Trading Automation Enhanced

Advanced cryptocurrency trading bot with intelligent momentum detection and multi-timeframe analysis.

📁 Project Structure

Main Trading System

File/Directory	Description
Trading-Automation/	Core trading automation system
├─ trading_automation.py	Main trading bot with enhanced momentum detection
├─ update_symbols.py	Symbol fetcher and market data updater
├─ secrets.py	API keys and configuration
symbols.yaml	USDC trading pairs and market data
trading.service	Linux systemd service configuration
symbols_update.service	Service for automatic symbol updates

Research & Analysis Tools

File	Description
Research/	Independent momentum analysis tools
├─ momentum_analyzer.py	Console-based momentum analysis (no deps)
├─ momentum_visualizer.py	Graphical momentum analysis (matplotlib)
├─ requirements_viz.txt	Dependencies for visualization tools
└─ README.md	Detailed research tools documentation

🚀 Quick Start

Prerequisites

pip install python-telegram-bot==22.2

Setup Process

1. Generate symbols.yaml - Update trading pairs and market data
2. Configure API keys - Set up Binance and Telegram credentials in secrets.py
3. Run the bot - Start the main trading automation
4. Keep symbol updater running - Maintain fresh market data

Research Tools

cd Research
python momentum_analyzer.py              # Console analysis (no dependencies)
pip install -r requirements_viz.txt      # Install visualization dependencies
python momentum_visualizer.py            # Graphical analysis

🧠 Enhanced Momentum Detection System

The bot now features an intelligent, adaptive momentum detection system that automatically adjusts to market conditions:

Key Features

• ATR-Based Dynamic Thresholds: Automatically adapts sensitivity based on market volatility
• Multi-Timeframe Confirmation: Requires signals across 1m, 5m, and 15m timeframes
• Multi-Indicator Fusion: Combines price momentum, RSI, volume spikes, and MA crossovers
• Symbol-Specific Calibration: Different thresholds for BTC, ETH, and altcoins
• Safety Bounds: Floor and cap values ensure stability across all market conditions

Volume Thresholds by Asset

• BTC: 660 USDC minimum volume
• ETH: 500,000 USDC minimum volume
• Other pairs: 300,000 USDC minimum volume

Adaptive Threshold Parameters

Timeframe	ATR Multiplier	Floor Threshold	Cap Threshold
1m	0.55	0.070%	1.5%
5m	0.70	0.100%	1.8%
15m	0.85	0.150%	2.0%

📈 Trading Strategy

Entry Conditions

The bot considers buying only when ALL of the following conditions are met:

• Multi-timeframe momentum: Positive momentum detected across 1m, 5m, and 15m timeframes
• Volume confirmation: Trading volume exceeds minimum thresholds for the asset class
• Multi-indicator confluence: Price momentum plus at least one additional indicator (RSI, volume spike, or MA crossover)
• Dynamic threshold validation: Price changes exceed ATR-based adaptive thresholds

Exit Strategy

The bot employs a sophisticated exit system:

• Trailing stop loss: Automatically adjusts stop levels as price moves favorably
• Maximum hold time: Prevents indefinite position holding in sideways markets
• Rapid reversal protection: Quick exit on sharp price reversals to minimize losses

Risk Management

• Position sizing: Calculated based on account balance and risk parameters
• Asset-specific limits: Different volume requirements for major vs minor pairs
• Volatility adaptation: Threshold sensitivity adjusts to current market conditions

📊 Performance Characteristics

The system is designed to:

• Catch strong trends: Enter during clear upward momentum phases
• Avoid false signals: Multi-timeframe confirmation reduces noise
• Adapt to volatility: Dynamic thresholds prevent over-trading in choppy markets
• Exit efficiently: Trailing stops and time limits protect profits

⚙️ Configuration & Tuning

Micro-Scalping Optimization

For high-frequency trading scenarios:

Threshold Multipliers (k):

• Start with k≈0.55–0.65 for 1m
• Use k≈0.7–0.8 for 5m
• Set k≈0.85–1.0 for 15m

Sensitivity Adjustments:

• Too many entries: Increase k values or floor thresholds
• Missing clean moves: Decrease k slightly on 1m/5m timeframes
• Volatile spikes: Keep cap thresholds ≤ 2% to avoid waiting indefinitely

Exit Optimization:

• Consider reducing MIN_PROFIT to ~0.8% for faster fills
• Tighten TRAIL_STOP to ~0.5% if spreads are tight
• Monitor slippage on rapid reversals

🔬 Research & Analysis

The Research/ directory contains powerful tools for analyzing and understanding the momentum detection system:

Console Analyzer

python Research/momentum_analyzer.py

• Zero external dependencies
• Reproduces exact trading bot logic
• Multi-symbol comparison
• Threshold adaptation demonstration

Graphical Visualizer

pip install -r Research/requirements_viz.txt
python Research/momentum_visualizer.py

• Interactive matplotlib charts
• Visual threshold analysis
• Market condition scenarios
• Performance comparison plots

Benefits

• No manual tuning required: System automatically adapts to market conditions
• Consistent performance: Works effectively across different asset classes
• Noise filtering: Multi-timeframe approach reduces false signals
• Market adaptive: Real-time threshold calculation based on recent volatility

🛠️ System Requirements

Dependencies

pip install python-telegram-bot==22.2

Optional (for research tools)

pip install -r Research/requirements_viz.txt

System Services (Linux)

• trading.service: Main bot service configuration
• symbols_update.service: Automatic market data updates

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⚠️ Risk Warning: This is a high-frequency trading bot designed for experienced traders. Always test thoroughly with small amounts before deploying significant capital. Past performance does not guarantee future results.