Quantitative Investment Strategies in Cryptocurrency Markets

Research on profitability of different investment strategies in cryptocurrency trading

Overview

Investigation of four quantitative trading strategies on cryptocurrency markets (Binance ETH-USDT perpetual contract). Demonstrates that traditional financial strategies require significant parameter optimization to succeed in crypto: unoptimized long-term strategies lost -3% to -20%, while optimized versions gained +9% to +21%.

Problem Statement

Cryptocurrency markets differ fundamentally from traditional finance: 24/7 trading, extreme volatility (10-20% daily swings), no circuit breakers, and immature market structure. Can established quantitative strategies from traditional finance work in crypto, and how critical is parameter optimization?

Methodology

Strategy Selection & Testing

Short-term (1-hour bars, May 9-14, 2020):

  1. SMA Crossover: Moving average signals
  2. EWMAC: Volatility-adjusted EMA crossover

Long-term (daily bars, May 1-Aug 1, 2020):

  1. Trend Following: Donchian + EMA + ATR stop
  2. MA Trend Following: EMA crossover + ATR stop

Optimization: Grid search (9-2,880 combinations) maximizing Sharpe Ratio, parallel execution across CPU cores

Results

Key Finding: Parameter optimization transforms losses into profits

Strategy Unoptimized Optimized Improvement
SMA Crossover +4.40% +6.25% +42%
EWMAC +6.13% +10.68% +74%
Trend Following -3.13% ❌ +9.53% ✓ +304%
MA Trend Following -20.84% ❌ +21.26% ✓ +202%

Best Performer: MA Trend Following (21.26% in 3 months, Sharpe 1.996)

Applications

  • Systematic crypto trading with optimized parameters
  • Risk management via ATR-based position sizing
  • Portfolio diversification with quantitative strategies
  • Backtesting framework for strategy validation

Achievements & Recognition

Key Metrics

  • 4 strategies tested, all profitable after optimization
  • 2,880 parameter combinations evaluated (max)
  • 21.26% return in 3 months (best strategy)
  • All optimized strategies beat 8.3% inflation and 7.15% conservative investments

Technical Stack

Python, Backtrader, pandas, numpy, matplotlib, multiprocessing

Team & Collaboration

Institution: College of Optoelectronic Engineering (Now: School of Instrument Science and Optical Engineering), National Chung Hsing University

References:

  • Carver, R. (2015). Systematic Trading
  • Clenow, A. (2013). Following the Trend

Timeline

Duration: September - December 2022 (4 months)