Added titles for method sections.

stocker/readme.md

@@ -58,11 +58,15 @@ The Stocker object includes 8 main methods for analyzing and predicting
 stock prices. Call any of the following on your stocker object, replacing
 `Stocker` with your object (for example `microsoft`):
 
+### Plot stock history
+
 `Stocker.plot_stock(start_date=None, end_date=None)`
 	
 Prints basic information and plots the history of the stock. The 
 default start and end dates are the extent of the data
 
+### Calculate profit from buy and hold strategy
+
 `Stocker.buy_and_hold(start_date=None, end_date=None, nshares=1)`
 
 Evaluates a buy and hold strategy from the start date to the end date
@@ -72,6 +76,8 @@ hold strategy, besides being the smartest choice, is also the simplest.
 We buy at the start date and hold to the end date. Prints the expected
 profit and plots the expected profit over time. 
 
+### Make prophet model with predictions for 1 year in future
+
 `model, future = Stocker.create_prophet_model(resample=False)`
 
 Make a Prophet Additive Model using 3 years of training data
@@ -86,6 +92,8 @@ To see the trends and patterns of the prophet model, call
 model.plot_components(future)
 plt.show()`
 
+### Find significant changepoints and try to correlate with Google search trends
+
 `Stocker.changepoint_date_analysis(term=None)`
 
 Finds the most significant changepoints in the dataset from a prophet model 
@@ -105,6 +113,8 @@ term is specified, the term default to "ticker stock". You can use
 this to determine if the stock price is related to certain search terms or if the 
 changepoints coincide with particular searches. 
 
+### Find the best chnagepoint prior scale graphically
+
 `Stocker.changepoint_prior_analysis(changepoint_priors=[0.001, 0.05, 0.1, 0.2], 
 olors=['b', 'r', 'grey', 'gold'])`
 
@@ -123,6 +133,8 @@ and can be changed using `Stocker.changepoint_prior_scale = 0.05`
 Altering the changepoint prior scale can have a significant effect on predictions,
 so try a few different values to see how they alter models.
 
+### Quantitaively compare different changepoint prior scales
+
 `Stocker.changepoint_prior_validation(changepoint_priors = [0.001, 0.05, 0.1, 0.2])`
 
 Similar to the changepoint prior analysis except quantifies the differences between 
@@ -135,6 +147,8 @@ and the uncertainty is the upper estimate minus the lower estimate in dollars as
 A graph of these results is also produced. This method is useful for choosing
 a proper cps in combination with the analysis graphical results. 
 
+### Evalaute the Prophet model predictions for 2017 against real prices and compare profits
+
 `Stocker.evaluate_prediction(nshares=1000)`
 
 Evalutes a trading strategy informed by the prophet model for 
@@ -154,6 +168,8 @@ profit from the model strategy, and the profit from a buy and hold strategy over
 same period. Graphs of the predictions versus the actual values and the expected 
 profit from both strategies over time are also displayed. 
 
+### Predict future prices
+
 `Stocker.predict_future(days=30)`
 
 Makes a prediction for the specified number of days in the future.