{"id":18537,"date":"2026-04-30T18:22:02","date_gmt":"2026-04-30T18:22:02","guid":{"rendered":"https:\/\/www.simtrade.fr\/blog_simtrade\/?p=18537"},"modified":"2026-04-30T18:24:27","modified_gmt":"2026-04-30T18:24:27","slug":"rise-algorithmic-trading-simple-strategies-machine-learning","status":"publish","type":"post","link":"https:\/\/www.simtrade.fr\/blog_simtrade\/rise-algorithmic-trading-simple-strategies-machine-learning\/","title":{"rendered":"The Rise of Algorithmic Trading: From Simple Strategies to Machine Learning"},"content":{"rendered":"\n\"Anis<\/a>\n\n

In this article, Anis MAAZ<\/a> (ESSEC Business School, Global Bachelor in Business Administration (GBBA), 2023-2027) explains how algorithmic trading works, from rule-based strategies like market making, arbitrage, and momentum to modern machine learning models and the systems that run them. The goal of this post is to give a clear, realistic overview of today\u2019s algo landscape, its methods, data and infrastructure needs, and the risks and controls traders must understand before building or adopting an automated strategy. <\/p>\n\n\n

What \u201calgorithmic trading\u201d means<\/h2>\n\n

Algorithmic trading is the use of computer programs to make and execute trading decisions according to predefined rules. These rules can be simple, such as splitting a large order into smaller pieces to reduce market impact, or more sophisticated, such as detecting short term patterns in prices, volumes, or order book dynamics. The goal is not necessarily to trade fast, but to trade systematically and consistently, removing emotion and human latency from the process. <\/p> \n\n

Algorithmic trading now dominates global markets. According to JP Morgan and Bloomberg estimates, it accounts for roughly 60\u201373% of U.S. equity trading volume, 40\u201350% in European equities, around 80% in FX spot markets (BIS Triennial Survey, 2022), and over 70% in futures markets. The evolution has been dramatic: less than 15% of U.S. equity volume in the early 2000s, past 50% by 2008, and a peak above 70% during 2009\u20132012 with the rise of high-frequency trading. It has since stabilized between 60% and 75% as regulation tightened and the industry consolidated around a few dominant players. <\/p>\n\n

Why it grew so fast? <\/h2>\n\n

Three forces drove adoption. First, markets became electronic and faster, so speed and precision started to matter in everyday execution. Second, data and computing became cheap: brokers and exchanges exposed APIs, cloud resources got affordable, and open-source libraries appeared. Third, microstructure itself changed: most trading now occurs on limit order books where tiny, frequent price changes reward consistency and careful cost control. Together, these factors made rules based automation both feasible and attractive for firms and independent traders. <\/p> \n\n

The ecosystem is driven by several types of players: high-frequency trading firms (Citadel Securities, Virtu Financial, Jump Trading, Jane Street) that dominate market making and short-term arbitrage; quantitative hedge funds (Renaissance Technologies, Two Sigma, D.E. Shaw) that run systematic strategies on longer horizons; investment banks (Goldman Sachs, JP Morgan) operating algorithmic execution desks for clients; asset managers (BlackRock, Vanguard) using algorithms for portfolio rebalancing; and a fast-growing retail segment leveraging platforms like Interactive Brokers, Alpaca, or MetaTrader. <\/p>\n\n

How a typical algorithmic setup works (without jargon) <\/h2>\n\n

Under the hood, most systems share four components. A signal suggests “buy,” “sell,” or “do nothing,” based on patterns the designer expects to repeat. Risk controls limit position size, daily losses, and exposure across instruments, and can stop the system if limits are hit. An execution module decides how to place orders, market or limit, how aggressively to join or improve the queue, and how to behave in volatile moments. Finally, a testing loop checks ideas on past data (backtests), then in small live trials (forward tests), and monitors production to catch problems or errors early. This last step is the most important one to verify the algorithm really works before committing significant capital. <\/p> \n

Machine learning, when used, lives mainly in the signal step: models learn patterns from large datasets such as order book features or news sentiment. It can improve accuracy, but it also adds failure modes such as overfitting (the model memorizes the past instead of learning real patterns) and model drift (the market changes and the model becomes obsolete), so governance and validation become central. Academic research highlights both sides of this automation: Hendershott, Jones, and Menkveld (2011) show that algorithmic trading improves liquidity and makes quotes more informative; Brogaard, Hendershott, and Riordan (2014) find that high-frequency traders contribute to price discovery; but Kirilenko et al. (2017), studying the 2010 Flash Crash, demonstrate how automated systems can amplify volatility during stress episodes.<\/p>\n\t\n

What algorithms actually do: strategy families in practice<\/h2>\n\n\n