EURUSD Sentiment Trading Algorithm

A sophisticated algorithmic trading system that combines sentiment analysis with technical indicators to trade the EURUSD currency pair. This algorithm was developed for a private institutional client and demonstrates advanced quantitative trading techniques using alternative data sources.

Algorithm Overview

The system integrates multiple data streams to make trading decisions:

• Sentiment Analysis: External sentiment scores from proprietary data sources
• Technical Indicators: Wave-based technical analysis (wave1b indicator)
• Time-to-Live (TTL): Configurable sentiment persistence modeling
• Risk Management: Dynamic position sizing and exit strategies

Key Features

Multi-Modal Strategy

• Dual Signal Confirmation: Requires both positive sentiment and favorable technical conditions
• Configurable Thresholds: Optimizable parameters for entry and exit conditions
• Directional Flexibility: Supports both long and short trading strategies

Advanced Data Integration

• External Data Sources: Real-time sentiment data via Dropbox integration
• Custom Data Processing: Pandas-based data pipeline with datetime parsing
• Multiple Data Sets: Support for various sentiment models (decaying, repeated, TTL-based)

Realistic Backtesting

• Custom Fee Model: Implements realistic trading costs
• Slippage Modeling: Accounts for market impact and execution costs
• Multiple Time Periods: In-sample and out-of-sample testing capabilities

Technical Implementation

Core Algorithm Logic

# Entry Conditions
if wave1b > entry_threshold_long and sentiment_score > sentiment_long:
    SetHoldings("EURUSD", 1.0)  # Long position

elif wave1b < entry_threshold_short and sentiment_score < sentiment_short:
    SetHoldings("EURUSD", -1.0)  # Short position

# Exit Conditions with TTL Support
if ttl_enabled:
    sentiment_value = sentiment_score_with_ttl
else:
    sentiment_value = current_sentiment_score

Data Processing Pipeline

• CSV Data Ingestion: Automated download and parsing of external data
• Time Series Alignment: Precise datetime matching between price and sentiment data
• Missing Data Handling: Robust error handling for data quality issues

Performance Monitoring

• Visual Trade Tracking: Real-time scatter plots for trade visualization
• Parameter Optimization: Built-in parameter sweep capabilities
• Backtest Validation: Multiple time period testing for robustness

Strategy Components

Sentiment Analysis Integration

• Proprietary Sentiment Scores: External sentiment data with configurable persistence
• Multi-Timeframe Analysis: Support for different sentiment decay models
• Signal Filtering: Sentiment threshold-based trade filtering

Technical Analysis

• Wave-based Indicators: Custom wave1b technical indicator
• Trend Following: Directional bias based on technical momentum
• Mean Reversion: Counter-trend exit strategies

Risk Management

• Position Sizing: Full capital allocation with risk-adjusted sizing
• Stop Loss Logic: Technical indicator-based exit rules
• Market Impact Modeling: Realistic slippage and fee calculations

Configuration Options

The algorithm offers extensive customization:

• Backtest Periods: In-sample, out-of-sample, or combined testing
• Data Sources: Multiple sentiment data sets with different characteristics
• Strategy Parameters: Configurable entry/exit thresholds
• Risk Settings: Adjustable position sizing and stop-loss levels

Performance Characteristics

• Market: EURUSD (Major Currency Pair)
• Frequency: Minute-level execution with real-time data processing
• Strategy Type: Multi-factor quantitative with sentiment integration
• Backtesting Period: September-October 2022 with multiple validation windows

Technology Stack

• QuantConnect Platform: Cloud-based algorithmic trading infrastructure
• Python: Core algorithm development with pandas and numpy
• External Data Integration: Real-time sentiment data via API
• Custom Models: Proprietary fee and slippage modeling
• Visualization: Real-time trade plotting and performance monitoring