Multi-Timeframe Trading Strategy

A sophisticated trading system that implements a multi-timeframe strategy using Python, with both backtesting and live trading capabilities on Binance Testnet.

Project Overview

This project implements a trading strategy that combines multiple timeframes (15-minute entries with 1-hour confirmations) to make trading decisions. The system includes both backtesting capabilities using backtesting.py and live trading functionality through Binance Testnet API.

Key Features

• Multi-timeframe strategy implementation (15m entries, 1h confirmations)
• Comprehensive backtesting system with detailed trade logging
• Live trading integration with Binance Testnet
• Trade comparison and analysis tools
• Modular, class-based architecture for maintainability and extensibility

Project Structure

├── README.md
├── requirements.txt
├── .env.example              # Template for API credentials
├── run_backtest.py           # Run backtesting
├── run_live.py               # Run live trading
├── config/
│   └── config.py             # Configuration settings and API keys
├── src/
│   ├── strategy/
│   │   ├── base.py           # Base strategy class
│   │   └── multi_tf.py       # Multi-timeframe strategy implementation
│   ├── backtesting/
│   │   ├── backtest.py       # Backtesting engine
│   │   └── analyzer.py       # Backtest results analysis
│   ├── trading/
│   │   ├── exchange.py       # Binance API wrapper
│   │   └── executor.py       # Trade execution logic
│   └── utils/
│       ├── logger.py         # Logging utilities
│       ├── indicator.py      # Technical indicators
│       └── data.py           # Data handling utilities
└── data/
    ├── historical_data.csv   # Historical price data for backtesting
    ├── backtest_trades.csv   # Backtest trade logs
    └── live_trades.csv       # Live trading logs

Quick Start

1. Installation

# Create and activate virtual environment
python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Run Backtest

The backtesting system is ready to use with the included historical data:

python run_backtest.py

Expected output:

==================================================
BACKTEST RESULTS
==================================================
Total Trades:      1
Win Rate:          0.00%
Equity Final:      $99,680.59
Return:            -0.32%
Max Drawdown:      -0.32%
Sharpe Ratio:      N/A
==================================================

Trades are automatically saved to data/backtest_trades.csv.

3. Set Up Live Trading (Optional)

⚠️ WARNING: Live trading executes real orders on Binance Testnet. Never use real API keys!

Step 1: Get Testnet API Keys

1. Visit Binance Testnet
2. Create an account or log in
3. Generate API Key and Secret

Step 2: Configure Environment

# Copy the example environment file
cp .env.example .env

# Edit .env and add your testnet credentials
nano .env  # or use your preferred editor

Update .env with your credentials:

BINANCE_API_KEY=your_testnet_api_key_here
BINANCE_API_SECRET=your_testnet_api_secret_here

Step 3: Test Connection

python run_live.py

This will verify your API connection and display current balance without executing trades.

Strategy Details

The strategy combines signals from two timeframes:

• 15-minute timeframe for entry signals
• 1-hour timeframe for trade confirmation (simulated by checking every 4th bar)

Entry Signals

• Long Entry: RSI < 40 (oversold) on a confirmation bar (every 4 bars)
• Short Entry: RSI > 60 (overbought) on a confirmation bar

Exit Signals

• Long Exit: RSI > 70 (overbought) OR price drops 0.5% below SMA50
• Short Exit: RSI < 30 (oversold) OR price rises 0.5% above SMA50

Risk Management

• Position sizing: 95% of available equity per trade
• Commission: 0.2% per trade (backtesting)
• Live trading: Uses RISK_PER_TRADE from config (default 1% of balance)

Configuration

Edit config/config.py to customize:

SYMBOL = "BTCUSDT"           # Trading pair
ENTRY_TIMEFRAME = "15m"      # Entry signal timeframe
CONFIRM_TIMEFRAME = "1h"     # Confirmation timeframe
RISK_PER_TRADE = 0.01        # Risk 1% per trade (live trading)

Trade Logging

Backtest Trades (data/backtest_trades.csv)

Logged automatically during backtesting with columns:

• Size, Entry/Exit Bar, Entry/Exit Price
• PnL, Commission, Return %
• Entry/Exit Time, Duration
• Indicator values at entry and exit

Live Trades (data/live_trades.csv)

Logged when executing live trades with columns:

• Timestamp, Side (BUY/SELL)
• Quantity, Price
• Order ID, Status

Development Guidelines

Adding New Indicators

Add indicator functions to src/utils/indicator.py:

def add_macd(df, fast=12, slow=26, signal=9):
    """Add MACD indicator to dataframe."""
    # Implementation here
    return df

Customizing the Strategy

Extend or modify src/strategy/multi_tf.py:

1. Override init() to add custom indicators
2. Modify next() to implement your entry/exit logic
3. Adjust parameters like rsi_period, sma_period, confirm_interval

Testing

Always test thoroughly:

1. Backtest first: Validate strategy logic with historical data
2. Paper trade: Use Binance Testnet before real trading
3. Monitor logs: Check trading.log for live trading activity

Troubleshooting

Common Issues

"Broker canceled order due to insufficient margin"

• Solution: Increase cash parameter in backtest.py or reduce position size

"Failed to connect to Binance Testnet"

• Verify API keys in .env file
• Check that keys are from testnet.binance.vision (not regular Binance)
• Ensure internet connection is active

"No trades executed in backtest"

• Check if historical data has enough rows (needs 50+ for SMA calculation)
• Adjust RSI thresholds if market conditions don't trigger signals
• Verify confirm_interval aligns with data frequency

Dependencies

Key packages used:

• backtesting - Backtesting framework
• python-binance - Binance API wrapper
• pandas - Data manipulation
• numpy - Numerical computations
• ta - Technical Analysis library
• python-dotenv - Environment variable management

Install all dependencies:

pip install -r requirements.txt

Performance Metrics

The analyzer displays:

• Total Trades: Number of completed trades
• Win Rate: Percentage of profitable trades
• Equity Final: Final portfolio value
• Return: Total return percentage
• Max Drawdown: Largest peak-to-trough decline
• Sharpe Ratio: Risk-adjusted return metric

Safety Notes

• ⚠️ NEVER commit .env file or API keys to version control
• ⚠️ Always test on Testnet before using real funds
• ⚠️ Monitor live trades actively; automated trading carries risk
• ⚠️ Start with small position sizes

Future Enhancements

• Real-time data streaming for live trading
• Multiple symbol support
• Advanced order types (limit, stop-loss)
• Telegram/Discord notifications
• Performance dashboard with charts
• Parameter optimization using .optimize()

Author

Implementation by: Devansh

Acknowledgments

• Binance for providing the Testnet API
• backtesting.py library contributors
• Technical Analysis library contributors

License

This project is for educational purposes. Use at your own risk.