Alpaca Trader
Advanced algorithmic trading bot built for Alpaca Markets.
Designed for automated strategy execution, multi-layer technical analysis, and research-driven trading experimentation.
Supports configurable strategies, risk management automation, and detailed performance logging.
🚀 Quick Start
Clone and install:
git clone https://github.com/YOUR_REPO/alpaca-trader.git
cd alpaca-trader
pip install -r requirements.txt
Run:
python3 run.py
On first launch, the bot will create:
alpaca_trader/.env
Add your Alpaca API keys:
APCA_API_KEY_ID="your_key"
APCA_API_SECRET_KEY="your_secret"
APCA_API_BASE_URL="https://paper-api.alpaca.markets"
🎯 Features
Core Trading Engine
- Automated signal evaluation loop
- Multi-strategy architecture
- Risk-aware position sizing
- Market regime detection
- Config-driven behavior (no code changes required)
Technical Indicators
- SMA / EMA
- RSI
- MACD
- ADX
- ATR
- Bollinger Bands
- Multi-timeframe signal confirmation
Strategy System
Supports multiple strategy modes:
- Moving Average crossover (default)
- Opening Range + Fair Value Gap (OR/FVG)
- Regime-filtered execution
Risk Management
- ATR-based stop loss
- Trailing stop logic
- Multi-level take profits
- Risk-per-trade sizing
- Max drawdown protection
- Risk/reward validation
- Position hold-time limits
Execution Controls
- Market or limit orders
- Slippage simulation
- Commission modeling
- Cash account compatibility
- T+1 settlement handling
- PDT rolling 5-day window enforcement
Market Filters
- Market regime classification
- Volume filters
- 200 SMA trend filter
- VIX volatility filter
- Candle confirmation
- MACD confirmation layer
Analytics & Logging
Automatically generates:
alpaca_trader/
├── trading.log
├── debug.log
├── trades.csv
├── signals.csv
├── performance.csv
├── indicators.csv
├── session.csv
├── pdt_tracker.csv
🧠 Strategy Overview
Moving Average Strategy
Primary signal generated when:
- Short MA crosses long MA
- Trend filters confirm
- Risk/reward meets threshold
- Market regime supports trade
Optional confirmation:
- MACD alignment
- RSI thresholds
- Volume confirmation
Opening Range + Fair Value Gap Strategy
Designed for intraday momentum:
- Detect opening range window.
- Identify Fair Value Gap structures.
- Validate volume and direction.
- Execute with ATR-based risk controls.
Configurable parameters:
- Opening range duration
- Minimum gap size
- Entry timeframe
- Risk/reward target
- Maximum entry window
⚙️ Configuration
All trading behavior controlled via:
alpaca_trader/config.json
Key sections:
Strategy
STRATEGY_MODE
OR_FVG_ENABLED
OR_FVG_OPENING_RANGE_MINUTES
OR_FVG_MIN_GAP_SIZE
Risk
RISK_PER_TRADE
ATR_STOP_MULTIPLIER
MAX_DRAWDOWN
MIN_RISK_REWARD
Filters
REGIME_DETECTION
USE_200_SMA_FILTER
USE_VIX_FILTER
MULTIFRAME_FILTER
Execution
USE_LIMIT_ORDERS
LIMIT_ORDER_TIMEOUT
SLIPPAGE_PCT
COMMISSION_PCT
🏗 Architecture
alpaca_trader/
├── api.py # Alpaca API interface
├── engine.py # Core trading loop
├── indicators.py # Technical analysis
├── filters.py # Market condition filters
├── risk.py # Risk & position sizing
├── utils.py # Helpers
├── cli.py # CLI interface
├── config.json # Main configuration
🔄 How It Works
- Load configuration and API credentials
- Fetch historical market data
- Calculate indicators
- Evaluate market regime
- Generate trading signals
- Validate risk constraints
- Execute trades via Alpaca API
- Log analytics data
📊 Design Philosophy
- Config-first architecture
- Strategy isolation
- Risk before execution
- Modular extensibility
- Research-friendly logging
⚠️ Important Notes
- Use paper trading first.
- Algorithmic trading involves financial risk.
- No strategy guarantees profit.
🛠 Roadmap (Example)
- Strategy plug-in system
- ML signal scoring
- Portfolio-level risk controls
- Multi-symbol scanning
- Performance dashboard
Disclaimer
This software is provided for educational and research purposes only.
Not financial advice.