Strategy: mean reversion

Based on the knowledge sources provided, I can guide you on how to implement some of the rules mentioned in your strategy using QuantConnect's LEAN engine. However, I don't have the exact implementation for the “2.11 Sharpe” mean reversion strategy you mentioned.

Here's how you can implement some of the rules in Python:

1. Compute the rolling mean of High minus Low over the last 25 days:

rolling_mean = history['high'].sub(history['low']).rolling(window=25).mean()

1. Compute the IBS indicator: (Close - Low) / (High - Low);

ibs = (history['close'].sub(history['low'])).div(history['high'].sub(history['low']))

1. Compute a lower band as the rolling High over the last 10 days minus 2.5 x the rolling mean of High minus Low:

lower_band = history['high'].rolling(window=10).max().sub(2.5 * rolling_mean)

1. Go long whenever SPY closes under the lower band and IBS is lower than 0.3:

go_long = (history['close'] < lower_band) & (ibs < 0.3)

1. Close the trade whenever the SPY close is higher than yesterday's high:

close_trade = history['close'] > history['high'].shift()

1. Also, close the trade whenever the price is lower than the 300-SMA:

sma_300 = history['close'].rolling(window=300).mean()
close_trade = close_trade | (history['close'] < sma_300)

Please note that this is a simplified version and you might need to adjust it according to your needs. Also, remember to handle the data properly, especially when dealing with multiple symbols.

For more detailed information on how to use QuantConnect's API, you can refer to the official documentation.

If you're still having trouble, I recommend posting your question on the QuantConnect Forum where the community and the QuantConnect team can provide more specific help.