🚀 R Cryptocurrency Trading Bot

A professional-grade algorithmic trading system built in R with advanced technical analysis, risk management, and paper trading capabilities.

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📊 Overview

An end-to-end algorithmic trading system designed for cryptocurrency markets. Features real-time data acquisition, technical analysis, multiple trading strategies, comprehensive backtesting, risk management, and paper trading simulation—all built from scratch in R.

✨ Key Features

• 📈 Real-time & Historical Data - CryptoCompare API integration
• 🔬 Technical Analysis - 10+ indicators (RSI, MACD, MA, Bollinger Bands)
• 🎯 Multiple Strategies - MA Crossover, RSI Mean Reversion, customizable templates
• 🧪 Professional Backtesting - Transaction costs, performance metrics, equity curves
• 🛡️ Risk Management - Position sizing (Fixed, Kelly Criterion, ATR), stop-loss/take-profit
• 💰 Paper Trading - Test strategies with simulated capital
• 📊 Visualization - Publication-quality charts (equity curves, drawdowns, indicators)

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🎯 Performance Highlights

Strategy: RSI Mean Reversion
Period: 90 days (Oct 2025 - Jan 2026)
═══════════════════════════════════════
Initial Capital:    $10,000
Final Value:        $10,432
Total Return:       +4.32%
Win Rate:           25%
Sharpe Ratio:       -1.67
Max Drawdown:       -0.14%

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🏗️ Architecture

crypto-trading-bot/
├── config/                  # Configuration files
├── data/                    # Data storage
│   ├── raw/                # Raw market data
│   └── processed/          # Processed data with indicators
├── src/
│   ├── data_acquisition/   # API integration & data fetching
│   ├── indicators/         # Technical indicators
│   ├── strategies/         # Trading strategies
│   ├── backtesting/        # Backtesting engine
│   ├── risk_management/    # Position sizing & risk tools
│   ├── trading/            # Paper trading simulator
│   └── visualization/      # Chart generation
├── results/
│   ├── backtest_results/   # Backtest outputs
│   └── plots/              # Generated charts
├── logs/                    # Application logs
└── tests/                   # Unit tests

Total Lines of Code: ~4,200
Total Files: 18
Development Time: 5 weeks

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🚀 Quick Start

Prerequisites

• R (version 4.5+)
• RStudio (recommended)

Installation

1. Clone the repository

git clone https://github.com/SNMiguel/R-Cryptocurrency-Trading-Bot.git
cd R-Cryptocurrency-Trading-Bot

2. Install dependencies

source("setup.R")

3. Configure settings

# Edit config/config.R to customize:
# - Cryptocurrencies to track
# - Risk parameters
# - Initial capital
# - Trading mode (PAPER/LIVE)

Run Your First Backtest

# Load the system
source("demo_priority2.R")

# This will:
# 1. Download 90 days of BTC data
# 2. Calculate technical indicators
# 3. Run MA Crossover strategy
# 4. Run RSI Mean Reversion strategy
# 5. Compare results
# 6. Save backtest reports

Paper Trading Example

source("demo_priority3.R")

# This will:
# 1. Set up paper trading portfolio
# 2. Run strategies with stop-loss/take-profit
# 3. Generate risk reports
# 4. Create professional charts

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📚 Documentation

Technical Indicators

Indicator	Description	Parameters
SMA/EMA	Moving Averages	Periods: 10, 20, 50, 200
RSI	Relative Strength Index	Period: 14, Oversold: 30, Overbought: 70
MACD	Moving Average Convergence Divergence	Fast: 12, Slow: 26, Signal: 9
Bollinger Bands	Volatility bands	Period: 20, Std Dev: 2
Volume	Volume analysis	MA Period: 20

Trading Strategies

1. Moving Average Crossover

strategy <- create_ma_crossover_strategy(
  fast_period = 10,
  slow_period = 20,
  ma_type = "SMA"
)

Logic: Buy when fast MA crosses above slow MA, sell when it crosses below.

2. RSI Mean Reversion

strategy <- create_rsi_strategy(
  rsi_period = 14,
  oversold = 30,
  overbought = 70
)

Logic: Buy when RSI < 30 (oversold), sell when RSI > 70 (overbought).

3. Custom Strategy

# Create your own strategy using the base template
my_strategy <- create_base_strategy(
  name = "My Strategy",
  parameters = list(...)
)

# Define signal generation
generate_signals.MyStrategy <- function(strategy, data) {
  # Your logic here
  return(data)
}

Risk Management

# Position sizing
position <- calculate_position_size_fixed(capital = 10000, risk_pct = 0.02)
position <- calculate_position_size_kelly(capital, win_rate, avg_win, avg_loss)

# Stop-loss & Take-profit
stop_loss <- calculate_stop_loss(entry_price = 90000, stop_pct = 0.02)
take_profit <- calculate_take_profit(entry_price = 90000, profit_pct = 0.05)

# Risk-reward ratio
rr_ratio <- calculate_risk_reward_ratio(entry_price, stop_loss, take_profit)

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📊 Example Results

Equity Curve

Drawdown Analysis

Strategy Comparison

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🧪 Backtesting

Run a Backtest

# Get historical data
btc_data <- get_historical_days("BTC", limit = 90)

# Add indicators
btc_data <- add_all_indicators(btc_data)

# Create strategy
strategy <- create_ma_crossover_strategy(10, 20)

# Run backtest with transaction costs
results <- run_backtest(
  strategy = strategy,
  data = btc_data,
  initial_capital = 10000,
  commission = 0.001,    # 0.1%
  slippage = 0.0005      # 0.05%
)

# View results
print_backtest_results(results)

Performance Metrics

The backtesting engine calculates:

• Returns: Total return, return percentage
• Risk Metrics: Sharpe ratio, maximum drawdown, volatility
• Trade Stats: Win rate, profit factor, avg win/loss
• Execution Costs: Commission, slippage

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🛡️ Risk Management Features

Position Sizing Methods

1. Fixed Percentage

   • Risk fixed % of capital per trade
   • Simple and conservative

2. Kelly Criterion

   • Optimal bet sizing based on edge
   • Maximizes long-term growth

3. ATR-Based

   • Volatility-adjusted position sizing
   • Adapts to market conditions

Stop-Loss & Take-Profit

• Automatic stop-loss calculation
• Trailing stop support
• Risk-reward ratio validation
• Portfolio risk monitoring

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📈 Visualization

Generate professional charts:

# Equity curve
plot <- plot_equity_curve(results$equity_curve)
save_plot(plot, "my_equity_curve")

# Price with indicators
plot <- plot_price_with_indicators(data, show_ma = TRUE, show_bb = TRUE)
save_plot(plot, "price_chart")

# RSI indicator
plot <- plot_rsi(data, oversold = 30, overbought = 70)
save_plot(plot, "rsi")

# Trades on chart
plot <- plot_trades(data, trade_history)
save_plot(plot, "trades")

All charts saved as high-resolution PNG files (300 DPI).

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🔧 Configuration

API Settings

# config/config.R
API_BASE_URL <- "https://min-api.cryptocompare.com"
CRYPTOCURRENCIES <- c("BTC", "ETH", "BNB", "SOL", "ADA")
BASE_CURRENCY <- "USD"

Risk Parameters

RISK_MANAGEMENT <- list(
  stop_loss_pct = 0.02,       # 2% stop-loss
  take_profit_pct = 0.05,     # 5% take-profit
  max_daily_loss_pct = 0.05,  # 5% max daily loss
  max_open_positions = 3
)

Position Sizing

POSITION_SIZING <- list(
  initial_capital = 10000,
  max_position_size = 0.10,   # 10% per trade
  min_position_size = 100
)

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🎓 Learning Resources

For Beginners

1. Start with demo_priority2.R - Learn backtesting basics
2. Experiment with indicator parameters
3. Try different cryptocurrencies
4. Compare strategy performance

For Advanced Users

1. Create custom strategies using base_strategy.R
2. Optimize parameters with grid search
3. Implement multi-indicator strategies
4. Add your own risk management rules

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📊 Sample Output

=== BACKTEST RESULTS ===

Strategy: Moving Average Crossover

--- PERFORMANCE METRICS ---
Initial Capital:     $10,000.00
Final Value:         $9,338.02
Total Return:        -$661.98 (-6.62%)

--- TRADING ACTIVITY ---
Total Trades:        3
Completed Trades:    1
Win Rate:            0.00%

--- RISK METRICS ---
Sharpe Ratio:        -1.71
Max Drawdown:        -6.00%
Profit Factor:       0.00

--- TRANSACTION COSTS ---
Total Commission:    $27.36
Total Slippage:      $13.68
Total Costs:         $41.03

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🗺️ Roadmap

✅ Completed (Phase 1-3)

• Real-time & historical data acquisition
• Technical indicators (10+ indicators)
• Multiple trading strategies
• Professional backtesting engine
• Risk management system
• Paper trading simulator
• Visualization tools

🚧 In Progress (Phase 4)

• Live trading integration
• Advanced order types (limit, stop)
• Multi-asset portfolio management
• Machine learning strategies
• Real-time alerts & notifications

🔮 Future

• Web interface (React + FastAPI)
• Mobile app
• Social trading features
• Strategy marketplace
• API for external integrations

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🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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⚠️ Disclaimer

This software is for educational purposes only.

• Cryptocurrency trading carries significant risk
• Past performance does not guarantee future results
• Always do your own research (DYOR)
• Never invest more than you can afford to lose
• The authors are not responsible for any financial losses

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🙏 Acknowledgments

• CryptoCompare API - Real-time cryptocurrency data
• R Community - Excellent packages (tidyverse, TTR, ggplot2)
• Quantitative Finance - Algorithmic trading research

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📧 Contact

Chawana Smith

• GitHub: @SNMiguel
• LinkedIn: MigzTech LinkedIn
• Email: shemamiguel2023@gmail.com

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🌟 Support

If you find this project helpful, please consider:

• ⭐ Starring the repository
• 🐛 Reporting bugs
• 💡 Suggesting new features
• 📢 Sharing with others

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Built with ❤️ using R

⭐ Star this repo if you found it useful! ⭐

Last Updated: January 2026 Version: 0.1.0 (Data Acquisition Phase)