Backtesting and quantitative method
A backtest proves nothing until you know how many variants were tried before it. Every article in this section tackles the same question: how to tell a real signal from an artefact produced by the research process itself.
It covers the biases that inflate historical results (data-snooping, look-ahead, survivorship), the validation methods that hold up against them (walk-forward, purged cross-validation), and statistical corrections such as the Deflated Sharpe Ratio.
Why the Sharpe ratio isn't enough to judge a strategy
A high Sharpe can hide a catastrophic drawdown or pure luck from multiple trials. Here are the blind spots of the most-used ratio in quant finance, and how to fill them.
New free ebook: Systematic Portfolio Optimization
A free reference guide on the complete institutional quant pipeline: RMT spectral filtering, shrinkage, risk parity, regime detection and Half-Kelly sizing.
Data-snooping in trading: the beginner quant's #1 trap
Testing 1000 variants then keeping the best guarantees a nice backtest… and a live letdown. Here's how to spot data-snooping and protect yourself with the Deflated Sharpe Ratio.
This week in markets (Aug 3–9, 2026): quant dispersion, a cautious ECB
Magnificent Seven earnings, a striking performance gap between quant funds, and the ECB holding steady: this week's signal, without the noise.
Walk-Forward Analysis: Validating a Backtest Without Falling Into Overfitting
Walk-forward analysis reproduces how a strategy would actually be re-optimized over time. Here's how it works and why it detects overfitting better than a simple in-sample/out-of-sample split.
Survivorship Bias in Backtesting: Why Your Results Are Probably Too Optimistic
Testing a strategy only on companies and funds still active today artificially inflates performance. Here's how to detect and correct survivorship bias.
Portfolio Rebalancing: The 5/25 Rule Explained
Why your allocation drifts over time, and how the 5/25 rule helps decide when to rebalance without overreacting.
Confirmation Bias in Trading: How It Skews Your Decisions
Confirmation bias pushes discretionary traders to see only what validates their position. A breakdown of the mechanism and concrete techniques to guard against it.
Why Backtest Results Fail in Live Trading: 7 Hidden Pitfalls
Seven concrete, well-documented reasons backtested performance fails to replicate live, from survivorship bias to overfitting, with mitigations for each.
Walk-Forward Analysis vs Cross-Validation for Time Series
Why K-Fold cross-validation leaks future data in trading backtests, and how walk-forward analysis and CPCV provide a valid alternative for time series.
Survivorship Bias in Backtesting: Why It's a Fatal Flaw
Understand how survivorship bias inflates equity backtest returns and how point-in-time data and delisting adjustments fix it.
Modeling Slippage and Commissions in Algorithmic Backtests
Learn why zero-cost fill assumptions inflate backtests and how to model slippage, spread, and commissions realistically in Python.
How to Avoid Overfitting in Algorithmic Trading Strategies
Practical techniques to reduce overfitting in algorithmic trading: temporal splits, parameter reduction, regularization, and heatmap diagnostics.
Best Python Libraries for Backtesting Trading Strategies
A technical comparison of VectorBT, Backtrader, Zipline-Reloaded and QuantConnect/LEAN for Python backtesting, covering architecture, speed and realism trade-offs.