702 lines
21 KiB
Markdown
702 lines
21 KiB
Markdown
# Modular Chart Layers System
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The Modular Chart Layers System is a flexible, strategy-driven chart system that supports technical indicator overlays, subplot management, and future bot signal integration. This system replaces basic chart functionality with a modular architecture that adapts to different trading strategies and their specific indicator requirements.
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## Table of Contents
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- [Overview](#overview)
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- [Architecture](#architecture)
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- [Quick Start](#quick-start)
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- [Components](#components)
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- [User Indicator Management](#user-indicator-management)
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- [Configuration System](#configuration-system)
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- [Example Strategies](#example-strategies)
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- [Validation System](#validation-system)
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- [API Reference](#api-reference)
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- [Examples](#examples)
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- [Best Practices](#best-practices)
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## Overview
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### Key Features
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- **Modular Architecture**: Chart layers can be independently tested and composed
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- **User Indicator Management**: Create, edit, and manage custom indicators with JSON persistence
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- **Strategy-Driven Configuration**: JSON-based configurations for different trading strategies
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- **Comprehensive Validation**: 10+ validation rules with detailed error reporting
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- **Example Strategies**: 5 real-world trading strategy templates
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- **Indicator Support**: 26+ professionally configured indicator presets
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- **Extensible Design**: Easy to add new indicators, strategies, and chart types
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### Supported Indicators
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**Trend Indicators:**
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- Simple Moving Average (SMA) - Multiple periods
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- Exponential Moving Average (EMA) - Multiple periods
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- Bollinger Bands - Various configurations
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**Momentum Indicators:**
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- Relative Strength Index (RSI) - Multiple periods
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- MACD - Various speed configurations
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**Volume Indicators:**
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- Volume analysis and confirmation
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## Architecture
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```
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components/charts/
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├── indicator_manager.py # User indicator CRUD operations
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├── indicator_defaults.py # Default indicator templates
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├── config/ # Configuration management
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│ ├── indicator_defs.py # Indicator schemas and validation
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│ ├── defaults.py # Default configurations and presets
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│ ├── strategy_charts.py # Strategy-specific configurations
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│ ├── validation.py # Validation system
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│ ├── example_strategies.py # Real-world strategy examples
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│ └── __init__.py # Package exports
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├── layers/ # Chart layer implementation
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│ ├── base.py # Base layer system
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│ ├── indicators.py # Indicator overlays
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│ ├── subplots.py # Subplot management
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│ └── signals.py # Signal overlays (future)
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├── builder.py # Main chart builder
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└── utils.py # Chart utilities
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dashboard/ # Modular dashboard integration
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├── layouts/market_data.py # Chart layout with controls
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├── callbacks/charts.py # Chart update callbacks
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├── components/
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│ ├── chart_controls.py # Reusable chart configuration panel
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│ └── indicator_modal.py # Indicator management UI
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config/indicators/
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└── user_indicators/ # User-created indicators (JSON files)
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├── sma_abc123.json
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├── ema_def456.json
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└── ...
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```
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## Dashboard Integration
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The chart system is fully integrated with the modular dashboard structure:
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### Modular Components
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- **`dashboard/layouts/market_data.py`** - Chart layout with strategy selection and indicator controls
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- **`dashboard/callbacks/charts.py`** - Chart update callbacks with strategy handling
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- **`dashboard/components/chart_controls.py`** - Reusable chart configuration panel
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- **`dashboard/components/indicator_modal.py`** - Complete indicator management interface
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### Key Features
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- **Strategy Dropdown**: Auto-loads predefined indicator combinations
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- **Real-time Updates**: Charts update immediately with indicator changes
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- **Modular Architecture**: Each component under 300 lines for maintainability
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- **Separated Concerns**: Layouts, callbacks, and components in dedicated modules
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### Usage in Dashboard
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```python
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# From dashboard/layouts/market_data.py
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from components.charts.config import get_available_strategy_names
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from components.charts.indicator_manager import get_indicator_manager
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# Get available strategies for dropdown
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strategy_names = get_available_strategy_names()
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strategy_options = [{'label': name.replace('_', ' ').title(), 'value': name}
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for name in strategy_names]
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# Get user indicators for checklists
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indicator_manager = get_indicator_manager()
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overlay_indicators = indicator_manager.get_indicators_by_type('overlay')
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subplot_indicators = indicator_manager.get_indicators_by_type('subplot')
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```
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For complete dashboard documentation, see [Dashboard Modular Structure (`../dashboard-modular-structure.md`)](../dashboard-modular-structure.md).
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## User Indicator Management
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The system includes a comprehensive user indicator management system that allows creating, editing, and managing custom technical indicators.
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### Features
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- **Interactive UI**: Modal dialog for creating and editing indicators
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- **Real-time Updates**: Charts update immediately when indicators are toggled
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- **JSON Persistence**: Each indicator saved as individual JSON file
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- **Full CRUD Operations**: Create, Read, Update, Delete functionality
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- **Type Validation**: Parameter validation based on indicator type
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- **Custom Styling**: Color, line width, and appearance customization
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### Quick Access
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- **📊 [Complete Indicator Documentation (`indicators.md`)](./indicators.md)** - Comprehensive guide to the indicator system
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- **⚡ [Quick Guide: Adding New Indicators (`adding-new-indicators.md`)](./adding-new-indicators.md)** - Step-by-step checklist for developers
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### Current User Indicators
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| Indicator | Type | Parameters | Display |
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|-----------|------|------------|---------|
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| Simple Moving Average (SMA) | `sma` | period (1-200) | Overlay |
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| Exponential Moving Average (EMA) | `ema` | period (1-200) | Overlay |
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| Bollinger Bands | `bollinger_bands` | period (5-100), std_dev (0.5-5.0) | Overlay |
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| Relative Strength Index (RSI) | `rsi` | period (2-50) | Subplot |
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| MACD | `macd` | fast_period, slow_period, signal_period | Subplot |
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### Usage Example
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```python
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# Get indicator manager
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from components.charts.indicator_manager import get_indicator_manager
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manager = get_indicator_manager()
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# Create new indicator
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indicator = manager.create_indicator(
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name="My SMA 50",
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indicator_type="sma",
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parameters={"period": 50},
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description="50-period Simple Moving Average",
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color="#ff0000"
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)
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# Load and update
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loaded = manager.load_indicator("sma_abc123")
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success = manager.update_indicator("sma_abc123", name="Updated SMA")
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# Get indicators by type
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overlay_indicators = manager.get_indicators_by_type("overlay")
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subplot_indicators = manager.get_indicators_by_type("subplot")
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```
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## Quick Start
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### Basic Usage
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```python
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from components.charts.config import (
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create_ema_crossover_strategy,
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get_strategy_config,
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validate_configuration
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)
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# Get a pre-built strategy
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strategy = create_ema_crossover_strategy()
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config = strategy.config
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# Validate the configuration
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report = validate_configuration(config)
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if report.is_valid:
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print("Configuration is valid!")
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else:
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print(f"Errors: {[str(e) for e in report.errors]}")
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# Use with dashboard
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# chart = create_chart(config, market_data)
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```
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### Custom Strategy Creation
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```python
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from components.charts.config import (
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StrategyChartConfig,
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SubplotConfig,
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ChartStyle,
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TradingStrategy,
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SubplotType
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)
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# Create custom strategy
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config = StrategyChartConfig(
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strategy_name="My Custom Strategy",
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strategy_type=TradingStrategy.DAY_TRADING,
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description="Custom day trading strategy",
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timeframes=["15m", "1h"],
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overlay_indicators=["ema_12", "ema_26", "bb_20_20"],
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subplot_configs=[
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SubplotConfig(
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subplot_type=SubplotType.RSI,
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height_ratio=0.2,
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indicators=["rsi_14"]
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)
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]
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)
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# Validate and use
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is_valid, errors = config.validate()
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```
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## Components
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### 1. Configuration System
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The configuration system provides schema validation, default presets, and strategy management.
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**Key Files:**
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- `indicator_defs.py` - Core schemas and validation
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- `defaults.py` - 26+ indicator presets organized by category
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- `strategy_charts.py` - Complete strategy configurations
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**Features:**
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- Type-safe indicator definitions
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- Parameter validation with ranges
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- Category-based organization (trend, momentum, volatility)
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- Strategy-specific recommendations
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### 2. Validation System
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Comprehensive validation with 10 validation rules:
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1. **Required Fields** - Essential configuration validation
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2. **Height Ratios** - Chart layout validation
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3. **Indicator Existence** - Indicator availability check
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4. **Timeframe Format** - Valid timeframe patterns
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5. **Chart Style** - Color and styling validation
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6. **Subplot Config** - Subplot compatibility check
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7. **Strategy Consistency** - Strategy-timeframe alignment
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8. **Performance Impact** - Resource usage warnings
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9. **Indicator Conflicts** - Redundancy detection
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10. **Resource Usage** - Memory and rendering estimates
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**Usage:**
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```python
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from components.charts.config import validate_configuration
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report = validate_configuration(config)
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print(f"Valid: {report.is_valid}")
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print(f"Errors: {len(report.errors)}")
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print(f"Warnings: {len(report.warnings)}")
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```
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### 3. Example Strategies
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Five professionally configured trading strategies:
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1. **EMA Crossover** (Intermediate, Medium Risk)
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- Classic trend-following with EMA crossovers
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- Best for trending markets, 15m-4h timeframes
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2. **Momentum Breakout** (Advanced, High Risk)
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- Fast indicators for momentum capture
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- Volume confirmation, best for volatile markets
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3. **Mean Reversion** (Intermediate, Medium Risk)
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- Oversold/overbought conditions
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- Multiple RSI periods, best for ranging markets
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4. **Scalping** (Advanced, High Risk)
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- Ultra-fast indicators for 1m-5m trading
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- Tight risk management, high frequency
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5. **Swing Trading** (Beginner, Medium Risk)
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- Medium-term trend following
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- 4h-1d timeframes, suitable for part-time traders
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## Configuration System
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### Indicator Definitions
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Each indicator has a complete schema definition:
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```python
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@dataclass
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class ChartIndicatorConfig:
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indicator_type: IndicatorType
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parameters: Dict[str, Any]
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display_name: str
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color: str
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line_style: LineStyle
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line_width: int
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display_type: DisplayType
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```
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### Strategy Configuration
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Complete strategy definitions include:
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```python
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@dataclass
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class StrategyChartConfig:
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strategy_name: str
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strategy_type: TradingStrategy
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description: str
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timeframes: List[str]
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layout: ChartLayout
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main_chart_height: float
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overlay_indicators: List[str]
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subplot_configs: List[SubplotConfig]
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chart_style: ChartStyle
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```
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### Default Configurations
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26+ indicator presets organized by category:
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- **Trend Indicators**: 13 SMA/EMA presets
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- **Momentum Indicators**: 9 RSI/MACD presets
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- **Volatility Indicators**: 4 Bollinger Bands configurations
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Access via:
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```python
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from components.charts.config import get_all_default_indicators
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indicators = get_all_default_indicators()
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trend_indicators = get_indicators_by_category(IndicatorCategory.TREND)
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```
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## Example Strategies
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### EMA Crossover Strategy
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```python
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from components.charts.config import create_ema_crossover_strategy
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strategy = create_ema_crossover_strategy()
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config = strategy.config
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# Strategy includes:
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# - EMA 12, 26, 50 for trend analysis
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# - RSI 14 for momentum confirmation
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# - MACD for signal confirmation
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# - Bollinger Bands for volatility context
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```
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### Custom Strategy Creation
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```python
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from components.charts.config import create_custom_strategy_config
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config, errors = create_custom_strategy_config(
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strategy_name="My Strategy",
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strategy_type=TradingStrategy.MOMENTUM,
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description="Custom momentum strategy",
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timeframes=["5m", "15m"],
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overlay_indicators=["ema_8", "ema_21"],
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subplot_configs=[{
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"subplot_type": "rsi",
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"height_ratio": 0.2,
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"indicators": ["rsi_7"]
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}]
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)
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```
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## Validation System
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### Comprehensive Validation
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```python
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from components.charts.config import validate_configuration
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# Full validation with detailed reporting
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report = validate_configuration(config)
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# Check results
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if report.is_valid:
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print("✅ Configuration is valid")
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else:
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print("❌ Configuration has errors:")
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for error in report.errors:
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print(f" • {error}")
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# Check warnings
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if report.warnings:
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print("⚠️ Warnings:")
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for warning in report.warnings:
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print(f" • {warning}")
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```
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### Validation Rules Information
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```python
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from components.charts.config import get_validation_rules_info
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rules = get_validation_rules_info()
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for rule, info in rules.items():
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print(f"{info['name']}: {info['description']}")
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```
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## API Reference
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### Core Classes
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#### `StrategyChartConfig`
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Main configuration class for chart strategies.
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**Methods:**
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- `validate()` → `tuple[bool, List[str]]` - Basic validation
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- `validate_comprehensive()` → `ValidationReport` - Detailed validation
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- `get_all_indicators()` → `List[str]` - Get all indicator names
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- `get_indicator_configs()` → `Dict[str, ChartIndicatorConfig]` - Get configurations
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#### `StrategyExample`
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Container for example strategies with metadata.
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**Properties:**
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- `config: StrategyChartConfig` - The strategy configuration
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- `description: str` - Detailed strategy description
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- `difficulty: str` - Beginner/Intermediate/Advanced
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- `risk_level: str` - Low/Medium/High
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- `market_conditions: List[str]` - Suitable market conditions
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### Utility Functions
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#### Configuration Access
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```python
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# Get all example strategies
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get_all_example_strategies() → Dict[str, StrategyExample]
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# Filter by criteria
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get_strategies_by_difficulty("Intermediate") → List[StrategyExample]
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get_strategies_by_risk_level("Medium") → List[StrategyExample]
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get_strategies_by_market_condition("Trending") → List[StrategyExample]
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# Get strategy summary
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get_strategy_summary() → Dict[str, Dict[str, str]]
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```
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#### JSON Export/Import
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```python
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# Export to JSON
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export_strategy_config_to_json(config) → str
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export_example_strategies_to_json() → str
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# Import from JSON
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load_strategy_config_from_json(json_data) → tuple[StrategyChartConfig, List[str]]
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```
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#### Validation
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```python
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# Comprehensive validation
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validate_configuration(config, rules=None, strict=False) → ValidationReport
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# Get validation rules info
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get_validation_rules_info() → Dict[ValidationRule, Dict[str, str]]
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```
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## Examples
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### Example 1: Using Pre-built Strategy
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```python
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from components.charts.config import get_example_strategy
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# Get a specific strategy
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strategy = get_example_strategy("ema_crossover")
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print(f"Strategy: {strategy.config.strategy_name}")
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print(f"Difficulty: {strategy.difficulty}")
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print(f"Risk Level: {strategy.risk_level}")
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print(f"Timeframes: {strategy.config.timeframes}")
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print(f"Indicators: {strategy.config.overlay_indicators}")
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# Validate before use
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is_valid, errors = strategy.config.validate()
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if is_valid:
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# Use in dashboard
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pass
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```
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### Example 2: Creating Custom Configuration
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```python
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from components.charts.config import (
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StrategyChartConfig, SubplotConfig, ChartStyle,
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TradingStrategy, SubplotType, ChartLayout
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)
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# Create custom configuration
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config = StrategyChartConfig(
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strategy_name="Custom Momentum Strategy",
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strategy_type=TradingStrategy.MOMENTUM,
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description="Fast momentum strategy with volume confirmation",
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timeframes=["5m", "15m"],
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layout=ChartLayout.MAIN_WITH_SUBPLOTS,
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main_chart_height=0.65,
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overlay_indicators=["ema_8", "ema_21", "bb_20_25"],
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subplot_configs=[
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SubplotConfig(
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subplot_type=SubplotType.RSI,
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height_ratio=0.15,
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indicators=["rsi_7"],
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title="Fast RSI"
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),
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SubplotConfig(
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subplot_type=SubplotType.VOLUME,
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height_ratio=0.2,
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indicators=[],
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title="Volume Confirmation"
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)
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],
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chart_style=ChartStyle(
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theme="plotly_white",
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candlestick_up_color="#00d4aa",
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candlestick_down_color="#fe6a85"
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)
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)
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# Comprehensive validation
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report = config.validate_comprehensive()
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print(f"Validation: {report.summary()}")
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```
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### Example 3: Filtering Strategies
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```python
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from components.charts.config import (
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get_strategies_by_difficulty,
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get_strategies_by_market_condition
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)
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# Get beginner-friendly strategies
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beginner_strategies = get_strategies_by_difficulty("Beginner")
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print("Beginner Strategies:")
|
||
for strategy in beginner_strategies:
|
||
print(f" • {strategy.config.strategy_name}")
|
||
|
||
# Get strategies for trending markets
|
||
trending_strategies = get_strategies_by_market_condition("Trending")
|
||
print("\nTrending Market Strategies:")
|
||
for strategy in trending_strategies:
|
||
print(f" • {strategy.config.strategy_name}")
|
||
```
|
||
|
||
### Example 4: Validation with Error Handling
|
||
|
||
```python
|
||
from components.charts.config import validate_configuration, ValidationLevel
|
||
|
||
# Validate with comprehensive reporting
|
||
report = validate_configuration(config)
|
||
|
||
# Handle different severity levels
|
||
if report.errors:
|
||
print("🚨 ERRORS (must fix):")
|
||
for error in report.errors:
|
||
print(f" • {error}")
|
||
|
||
if report.warnings:
|
||
print("\n⚠️ WARNINGS (recommended fixes):")
|
||
for warning in report.warnings:
|
||
print(f" • {warning}")
|
||
|
||
if report.info:
|
||
print("\nℹ️ INFO (optimization suggestions):")
|
||
for info in report.info:
|
||
print(f" • {info}")
|
||
|
||
# Check specific validation rules
|
||
height_issues = report.get_issues_by_rule(ValidationRule.HEIGHT_RATIOS)
|
||
if height_issues:
|
||
print(f"\nHeight ratio issues: {len(height_issues)}")
|
||
```
|
||
|
||
## Best Practices
|
||
|
||
### 1. Configuration Design
|
||
|
||
- **Use meaningful names**: Strategy names should be descriptive
|
||
- **Validate early**: Always validate configurations before use
|
||
- **Consider timeframes**: Match timeframes to strategy type
|
||
- **Height ratios**: Ensure total height ≤ 1.0
|
||
|
||
### 2. Indicator Selection
|
||
|
||
- **Avoid redundancy**: Don't use multiple similar indicators
|
||
- **Performance impact**: Limit complex indicators (>3 Bollinger Bands)
|
||
- **Category balance**: Mix trend, momentum, and volume indicators
|
||
- **Timeframe alignment**: Use appropriate indicator periods
|
||
|
||
### 3. Strategy Development
|
||
|
||
- **Start simple**: Begin with proven strategies like EMA crossover
|
||
- **Test thoroughly**: Validate both technically and with market data
|
||
- **Document well**: Include entry/exit rules and market conditions
|
||
- **Consider risk**: Match complexity to experience level
|
||
|
||
### 4. Validation Usage
|
||
|
||
- **Use comprehensive validation**: Get detailed reports with suggestions
|
||
- **Handle warnings**: Address performance and usability warnings
|
||
- **Test edge cases**: Validate with extreme configurations
|
||
- **Monitor updates**: Re-validate when changing configurations
|
||
|
||
### 5. Performance Optimization
|
||
|
||
- **Limit indicators**: Keep total indicators <10 for performance
|
||
- **Monitor memory**: Check resource usage warnings
|
||
- **Optimize rendering**: Consider visual complexity
|
||
- **Cache configurations**: Reuse validated configurations
|
||
|
||
## Error Handling
|
||
|
||
### Common Issues and Solutions
|
||
|
||
1. **"Indicator not found in defaults"**
|
||
```python
|
||
# Check available indicators
|
||
from components.charts.config import get_all_default_indicators
|
||
available = get_all_default_indicators()
|
||
print(list(available.keys()))
|
||
```
|
||
|
||
2. **"Total height exceeds 1.0"**
|
||
```python
|
||
# Adjust height ratios
|
||
config.main_chart_height = 0.7
|
||
for subplot in config.subplot_configs:
|
||
subplot.height_ratio = 0.1
|
||
```
|
||
|
||
3. **"Invalid timeframe format"**
|
||
```python
|
||
# Use standard formats
|
||
config.timeframes = ["1m", "5m", "15m", "1h", "4h", "1d", "1w"]
|
||
```
|
||
|
||
## Testing
|
||
|
||
The system includes comprehensive tests:
|
||
|
||
- **112+ test cases** across all components
|
||
- **Unit tests** for individual components
|
||
- **Integration tests** for system interactions
|
||
- **Validation tests** for error handling
|
||
|
||
Run tests:
|
||
```bash
|
||
uv run pytest tests/test_*_strategies.py -v
|
||
uv run pytest tests/test_validation.py -v
|
||
uv run pytest tests/test_defaults.py -v
|
||
```
|
||
|
||
## Future Enhancements
|
||
|
||
- **✅ Signal Layer Integration**: Bot trade signals and alerts - **IMPLEMENTED** - See [Bot Integration Guide (`bot-integration.md`)](./bot-integration.md)
|
||
- **Custom Indicators**: User-defined technical indicators
|
||
- **Advanced Layouts**: Multi-chart and grid layouts
|
||
- **Real-time Updates**: Live chart updates with indicator toggling
|
||
- **Performance Monitoring**: Advanced resource usage tracking
|
||
|
||
## Bot Integration
|
||
|
||
The chart system now includes comprehensive bot integration capabilities:
|
||
|
||
- **Real-time Signal Visualization**: Live bot signals on charts
|
||
- **Trade Execution Tracking**: P&L and trade entry/exit points
|
||
- **Multi-Bot Support**: Compare strategies across multiple bots
|
||
- **Performance Analytics**: Built-in bot performance metrics
|
||
|
||
📊 **[Complete Bot Integration Guide (`bot-integration.md`)](./bot-integration.md)** - Comprehensive documentation for integrating bot signals with charts
|
||
|
||
## Support
|
||
|
||
For issues, questions, or contributions:
|
||
|
||
1. Check existing configurations in `example_strategies.py`
|
||
2. Review validation rules in `validation.py`
|
||
3. Test with comprehensive validation
|
||
4. Refer to this documentation
|
||
|
||
The modular chart system is designed to be extensible and maintainable, providing a solid foundation for advanced trading chart functionality.
|
||
---
|
||
|
||
*Back to [Modules Documentation](../README.md)* |