📈 Backtest Strategy

A comprehensive Python-based backtesting suite for algorithmic trading strategies. This project allows traders and developers to evaluate and optimize trading strategies using historical data, ensuring data-driven decision-making before live deployment.

---

✅ Features

• 🔄 Delta Trading Algorithm Backtester A robust backtesting engine featuring:

  • Historical data fetching via ccxt
  • Technical indicators: Moving Averages, ATR
  • Position sizing with the Kelly Criterion
  • Trade simulation & performance analytics
  • Interactive & static plots for visual analysis

• 📊 Trend Following Strategy Classic trend-following strategy using:

  • Multiple moving averages
  • Session high/low breakouts
  • Configurable stop-loss and take-profit (via backtesting.py)

• 📉 VWAP Trading Strategies Includes two variations:

  • Basic VWAP entries & exits
  • Enhanced VWAP + RSI & ATR filtering for improved risk management

• 📌 Multi-Indicator Strategy Combines Supertrend, MACD, and VWAP for a confluence-based approach.

• 📂 Historical Data Integration Fetches accurate OHLCV data from multiple exchanges (Delta, Binance, etc.) using ccxt.

• 📈 Performance Analysis & Visualization Generates detailed performance statistics: profitability, drawdown, trade statistics, and equity curves.

---

🗂️ Project Structure

.
├── 3cnfo.py                      # Multi-indicator strategy (Supertrend, MACD, VWAP)
├── backtest_results.png          # Example screenshot of backtest results
├── backtest.py                   # Delta Trading Algorithm backtester
├── imp.py                        # Enhanced VWAP strategy
├── Trend Following Backtest.py   # Trend Following strategy backtester
├── VWAPHLC3Strategy.html         # HTML plot output for VWAP strategy
├── VWAPStrategy.html             # HTML plot output for VWAP strategy
└── vwep.py                       # Basic VWAP strategy

---

⚙️ Prerequisites

Ensure you have the following installed:

• Python 3.x
• pip (Python package installer)

---

🛠️ Tech Stack

• Python – Core language
• Pandas – Data manipulation & analysis
• NumPy – Numerical computations
• CCXT – Cryptocurrency exchange data fetching
• Matplotlib – Static plots
• backtesting.py – Backtesting framework
• pandas_ta – Technical indicators

---

🚀 Getting Started

1️⃣ Clone the Repository

git clone https://github.com/your_username/backtest-strategy.git
cd backtest-strategy

(Replace your_username/backtest-strategy.git with your actual repository URL)

2️⃣ Install Dependencies

pip install pandas numpy ccxt matplotlib backtesting pandas_ta

---

▶️ Running Backtests

Run Delta Trading Algorithm Backtest:

python backtest.py

Run Trend Following Backtest:

python "Trend Following Backtest.py"

Each script will output performance statistics and generate plots/HTML files (e.g., backtest_results.png or backtest.html).

---

⚡ Configuration

You can customize:

• SYMBOL (e.g., BTC/USDT)
• TIMEFRAME (e.g., 1h, 4h)
• EXCHANGE_ID (e.g., binance, kraken)
• LOT_SIZE

🔑 Environment Variables for API Keys

Linux/macOS:

export DELTA_API_KEY="your_actual_delta_api_key"

Windows:

set DELTA_API_KEY="your_actual_delta_api_key"

Access in Python:

import os
API_KEY = os.getenv('DELTA_API_KEY', 'YOUR_API_KEY_DEFAULT_IF_NOT_SET')

❓ FAQ

Q: How do I change the cryptocurrency symbol or timeframe? A: Edit configuration variables (SYMBOL, TIMEFRAME, etc.) at the top of each Python script.

Q: Can I use different exchanges? A: Yes. Change EXCHANGE_ID to supported exchanges in ccxt (e.g., binance, kraken, bybit).

Q: Why multiple backtesting scripts? A: Each represents a unique trading strategy or backtesting approach.

Q: How to interpret results? A: Check equity curves, drawdown, and profitability metrics in generated plots and console outputs.