# Architecture Components Documentation

## Overview

The Cycles framework has been refactored into a modular architecture with specialized components for different aspects of the backtesting workflow. This document provides detailed information about the new architectural components and how to use them.

## 🏗️ Component Architecture

```
New Components:
├── 🎯 BacktestApplication      # Main workflow orchestration
├── ⚙️ ConfigManager           # Configuration management
├── 📊 ResultsProcessor        # Results processing & metrics
├── 🚀 BacktestRunner          # Backtest execution logic
└── 📦 TimeframeTask           # Individual task encapsulation
```

---

## ⚙️ ConfigManager

**Purpose**: Centralized configuration loading, validation, and access

### Features
- **Automatic Validation**: Validates configuration structure and required fields
- **Type-Safe Access**: Property-based access to configuration values
- **Smart Defaults**: Automatic fallbacks (e.g., current date for stop_date)
- **Reusable Configs**: Generate configurations for different components

### Basic Usage

```python
from cycles.utils import ConfigManager

# Initialize with config file
config_manager = ConfigManager("configs/config_bbrs.json")

# Access configuration properties
start_date = config_manager.start_date
initial_usd = config_manager.initial_usd
timeframes = config_manager.timeframes

# Get specialized configurations
strategy_config = config_manager.get_strategy_manager_config()
task_config = config_manager.get_timeframe_task_config("15min")
```

### Configuration Properties

| Property | Type | Description |
|----------|------|-------------|
| `start_date` | `str` | Backtest start date |
| `stop_date` | `str` | Backtest end date (auto-defaults to current) |
| `initial_usd` | `float` | Initial portfolio value |
| `timeframes` | `List[str]` | List of timeframes to test |
| `strategies_config` | `List[Dict]` | Strategy configurations |
| `combination_rules` | `Dict` | Signal combination rules |

### Configuration Methods

```python
# Get strategy manager configuration
strategy_config = config_manager.get_strategy_manager_config()
# Returns: {"strategies": [...], "combination_rules": {...}}

# Get timeframe-specific task configuration
task_config = config_manager.get_timeframe_task_config("15min")
# Returns: {"initial_usd": 10000, "strategies": [...], "combination_rules": {...}}
```

### Error Handling

```python
try:
    config_manager = ConfigManager("invalid_config.json")
except FileNotFoundError as e:
    print(f"Config file not found: {e}")
except ValueError as e:
    print(f"Invalid configuration: {e}")
```

---

## 📊 ResultsProcessor & BacktestMetrics

**Purpose**: Unified processing, aggregation, and analysis of backtest results

### BacktestMetrics (Static Utilities)

```python
from cycles.utils import BacktestMetrics

# Calculate trade-level metrics
trades = [{"profit_pct": 0.05}, {"profit_pct": -0.02}]
trade_metrics = BacktestMetrics.calculate_trade_metrics(trades)
# Returns: {n_trades, win_rate, max_drawdown, avg_trade, ...}

# Calculate portfolio-level metrics
portfolio_metrics = BacktestMetrics.calculate_portfolio_metrics(trades, 10000)
# Returns: {initial_usd, final_usd, total_fees_usd, total_return}
```

### ResultsProcessor (Instance-Based)

```python
from cycles.utils import ResultsProcessor

processor = ResultsProcessor()

# Process single backtest results
summary_row, trade_rows = processor.process_backtest_results(
    results=backtest_results,
    timeframe="15min", 
    config=task_config,
    strategy_summary=strategy_summary
)

# Aggregate multiple results
aggregated = processor.aggregate_results(all_result_rows)

# Create metadata for output files
metadata_lines = processor.create_metadata_lines(config_manager, data_1min)
```

### Available Metrics

#### Trade Metrics
- `n_trades`: Total number of trades
- `n_winning_trades`: Number of profitable trades
- `win_rate`: Percentage of winning trades
- `total_profit`: Sum of all profitable trades
- `total_loss`: Sum of all losing trades
- `avg_trade`: Average trade return
- `profit_ratio`: Ratio of total profit to total loss
- `max_drawdown`: Maximum portfolio drawdown

#### Portfolio Metrics
- `initial_usd`: Starting portfolio value
- `final_usd`: Ending portfolio value
- `total_fees_usd`: Total trading fees
- `total_return`: Overall portfolio return percentage

---

## 🚀 BacktestRunner & TimeframeTask

**Purpose**: Modular backtest execution with clean separation of concerns

### BacktestRunner

```python
from cycles.utils import BacktestRunner

runner = BacktestRunner()

# Run single timeframe backtest
summary_row, trade_rows = runner.run_single_timeframe(
    data_1min=market_data,
    timeframe="15min",
    config=task_config,
    debug=False
)
```

#### BacktestRunner Methods

| Method | Purpose | Returns |
|--------|---------|---------|
| `run_single_timeframe()` | Execute backtest for one timeframe | `(summary_row, trade_rows)` |
| `_create_strategy_manager()` | Create strategy manager from config | `StrategyManager` |
| `_setup_backtester()` | Setup backtester with data and strategies | `Backtest` |
| `_execute_backtest()` | Run the backtest | `Dict[results]` |
| `_handle_debug_plotting()` | Handle debug mode plotting | `None` |

### TimeframeTask

**Purpose**: Encapsulates a single timeframe backtest task for easy execution and parallelization

```python
from cycles.utils import TimeframeTask, create_timeframe_tasks

# Create individual task
task = TimeframeTask(
    timeframe="15min",
    data_1min=market_data,
    config=task_config
)

# Execute task
summary_row, trade_rows = task.execute(debug=False)

# Create multiple tasks from configuration
tasks = create_timeframe_tasks(
    timeframes=["5min", "15min", "1h"],
    data_1min=market_data,
    config_manager=config_manager
)

# Execute all tasks
for task in tasks:
    results = task.execute()
```

### Parallel Execution

```python
import concurrent.futures
from cycles.utils import create_timeframe_tasks

# Create tasks
tasks = create_timeframe_tasks(timeframes, data_1min, config_manager)

# Execute in parallel
with concurrent.futures.ProcessPoolExecutor(max_workers=4) as executor:
    futures = {executor.submit(task.execute, False): task for task in tasks}
    
    for future in concurrent.futures.as_completed(futures):
        task = futures[future]
        try:
            summary_row, trade_rows = future.result()
            print(f"Completed: {task.timeframe}")
        except Exception as e:
            print(f"Failed: {task.timeframe} - {e}")
```

---

## 🎯 BacktestApplication

**Purpose**: Main application orchestration class that coordinates the entire workflow

### Complete Workflow

```python
from cycles.application import BacktestApplication

# Simple usage
app = BacktestApplication("configs/config_combined.json")
app.run(debug=False)
```

### Workflow Steps

```python
class BacktestApplication:
    def run(self, debug=False):
        # 1. Load data
        data_1min = self.load_data()
        
        # 2. Create tasks
        tasks = self.create_tasks(data_1min)
        
        # 3. Execute tasks (parallel or debug mode)
        results_rows, trade_rows = self.execute_tasks(tasks, debug)
        
        # 4. Save results
        self.save_results(results_rows, trade_rows, data_1min)
```

### Custom Application

```python
from cycles.application import BacktestApplication

class CustomBacktestApplication(BacktestApplication):
    def execute_tasks(self, tasks, debug=False):
        # Custom execution logic
        # Maybe with custom progress tracking
        results = []
        for i, task in enumerate(tasks):
            print(f"Processing {i+1}/{len(tasks)}: {task.timeframe}")
            result = task.execute(debug)
            results.append(result)
        return results
    
    def save_results(self, results_rows, trade_rows, data_1min):
        # Custom result saving
        super().save_results(results_rows, trade_rows, data_1min)
        # Additional custom processing
        self.send_email_notification(results_rows)

# Usage
app = CustomBacktestApplication("config.json")
app.run()
```

---

## 🔧 Component Integration Examples

### Simple Integration

```python
# All-in-one approach
from cycles.application import BacktestApplication

app = BacktestApplication("config.json")
app.run(debug=False)
```

### Modular Integration

```python
# Step-by-step approach using individual components
from cycles.utils import ConfigManager, BacktestRunner, ResultsProcessor

# 1. Configuration
config_manager = ConfigManager("config.json")

# 2. Data loading (using existing storage utilities)
from cycles.utils import Storage
storage = Storage()
data_1min = storage.load_data('btcusd_1-min_data.csv', 
                              config_manager.start_date, 
                              config_manager.stop_date)

# 3. Execution
runner = BacktestRunner()
all_results = []

for timeframe in config_manager.timeframes:
    task_config = config_manager.get_timeframe_task_config(timeframe)
    summary_row, trade_rows = runner.run_single_timeframe(
        data_1min, timeframe, task_config
    )
    all_results.extend(trade_rows)

# 4. Processing
processor = ResultsProcessor()
final_results = processor.aggregate_results(all_results)
```

### Custom Workflow

```python
# Custom workflow for specific needs
from cycles.utils import ConfigManager, BacktestRunner

config_manager = ConfigManager("config.json")
runner = BacktestRunner()

# Custom data preparation
custom_data = prepare_custom_data(config_manager.start_date)

# Custom configuration modification
for strategy in config_manager.strategies_config:
    if strategy['name'] == 'default':
        strategy['params']['stop_loss_pct'] = 0.02  # Custom stop loss

# Custom execution with monitoring
for timeframe in config_manager.timeframes:
    print(f"Starting backtest for {timeframe}")
    config = config_manager.get_timeframe_task_config(timeframe)
    
    try:
        results = runner.run_single_timeframe(custom_data, timeframe, config)
        process_custom_results(results, timeframe)
    except Exception as e:
        handle_custom_error(e, timeframe)
```

---

## 🎨 Extension Points

### Custom Configuration Manager

```python
from cycles.utils import ConfigManager

class CustomConfigManager(ConfigManager):
    def _validate_config(self):
        super()._validate_config()
        # Additional custom validation
        if self.config.get('custom_field') is None:
            raise ValueError("Custom field is required")
    
    @property
    def custom_setting(self):
        return self.config.get('custom_field', 'default_value')
```

### Custom Results Processor

```python
from cycles.utils import ResultsProcessor

class CustomResultsProcessor(ResultsProcessor):
    def process_backtest_results(self, results, timeframe, config, strategy_summary):
        # Call parent method
        summary_row, trade_rows = super().process_backtest_results(
            results, timeframe, config, strategy_summary
        )
        
        # Add custom metrics
        summary_row['custom_metric'] = self.calculate_custom_metric(trade_rows)
        
        return summary_row, trade_rows
    
    def calculate_custom_metric(self, trades):
        # Custom metric calculation
        return sum(t['profit_pct'] for t in trades if t['profit_pct'] > 0.05)
```

---

## 🚀 Migration Guide

### From Old main.py

**Before (Old main.py)**:
```python
# Scattered configuration
config_file = args.config or "configs/config_default.json" 
with open(config_file, 'r') as f:
    config = json.load(f)

# Complex processing function
results = process_timeframe_data(data_1min, timeframe, config, debug)

# Manual result aggregation
all_results = []
for task in tasks:
    results = process(task, debug)
    all_results.extend(results)
```

**After (New Architecture)**:
```python
# Clean application approach
from cycles.application import BacktestApplication
app = BacktestApplication(config_file)
app.run(debug=debug)

# Or modular approach
from cycles.utils import ConfigManager, BacktestRunner
config_manager = ConfigManager(config_file)
runner = BacktestRunner()
results = runner.run_single_timeframe(data, timeframe, config)
```

### Benefits of Migration

1. **🧹 Cleaner Code**: Reduced complexity and better organization
2. **🔄 Reusability**: Components can be used independently
3. **🧪 Testability**: Each component can be tested in isolation
4. **🛠️ Extensibility**: Easy to extend and customize components
5. **📈 Maintainability**: Clear separation of concerns

---

**Note**: All new components maintain full backward compatibility with existing configuration files and output formats.