Articles on quantitative trading

Primitives: a video editor for quantitative research A trading strategy is, underneath everything, a flow of data. The timeline is a leakage detector The easiest person to fool is yourself, and a backtest is one of the most efficient self-fooling machines ever built. Walk-forward, anchors, and the honest holdout There is a particular kind of self-deception that quantitative research invites. Quantin: a research assistant, not a strategy oracle There is a particular request that every AI-in-finance product seems built to invite: "find me a strategy that makes money." It is a natural thing to want, and a language model will happily… Lenses: asking better questions of your research The most dangerous moment in research is the one right after you see a good number. Deep agentic workflows over your research Most uses of an AI assistant in research are one-shot: you ask a question, you get an answer, and the answer sounds confident whether or not it is right. Why most backtests are false Here is an uncomfortable thing to sit with before you trust any backtest, including your own: if you try enough strategies, some of them will look brilliant for no reason at all. The Deflated Sharpe Ratio: correcting for how many tries you took Here is an uncomfortable fact about the Sharpe ratio. Pseudo-mathematics and financial charlatanism There is a particular way to be wrong in quantitative finance that does not look like being wrong. Fooled by randomness in markets Markets produce a lot of numbers and not much signal. A reading list for the honest quant Every idea behind QuanterLab was borrowed.

Platform Overview QuanterLab is a research platform organised around the top ribbon. Indicator Strategies Workflow The Indicator Strategies tab is where you combine any indicators freely — no family constraints. Basic Strategies Workflow The Basic Strategies tab is the starting point. Stochastic Methods Workflow The Stochastic Methods tab is the model-driven cousin of Mean Reversion. Factor Models Workflow The Factor Models tab is the fundamental-research side of the platform. Supplementary Data Workflow The Supplementary Data tab is the context layer. Getting Started: Factor Models The Factor Models section evaluates stocks based on their financial statements, valuation metrics, and macroeconomic context. Getting Started: My Projects My Projects is the organizational hub of QuanterLab. Getting Started: Paper Trading Paper trading lets you test strategies with virtual capital in real-time market conditions. Getting Started: Portfolio Visualizer The Portfolio Visualizer (PV100FAWF) is a real-time portfolio tracking dashboard.

Confirmation Layers Entry signals from RSI and Bollinger Bands can be improved through additional confirmation layers. Composite Scoring System Rather than relying on any single indicator, QuanterLab combines multiple metrics into a composite score ranging from 0 to 100. What Sharpe Ratio Actually Tells You (and Doesn't) The Sharpe ratio is the most widely cited number in quantitative trading — and the most widely misread. In-Sample, Out-of-Sample, and Why It Matters Every backtest you run lives in one of two states: in-sample (you optimized parameters using this data) or out-of-sample (you did not). Walk-Forward Validation: Anchored vs Rolling Windows, and Which to Use Walk-forward validation is the gold standard for testing whether a strategy will work going forward. Robustness Sweeps and Stable Plateaus The single best parameter setting in any backtest is almost always the wrong one to trade. Deflated Sharpe Ratio: Formula, Intuition, and a Worked Example The Sharpe ratio you see at the end of a parameter sweep is biased upward — sometimes by a lot. Bootstrap Confidence Intervals for Backtests A backtest produces a single point estimate — Sharpe 1.8, max DD 12%, win rate 62%. Those numbers are estimates from a finite sample, and like any estimate they have uncertainty around them. The Time Travel Workflow Time Travel is QuanterLab's feature for doing rigorous walk-forward research without leaking the future into your decisions. Survivorship Bias in Equity Backtests If you backtest a strategy on today's S&P 500, you are testing on the survivors — the companies that made it. Look-Ahead Bias: The Silent Killer Look-ahead bias occurs when a backtest uses information that would not actually have been available at the time of the simulated trade. Transaction Costs and Slippage Modeling The single most common reason a backtested strategy fails in production is transaction-cost underestimation. p-Hacking and Curve-Fitting in Quant Research If you torture the data long enough, it will confess to anything. p-hacking — the practice of running many tests and reporting only the ones that worked — is the largest source of false… Position Sizing: Fixed, Volatility-Targeted, and Kelly The same edge can produce wildly different equity curves depending on how you size positions. Kelly Criterion: Full, Half, and Capped The Kelly criterion gives the bet size that maximizes long-run logarithmic wealth. Drawdown, Max DD Duration, and Recovery Drawdown is the gap between current equity and the highest equity ever reached. Cookbook: From Backtest to Walk-Forward The transition from "ran a backtest" to "actually validated a strategy" is the single most common gap in retail quant research. Cookbook: Reading the Robustness Heatmap The robustness heatmap is the single most informative chart in QuanterLab's validation toolkit — and one of the most often misread. Static Grid Search Optimization Static grid search is the simplest and most-transparent way to find good parameters for a strategy: enumerate every combination on a defined grid, run the backtest at each cell, and pick… Per-Regime Optimization Markets behave differently in different regimes. Dynamic Mean Optimization Static thresholds work when the underlying indicator has a stable typical level — but most indicators don't. Regression-Based Optimization (Quantile Regression) The most flexible of QuanterLab's optimization modes lets market features predict optimal thresholds. Cookbook: Interpreting Walk-Forward Results A walk-forward run produces a lot of numbers — composite Sharpe, per-fold breakdown, parameter stability, decay ratio.

Mean Reversion: Overview & Theory Mean reversion is a financial theory suggesting that asset prices and historical returns eventually revert to their long-term average or mean level. RSI Applications in Mean Reversion The Relative Strength Index (RSI) is one of the most widely used oscillators for identifying overbought and oversold conditions. Bollinger Bands in Mean Reversion Bollinger Bands are volatility-adjusted price channels that adapt to market conditions. Mean Reversion Platform Workflow This guide walks through the practical workflow for using QuanterLab's mean reversion tools, from scanning for candidates to building and testing strategies.

Momentum: Overview & Theory Momentum is one of the oldest and most robust anomalies in financial markets. Momentum: Core Indicators Momentum strategies rely on indicators that measure the speed, direction, and strength of price movements. Momentum: Platform Workflow QuanterLab provides a complete workflow for momentum-based trading, from identifying trending stocks to backtesting strategies and deploying them for live paper trading.

Crossover: Overview & Theory Crossover strategies trade the moment when one indicator crosses another — fast moving average crossing slow, MACD line crossing signal, price crossing a band. Crossover: Core Indicators The crossover signal's quality depends almost entirely on the choice of indicators being crossed. When Crossovers Lie The same crossover rule that quietly makes money in 2017 quietly loses money in 2015.

Breakout: Overview & Theory A breakout is the moment when price exits a consolidation range with momentum. Breakout: Core Indicators The choice of range definition is the most important parameter in any breakout strategy. Volume Confirmation in Breakouts A breakout without volume is suspicious. The single most reliable filter for breakout strategies — across decades of empirical research — is the requirement that the breakout bar trades…

The Universal Builder Approach The Universal Strategy Builder (UB001UNIV) is QuanterLab's most flexible strategy module. Stacking Signals: Compose Without Overfitting The most common improvement in any strategy is adding a confirmation filter. Indicator Reference UB001UNIV ships with 50+ technical indicators organized into families. Custom Rules and Conditional Logic UB001UNIV's rule engine lets you compose entry and exit conditions using boolean logic — AND, OR, NOT — across any indicators. When to Add a Confirmation Filter Adding a confirmation filter to a strategy is the most-suggested fix for any disappointing backtest.

Hurst Exponent: Regime Detection The Hurst Exponent is a statistical measure used to classify time series as trending, mean-reverting, or random. The Variance Ratio Test: VR < 1, Mean Reversion, and How to Read It The Variance Ratio test is a statistical method for detecting whether a time series follows a random walk or exhibits mean-reverting characteristics. Half-Life: Speed of Mean Reversion The Half-Life of mean reversion measures how quickly prices tend to return to their average level. Market Regime Detection Markets alternate between trending and ranging regimes. The Ornstein-Uhlenbeck Process: Mean-Reversion Math, Half-Life, and Trading Use The Ornstein-Uhlenbeck (OU) process is the mathematical heart of QuanterLab's stochastic mean-reversion module. Kalman Filter: Mean Reversion The Kalman filter generalizes OU mean-reversion to handle time-varying parameters. Kalman Filter: Trend Following The same Kalman filter that powers mean-reversion strategies can be reconfigured to estimate trend instead — a smooth, adaptive estimate of the underlying direction of price, with much less… Cointegration & Pairs Trading Two stocks that share a fundamental driver (industry, geography, business model) tend to move together, but their prices drift apart and back over short horizons. ADF vs KPSS: How the Two Stationarity Tests Disagree, and What It Means Before you fit an OU model, run a Z-score strategy, or build a pairs trade, you need to know whether the underlying series is actually stationary. Hidden Markov Models for Regime Detection Markets behave differently in different regimes — bull, bear, choppy, high-volatility — and the same strategy can be profitable in one regime and disastrous in another. GARCH Volatility Forecasting Volatility is not constant. It clusters in time — high-vol periods tend to be followed by more high-vol periods, low-vol by more low-vol. GARCH (Generalized Autoregressive Conditional… GARCH Monte Carlo for Risk Assessment A backtest gives you one historical realization of how a strategy performed. Z-Score Entry/Exit Bands The Z-score is the most-used signal in mean-reversion trading. Optimal Trading Bands The standard Z-score bands at ±2 are heuristic — they work, but there is no theoretical reason they are optimal.

Quantitative Fundamentals: Theory Quantitative fundamental analysis applies systematic, data-driven methods to financial statement data. Quantitative Fundamentals: Platform Usage This guide walks through using QuanterLab's Factor Models tools, from the Multi-Factor Screener through the deep-dive research units. Financial Ratios: Essential Guide Financial ratios convert raw financial statement data into comparable metrics. Financial Ratios: Interpreting Results Understanding what the numbers mean is as important as calculating them. Scoring Methodology: Theory The scoring methodology transforms raw financial data into a single comparable number for each stock. Scoring Methodology: Configuring the Screener This guide covers practical configuration of the Multi-Factor Screener — how to adjust metric weights, choose normalization methods, and interpret the output tabs. Portfolio Construction: Theory Portfolio construction determines how much capital to allocate to each stock. Portfolio Construction: Using the Optimizers This guide covers the practical steps for using QuanterLab's three portfolio optimization modules and its two risk analysis tools. The Fama-French 3-Factor Model The Fama-French 3-Factor Model is the most influential extension of the Capital Asset Pricing Model (CAPM) in modern finance. Carhart 4-Factor Model: Formula, UMD Momentum, and When to Use It The Carhart 4-Factor Model adds momentum to the Fama-French 3-factor framework.

Macro Indicators in Quant Strategies Macro indicators — yield curves, inflation prints, employment data, central bank actions — are publicly available, well-defined, and update on regular schedules. Calendar Anomalies Some of the most persistent patterns in equity returns are tied to calendar dates rather than fundamentals or momentum. Sentiment & Alt Data: What's Real, What's Not "Alternative data" is the marketing label for everything that isn't price, volume, or fundamentals: news sentiment, social media chatter, satellite imagery, credit-card flows, web-scraping…

The Strategy Health Card The Strategy Health Card is the entry point to the Post-Mortem view in FM103APSX. It compresses a multi-year, multi-period factor backtest into a single 0–100 composite score, three… Verdict Thresholds: From Score to Action The Strategy Health Card converts a numeric composite into a four-level verdict: HEALTHY, MONITOR, REVIEW, RECONSIDER. The bucketing is intentionally coarse because a single score does not… Auto-Warnings: How the Engine Flags Issues The Strategy Health Card surfaces the four highest-severity auto-warnings on its header. Factor Decay and Half-Life Factor decay is the rate at which a factor's predictive power dissipates over time. Brinson Attribution: The 1986 Model, Explained Brinson attribution answers the question every portfolio review eventually reaches: did we win because we overweighted the right segments, or because we picked the right securities inside… Information Coefficient (IC): Formula, Spearman vs Pearson, and What Counts as Good The Information Coefficient (IC) is the workhorse statistic of cross-sectional factor analysis. Factor Crowdedness and Spread Compression A factor becomes "crowded" when enough capital is chasing it that the very act of chasing erodes the edge. Brinson Factor Attribution Brinson attribution decomposes a portfolio's realised return into contributions from each factor exposure. Specific (Idiosyncratic) Return Specific return — also called idiosyncratic, residual, or alpha — is the portion of a strategy's return that a named factor model cannot explain. Concentration: HHI and Effective N A portfolio with 50 holdings looks diversified. Drawdown Forensics: Episode-Level Attribution "Max drawdown was −18%" is a single number. Regime-Conditional Performance A strategy that delivered +12% CAGR over 10 years may have delivered +40% in 2020 and −8% in every other year. Style Box Analysis (Morningstar 3&times;3) The Morningstar Style Box is a 3×3 grid that classifies equity portfolios on two axes: size (large / mid / small market cap) and value-growth orientation. Ridge Factor Risk Decomposition The Risk Decomposition sub-pill answers: "how much of my portfolio's return variance is explained by factor exposures, and how much remains as idiosyncratic risk?" The engine fits a ridge… Macro Regime Classification FM103 classifies each rebalance period into a macro regime using three signals: the CBOE Volatility Index (VIX), the 10Y–2Y Treasury yield spread, and the S&P 500 trend (200-day… Transaction Cost Modeling: Spread + Impact A backtest reporting +14% CAGR with 200% annual turnover is reporting a gross number. Tax Drag: ST/LT Capital Gains For a taxable account, every realised gain triggers a tax. Capacity & Liquidity: ADV-Based Ceiling A strategy that backtests beautifully on $100k of capital may be undeployable at $10M. The constraint is liquidity — the average daily volume (ADV) of the holdings limits how much capital… Counterfactual Sweep: Top-N and Stride Robustness The Counterfactual Sweep re-runs the same strategy across a grid of parameter values — varying top-N (number of holdings) or rebalance stride (how many periods between rebalances) — and… Parametric Stress Tests Stress tests apply hypothetical adverse shocks to the latest portfolio holdings and report the resulting one-period loss. Complete Overhaul: Signal-Guided Re-Ranking The Complete Overhaul tool sits in the Autopsy mode (not the Post-Mortem mode) of FM103, but it is fundamentally a diagnostic exercise. Spearman vs Pearson for the IC: Why Rank Correlation Wins The Information Coefficient (see IC) and most cross-sectional factor measurements use Spearman rank correlation rather than Pearson. Bootstrap Resampling for Monte Carlo The Autopsy mode's Monte Carlo sub-pill simulates the range of plausible period returns by bootstrap resampling the daily returns of the portfolio's holdings. Ridge Regression for Factor Decomposition The Risk Decomposition sub-pill fits a ridge regression of portfolio returns onto factor returns. Mean-Reversion Half-Life: The ln(2)/kappa Formula, Derived "Half-life" appears in two distinct contexts in QuanterLab: the mean-reversion half-life of a price series (in stochastic methods, SC001STCB) and the factor decay half-life of a…