In the chart example featuring Grasim, you can observe two successful 3BB trades that unfolded seamlessly.
Upon zooming out, the broader perspective reveals a discernible uptrend in the market.
The 3BB strategy excels in offering sell entry signals at the market’s peaks, effectively capturing mean reversion trades, and then providing exit signals near the median Bollinger Band. But, after a period of consolidation, the stock once again breaks out of the previous resistance and continues on the path of Uptrend.
Now the question ariese –
The Entropy Alpha Strategy employs advanced Machine Learning techniques to augment its trading capabilities. This innovative approach leverages historical trade data from the 3BB Strategy to make informed trading decisions.
The Machine Learning model sifts through the trade history to pinpoint trades that would have been more profitable if one had entered them at the time of the original trade’s exit. This valuable insight helps traders recognize opportunities to maximize profits by timing their entries strategically.
Equally important, the model identifies trades with a high probability of hitting the stop loss. This risk assessment empowers traders to exercise caution or consider alternative strategies when approaching potentially risky trades.
The Entropy Alpha Strategy harnesses the predictive power of Machine Learning to refine trading decisions. Two notable models play pivotal roles in this strategy:
Regression analysis is a core component of the Machine Learning framework employed within the Entropy Alpha Strategy. This statistical method is used to examine relationships between variables, making it an invaluable tool for identifying profitable trades and managing risk.
In addition to regression analysis, the Entropy Alpha Strategy employs k-means clustering as part of its Machine Learning arsenal.
These two Machine Learning techniques work in tandem within the Entropy Alpha Strategy: