Darkpool Demystified - Create Winning Strategies using Darkpool Data

November 6th, 2021

According to Investopedia, A dark pool (DP) is a privately organized financial forum or exchange for trading securities. Dark pools allow institutional investors to trade without exposure until after the trade has been executed and reported. One of the reasons a dark pool is needed is to avoid the impact of very large trades on financial markets. An extremely large trade, if filled on normal exchanges such as NASDAQ/NYSE, can cause a massive effect on the stock price. This creates a need for off exchange trades and dark pools fill that role.

Although dark pool trades take place off exchange, there are still many benefits of knowing about them. Knowing when and at what price a darkpool trade occurred can provide great insights to a trader, as we will discuss in this guide.

But isn't Darkpool Data Supposed to be Private?

Before talking about anything, this is the first question that requires answering. Darkpool data is inherently secret but some efforts have been made recently to make darkpool data publicly available. A few vendors and services have emerged that can provide darkpool data feeds to retail traders. Although the data costs are huge, the strategies we can build can be worth the price. One of the most important things to know about dark pool data is that it never comes with information on the trade side. That means we never know whether a trade was a bought or a sold position. We can only make assumptions based on how price reacts afterwards.

One of the most important things to know about dark pool data is that it never comes with information on the trade side. That means we never know whether a trade was a bought or a sold position. We can only make assumptions based on how price reacts afterwards.

A side note here - in Tradytics Darkpool Tools, we also consider very large Block Trades in our DP data as well. Block trades are filled on normal exchanges, which means we do know whether they were filled on the bid (Sell) or the ask (Buy).

Darkpool Trading Strategies

This section is going to be the core of this blog post. Since we do not know the direction of darkpool trades, it can be tricky to effectively use them to form trading strategies. However, as we will see in this guide, there are a few ways we can make use of this data and create winning strategies.

A darkpool trade consists of three parts - price at which the trade took place, number of shares traded, and the total value of the trade. This is all the information we have to work with. For Block Trades, we do know the direction of the trade as well. Let us now discuss a few strategies using DP data.

1. Support & Resistance Levels

One of the most common usecases of DP data is to create support and resistance (SR) levels. These levels can help traders time their entries and exits. For instance, buying on support and selling on a resistance, as well as buying on the break of a resistance, or selling on the break of a support, are all valid strategies that traders use. The hard part can sometimes be to find strong SR levels. That is where darkpool data can massively help.

The image above shows an example of SR levels. Resistance is a region at which price finds supply, and cannot keep going up, thereby reversing to the downside. In contrast, support is a region where price can find demand and reverse to the upside. It is quite easy to see this in retrospect but creating these levels for future purposes can sometimes be challenging and DP data helps solve that challenge nicely.

It is quite easy to see SR levels in retrospect but creating them in real time for future purposes can sometimes be challenging and DP data helps solve that challenge nicely.

In order to create SR levels using DP data, we take note of the biggest trades, and create levels based on them. Next, whenever price reaches those levels, we expect buyers or sellers to step in and take control of the price. It is important to understand the reasoning behind this before applying it to your trading. We discuss it next with an example of $GME.

Gamestop (GME) Case Study

The image above shows an example of a Gamestop ($GME) DP trade that took place on November 1st, 2021 with a value of 48 million dollars. Right after the trade took place, price started to move up. Based on this, we can assume that it might have been a bought position. Given this information, we can now hypothesize a few things. If price ever goes below the 193 level where the trade took place, the institution or the large player who made the trade would go into a loss. Obviously they wouldn't want that. Therefore, what generally happens is that they will start to buy more and step in to try to send the price upwards. That entire process is what can make this a potential support level.

In the image above, we can clearly see that after the DP trade, when the price came down to the 193 level, it immediately found support at that level and reversed back. That demonstrates the power of darkpool data as SR levels. As a trader, whenever price comes down to such levels, we can buy expecting the price to go up again.

As a trader, whenever price comes back to large darkpool levels, we can buy/sell accordingly expecting the price to reverse.

Square (SQ) Case Study

Similar to how DP trades can act as support levels, they can also act as resistance levels. In the figure below, we can see that the price started to go down as soon as the dark pool trade took place. Afterwards, price tried to cross that level two times and failed right away thus acting as a strong resistance level.

Tips & Tricks

We have discussed some examples, but analyzing DP data can sometimes be more of an art than a science. Therefore, we are noting some of the observations we have made while using DP data for SR levels at Tradytics.


    There is a general rule with SR levels - anything that has acted as a resistance, once crossed, will act as a support. The same applies to DP based SR levels as well.


    We say this all the time - when you are creating your trading plays, it is always a good idea to find as many confluence factors as you possibly can. What if we had a horizontal support, plus a DP support, plus some other bullish factor - that would be great.


    Very large darkpool prints can often become stronger levels compared to smaller prints. Size here is relative - a 100 million dollar print for $SPY is not very big, but for a smaller cap like $AMC, it is.

2. Trend Analysis

Trend analysis is another usecase of DP data, but only via block trades. There might be some ways of identifying trends using raw DP trades. But since we have trade side information for block trades, it is much easier to use them for trend identification. Basically, we want to identify points where block trades sentiment suddenly shifts in the opposite direction, and use them to identify trends.

Basically, we want to find periods where block trades sentiment suddenly shifts in the opposite direction, and use them to identify trends.

The image above shows an example of trend identification using our DP sentiment widget. We track block trades sentiment every day and create a chart for the cumulative sentiment over the last month. This can help us identify turning points in stocks. A trend shift happens when smart money and institutions aggressively start going in the opposite direction to the historical block trades sentiment thereby creating a shift in it. For instance, if most block trades were sold positions last week, but there is a sudden large increase in buying activity this week, that can create a trend shift.

In the case of Tesla ($TSLA), we had a trend shift to the bullish side on October 18th, 2021. That was also the start of an uptrend which led to a 30%+ move in the stock in the next 2-3 weeks.

Another example of trend shift is given in the image below for Peloton ($PTON). This is a slightly stretched example since $PTON had earnings after the trend shift and earnings are a bit hard to always get right. However, just 2 days before earnings (November 4th), the darkpool sentiment changed to bearish. After earnings came out, price dumped 30%. These two examples hopefully gives everyone a glimpse of how powerful darkpool data can be.

3. Darkpool Volume Gaps

Darkpool data can also be used to identify the speed and momentum at which price can move. The image below illustrates this concept with Boeing ($BA). We can see that there are two large darkpool levels at 205 and 211 but the volume between those levels is very small. That can mean little to no resistance on a move up from 205 to 211 since there are no institutions or smart money who has traded in that range. On the right side, we can actually see that price moved very fast from 205 to 212 without any clear resistance.

Volume shelf is a concept that comes from price action analysis, but can be easily applied to darkpool levels as well, as we have shown here. Volume gaps work with both upward and downward moves.

Final Thoughts

That is it for this blog post, we have discussed three different ways darkpool data can be used to create trading strategies. Not having the ability to know the direction of a darkpool trade can be tricky. However, there are ways out there to use DP data to your advantage and create an edge for your trading, as we have discussed in this blog post. Because of the inherent limitations of DP data, it is a good idea to always couple your DP based due diligence with other data such as Options Flow, Technical Analysis, etc.

We hope this guide will prove useful to you. If you are looking to access darkpool data and graphics that were discussed in this post, please visit our Darkpool Tools at Tradytics.