Implementation of a trading algorithm with a focus on volume profiles - Kohout, Vít; Zeman, Václav; Chudán, David; Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky (2025)
Thesis investigates the incremental value of integrating advanced analytics into classical intraday trading strategies. To systematically assess the impact of increasing analytical complexity, a tiered framework of nine strategies was developed spanning baseline (price-only), volume-enhanced, and deep learning-enhanced archetypes. Temporal Fusion Transformer model is implemented to provide 15-minute ahead volume forecasts for the most advanced strategies and everything is rigorously backtested on NASDAQ-100 securities. The empirical results demonstrate a key trade-off: while incorporating volume-based parameters did not significantly increase profitability, it offered a powerful, statistically significant advantage in risk management by consistently reducing maximum drawdowns. Conversely, the hypothesis that a sophisticated transformer-based forecast would further improve performance was rejected, as the strategies operating on predicted volume failed to outperform their simpler, heuristic-based counterparts. Analysis also validates that strategy performance is highly regime-dependent, particularly for mean-reversion and momentum approaches. It is concluded that the primary contribution of adding volume-based complexity in this context is defensive, serving to preserve capital rather than amplify return, and highlight a critical trade-off between the benefits and implementation costs of deploying advanced predictive models in intraday trading strategies on higher frequency data.
Vedoucí práce: Václav Zeman
Bakalářská práce (Bc.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2025
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2025
Způsob přístupu: Internet
Keywords: data analytics [obor bakal. práce]; bakalářské práce; bachelor's theses