Trading Bot System

This research by Sefirot Financial Research investigates how the statistical properties of daily return distributions - specifically the presence of fat tails - can be systematically exploited through an automated trading strategy. Using 15 years of QQQ data (2010 - 2025), the study develops and backtests a long-only bot that applies a disciplined stop-loss mechanism to asymmetrically manage downside risk while preserving upside exposure. By examining how the return distribution shifts when extreme negative events are truncated, the report offers a rigorous yet accessible framework for understanding structural edge in systematic trading.

Key topics covered:

• Fat tails in financial return distributions and why they represent an exploitable inefficiency

• Distributional analysis of QQQ daily returns with and without stop-loss application

• Backtest results across 15 years: strategy vs. benchmark (+1,567% vs +1,238%)

• Technical architecture of the bot: entry/exit timing, intraday vs. overnight position logic

• Honest assessment of implementation constraints and their impact on real-world performance

• Key takeaway: outperformance is generated not through market prediction, but through asymmetric loss truncation applied to a structurally right-skewed distribution