Paper: arXiv 2508.16378

Authors: Qizhao Chen

Abstract

This paper presents a dynamic cryptocurrency portfolio optimization strategy that integrates technical indicators and sentiment analysis to enhance investment decision-making. The proposed method employs the 14-day Relative Strength Index (RSI) and 14-day Simple Moving Average (SMA) to capture market momentum, while sentiment scores are extracted from news articles using the VADER (Valence Aware Dictionary and sEntiment Reasoner) model, with compound scores quantifying overall market tone. The large language model Google Gemini is used to further verify the sentiment scores predicted by VADER and give investment decisions. These technical indicator and sentiment signals are incorporated into the expected return estimates before applying mean-variance optimization with constraints on asset weights. The strategy is evaluated through a rolling-window backtest over cryptocurrency market data, with Bitcoin (BTC) and an equal-weighted portfolio of selected cryptocurrencies serving as benchmarks. Experimental results show that the proposed approach achieves a cumulative return of 38.72, substantially exceeding Bitcoin’s 8.85 and the equal-weighted portfolio’s 21.65 over the same period, and delivers a higher Sharpe ratio (1.1093 vs. 0.8853 and 1.0194, respectively). However, the strategy exhibits a larger maximum drawdown (-18.52%) compared to Bitcoin (-4.48%) and the equal-weighted portfolio (-11.02%), indicating higher short-term downside risk. These results highlight the potential of combining sentiment and technical signals to improve cryptocurrency portfolio performance, while also emphasizing the need to address risk exposure in volatile markets.

Complexity vs Empirical Score

  • Math Complexity: 4.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper uses standard mean-variance optimization (MVO) and basic signal processing (RSI, SMA) without deep mathematical derivations, keeping complexity moderate. It scores high on empirical rigor due to a detailed five-year rolling-window backtest against benchmarks, reporting multiple financial metrics (cumulative return, Sharpe ratio, max drawdown).

Research Flowchart

  flowchart TD
  A["Research Goal: Enhance Cryptocurrency Portfolio<br/>Optimization using Sentiment & Technical Signals"] --> B["Data Collection & Feature Extraction"]
  B --> C["Computational Processing"]
  C --> D["Portfolio Optimization"]
  D --> E["Backtesting & Evaluation"]
  E --> F["Key Findings & Outcomes"]
  
  subgraph B ["Inputs"]
      B1["Historical Price Data"]
      B2["News Articles"]
  end
  
  subgraph C ["Signal Generation"]
      C1["Technical Indicators<br/>14-day RSI & SMA"]
      C2["Sentiment Analysis<br/>VADER Model"]
      C3["LLM Verification<br/>Google Gemini"]
  end
  
  subgraph D ["Model"]
      D1["Mean-Variance Optimization<br/>with Constraints"]
  end
  
  subgraph E ["Validation"]
      E1["Rolling-Window Backtest"]
  end
  
  subgraph F ["Results"]
      F1["High Returns (38.72%)"]
      F2["High Sharpe Ratio (1.1093)"]
      F3["Higher Max Drawdown<br/>(-18.52%)"]
  end
  
  B1 & B2 --> C1
  B2 --> C2
  C2 --> C3
  C1 & C3 --> D1
  D1 --> E1
  E1 --> F1
  E1 --> F2
  E1 --> F3

Cited References (22)

  1. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text (2014) — link
  2. Cryptocurrency-portfolios in a mean-variance framework (2019) — link
  3. Comparative Study of Portfolio Optimization Models for Cryptocurrency and Stock Markets (2025) — link
  4. Bitcoin Price Prediction Using Sentiment Analysis and Empirical Mode Decomposition (2025) — link
  5. Sentiment-Driven Bitcoin Price Range Forecasting: Enhancing CART Decision Trees With High-Dimensional Indicators and Twitter Dynamics (2025) — link
  6. Stock Price Prediction Using LLM-Based Sentiment Analysis (2024) — link
  7. Cryptocurrency Price Prediction using Forecasting and Sentiment Analysis (2022) — link
  8. BERT ’s sentiment score for portfolio optimization: a fine-tuned view in Black and Litterman model (2022) — link
and 14 more
  1. A Novel Approach to Portfolio Construction: An Application of FinBERT Sentiment Analysis and Credibilistic CVaR Criterion (2025) — link
  2. Deep Leaming for Stock Market Prediction Using Technical Indicators and Financial News Articles (2018) — link
  3. Stock Market Prediction System Using LSTM with Technical Indicators as Voters (2023) — link
  4. Stock Price Prediction with LLM-Guided Market Movement Signals and Transformer Model (2025) — link
  5. Stock Price Change Prediction Using Prompt-Based LLMs with RL-Enhanced Post-Hoc Adjustments (2025) — link
  6. Explore the Use of Prompt-Based LLM for Credit Risk Classification (2025) — link
  7. Sentiment-Aware Stock Price Prediction with Transformer and LLM-Generated Formulaic Alpha (2025) — link
  8. Optimal Markowitz portfolio using returns forecasted with time series and machine learning models (2025) — link
  9. Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models — → 2508.14999
  10. A novel sentiment correlation-based method with dual transformer model for stock price prediction (2025) — link
  11. Financial Sentiment Analysis: Techniques and Applications (2024) — link
  12. Qwen-Fin: Chinese Financial Sentiment Analysis Based on Fine-Tuned Large Language Model (2024) — link
  13. Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy (2009) — link
  14. Predicting Future Stock Prices Using Historical Data: A Machine Learning Approach (2025) — link

1 of 22 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.