Traider

🧠 Overview

Traider is an automated crypto trading bot for Binance, built around a dynamic, risk-managed hybrid strategy. It features a robust PnL tracking system, persistent trade logging, and a powerful analysis toolkit.

• Dynamic Strategy Selection: Switches between breakout and grid trading based on market volatility (ATR).
• Active Position Monitoring: A background service actively monitors placed orders to track the entire lifecycle of a trade.
• Automated PnL Calculation: Automatically calculates and logs the realized Profit & Loss for every completed trade.
• Persistent Trade Logging: Saves a detailed history of all live and testnet trades to live_trades.csv and testnet_trades.csv.
• In-depth Analysis: Comes with a script to analyze your trade logs and generate detailed performance reports.
• Multi-Currency Support: The provided Docker Compose setup allows for running separate, optimized bot instances for multiple trading pairs simultaneously.
• Enhanced Backtesting: Features realistic backtesting with dynamic slippage, latency simulation, and exchange constraints.
• Parameter Optimization: Automated parameter optimization using Optuna to find the most profitable strategy configurations.
• Sentiment Analysis Integration: Deep sentiment integration for more informed trading decisions based on market sentiment.
• Live Sentiment & Safety Gate: Real-time sentiment from CryptoPanic, NewsAPI, RSS, plus Fear & Greed Index to gate trades.

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📦 Features

• Binance API live and testnet trading support
• Persistent CSV Trade Logging
• Automated PnL Calculation & Performance Analysis
• Active Order Monitoring & Position Management
• Dynamic strategy selection using ATR
• Breakout and grid trading strategies
• Advanced Sentiment Analysis for risk management and position sizing
• Realistic Backtesting Engine with dynamic slippage, latency simulation, and exchange constraints
• Parameter Optimization with Optuna, including walk-forward validation
• Historical Data Acquisition for sentiment and price data
• Bollinger Bands integration for enhanced signal generation
• Comprehensive Performance Metrics for strategy evaluation

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⚙️ Installation

1. Clone the repo:

   git clone https://github.com/iyeque/Traider.git
   cd Traider
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Create your .env file:

   cp .env.example .env
   

4. Add your API keys and configure your strategy in the .env file. Set BINANCE_ENV to TESTNET to use the testnet.

Environment variables for live sentiment

• CRYPTOPANIC_API_KEY (optional, recommended)
• NEWSAPI_KEY (optional) If not provided or sources return no data, the bot falls back to RSS feeds and a neutral score.

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💻 Running the Bot

With Docker (Recommended for Multi-Currency)

The docker-compose.yml is pre-configured to run four bot instances, one for each currency (BNB, BTC, ETH, SOL), with individually tuned parameters.

docker-compose up -d

View logs for a specific bot:

docker-compose logs -f traider-bot-btc

Natively

When you run the bot natively, it starts the main trading logic and the background OrderMonitor to track trade executions and PnL.

python main.py

The bot evaluates market safety each cycle using live sentiment and the Fear & Greed Index (see below). Tune thresholds in config.

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📊 Monitoring and Analysis

This project provides several tools to monitor your bot's activity and analyze its performance.

1. Trade Log Analysis (Recommended)

The most powerful analysis tool is the analyze_trades.py script. It reads your CSV trade logs and generates a comprehensive performance report with key metrics like Net Profit, Win Rate, and Profit Factor, along with a PnL distribution chart.

• Live Trades: live_trades.csv
• Testnet Trades: testnet_trades.csv

How to run the analysis:

# To analyze your live trades
py backtest/analyze_trades.py live_trades.csv

# To analyze your testnet trades
py backtest/analyze_trades.py testnet_trades.csv

2. Enhanced Backtesting

The backtesting engine (backtest/backtest.py) now provides more realistic simulations with:

• Dynamic Slippage: Slippage calculated based on trade quantity
• Latency Simulation: Realistic execution delays
• Exchange Constraints: MIN_NOTIONAL and LOT_SIZE checks
• Global Drawdown Control: Stops trading if maximum drawdown is hit
• Trade Count Limit: Limits the number of trades in a backtest
• Sentiment Integration: Uses historical sentiment data
• Comprehensive Metrics: Detailed performance metrics

3. Live Sentiment Integration

Live sentiment is fetched by data_acquisition/fetch_live_sentiment.py (CryptoPanic, NewsAPI, RSS) and the Fear & Greed Index. It is consumed by bot/sentiment_engine.is_market_safe() which updates LiveTradingStats.

• Safety gate: main.py calls is_market_safe(min_sentiment, min_fear_greed) before running strategies.
• Signal context: breakout_strategy pulls the latest value via LiveTradingStats().get_sentiment().
• Configuration: tune SENTIMENT_THRESHOLD_POSITIVE, SENTIMENT_THRESHOLD_NEGATIVE, and FEAR_GREED_THRESHOLD in config.

3. Real-time Dashboard

The dashboard provides a simple, real-time web interface to see high-level stats of a single running bot instance.

How to run it:

# Run this in a separate terminal
py manual_dashboard.py

Then open your browser to http://127.0.0.1:8181.

3. Raw Account Data Check

For a quick, on-demand check of your raw testnet account data (all balances and full trade history from the exchange), use the check_testnet.py script.

py check_testnet.py

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🧪 Automated Testing

Unit tests are provided for core modules to ensure reliability and correctness:

• bot/test_strategy.py: Tests the signal generation logic in strategy.py.

Run all tests before deploying or running the bot to catch bugs early:

python -m unittest discover bot

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📁 Project Structure

traider/
├── bot/
│   ├── strategy.py        # Signal generator & indicators
│   ├── trading.py         # Executes trades with SL/TP
│   ├── grid.py            # Grid ladder logic
│   ├── sentiment_engine.py# Live sentiment & market safety gate
│   ├── position_manager.py# Singleton to manage open positions
│   ├── order_monitor.py   # Background service to track fills and PnL
│   ├── trade_logger.py    # Handles writing trades to CSV
│   ├── news_utils.py      # News data processing
│   ├── rss_utils.py       # RSS feed processing
│   └── ...
├── backtest/
│   ├── backtest.py        # Enhanced backtesting engine
│   ├── optimize_params.py # Parameter optimization with Optuna
│   ├── analyze_trades.py  # Trade analysis script
│   ├── optimization_results/ # Stores optimization results
├── data_acquisition/      # Scripts for fetching market data
│   ├── fetch_historical_data.py # Fetches historical price/sentiment data
│   ├── fetch_live_sentiment.py  # Fetches live sentiment & Fear & Greed Index
├── .env.example           # Example environment variables
├── config.py              # Configuration management
├── live_trades.csv        # Auto-generated log for live trades
├── testnet_trades.csv     # Auto-generated log for testnet trades
├── check_testnet.py       # Utility to check testnet account
├── main.py                # Main bot runner
├── manual_dashboard.py    # Real-time monitoring dashboard
├── CHANGELOG.md           # Detailed change history
└── README.md

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🔐 Security Notes

• Never commit your .env file or API keys.
• Always use IP whitelisting on the Binance API.
• Start with small amounts for live testing or use the Binance testnet.

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🛡️ Disclaimer

This is NOT financial advice. You are fully responsible for your own trades, wins, and losses. Use it at your own risk.