I think the times of large players doing unsofisticated executions are long gone. Even the time of unsofisticated algos are gone (see below article). And, as @bone said, there's so much hedging and cross activity that figuring out the intent is impossible. Add to that that whales most of the time don't have the urgency to get in and out because they can use their size if necessary. I don't have access to whales, so just my opinion.
However, it doesn't mean that all these things(mentioned in title) are useless, I just think you are not dealing with whales, but with sharks, tunas, and sardines.

September 23, 2019
New and Improved Algorithms Empower the Buy Side
Larry Tabb
TABB Group
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Little more than a decade after early trading algorithms transformed the markets, algos account for nearly half of US institutional equity buy-side trading volume. But technology has turned over again and a new generation of algos is emerging. Brokers are rethinking electronic execution and reinvesting to create models that improve performance in ways that previously were impossible.
While computer-based trading technology dates back to the early 1970s, modern algorithmic trading was kick-started by the seminal paper, “Optimal Execution of Portfolio Transactions,” by published in 1999 by mathematicians Robert Almgren and Neil Chriss. Their work considered how to execute portfolio-driven transactions that would reduce volatility and lower costs, leveraging computer algorithms to define efficient trading strategies.
By 2007, algorithms known by names such as Sniper and Guerilla had become prominent in securities trading, and algorithmic trading became a major factor in how exchanges and markets operated. Another wave of algos was launched in the early 2010s, including strategies such as Dagger, Sonar and Stealth. These algos evolved past simple time and price averaging to take advantage of the increasing sophistication of dark liquidity and new exchange and ATS order types, as well as the increasing amount of available data and analytics. While these algorithmic tools were revolutionary, technology has turned over again, pushing firms to rethink electronic execution and reinvest to create models that improve performance in ways that previously were impossible.
Today, trading algorithms are critical to institutional equity traders. Not only do they account for nearly half of US institutional equity buy-side trading volume, they also are the most important aspect of the brokerage business, surpassing high-touch sales trading services and block liquidity in driving buy-side order flow (see Exhibits 1 and 2, below).
Exhibit 1: Buy-Side Order Flow Routing by Channel
Source: TABB Group
Exhibit 2: Buy-Side Order Flow Priorities
Source: TABB Group
As a result, brokers are re-investing in their infrastructure and trying to leverage new technologies and data science to make their trading algorithms better, more efficient and more effective.
New trading technologies, combined with new machine learning and automated intelligence tools, have enabled brokers and algo providers to recalibrate and retarget their strategies as they begin to launch the next generation of algorithms. These new and upgraded algos are being developed to meet stricter performance metrics and benchmark standards, and algorithmic trading is now poised to make a great leap forward in complexity and sophistication. In addition, trading algorithms are increasingly customizable, which enable these newer algos – in conjunction with high-frequency trading capabilities, advanced communications and more sophisticated analytics – to support almost individually defined execution strategies.
Overall, the benefits of next-gen algos include transparency and better relationship management for traders with their clients, as well as a more competitive posture and flexible nature in how they interact with the market. With a transparent next-gen algo, a user can see his performance in real time and adjust his trading protocol for greater efficiency and better performance. That transparency, and being able to show that performance to clients, makes the foundation of a trader’s relationships with his clients much stronger. It also may enable clients to work more collaboratively with their traders or brokers as a result.
Today, next-gen algos provide the markets with flexibility, visibility and sophistication that just didn’t exist in the first wave of modern trading algorithms inspired by Almgren and Chriss in 1999. The evolution of algorithms and the newest generation of strategies empower users; algo providers’ clients aren’t likely to settle for models with fixed, inflexible structures, which once were all that was available.
However, it doesn't mean that all these things(mentioned in title) are useless, I just think you are not dealing with whales, but with sharks, tunas, and sardines.

September 23, 2019
New and Improved Algorithms Empower the Buy Side
Larry Tabb
TABB Group
Follow | Profile | More
Little more than a decade after early trading algorithms transformed the markets, algos account for nearly half of US institutional equity buy-side trading volume. But technology has turned over again and a new generation of algos is emerging. Brokers are rethinking electronic execution and reinvesting to create models that improve performance in ways that previously were impossible.
While computer-based trading technology dates back to the early 1970s, modern algorithmic trading was kick-started by the seminal paper, “Optimal Execution of Portfolio Transactions,” by published in 1999 by mathematicians Robert Almgren and Neil Chriss. Their work considered how to execute portfolio-driven transactions that would reduce volatility and lower costs, leveraging computer algorithms to define efficient trading strategies.
By 2007, algorithms known by names such as Sniper and Guerilla had become prominent in securities trading, and algorithmic trading became a major factor in how exchanges and markets operated. Another wave of algos was launched in the early 2010s, including strategies such as Dagger, Sonar and Stealth. These algos evolved past simple time and price averaging to take advantage of the increasing sophistication of dark liquidity and new exchange and ATS order types, as well as the increasing amount of available data and analytics. While these algorithmic tools were revolutionary, technology has turned over again, pushing firms to rethink electronic execution and reinvest to create models that improve performance in ways that previously were impossible.
Today, trading algorithms are critical to institutional equity traders. Not only do they account for nearly half of US institutional equity buy-side trading volume, they also are the most important aspect of the brokerage business, surpassing high-touch sales trading services and block liquidity in driving buy-side order flow (see Exhibits 1 and 2, below).
Exhibit 1: Buy-Side Order Flow Routing by Channel
Source: TABB Group
Exhibit 2: Buy-Side Order Flow Priorities
Source: TABB Group
As a result, brokers are re-investing in their infrastructure and trying to leverage new technologies and data science to make their trading algorithms better, more efficient and more effective.
New trading technologies, combined with new machine learning and automated intelligence tools, have enabled brokers and algo providers to recalibrate and retarget their strategies as they begin to launch the next generation of algorithms. These new and upgraded algos are being developed to meet stricter performance metrics and benchmark standards, and algorithmic trading is now poised to make a great leap forward in complexity and sophistication. In addition, trading algorithms are increasingly customizable, which enable these newer algos – in conjunction with high-frequency trading capabilities, advanced communications and more sophisticated analytics – to support almost individually defined execution strategies.
Overall, the benefits of next-gen algos include transparency and better relationship management for traders with their clients, as well as a more competitive posture and flexible nature in how they interact with the market. With a transparent next-gen algo, a user can see his performance in real time and adjust his trading protocol for greater efficiency and better performance. That transparency, and being able to show that performance to clients, makes the foundation of a trader’s relationships with his clients much stronger. It also may enable clients to work more collaboratively with their traders or brokers as a result.
Today, next-gen algos provide the markets with flexibility, visibility and sophistication that just didn’t exist in the first wave of modern trading algorithms inspired by Almgren and Chriss in 1999. The evolution of algorithms and the newest generation of strategies empower users; algo providers’ clients aren’t likely to settle for models with fixed, inflexible structures, which once were all that was available.