Composing Ensembles of Instrument-Model Pairs for Optimizing Profitability in Algorithmic Trading
ArXiv ID: 2411.13559 “View on arXiv”
Authors: Unknown
Abstract
Financial markets are nonlinear with complexity, where different types of assets are traded between buyers and sellers, each having a view to maximize their Return on Investment (ROI). Forecasting market trends is a challenging task since various factors like stock-specific news, company profiles, public sentiments, and global economic conditions influence them. This paper describes a daily price directional predictive system of financial instruments, addressing the difficulty of predicting short-term price movements. This paper will introduce the development of a novel trading system methodology by proposing a two-layer Composing Ensembles architecture, optimized through grid search, to predict whether the price will rise or fall the next day. This strategy was back-tested on a wide range of financial instruments and time frames, demonstrating an improvement of 20% over the benchmark, representing a standard investment strategy.
Keywords: Ensemble Learning, Grid Search, Price Direction Prediction, Trading Strategy, Backtesting, Multi-Asset
Complexity vs Empirical Score
- Math Complexity: 4.0/10
- Empirical Rigor: 8.5/10
- Quadrant: Street Traders
- Why: The paper describes a practical ensemble architecture with grid search and benchmark comparisons, emphasizing backtesting and profitability metrics; the mathematics is largely applied ML rather than theoretical derivations.
flowchart TD
A["Research Goal:<br>Predict Daily Price Direction<br>to Maximize Profitability"] --> B["Data Inputs:<br>Multi-Asset Market Data<br>+ Technical Indicators"]
B --> C["Methodology:<br>Two-Layer Ensemble Architecture<br>+ Grid Search Optimization"]
C --> D["Process:<br>Train Base Models<br>Combine Predictions via Meta-Learner"]
D --> E["Validation:<br>Backtesting on<br>Multiple Instruments & Timeframes"]
E --> F["Outcome:<br>20% Improvement over<br>Benchmark Strategy"]