import backtrader as bt
import os


class SMACrossOverStrategy(bt.Strategy):
    params = (
        ('fastPeriod', int(os.environ['fast'])),
        ('slowPeriod', int(os.environ['slow'])),
        ('stopLoss', 2),
        ('takeProfit', 5)
    )
    
    def __init__(self):
        self.fast_ma = bt.indicators.SimpleMovingAverage(
            self.data.close, 
            period=self.params.fastPeriod
        )
        self.slow_ma = bt.indicators.SimpleMovingAverage(
            self.data.close, 
            period=self.params.slowPeriod
        )
        
        self.crossover = bt.indicators.CrossOver(self.fast_ma, self.slow_ma)
        
        self.entry_price = None
        self.stop_loss = None
        self.take_profit = None
        
    def next(self):
        if not self.position:
            if self.crossover > 0:
                close = self.data.close[0]
                self.buy()
                self.entry_price = close
                
                self.stop_loss = close * (1 - self.params.stopLoss / 100)
                self.take_profit = close * (1 + self.params.takeProfit / 100)
        else:
            close = self.data.close[0]
        
            if close <= self.stop_loss or close >= self.take_profit:
                self.close()
                self.entry_price = None
                self.stop_loss = None
                self.take_profit = None
                return
            
            if self.crossover < 0:
                self.close()
                self.entry_price = None
                self.stop_loss = None
                self.take_profit = None

def cerebro_init(cerebro: bt.Cerebro):
    cerebro.addsizer(bt.sizers.PercentSizer, percents=95)

# No driver code (e.g. cerebro.run()) needed
# Backtrader Documentation: https://www.backtrader.com/docu/quickstart/quickstart/

Each project must include a cerebro_init function. This lets the extension handle data loading and configuration automatically before running your backtest.

import vectorbt as vbt


# Build portfolio args 
def portfolio_init(close) -> dict:
    portfolio_kwargs = {}
    
    fast_ma = vbt.MA.run(close, 10)
    slow_ma = vbt.MA.run(close, 50)

    portfolio_kwargs['close'] = close
    portfolio_kwargs['entries'] = fast_ma.ma_crossed_above(slow_ma)
    portfolio_kwargs['exits'] = fast_ma.ma_crossed_below(slow_ma)
    
    return portfolio_kwargs

# No driver code (e.g. vbt.Portfolio.from_signals()) needed
# VectorBT Documentation: https://vectorbt.dev/

Each project must include a portfolio_init function. The extension uses this to load data, set parameters, and run your backtest automatically.

# Write backtesting code with your custom engine here.
# Don't forget to print your backtest results so that Backtest Manager can capture them.

print("t: {
    'ref': 0, 
    'datetime': "2024-05-30T14:00:00+00:00", 
    'price': 10202.0, 
    'size': 2, 
    'value': 20404.0, 
    'commission': 20.4, 
    'side': 'long'
}")

print("t: {
    'ref': 0,
    'datetime': "2024-05-31T00:00:00+00:00",
    'price': 10302,
    'size': 2,
    'pnl': 200.0,
    'pnlcomm': 179.6,
    'commission': 20.4,
    'hold_bars': 10
}")

print("e: { 
    'datetime': "2024-05-30T14:00:00+00:00", 
    'value': 3000000 
}")