You’ve probably seen a graphic similar to this one that shows the road capacity and throughput given different modes of transport. This is often used to show support for public transit subsidies which perhaps some places should do more of.
Note that these type of images always the theoretical capacity if the bus was always full compared to the actual average number of passengers in cars (1.5 it seems). The visualizations would be much less impressive if it showed actually existing ridership levels for the alternate transit modes.
Another way to view this issue, though, is how many actual car trips are avoided by the existence of various modes and services? When viewed this way, of course, the bus is not that efficient at all especially when you realize that bus ridership cannibalizes bike and pedestrian usage at a significant rate. Anecdotally this happened almost overnight here in Chapel Hill/Carrboro when the buses went fare free several years ago. See this article about Finland resisting the urge to make public transit free for precisely this reason.
Which Produces Most Car Trip Replacement?
The service that I imagine is by far the most efficient in terms of reducing car trips is one that a lot of people typically complain about – the Amazon delivery truck. Of course they seem like they’re everywhere but you can fit a lot of packages in each one and I imagine Amazon is ruthlessly efficient at using the full capacity. You can fit far more packages in a truck than people in a bus and although not every package represents a car trip replacement I suspect that the ratio is quite strong. You’d also have to account for the fact that without Amazon we wouldn’t acquire quite as much stuff. Reviewing our own purchase history makes it clear to me that Amazon is reducing our family’s car trips.
If Amazon went away tomorrow, how many new car trips would emerge? A lot I bet. Has anyone seen any studies on this phenomenon?
Last year I made some big changes in how I trade my main 3 strategies. Each year I go back and review all the trades I took and see where I could improve. Are there any adjustments I can make to get better? Did any strategy perform poorly relative to what I expected? This is a good time to reflect on the changes I made and see how they played out.
First of all, here’s an equity curve (in R multiples) for the entire decade. The total R for the decade was 2011.9. This includes probably 10 strategies or so I’ve traded during that period. Pretty good. When you look at an equity curve zoomed out like this it looks smooth but when you zoom in you notice some drawdowns. One of the great benefits of using R multiples to measure performance is that it normalizes performance no matter the risk amount you’re using for a particular strategy. At any given point in time I may be at full risk in one strategy but a small fraction of that in another. Here’s a post where I go into detail about my process for sizing up.
My Gappers Strategy for 2019
Although this curve looks ok by the end of the year, in March it looked terrible! That is the time where I took a step back and reevaluated what I was doing with this strategy. Keep in mind that the basic strategy is one that I’ve traded for over a decade now. I’ve made some pretty drastic changes to the strategy over the years but the fundamental strategy is exactly the same. So what did I do in March?
I started looking at my backtest in a completely different way. I removed the stops! (I know, I know – bear with me.) My workflow from the backtest went as follows:
Create a backtest with very little filtering of trades – includes stops and targets.
The Filter Phase: Apply filters to determine which trades from the backtest I’ll actually end up trading going forward.
The Filter Phase is the hardest part and the one that takes the most energy and experience to do well. The set of trades that I end up actually trading turns out to be a pretty small percentage of the total backtest. I realized that I was doing my filtering after the stop had been applied to the trade. This has the affect of ignoring some valuable information for each trade. Here’s my new workflow that I’m using now:
Create a backtest with very little filtering of trades – NO STOPS OR TARGETS.
The Filter Phase: Apply filters to determine which trades from the backtest I’ll actually end up trading going forward.
Determine stops and targets to use.
Notice that the stops and targets are applied at the end instead of the very beginning. Why does this matter? The difference is subtle but turns out to be very important.
Let’s take two types of trades from the backtest. They both end up stopping out but the first type stops out but just barely and then continues in the desired direction. Here’s a theoretical example of the first type: LVGO from 1/14. It barely hit the stop price of 28.99 only to continue on.
The second type stops out and then continues its plunge finishing the day well below (assuming a long trade) the stop. A good example of this is STT from 1/17. It hit the stop of 83.32 and continued south for the remainder of the day.
In my original workflow, these two types of trades were considered equivalent and my filter phase would not notice any difference among them. In fact they are very, very different! My updated workflow accounts for this difference – I consider it like adding another full dimension to my backtesting routine.
I’m hoping to continue sizing up this strategy in 2020.
My other main strategy I’ve been trading largely unchanged since 2015 or so. Here’s the equity curve for 2019 for strategy B.
This doesn’t look great but when I zoom out and look at the chart from 2015 to present it looks fine having gained 400 R. This strategy is actually quite flexible. It’s designed to use variable position sizing based on the quality of the setup. I haven’t implemented that yet (I use same size for each trade in the strategy) but I hope to do this in 2020 which I’ll write about in a future post.
I hope you traded well in 2019 and you trade even better in 2020! If you have any questions feel free to contact me.
The next interview in the series is with Anne-Marie Baiynd, a long time trader, coach, and author of The Trading Book (Amazon link). I haven’t yet met Anne-Marie in person but we’ve interacted on social media quite a bit and I think her Twitter presence is great. I know several folks in the trading industry that have met her in person and have nothing but good things to say about her.
Her bio tells me she’s had a wide range of experiences before planting her trading roots: neuroscience, mathematics, econometrics, and recruiting – plus she’s started several companies along the way.
Here are her responses:
What is the most overrated trading advice?
“You’ve got to take the emotion out of trading”
Though people are becoming more sensible about this, I remember when I started almost two decades ago, this was all the rage. The fact is that trading should be disciplined but humans cannot operate without emotion, so people end up trying to stifle their emotions only to see them explode in other aspects of their lives or their trading accounts. Emotion has to be managed – but it cannot be squelched in the trading environment.
What is the most underrated trading advice?
“Learn to paper trade strategies properly before you trade in the live environment”
Everyone wants to be in on the action before they actually know what to do. Myself included. Granted there are things you learn in the live trading space that can only be assessed there, but jumping in with real money before you establish proper risk protocol and strategy execution is like putting on slalom skis for the first time and getting dropped off atop an Olympic ski jump….with almost as disastrous results. Learn to trade before you actually trade.
What’s a non-trading related book that’s influenced you recently?
An excellent study of the decision systems we have and how we war with them in the realms of our personal biases. It helped so much in the understanding of the thought process that goes into the decision-making business of trading.
I’m starting a trader interview series. The series will not be a long form, exhaustive list of questions about how and why the interviewee got into trading or how their upbringing did or did not contribute to their success. There will be just three simple questions designed to reveal how the trader separates themselves from the trading herd.
There’s a lot of trading advice floating around out there. Some of it’s great, some of it’s terrible, and a lot of it is so ingrained in the collective trader psyche that it becomes meaningless or cliche. Some of it applies only to a certain type of trader (“sell in May and go away”) and some of it is so common that no one would dare question it (“add to your winners, cut your losers short”).
The first two questions apply to the collective trading advice that is present all around us. What is the most overrated trading advice? What is the most underrated trading advice? I expect I’ll get some pretty contrarian takes on these questions even for the already contrarian bunch that traders are.
The last question is designed to bring out a learning suggestion – a book recommendation. Not the same tired question (“what’s the best trading book to buy?” – it’s One Good Trade by the way) but a different and much more interesting question. What’s a non trading book you’ve read recently that INDIRECTLY helped your trading? In other words, a non trading book that you were surprised to find lessons that you could apply to your trading or just improved your life or understanding of the world in some way.
Who is Sean McLaughlin?
I appreciate my friend Sean McLaughlin (@chicagosean) for agreeing to participate and being the inaugural interviewee. I don’t remember the first time I actually met Sean but it feels like I’ve known him a long time. He’s brutally honest about his trading and that comes out in his blog and his tweets. Sean has a great voice and hosts at least two podcasts that are worth checking out. He hosts a Denver traders’ meet up which I understand is awesome – I wish there was something like that in my area. Sean came to Trade-Ideas via StockTwits and he’s been an awesome addition. Here are Sean’s answers to the questions…
What is the most overrated trading advice?
Journaling. Yeah. I said it.
Look, I journal. But I don’t do it every day. And I certainly don’t do it for every trade. I do it when I’m called to do it. I don’t set a timer, and I don’t force myself to eat spinach whenever I don’t journal. Deep introspection is important for traders. But the means in which we all get there varies widely. For some, its meditation. For others its exercise. I might like to hike. Or maybe inspiration hits you best when you’re in the shower or on long drives? Whatever works for you, do more of it. At the end of the day, we all need time to think and strategize and re-energize away from the screens. Whatever that means to you and however that works for you — rock on with your Spirit Animal self.
What is the most underrated trading advice?
The X’s and O’s of trading are important, no question. But no matter how efficient you are at learning how to be a better trader, and no matter how quickly your assent to trading mastery, none of it will matter — NONE OF IT — if you don’t get your personal financial house in order and establish systems and best practices to systematically save money, minimize tax impacts, aggressively pay down and ultimately avoid debt, and live within your means with a sensible budget. No amount of income you might derive from the markets will matter if you don’t take care of this stuff first and make it a priority throughout your journey. Take it from me. I’ve lived the nightmare.
What’s a non-trading related book that’s influenced you recently?
I TEACH YOU TO BE RICH, Ramit Sethi. Terrible title, but life-changing approach to personal finances for me. The introduction to YNAB within this book alone was worth the $10 cost. This one book has opened my mind to so many new things to be aware of and be systematic about and can be directly linked to an ever-increasing positive slope to my personal net worth, self-esteem, productivity, health, family relationships around money, and ultimately my success as a trader. All these things are linked. The sooner we accept this, the sooner we thrive in all facets of our careers and life.
Questions for Sean?
Ask them in the comments or hit him up on Twitter. Thanks Sean!
Do you know someone who you think would make a good interviewee? Contact me!
When evaluating trading systems, how do you decide what frequency of trades is best? It seems like an easy question but there are several factors to consider and many of these decisions can be the difference between a successful trading system and one that’s not worth your time. Here’s my framework for determining the right number of trades for a model.
As Often As Possible? Maybe!
On one hand if your trading system has a significant edge then theoretically you should trade it as often as possible – no filtering at all, right? In practice though there is typically some filtering that needs to take place.
Consider the chart above. You have your trading idea, say, new highs on high relative volume. Most trading systems should start out with a basic trading edge – a strong theory that is supported by looking at a large number of trades in a backtest.
When done properly, the more filtering you do to your trading system the higher the profit per trade but the lower the total profit from the system. This is because proper filtering will filter out the worst trades at each point along the x-axis. (I estimate that 90% of my edge comes from my ability to choose filters that make sense – I’ll write about this soon.)
Imagine you can determine with decent accuracy which trades will be profitable and which will not. If you were really concerned about the win rate (unhealthily so!) then you could reduce the system to just a single trade that was highly likely to be profitable. But at what cost? Total profit of course. So you can see there’s a tension between total profit and profit per trade when you are able to meaningfully filter your trading system.
What’s the Right Balance Between Total Profit and Profit Per Trade?
This is the ultimate question and where most of your work occurs. There is an art to coming up with the right balance and a lot of what you choose will come from experience. Depending on your situation the more conservative approach would be to apply more filtering at first – that is, choose a point nearer to the right side of the chart. The more aggressive approach would be to choose a point nearer to the left side.
There are good arguments to be made for either direction. If you have more trading experience then the more aggressive approach can be quite valuable. The more quickly you gather live feedback from the market the more quickly you’ll be able to iterate and understand more about how your trading system acts in the actual market and how that differs from your theoretical backtest. Of course there are costs associated with this and as I’ve written before there’s always the real temptation to scale a trading system up too quickly (something I’ve done many times) since you’re typically overly optimistic at this point in the system development cycle.
If you choose a point towards the right of the chart you’ll have less risk but that comes with the cost of having to wait potentially a long time before the feedback comes (i.e. trade signals appear). Of course there is no “right” answer – you’ll have to gauge for yourself what makes sense given the nature of the strategy and your personal preferences.
Other Important Factors to Consider
How will this affect the systems you’re already trading?
If you already have a trading system that you’re trading how will these new trades affect it? Will the trade times overlap? Will this new system be a distraction? Is the trade frequency of the new system dramatically different than your current one? If so trading the new system might be a serious distraction from the trading you’re already doing. If you’re mentally burrowed down trading in the zone in one system and then a signal from another comes along you might underestimate just how much of a distraction that can be.
How fast is too fast?
Like the video clip from I Love Lucy above implies, there’s a point where you’ll be overwhelmed by the number of trades. There’s a wide variety of capacities among traders – some can easily handle more trades and others find it difficult. This can be alleviated by automating your entries, your exits, or both – something I highly recommend. Also note that you should err on the side of fewer strategies if given the choice. For example, would you prefer to take 10 trades from a single strategy or one trade from 10 different strategies? It should be obvious that 10 trades from a single strategy is preferable.
How slow is too slow?
Another factor to consider is what’s your threshold for the lowest frequency? I think about this in terms of trades per day. I try to keep my trading systems above 0.5 trades per day if possible. Why? I find that anything less than that and it’s not frequent enough to keep my attention. I need to be focused on the strategies I trade – understanding fully why they should be successful. There’s something about the repetition of trades in a system that keeps this focus in the forefront of your mind. As the frequency of trades drops it’s harder and harder to recall that focus quickly.
What about buying power?
A non-obvious factor when you add a trading system is the buying power that is required. The more trades in the system the more buying power needed. Combine this with systems you’re already trading and this could become a problem quicker than you realize.
It’s quite frustrating to run out of buying power just before some winning trade signals materialize! One thing I do is keep a log of buying power over time. This updates in real time as I’m trading. I can go back and see when I had the least amount of buying power available over a period of time and I can plan accordingly.
Which day had the most trades?
Trades per day is a good benchmark, but that number hides a very important piece of information – what was the day with the highest number of trades? This can vary dramatically between systems even with the same trades per day. It’s really important to plan ahead and visualize what those days would have felt like. Would you have been able to keep up with all the trades? Would you have run out of buying power?
It’s up to you!
It’s clear that once you take all these factors into consideration the choice for trade frequency for a trading strategy is a very personal one and there will not be a one size fits all answer for every trader.
Do you approach things differently? Let me know in the comments.
In this case the trader thinks he’s exiting early because he’s worried about a big loss. Big losses, of course, are uncomfortable. You have to be able to tolerate them to catch the profitable trades. The problem is not the occasional big loss – the most difficult part of trading is that you can’t predict when there will be a large concentration of losing trades (a drawdown).
If you find yourself deviating from your plan and exiting your positions too early or too late it could be one of two things that’s occurring:
You are trading too big
You don’t have confidence in your trading plan (or you don’t have one to begin with)
If the trader is mostly comfortable taking losses and is still worried about big losses then he should reduce his trading size to something where he can think more clearly about each trade.
Just as common a situation though is that the trader hasn’t put in the work and research required to understand their exit strategy. If you find yourself second guessing your plan and overriding your pre-planned exit then the best course of action is to test different exit scenarios and see which exit strategy works best over a large number of your trades.
If you don’t know which exit strategies work best for your strategy then you should drop what you’re doing and do the research required to come up with that answer.
Here’s an example from one of my trading strategies. This strategy enters early in the day and holds the trades until the end of the trading day if a stop loss or target isn’t hit. The result is a lot of waiting for positions to play out. Any one trade looks erratic and obvious in hindsight but I know that if I exit earlier than the end of the day I will be leaving money on the table across a large number of trades. There is no guesswork on my part – the numbers don’t lie.
Let’s say you’re designing a trading system to trade the SPY every day by buying (or selling short) the open of the second 1 minute bar and holding until the market close. The one thing you can vary is your position size — so you can choose to use large size some days, trade zero shares some days (i.e. don’t take a position at all), or trade a negative number of shares (i.e. sell short).
These are the parameters of the system — you can’t change anything else about the mechanics of the system. Let’s say you have to follow my good friend Sean McLaughlin’s advice and ignore the news:
What information would you look at to inform your decision about what position size to use on a given day? There are a lot of technical indicators and data points you could look at — literally thousands! You can’t look at all of them, so how do you decide which ones are important? One was I will evaluate data points is to look at the correlation. Which indicators are most correlated with the profit of the system. The higher the correlation, the stronger the relationship with profit.
For this system, I picked 4 indicators and posed a question on Twitter asking which one people believed was the most correlated with profit.
Here are the indicators:
% gap from yest close: how big was the gap between yesterday’s daily close price and today’s open.
The Range of the 1st 1 minute bar of the day: the high price minus the low price of the first one minute bar.
Close of first 1 minute bar in relation to the range: where does the bar close in relation to the range of the first bar? Think of it as a value between 0 and 100 where 0 means it closed at the low and 100 means it closed at the high of the range of the bar.
Close of bar in relation to the open of the bar: where did the bar close in relation to the open price? This is related to the overall range but different. The bar could close at the same price as the open but both those prices could be equal to the high of the bar. Think of this as a value between -100 and 100 with 0 being a “doji”, a value of 100 being a solid green bar with no wicks, and a value of -100 being a solid red bar with no wicks. Here’s a good example of bars that close similarly in the range of each bar but quite differently in relation to the open price.
So which do you think is most important for this trading system? Here were the answers given:
Most thought the gap would be the most important. Surprisingly though that is the least important of the 4 variables. It turns out the range of that first bar is the most important factor of the 4.
This makes sense in light of a general rule of thumb when determining the value of a data point: all things equal the recency of the data is more likely to be important. In this case the gap is the least “recent” of the data points — it includes the open of the day and yesterday’s close both of which are in the past compared to the other indicators.
Mike Bellafiore asked this question on Twitter yesterday. It’s a great question that almost every trader deals with at some point.
There are a lot of thoughtful replies in that thread with some good advice: step away from your trading desk, trade with half your normal position size, stop and go exercise, etc. Of course these are all things you should do if you find yourself in this situation. All of these “solutions,” though, are temporary band-aids for a more permanent problem. If you don’t address the deeper problem you’re likely to continue to find yourself in these difficult trading situations.
The Deeper Problem and How to Fix It
What is the root cause of trading on tilt? It’s really a symptom of a much deeper problem that you should try to solve: you don’t understand your trading system well enough.
If you’re trading on tilt then it likely means that the market has put you in a situation that you didn’t realize could happen ahead of time. “I had no idea this many trades could go against me in one day!” or “Who knew that I could lose so much money in a single trade!” Having these thoughts in the middle of a trading day is a major flaw in your system that must be addressed immediately. How?
The fundamental problem is that you haven’t done enough work outside of the trading day to fully understand your trading system and why it works. What are the best and worst days that your system is going to have? Under what types of markets does your system work well? When does it perform most poorly? You can’t predict the future but you can look in the past and get a really good idea about the range of performance you can reasonably expect from your system. If something surprises you during the trading day, it’s likely that you’ve skipped this arduous but critically important step in preparation.
Peer into the Past
Historical market data is really easy to get access to now. Go back and backtest your system for as long as you can go back. Look at the equity curves — are they smooth or do they have spikes? (And be careful, if you look over a large enough timeframe all curves look smooth!)
Here’s an important step that most traders skip — look at the performance by day of your trading system. What were the best days? The worst days? What days did it trade the most? How many days are there with no trades? Look at the trades it took on those days and truly visualize what trading that day would have felt like with size. Could you have kept up on the days where it traded a ton? What would you have done on the days with the biggest intraday drawdowns?
This is the hard work required to be prepared for any given trading day.
Easy for you to Say, Dave — You’re an Automated Trader — I’m a Discretionary Trader!
What about traders that trade with their instincts? “You can’t backtest my super-duper trading discretion. I trade with my extraordinary feel for the markets.”
I know plenty of traders like this and while it seems like these are fundamentally different styles, they’re really very similar: You get a signal where you’re confident you have a trading edge and you take the trade. Sounds pretty simple and in a lot of ways it is.
How you attain that confidence in your edge varies from trader to trader. Confidence for a discretionary trader comes from experience and trading results over time. This is more valuable than any backtest but it takes time to develop that experience.
If you get into situations where you end up trading on tilt, then you’re probably trading with too much size and you should reduce your size until you gain more experience with your system.
Of course there are no easy answers but if it were easy everyone would do it!
Here at Trade-Ideas we’re in the process of releasing our paper trading platform. OK, so you can trade fake money — what’s the big deal? As someone who has traded the markets for 15+ years I can say with certainty that when used properly Trade-Ideas Paper Trading can take your trading to the next level. Here’s how to do it.
A Successful Trader’s Worst Habit
I can honestly say that I’m a successful trader having been profitable all but 2 years of the last 15. I’m always doing things to improve my trading which mostly involves brainstorming different strategies to efficiently make money. At any given time I have several strategies in various stages of development.
The biggest mistake I end up making is scaling up size too quickly. I spend too little time paper trading a newer strategy. Backtesting is awesome, but you need to spend time watching trades play out in real time to fully understand the WHY — why your strategy looks great in a backtest, why the strategy makes money, and what is the best way to trade it.
Why is this Stage Hard?
I realized over time that I was spending too little time Paper Trading these ideas. Part of the reason was that I was in a hurry to trade the strategy because I thought it had a limited shelf life — I thought the strategy’s edge would evaporate, so I needed to trade it now while it had edge. I now build strategies from the ground up to have staying power — edges that last for the long term.
The Technical Reason I Didn’t Paper Trade Enough
I slowly realized that the main reason I wasn’t paper trading enough was actually pretty simple technical reason. It’s impossible to have a Paper Trading platform open at the same time as your regular live trading platform — maybe not literally “impossible” but at the least VERY, VERY, VERY awkward. It’s so awkward that I found myself skipping that stage since it required logging into a separate account at Interactive Brokers and having TWO instances of TWS running at the same time. Do you want real time data in both your trading platform and your paper trading environment? That’s hard or impossible in most trading platforms!
Paper Trading should be SIMPLE and easily available alongside your real trading platform. You shouldn’t have to pick and choose whether you’ll be live trading or paper trading on a given day.
Enter Trade-Ideas Paper Trading
We’ve built the Trade-Ideas Paper Trading platform from the ground up to operate seamlessly alongside your actual trades. No awkward convoluted hoops to jump through, no sacrificing real time data, and no picking or choosing which platform to run. It shows up as just another account in Brokerage Plus.
Paper Trading Should Work in your Normal Trading Routine
Paper Trading should be available right at your fingertips. When you’re in the middle of a trading day, you need to be able to make split second decisions and fumbling around with another instance of your trading platform just to send a paper trade is unacceptable.
That’s why we put Paper Trading right alongside your real trading — seamlessly available right in your normal trading workflow. Add a strategy in Brokerage Plus like you normally do with all your trading instructions and position sizing predefined and Paper Trading conveniently shows up as another account to point to.
Then when you want to make a paper trade, just right click in almost any window in Trade-Ideas Pro and access the trade menu and choose your strategy. Easy!
Track Paper Trading Performance
When you make paper trades in Brokerage Plus, you’ll see your positions right along side your live trades in the Positions tab. You can monitor performance across all your trades live and paper or you use the Account filter to show just your live trades or just your paper trades.
Paper Trading has to be an important part of what you do. As a trader you MUST be continuously learning and adapting and paper trading has to be an important part of your improvement journey.
(Now is the perfect time to give Trade-Ideas Paper Trading a spin in our Test Drive.)
(Alternate Title: A Toolkit for Making Political Opponents Look Bad Using Stock Market Data)
This tweet came across my Twitter feed a couple days ago.
if you like to keep score, here's the change in Dow Jones Industrial Average from Inauguration Day to Aug 12 of their 3rd year in the White House: GHWB: +34.2% Clinton: +42.4% GWB: -11.9% Obama: +41.7% Trump: +30.1%
The first thing I thought was this was an interesting fact and somewhat counter intuitive — we know how much Trump likes to crow about the stock market when it’s doing well. Quickly my skepticism kicked in and I thought: why did he choose inauguration day and not election day? And also why August 12th and not some other random day? This screamed to me “well chosen example” like a lot of stock market snake oil.
Being a market and data nerd I guessed there was a very good chance you could choose a date like August 12th to make any of these 5 presidents look good compared to the others. I took a look at the data.
It turns out you can use a wide variety of dates to make any one of them look awesome compared to the others (except for one).
Election Day versus Inauguration Day
There’s a lot of time after the election prior to the inauguration of a U.S. President, on average it’s 75.2 days. A lot can happen in that time! Depending on which President you want to look good then you should carefully choose either Election Day or Inauguration Day as this makes a huge difference in the “performance.” A big part of this is that during the time between Obama’s election and inauguration the Dow dropped 17% and during the same time for Trump it rose 8.5%.
I looked at every day for the Dow Jones Industrial Average during these five presidencies. There have been 1009 days since Trump’s election, so I looked at every value 1 though 1009 to see which president’s “score” looked best by comparing the gap between each president and the second best at that point during each presidency. I used either election day or inauguration day as a reference point depending on which one produced the biggest gap between the leader and second place in percentage terms.
There’s a lot of different ways to look at these results and you’ll want to choose carefully depending on your political bias.
The most days with the largest return among these U.S. Presidents:
Trump: 425 days Obama: 371 days George H. W. Bush: 171 days Bill Clinton: 41 days George W. Bush: 0 days
That settles it, right?!?! Not really. The average gap between the leader and second place on any given day varies a lot. Looking that that:
Obama: 15.6% difference Trump: 8.1% difference George H. W. Bush: 3.4% difference Bill Clinton: 3.3% difference George W. Bush: N/A
So as you can see, it’s easy to paint a picture that makes your favorite political party/candidate look good. Eyeballing the chart I think you could even make George W. Bush’s term look good by picking his second term or a particular year as a starting point.
Why August 12th?
So why did the John Harwood choose August 12 for the tweet above? It turns out Clinton had just taken a narrow lead over Trump and then Barack Obama had just taken an even more narrow lead over Clinton. See the area on the right hand side of this chart. Excellent timing indeed!
OK, what’s the logical takeaway from the above chart? My conclusion confirms my prior assumption: you cannot create a trading system that uses the party of the current U.S. President as any sort of meaningful input. In other words, there’s not very much correlation between market performance and political party, as much as either party wishes it were so.
If you think there is (and I often hear many claiming there is!) then your conclusion should be obvious: buy the market when your candidate wins and short the market when the other party’s candidate wins. Should be easy money!
My advice: ignore the news — especially political news and commentary. As Bryan Caplan likes to say: “News is the lie that something important happens every day.”