Cycles - Advanced Trading Strategy Backtesting Framework

A sophisticated Python framework for backtesting cryptocurrency trading strategies with multi-timeframe analysis, strategy combination, and advanced signal processing.

Features

• Multi-Strategy Architecture: Combine multiple trading strategies with configurable weights and rules
• Multi-Timeframe Analysis: Strategies can operate on different timeframes (1min, 5min, 15min, 1h, etc.)
• Advanced Strategies:
  • Default Strategy: Meta-trend analysis using multiple Supertrend indicators
  • BBRS Strategy: Bollinger Bands + RSI with market regime detection
• Flexible Signal Combination: Weighted consensus, majority voting, any/all combinations
• Precise Stop-Loss: 1-minute precision for accurate risk management
• Comprehensive Backtesting: Detailed performance metrics and trade analysis
• Data Visualization: Interactive charts and performance plots

Quick Start

Prerequisites

• Python 3.8+
• uv package manager (recommended)

Installation

# Clone the repository
git clone <repository-url>
cd Cycles

# Install dependencies with uv
uv sync

# Or install with pip
pip install -r requirements.txt

Running Backtests

Use the uv run command to execute backtests with different configurations:

# Run default strategy on 5-minute timeframe
uv run .\main.py .\configs\config_default_5min.json

# Run default strategy on 15-minute timeframe
uv run .\main.py .\configs\config_default.json

# Run BBRS strategy with market regime detection
uv run .\main.py .\configs\config_bbrs.json

# Run combined strategies
uv run .\main.py .\configs\config_combined.json

Configuration Examples

Default Strategy (5-minute timeframe)

uv run .\main.py .\configs\config_default_5min.json

BBRS Strategy with Multi-timeframe Analysis

uv run .\main.py .\configs\config_bbrs_multi_timeframe.json

Combined Strategies with Weighted Consensus

uv run .\main.py .\configs\config_combined.json

Configuration

Strategies are configured using JSON files in the configs/ directory:

{
    "start_date": "2024-01-01",
    "stop_date": "2024-01-31",
    "initial_usd": 10000,
    "timeframes": ["15min"],
    "stop_loss_pcts": [0.03, 0.05],
    "strategies": [
        {
            "name": "default",
            "weight": 1.0,
            "params": {
                "timeframe": "15min"
            }
        }
    ],
    "combination_rules": {
        "entry": "any",
        "exit": "any",
        "min_confidence": 0.5
    }
}

Available Strategies

1. Default Strategy: Meta-trend analysis using Supertrend indicators
2. BBRS Strategy: Bollinger Bands + RSI with market regime detection

Combination Rules

• Entry: any, all, majority, weighted_consensus
• Exit: any, all, priority (prioritizes stop-loss signals)

Project Structure

Cycles/
├── configs/                 # Configuration files
├── cycles/                  # Core framework
│   ├── strategies/         # Strategy implementation
│   │   ├── base.py        # Base strategy classes
│   │   ├── default_strategy.py
│   │   ├── bbrs_strategy.py
│   │   └── manager.py     # Strategy manager
│   ├── Analysis/          # Technical analysis
│   ├── utils/             # Utilities
│   └── charts.py          # Visualization
├── docs/                   # Documentation
├── data/                   # Market data
├── results/               # Backtest results
└── main.py               # Main entry point

Documentation

Detailed documentation is available in the docs/ directory:

• Strategy Manager - Multi-strategy orchestration and signal combination
• Strategies - Individual strategy implementations and usage
• Timeframe System - Advanced timeframe management and multi-timeframe strategies
• Analysis - Technical analysis components
• Storage Utils - Data storage and retrieval
• System Utils - System utilities

Examples

Single Strategy Backtest

# Test default strategy on different timeframes
uv run .\main.py .\configs\config_default.json      # 15min
uv run .\main.py .\configs\config_default_5min.json # 5min

Multi-Strategy Backtest

# Combine multiple strategies with different weights
uv run .\main.py .\configs\config_combined.json

Custom Configuration

Create your own configuration file and run:

uv run .\main.py .\configs\your_config.json

Output

Backtests generate:

• CSV Results: Detailed performance metrics per timeframe/strategy
• Trade Log: Individual trade records with entry/exit details
• Performance Charts: Visual analysis of strategy performance (in debug mode)
• Log Files: Detailed execution logs

License

[Add your license information here]

Contributing

[Add contributing guidelines here]