tradingbot.py

from lumibot.brokers import Alpaca
from lumibot.backtesting import YahooDataBacktesting
from lumibot.strategies.strategy import Strategy
from lumibot.traders import Trader
from datetime import datetime
from alpaca_trade_api import REST
from timedelta import Timedelta
from finbert_utils import estimate_sentiment

API_KEY="PKMRT4JWP9AD905HBDO0"
API_SECRET="9W7V2GMOhOC3yzf3dIeBdOyuiueIfpfJTtYYsSRl"
BASE_URL="https://paper-api.alpaca.markets/v2"
ALPACA_CREDS={
    "API_KEY":API_KEY,
    "API_SECRET":API_SECRET,
    "PAPER":True
}

class MLTrader(Strategy):
    def initialize(self,symbol:str="SPY",cash_at_risk:float=0.5):
        self.symbol=symbol
        self.sleeptime="24H"
        self.last_trade=None
        self.cash_at_risk=cash_at_risk
        self.api=REST(base_url=BASE_URL,key_id=API_KEY,secret_key=API_SECRET)
        
    def position_sizing(self):
        cash=self.get_cash()
        last_price=self.get_last_price(self.symbol)
        quantity=round(cash*self.cash_at_risk/last_price,0)
        return cash,last_price,quantity
    
#This formula guides how much of our cash balance we use per trade. cash_at_risk of 0.5 menas that for each trade we are using 50% of our remaining cash balance
    def get_dates(self):
        today=self.get_datetime()
        three_days_prior=today-Timedelta(days=3)
        return today.strftime('%Y-%m-%d'),three_days_prior.strftime('%Y-%m-%d')
    
    def get_sentiment(self):
        today,three_days_prior =self.get_dates()
        news=self.api.get_news(symbol=self.symbol,
                                start=three_days_prior,
                                end=today)
        news=[ev.__dict__["_raw"]["headline"] for ev in news]
        probability, sentiment=estimate_sentiment(news)
        return probability, sentiment, news 
        
            
    def on_trading_iteration(self):
        cash,last_price,quantity=self.position_sizing()
        probability, sentiment, news=self.get_sentiment()
        if cash>last_price:
            if sentiment=="positive" and probability> 0.999:
                #print(probability, sentiment, news)
                if self.last_trade=="sell":
                    self.sell_all()
                order=self.create_order(
                    self.symbol,
                    quantity,
                    "buy",
                    type="market",
                    take_profit_price=last_price*1.20,
                    stop_loss_price=last_price*0.95,
                    )
                self.submit_order(order)
                self.last_trade="buy"
            
            elif sentiment=="negative" and probability> 0.999:
                #print(probability, sentiment, news)
                if self.last_trade=="buy":
                    self.sell_all()
                order=self.create_order(
                    self.symbol,
                    quantity,
                    "sell",
                    type="market",
                    take_profit_price=last_price*0.8,
                    stop_loss_price=last_price*1.05,
                    )
                self.submit_order(order)
                self.last_trade="sell"
            
start_date=datetime(2020,1,1)
end_date=datetime(2024,4,5)
broker=Alpaca(ALPACA_CREDS)

strategy=MLTrader(name="mlstrat",
                    broker=broker,
                    parameters={"symbol":"SPY",
                            "cash_at_risk":0.5}
                )

strategy.backtest(
    YahooDataBacktesting,
    start_date,
    end_date,
    parameters={"symbol":"SPY","cash_at_risk":0.5}
)