Overview Leaderboard Methodology
Industry Report

Trading Apps

Mobile-first applications that offer commission-free or low-cost stock and crypto trading with simplified interfaces, social features, and fractional shares.

Brands tracked: 42
Brands analyzed: 42
Last updated: 2026-04-23
Model: OpenAI GPT-5
Prompts: 317
Total responses: 1,645
Top Brand Overall?
Fidelity
89/100

Highest overall AI Visibility Score in this industry.

LBA Leader?
Interactive Brokers
92

Highest score on the LBA metric in this industry.

Authority Leader?
Charles Schwab
88

Highest score on the Authority metric in this industry.

TOM Leader?
Robinhood
98

Highest score on the TOM metric in this industry.

Fidelity is the default answer in AI responses for Trading Apps

Fidelity and Charles Schwab together dominate AI responses for Trading Apps. Both brands consistently surface unprompted, with the model treating them as the default answers for most category queries. Brands outside the top two face a structural disadvantage: users are usually given these two before the model even considers alternatives.

Brand Leaderboard All 42 Trading Apps brands ranked

Ranked by overall AI Visibility Score (smoothed geometric mean of LBA, Authority, and TOM with an LBA-based floor on Authority and TOM, see methodology). Click any brand for the full report.

# Brand LBA Authority TOM Overall
1
Fidelity
fidelity.com
 90
84
93
89
2
Charles Schwab
schwab.com
 88
88
81
86
3
Robinhood
robinhood.com
 67
68
98
76
4
Webull
webull.com
 72
56
71
66
5
Interactive Brokers
interactivebrokers.com
 92
28
53
52
6
Thinkorswim
thinkorswim.com
 87
37
30
46
7
E*TRADE
etrade.com
 74
13
55
39
8
SoFi
sofi.com
 70
13
60
39
9
eToro
etoro.com
 72
7
37
29
10
Public
public.com
 73
7
22
25
11
Coinbase
coinbase.com
 80
8
16
24
12
moomoo
moomoo.com
 73
7
22
24
13
Betterment
betterment.com
 83
12
8
22
14
Wealthfront
wealthfront.com
 85
11
8
22
15
Zerodha
zerodha.com
 86
9
9
21
16
Kraken
kraken.com
 74
7
12
20
17
M1
m1.com
 67
9
8
19
18
Revolut
revolut.com
 76
8
8
19
19
Vanguard
vanguard.com
 78
8
8
19
20
Gemini
gemini.com
 72
7
7
18
21
Stash
stash.com
 75
8
8
18
22
Trade Republic
traderepublic.com
 73
7
7
18
23
XTB
xtb.com
 72
7
7
18
24
Acorns
acorns.com
 71
7
7
17
25
Groww
groww.in
 71
7
7
17
26
Questrade
questrade.com
 71
7
7
17
27
Scalable Capital
scalable.capital
 69
7
7
17
28
Sharesies
sharesies.com
 68
7
7
17
29
tastytrade
tastytrade.com
 72
7
7
17
30
T
Tiger Brokers
tigerbrokers.com
 70
7
7
17
31
Upstox
upstox.com
 69
7
7
17
32
DEGIRO
degiro.com
 63
6
6
16
33
Freetrade
freetrade.io
 65
6
6
16
34
Trading 212
trading212.com
 67
7
7
16
35
Cash App Taxes
cash.app
 59
6
6
15
36
Firstrade
firstrade.com
 58
6
6
15
37
Stake
hellostake.com
 60
6
6
15
38
BUX
bux.com
 56
6
6
14
39
Lightyear
lightyear.ai
 47
5
5
12
40
NAGA
naga.com
 48
5
5
12
41
Wealthsimple Tax
wealthsimple.com
 49
5
5
12
42
Freedom24
freedom24.com
 42
4
4
11

How is this calculated? Methodology

Every brand in this leaderboard is scored against the same set of 317 shared Trading Apps prompts. The same prompts, same model, same iterations. So differences in scores reflect actual differences in AI visibility, not differences in measurement.

Overall AI Visibility Score
Smoothed geometric mean of LBA, Authority and TOM. Authority and TOM are floored at LBA × 0.1 before the geometric mean (the same floor used in the per-metric cards above, so brand cards and the composite tell the same story). Formula: composite = ((LBA + 5)(Authority + 5)(TOM + 5))^(1/3) - 5. The floor keeps brands the model clearly recognises but doesn't yet recommend from collapsing to zero, while a single genuinely weak metric still pulls the composite down. Full methodology.
Shared industry prompts
For Authority and TOM, all brands in the industry are scored against the same 317 category prompts (e.g. "best SEO tools for agencies"). This makes brand-to-brand comparisons valid - everyone faces identical inputs. LBA prompts are per-brand because they ask brand-specific questions.
Latent Brand Association (LBA)
5 brand probes + 1 control prompt, each run 5 times in recall mode (no web search). LBA = quality × meta × stability × share × recognition × 100. Read the full LBA methodology →
LLM Authority
50 organic category prompts (discovery, comparison, problem and transactional intents), each run once in recall mode and once in retrieval mode. Score = frequency × log-decayed prominence × intent weight, then 50/50 averaged across the two modes. Read the full Authority methodology →
Top of Mind (TOM)
15 high-volume discovery prompts (sourced from Keywords Everywhere search-volume data), each run 5 times in pure recall mode (no web). Score = frequency × (0.5 + 0.5 × log-prominence), volume-weighted. Read the full TOM methodology →