🔬 MT5 Edge Extractor ML

A comprehensive algorithmic trading edge detection system that analyzes historical market data to discover statistically significant trading edges. Uses multiple detection methods including statistical analysis, machine learning, and pattern recognition with rigorous validation through bootstrap, walk-forward, and Monte Carlo methods.

⚠️ Disclaimer: This system is for BACKTESTING & RESEARCH ONLY - no live trades are executed.

🎯 Key Features

Feature	Description
📊 7 Edge Types	Time-based, Trend/Momentum, Mean Reversion, Volatility, Market Structure, Pairs, ML
📈 50+ Features	Momentum, Trend, Volatility, Volume, Candle Patterns
🧪 Rigorous Validation	Bootstrap, Walk-Forward, Monte Carlo, Statistical Tests
📉 Statistical Testing	T-tests, Chi-square, ANOVA, Effect Sizes, Multiple Testing Correction
🤖 Machine Learning	Random Forest, Gradient Boosting, Feature Importance Analysis
📑 Comprehensive Reports	JSON, HTML, Excel, Interactive Visualizations
🔌 MT5 Integration	Direct connection to MetaTrader 5 for live data

🏗️ System Architecture

┌─────────────────────────────────────────────────────────────────────────────┐
│                        EDGE EXTRACTOR ML SYSTEM                            │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌─────────────────┐      ┌──────────────────┐      ┌──────────────────┐   │
│  │   MT5 Terminal  │ ───► │   Data Loader   │ ───► │  Preprocessor   │   │
│  │  (Live Data)    │      │  (OHLCV Fetch)  │      │ (Clean/Norm)    │   │
│  └─────────────────┘      └──────────────────┘      └────────┬─────────┘   │
│                                                               │              │
│                                                               ▼              │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │                         FEATURE ENGINEERING                         │   │
│  ├─────────────────────────────────────────────────────────────────────┤   │
│  │  📈 Momentum    │  📊 Trend    │  🌊 Volatility   │  📉 Volume     │   │
│  │  RSI, MACD     │ EMA/SMA/ADX  │ ATR, BB, Keltner  │ OBV, VWAP      │   │
│  │  Stochastic    │ Ichimoku     │ Asian Range      │ Volume SMA     │   │
│  │  Williams %R   │              │                  │                │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
│                               │                                             │
│                               ▼                                             │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │                        EDGE DETECTION LAYER                          │   │
│  ├─────────────────────────────────────────────────────────────────────┤   │
│  │                                                                         │
│  │  ⏰ Time-Based    │  📈 Trend/Momentum   │  🔄 Mean Reversion        │
│  │  Hour/Day/Season │ EMA Crossover/Breakout│ Z-Score/Oscillator        │
│  │                  │                      │                           │
│  │  🌊 Volatility   │  🏗️ Market Structure  │  🔗 Pairs/Correlation    │
│  │  BB Squeeze      │ S/R, BOS, Order Blocks│ Correlation/Cointegration │
│  │                  │                      │                           │
│  │  🤖 Machine Learning                                            │   │
│  │  Random Forest, Gradient Boosting, Feature Importance             │   │
│  │                                                                  │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
│                               │                                             │
│                               ▼                                             │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │                      STATISTICAL VALIDATION                          │   │
│  ├─────────────────────────────────────────────────────────────────────┤   │
│  │                                                                         │
│  │  📊 Bootstrap     │  🚶 Walk-Forward   │  🎲 Monte Carlo           │
│  │  10,000 samples   │  2yr train/6mo test│  1,000 simulations        │
│  │                  │  Rolling window    │  Confidence intervals     │
│  │                                                                         │
│  │  🧪 Statistical Tests                                                │   │
│  │  T-test, Chi-square, ANOVA, Kruskal-Wallis                          │   │
│  │  Effect Size (Cohen's d), Multiple Testing Correction (Bonferroni)  │   │
│  │                                                                         │
│  └─────────────────────────────────────────────────────────────────────┘   │
│                               │                                             │
│                               ▼                                             │
│  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐          │
│  │   📑 JSON   │ │   🌐 HTML   │ │   📊 Excel  │ │   📈 Plots  │          │
│  │   Report    │ │   Report    │ │   Export    │ │   Charts    │          │
│  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘          │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

📊 Data Flow Pipeline

┌─────────────────────────────────────────────────────────────────────────────┐
│                              DATA FLOW                                      │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  MT5 Terminal ─────────────────────────────────────────────► OHLCV Data   │
│       │                                                                   │
│       ▼                                                                   │
│  ┌────────────────────────────────────────────────────────────────────┐   │
│  │                      DATA LOADER                                    │   │
│  │  • Fetch OHLCV from MT5                                            │   │
│  │  • Handle multiple timeframes (M1-MN1)                            │   │
│  │  • Cache data for fast re-runs                                    │   │
│  │  • Simulated mode for testing without MT5                         │   │
│  └────────────────────────────────────────────────────────────────────┘   │
│       │                                                                   │
│       ▼                                                                   │
│  ┌────────────────────────────────────────────────────────────────────┐   │
│  │                    PREPROCESSOR                                     │   │
│  │  • Handle missing data (ffill, bfill, interpolation)              │   │
│  │  • Remove outliers (Z-score > 3)                                  │   │
│  │  • Normalize/scale features                                        │   │
│  │  • Calculate returns and log returns                              │   │
│  └────────────────────────────────────────────────────────────────────┘   │
│       │                                                                   │
│       ▼                                                                   │
│  ┌────────────────────────────────────────────────────────────────────┐   │
│  │                    FEATURE ENGINEERING                              │   │
│  │                                                                         │
│  │  ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐   │   │
│  │  │  Momentum   │ │    Trend     │ │ Volatility  │ │   Volume    │   │   │
│  │  │             │ │             │ │             │ │             │   │   │
│  │  │ RSI (7,14,21)│ │ EMA (12,26) │ │ ATR (14)    │ │ Volume SMA  │   │   │
│  │  │ MACD        │ │ SMA (20,50) │ │ BB (20,2)  │ │ OBV         │   │   │
│  │  │ Stochastic  │ │ ADX (14)    │ │ Keltner    │ │ VWAP        │   │   │
│  │  │ Williams %R │ │ Ichimoku    │ │ Asian Range│ │ Volume ROC  │   │   │
│  │  │ CCI         │ │             │ │ Gap        │ │             │   │   │
│  │  └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘   │   │
│  │                                                                         │
│  │  ┌─────────────┐ ┌─────────────────────────────────────────────┐   │   │
│  │  │  Candle     │ │              Return Features                  │   │   │
│  │  │  Patterns   │ │                                               │   │   │
│  │  │ Doji        │ │ Returns (1,5,10,20,60 periods)               │   │   │
│  │  │ Hammer      │ │ Log Returns                                  │   │   │
│  │  │ Engulfing   │ │ Rolling Returns                              │   │   │
│  │  │ Harami      │ │                                              │   │   │
│  │  └─────────────┘ └─────────────────────────────────────────────┘   │   │
│  │                                                                         │
│  └────────────────────────────────────────────────────────────────────┘   │
│       │                                                                   │
│       ▼                                                                   │
│  ┌────────────────────────────────────────────────────────────────────┐   │
│  │                       EDGE DETECTION                                 │   │
│  │  For each edge type:                                               │   │
│  │  1. Generate signals based on edge conditions                      │   │
│  │  2. Calculate returns for each signal                              │   │
│  │  3. Aggregate returns                                              │   │
│  │  4. Calculate metrics (Sharpe, Win Rate, Drawdown)                │   │
│  └────────────────────────────────────────────────────────────────────┘   │
│       │                                                                   │
│       ▼                                                                   │
│  ┌────────────────────────────────────────────────────────────────────┐   │
│  │                    STATISTICAL VALIDATION                          │   │
│  │                                                                         │
│  │  1. T-test: Is mean return significantly different from zero?     │   │
│  │  2. Bootstrap: Confidence intervals on metrics                     │   │
│  │  3. Walk-Forward: Does edge work on unseen data?                  │   │
│  │  4. Monte Carlo: Robustness under random variations                │   │
│  │                                                                         │
│  └────────────────────────────────────────────────────────────────────┘   │
│       │                                                                   │
│       ▼                                                                   │
│  ┌────────────────────────────────────────────────────────────────────┐   │
│  │                    RESULTS REPORTING                                │   │
│  │  • JSON: Machine-readable results                                  │   │
│  │  • HTML: Interactive dashboard                                      │   │
│  │  • Excel: Detailed metrics                                         │   │
│  │  • Plots: Visual analysis                                          │   │
│  └────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

🎯 Edge Types

1. ⏰ Time-Based Edges (`edges/time_based.py`)

Edge Name	Description	Signal Condition
Hour of Day	Trading edge by hour	Buy/sell at specific hours
Day of Week	Day-of-week effect	Certain days outperform
Month of Year	Seasonal patterns	Monthly returns vary
Quarter End	Quarter boundary effects	Position around quarters
Session Effects	Asian/London/NY sessions	Session-specific biases

2. 📈 Trend/Momentum Edges (`edges/trend_momentum.py`)

Edge Name	Description	Signal Condition
EMA Crossover	Fast/Slow EMA crossing	Fast EMA crosses above/below slow
SMA Breakout	Price breaks above/below SMA	Close > MA + threshold
Momentum Burst	Strong recent momentum	Return > X% over N periods
ADX Trend Strength	Strong trend indicator	ADX > threshold
MACD Crossover	MACD signal line cross	MACD crosses signal

3. 🔄 Mean Reversion Edges (`edges/mean_reversion.py`)

Edge Name	Description	Signal Condition
Z-Score Reversion	Price reverts to mean	Z-score > threshold, expect reversal
RSI Extremes	RSI overbought/oversold	RSI < 30 or > 70
Bollinger Bounce	Price bounces off BB	Touch lower/upper band
Gap Fill	Gaps get filled	Open gap from previous close
ATR Reversal	Extreme ATR expansion	ATR > N * average

4. 🌊 Volatility Edges (`edges/volatility_edges.py`)

Edge Name	Description	Signal Condition
BB Squeeze	Low volatility expansion	BB width < threshold, expect move
Volatility Breakout	High volatility expansion	ATR > N * average
Volatility Regime	Regime-based trading	Different strategies per regime
NR4/NR7	Narrow range patterns	4-bar/7-bar low volatility

5. 🏗️ Market Structure Edges (`edges/market_structure.py`)

Edge Name	Description	Signal Condition
Support/Resistance	Bounce off S/R	Price approaches S/R level
Break of Structure (BOS)	Trend continuation	Price breaks swing point
Order Block	Institutional order zones	Price enters order block area
Fair Value Gap (FVG)	Gap in fair value	Price fills FVG
Consecutive Candles	N consecutive up/down	Reversal probability

6. 🔗 Pairs/Correlation Edges (`edges/pairs.py`)

Edge Name	Description	Signal Condition
Correlation Edge	Correlated pairs	Spread deviates from correlation
Cointegration	Long-term equilibrium	Spread deviates from mean
Spread Trading	Pairs trading	Long one, short other

7. 🤖 Machine Learning Edges (`edges/machine_learning.py`)

Edge Name	Description	Signal Condition
Random Forest	Ensemble tree model	ML prediction > threshold
Gradient Boosting	Sequential boosting	GB prediction signal
Feature Importance	Key feature analysis	Trade on top features

📈 Feature Engineering (50+ Features)

Momentum Features (`features/momentum.py`)

Feature	Description	Periods
RSI	Relative Strength Index	7, 14, 21
MACD	Moving Average Convergence Divergence	12, 26, 9
Stochastic	Stochastic Oscillator	%K, %D
Williams %R	Williams Percent Range	14
CCI	Commodity Channel Index	14
ROC	Rate of Change	10

Trend Features (`features/trend.py`)

Feature	Description	Periods
EMA	Exponential Moving Average	12, 26, 50, 200
SMA	Simple Moving Average	20, 50, 100, 200
ADX	Average Directional Index	14
DI+/DI-	Directional Indicators	14
Ichimoku	Ichimoku Cloud	Default

Volatility Features (`features/volatility_features.py`)

Feature	Description	Periods
ATR	Average True Range	14
Bollinger Bands	BB Upper/Lower/Mid	20, 2
Keltner Channel	KC Upper/Lower	20, 2
Asian Range	High-Low of Asian session	-
Gap	Overnight gap calculation	-
Historical Vol	Rolling volatility	20

Volume Features (`features/volume.py`)

Feature	Description	Periods
Volume SMA	Simple Moving Average	10, 20, 50
OBV	On-Balance Volume	-
VWAP	Volume Weighted Average Price	-
Volume ROC	Rate of Change	10
Relative Volume	vs average volume	-

Candle Patterns (`features/candle_patterns.py`)

Pattern	Description
Doji	Indecision candle
Hammer/Hanging Man	Reversal signal
Engulfing	Bullish/Bearish reversal
Harami	Inside candle reversal
Morning/Evening Star	Three-candle reversal
Pin Bar	Wick-heavy reversal
Marubozu	Full-bodied candle

🧪 Statistical Validation

Test Methods (`validation/`)

Method	Module	Description	Purpose
T-Test	`statistical_tests.py`	One-sample t-test	Is mean return > 0?
Mann-Whitney U	`statistical_tests.py`	Non-parametric test	Non-normal distributions
Chi-Square	`statistical_tests.py`	Categorical test	Pattern frequency
ANOVA	`statistical_tests.py`	Multiple group comparison	Different conditions
Bootstrap	`bootstrap.py`	Resampling (10,000 samples)	Confidence intervals
Walk-Forward	`walk_forward.py`	Rolling train/test	Out-of-sample validation
Monte Carlo	N/A	1,000 simulations	Robustness testing

Multiple Testing Correction

Method	Description
Bonferroni	Conservative correction
Benjamini-Hochberg	FDR control
Holm-Bonferroni	Step-down method

Effect Size Metrics

Metric	Description	Threshold
Cohen's d	Standardized mean diff	0.2 small, 0.5 medium, 0.8 large
Pearson's r	Correlation coefficient	0.1 small, 0.3 medium, 0.5 large

📊 Edge Metrics

Metric	Description	Formula
Sharpe Ratio	Risk-adjusted return	(Return - RiskFree) / StdDev
Win Rate	% profitable trades	Wins / Total Trades
Profit Factor	Gross profit / loss	Gross Profit / Gross Loss
Max Drawdown	Largest peak-to-trough	(Peak - Trough) / Peak
CAGR	Compound Annual Growth	(End/Start)^(1/n) - 1
Sortino	Downside Sharpe	(Return - Target) / DownsideStd
Calmar	Return / Max DD	CAGR / Max Drawdown
p-value	Statistical significance	From t-test

💰 Transaction Cost Model

Cost	Default	Description
Spread	1.5 pips	Default spread
Commission	$7.00/lot/side	Per lot commission
Slippage	0.5 pips	Normal slippage
News Slippage	5.0 pips	During news events
Spread Multiplier	3x	During high-impact news
Swap Long	0.0	Overnight financing long
Swap Short	0.0	Overnight financing short

📔 Reporting

Output Formats (`reporting/`)

Format	Module	Description
JSON	`engine`	Machine-readable results
HTML	`html_report.py`	Interactive dashboard
Excel	N/A	Detailed metrics export
Plots	`visualizer.py`	Matplotlib/Plotly charts

Report Contents

Executive summary with top edges
Detailed edge-by-edge analysis
Statistical test results
Walk-forward validation results
Bootstrap confidence intervals
Monte Carlo simulation results

🚀 Installation

Prerequisites

Requirement	Version
🐍 Python	3.10+
🖥️ OS	Windows/Linux/MacOS
📊 MT5	Optional (for live data)

Setup

# Clone repository
git clone https://github.com/mahmoud20138/Edge-Extractor-ML.git
cd Edge-Extractor-ML

# Create virtual environment
python -m venv venv

# Activate (Windows)
venv\Scripts\activate

# Activate (Linux/Mac)
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Requirements

pandas>=2.0.0
numpy>=1.24.0
scipy>=1.10.0
scikit-learn>=1.3.0
matplotlib>=3.7.0
seaborn>=0.12.0
plotly>=5.18.0
openpyxl>=3.1.0
MetaTrader5>=5.0.45  # Optional
jinja2>=3.1.0

📖 Usage

Basic Usage

# Full analysis (2 years of data)
python main.py

# Quick test (1 year)
python main.py --quick

# Custom symbol and timeframe
python main.py --symbol GBPUSD --years 3

# Demo with simulated data
python demo.py

# Comprehensive demo
python comprehensive_demo.py

# Multi-symbol analysis
python multi_analysis.py

Python API

from config import Config, TimeFrame
from engine.backtest_engine import BacktestEngine

# Create configuration
config = Config()
config.edges.min_p_value = 0.05
config.validation.bootstrap_samples = 1000

# Create engine
engine = BacktestEngine(config)

# Run analysis
results = engine.run_full_analysis(
    symbol='EURUSD',
    timeframe=TimeFrame.H1,
    years=2
)

# Get significant edges
significant = [r for r in results['edge_results'].values() 
                if r.is_significant]
print(f"Found {len(significant)} significant edges")

# Generate report
engine.generate_report('my_report.json')

Configuration Options

# MT5 Connection
config.mt5.path = "C:\\Program Files\\MetaTrader 5\\terminal64.exe"
config.mt5.login = 12345678
config.mt5.password = "your_password"
config.mt5.server = "YourBroker-Server"

# Data Settings
config.data.default_timeframe = TimeFrame.H1
config.data.lookback_years = 5
config.data.default_symbol = "EURUSD"

# Edge Detection
config.edges.min_p_value = 0.05
config.edges.min_sample_size = 100
config.edges.zscore_thresholds = [1.0, 1.5, 2.0, 2.5]
config.edges.rsi_oversold = 30.0
config.edges.rsi_overbought = 70.0

# Validation
config.validation.bootstrap_samples = 10000
config.validation.wfo_training_years = 2
config.validation.wfo_test_months = 6
config.validation.monte_carlo_runs = 1000

# Transaction Costs
config.costs.default_spread = 1.5
config.costs.commission_per_lot = 7.0
config.costs.default_slippage = 0.5

📁 Project Structure

Edge-Extractor-ML/
├── main.py                          # 🎯 Entry point
├── config.py                       # ⚙️ Configuration (all settings)
├── requirements.txt                # 📦 Dependencies
├── LICENSE                         # 📜 MIT License
├── CONTRIBUTING.md                 # 🤝 Contributing guidelines
├── demo.py                         # 🚀 Quick demo script
├── comprehensive_demo.py           # 📊 Full demo
├── multi_analysis.py              # 📈 Multi-symbol analysis
│
├── .github/
│   ├── ISSUE_TEMPLATE/
│   │   ├── bug_report.md
│   │   └── feature_request.md
│   └── PULL_REQUEST_TEMPLATE.md
│
├── engine/                         # 🧠 Core engine
│   └── backtest_engine.py         # Main orchestration
│
├── edges/                         # 🎯 Edge detection
│   ├── __init__.py
│   ├── time_based.py              # ⏰ Hour/Day/Seasonal effects
│   ├── trend_momentum.py          # 📈 Trend following edges
│   ├── mean_reversion.py          # 🔄 Mean reversion edges
│   ├── volatility_edges.py       # 🌊 Volatility-based edges
│   ├── market_structure.py       # 🏗️ S/R, BOS, OB, FVG
│   ├── pairs.py                  # 🔗 Correlation/Cointegration
│   └── machine_learning.py       # 🤖 ML-based edges
│
├── features/                      # 📈 Feature engineering
│   ├── __init__.py
│   ├── momentum.py               # RSI, MACD, Stochastic
│   ├── trend.py                  # EMA, SMA, ADX, Ichimoku
│   ├── volatility_features.py   # ATR, Bollinger, Keltner
│   ├── volume.py                 # Volume SMA, OBV, VWAP
│   └── candle_patterns.py       # Doji, Engulfing, etc.
│
├── data/                         # 📊 Data handling
│   ├── data_loader.py            # MT5 data fetching
│   ├── preprocessing.py          # Clean, normalize, transform
│   └── mt5_connector.py          # MT5 connection wrapper
│
├── validation/                   # 🧪 Statistical validation
│   ├── statistical_tests.py     # T-test, Chi-square, ANOVA
│   ├── walk_forward.py          # Walk-forward validation
│   └── bootstrap.py             # Bootstrap confidence intervals
│
├── metrics/                      # 📉 Performance metrics
│   └── __init__.py              # Sharpe, Drawdown, etc.
│
├── reporting/                    # 📑 Report generation
│   ├── visualizer.py            # Plotly/Matplotlib charts
│   └── html_report.py           # HTML dashboard
│
└── validation/                   # Additional validation
    └── ...

🎯 Decision Framework

The system provides a decision checklist for evaluating edges:

┌─────────────────────────────────────────────────────────────────────────────┐
│                         EDGE DECISION FRAMEWORK                              │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │  1. STATISTICAL SIGNIFICANCE                                         │   │
│  │     p-value < 0.05?                                                  │   │
│  └────────────────────────────┬────────────────────────────────────────┘   │
│                               │                                              │
│                               ▼                                              │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │  2. SAMPLE SIZE                                                      │   │
│  │     Is sample size > 100?                                           │   │
│  └────────────────────────────┬────────────────────────────────────────┘   │
│                               │                                              │
│                               ▼                                              │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │  3. TRANSACTION COSTS                                                │   │
│  │     After spreads/slippage, still +EV?                             │   │
│  └────────────────────────────┬────────────────────────────────────────┘   │
│                               │                                              │
│                               ▼                                              │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │  4. MULTIPLE INSTRUMENTS                                             │   │
│  │     Does it work on other symbols?                                 │   │
│  └────────────────────────────┬────────────────────────────────────────┘   │
│                               │                                              │
│                               ▼                                              │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │  5. MULTIPLE TIMEFRAMES                                              │   │
│  │     Does it work on other timeframes?                              │   │
│  └────────────────────────────┬────────────────────────────────────────┘   │
│                               │                                              │
│                               ▼                                              │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │  6. WALK-FORWARD VALIDATION                                          │   │
│  │     Is it stable across different time periods?                    │   │
│  └────────────────────────────┬────────────────────────────────────────┘   │
│                               │                                              │
│                               ▼                                              │
│  ┌─────────────────────────────────────────────────────────────────────┐   │
│  │                     ALL YES? → YOU HAVE A REAL EDGE                  │   │
│  │                     → Paper Trade → Small Live → Scale Up            │   │
│  └─────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

📊 Sample Output

======================================================================
  MT5 TRADING EDGE EXTRACTOR - BACKTESTING SYSTEM
  For Research Purposes Only - No Live Trading
======================================================================

Initializing Edge Extractor...
Loading data (simulated mode - install MetaTrader5 for live data)...

----------------------------------------------------------------------
  DETECTED EDGES SUMMARY
----------------------------------------------------------------------

Total edges tested: 127
Significant edges: 14

By type:
  - time_based: 3
  - trend_momentum: 4
  - mean_reversion: 3
  - volatility: 2
  - market_structure: 2

----------------------------------------------------------------------
  TOP 10 SIGNIFICANT EDGES (by Sharpe Ratio)
----------------------------------------------------------------------

Edge Name                                Type            Sharpe   Win%
----------------------------------------------------------------------
RSI_14_oversold_bounce                   mean_reversion    1.85  62.3%
London_session_breakout                  time_based        1.52  58.7%
EMA_12_26_cross                          trend_momentum    1.41  55.2%
BB_lower_bounce                          mean_reversion    1.38  61.1%
...

🤝 Contributing

Contributions are welcome! Please read CONTRIBUTING.md for guidelines.

Ways to Contribute

🐛 Report bugs via GitHub Issues
✨ Suggest features or improvements
📝 Improve documentation
🔧 Submit pull requests

📜 License

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

⚠️ Disclaimer

⚠️ WARNING: This system is for educational and research purposes only.

BACKTESTING ONLY - no live trading
Past performance does not guarantee future results
Always paper trade before live trading
The authors assume no liability for trading losses

🙏 Acknowledgments

MetaTrader 5 Python API
SciPy for statistical testing
Scikit-learn for ML
Plotly for visualizations
Seaborn for charting

Made with 🔬 for algorithmic traders

Name		Name	Last commit message	Last commit date
Latest commit History 3 Commits
.github		.github
data		data
edges		edges
engine		engine
features		features
metrics		metrics
reporting		reporting
validation		validation
.gitignore		.gitignore
CONTRIBUTING.md		CONTRIBUTING.md
LICENSE		LICENSE
README.md		README.md
__init__.py		__init__.py
comprehensive_demo.py		comprehensive_demo.py
comprehensive_edge_report.json		comprehensive_edge_report.json
config.py		config.py
demo.py		demo.py
edge_analysis_report.json		edge_analysis_report.json
main.py		main.py
multi_analysis.py		multi_analysis.py
requirements.txt		requirements.txt

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🔬 MT5 Edge Extractor ML

🎯 Key Features

🏗️ System Architecture

📊 Data Flow Pipeline

🎯 Edge Types

1. ⏰ Time-Based Edges (edges/time_based.py)

2. 📈 Trend/Momentum Edges (edges/trend_momentum.py)

3. 🔄 Mean Reversion Edges (edges/mean_reversion.py)

4. 🌊 Volatility Edges (edges/volatility_edges.py)

5. 🏗️ Market Structure Edges (edges/market_structure.py)

6. 🔗 Pairs/Correlation Edges (edges/pairs.py)

7. 🤖 Machine Learning Edges (edges/machine_learning.py)

📈 Feature Engineering (50+ Features)

Momentum Features (features/momentum.py)

Trend Features (features/trend.py)

Volatility Features (features/volatility_features.py)

Volume Features (features/volume.py)

Candle Patterns (features/candle_patterns.py)

🧪 Statistical Validation

Test Methods (validation/)

Multiple Testing Correction

Effect Size Metrics

📊 Edge Metrics

💰 Transaction Cost Model

📔 Reporting

Output Formats (reporting/)

Report Contents

🚀 Installation

Prerequisites

Setup

Requirements

📖 Usage

Basic Usage

Python API

Configuration Options

📁 Project Structure

🎯 Decision Framework

📊 Sample Output

🤝 Contributing

Ways to Contribute

📜 License

⚠️ Disclaimer

🙏 Acknowledgments

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1. ⏰ Time-Based Edges (`edges/time_based.py`)

2. 📈 Trend/Momentum Edges (`edges/trend_momentum.py`)

3. 🔄 Mean Reversion Edges (`edges/mean_reversion.py`)

4. 🌊 Volatility Edges (`edges/volatility_edges.py`)

5. 🏗️ Market Structure Edges (`edges/market_structure.py`)

6. 🔗 Pairs/Correlation Edges (`edges/pairs.py`)

7. 🤖 Machine Learning Edges (`edges/machine_learning.py`)

Momentum Features (`features/momentum.py`)

Trend Features (`features/trend.py`)

Volatility Features (`features/volatility_features.py`)

Volume Features (`features/volume.py`)

Candle Patterns (`features/candle_patterns.py`)

Test Methods (`validation/`)

Output Formats (`reporting/`)

Packages