Quant development and analysis

Background

Areas of interest

Quantitative finance

Calculus, ODEs/PDEs/SDEs, linear/matrix algebra, probability distributions, statistical inference, Taylor series, transition density functions, Fokker-Planck and Kolmogorov equations, stochastic calculus, Itô’s lemma, martingales, binomial pricing models, Bayesian methods, margin accounts, leverage, macroeconomic event analysis

Quantitative risk and return

MPT, CAPM, portfolio optimisation, Basel III, VaR, collateral and margins, liquidity ALM, GARCH/ARCH, diversification, drawdowns, win rates, position sizing, trend following and mean reversion, profit distribution, MAE/MFE, walk-forward testing

Equities and currencies

The Black-Scholes model, options pricing, delta hedging, advanced Greeks, no-arbitrage principles, derivatives market practice, volatility arbitrage, pairs trading, finite-difference methods, advanced/non-probabilistic volatility models and FX options.

Supervised ML

Regression models (linear, penalised: lasso, ridge, elastic net; logistic, SoftMax), KNN, naïve Bayes, SVM, decision trees, ensemble models (bagging, boosting), hyperparameter tuning, decision trees, multi-AI model strategies, deep learning (NNs, RNNs, LSTMs).

Unsupervised ML

Clustering (K-means, SOMs), dimensionality reduction (t-SNE, UMAP, PCA), autoencoders, NLP, reinforcement learning, AI-based algorithm trading strategies.

Fixed income and credit

Fixed-income products, yield, duration, convexity, stochastic interest rate models, probabilistic methods for interest rates, calibration and data analysis, HJM, LMM, structural models, hazard rate implementations, credit risk and derivatives, XVA (CVA, DVA, FVA, MVA), CDS pricing, market approach, risk of default, copula models

AI/ML in portfolio management

ML/AI in portfolio rebalancing, AI-enabled Markowitz portfolio optimisation, strategies outperforming SPX CAGR/max drawdown, Monte Carlo analysis, training/testing data optimisation to ML/AI strategies, curve fitting, QuantConnect

Calibration

Core performance stats

  • CAGR / annualised return
  • Annualised volatility
  • Sharpe and Sortino
  • Max drawdown (MDD) and time to recovery
  • Calmar (CAGR / MDD)
  • Profit factor (gross profits / gross losses)
  • Expectancy per trade (average P&L per trade)
  • Hit rate (win %), but also average win / average loss (payoff ratio)
  • Tail losses: 1% / 5% worst trade, worst day, worst month
  • VaR / Expected Shortfall

Trade statistics

  • Number of trades, trades/year (turnover)
  • Average holding period, median holding period
  • Time in market (% of time invested)
  • Entry efficiency: average move after entry (e.g., 1 day post-entry return)
  • Market regime split: performance in uptrend/downtrend/sideways regimes
  • Long vs short breakdown
  • Concentration: fraction of P&L coming from top 5 trades / top 1% of days

Statistical significance

  • Market microstructure (phase A, B, C or D)
  • T-stat of mean daily strategy returns (or of trade returns)
  • P-value (with caution), and confidence intervals for Sharpe/CAGR
  • Bootstrapped performance: distribution of Sharpe / CAGR
  • Permutation test: shuffle returns
  • Multiple-testing correction / Deflated Sharpe Ratio

Out-of-sample robustness

  • Walk-forward results (e.g., train 2 years, test 6 months, rolling)
  • In-sample vs out-of-sample gap (Sharpe, CAGR, MDD deltas)
  • Stability of chosen parameters over time
  • Parameter neighbourhood robustness (plateau vs single spike).

Risk and exposure diagnostics

  • Beta to benchmark (e.g., SPY for equities) and alpha (CAPM regression)
  • Correlation to the benchmark and to a simple alternative (buy-and-hold)
  • Volatility of strategy vs underlying
  • Downside capture/upside capture
  • Exposure profile: average notional, leverage used, gross/net exposure if long/short

Practical tradability checks

  • Average daily $ volume (liquidity proxy)
  • Capacity estimate: impact-aware max position size (rough heuristic is fine)
  • Turnover vs liquidity: turnover / ADV
  • Slippage sensitivity: performance at 0.5x / 1x / 2x assumed slippage

Benchmarking and “incremental value”

  • Excess return vs buy-and-hold
  • Excess Sharpe vs buy-and-hold
  • Information ratio vs benchmark
  • Drawdown improvement vs benchmark
  • “Edge per unit turnover” (net return/turnover)