Strategy

To implement your Pine Script strategy in Python using QuantConnect, you can follow these steps, considering the slippage and fee models you mentioned:

1. Set Up the Algorithm Framework: Define the initialize method to set the start and end dates, initial capital, and the securities you want to trade. You can use the add_equity method to add securities.

2. Implement Slippage Model: Since you mentioned adding 10-15 ticks of slippage, you can use the ConstantSlippageModel or create a custom slippage model to simulate this. Here's an example of setting a constant slippage model:

    class MyAlgorithm(QCAlgorithm):
        def initialize(self):
            self.set_start_date(2000, 1, 1)
            self.set_end_date(2020, 1, 1)
            self.set_cash(100000)
            security = self.add_equity("SPY", Resolution.DAILY)
            security.set_slippage_model(ConstantSlippageModel(0.001))  # Adjust the slippage percentage as needed
   

3. Handle Fees: If your broker is fee-less, you can use a fee model that reflects this.

4. Define Trade Execution: Use the on_data method to define how trades are executed based on your strategy logic. Since your strategy averages 2 trades a day, ensure your logic aligns with this frequency.

5. Backtest the Strategy: Run backtests on QuantConnect to evaluate the performance of your translated strategy, using historical data to simulate trades and analyze results.

6. Optimize and Iterate: Based on backtest results, refine your strategy by adjusting parameters, improving logic, or optimizing performance to achieve desired outcomes.

This approach will help you translate your Pine Script strategy into a QuantConnect algorithm, considering the slippage and fee conditions you modeled in your backtest.

Sources: Initialization - QuantConnect.com, Custom Models P E P8 Algorithm, QuantConnect Paper Trading - QuantConnect.com, Key Concepts - QuantConnect.com, Key Concepts - QuantConnect.com

Your backtest looks solid:

• Net Profit: Strong result at $217.96M, indicating good growth.
• Win Rate (21.16%): A bit low; refining entries/exits could help boost profitability.
• Max Drawdown (32.14%): High; consider volatility-adjusted position sizing to reduce risk during volatile periods. Using a 14-period ATR can help dynamically adjust position size in response to market conditions, smoothing the equity curve. Here’s a quick example:

atr = self.ATR(self.symbol, 14)
if atr.IsReady:
    position_size = (self.portfolio_target_risk / atr.Current.Value) * self.Portfolio[self.symbol].Price
    self.SetHoldings(self.symbol, position_size)

• Profit Factor (1.178): Decent, but raising it could further strengthen returns.