What is Lumibot?¶

Lumibot is a Python library made by Lumiwealth (https://www.lumiwealth.com) that allows you to create trading strategies and backtest them. It also allows you to run your strategies live on a paper trading account. You can also use Lumibot to run your strategies live on a real trading account, but we recommend you start with paper trading first.

Lumibot is designed to be easy to use, but also powerful. It is designed to be used by both beginners and advanced users. It is also designed to be flexible, so you can use it to create any kind of trading strategy you want. It is also designed to be fast, so you can backtest your strategies quickly.

Getting Started¶

Welcome to Lumibot! This guide will help you get started with Lumibot, we hope you enjoy it!

Here are the steps to get started using the Alpaca broker, if you want to use a different broker, you can see the list of supported brokers under the brokers section.

Install the package on your computer

pip install lumibot

import the following modules:

# importing the trader class
from lumibot.traders import Trader
# importing the alpaca broker class
from lumibot.brokers import Alpaca

Create an Alpaca paper trading account: https://app.alpaca.markets/paper/dashboard/overview (you can also use other brokers, but Alpaca is easiest to get started with)
Copy your API_KEY and API_SECRET from alpaca dashboard and create a Config class like this:

ALPACA_CONFIG = {
    # Put your own Alpaca key here:
    "API_KEY": "YOUR_ALPACA_API_KEY",
    # Put your own Alpaca secret here:
    "API_SECRET": "YOUR_ALPACA_SECRET",
    # If you want to go live, you must change this. It is currently set for paper trading
    "ENDPOINT": "https://paper-api.alpaca.markets"
}

Create a strategy class (See strategy section) e.g. class MyStrategy(Strategy) or import an example from our libraries, like this:

class MyStrategy(Strategy):
    # Custom parameters
    parameters = {
        "symbol": "SPY",
        "quantity": 1,
        "side": "buy"
    }

    def initialize(self, symbol=""):
        # Will make on_trading_iteration() run every 180 minutes
        self.sleeptime = "180M"

    def on_trading_iteration(self):
        symbol = self.parameters["symbol"]
        quantity = self.parameters["quantity"]
        side = self.parameters["side"]

        order = self.create_order(symbol, quantity, side)
        self.submit_order(order)

Instantiate the Trader, Alpaca and strategy classes like so:

trader = Trader()
broker = Alpaca(ALPACA_CONFIG)
strategy = MyStrategy(name=strategy_name, budget=budget, broker=broker, symbol="SPY")

Backtest the strategy (optional):

backtesting_start = datetime(2020, 1, 1)
backtesting_end = datetime(2020, 12, 31)
strategy.backtest(
    YahooDataBacktesting,
    backtesting_start,
    backtesting_end,
    parameters= {
        "symbol": "SPY"
    },
)

Run the strategy:

trader.add_strategy(strategy)
trader.run_all()

And that’s it! Now try modifying the strategy to do what you want it to do.

Here it is all together:

from datetime import datetime

from lumibot.backtesting import YahooDataBacktesting
from lumibot.brokers import Alpaca
from lumibot.strategies.strategy import Strategy
from lumibot.traders import Trader


ALPACA_CONFIG = {
    # Put your own Alpaca key here:
    "API_KEY": "YOUR_ALPACA_API_KEY",
    # Put your own Alpaca secret here:
    "API_SECRET": "YOUR_ALPACA_SECRET",
    # If you want to go live, you must change this. It is currently set for paper trading
    "ENDPOINT": "https://paper-api.alpaca.markets"
}


class MyStrategy(Strategy):
    # Custom parameters
    parameters = {
        "symbol": "SPY",
        "quantity": 1,
        "side": "buy"
    }

    def initialize(self, symbol=""):
        # Will make on_trading_iteration() run every 180 minutes
        self.sleeptime = "180M"

    def on_trading_iteration(self):
        symbol = self.parameters["symbol"]
        quantity = self.parameters["quantity"]
        side = self.parameters["side"]

        order = self.create_order(symbol, quantity, side)
        self.submit_order(order)


trader = Trader()
broker = Alpaca(ALPACA_CONFIG)
strategy = MyStrategy(
    broker=broker,
    parameters= {
        "symbol": "SPY"
    })

# Backtest this strategy
backtesting_start = datetime(2020, 1, 1)
backtesting_end = datetime(2020, 12, 31)
strategy.backtest(
    YahooDataBacktesting,
    backtesting_start,
    backtesting_end,
    # You can also pass in parameters to the backtesting class, this will override the parameters in the strategy
    parameters= {
        "symbol": "SPY"
    },
)

# Run the strategy live
trader.add_strategy(strategy)
trader.run_all()

Or you can download the file here: https://github.com/Lumiwealth/lumibot/blob/master/example_strategies/simple_start_single_file.py

Adding Trading Fees¶

If you want to add trading fees to your backtesting, you can do so by setting up your backtesting like this:

# Create two trading fees, one that is a percentage and one that is a flat fee
trading_fee_1 = TradingFee(flat_fee=5) # $5 flat fee
trading_fee_2 = TradingFee(percent_fee=0.01) # 1% trading fee

# Backtest this strategy
backtesting_start = datetime(2020, 1, 1)
backtesting_end = datetime(2020, 12, 31)
strategy.backtest(
    YahooDataBacktesting,
    backtesting_start,
    backtesting_end,
    # You can also pass in parameters to the backtesting class, this will override the parameters in the strategy
    parameters= {
        "symbol": "SPY"
    },
    buy_trading_fees=[trading_fee_1, trading_fee_2],
    sell_trading_fees=[trading_fee_1, trading_fee_2],
)

Profiling to Improve Performance¶

Sometimes you may want to profile your code to see where it is spending the most time and improve performance.

We recommend using the yappi library to profile your code. You can install it with the following command in your terminal:

pip install yappi

Once installed, you can use yappi to profile your code like this:

import yappi

# Start the profiler
yappi.start()

#######
# Run your code here, eg. a backtest
#######
MachineLearningLongShort.backtest(
    PandasDataBacktesting,
    backtesting_start,
    backtesting_end,
    pandas_data=pandas_data,
    benchmark_asset="TQQQ",
)

# Stop the profiler
yappi.stop()

# Save the results
threads = yappi.get_thread_stats()
for thread in threads:
    print(
        "Function stats for (%s) (%d)" % (thread.name, thread.id)
    )  # it is the Thread.__class__.__name__
    yappi.get_func_stats(ctx_id=thread.id).save(
        f"profile{thread.name}.out", type="pstat"
    )

This will create a .out file for each thread that you can then analyze to see where your code is spending the most time.

Viewing the Results¶

We recommend using snakeviz to view the results. You can install it with pip install snakeviz. You can then use it to view the results like this:

pip install snakeviz

snakeviz profile_MainThread.out

I personally like changing the style to sunburst, I find it to be easier to understand.