Blog
Data-driven articles on quant strategies, backtesting, algorithmic investing, and building edge in financial markets.
Moving averages are the backbone of technical analysis. Learn the difference between SMA and EMA, how to combine them for signals, and the pitfalls to avoid.
Walk-forward analysis separates genuine strategy edges from curve-fitted noise. Learn how to implement it, interpret the results, and why it is the most trusted validation method in quant trading.
Quantitative trading uses mathematical models, statistical analysis, and algorithms to make investment decisions. Here is everything you need to know to get started.
Choosing the right backtesting library shapes everything: development speed, strategy complexity, and result reliability. Here is a practical comparison of the leading Python frameworks for retail and institutional quant traders.
Most traders lose money not because they pick the wrong stocks, but because they size positions incorrectly. Learn the frameworks that protect capital and extend trading longevity.
Overfitting is the silent killer of quantitative trading strategies. A strategy that perfectly explains historical data often has zero predictive power going forward. Learn how to protect yourself.
Backtesting is how serious traders validate ideas before risking capital. Learn the step-by-step process, common pitfalls, and how to interpret results correctly.
Mean reversion is one of the most statistically robust phenomena in financial markets. Learn how to identify reversion setups, measure statistical deviation, and manage the risk of trending markets.
Algorithmic trading is no longer just for hedge funds. Learn how retail investors can leverage systematic strategies, the tools available, and realistic expectations.
Momentum is one of the most replicated findings in finance. Assets that have outperformed recently tend to continue outperforming. Learn the mechanics, timing, and risk controls behind momentum strategies.
Factor investing is the systematic approach to capturing specific return drivers that have been proven by decades of research. Learn what the major factors are, how to measure them, and how to combine them.
Trading signals tell you when to enter, exit, or stay in a position. Learn how buy, sell, and hold signals are generated, what makes them reliable, and how to act on them.
Monte Carlo simulation reveals the full distribution of possible outcomes for your trading strategy — not just the single historical path. Learn how to use it to set realistic expectations and size positions correctly.
Your backtest is only as good as your data. Bad data produces misleading results, no matter how sophisticated your strategy. Here is a complete guide to the best free and paid data sources for equity, futures, and crypto backtesting.
From backtesting platforms to fundamental screeners, here is a comprehensive comparison of the best tools for finding, validating, and acting on investment ideas in 2026.
Markowitz's efficient frontier is 70 years old and still the foundation of quantitative portfolio construction. Learn how it works, where it breaks down, and what practitioners use instead.
The Sharpe ratio is the most widely used metric for evaluating investment performance. Learn what it measures, how to calculate it, and how to use it to compare strategies.
Knowing your return is not enough. Understanding where it came from — and where you are losing edge — requires portfolio analytics. Learn the tools and metrics that separate serious traders from guessers.
Gilito tests 100,000,000+ strategies per asset daily so you get data-driven buy/sell/hold signals — not opinions.