AI Explorer Session

Iteration 1 (spot_checks)

Initial exploration to understand how different trading strategies (Buy and Hold, Mean Reversion, Moving Average) and their parameters (thresholds, windows, commissions, position sizes) impact profit across various market conditions, identifying which parameters have the strongest effects on returns

Analysis & Proposal (Iteration 2) - Grok Code (10s)

Initial experiments show MovingAverageFeature achieving the highest total_return at 0.35 when using shorter windows and smaller position sizes, while BuyAndHoldFeature and MeanReversionFeature perform poorly with negative returns. Fixed and Random period features also yield negative results, indicating the need for better period selection or strategy optimization.

Key Insights:

• MovingAverageFeature outperforms others when tuned with short_window=5, long_window=20, and position_size=0.2, suggesting potential for further optimization.
• MeanReversionFeature shows negative returns across tested configurations, but default parameters may not be optimal and require systematic tuning.
• Period features (FixedPeriod and RandomPeriod) tested so far result in losses, potentially due to unfavorable market conditions in the selected periods.

Confidence: 50% - Moderate confidence as we're building on the only positive result found, but still exploring parameter space for this strategy.

Next Step (gridsearch): Gridsearch to optimize MovingAverageFeature parameters around the best-performing configuration (short_window=5, long_window=20, position_size=0.2), exploring ranges that could improve total_return while keeping the strategy fixed. Estimated 120 experiments to systematically sweep numeric parameters, leveraging fast execution times.

Analysis & Proposal (Iteration 3) - Grok Code (10s)

The MovingAverageFeature strategy has shown the only positive total_return at 0.35%, significantly outperforming other strategies like MeanReversion and BuyAndHold which yielded negative returns. The best configuration uses short_window=5, long_window=20, and position_size=0.2, suggesting potential for further optimization of these numeric parameters.

Key Insights:

• MovingAverageFeature is the most promising strategy with the only positive return, indicating it should be the focus for optimization.
• MeanReversionFeature configurations tested so far have negative returns, but its parameters (thresholds, lookback_period) have not been systematically optimized.
• BuyAndHold and period variations show poor performance, suggesting they are not viable for profit maximization.

Confidence: 60% - Moderate confidence as MovingAverage shows promise, but we need to confirm optimal parameter values through systematic testing.

Next Step (gridsearch): Using gridsearch to systematically optimize the numeric parameters of the MovingAverageFeature strategy around the best-performing values (short_window=5, long_window=20, position_size=0.2), exploring 2-3 parameters with reasonable ranges to find optimal combinations. Estimated 150 experiments to balance thoroughness with fast execution time.

Analysis & Proposal (Iteration 4) - Grok Code (12s)

The MovingAverageFeature strategy shows the most promise with positive total_return values, particularly at short_window=5, long_window=20, and position_size=0.5 achieving 0.88, while other strategies like MeanReversion and BuyAndHold consistently show negative returns.

Key Insights:

• MovingAverageFeature is the only strategy yielding positive total_return, indicating it's worth further optimization.
• MeanReversion and BuyAndHold strategies have been tested with various parameters but remain unprofitable.
• Parameter tuning around the best MovingAverage configuration could improve results further, as default values haven't been fully explored.

Confidence: 60% - Moderate confidence as MovingAverage shows clear promise, but we need systematic parameter optimization to confirm optimal values.

Next Step (gridsearch): Use gridsearch to systematically optimize the numeric parameters of MovingAverageFeature around the best-performing configuration (short_window=5, long_window=20, position_size=0.5) to find optimal values for maximizing total_return. This should explore variations in short_window (3-10), long_window (15-50), and position_size (0.1-0.5), estimating 320 experiments based on fast execution times.

Analysis & Proposal (Iteration 5) - Grok Code (9s)

MovingAverageFeature has shown the most promise with positive total returns up to 0.88, while other strategies like MeanReversion and BuyAndHold have produced negative or zero results. The best configuration uses short_window=5, long_window=20, and position_size=0.5. Systematic optimization of MovingAverage parameters is needed to confirm if higher returns are possible.

Key Insights:

• MovingAverageFeature outperforms other strategies tested, with the best result achieving 0.88 total return
• Position size significantly impacts returns, with 0.5 yielding the highest result so far
• Other strategies like MeanReversion and BuyAndHold have not been optimized thoroughly and show negative results

Confidence: 60% - Moderate confidence as MovingAverage shows clear promise, but we need systematic parameter optimization to confirm optimal values and explore if higher returns are achievable

Next Step (gridsearch): Using gridsearch to systematically optimize MovingAverageFeature parameters around the current best values (short_window=5, long_window=20, position_size=0.5). This will refine the parameter space to find potentially better combinations, with an estimated 160 experiments based on the ranges. Expected to take approximately 30 minutes given the 11.35s average experiment time.

Analysis & Proposal (Iteration 6) - Grok Code (7s)

MovingAverageFeature consistently outperforms other strategies, achieving positive returns up to 1.12% with optimal parameters, while MeanReversion and BuyAndHold strategies result in significant losses. The best configuration uses short_window=5, long_window=20, and position_size=0.7 with barebones mode.

Key Insights:

• MovingAverageFeature is the superior strategy, with all top-performing configurations using this feature.
• Lower short_window (around 5) combined with moderate long_window (around 20) yields best results.
• Higher position_size (up to 0.7) improves returns, suggesting risk tolerance benefits performance.

Confidence: 70% - High confidence in MovingAverageFeature as the best strategy, with systematic refinement likely to find incremental improvements.

Next Step (gridsearch): Using gridsearch to refine MovingAverageFeature parameters around the best configuration (short_window=5, long_window=20, position_size=0.7) to potentially improve beyond 1.12% total_return. This will test tighter ranges for optimal values, estimating 275 experiments.