Update README.md

Author: brookswoolf

README.md

@@ -21,7 +21,7 @@ Enjoy! ^_^
 [This](https://github.com/brookswoolf/Trading-Algorithms/blob/master/Constrained%20Model/Alpha%20Analysis.ipynb) notebook was initially made to test different fundamental alpha theories. I wanted to make a notebook that was concise, but also very informational. You can come up with any combination of different fundamental values, ratios, and any other Morningstar data you like. This book uses tear sheets to analyze the effect of the factors and returns across different sectors and portfolios. When analyzing the predictive value of an alpha factor, it is common to look at the mean information coefficient. The information coefficient essentially represents the correlation between the predicted values of a stock and the actual outcome. At the bottom, you can also visualize the 'decay' of your alpha factor across the data set by plotting the mean information coefficient.
 
 ### The Futures Breakout Trading Model 
-[This](https://github.com/brookswoolf/Trading-Algorithms/tree/master/Futures%20Breakout%20Model) trading model was inspired by a book about the teachings of Richard Dennis and his trading partner Bill Eckhardt during the Turtle Experiment in 1983. The model trades ten different futures markets in an attempt to capture channel breakout patterns. In the experiment, they took 14 traders and taught them a complete rule-based trading system for determining markets, position sizing, entries, stops, exits, and a handful of miscellaneous (and useful) trading tactics that can be used to leverage different market nuances. Over the course of five years of trading, the Turtle group had reportedly earned more than $175 million. Visit [this](https://github.com/brookswoolf/Trading-Algorithms/blob/master/Futures%20Breakout%20Model/The%20Original%20Turtle%20Trading%20Rules.pdf) for the complete book.  
+[This](https://github.com/brookswoolf/Trading-Algorithms/tree/master/Futures%20Breakout%20Model) trading model was inspired by a book about the teachings of Richard Dennis and his trading partner Bill Eckhardt during the Turtle Experiment in 1983. The model trades ten different futures markets in an attempt to capture channel breakout patterns. In the experiment, they took 14 traders and taught them a complete rule-based trading system for determining markets, position sizing, entries, stops, exits, and a handful of miscellaneous (and useful) trading tactics that can be used to leverage different market nuances. Over the course of five years of trading, the Turtle group had reportedly earned more than $175 million. Click [here](https://github.com/brookswoolf/Trading-Algorithms/blob/master/Futures%20Breakout%20Model/The%20Original%20Turtle%20Trading%20Rules.pdf) for the complete book.  
 
 ### The Random Forest Regression Model 
 [This](https://github.com/brookswoolf/Trading-Algorithms/tree/master/Random%20Forest%20Regression) was something that I wanted to develop in order to better understand the application of machine learning in a quantitative finance. Although basic in its actual application, I feel like this helped me understand the actual objective that machine learning models try to accomplish. The idea of implementing trading strategies based on machine learning has been growing with popularity, however perfecting a model that relies heavily on a machine learned alpha vector is still very difficult even amongst seasoned professionals. That being said, I feel that this is the direction that we are headed, and in the near future this type of model is going to be implemented across all firms in the industry, some way or another.