🚀 LeverageBack

LeverageBack is a high-performance quantitative backtesting suite designed to simulate leveraged investment strategies. It transforms historical market data into actionable risk-adjusted insights, featuring an AI-powered advisory layer to help investors navigate the complexities of leveraged volatility.

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🎯 Key Features

• 📈 Leveraged Simulation Engine: Accurately simulate daily-resetting leverage (e.g., 2x, 3x) and observe the effects of volatility decay and compounding.
• 🤖 AI Advisor: Integrated intelligence that analyzes backtest results to provide strategic commentary and risk warnings.
• 📊 Advanced Visualization: Interactive charts powered by Plotly, including drawdown analysis, equity curves, and rolling volatility.
• ⚙️ Flexible Parameterization: Customize management fees, borrow costs, leverage ratios, and rebalancing frequencies.
• 📉 Risk Metrics Suite: Instant calculation of Sharpe Ratio, Sortino Ratio, Maximum Drawdown (MDD), and Calmar Ratio.

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🛠️ Tech Stack

• Frontend: Streamlit – For a sleek, responsive analytical dashboard.
• Data Engine: Pandas & NumPy – High-speed vectorization for financial time-series.
• Graphics: Plotly – Interactive financial charting.
• Market Data: yfinance – Reliable end-of-day market data.

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📊 Quantitative Logic

LeverageBack calculates the daily leveraged return ($R_{lev}$) by factoring in the leverage ratio ($L$), borrowing costs ($C_b$), and expense ratios ($E$):

$$R_{lev, t} = (R_{base, t} \times L) - [(L - 1) \times \frac{C_b}{365}] - \frac{E}{365}$$

The model explicitly accounts for the Volatility Decay inherent in daily rebalanced leveraged ETFs, providing a realistic view of long-term "slippage."

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🚀 Quick Start

1. Installation

Clone the repository and install the dependencies:

git clone [https://github.com/Seanyim/LeverageBack.git](https://github.com/Seanyim/LeverageBack.git)
cd LeverageBack
pip install -r requirements.txt