Advanced Trading Strategies
1. Trend Following Strategies
We analyze financial time series spanning 10–20 years to uncover underlying market direction using advanced quant models. Techniques include Genetic Algorithms, Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), which allow adaptive, non-linear modeling of price dynamics. These models are particularly effective in capturing persistent momentum across asset classes.
2. Mean Reversion Strategies (Statistical Arbitrage)
Statistical arbitrage is a market-neutral strategy that involves matching long and short positions in highly correlated instruments—such as pairs of stocks, ETFs, or options. We compute the half-life of mean reversion using an Ornstein-Uhlenbeck process, allowing precise calibration of historical lookback windows. Cointegration analysis forms the core of identifying tradeable spreads.
3. Volatility Arbitrage Strategies
These strategies exploit mispricings between implied and realized volatility. Due to the volatility smile, out-of-the-money options often trade at inflated prices. By shorting high-IV strikes and longing lower-IV counterparts, we construct delta-neutral portfolios. Greeks—especially Vega, Gamma, and Theta— are dynamically monitored to manage risk and capture time decay asymmetries.
4. Gamma Scalping Strategies
Gamma scalping profits from large movements in the underlying while maintaining delta neutrality. Short options positions typically have negative gamma, while long positions provide positive gamma. As delta changes with price, we adjust hedges dynamically. Positive gamma helps traders outperform delta forecasts by profiting more on large moves and losing less on small reversals.
5. Machine Learning-Based Strategies
Predicting market direction is notoriously noisy. We employ Deep Neural Networks (DNNs) with multiple hidden layers to classify market states in a binary manner—upward or downward. Additionally, we apply K-Means clustering, an unsupervised algorithm, to segment market regimes without labeled data. This approach is useful in real-world settings where human labeling is infeasible.