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🪙 Crypto Carry Trade Project

EPFL - Financial Engineering MA2 - FIN-413
Spring Semester 2025
Professor: Dimitrios Karyampas
Group 7
Authors:

  • Matthias Wyss (SCIPER 329884)
  • Loris Tran (SCIPER 341214)
  • Massimo Berardi (SCIPER 345943)
  • Vincent Ventura (SCIPER 302810)
  • Alexandre Huou (SCIPER 342227)

📘 Project Overview

This project investigates the implementation and performance of various delta-neutral crypto carry trade strategies using perpetual futures and DeFi innovations like staking and Pendle Finance.

We analyze the historical profitability, market resilience, and risk characteristics of the strategies across different market regimes, including the 2021 bull market, the Luna collapse, the FTX crisis and the ETF bullrun.


📄 Read the full Project Report here


📈 Strategies Implemented

1. Classical Carry Trade

  • Long spot (BTC or ETH), short perpetuals
  • Captures funding rate as passive yield
  • Delta-neutral, relies on positive funding rate

2. Staking-Enhanced Carry (ETH + Lido)

  • Stake ETH via Lido to earn staking APR
  • Hedge price exposure via shorting perpetuals
  • Combines funding rate + staking rewards

3. USD-Settled Carry via Pendle (PT-stETH)

  • Buy Pendle PT-stETH (discounted staked ETH)
  • Short ETH perpetuals to neutralize price exposure
  • Realize fixed yield in USD terms at maturity
  • Only fully delta-neutral at maturity

🧪 Backtesting & Analysis

We conducted backtests from 2019 to 2024, analyzing performance over different market regimes:

  • 📈 Bull Market (2021)
  • 💥 Luna Collapse
  • 🧨 FTX Collapse
  • 🚀 ETF Bull Market (2024)

Metrics include:

  • Cumulative funding returns
  • Annualized funding rates
  • Funding rate distributions
  • Strategy resilience to market shocks
  • Underlying asset drawdown
  • Strategy drawdown

All data was collected from Binance, CoinGlass API and Dune Analytics.


📂 Repository Structure

├── data/                # Raw and processed datasets, includes some plots for Question 2
├── src/                 # Python scripts implementing the crypto carry strategies
├── plots/               # Output plots for Questions 3 and 4
├── q2.ipynb             # Notebook for plots and analysis related to Question 2
├── q3.ipynb             # Main notebook generating data for Questions 3 and 4
├── q3_bis.ipynb         # Supplementary visualizations for Question 3
├── q3_staking.ipynb     # ETH staking analysis (not included in the final report)
├── q4.ipynb             # Notebook for visualizations and insights for Question 4
├── Project_report.pdf   # Final project report (PDF)
└── README.md            # Project overview and structure

🔍 Key Findings

  • Funding rates for BTC and ETH were positive over 85% of the time, especially during bull markets.
  • ETH carry strategies enhanced with staking outperform pure funding-based strategies by approximately 3.9% on average.
  • Pendle-based carry trades provide fixed yield in USD, offering an attractive option for risk-averse investors.
  • Carry strategies remain resilient during market stress, but risk management is crucial, particularly regarding liquidation risk and funding rate volatility.
  • Simulations show that carry strategies with dynamic leverage adjustment reduce drawdowns while maintaining competitive returns.

🛠️ Dependencies

  • Python ≥ 3.9
  • pandas, numpy, matplotlib, requests
  • Jupyter for notebooks
  • Dune API key
  • CoinGlass API key

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Quantitative analysis of crypto carry trade strategies (funding rates, staking, Pendle) on BTC/ETH, using historical data and visual insights.

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