# quantconnect-lean

> Build algorithmic trading strategies with QuantConnect LEAN engine. Use when creating trading algorithms, backtesting strategies, implementing technical indicators, setting up live trading, or using the LEAN CLI. Covers Python algorithm development with support for equities, options, futures, forex, and crypto.

- Author: py77
- Repository: py77/quantio
- Version: 20260125194137
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-06
- Source: https://github.com/py77/quantio
- Web: https://mule.run/skillshub/@@py77/quantio~quantconnect-lean:20260125194137

---

---
name: quantconnect-lean
description: Build algorithmic trading strategies with QuantConnect LEAN engine. Use when creating trading algorithms, backtesting strategies, implementing technical indicators, setting up live trading, or using the LEAN CLI. Covers Python algorithm development with support for equities, options, futures, forex, and crypto.
---

# QuantConnect LEAN Trading

Build algorithmic trading strategies using the QuantConnect LEAN engine.

## Quick Start

Create a new algorithm project:
```bash
pip install lean
lean init
lean project-create "My Strategy" --language python
```

## Workflow Decision Tree

1. **Classic Algorithm** → Simple, direct control
   - Override `Initialize()` and `OnData()`
   - Good for straightforward strategies

2. **Framework Algorithm** → Modular, reusable components
   - Use Universe Selection, Alpha, Portfolio, Risk, Execution modules
   - Good for complex, multi-asset strategies

Choose Classic unless you need component reusability or are building institutional-grade systems.

## Core Workflow

```
Research → Backtest → Optimize → Live
   ↓          ↓          ↓        ↓
lean     lean       lean      lean
research backtest   optimize  live
```

## Classic Algorithm Structure

```python
from AlgorithmImports import *

class MyAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2020, 1, 1)
        self.SetEndDate(2023, 1, 1)
        self.SetCash(100000)
        
        self.symbol = self.AddEquity("SPY", Resolution.Daily).Symbol
        self.sma = self.SMA(self.symbol, 20)
    
    def OnData(self, data):
        if not self.sma.IsReady:
            return
            
        if self.Securities[self.symbol].Price > self.sma.Current.Value:
            self.SetHoldings(self.symbol, 1)
        else:
            self.Liquidate(self.symbol)
```

## Key Patterns

### Scheduled Events
```python
def Initialize(self):
    self.Schedule.On(
        self.DateRules.EveryDay(self.symbol),
        self.TimeRules.AfterMarketOpen(self.symbol, 30),
        self.Rebalance
    )

def Rebalance(self):
    # Execute at scheduled time
    pass
```

### Multiple Indicators
```python
def Initialize(self):
    self.sma_fast = self.SMA(self.symbol, 10)
    self.sma_slow = self.SMA(self.symbol, 30)
    self.rsi = self.RSI(self.symbol, 14)
    self.macd = self.MACD(self.symbol, 12, 26, 9)
```

### Risk Management
```python
def OnData(self, data):
    # Position sizing based on volatility
    atr = self.ATR(self.symbol, 14).Current.Value
    risk_per_trade = self.Portfolio.TotalPortfolioValue * 0.01
    position_size = risk_per_trade / atr
    
    # Stop loss
    if self.Portfolio[self.symbol].UnrealizedProfitPercent < -0.02:
        self.Liquidate(self.symbol)
```

## References

- **CLI Commands**: See [cli-commands.md](references/cli-commands.md) for full LEAN CLI reference
- **Algorithm Templates**: See [algorithm-templates.md](references/algorithm-templates.md) for strategy patterns
- **Framework Components**: See [framework-components.md](references/framework-components.md) for modular components

## Asset Templates

Use the templates in `assets/templates/` as starting points:
- `basic_algorithm.py` - Minimal starter template
- `momentum_strategy.py` - SMA crossover momentum strategy