Quant Trading Challenge Prep

Implement a reusable function to compute rolling volatility and visualize volatility clustering.

Implement a signal-to-noise estimation utility to assess the predictability of a time series.

Build simple signal extraction filters (e.g., moving averages, momentum) as reusable functions.

Implement a function to test if a trading signal's mean return is statistically significant using a t-test.

Formulate a portfolio optimization problem with an objective function (e.g., maximize Sharpe ratio) and constraints (e.g., weights sum to 1).

Implement a mean-variance optimization solver for a multi-asset portfolio using `scipy.optimize.minimize`.

Visualize the efficient frontier for a portfolio to understand the risk-return tradeoff.

Build a reusable feature engineering pipeline for financial time series (e.g., lags, rolling stats, momentum, rank transforms).

Implement a walk-forward cross-validation utility to prevent lookahead bias in time series models.

Configure a gradient boosting model for financial prediction, addressing low signal-to-noise and non-stationarity.

Evaluate prediction quality using information coefficient and rank correlation.

Convert model predictions into discrete trading signals with defined entry and exit rules.

Build a vectorized backtesting framework to simulate strategy P&L, incorporating transaction costs.

Implement utility functions to calculate the Sharpe ratio and Calmar ratio from a series of strategy returns.

Implement a function to calculate and visualize the maximum drawdown of a strategy.

Apply a simplified Kelly criterion for dynamic position sizing based on signal conviction.

Apply Principal Component Analysis (PCA) to a matrix of correlated features to create decorrelated factors.

Develop a regime-detection model (e.g., using volatility) to enable state-dependent strategies.

Estimate confidence intervals for strategy performance metrics using bootstrap resampling.

Perform out-of-sample testing on held-back data to validate strategy robustness against overfitting.

Explain the mathematical formulation of Mean-Variance Optimization and the concept of the efficient frontier.

Derive the mathematical basis of the simplified Kelly criterion for optimal bet sizing.

Analyze the bias-variance tradeoff specifically in the context of low signal-to-noise financial prediction.

Explain the geometric interpretation of PCA as a rotation of the coordinate system to align with maximum variance.