What Is Quant Investing? A Complete Guide to Algorithmic and Systematic Trading [2026]

1. What is quant investing?

Quant investing is a systematic investment approach that makes decisions using mathematical and statistical models. Its name comes from “quantitative.” It differs from traditional fundamental and technical analysis in its reliance on data and mathematics rather than intuition.

Quant investing has a history of several decades on Wall Street. James Simons's Renaissance Technologies Medallion Fund recorded an average annual return of 66% from 1988 to 2018, substantially exceeding Warren Buffett's returns. Quant strategies were the secret behind this performance.

2. Types of quant strategies

① Momentum strategies

These follow the premise that “the strong become stronger, and the weak become weaker.” They buy assets with strong gains over the last N days and sell or short those that have fallen. Momentum is a central strategy for Commodity Trading Advisor (CTA) funds, with effectiveness demonstrated across different markets over decades.

NOONOO TRADING's core strategy also follows momentum. Instead of using simple price momentum, however, 100 AI agents each measure momentum across different timeframes and indicators to make more refined assessments.

② Mean reversion strategies

These assume that prices return to their average after moving far away from it. Bollinger Bands are a typical mean reversion tool: sell at the upper band and buy at the lower band. This can work in sideways markets but produce large losses during strong trends.

③ Statistical arbitrage

This strategy seeks to profit when the price relationship between two related assets temporarily diverges. For example, a statistically significant deviation in the BTC-to-ETH price ratio can lead to simultaneous long and short positions.

④ Factor investing

Factor investing identifies factors that influence returns and constructs a portfolio exposed to them. Common factors include value, momentum, volatility and liquidity.

3. Backtesting: The core of strategy validation

A major advantage of quant strategies is that historical data can be used to evaluate their performance in advance. This is called backtesting.

Good backtesting requires the following:

• A sufficient period — At least 2–3 years of data, ideally more than 5 years.
• Realistic costs — Slippage, commissions, funding fees and other costs.
• Out-of-sample (OOS) validation — Test on data that was not used for training.
• Walk-forward optimization — Continuous validation using rolling windows.

4. Essential risk-management techniques

The Kelly criterion

This formula determines a mathematically optimal bet size. Based on win rate and the risk-reward ratio, it calculates the position size that maximizes wealth over the long term. In practice, traders commonly use 1/4 to 1/2 of the Kelly value, or quarter Kelly to half Kelly, because full Kelly produces excessive volatility.

Volatility scaling

Position size decreases when market volatility rises and increases when volatility falls. Dynamic adjustments based on Average True Range (ATR) aim to maintain consistent risk across market conditions.

Kill switch: Maximum drawdown limit

This safety mechanism automatically stops all trading when cumulative losses exceed a predefined limit, such as -8%. NOONOO TRADING calls it the “Sentinel Shield,” intended to protect assets from catastrophic losses.

5. How individuals can begin quant investing

1. Learn basic Python — A fundamental quant tool; learn Pandas and NumPy.
2. Understand indicators — Basic technical indicators such as RSI, MACD, Bollinger Bands and ATR.
3. Use a backtesting framework — Options include Backtrader and QuantConnect.
4. Start live tests with a small amount — Move from paper trading to the minimum practical live size.
5. Alternatively, use an established system — If building one yourself is difficult, refer to signals from an established system such as NOONOO TRADING.