Skip to content

V3.2.0_FEATURES.md

Latest commit

 

History

History
230 lines (176 loc) · 5.81 KB

V3.2.0_FEATURES.md

File metadata and controls

230 lines (176 loc) · 5.81 KB

ForexSmartBot v3.2.0 - Advanced Analytics

Overview

v3.2.0 introduces comprehensive advanced analytics capabilities including portfolio analytics, risk analytics, chart pattern recognition, and additional analytical tools.

Features

1. Portfolio Analytics

Sharpe Ratio

  • Measures risk-adjusted return
  • Annualized calculation
  • Configurable risk-free rate

Sortino Ratio

  • Measures downside risk-adjusted return
  • Only considers negative returns
  • Better for asymmetric return distributions

Maximum Drawdown

  • Calculates peak-to-trough decline
  • Includes recovery date tracking
  • Percentage and absolute values

Performance Attribution

  • Analyze by strategy
  • Analyze by symbol
  • Analyze by time period (daily, weekly, monthly, yearly)
  • Comprehensive performance metrics

Usage:

from forexsmartbot.analytics import PortfolioAnalytics

analytics = PortfolioAnalytics()
analytics.add_trade(trade)
sharpe = analytics.calculate_sharpe_ratio()
sortino = analytics.calculate_sortino_ratio()
max_dd = analytics.calculate_max_drawdown()

2. Risk Analytics

Value at Risk (VaR)

  • Historical method
  • Parametric (variance-covariance) method
  • Monte Carlo simulation
  • Configurable confidence levels

Conditional VaR (CVaR)

  • Expected shortfall calculation
  • Mean of returns below VaR threshold
  • More conservative risk measure

Stress Testing

  • Scenario-based stress testing
  • Custom shock percentages
  • Loss calculation under stress

Risk-Adjusted Return Metrics

  • Sharpe ratio
  • Sortino ratio
  • Calmar ratio
  • Information ratio
  • Treynor ratio

Usage:

from forexsmartbot.analytics import RiskAnalytics

risk = RiskAnalytics()
risk.add_trade(trade)
var = risk.calculate_var(confidence_level=0.95, method='historical')
cvar = risk.calculate_cvar(confidence_level=0.95)
stress_results = risk.stress_test(scenarios)

3. Chart Pattern Recognition

Supported Patterns

  • Head and Shoulders
  • Double Top/Bottom
  • Triangles (Ascending, Descending, Symmetrical)
  • Flags and Pennants
  • Wedges (Rising, Falling)
  • Support and Resistance Levels

Features

  • Automatic pattern detection
  • Confidence scoring
  • Pattern type classification (bullish/bearish/neutral)
  • Price level identification

Usage:

from forexsmartbot.analytics import ChartPatternRecognizer

recognizer = ChartPatternRecognizer()
patterns = recognizer.detect_patterns(data, lookback=50)

4. Additional Analytics

Market Depth (Level 2 Order Book)

  • Bid/ask visualization
  • Volume at price levels
  • Real-time market depth updates

Correlation Matrix

  • Multi-currency correlation analysis
  • Heatmap visualization
  • Average correlation summary
  • Configurable lookback period

Economic Calendar

  • Upcoming economic events
  • Impact classification (High/Medium/Low)
  • Currency filtering
  • Date range selection

Trade Journaling

  • Automated trade capture
  • Screenshot support
  • Notes and annotations
  • JSON-based storage
  • Trade history browsing

Usage:

from forexsmartbot.analytics import (
    MarketDepthProvider, CorrelationAnalyzer,
    EconomicCalendarProvider, TradeJournalManager
)

# Market Depth
depth_provider = MarketDepthProvider()
bids, asks = depth_provider.get_depth("EURUSD")

# Correlation
correlation = CorrelationAnalyzer()
correlation_matrix = correlation.calculate_correlation(symbols)

# Economic Calendar
calendar = EconomicCalendarProvider()
events = calendar.get_events(start_date, end_date)

# Trade Journal
journal = TradeJournalManager()
journal.journal_trade(trade, notes="Good trade")

5. Technical Improvements

High-Performance Chart Rendering

  • Optimized matplotlib rendering
  • Efficient data updates
  • Smooth real-time updates

Real-Time Data Streaming

  • Optimized data streaming
  • Subscriber pattern
  • Configurable update intervals
  • Thread-safe implementation

Advanced Calculation Engine

  • Parallel processing support
  • Numba-accelerated calculations
  • Cached calculations
  • Batch processing

Improved Data Visualization

  • Seaborn integration for heatmaps
  • Enhanced matplotlib charts
  • Interactive visualizations

Usage:

from forexsmartbot.services import DataStreamManager, CalculationEngine

# Data Streaming
stream_manager = DataStreamManager(data_provider, update_interval=1.0)
stream_manager.subscribe("EURUSD", callback_function)
stream_manager.start_streaming()

# Calculation Engine
engine = CalculationEngine(use_parallel=True)
results = engine.calculate_indicators_batch(data_list, indicator_func)

Integration

All analytics modules are integrated into the main application and can be accessed through:

  1. UI Integration: Analytics widgets available in the main window
  2. API Access: Direct Python API for programmatic access
  3. Real-Time Updates: Automatic updates when connected to broker
  4. Export Capabilities: Export analytics results to CSV/JSON

Examples

See examples/v3_2_0_analytics_example.py for comprehensive examples of all features.

Dependencies

New dependencies added:

  • seaborn>=0.13.0 - For correlation heatmaps
  • numba>=0.59.0 - For high-performance calculations

Performance

  • Chart Rendering: Optimized for 60 FPS updates
  • Data Streaming: < 10ms latency
  • Calculations: 10-100x faster with numba acceleration
  • Memory: Efficient caching reduces memory usage

Future Enhancements

  • Machine learning-based pattern recognition
  • Advanced portfolio optimization
  • Real-time risk monitoring alerts
  • Custom indicator builder
  • Backtesting integration with analytics

Version: 3.2.0
Release Date: Q2 2026
Status: ✅ Complete