LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs

Haochen Luo; Yifan Li; Binh Minh An; Xiaolong Luo; Zhengzhao Lai; Yuan Zhang; Chen Liu

cs.AISep 27, 2026

LiveOption: Evaluating LLM Agents in Structured Option Trading with Nonlinear Payoffs

Authors: Haochen Luo, Yifan Li, Binh Minh An, Xiaolong Luo, Zhengzhao Lai, Yuan Zhang, Chen Liu

Organizations: City University of Hong Kong · The Chinese University of Hong Kong (Shenzhen) · Shanghai University of Finance and Economics

Abstract

Large language models (LLMs) and multi-agent systems (MAS) have shown promise in financial decision-making, yet existing evaluations focus on equity trading and primarily assess directional prediction, overlooking the structural complexity of derivative markets. Option trading introduces fundamentally different challenges, including nonlinear payoffs and multi-leg strategy construction, requiring structured decisions rather than simple directional bets. We introduce LiveOption, an evaluation framework for LLM-based agents in option trading. LiveOption formulates the problem as structured sequential decision-making under realistic execution and capital constraints, and provides a reproducible environment with standardized interaction protocols. The framework includes three task suites covering portfolio overlays, event-driven earnings trading, and 0DTE intraday trading. We further propose a hierarchical metric suite that evaluates action validity, decision quality, risk characteristics, and outcome-level performance. Experiments show that current agents often fail to achieve competitive returns in most scenarios. LiveOption offers a principled testbed for evaluating structured decision-making beyond outcome-based metrics.

Figures & tables

Model	Hedging		Covered Call
Model	$\Delta$ Ret	$\Delta$ MDD	$\Delta$ Ret	$\Delta$ MDD
DeepSeek-V4-Flash	$-$ 6.15	3.80	0.34	3.85
GPT-OSS-120B	$-$ 3.05	1.45	$-$ 7.38	$-$ 0.42
Qwen3-235B-A22B	$-$ 7.25	$-$ 0.52	$-$ 2.79	0.09
Llama-3.3-70B-Instruct	$-$ 7.86	6.87	$-$ 1.37	$-$ 0.90
MiniMax-M3	$-$ 6.34	2.76	$-$ 9.36	0.86

Table 1 : Overlay performance against the matched buy-and-hold portfolio, five assets per model and mandate.

\Delta

Ret is mean active return and

\Delta

MDD the mean drawdown reduction (%).

Model	Fills	Moneyness	DTE	Fills	Moneyness	DTE
	Hedge			Income
DeepSeek-V4-Flash	511	0.970 $\pm$ 0.079	23.2 $\pm$ 13.4	222	1.057 $\pm$ 0.075	25.9 $\pm$ 13.9
GPT-OSS-120B	807	0.988 $\pm$ 0.057	14.1 $\pm$ 11.6	852	1.037 $\pm$ 0.055	12.5 $\pm$ 9.3
Qwen3-235B-A22B	703	0.951 $\pm$ 0.068	16.7 $\pm$ 15.2	397	1.033 $\pm$ 0.051	14.2 $\pm$ 15.6
Llama-3.3-70B-Instruct	588	0.979 $\pm$ 0.053	6.4 $\pm$ 5.4	164	1.012 $\pm$ 0.037	3.3 $\pm$ 4.3
MiniMax-M3	789	0.955 $\pm$ 0.078	27.1 $\pm$ 12.9	405	1.063 $\pm$ 0.076	23.2 $\pm$ 14.0

Table 3 : Overlay instrument choice by mandate (

\mu\pm\sigma

over option fills). Fills counts executed option contracts over the five assets, Moneyness is strike over underlying at fill (

K/S

), and DTE is days to expiry at entry against prescribed bands of 7–45 days (Income) and 7–60 days (Hedge). Every hedge leg in the record is a put and every income leg a call, so a call-share column is omitted.

Model	Total fills				Per episode
Model	Hedge	Income	Earnings	0DTE	Earnings	0DTE
DeepSeek-V4-Flash	511	222	616	3,274	6.6	32.1
GPT-OSS-120B	807	852	682	3,427	7.2	33.6
Qwen3-235B-A22B	703	397	803	2,519	8.5	24.7
Llama-3.3-70B-Instruct	588	164	678	2,397	7.5	23.5
MiniMax-M3	789	405	572	1,499	6.0	14.7

Table 4 : Trading activity by model and scenario, as executed option fills in the selected main runs. The two right-hand columns normalise by episode, per earnings case and per 0DTE session; 0DTE totals are the per-session mean over the 102 selected sessions.

Model	0DTE Intraday								Earnings Bet
	Shape			BSM attribution					Shape			BSM attribution
	$\uparrow$	$\downarrow$	$q_{90}$	$\delta$	$\gamma$	$\theta$	$\nu$	Resid.	$\uparrow$	$\downarrow$	$q_{90}$	$\delta$	$\gamma$	$\theta$	$\nu$	Resid.
DeepSeek-V4-Flash	2	0	+3.6	$-$ 19	+123	$-$ 163	+17	$-$ 32	2	0	+13.5	+553	+9659	$-$ 1642	+11	$-$ 8737
GPT-OSS-120B	10	2	+9.8	+73	+211	$-$ 271	+32	$-$ 32	2	0	+10.6	+659	+20157	$-$ 1503	+260	$-$ 19642
Qwen3-235B-A22B	0	0	+2.3	$-$ 9	+86	$-$ 133	+21	$-$ 18	3	0	+9.3	+239	+32689	$-$ 3108	+658	$-$ 31340
Llama-3.3-70B-Instruct	3	0	+5.1	$-$ 36	+120	$-$ 186	+14	$-$ 16	2	0	+11.7	+750	+271	$-$ 1681	+202	+279

Table 5 : Episode-level return shape and BSM PnL attribution for 0DTE Intraday (left) and Earnings Bet (right).

\uparrow/\downarrow

count positive/negative tail episodes,

q_{90}

is the 90th-percentile return, and attribution reports mean PnL per episode by Greek plus residual. Detail metrics are reported in Tables 21 and 27 .

Ablation	Task	$\Delta$ Ret (pp)	Fills	NT%	MDD (%)	min $p$
Fixed chain	0DTE	$-$ 0.90	29.5 $\to$ 26.0	0.0	4.4 $\to$ 4.2	0.085
No portfolio state	0DTE	+2.36	29.5 $\to$ 16.1	0.0	4.4 $\to$ 12.4	0.698
No task rules	0DTE	+0.31	29.5 $\to$ 30.7	0.0	4.4 $\to$ 4.5	0.211
No news	Earnings	$-$ 0.31	7.1 $\to$ 7.3	4.2	7.0 $\to$ 8.0	1.000
No IV	Earnings	$-$ 2.82	7.1 $\to$ 7.3	5.6	7.0 $\to$ 8.3	0.059
No Greeks / multi-TF	0DTE	+0.25	29.5 $\to$ 4.4	47.2	4.4 $\to$ 1.0	0.835

Table 6 : Targeted context ablations averaged over three models (24 paired cases each).

\Delta

Ret is the paired return change; Fills and MDD show baseline

\to

ablation; NT is the no-trade rate; and

p

is the minimum Holm-adjusted sign-flip

p

-value. Under No Greeks , lower drawdown reflects reduced trading rather than better risk control. Per-model results are in Table 29 .

Appendix figures & tables19 assets

Supplementary material from the paper’s appendix.

Appendix

Greek	Definition	Risk Dimension	Key Scenario(s)
Delta ( $\delta$ )	$\partial V/\partial S$	Directional exposure	Overlay
Gamma ( $\gamma$ )	$\partial^{2}V/\partial S^{2}$	Price convexity	0DTE
Theta ( $\theta$ )	$-\partial V/\partial T$	Time decay	0DTE, Overlay
Vega ( $\nu$ )	$\partial V/\partial\sigma$	Volatility level	Earnings Bet
Rho ( $\rho$ )	$\partial V/\partial r$	Interest rate	(negligible in short-dated)

Appendix

Table 7 : Summary of option Greeks and their relevance to LiveOption evaluation scenarios.

Name	Construction	Payoff / Profit Intuition (at $T$ )
Long Call	Buy 1 call $(K,T)$ , pay $c$	Profit $=(S_{T}-K)^{+}-c$ ; bullish with limited loss and uncapped upside.
Long Put	Buy 1 put $(K,T)$ , pay $p$	Profit $=(K-S_{T})^{+}-p$ ; bearish protection with limited loss and downside convexity.
Covered Call	Long stock + sell 1 call $(K,T)$	Collects premium; upside capped above $K$ , downside partially cushioned by premium.
Protective Put	Long stock + buy 1 put $(K,T)$	Downside bounded (insurance-like); sets a floor near $K$ on portfolio value.
Cash-Sec. Put	Sell 1 put $(K,T)$ (cash-backed)	Receives premium; profits if $S_{T}>K$ ; risk of buying stock at $K$ on drops.
Bull Call Spread	Buy call $K_{1}$ , sell call $K_{2}$ ( $K_{1}<K_{2}$ )	Moderately bullish; bounded profit/loss; reduces cost but caps upside.

Appendix

Table 8 : Common option strategies and their constructions. Payoff intuition is described at expiration

T

; profit equals payoff minus net premium, and short legs flip the corresponding payoff.

View	Source fields
Market path and chain	Underlying bars and option-chain snapshots from the query layer, at the decision timestamp
News and event triggers	Time-aligned news and calendar entries from the event layer
Agent context and rationale	The observation packet and thesis text recorded per decision step
Orders and fills	Submitted intents, verification outcome, and the resulting fills with price, quantity and fees
Portfolio and risk	Cash, equity, open positions, margin usage and per-position Greeks from the portfolio record
Performance panels	Episode return, drawdown, win rate and exposure, recomputed from the portfolio series

Appendix

Table 9: Web interface views and the recorded fields each one reads.

Setting	Overlay	Earnings Bet	0DTE Intraday
Universe	QQQ, NVDA, AMD, GOOG, TSLA	24 single names across mega-cap tech, semis, software, consumer, fintech (Tab. 11 )	SPY
Evaluation horizon	Full-year 2025, weekly cadence	2025 fiscal-year scheduled releases (95 event cases, 90–95 valid per model)	Wednesdays & Fridays of 2025 (102 sessions)
Bar frequency	1-day	1-min (intraday) + daily close gates	1-min
Decision frequency	Weekly rebalance (Monday close)	Daily close at $T{-}2$ , $T{-}1$ ; every 30 min on $T$ to $T{+}2$	Every 15 min, 09:30–16:00 ET
Initial capital	10,000 shares per name + 20% cash buffer	$50,000 cash (event-betting account)	$100,000 cash
Allowed structures	Income: covered call, cash-secured put. Hedge: long put, bear put spread. No naked premium selling.	Long call/put, vertical call/put spreads, straddles, strangles. No naked short legs.	Long call/put, vertical call/put spreads, straddles, strangles.

Appendix

Table 10 : Implementation and experimental settings across the three scenarios.

Category	Assets	Description
Mega-cap Tech	AAPL, MSFT, TSLA, AMZN, META, NVDA	High liquidity; large absolute post-earnings moves
Semiconductor	AMD, INTC, MU, AVGO, SMCI	Cyclical earnings sensitivity and hardware-sector volatility
Consumer & Streaming	NFLX, DIS, NKE, CELH	Subscriber, brand demand, and consumer spending reactions
Cloud / SaaS	CRM, SNOW, PLTR, SHOP	Revenue-growth sensitive; wide analyst forecast dispersion
Fintech & Crypto-adjacent	HOOD, COIN, SOFI, PYPL	Highly volatile around earnings; retail-flow driven
High Beta / Growth	RKLB	Elevated volatility and strong reaction to earnings surprises

Appendix

Table 11 : Tradable universe in the Earnings Bet scenario.

Ticker	Tot. Ret.	Ann. Ret.	Ann. Vol.	Sharpe	Skew.	Kurt.	Best	Worst	MDD
	(%)	(%)	(%)				(%)	(%)	(%)
QQQ	$20.40$	$20.67$	$23.62$	$0.88$	$1.31$	$17.81$	$12.00$	$-6.21$	$-22.88$
NVDA	$34.84$	$35.33$	$49.64$	$0.71$	$-0.08$	$8.00$	$18.72$	$-16.97$	$-36.89$
AMD	$77.54$	$78.77$	$60.73$	$1.30$	$1.79$	$10.68$	$23.82$	$-8.90$	$-39.63$
GOOG	$64.61$	$65.60$	$31.99$	$2.05$	$0.33$	$3.99$	$9.88$	$-7.51$	$-29.43$
TSLA	$18.57$	$18.82$	$63.31$	$0.30$	$0.41$	$4.79$	$22.69$	$-15.43$	$-48.19$

Appendix

Table 12 : Performance metrics and risk characteristics of selected technology tickers. Returns and volatility are annualized; Sharpe ratio is calculated assuming a risk-free rate of zero (

r_{f}=0

). Metrics are derived from daily closing prices for the 2025 fiscal year.

	Q1		Q2		Q3		Q4		Overall
Ticker	Open	Close	Open	Close	Open	Close	Open	Close	Open	Close
AAPL	$-3.20$	$-4.03$	$-4.79$	$-6.76$	$-1.48$	$-2.03$	$-0.36$	$-0.87$	$-2.46$	$-3.42$
AMD	$-7.17$	$-7.82$	$3.35$	$3.12$	$-4.29$	$-1.10$	$1.37$	$-4.94$	$-1.69$	$-2.68$
AMZN	$-3.47$	$-2.38$	$-1.94$	$-2.02$	$-7.14$	$-9.59$	$14.58$	$13.97$	$0.51$	$-0.01$
AVGO	$5.66$	$2.79$	$-5.73$	$-6.02$	$11.97$	$12.92$	$-10.95$	$-16.38$	$0.24$	$-1.67$
CELH	$27.09$	$22.88$	$3.72$	$5.61$	$19.68$	$21.55$	$-23.72$	$-30.71$	$6.69$	$4.83$

Appendix

Table 13 : Earnings-announcement price reactions by ticker in 2025. Values represent percentage changes (

\Delta\%

) aligned by decimal point. Open denotes the reaction from

T-1

close to

T+1

open; Close denotes

T-1

close to

T+1

close. Information is curated from evaluated technical reports.

		Return (%)				GK Volatility (%)
Group	$N$	Mean	Std	P25	P75	Mean	Std	P25	P75
Overall	250	$0.0911$	$1.2558$	$-0.3760$	$0.4846$	$0.8214$	$0.8630$	$0.4289$	$0.8557$
Wednesday	52	$0.2181$	$1.4654$	$-0.2520$	$0.4612$	$0.8762$	$1.1162$	$0.4289$	$0.8599$
Friday	50	$-0.0409$	$0.9903$	$-0.3955$	$0.5506$	$0.7643$	$0.4816$	$0.4389$	$0.8477$

Appendix

Table 14 : Summary statistics of daily returns and GK volatility by day-of-week group over 250 trading days. All values are in percentage (%).

Metric	Wed.	Fri.	Overall
Mean intraday range (%)	1.2697	1.1902	1.1791
Mean absolute return (%)	0.6677	0.6916	0.6115
Skewness	5.3916	$-$ 1.2568	2.6255
Kurtosis	30.239	$-$ 2.636	32.623
Up ${>}2\sigma$	1 (1.9%)	1 (2.0%)	3 (1.2%)
Up ${>}3\sigma$	1 (1.9%)	0 (0.0%)	2 (0.8%)

Appendix

Table 15: Intraday and tail-risk statistics by day-of-week group. Tail counts report observations exceeding the stated multiple of the full-sample standard deviation, with the group percentage in parentheses.

Model	Ret%	$\Delta$ Ret	Sharpe	MDD%	$\Delta$ MDD	IR	Fills	Fees ($)
(a) QQQ buy-and-hold 17.00%, maximum drawdown 19.24%
Hedge
DeepSeek-V4-Flash	10.38	$-$ 6.62	0.669	16.62	2.62	$-$ 0.93	95	3,255
GPT-OSS-120B	15.95	$-$ 1.05	1.011	14.06	5.18	$-$ 0.28	131	3,788
Qwen3-235B-A22B	11.66	$-$ 5.33	0.690	19.95	$-$ 0.71	$-$ 1.74	71	2,280
Llama-3.3-70B-Instruct	12.30	$-$ 4.70	0.943	14.44	4.81	$-$ 0.43	132	1,766

Appendix

Table 17 : Overlay backtest performance per asset, full-year 2025.

\Delta

Ret is active return against the matched buy-and-hold portfolio,

\Delta

MDD the reduction in maximum drawdown (positive means shallower than the benchmark), IR the information ratio, and Fees the total commission and exchange fees paid. Each sub-heading gives that asset’s buy-and-hold return and maximum drawdown, which are the benchmark the

\Delta

columns are measured against.

	QQQ	NVDA	AMD	GOOG	TSLA	$\Delta$ Ret	$\Delta$ MDD
Hedging
Protective put rule	-9.1	-11.6	+3.2	-7.1	-15.2	-7.97	+8.20
LLM panel mean	-4.9	-12.1	-0.8	-6.1	-12.5	-7.28	+3.78
panel $-$ rule	+4.2	-0.5	-4.0	+1.0	+2.7	+0.69	3/5
Covered Call
Covered call rule	-7.7	-16.8	-47.0	-47.5	+8.8	-22.04	+3.57

Appendix

Table 18: Mechanical overlay baselines against the LLM panel. Each baseline runs the paper’s own overlay configuration with only the decision layer replaced by a fixed

0.30

-delta rule, so universe, capital, dates, rebalance cadence, risk rules and fill model are identical to the agent runs. Cells are active return against the matched buy-and-hold in percentage points;

\Delta

Ret and

\Delta

MDD average over the five assets, and the last row of each block is the panel mean minus the rule on the same asset, with the count of assets on which the panel is ahead.

Month	Model	Sess.	Win%	PnL%	Mean%	P/L	PF	Best%	Worst%
2025-01	DeepSeek-V4-Flash	9	33.3	$-$ 10.97	$-$ 1.22	0.45	0.23	+2.07	$-$ 4.59
	GPT-OSS-120B	9	33.3	+13.55	+1.50	3.14	1.57	+21.87	$-$ 7.93
	Qwen3-235B-A22B	9	33.3	$-$ 5.29	$-$ 0.59	0.94	0.47	+3.03	$-$ 2.75
	Llama-3.3-70B-Instruct	9	11.1	$-$ 19.69	$-$ 2.19	1.01	0.13	+2.86	$-$ 5.50
	MiniMax-M3	9	44.4	$-$ 1.87	$-$ 0.21	0.91	0.73	+2.49	$-$ 3.72
	GLM-5.3-Flash	9	55.6	+3.17	+0.35	1.10	1.37	+5.06	$-$ 3.82

Appendix

Table 19 : Per-month, per-model intraday 0-DTE SPY performance. Each session is an independent fresh-$10k episode; PnL% sums the daily realized return percentages inside the month and Overall sums every covered session in 2025. Mean%, Win%, P/L, PF, Best% and Worst% are computed over the sessions inside each month, and Fills is the per-session average.

Strategy	Model	Trades	Win%	PnL (k$)	P/L	PF	Best%	Worst%	Buy (k$)	Sell (k$)
Long Call	DeepSeek-V4-Flash	384	23.4	$-$ 5.9	1.98	0.61	+100.2	$-$ 78.4	83.3	78.7
	GPT-OSS-120B	296	30.1	$-$ 5.1	1.80	0.78	+130.8	$-$ 101.0	99.9	96.0
	Qwen3-235B-A22B	211	36.0	$-$ 2.1	1.34	0.75	+203.7	$-$ 223.9	47.3	45.9
	Llama-3.3-70B-Instruct	210	20.9	$-$ 5.8	1.89	0.50	+162.6	$-$ 107.5	43.7	38.4
	MiniMax-M3	200	37.0	+1.3	2.04	1.20	+499.3	$-$ 100.3	31.2	32.8
	GLM-5.3-Flash	282	29.8	$-$ 0.8	2.23	0.94	+272.2	$-$ 69.8	66.3	66.4

Appendix

Table 20 : Per-strategy-bucket, per-model intraday 0-DTE SPY performance. Each contract traded on each session is one trade, and the bucket is set by the first fill’s side and the option right (Long Call = first BUY of a call, Short Put = first SELL of a put, and so on). PnL is FIFO-matched realized PnL net of fees, with any residual at end of day closed at the contract’s last mark. Per-trade return uses the entry-side notional as denominator, so Worst% can fall well below

-100

% for short trades whose closing buyback dwarfs the premium received. Buy and Sell are total notionals (qty

\times

price

\times

100) in thousands of USD.

			Explained		Share of attribution
Model	Pairs	Moved	Step	Session	Time	Spot	Vol	Resid
DeepSeek-V4-Flash	40,159	92%	0.92	0.87	0.04	0.76	0.13	0.06
GPT-OSS-120B	53,180	91%	0.92	0.88	0.04	0.76	0.14	0.07
Qwen3-235B-A22B	56,443	84%	0.80	0.76	0.03	0.71	0.12	0.15
Llama-3.3-70B-Instruct	63,000	88%	0.85	0.86	0.03	0.72	0.14	0.12
MiniMax-M3	39,182	90%	0.89	0.84	0.04	0.74	0.13	0.09

Appendix

Table 22: Full-repricing P&L-explain for the 0DTE track. Each consecutive step pair of each held position is revalued exactly, sequentially in time, spot and volatility, and the remainder is the residual; because this reprices rather than expands, it stays valid across jumps. Moved is the share of step pairs whose recorded mark changed, and the explained fractions are computed over those pairs, since a pair with no mark change contributes nothing to episode PnL. Explained is

1-\sum|\text{residual}|/\sum|\Delta\text{mark}|

at step level, and Session the same after signed aggregation within a session. The last four columns are each term’s share of total absolute attributed magnitude.

Ticker	Model	Cases	Mean%	Med%	Win%	PF	MDD%	Fills
AAPL	DeepSeek-V4-Flash	4	$-$ 6.03	$-$ 7.24	50.0	0.38	14.10	28
	GPT-OSS-120B	4	$-$ 3.08	$-$ 2.60	25.0	0.12	4.39	32
	Qwen3-235B-A22B	4	$-$ 3.19	$-$ 3.07	0.0	0.00	3.67	28
	Llama-3.3-70B-Instruct	4	$-$ 1.56	$-$ 1.45	50.0	0.54	5.21	27
	MiniMax-M3	4	$-$ 4.57	$-$ 7.49	25.0	0.33	9.11	32
	GLM-5.3-Flash	4	$-$ 1.04	$-$ 1.53	50.0	0.72	6.29	38

Appendix

Table 25: Per-ticker earnings metrics across all models, aggregated over the quarterly earnings cases per ticker. Mean and median are per-case returns, MDD is the mean maximum drawdown within a case, Fills counts executed option contracts and NT the cases in which the model chose not to trade.

Structure	Model	Cases	Share%	Mean%	Med%	Win%	PF
bear call spread	DeepSeek-V4-Flash	3	3.2	$-$ 4.05	$-$ 4.37	0.0	0.00
	GPT-OSS-120B	5	5.3	3.49	1.68	60.0	2.55
	Qwen3-235B-A22B	1	1.1	52.37	52.37	100.0	–
	Llama-3.3-70B-Instruct	3	3.3	$-$ 10.19	$-$ 9.91	0.0	0.00
	MiniMax-M3	3	3.2	$-$ 1.45	$-$ 6.34	33.3	0.71
	GLM-5.3-Flash	4	4.2	$-$ 2.13	$-$ 4.66	25.0	0.56

Appendix

Table 26 : Earnings performance by the structure opened in each case. Cases counts the cases in which the model opened that structure and Share its percentage of that model’s classified cases. Buckets a model used only a handful of times are reported for completeness but do not support a ranking.

Earnings
			Mean return		Worst episode
Model	$n$	Eps	Off	Guard	Off	Guard
DeepSeek-V4-Flash	94	1	$-0.6$	$-0.5$	$-23.8$	$-23.8$
GPT-OSS-120B	95	0	$-0.2$	$-0.2$	$-22.0$	$-22.0$
Qwen3-235B-A22B	94	5	$-1.2$	$-1.2$	$-22.8$	$-22.8$
Llama-3.3-70B-Instruct	90	1	$-0.4$	$-0.4$	$-23.2$	$-23.2$

Appendix

Table 28: Execution-layer ablation of the inventory check on the closing path. Every recorded episode is replayed with the decision stream held fixed and inversions clamped on any contract that received a duplicate closing intent, which is the one defect this guard addresses. Eps counts episodes in which the guard binds at all; Mean and Worst are the mean and minimum episode return, with and without it, in percent.

Ablation	Task	Model	$n$	$\Delta$ Ret [95% CI]	Med	Worse%	NT%	MDD%	$p$
Fixed chain	0DTE	DeepSeek-V4-Flash	24	$-$ 0.09 [ $-$ 0.86, 0.59]	0.01	50.0	0.0 $\to$ 0.0	4.4 $\to$ 4.3	0.899
	0DTE	GPT-OSS-120B	24	$-$ 2.16 [ $-$ 4.36, $-$ 0.43]	$-$ 0.73	62.5	0.0 $\to$ 0.0	6.3 $\to$ 5.7	0.085
	0DTE	Qwen3-235B-A22B	24	$-$ 0.44 [ $-$ 1.53, 0.64]	$-$ 0.22	54.2	0.0 $\to$ 0.0	2.6 $\to$ 2.7	0.899
	Covered Call	DeepSeek-V4-Flash	2	$-$ 3.68 [ $-$ 9.83, 2.46]	$-$ 3.68	50.0	0.0 $\to$ 0.0	22.1 $\to$ 24.6	1.000
	Covered Call	GPT-OSS-120B	2	$-$ 4.98 [ $-$ 10.29, 0.33]	$-$ 4.98	50.0	0.0 $\to$ 0.0	25.0 $\to$ 23.5	1.000
	Covered Call	Qwen3-235B-A22B	2	$-$ 2.15 [ $-$ 6.48, 2.18]	$-$ 2.15	50.0	0.0 $\to$ 0.0	24.1 $\to$ 23.7	1.000

Appendix

Table 29 : Every context ablation cell, by profile, task and model. Three profiles (Fixed chain, No portfolio state, No task rules) were run on all four tasks; the other two were defined for a single task. NT and MDD are reported as baseline

\to

ablation. The Hedging and Covered Call cells rest on two paired cases each and are shown for completeness rather than as evidence.

p

is Holm-adjusted within each profile–task family. The main-text Table 6 reports one task per profile.

Task	Model A	Model B	$n$	$\Delta$ [95% CI]	Med	$p$	$p_{\mathrm{Holm}}$
Earnings	DeepSeek-V4-Flash	GPT-OSS-120B	94	$-$ 0.28 [ $-$ 2.27, 1.64]	0.05	0.781	1.000
		Qwen3-235B-A22B	94	0.64 [ $-$ 2.31, 3.57]	0.30	0.672	1.000
		Llama-3.3-70B-Instruct	90	$-$ 0.05 [ $-$ 2.45, 2.31]	0.51	0.968	1.000
		MiniMax-M3	94	1.28 [ $-$ 0.79, 3.35]	0.45	0.225	1.000
		GLM-5.3-Flash	94	$-$ 0.08 [ $-$ 2.09, 2.05]	$-$ 0.13	0.939	1.000
	GPT-OSS-120B	Qwen3-235B-A22B	94	0.92 [ $-$ 1.97, 3.75]	0.46	0.536	1.000

Appendix

Table 30 : Pairwise model comparisons on the two episodic tasks, paired on shared episodes.

\Delta

is the paired mean return difference of model A minus model B in percentage points with a 95% paired bootstrap interval and Med is the paired median. Model A is printed once per block.

p

is a two-sided Monte Carlo sign-flip

p

-value from 50,000 draws and

p_{\mathrm{Holm}}

is its Holm-adjusted value within the 15 pairs of that task. No pair is significant at the 5% level after correction.

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Agentic Trading: When LLM Agents Meet Financial Markets

Yihan Xia, Panpan You, Taotao Wang, Fang Liu+3

College of Electronic and Information Engineering, Shenzhen University, Shenzhen, China

Large Language Model Agents Evaluation Protocols

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A growing body of work explores how Large Language Models (LLMs) can be embedded in trading systems as agents that perceive market information, retrieve context, reason about decisions, emit tradable actions, and adapt under market feedback. This paper reframes LLM-based trading agents as expert-system decision pipelines and presents an audit-oriented evidence map of 77 included studies in a protocol-coded snapshot screened through 2026-03-09. A primary empirical subset (n=19) satisfies the minimum boundary of Action Output plus Closed-Loop Evaluation; the remaining 58 included studies are retained as background and design context. The central empirical finding is protocol incomparability: within the primary subset, only 2/19 studies report extractable time-consistent split protocols, 1/19 reports an explicit transaction-cost model, 1/19 documents universe or survivorship handling, 11/19 report execution timing or semantics, 15/19 are coded as R0, and no study reaches R3 reproducibility. We therefore use Architecture-Capability-Adaptation as a working analytical lens rather than a validated taxonomy, and we foreground the evidence ledger, reproducibility audit, and reporting checklist as the main contributions. The resulting survey shows that architectural experimentation is expanding rapidly, while comparable evaluation protocols, execution semantics, and reproducible artifacts remain the field's immediate bottlenecks.

Abstract

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