Automated Day Trading Strategies: Feasibility, Challenges, and Beyond Emotional Excuses

:
According to MQL5, high-performing automated trading systems reported growth rates ranging from 504% to 3,052% in 2024-2025, with some achieving 90%+ signal win rates and under 5% maximum drawdowns. Research indicates algorithmic trading accounts for 60-75% of U.S. equity trading volume (institutional preference) and the market grows at an 11.23% CAGR (2021-2026). Limitations include extreme volatility risks, backtesting overfitting (per Trading Strategy Development & Backtesting Mastery Course), regulatory constraints, and infrastructure needs.

:
Reddit users highlight: “Coding something that is actually profitable is very difficult” (Reddit Discussion). Another user notes: “Emotions weren’t the issue—creating a profitable system is; emotions are an easy excuse for not having one.” A minority report success: “Simple enough strategy can be automated… I made my bot using Claude code (software background helped).” Some argue discretion is essential: “Your strategy won’t be profitable without discretion.”

:
Research and social media align: Automated systems have potential but require a robust, adaptable strategy beyond coding. Institutional success (research) vs. retail struggles (discussions) underscore the gap in strategy validation. Investment implications: Prioritize strategy development/validation before automation; simple rules-based strategies are feasible. Overfitting and volatility demand ongoing monitoring.