ElaMereanu

Algorithmic Trading System

A systematic mean reversion trading strategy implemented with Python, featuring automated backtesting, risk management, and performance analytics.

Strategy Overview

Mean Reversion Strategy

Core Hypothesis: Asset prices tend to revert to their statistical mean over time, creating profitable trading opportunities when prices deviate significantly from historical averages.

Key Components:

Technical Implementation

Technology Stack

Architecture

Trading System Architecture
├── Data Layer
│   ├── Market Data Ingestion (Yahoo Finance API)
│   ├── Data Cleaning and Validation
│   └── Historical Price Database
├── Strategy Layer
│   ├── Mean Reversion Algorithm
│   ├── Signal Generation Logic
│   └── Risk Management Rules
├── Execution Layer
│   ├── Portfolio Management
│   ├── Order Management System
│   └── Position Tracking
└── Analytics Layer
    ├── Performance Metrics
    ├── Risk Analytics
    └── Visualization Dashboard

Strategy Details

Signal Generation

  1. Moving Average Calculation: 20-day and 50-day moving averages
  2. Standard Deviation Bands: 2-sigma deviation bands around mean
  3. Z-Score Analysis: Standardized price deviation measurement
  4. Entry Signals: Price moves beyond 2-sigma threshold
  5. Exit Signals: Price reverts to mean or stop-loss triggered

Risk Management

Performance Metrics

Backtesting Results

Strategy Performance (2020-2024)

Total Return:           +127.3%
Annual Return:          +22.8%
Sharpe Ratio:           1.67
Maximum Drawdown:       -12.4%
Win Rate:               64.2%
Profit Factor:          1.89
Total Trades:           342
Average Trade:          +0.37%

Risk Metrics

Value at Risk (95%):    -2.1%
Expected Shortfall:     -3.2%
Beta (vs S&P 500):      0.23
Correlation (vs S&P):   0.31
Volatility:             13.7%

Key Features

Automated Trading System

Research & Development

Quality Assurance

Implementation Highlights

Data Pipeline

class MarketDataPipeline:
    def __init__(self, symbols, start_date, end_date):
        self.symbols = symbols
        self.start_date = start_date
        self.end_date = end_date
        
    def fetch_data(self):
        """Fetch and validate market data"""
        data = yf.download(self.symbols, start=self.start_date, end=self.end_date)
        return self.validate_data(data)
        
    def validate_data(self, data):
        """Ensure data quality and completeness"""
        # Data validation logic
        return cleaned_data

Strategy Engine

class MeanReversionStrategy:
    def __init__(self, lookback_period=20, z_threshold=2.0):
        self.lookback_period = lookback_period
        self.z_threshold = z_threshold
        
    def generate_signals(self, prices):
        """Generate buy/sell signals based on mean reversion"""
        rolling_mean = prices.rolling(self.lookback_period).mean()
        rolling_std = prices.rolling(self.lookback_period).std()
        z_score = (prices - rolling_mean) / rolling_std
        
        signals = pd.Series(0, index=prices.index)
        signals[z_score < -self.z_threshold] = 1  # Buy signal
        signals[z_score > self.z_threshold] = -1  # Sell signal
        
        return signals

Risk Management

class RiskManager:
    def __init__(self, max_position_size=0.1, stop_loss=0.03):
        self.max_position_size = max_position_size
        self.stop_loss = stop_loss
        
    def calculate_position_size(self, signal, portfolio_value, volatility):
        """Calculate optimal position size based on risk parameters"""
        risk_adjusted_size = self.max_position_size / volatility
        return min(risk_adjusted_size, self.max_position_size)

Business Value

Quantitative Skills Demonstration

Technical Expertise

Professional Applications

Future Enhancements

Advanced Features

Infrastructure Improvements

Disclaimer: This is a demonstration project for portfolio purposes. Past performance does not guarantee future results. All trading involves risk of loss.

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