Quant Tools

Build composite scoring models that quantify value, quality, growth, and stability across Indian equities. Rank stocks using multi-factor frameworks including capital efficiency, earnings consistency, cash flow reliability, and business reinvestment quality. Supports sector-neutral normalization and user-defined factor formulas for custom alpha signals.

Decompose and measure portfolio risk using GARCH/EGARCH/EWMA volatility models, rolling beta analysis across multiple indices, and comprehensive drawdown metrics. Calculate VaR/CVaR, systematic vs idiosyncratic risk splits, stress beta during crisis regimes, and capital preservation scores — all calibrated for Indian market conditions.

Construct and optimize portfolios using mean-variance, risk parity, hierarchical risk parity, Black-Litterman, and CVaR-optimized strategies. Includes volatility-aware and cluster-based construction, sector-aware optimization, tax-aware rebalancing for India (LTCG/STCG), and transaction cost minimization with threshold-based rebalancing logic.

Analyze rolling, conditional, and regime-based correlations between individual assets and portfolios to understand diversification, co-movement, and structural breaks. Includes autocorrelation, cointegration testing, correlation stability scoring, and breakdown analysis across bull/bear regimes.

Identify, measure, and visualize factor exposures across value, momentum, quality, size, and low-volatility dimensions. Run factor attribution, regression, and loadings analysis. Detect style drift, factor crowding, and timing signals. Visualize risk decomposition maps, exposure treemaps, and multi-factor model outputs.

Model and forecast financial time series using ARIMA, GARCH, VAR, state space models, and hybrid ARIMA+GARCH approaches. Perform wavelet decomposition, spectral analysis, trend and seasonality decomposition, structural break detection, unit root tests, and cointegration testing. Evaluate forecast accuracy with standard metrics.

Identify cointegrated and distance-based pairs, calculate optimal hedge ratios, analyze spread dynamics, and generate z-score entry/exit signals. Estimate mean-reversion half-life, build portfolios of pairs, model transaction costs, and backtest complete pairs trading strategies with realistic execution assumptions.

Break down portfolio returns into their sources using Brinson, factor-based, and sector attribution models. Quantify asset selection, allocation, and interaction effects. Generate attribution waterfalls, rolling attribution over time, multi-period compounded attribution, and risk-adjusted contribution analysis.

Detect and classify market regimes — bull/bear, high/low volatility, trend vs mean-reversion — using Hidden Markov Models and statistical methods. Estimate regime transition probabilities, visualize regime timelines, build regime-conditional strategies, and generate early warning signals for regime changes.

Analyze sector rotation, momentum, valuation, and risk decomposition across Indian market sectors and industries. Measure industry relative strength across multiple time horizons, build sector correlation matrices, optimize sector allocation, detect cross-sector factor exposures, and generate sector timing signals.

Run rigorous strategy backtests with India-specific transaction costs (STT, brokerage, slippage), survivorship-bias-free data, and point-in-time fundamentals. Use technical indicators (SMA cross, EMA cross, RSI, MACD, Bollinger Bands, momentum, z-score reversion) as backtest signals — individually or combined with AND/OR/majority logic. Includes Monte Carlo simulation, walk-forward analysis, regime-conditioned backtests, stress-scenario replay, circuit breaker simulation, alpha decay analysis, and multi-horizon equity curve generation.

Leverage machine learning for stock clustering, anomaly detection, return prediction, and peer group auto-detection. Use SHAP-based interpretation to explain model outputs, run probabilistic forecasts, build ensemble models, discover regimes via HMM/GMM, and score stocks with rank-based ML models.

Measure bid-ask spreads, order imbalance, depth-weighted liquidity, and execution quality to understand real-world trading costs and market impact. Analyze spread stability, volume concentration, trade size distribution, liquidity stress scores, slippage probability bands, and price impact asymmetry across market conditions.

Analyze implied vs realized volatility spreads, skew dynamics, term structure, and volatility risk premium for derivatives strategies. Monitor put-call open interest balance, max pain dynamics, delta-neutral screening, Greeks-aware portfolio impact, event-driven IV shifts, and margin utilization modeling.