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A Novel Bitcoin and Gold Prices Prediction Method Using an LSTM-P Neural Network Model.

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Greedy Strategies with Multiobjective Optimization for Investment Portfolio Problem Modeling.

Xinchen Zhang¹, Linghao Zhang¹, Qincheng Zhou²

¹School of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210046, China.

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|May 31, 2022

Summary

This study presents a quantitative trading model for Bitcoin and gold, aiming to maximize profit and minimize risk using multiobjective optimization and trend indicators. The model offers scalable, personalized strategies for diverse investor preferences.

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Area of Science:

Quantitative Finance
Computational Economics
Investment Strategy

Background:

Portfolio investment aims to balance risk and return through capital allocation.
Analyzing historical price movements of Bitcoin and gold is crucial for developing effective trading strategies.
Existing models may not adequately address diverse investor risk tolerances.

Purpose of the Study:

To develop a quantitative model for optimizing Bitcoin and gold trading strategies.
To maximize investment profits while minimizing associated risks.
To provide tailored trading recommendations for different investor profiles.

Main Methods:

Utilized multiobjective optimization models with greedy strategies.
Incorporated popular trend indicator strategies for price trend prediction.
Segmented investors into aggressive, advanced, balanced, and cautious groups.

Main Results:

The model demonstrates scalability by accommodating various trading preferences.
Sensitivity analysis was performed to evaluate model robustness.
The impact of trading commissions on model outcomes was assessed.

Conclusions:

The developed quantitative model offers a versatile framework for Bitcoin and gold investment.
The model provides adaptable trading strategies for diverse investor risk appetites.
The approach is applicable across various investment scenarios and market conditions.