⚡ [performance] Optimize max_drawdown calculation to O(N)

[refraction-ray]

💡 What:

Optimized the max_drawdown method in xalpha/indicator.py by replacing a nested loop $O(N^2)$ algorithm with a single-pass $O(N)$ peak-tracking algorithm. The implementation also switches from iterrows() to itertuples(index=False) for more efficient Pandas iteration.

🎯 Why:

The previous $O(N^2)$ implementation caused significant performance bottlenecks as the price series grew, making drawdown analysis of long-term portfolios or high-frequency data unnecessarily slow.

📊 Measured Improvement:

Using a benchmark with 2000 random data points:

• Baseline (Original $O(N^2)$): ~1.06 seconds
• Optimized ($O(N)$): ~0.00047 seconds
• Improvement: Approximately 2200x speedup.

The optimization preserves the original behavior, including chronological tie-breaking and raising a ValueError when given fewer than 2 data points.

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PR created automatically by Jules for task 9225084732163863234 started by @refraction-ray

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