7. Twelve trading approaches: which tool each type of strategy uses
Level 1: Foundations of Algorithmic Trading
There is no single 'correct' way to trade the markets. There are different approaches, each with its own tools and its own logic, and most experienced traders end up combining several rather than clinging to one.
Technical analysis relies on historical price, chart patterns and technical indicators — moving averages, RSI, MACD — under the idea that past price behavior, shaped by the collective psychology of participants, tends to repeat.
Fundamental analysis, by contrast, ignores the chart and focuses on the intrinsic value of the asset: it examines financial statements, sector trends and macroeconomic factors to determine whether a stock is undervalued or overvalued.
The CANSLIM method, developed by William O'Neil, blends fundamental analysis with momentum indicators, looking for companies with strong earnings growth and leadership in their sector.
Econometrics applies statistical methods to economic and financial data, and underpins a large share of the models used to forecast prices, volatility and other market factors.
Machine learning identifies patterns in large volumes of data and uses them to predict future behavior, with the advantage of capturing non-linear relationships that other methods miss.
Portfolio optimization seeks to build a combination of assets that maximizes return for a given level of risk, following Harry Markowitz's Modern Portfolio Theory — balancing risk and reward by spreading capital across different assets instead of concentrating it in one.
Game theory applies strategic thinking to competitive markets: a trader using this approach does not only watch the price, but tries to anticipate how the other participants will react to their own decisions.
Rules-based trading follows a strict, predefined set of conditions — rules of the form 'if this happens, then do that' — built on technical or fundamental indicators.
Algorithmic trading automates the execution of any of the above approaches through programmed systems, capable of operating at speeds and volumes no human could manage by hand. Within it live strategies of different kinds: trend following, which buys when price rises and sells when it falls; mean reversion, which bets that price will return to its historical average; and statistical arbitrage, which exploits small price inefficiencies between related assets.
Quantitative trading uses mathematical models and large volumes of data to find opportunities, backing every strategy with rigorous backtesting before risking a single real euro.
And sentiment analysis uses textual data — news, social media, earnings reports — to gauge the mood of the market and anticipate how it will react to certain events.
None of these approaches is superior in the abstract. Technical and fundamental analysis are the most traditional; machine learning and algorithmic trading are the most recent and the ones dominating the current conversation. Understanding the strengths and limits of each is what later allows you to choose, with sound judgment, which one — or which combination — fits the strategy you want to build.