• To maintain liquidity, It scans the stocks which are in derivatives.
• 3BB Scanners and in general, Entropy does not work when there is fundamental news.

Scanner History –

Entropy Scanner is made with two scanners –

• Entropy 3BB Strategy Watchlist Scanner
• Entropy 3BB Strategy Trigger Scanner

The Entropy 3BB Strategy Watchlist Scanner scans the stocks in 3BB Scanner and provides a pre-listed “Watchlist” of potential trade entries for Entropy 3BB Strategy

Here is a list of some of those additional parameters like –

• Weekend Strategy Validity in 15 Minutes and 5 Minutes Timeframe (Case Basis)
• Narrow Range (Compression i.e Inside Bars and modified NR Theory)
• Bollinger Width
• Proper 3BB Pattern Formation
• Econominc News i.e. It scans data for news and give them weightage.
• Candlestick Type in some cases
• Dynamic Entry: The entry parameters are dynamic based on market conditions. For Example, in a consolidating market, We need to loosen up on the validity of weekend logics while it is vice-versa for the case of the market at the time of results season because it becomes highly volatile.

Initially started as a Price Action Scanner, later it pivoted to a machine learning model named K-Nearest Neighbors (KNN) Model.

Scanner History –

We have also added a few more filters and conditions to minimize false breakouts. It also checks an immediate daily trends to ensure more profitability.

KNN Model

K-Nearest Neighbors (KNN) is a simple yet effective machine learning algorithm used for classification and regression tasks. The algorithm is based on the concept that similar data points are likely to have similar outcomes.

In KNN, the model finds the K-nearest data points to a new data point and assigns a label to the new point based on the labels of its K-nearest neighbors. The value of K is a hyperparameter that can be tuned to improve the model’s accuracy.

For example, if we have a dataset of stocks and we want to predict whether a particular stock will go up or down, we can use KNN. We would first preprocess the data and extract the relevant features such as the stock’s price, volume, etc. We would then split the dataset into training and testing sets.

To make a prediction for a new stock, we would find the K-nearest stocks to the new stock based on the extracted features. We would then assign a label to the new stock based on the labels of its K-nearest neighbors. For instance, if the majority of its K-nearest neighbors have gone up in the past, we would predict that the new stock will also go up.

KNN is easy to implement and can work well with small datasets. However, it can become computationally expensive for large datasets as the algorithm has to calculate the distance between each data point. In addition, choosing the value of K can be challenging, and the algorithm can be sensitive to the scale of the data.

Entropy 3BB Strategy –

Entry: Any moment a stock comes into this scanner, one can immediately put short trade at the low of the previous candle’s low in 15 minutes timeframe. Once triggered,

Stop Loss: The Stop Loss should be updated to the high of that very day.

Exit Strategy: There are several exit strategies.

• Exit when price cross-median of 1SD Bollinger band on 15 minutes time frame.
• Exit after 30 minutes irrespective of the Profit/Loss.
• Let it auto square off at day’s end! (Recommended)
• TSL with Weekend or Bounce Strategy!
• Exit at 14:50 (We follow this.)

Entropy Alpha Strategy –

Unlike Entropy 3BB Strategy Scanner which uses two scanners which gives the watchlist first and then post the trades if it is triggered, The Entropy Alpha Strategy Scanner just posts when the trade is triggered.

Just take it when the Scanner Posts it.

Entropy 3BB Strategy –

Here go the Past trades that triggered from the watchlist of Entropy scanner –

You can backtest using the Bar Replay Method of any charting software like Tradingview with your exit strategy.

Entropy Alpha Strategy –

The Entropy Alpha Strategy is an exclusive trading approach based on the principles of the Entropy System, which revolves around probability distribution theories, with Bollinger Bands serving as its primary tool.

This is a comprehensive page showcasing the historical performance along with various analytics of Entropy Alpha trades.