Examples of Portfolio and Signal Strategies in a Lumibot Collection
Summary
This document catalogs example strategies for a Python trading framework. The collection spans stock buy-and-hold, moving-average crossover, stock momentum rotation, a traditional stock-and-bond allocation, equal-weight Bitcoin and Ethereum, a short options strangle, and rebalancing when portfolio weights drift past a threshold. Together, the examples illustrate a range of basic signal rules and portfolio allocation approaches across several asset classes.
The page also describes a contribution workflow and a minimal strategy template, including a daily iteration schedule and a historical backtest using a sample data source. These are practical starting points for learning how strategies are organized, but the page does not explain the individual rules in depth or provide return, risk, or benchmark results. The listed strategies should therefore be treated as examples to inspect and test, not as evidence of profitable approaches. Data source requirements and the behavior of each strategy would need further review before applying them in live trading.
Key ideas
- The collection includes trend signals, momentum rotation, allocation strategies, and drift-based rebalancing.
- Its examples cover stocks, bonds, cryptocurrency, options, and multi-asset portfolios.
- The template demonstrates a daily trading iteration and a historical backtesting setup.
- The page provides no performance results or detailed evaluation of the listed strategies.
- Users would need to inspect data requirements and test each strategy before live use.
Tags
Full text
# Lumibot Community Strategies
# Lumibot Community Strategies
A collection of trading strategies for [Lumibot](https://github.com/Lumiwealth/lumibot). Browse, fork, backtest, and share your own.
## Strategies
| Strategy | Asset Class | Description |
|----------|------------|-------------|
| [Stock Buy and Hold](strategies/stock_buy_and_hold.py) | Stocks | Simple buy-and-hold with portfolio allocation |
| [SMA Crossover](strategies/sma_crossover.py) | Stocks | Classic moving average crossover signal |
| [Stock Momentum](strategies/stock_momentum.py) | Stocks | Momentum rotation across a stock universe |
| [Classic 60/40](strategies/classic_60_40.py) | Stocks + Bonds | Traditional 60% stocks, 40% bonds with rebalancing |
| [Crypto 50/50](strategies/crypto_50_50.py) | Crypto | Equal-weight Bitcoin and Ethereum |
| [Options Strangle](strategies/options_strangle.py) | Options | Short strangle strategy for premium collection |
| [Drift Rebalancer](strategies/drift_rebalancer.py) | Multi-Asset | Rebalance portfolio when drift exceeds threshold |
## Quick Start
```bash
pip install lumibot
# Run any strategy
python strategies/sma_crossover.py
```
## How to Contribute
We welcome community strategies! To submit yours:
1. Fork this repo
2. Add your strategy to the `strategies/` folder
3. Include a docstring with:
- Strategy description (1-2 sentences)
- Asset class (stocks, options, crypto, futures, forex)
- Required data source (Yahoo is free, or specify ThetaData/Polygon/etc.)
- Expected behavior (what it buys/sells and why)
4. Make sure it runs with `python strategies/your_strategy.py`
5. Open a pull request
### Strategy Template
```python
"""
My Strategy Name
Asset class: Stocks
Data source: Yahoo Finance (free)
Description: Brief description of what this strategy does.
"""
from datetime import datetime
from lumibot.strategies import Strategy
from lumibot.backtesting import YahooDataBacktesting
class MyStrategy(Strategy):
parameters = {}
def initialize(self):
self.sleeptime = "1D"
def on_trading_iteration(self):
# Your strategy logic here
pass
if __name__ == "__main__":
MyStrategy.backtest(
YahooDataBacktesting,
datetime(2023, 1, 1),
datetime(2024, 1, 1),
)
```
## Resources
- [Lumibot Documentation](https://lumibot.lumiwealth.com/)
- [Lumibot GitHub](https://github.com/Lumiwealth/lumibot)
- [BotSpot Platform](https://botspot.trade/) (deploy strategies with AI, no code required)
- [Discord Community](https://discord.gg/TmMsJCKY3T)
## License
MITShown in full with attribution under the source's licence. Licence: MIT
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.