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product#llm🏛️ OfficialAnalyzed: Jan 4, 2026 14:54

User Experience Showdown: Gemini Pro Outperforms GPT-5.2 in Financial Backtesting

Published:Jan 4, 2026 09:53
1 min read
r/OpenAI

Analysis

This anecdotal comparison highlights a critical aspect of LLM utility: the balance between adherence to instructions and efficient task completion. While GPT-5.2's initial parameter verification aligns with best practices, its failure to deliver a timely result led to user dissatisfaction. The user's preference for Gemini Pro underscores the importance of practical application over strict adherence to protocol, especially in time-sensitive scenarios.
Reference

“"GPT5.2 cannot deliver any useful result, argues back, wastes your time. GEMINI 3 delivers with no drama like a pro."”

Permalinkr/OpenAI
Paper#Finance, AI, Time Series Prediction🔬 ResearchAnalyzed: Jan 3, 2026 19:51

Gold Price Prediction with LSTM, MLP, and GWO

Published:Dec 27, 2025 14:32
1 min read
ArXiv

Analysis

This paper addresses the challenging task of gold price forecasting using a hybrid AI approach. The combination of LSTM for time series analysis, MLP for integration, and GWO for optimization is a common and potentially effective strategy. The reported 171% return in three months based on a trading strategy is a significant claim, but needs to be viewed with caution without further details on the strategy and backtesting methodology. The use of macroeconomic, energy market, stock, and currency data is appropriate for gold price prediction. The reported MAE values provide a quantitative measure of the model's performance.
Reference

“The proposed LSTM-MLP model predicted the daily closing price of gold with the Mean absolute error (MAE) of $ 0.21 and the next month's price with $ 22.23.”

PermalinkArXiv
Research Paper#Cryptocurrency Trading, Algorithmic Trading, Backtesting🔬 ResearchAnalyzed: Jan 3, 2026 20:00

AutoQuant: Auditable Framework for Crypto Futures Strategy Tuning

Published:Dec 27, 2025 05:46
1 min read
ArXiv

Analysis

This paper addresses the fragility of backtests in cryptocurrency perpetual futures trading, highlighting the impact of microstructure frictions (delay, funding, fees, slippage) on reported performance. It introduces AutoQuant, a framework designed for auditable strategy configuration selection, emphasizing realistic execution costs and rigorous validation through double-screening and rolling windows. The focus is on providing a robust validation and governance infrastructure rather than claiming persistent alpha.
Reference

“AutoQuant encodes strict T+1 execution semantics and no-look-ahead funding alignment, runs Bayesian optimization under realistic costs, and applies a two-stage double-screening protocol.”

PermalinkArXiv