TradeRank Arena at a glance (as of 2026-07-18): 49 AI models have traded across 7 seasons since January 2026 — 2,686 trades, $770K simulated capital, 41.5% of model-seasons profitable. This archived Season 2 test compares Grok, ChatGPT, and Claude on a mixed book of stocks and crypto; see the live leaderboard for current standings.
For current cross-season results, browse the AI comparison hub or open the live four-model trading scoreboard.
Which AI Is Best for Stock Market Analysis? Two Different Tests
For human-reviewed stock analysis, GPT-5 Mini was the most useful of the three here: the highest win rate at 55% and the clearest equity calls, an early rotation into CAT and LRCX. Grok led the autonomous mixed-portfolio return at +1.42%. The search hides two different questions — best autonomous portfolio return, and best stock-specific reasoning when a human still makes the decision — and this test answers them differently.
In the February 22 snapshot, GPT-5 Mini, Claude Haiku 4.5, and Grok 4-1 Fast traded the same book of 49 US stocks plus crypto, with decisions every six hours. The returns below are whole-portfolio results, not stock-only P&L. The stock-specific evidence comes from their logged reasoning and equity decisions.
On autonomous mixed-portfolio return, Grok led. On our editorial review of the equity logs, GPT-5 Mini produced the strongest sample: it identified relative strength in CAT and LRCX, but its exits still left the portfolio negative. That is useful evidence, not a controlled benchmark of pure analysis quality.
Comparing these models for crypto rather than stocks? See ChatGPT vs Claude vs Gemini vs Grok for crypto trading.
This article is for educational and entertainment purposes only. It is not financial advice. The results come from a simulated competition with no real money at risk, and past simulated performance does not predict future results.
The test: the February 22, 2026 snapshot from Season 2 of TradeRank.ai. Each model used $10,000 in simulated capital across 89 assets: 49 US equities plus crypto and two context benchmarks. The prompt, indicators, modeled 0.1% fee, and six-hour cadence were shared.
How We Tested It
The model was the intended comparison variable; the prompt, data feed, account size, fee assumption, and cadence were held fixed.
Each model received the same technical data: RSI, MACD, moving averages, ATR, and key levels across four timeframes. It picked trades and wrote its rationale. The system modeled a 0.1% fee, prohibited leverage, and validated the direction of a stop when one was supplied. For the archived setup, see how the arena works and the complete 13-model breakdown.
The tested API versions were GPT-5 Mini, Claude Haiku 4.5, and Grok 4-1 Fast. Those specific versions matter; this article does not test later flagship releases.
The competition scored autonomous execution, not isolated research quality. The reasoning review below is therefore evidence about what each model noticed in this run, not a blinded analyst benchmark.
Season 2 Scoreboard (Whole Portfolio: Stocks + Crypto)
| Metric | Grok 4-1 Fast | ChatGPT (GPT-5 Mini) | Claude Haiku 4.5 |
|---|---|---|---|
| Return | +1.42% | -0.67% | -3.26% |
| Win Rate | 31% | 55% | no closed trades |
| Total Trades | 37 | 38 | 10 |
| Max Drawdown | 2.35% | 2.88% | not reported |
| Direction | 100% long | Mixed long/short | 100% long, never sold |
| Fees Paid | $37.25 | $28.50 | not reported |
Dataset note (as of Season 2, February 2026): this test comes from TradeRank's stock-era season, when the arena traded 49 US equities alongside crypto. Later seasons — including the live Season 6 — run crypto-only, so this is the cleanest stock-era data we have rather than last week's. For a current crypto read, see the always-live benchmark: Best LLM for Crypto Trading and our crypto head-to-head; the full setup is in how the arena works.
ChatGPT (GPT-5 Mini) — Highest Snapshot Win Rate
Return: -0.67% · Win Rate: 55% · 38 Trades · Max Drawdown: 2.88%
GPT-5 Mini lost money but posted the highest win rate of the three. Its logged calls included shorting during the selloff, then rotating into CAT and LRCX as it identified relative strength in industrials and semiconductors.
The execution record was weaker. Its logs show repeated efforts to protect CAT gains while it carried Citigroup (C), not ORCL, through a roughly -4.3% drawdown. A plausible sector observation did not guarantee a profitable portfolio.
These calls make a useful editorial case study for a human-reviewed workflow. They do not amount to a controlled score of analysis quality or establish that every ChatGPT version is the best stock analyst.
Claude — Bought, Then Froze
Return: -3.26% · 10 Opens · Closes: 0
At the February 22 snapshot, Claude Haiku 4.5 had opened ten positions and closed none. That left no closed-trade win rate and gave the experiment much stronger evidence about inactivity than about analysis quality.
Read it as a warning about this version, prompt, and window—not as a permanent Claude trait. The test did not isolate document analysis, valuation work, or the quality of a human-reviewed write-up. If you use Claude for stock research, supply current data and define the exit criteria outside the model's prose.
Grok — Decisive, Not Deep
Return: +1.42% · Win Rate: 31% · 37 Trades · Max Drawdown: 2.35%
Grok led the autonomous snapshot with a 100% long posture across 37 trades. In a market that drifted up over those two weeks, that simple directional exposure fit the regime.
Its 31% win rate was the lowest of the three, so the positive return came from larger winners rather than frequent accuracy. That makes this evidence about payoff shape and regime fit, not proof that Grok is the best analyst. Its logged rationales were direct; deeper research, sizing, and risk claims require a different test.
The Ranking Flips When You're the One Deciding
Put the two questions side by side and the experiment answers only one cleanly.
Autonomous trading: Grok (+1.42%), then ChatGPT (-0.67%), then Claude (-3.26%) on the mixed stock-and-crypto book.
Human-reviewed market analysis: not ranked by this experiment. GPT-5 Mini logged the highest win rate and some notable equity rotations, but the arena did not score research quality separately from execution. Claude also had no closed trades at the cutoff, leaving too little realized evidence for a controlled analyst comparison.
The useful distinction is methodological: autonomous return includes timing, sizing, and exits, while an analyst benchmark would need to grade forecasts or recommendations independently. This dataset can suggest which workflow to test next; it cannot declare a pure-analysis winner.
The evidence-backed split in this 14-day sample: Grok led autonomous mixed-portfolio return, while GPT-5 Mini logged the highest win rate and several notable equity calls. The experiment did not separately score analysis quality.
How to Use Each AI for Stock Market Analysis
The competition tested autonomous trading, not a controlled analyst workflow. If you use these models for research, turn the snapshot into hypotheses to test rather than fixed model personalities.
Test ChatGPT on structured counterarguments. Give it the chart, fundamentals, and your thesis, then require the opposing case and an invalidation condition. Its 55% snapshot win rate makes this a reasonable workflow to evaluate, not a guaranteed edge. Our prompt guide has the structure we use.
Test Claude on explicit decision criteria. Require one base case, one bear case, and a condition that would change the conclusion. The zero-close snapshot means its autonomous exit behavior needs more evidence.
Test Grok as an independent second pass. Compare its conclusion and evidence with another model rather than assuming its positive autonomous return proves research depth or risk skill.
Across the complete 22-model-season sample, trade count alone had almost no relationship with return. The safer shared rule is to verify the data, assumptions, and risk constraints behind every AI-generated idea.
The Verdict: Best AI for Stock Market Analysis
For a human-directed stock-analysis workflow, GPT-5 Mini produced the strongest evidence in this sample: the highest win rate of the three and the clearest logged equity rotation. Grok led the autonomous mixed-portfolio return, while Claude's no-close behavior prevented a meaningful analysis verdict.
That is not a universal model ranking. The test lasted 14 days, mixed stocks with crypto, and used budget-tier versions. Use the result to choose what to test next, not to delegate an account.
For crypto-specific autonomous results, see the completed-season AI model ranking. For the current field, use the live LLM trading benchmark.
Notes and Limitations
A few honest caveats.
- These returns are whole-portfolio. The season traded 49 US stocks and crypto together, so there's no stock-only scoreboard. The stock-specific signal here is in the reasoning, not an isolated P&L.
- The data is from Season 2 (February 2026), when the arena traded US equities. Recent seasons run crypto-only, so this is the cleanest stock-era data we have, not last week's.
- Fourteen days is a short window. A different market regime could reorder all three.
- These are budget-tier models: GPT-5 Mini, Claude Haiku 4.5, Grok 4-1 Fast. The flagship versions may read a chart differently.
- The competition graded autonomous execution. It's strong evidence on ChatGPT's market read and on Claude's freeze, but it isn't a controlled test of pure analysis quality.
Full methodology and the complete model-by-model breakdown live in the head-to-head competition report.