Update README.md

Author: brookswoolf

README.md

@@ -15,7 +15,7 @@ Enjoy! ^_^
 [This](https://github.com/brookswoolf/Trading-Algorithms/blob/master/Multi-Factor%20Model/The%20Quality%20Score%20-%20Extracting%20'Quality'%20From%20Your%20Pipeline.ipynb) notebook is the first part of the main project that I developed using concepts I learned from my education and reading numerous whitepapers in my profession. This notebook tries to recreate the 'quality score', which over the past 15 years has shown to be the driving return factor across multiple different benchmarks. A company is considered 'quality' when they have a combination of an ethical and forward-thinking management, solid financials (i.e. profitable, low debt, consistent cash flow), and good market positioning. That can be hard to define and the market is always changing so I wanted to make something that can be fully customized and educational.
 
 ### The Constrained Model 
-[This](https://github.com/brookswoolf/Trading-Algorithms/tree/master/Constrained%20Model) is an algorithm that I created using the defined contest criteria for Quantopian. This algorithm has more adjustable constraint and optimization settings compared to the Multi Factor Model simply because it is focused on those objectives. The Multi Factor Model is better for backtesting different alpha factor theories using different combinations of factors. The tested alpha factor from the Multi Factor Model can be easily plugged into this model and tested using the costrained criteria. This model can also be seen as a conservative approach to reviewing the actual strength of your alpha factor in a real world setting. The alpha factor was initially researched in the below notebook: 
+[This](https://github.com/brookswoolf/Trading-Algorithms/tree/master/Constrained%20Model) is an algorithm that I created using the defined contest criteria for Quantopian. This algorithm has more adjustable constraint and optimization settings compared to the Multi-Factor Model simply because it is focused on those objectives. The Multi-Factor Model is better for backtesting different alpha factor theories using different combinations of factors. The tested alpha factor from the Multi-Factor Model can be easily plugged into this model and tested using the costrained criteria. This model can also be seen as a conservative approach to reviewing the actual strength of your alpha factor in a real world setting. The alpha factor was initially researched in the below notebook: 
 
 ##### "Alpha Analysis"
 [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.