One overlooked aspect to trading is commissions and their costs to the trader. Today most brokers offer very little in terms of competitive advantage compared to other brokers for most strategies. Some brokers do offer software advantages, like Tradestation is better for programmers who want to learn Easy Language and backtest trading systems, and Tastyworks offers software tailored for those looking to trade on their research.
Commissions are often an overlooked aspect to trading and can nullifying trading strategies, as a certain commission structure can penalize smaller accounts if trade size is fixed, and can be more advantageous to other trading strategies.
Commissions typically have buy and sell execution costs. Often daytrader strategies pay high commissions due to the frequency of trade execution that ultimately can be expensive over the lifetime of the strategy.
This was something that always baffled me after reading “Flash Boys: A Wall Street Revolt,” by Michael Lewis, is how could traders who essentially make 1000s of trades of day, hoping for a fraction of a penny in profits (very small avg % gain per trade as well) overcome the commissions hurdle. It wasn’t addressed in this book unfortunately, but I assume it was the fact they paid a huge initial exchange fee to have their servers housed in New Jersey, but also allowed them not to pay ANY commissions. Therefore they could microscalp trades for fractions of a penny. Their overhead cost was fixed (in this case millions of dollars paid to the exchange) and then the HFTs could be highly profitable if X amount of trades were executed.
On the other end of the spectrum, buy-and-hold strategies (like Buffett) prefer to never sell, so the commissions are very, very low as a percentage of gain on trade (mutli-year single trade).
Either way, size and trade frequency play a part in commission costs for most traders.
In the past commissions were much higher for investors. Technology has changed financial markets rapidly, and increased competition has reduced commissions much lower where they used to be ($50+ per stock trade 3 decades ago), and certainly many strategies in the past couldn’t have been viable in today’s market.
One way to determine the viability of a strategy is that commissions have to overcome the average dollar gain, and in most cases commissions scale with trade size so average percent gain on trade is an important metric. To me the average dollar gain on trade should not be effected materially by commissions. In accounting materiality is often considered 5%, so that is the amount I want to use to determine whether a trade should be executed based on expected average expected profit per trade. Also it should be noted that this is just one of many metrics to determine strategy or an individual trade’s viability.
Let’s look at an example using the commission structure of Tastyworks with the Iron Fly Agent (IFA) strategy:
Tastyworks charges around $1 per option contract, and fortunately does NOT charge ANY exit commission fees. Round trip per contract is roughly $0.50. Actual costs with certain other fees attached are roughly $4.54 per trade on 4 contracts.
$4.54 is the total cost of commission to enter and exit a trade, therefore its effect on the average dollar gain per trade should be IMMATERIAL or only 5% of the average gain we expect of a trade.
$4.54 / 0.05 = $90.80 average dollar profit needed to consider commissions immaterial.
Tastytrade has not posted average % gain per trade needed but they do post average dollar gain in some of their backtests which is $50 in a single lot on ETFs listed in the study “Dynamic Iron Flies.”
Meaning roughly 10% of an average dollar gain per trade goes to commission, something unfortunately that is not emphasized in these studies.
10% could be immaterial to most investors, but in my opinion, the strategy could change, and an edge could be lost, and commissions essentially give up edge in a trading strategy. It is up to each trader or investor to determine their material threshold for commissions and their expected return per trade.
Another way to look at the IFA strategy is to view it from a period of max gain to our expected target. Certain targets suggest we should only reach 25% of max profit. We must make a min of $90 to meet our immateriality threshold, therefore $90 is our 25% target.
$90.80 / 0.25 = $363.20 of minimum credit that needs to be received.
Meaning that on a 1 lot we need to collect a minimum of $363.20 of credit or typically $3.63 contract (round down) for our option IFA trade. This by the way UNDERASSUMES that we will hit our profit target practically every trade, which is not true since the strategy at best works 70% of the time or less.
True figures for IFA are probably upwards of $400 - $500 per 1 lot.
This means when evaluating the trade #3 for USO on 2019-01-07 where I collected $1.09 per one lot was WAY TOO LOW to breach the materiality threshold for trade viability. When in fact I needed to collect at least $3.63 per trade. The reason being is while max gain or loss is smaller due to the low price of the underlying, the commission structure stays fixed, and therefore average gain per trade needs to be higher.
USO is a low priced underlying, so it is debatable to change it to a straddle for BPR and “force” the trade, but I choose not to do that personally because it does not fit the strategy’s criteria.
These numbers can also let us know the minimum strategy size we need to properly execute the portfolio using a risk parameter of 1%. Let’s be conservative and assume we need $500 cr. This means for this strategy I would risk roughly $700 in BPR (number can vary based on the delta of the wings).
$700 max loss per trade / 0.01 risk on portfolio = $70,000 min portfolio size risk adjusted based on commission materiality threshold.
As you can see from the above example we can derive a lot of information based from our commissions: min trade size, and min portfolio size to name a few examples.
Let’s look at a theoretical equity trading strategy that has an average gain on trade of 3%, with a max risk of 1.5% stop per trade (3/1.5 = 2R trading system, gain 2 to risk 1).
Let’s assume the broker will charge you a flat fee per trade of $4.95 (like Fidelity).
Commission materiality threshold = 5%.
$4.95 / 0.05 = ~$100 gain per trade minimum.
$100 per trade minimum / 0.03 gain per trade = $3333 min capital invested per trade.
Risk of $50 per trade. (2R trading system)
$50 risk / 0.01 portfolio risk = $5000 min portfolio size
However we know that min position size is $3333. A minimum of 10 trading positions at any one time are assumed. So we calculate $3333 / 0.10 = $33,330 realistic portfolio size to assume 10 positions. $50 loss on a $33,330 strategy capital is 50/33300 or 0.15% drawdown.
Another way to calculate this is if we lose 20 trades in a row based on our system (win rate not assumed in this example) we get:
$50 * 20 = $1000 drawdown from min position size. From then it is up to the investor to determine how much of a drawdown from max portfolio size this should be.
A portfolio size of $10,000 would mean a drawdown of 10% ($1,000 / $10,000 = 10%) should not be unexpected.
With a $10k account with 10 positions and average risk per position is $50 we can assume max drawdown per trade would be $50 / $10000 = 0.5%.
I can keep going on, but based on number of trade occurrences, max loss potential, etc., you can start to build strategy’s risk, estimated annualized gain, and estimated size based around your own risk tolerances that uses some of this math. But having a materiality threshold for commissions is very important in determining the edge you lose per trade based from commissions.
Ultimately it is up the investor to determine how much of their average dollar gain per trade is to be taken by the broker on an assumed profitable strategy. Having realistic targets and stops on your trading system and knowing a realistic average gain per trade can help determine trade and strategy‘s viability. The more commissions you have to pay, especially in options, can certainly void out many strategies you thought were profitable, and can skew backtests if commissions (and slippage [BAS%]) are not taken into account. In other words, the profits you thought that were going into your pockets are instead going to your broker and the market makers’ pockets.