more so because BPR is reduced further.
Wings are always kept between 10 to 16 delta, or can be widened to 1.5x credit received, this will increase BPR.
In the case where many instances of high IVR might not exist to give a trade entry signal, the attitude might be “wings don’t need to be used” increasing BPR since “there are no trades.” However with a true delta neutral market view I should perceive that opportunities might suddenly arrive without notice or warning and wings should always accompany the trade to make sure BPR isn’t effecting the buying power to execute future trades for the strategy.
Duration in Trade:
One aspect of this strategy I like is I don’t stare at my monitor all day, it is mostly a semi-passive investment strategy after putting the trade on. Often weeks can take place before a trade target is hit with the GTC limit order. Trades are manage aggressively after 22 days to 10 DTE. I like this as I have other real life commitments and strategies I have to manage. I spend about 1 hour or less near 3pm EST reviewing the positions and looking for new opportunities.
Having a strategy fit your personality as a trader is very important. Watching intraday market fluxuations is not my idea of personal self-fulfillment, nor is it very productive.
Risk always exists in every strategy. While even trading with strategy edges of high IVR and ETFs.
One major risk of the strategy, which is similar to almost every strategy is multiple strategy trade positions moving against you at the same time.
Since this is a short based strategy, large losses can exist where meager gains can compare to large losses. This is offset by a higher win percentage.
If multiple trades move in to max loss it could cause significant drawdowns in the strategy. But this risk is mitigated by, the high unlikelihood that our CAC all correlate the same direction, the number of positions are at max, essentially IVR increases or volatility for a large portion of the active positions over the duration of the trade, and this means that volatility increases. While this scenario can likely happen during a crash-like market, the market could overshoot multiple CAC positions and even if a “bounce” where to happen it would not reach inside our strikes of the wings. I suspect a max drawdown of 30% could happen if this occurred, which is not impossible.
A more reasonable approach is to assume 5% of the trades reach 1.5x loss, and roughly 50% of trades will reach profit target. 25% of trades will be minor gain. The remainder or 15% will be a loss but not max loss.# trades Average P/L 50 0.25 12.50 25 0.10 2.50 15 (0.50) (7.50) 5 (1.50) (7.50) -
Potential outcomes for the strategy could be overall negative (due to slippage). This is the reason to forward test the strategy.
Options markets could become illiquid in the future for unseen circumstance (i.e. higher interest rates cause a lack of demand for options).
Risk of scaling:
I always believe in portfolio scaling which is why position size is critical to adjust or reduce position size as a portfolio grows to keep our 1-2% threshold in risk per trade. Which means adding money to the strategy or letting the strategy compound money is used.
The risk is always “you have to be more right than you were yesterday.” All strategies eventually fail, years of increasing volatility expansion could void the strategy, meaning volatility never mean reverts and remains elevated relative to itself for years or decades (that would be a wild world, no fight against entropy).
Scaling a strategy can cause larger drawdowns in the future.
This section will be expanded on in a future blog post because it is a deeper aspect to money management of a portfolio.
The hard backtest work for Iron Fly Agent has been done by the brilliant minds at TastyTrade to give the foundation for the strategy. Forward testing is the next step to take it from the backtest to forward test. I will start by using a small amount of money to test the strategy and implement the mechanics and trade signals.
You can follow this strategy and any other ones I am willing to share here on my personal blog where I will keep the strategy up to date. Subscribe to the newsletter to get real-time email alerts when I place a trade or a strategy modification has been made (an addition or subtraction to the ETF list).
Lots of topics on strategy development and risk management were touched on in this blog post, one most of the points can be expanded up on in great depth, and most likely will be in future blog posts.
The IFA strategy could work given the backtested data, but markets change; remaining patient and mechanical and not deviating from the trade signals is paramount. There is no guarantee in a strategy working even if the backtest has been proven to work, but the rub is to find something that never changes: human nature. I believe this strategy captures that, which is essentially selling high insurance premiums when investors are panicking to buy insurance in large, liquid, market sectors.