EdgeLab CLI

Algorithmic Trading Strategy Analysis Platform

EdgeLab CLI is a Python package for developing, testing, and analyzing algorithmic trading strategies using server-side backtesting and machine learning optimization.

🚀 Quick Start

# Install
pip install edgelab

# Sign up
edgelab auth signup

# Login
edgelab auth login

# Create workspace
edgelab init my-strategies

# Analyze strategy
cd my-strategies
edgelab analyze strategies/simple_rsi.py SPY 2023-01-01 2023-12-31

✨ Features

• 🔬 Server-Side Analysis - No local compute needed, runs on EdgeLab Cloud
• 📊 4 Analysis Engines - Backtest, Walk-Forward, Monte Carlo, Stress Testing
• 🤖 ML Optimization - Automatic parameter tuning using XGBoost
• 📈 Multiple Symbols - Test strategies across multiple stocks simultaneously
• 🎨 Rich Terminal UI - Beautiful formatted output with progress bars
• 🔐 Secure - JWT authentication, encrypted API communication

📦 Installation

pip install edgelab

Requirements:

• Python 3.11+
• Internet connection (for API access)

🎯 Usage

Authentication

# Create account
edgelab auth signup

# Login
edgelab auth login

# Check status
edgelab auth whoami

# Logout
edgelab auth logout

Workspace

# Initialize workspace with example strategies
edgelab init my-strategies

# Creates:
# my-strategies/
#   ├── strategies/
#   │   ├── simple_rsi.py
#   │   ├── ema_crossover.py
#   │   └── orb_breakout.py
#   └── results/

Strategy Analysis

# Single symbol
edgelab analyze strategies/my_rsi.py SPY 2023-01-01 2023-12-31

# Multiple symbols
edgelab analyze strategies/my_rsi.py SPY,TSLA,NVDA 2023-01-01 2023-12-31

# With ML optimization
edgelab analyze --ml strategies/my_ml_rsi.py SPY,QQQ,AAPL 2023-01-01 2023-12-31

# Different resolution
edgelab analyze --resolution 15m strategies/my_rsi.py SPY 2023-01-01 2023-12-31

Results Management

# List all analysis runs
edgelab results list

# Show detailed results
edgelab results show <workflow-id>

Strategy Management

# List all your strategies
edgelab strategies list

# Show strategy details
edgelab strategies show my_rsi v1

📝 Writing Strategies

from edgelab.core import Strategy, Bar, SignalType, Indicators

class MyRSI(Strategy):
    @property
    def name(self) -> str:
        return "my_rsi"

    @property
    def version(self) -> str:
        return "v1"

    def on_bar(self, bar: Bar) -> SignalType | None:
        rsi = Indicators.rsi(bar.close, period=14)

        if rsi < 30:
            return SignalType.LONG
        elif rsi > 70:
            return SignalType.SHORT
        else:
            return None

    def stop_loss(self, entry_price: float) -> float:
        return entry_price * 0.98  # 2% stop

    def take_profit(self, entry_price: float) -> float:
        return entry_price * 1.05  # 5% profit

🤖 ML Optimization

from edgelab.core import Strategy, Bar, SignalType, Indicators
from edgelab.ml import ml_optimizable, param_range

@ml_optimizable
class MyMLStrategy(Strategy):
    rsi_period = param_range(10, 20, default=14, step=2)
    oversold = param_range(20, 40, default=30, step=5)

    # ... rest of strategy

📚 Documentation

Full documentation: https://docs.edgelab.com

🆘 Support

• GitHub Issues: https://github.com/yourusername/edgelab-cli/issues
• Email: support@edgelab.com

📄 License

MIT License - see LICENSE file for details