← Overview Team 1

Round 1: Model Development

Approach

Model: XGBoost for non-linear relation capture while maintaining efficiency

Parameter Value
Tree Construction Histogram-based (faster, scalable)
Boosting Rounds 300
Max Depth 6
Learning Rate 0.05

Features (14 total)

Category Features
Momentum RSI, 7d Momentum
Moving Average MACD, EMA
Volatility Bollinger Bands, Volatility (1d, 7d)
Volume Average 7d Volume
Lagged Returns 1hr, 4hr, 1d, 7d

Results

Metric Value
In-sample Weighted Spearman 0.3113
Out-of-sample Weighted Spearman 0.1225

Gap suggests overfitting, but out-of-sample score remained strong given model simplicity.

Round 2: Trading Strategy

Design

Threshold-Based Approach:

  • Trade only predicted returns > 2% magnitude
  • 15-minute data (consistent with Round 1)
  • Leverage model’s accuracy on extreme predictions

Position Sizing:

  • Weighted by prediction magnitude (not evenly distributed)
  • Example: +5% predicted BTC return → larger BTC allocation

Evolution

Phase Issue Solution
Initial Underperformance after ~1 month Insufficient diversification
Fix Expanded trading list Include maximum perpetuals
Oct 10 Crash No extreme event handling Added stop-loss: -2% long, +2% short

Future Improvements

  • Replace fixed 2% threshold with volatility-based thresholds (Bollinger Bands)
  • Implement ML-based stop-loss mechanisms
  • Automate strategy parameters using indicators