Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Backtestable AI trading agents and Python algorithmic trading strategies for stocks, options, crypto, futures, forex, SEC filings, FRED macro data, and real brokers.
| Date | Stars |
|---|---|
| 2026-07-24 | 1839 |
| 2026-07-25 | 1844 |
| 2026-07-28 | 1844 |
| 2026-07-30 | 1844 |
| 2026-07-31 | 1862 |
| 2026-08-06 | 1862 |
Today
— stars today
This week
+18 stars this week
This month
— stars this month
Momentum
53.0
growth rate 0.98%/day
[](https://github.com/Lumiwealth/lumibot/actions/workflows/cicd.yaml)
[](https://github.com/Lumiwealth/lumibot/actions/workflows/cicd.yaml)
[](https://pypi.org/project/lumibot/)
[](https://pypi.org/project/lumibot/)
[](LICENSE)
# Lumibot
**Build, backtest, and run algorithmic trading strategies and AI agents in Python.**
**Full docs:** [lumibot.lumiwealth.com](https://lumibot.lumiwealth.com/) · **Managed cloud:** [BotSpot.trade](https://botspot.trade/sales?showLogin=1&utm_source=github&utm_medium=readme&utm_campaign=lumibot&utm_content=top_text_link&sample=lumibot_readme_deploy) · **MCP:** [BotSpot for AI coding agents](https://botspot.trade/agents?utm_source=github&utm_medium=readme&utm_campaign=lumibot&utm_content=top_mcp_link)
<p align="center">
<strong>🌐 Source and community</strong><br><br>
<a href="https://github.com/Lumiwealth/lumibot"><img src="docs/assets/community/github.svg" alt="GitHub" width="20" height="20"> GitHub</a>
<a href="https://www.reddit.com/r/BotSpotTrade/"><img src="docs/assets/community/reddit.svg" alt="Reddit" width="20" height="20"> Reddit Community</a>
<a href="https://discord.gg/lumiwealth"><img src="docs/assets/community/discord.svg" alt="Discord" width="20" height="20"> Discord Community</a>
</p>
<p align="center">
<img src="docs/assets/readme/lumibot_ai_trading_agents_overview.png" alt="Lumibot AI trading agents overview" width="100%">
</p>
## What You Can Build
- **Deterministic strategies:** normal Python logic, indicators, if statements, scheduled rules, position sizing, and risk controls.
- **AI-agent strategies:** one or more agents that reason through evidence, call tools, write memory, and optionally place orders.
- **Backtests:** replay historical data and simulated orders with artifacts you can inspect.
- **Paper or live trading:** reuse the same strategy code with real broker state and real order routing.
Start with the open-source docs, then deploy when you are ready: [Lumibot documentation](https://lumibot.lumiwealth.com/?utm_source=github&utm_medium=readme&utm_campaign=lumibot&utm_content=what_you_can_build_docs) · [Try a sample Lumibot strategy on BotSpot](https://botspot.trade/sales?showLogin=1&utm_source=github&utm_medium=readme&utm_campaign=lumibot&utm_content=what_you_can_build_botspot&sample=lumibot_readme_deploy)
## Quick Start
### Backtest a strategy
```bash
pip install lumibot
```
Save this as `my_strategy.py`:
```python
from datetime import datetime
from lumibot.strategies import Strategy
from lumibot.backtesting import YahooDataBacktesting
class MyStrategy(Strategy):
def on_trading_iteration(self):
if self.first_iteration:
aapl = self.create_order("AAPL", 10, "buy")
self.submit_order(aapl)
MyStrategy.backtest(
YahooDataBacktesting,
datetime(2023, 1, 1),
datetime(2024, 1, 1),
)
```
```bash
python my_strategy.py
```
### Run the same strategy with a paper broker
After the backtest works, keep the `MyStrategy` class and replace the final `MyStrategy.backtest(...)` call with a broker runner. This example uses Alpaca paper trading:
```bash
export ALPACA_API_KEY='your-alpaca-key'
export ALPACA_API_SECRET='your-alpaca-secret'
export ALPACA_IS_PAPER=true
python my_strategy.py
```
```python
import os
from lumibot.brokers import Alpaca
from lumibot.traders import Trader
ALPACA_CONFIG = {
"API_KEY": os.environ["ALPACA_API_KEY"],
"API_SECRET": os.environ["ALPACA_API_SECRET"],
"PAPER": os.environ.get("ALPACA_IS_PAPER", "true").lower() != "false",
}
broker = Alpaca(ALPACA_CONFIG)
strategy = MyStrategy(brokerExcerpt of 35,420 characters
Read on GitHub
3.4k
484
375
313
307
269
70
56
20
15
10
10
8
6
5
5
4
4
3
3
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0748a544c5e9cb3a, topic:ai-agents, topic:multi-agent, readme:ai agent
matched fp:0748a544c5e9cb3a, topic:finance, topic:quantitative-finance