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respondents trading 20 % to 30 % of value showed the steepest decline at 2.68 %. While these are interesting year-on-year comparative numbers, what is perhaps most impressive is the fact that there is an increase from 25 % to 33 % in the number of respondents indicating that they leverage algos to trade 80 % or more of their value, clearly validating the effectiveness and acceptance of algos as an integral part of their overall trading activities.
Diversity in types of algorithms used As in 2025, when evaluating the types of algorithms employed by traditional asset managers, VWAP, dark liquidity seeking and % volume( participation) strategies emerged as the top three( Figure 6).
The largest mover was implementation shortfall( basket), which showed a robust yearon-year increase of 6.75 % to 29.71 %. This recent jump could be attributed to the preference for basket trading over a single stock; the latter had a decrease of 2.54 %. Meanwhile, the use of target close and auction algos rose 6.5 % to 63 %, marking the shift where almost one quarter of all trading occurs in the closing hours of the market.
In terms of declines, % volume( participation) experienced the
Figure 5. Algorithm usage by value traded(% of responses) |
Percentage of respondents |
2026 |
2025 |
2024 |
unanswered |
3.43 |
2.96 |
4.91 |
0-5 % |
7.71 |
5.93 |
6.72 |
5-10 % |
4.57 |
6.91 |
5.94 |
10-20 % |
7.71 |
8.40 |
8.79 |
20-30 % |
5.71 |
8.40 |
9.04 |
30-40 % |
6.00 |
6.67 |
8.53 |
40-50 % |
7.14 |
8.89 |
8.79 |
50-60 % |
9.14 |
10.12 |
9.82 |
60-70 % |
8.29 |
8.15 |
5.43 |
70-80 % |
7.71 |
9.14 |
8.01 |
80 % and over |
32.57 |
24.44 |
24.03 |
Figure 6. Types of algorithms used(% of responses) Algo type |
2026 |
2025 |
2024 |
% Volume( Participation) |
70.00 |
73.58 |
68.48 |
Dark Liquidity Seeking |
75.71 |
74.32 |
77.26 |
Implementation Shortfall( Basket) |
29.71 |
22.96 |
25.06 |
Implementation Shortfall( Single Stock) |
48.57 |
51.11 |
54.01 |
TWAP |
43.43 |
38.02 |
36.69 |
VWAP |
82.29 |
81.23 |
79.07 |
Target Close / Auction Algos |
62.57 |
56.05 |
58.14 |
Other |
2.86 |
2.72 |
4.65 |
largest drop at-3.58 %, indicating a shift in trading practices where algorithms can exert influence while reducing participation in individual orders. This trend may also reflect greater trading diversification across ETFs, fixed income and foreign exchange.
Methodology Buy-side survey respondents were asked to give a rating for each algorithm provider on a numerical scale from 1.0( very weak) through to 7.0( excellent), covering 15 functional criteria. In general, 5.0( good) is the‘ default’ score of respondents. In total, 1,987 provider ratings were received from 518 individual respondents across 34 algo providers, yielding thousands of data points for analysis. Only the evaluations from clients who indicated that they were engaged in managing long-only strategies have been used to compile the provider profiles and overall market review information. Each evaluation was weighted according to three characteristics of each respondent: the value of assets under management, the proportion of business done using algorithms, and the number of different providers being used. In this way, the evaluations of the largest and broadest users of algorithms were weighted three times as much as those of the smallest and least experienced respondents. Finally, it should be noted that some responses provided by affiliated entities were ignored. A few other responses where the respondent could not be properly verified were also excluded. We hope that readers find this approach both informative and useful as they assess different capabilities in the future.
64 // TheTRADE // Q1 2026