By @delthetrader
5
88 ratings
The podcast currently has 9 episodes available.
If you’re new, subscribe for free! 👇 And listen to the intro post here. - Del
I'm a bit of a stickler when it comes to volume, maybe because it's at the center of my trading strategy. I often see people on Twitter refer to volume profile as a moving average or a plug n’ play strategy.
“I like your volume profile, bro” or “So happy I added volume profile.”
Let me help with a rant.
Volume analysis is the study of the lifeblood of the market auction. The objective is to understand what’s beneath the surface to make sense of what’s going on above.
To put it another way, price is an indicator of volume. Let that sink in. When you trade, are you trading price? Or are you really trading volume?
As traders, it’s imperative that we fully grasp the concept of volume and how it changes hands. You need to be at least aware of the methods used to analyze volume, as they could benefit your trading.
Volume tools
Volume Profile on its own is useless without the holistic analysis that goes along with it. It would be like learning to steer but not knowing how to push the pedal. Here are some of the more common tools available on most charting platforms.
* Volume at Time (A histogram along the x-axis)
* Volume at Price (A histogram along the y-axis, aka. “Volume Profile”)
* VWAP (Volume weighted average price)
* Bid/Ask Delta (Difference in volume between buyers are sellers)
Together, these indicators can help you get a picture of sentiment and how volume is distributed at different price points on the chart.
Volume at play
Ok, so why do you care to understand volume? Can’t you just trade price action? Yes! But if you use charts to make trading decisions (technical analysis), you may have noticed that stocks with large ranges or successful breakouts all have high relative volume.
Intraday, you may have also noticed that volume falls off from morning peaks, which is usually when you’ll have the strongest defining moves for the session.
Lastly, and most importantly, new traders can use volume profile to pinpoint conflict areas on the chart with much more accuracy than the traditional method of Support and Resistance—something I’ll do a separate post on.
In another post, I’ll do a deep dive on how to interpret and apply “Volume Profile (Volume at Price)” analysis to your trading.
These tools are all part of a larger analysis that can be very powerful in selecting conflict areas on a chart, but there’s more to this type of analysis than meets the eye.
The exciting part to me when I spent time learning about volume was a shift in how I thought about price action. When doing technical analysis, it’s less about what works and more about looking for commitment.
If price reflects the underlying volume, what characteristics of the volume activity would be most telling about the emotional or behavioral state of market participants? Do we even have the full picture?
The fourth dimension
You’ve been staring at fixed periods your whole life, Neo; there is another dimension. Spoiler… it’s important.
We look at price, time, and volume on a chart every day, but there is another axis. Velocity. This fourth dimension is overlooked on most charting platforms for stock traders. Don’t you think how much time price spent at each price level could be important?
Time Price Opportunity(TPO)
If you’re looking at charts with timeframes, you’re simply seeing price split arbitrarily by time periods. That split has nothing to do with the way price actually behaves. It serves no other purpose than to simplify data for us, which is fine.
What we’re missing is the ability to see how much time was spent at one price level vs. another. Sure, we could have sideways movement in a stock that tells us it’s spending a lot of time there. But a lot of volume may be deposited at a price point where price spent the least amount of time. That is arguably important information.
This is where TPO charts come in. Time Price Opportunity — commonly used in futures trading. In stock trading, I haven’t found the same popularity. Here’s an example of my use of TPO charts mixed with Volume Profile which played a big part in a decision to short NFLX.
TPO charts build a profile sequentially based on how much time is spent at each price level, commonly using the alphabet. They build from open to close and represent where price has spent the most or least time during a session.
I can do a deeper dive into using TPO charts in the future. I’ll also make future posts about the concept of velocity in day trading, and how I use it to complete the story.
In Closing
Here are a couple of platforms and addons that I can recommend.
* Sierra Chart (My current platform)
* Rancho Dinero (NinjaTrader add-on)
When you look at a price chart, realize that there is a volume distribution inside each time period. Imagine a tiny profile, very much fractal in nature.
For traders, the personality of each stock is different, and the best way to get to know them is through analyzing their volume.
📣 I put together a 5+ hour course that shows how I used these methods to day trade small-cap stocks. Check it out at delthetrader.com.
If you liked this issue and want to hear more about a specific topic. Let me know in the comments below.
— Del (twitter)
📣 Issues will now have podcasts attached, so I’m re-releasing this post with the added podcast.
If you’re new, subscribe for free! 👇 And read the intro post here.
You can have an edge in the way you approach the markets, a method you use, a setup you trade, or even a mindset. Whatever floats your boat, as long as it’s measurable. It also doesn’t have to be unique to you, it can be a common setup and maybe you just exploit it better or differently than the next trader.
The only certain thing is that if you’re trading without an edge, you’re the sucker at the poker table. Trading without knowing your edge is arguably just as bad.
It’s worthwhile investing some time and effort into refining your trading by collecting and exploring your own data.
It can also be a powerful way to build conviction early in your trading journey. Okay, dead horse beat. Let’s explore methods for collecting data.
The example above is a regression analysis I ran on a specific setup of mine—intended to be traded in a specific way, with specific parameters, under specific market conditions. Dots on the chart that stray too far from the trend-line are setups that are outliers, performing out of the norm.
This type of analysis is a great way to introduce rigor to the process of trade selection. The outcomes from taking those setups, win or lose, are then by nature more easily measured; improving your data collection overall. Sounds great! But it’s no small task like anything worthwhile in trading.
At the end of the day, we’re looking for a pattern in your trading or setups that we can reproduce to fine-tune your performance and better define your edge.
a. Define your setup
For the example below, we’ll use a simple bull flag breakout. I would boil down the 1-3 setups that you’re willing to spend a significant amount of time studying and pick one to start.
You need a defined setup to continue.
b. Make the setup reproducible
Your goal now is to convert everything that may be relevant to the outcome of your setup into a data point. Begin by assigning letters to each of the key points in your pattern. 🥳 You now have reference points for your spreadsheet.
X-A references a the start of your pattern, and F-G references the breakout. Now you can begin collecting data on the setups you trade across different stocks and referencing them in your spreadsheet.
Here’s an example from one of my sheets on what this data may look like.
You can see that I was able to calculate the MFRR, Maximum Favorable Risk to Reward, from this data set. Just one of the many data explorations you can do that can help inform your trading decisions.
Be sure to include external factors as well, like fundamental parameters, or anything that weighs on how you would take the setup during live trading. You want to model the data to be as wide as possible, then work towards narrowing it down.
c. Trade the setup 1,000 times
To extract statistically significant insights from your data, you need a large data set.
It’s only after 1,000 trades, traded in the same way, under the same conditions, that you are able to confidently assign a statistical probability.
Don’t worry, there are shortcuts. You can backtest setups manually—or with an algorithm— and walk trades forward to glean more data from your backtest results. This can be as simple or complicated as you want. Just stick to the goal of collecting good consistent data.
In the past I’ve used the ThinkorSwim on-demand feature, I’ve also manually walked the chart back on TradingView which works really well, and I’ve even gone as far as writing a python script to scrape what I need from stock data feeds.
d. Statistical exploration
Exploring data is a very large topic. I’ll cover a couple of examples to get you started but this area is where you’ll need to do some leg work and find the best analysis that fits your specific setups.
**Please note that I am not a data analyst, I’m simply sharing what works for me. Happy to take suggestions in the comments.
Linear Regression
Is there a correlation? The linear regression example I showed above is one method that, once you wrap your head around, is very easy to apply to any data with multiple variables to predict the relationship between different factors and output on a graph.
For example, plotting returns vs position sizing and running a regression may give you a high correlation which can give you insight into how position sizing affects your returns. The points on the chart would closely follow a linear regression, the tighter they follow the higher the correlation.
Using this method you can identify outliers to refine your setup or performance.
Here are some resources to get you started:
* Using Multiple Regression in Excel for Predictive Analysis
* Simple Linear Regression in Google Sheets
* Statistics 101: Linear Regression, The Very Basics
Standard Deviation and Mean
How do you know how far your data is straying from the mean? Standard deviations! 🤓
The standard deviation is the average amount of variability in your dataset. It tells you, on average, how far each value lies from the mean. A high standard deviation means that values are generally far from the mean, while a low standard deviation indicates that values are clustered close to the mean.
Let’s look at an example using trade data. The charts below show that returns are much more concentrated to the mean in the short term. As we go out to the monthly view, the mean shifts dramatically and we get a more sporadic return distribution.
As a trader, the insight would be to focus on the outliers and work on tightening up the longer-term return distribution.
Here are some helpful resources:
* How to Calculate Standard Deviation in Excel
* Understanding and calculating standard deviation
* Introduction to Statistics Using Google Sheets(this is a monstrous, but an extremely helpful document that everyone should save to disk.)
In closing
Don’t be overwhelmed by the complexity of this issue. It’s completely expected to be lost when it comes to this subject, trading is hard enough without having to do all of this spreadsheet statistical work. Think of it as something you’ll get better at as you learn.
The next time you trade, consider keeping a detailed log to make it easier on yourself in the future. You’ll regret not collecting data as early as possible.
💬 Happy to explore further in the comments! Also, if this is an interesting topic for everyone, I can try to get a statistician or data analyst on the next podcast episode to set us straight.
— Del (twitter)
📣 Issues will now have podcasts attached, so I’m re-releasing this post with the added podcast.
And if you’re new, subscribe for free! 👇
It’s been some time since the last podcast episode of Bear vs Pig was released back in 2018 (youtube playlist, apple podcasts, spotify). I really enjoyed picking the brains of some of the traders I admired while attempting to mine nuggets from them. I really only started the podcast because I wanted to selfishly improve my own trading… I guess that’s how good things are born, out of a need.
Since 2018, I’m now swing trading instead of day-trading piggies, and many of the guests on the show have gone on to find their own success in the community. It’s been great to watch them develop. (Shout out to @CiocanaTrader@SmashTheBid@jtraderco@risklifemike@RickyAnalog 🙏🏻)
Today, I’m reviving BVP. Not just into yet another podcast, but a resource for stock traders which will be centered around one simple goal.
Cut the fat and bring lean value to traders.
As a dad and trader, I don’t have time for b******t and neither do you if you’re trying to make money in the markets.
It will also be a much more personal experience this time around as I will be sharing my own strategy, play by plays, watchlist, and analysis to once again, continue to improve my own trading. Interviews will be relevant, hard to find, nugget-filled explorations in trying to talk to who’s ‘doing it right’ to extract value for us, the nugget miners.
What to expect
* Active trading focus (i.e. not investing).
* Nightly(ish) watchlists.
* Weekend market reviews and setups.
* Interviews with traders (no schedule yet).
* Issues about trading, risk, management, and process.
* Technical deep dives (fun!).
Cost?
It’ll be free for now. If people find this content valuable, I’m sure we’ll come up with a way to support BVP together.
💙
— @delthetrader
The podcast currently has 9 episodes available.