Shared agent run on NexusTrade
AI Model Competition: Discovering the Best Trading Strategy
A head-to-head experiment between 10 AI models (GPT, Claude, Gemini) to create trading strategies. Features out-of-sample validation and a leaderboard ranking models by total return and risk-adjusted performance.
User: Use GPT-5.2, Opus 4.6, Sonnet 4.6, Gemini 3 Pro, Gemini 3.1 Pro, GPT5-mini, Gemini 3 Flash, Minimax 2.5, GLM-5, and Kimi K2.5 to independently try to create the best trading strategy in terms of raw gains and risk-adjusted returns. Which model produces the consistently best strategies after 15 iterations? Create the strategy based on data before 01/01/2024, then at the end, we'll use 01/01/2024 to today as a test set for the best trading strategies we found
Aurora: This is a fantastic experiment — a **model vs. model strategy competition**! You want 10 different AI models to independently explore and create the best trading strategies, then compare their results head-to-head. Let me design this rigorously. ### The Core Question Which AI model, given the same goal and iteration budget, consistently produces the best trading strategies in terms of raw total return and risk-adjusted returns (Sortino Ratio)? ### Experiment Design **Training Period:** All strategy development uses data **before January 1, 2024**. **Test Period (Out-of-Sample):** January 1, 2024 → Today (Feb 21, 2026) — used ONLY at the end to validate the best strategies from each model. Each model will act as an independent **strategyExplorer** subagent with 15 iterations to research, create, backtest, analyze, and refine trading strategies. They are free to explore ANY approach — momentum, mean reversion, value, multi-factor, trend-following, etc. The best strategies will then be validated on unseen data. --- ### Step 1: Spawn 10 Independent Strategy Explorer Subagents Each subagent will independently: - Define competing hypotheses about what drives returns - Create 4-8 portfol…
User: Launching 10 subagents to explore strategies in parallel
User: Waiting for subagents to complete...
User: Waiting for subagents to complete...
Aurora: All 10 subagents complete. **Subagent 1**: Independently discover and refine the best trading strategy using data before 01/01/2024. Research market patterns, create multiple portfolios using DynamicRebalance, backtest them, and iterate to find the highest raw gains and Sortino Ratio. Present your top 2-3 winning portfolios. - Status: completed - Summary: **Strategy Research Complete: Quality Reversion with Trend Wins** Successfully designed, backtested, and refined a trading strategy for the 2020-2023 market cycle. Tested 4 core hypotheses (Momentum, Reversion, Growth, Value) across 7 portfolios and multiple optimization runs. ### Key Discoveries **The Winner:** **'Quality Reversion with Trend'** is the undisputed top performer. It achieved a **155.16% Total Return** (vs SPY 57%) with a **1.91 Sortino Ratio** and a low **19.09% Max Drawdown**. **Why It Works:** It combines three factors: 1. **Quality:** Filters for profitable S&P 500 companies (Net Income > 0). 2. **Reversion:** Buys oversold stocks (Lowest 14-day RSI). 3. **Trend:** Only buys when Price > 200-day SMA (avoids bear market crashes). **What Failed:** * **Pure Momentum:** Failed in the 2022 chop (37% return). * **Opt…
User: Backtest the following portfolios from 2024-01-01 to 2026-02-21 with $10,000 initial value, benchmarked against SPY: 1. 699a8fa50b667bcb9edc01cb (GPT-5.2 Winner) 2. 699a76960b667bcb9ed68c82 (Claude Opus 4.6 Winner) 3. 699a771d0b667bcb9ed6abce (Claude Sonnet 4.6 Winner) 4. 699a76ca0b667bcb9ed698ab (Gemini 3 Pro Winner) 5. 699a78fa0b667bcb9ed715c9 (Gemini 3.1 Pro Winner) 6. 699a779a0b667bcb9ed6c960 (GPT-5 Mini Winner) 7. 699a96140b667bcb9ede6902 (GLM-5 Winner)
Aurora: I'll create the backtest configurations for the winning portfolios from each model to evaluate their out-of-sample performance from January 2024 to today.
User: Analyze the out-of-sample backtest results for the following 7 portfolios from 2024-01-01 to 2026-02-21: 699aa0d30b667bcb9edffd44 (GPT-5.2), 699aa0d30b667bcb9edffd43 (Opus 4.6), 699aa0d30b667bcb9edffd45 (Sonnet 4.6), 699aa0d30b667bcb9edffd49 (Gemini 3 Pro), 699aa0d30b667bcb9edffd48 (Gemini 3.1 Pro), 699aa0d30b667bcb9edffd46 (GPT-5 Mini), and 699aa0d30b667bcb9edffd47 (GLM-5). Please provide a comparative summary of their performance metrics against each other and the SPY benchmark.
Aurora: The out-of-sample test results (January 1, 2024, to February 21, 2026) provide a clear picture of which AI models produced strategies that generalize well to new market conditions versus those that overfit to historical data. ### 1. Backtest Summary and Comparison The following table compares the performance of the winning strategies from each model against the **SPY** baseline during the test period. | Model | Portfolio Name | Total Return | Sharpe Ratio | Max Drawdown | vs. SPY Return | | :--- | :--- | :--- | :--- | :--- | :--- | | **Baseline** | **SPY (S&P 500)** | **45.05%** | **1.10** | **20.19%** | -- | | Gemini 3 Pro | Multi-Factor Quality | **73.67%** | **1.25** | **19.38%** | **+28.62%** | | Gemini 3.1 Pro | Quality Value - Low P/E | **68.01%** | **1.16** | **18.18%** | **+22.96%** | | Claude Sonnet 4.6 | Quality Momentum | **54.68%** | 0.97 | 21.98% | **+9.63%** | | GPT-5 Mini | Quality + Momentum | 42.68% | 0.84 | 22.83% | -2.37% | | Claude Opus 4.6 | Low Volatility Strategy | 29.45% | 0.85 | 20.84% | -15.60% | | GPT-5.2 | Quality Reversion with Trend | -1.61% | 0.05 | 27.75% | -46.66% | | GLM-5 | Aggressive Mean Reversion | -14.71% | -0.21 | 23.40% | -59.76% | ### 2. T…
User: ## Model-vs-Model Strategy Competition: Final Results This experiment tested 10 AI models in a head-to-head competition to discover and refine the best trading strategies. Each model acted as an independent subagent with a 15-iteration budget using data **before January 1, 2024**. The best strategy from each model was then tested on unseen data from **January 1, 2024, to February 21, 2026**. ### 🏆 The Leaderboard (Out-of-Sample Validation) | Rank | Model | Strategy Type | Total Return | Sharpe | Max Drawdown | vs. SPY Alpha | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | **1** | **Gemini 3 Pro** | Multi-Factor Quality | **73.67%** | **1.25** | **19.38%** | **+28.62%** | | **2** | **Gemini 3.1 Pro** | Quality Value | **68.01%** | **1.16** | **18.18%** | **+22.96%** | | **3** | **Claude Sonnet 4.6** | Quality Momentum | **54.68%** | 0.97 | 21.98% | **+9.63%** | | -- | **SPY Benchmark** | Buy & Hold | 45.05% | 1.10 | 20.19% | -- | | **4** | **GPT-5 Mini** | Quality + Momentum | 42.68% | 0.84 | 22.83% | -2.37% | | **5** | **Claude Opus 4.6** | Low Volatility | 29.45% | 0.85 | 20.84% | -15.60% | | **6** | **GPT-5.2** | Quality Reversion | -1.61% | 0.05 | 27.75% | -46.66% | | **…
Aurora: ## Model-vs-Model Strategy Competition: Final Results This experiment tested 10 AI models in a head-to-head competition to discover and refine the best trading strategies. Each model acted as an independent subagent with a 15-iteration budget using data **before January 1, 2024**. The best strategy from each model was then tested on unseen data from **January 1, 2024, to February 21, 2026**. ### 🏆 The Leaderboard (Out-of-Sample Validation) | Rank | Model | Strategy Type | Total Return | Sharpe | Max Drawdown | vs. SPY Alpha | | :--- | :--- | :--- | :--- | :--- | :--- | :--- | | **1** | **Gemini 3 Pro** | Multi-Factor Quality | **73.67%** | **1.25** | **19.38%** | **+28.62%** | | **2** | **Gemini 3.1 Pro** | Quality Value | **68.01%** | **1.16** | **18.18%** | **+22.96%** | | **3** | **Claude Sonnet 4.6** | Quality Momentum | **54.68%** | 0.97 | 21.98% | **+9.63%** | | -- | **SPY Benchmark** | Buy & Hold | 45.05% | 1.10 | 20.19% | -- | | **4** | **GPT-5 Mini** | Quality + Momentum | 42.68% | 0.84 | 22.83% | -2.37% | | **5** | **Claude Opus 4.6** | Low Volatility | 29.45% | 0.85 | 20.84% | -15.60% | | **6** | **GPT-5.2** | Quality Reversion | -1.61% | 0.05 | 27.75% | -46.66% | | **…