LEAN Engine — QuantConnect Algorithmic Trading

---

name: lean

description: Run QuantConnect LEAN backtests and manage US equity algorithm development. Use when asked to backtest a trading strategy, run a LEAN algorithm, analyze backtest results, download market data, or deploy to Interactive Brokers TWS. Covers algorithm creation, data management, config editing, and result analysis.

metadata:

{

"openclaw":

{

"requires": { "bins": ["dotnet"], "anyBins": ["python3", "python"], "env": ["LEAN_ROOT", "DOTNET_ROOT", "PYTHONNET_PYDLL"] },

"install":

[

{

"id": "dotnet",

"kind": "download",

"url": "https://dotnet.microsoft.com/download/dotnet/8.0",

"label": "Install .NET 8 SDK"

}

]

}

}

---

# LEAN Engine — QuantConnect Algorithmic Trading

Prerequisites & Setup

Required Environment Variables

| Variable | Purpose | Example |

|----------|---------|---------|

| `LEAN_ROOT` | Path to cloned LEAN repository | `/home/user/lean` |

| `DOTNET_ROOT` | Path to .NET SDK installation | `/home/user/.dotnet` |

| `PYTHONNET_PYDLL` | Path to Python shared library (required by LEAN's pythonnet) | `$LEAN_ROOT/.libs/libpython3.11.so.1.0` |

All three must be set before using this skill. Add to your shell profile:

export LEAN_ROOT="$HOME/lean"
export DOTNET_ROOT="$HOME/.dotnet"
export PATH="$PATH:$DOTNET_ROOT"
export PYTHONNET_PYDLL="$LEAN_ROOT/.libs/libpython3.11.so.1.0"

> **Note:** LEAN bundles its own Python shared library in `$LEAN_ROOT/.libs/`. If you built LEAN from source, the library should be there after `dotnet build`. If not, install `libpython3.11-dev` and point `PYTHONNET_PYDLL` to your system's `libpython3.11.so`.

First-Time Setup

1. **Install .NET 8 SDK:**

```bash

# Linux/macOS

wget https://dot.net/v1/dotnet-install.sh -O dotnet-install.sh

chmod +x dotnet-install.sh

./dotnet-install.sh --channel 8.0

export DOTNET_ROOT="$HOME/.dotnet"

export PATH="$PATH:$DOTNET_ROOT"

```

2. **Clone and build LEAN:**

```bash

git clone https://github.com/QuantConnect/Lean.git "$LEAN_ROOT"

cd "$LEAN_ROOT"

dotnet build QuantConnect.Lean.sln -c Debug

```

3. **Download initial market data:**

```bash

pip install yfinance pandas

python3 {baseDir}/scripts/download_us_universe.py --symbols sp500 --start 2020-01-01 --data-dir "$LEAN_ROOT/Data"

```

4. **Verify setup:**

```bash

ls "$LEAN_ROOT/Data/equity/usa/daily/" # Should list .zip files

ls "$LEAN_ROOT/Launcher/bin/Debug/" # Should contain QuantConnect.Lean.Launcher.dll

```

Environment

Quick Reference

Run a Backtest

1. Place algorithm in `$LEAN_ROOT/Algorithm.Python/YourAlgo.py`

2. Edit config to point to it:

```bash

# Update config.json — set these fields:

# "algorithm-type-name": "YourClassName"

# "algorithm-language": "Python"

# "algorithm-location": "../../../Algorithm.Python/YourAlgo.py"

```

3. Run:

```bash

export PATH="$PATH:$DOTNET_ROOT"

cd "$LEAN_ROOT/Launcher/bin/Debug"

dotnet QuantConnect.Lean.Launcher.dll

```

4. Results appear in stdout + `$LEAN_ROOT/Results/`

**Or use the helper script:**

bash {baseDir}/scripts/run_backtest.sh YourClassName YourAlgo.py

Config Editing

Edit `$LEAN_ROOT/Launcher/config.json` with these key fields:

| Field | Purpose | Example |

|-------|---------|---------|

| `algorithm-type-name` | Python class name | `"MyStrategy"` |

| `algorithm-language` | Language | `"Python"` |

| `algorithm-location` | Path to .py file | `"../../../Algorithm.Python/MyStrategy.py"` |

| `data-folder` | Market data path | `"../Data/"` |

| `environment` | Mode | `"backtesting"` or `"live-interactive"` |

For IB live trading, set environment to `"live-interactive"` and configure the

`ib-*` fields (account, username, password, host, port, trading-mode).

Data Management

**Check available data:**

ls "$LEAN_ROOT/Data/equity/usa/daily/"

**Data format:** ZIP files containing CSV. Each line:

`YYYYMMDD HH:MM,Open*10000,High*10000,Low*10000,Close*10000,Volume`

Prices are stored as integers (multiply by 10000). LEAN handles conversion internally.

**Download more data:**

python3 {baseDir}/scripts/download_us_universe.py --symbols sp500 --data-dir "$LEAN_ROOT/Data"

See `{baseDir}/references/data-download.md` for additional methods to expand the universe.

Writing Algorithms

LEAN Python algorithms inherit from `QCAlgorithm`:

from AlgorithmImports import *

class MyAlgo(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2024, 1, 1)
        self.SetEndDate(2025, 1, 1)
        self.SetCash(100_000)
        self.AddEquity("SPY", Resolution.Daily)
        self.SetBenchmark("SPY")
        self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage,
                               AccountType.Margin)

    def OnData(self, data):
        if not self.Portfolio.Invested:
            self.SetHoldings("SPY", 1.0)

Key API patterns:

Analyzing Results

After a backtest run, check:

ls "$LEAN_ROOT/Results/"
# Key files: *-log.txt, *-order-log.txt, *.json (statistics)

Rebuild LEAN (if source changes)

export PATH="$PATH:$DOTNET_ROOT"
cd "$LEAN_ROOT"
dotnet build QuantConnect.Lean.sln -c Debug

Security Notes

Config.json Safety

The `run_backtest.sh` script does **NOT** modify your original `config.json`. Instead, it:

1. Reads the original config as a template (read-only)

2. Creates a separate `config.backtest.json` with only algorithm fields changed (class name, file path, language, environment=backtesting)

3. Temporarily swaps it in for the LEAN run, then restores the original via a `trap` cleanup handler

The `configure_algo.py` helper performs the field substitution in an isolated output file. Your original config — including any Interactive Brokers credentials for live trading — is never modified.

**Modified fields (in the temp copy only):**

Network Access

The setup instructions involve network downloads:

All downloads are from well-known public sources. For maximum isolation, run setup in a container or VM.

Environment Variables

This skill requires the following environment variables at runtime:

These are declared in the skill metadata and must be set before use.

Troubleshooting