diff --git a/archives.html b/archives.html index 98dcbb6..cd0d38f 100644 --- a/archives.html +++ b/archives.html @@ -82,6 +82,8 @@

+
Wed 08 June 2016
+
Event Studies and Earnings Releases
Sun 15 May 2016
The Unfair Casino
Wed 06 April 2016
diff --git a/author/bradlee-speice.html b/author/bradlee-speice.html index 2295db2..93b41e9 100644 --- a/author/bradlee-speice.html +++ b/author/bradlee-speice.html @@ -82,6 +82,8 @@

+
Wed 08 June 2016
+
Event Studies and Earnings Releases
Sun 15 May 2016
The Unfair Casino
Wed 06 April 2016
diff --git a/author/bradlee-speice2.html b/author/bradlee-speice2.html index 2295db2..93b41e9 100644 --- a/author/bradlee-speice2.html +++ b/author/bradlee-speice2.html @@ -82,6 +82,8 @@

+
Wed 08 June 2016
+
Event Studies and Earnings Releases
Sun 15 May 2016
The Unfair Casino
Wed 06 April 2016
diff --git a/authors.html b/authors.html index 9b1aa87..96e8e13 100644 --- a/authors.html +++ b/authors.html @@ -82,6 +82,8 @@

Bradlee Speice

+
Wed 08 June 2016
+
Event Studies and Earnings Releases
Sun 15 May 2016
The Unfair Casino
Wed 06 April 2016
diff --git a/autocallable-bonds.html b/autocallable-bonds.html index d9df85f..b359b30 100644 --- a/autocallable-bonds.html +++ b/autocallable-bonds.html @@ -95,7 +95,7 @@ Because these are all very similar, we decided to demonstrate all 3 products at
In [1]:
-
using Gadfly
+
using Gadfly
@@ -131,10 +131,10 @@ Because these are all very similar, we decided to demonstrate all 3 products at
In [2]:
-
S0 = 102.2
+
S0 = 102.2
 nominal = 100
 q = 2.84 / 100
-σ = 15.37 / 100
+σ = 15.37 / 100
 term = [0, .49, .9, 1.21, 1.45, 1.69] / 100 + 1
 
 ###
@@ -187,7 +187,7 @@ Because these are all very similar, we decided to demonstrate all 3 products at
 
In [3]:

 

     

-
simulate_gbm = function(S0, μ, σ, T, n)
+
simulate_gbm = function(S0, μ, σ, T, n)
     # Set the initial state
     m = length(S0)
     t = T / n
@@ -196,7 +196,7 @@ Because these are all very similar, we decided to demonstrate all 3 products at
     
     # Build out all states
     for i=1:(n-1)
-        motion[:,i+1] = motion[:,i] .* exp((μ - σ^2/2)*t) .* exp(sqrt(t) * σ .* randn(m))
+        motion[:,i+1] = motion[:,i] .* exp((μ - σ^2/2)*t) .* exp(sqrt(t) * σ .* randn(m))
     end
     
     return motion
@@ -258,9 +258,9 @@ Because these are all very similar, we decided to demonstrate all 3 products at
 
In [4]:

 

     

-
initial = ones(5) * S0
+
initial = ones(5) * S0
 # Using μ=0, T=.25 for now, we'll use the proper values later
-motion = simulate_gbm(initial, 0, σ, .25, 200) 
+motion = simulate_gbm(initial, 0, σ, .25, 200) 
 
 display_motion(motion, .25)
 

@@ -1866,7 +1866,7 @@ fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-20")
 
In [5]:

 

     

-
forward_term = function(yearly_term)
+
forward_term = function(yearly_term)
     # It is assumed that we have a yearly term structure passed in, and starts at year 0
     # This implies a nominal rate above 0 for the first year!
     years = length(term)-1 # because we start at 0
@@ -1896,14 +1896,14 @@ fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-20")
 
In [6]:

 

     

-
# Example term structure taken from:
+
# Example term structure taken from:
 # http://www.treasury.gov/resource-center/data-chart-center/interest-rates/Pages/TextView.aspx?data=yield
 # Linear interpolation used years in-between periods, assuming real-dollar
 # interest rates
 forward_yield = forward_term(term)
 calculated_term2 = term[1] * forward_yield[1]
 
-println("Actual term[2]: $(term[2]); Calculated term[2]: $(calculated_term2)")
+println("Actual term[2]: $(term[2]); Calculated term[2]: $(calculated_term2)")
 

 
 

@@ -1940,11 +1940,11 @@ fig.select("#fig-3a6dd25ad25c4037a166889ee51bb151-element-20")
 
In [7]:

 

     

-
full_motion = ones(5) * S0
+
full_motion = ones(5) * S0
 full_term = vcat(term[1], forward_yield)
 for i=1:T
-    μ = (full_term[i] - 1 - q)
-    year_motion = simulate_gbm(full_motion[:,end], μ, σ, 1, n)
+    μ = (full_term[i] - 1 - q)
+    year_motion = simulate_gbm(full_motion[:,end], μ, σ, 1, n)
     full_motion = hcat(full_motion, year_motion)
 end
 
@@ -3501,14 +3501,14 @@ fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-20")
 
In [8]:

 

     

-
full_simulation = function(S0, T, n, m, term)
+
full_simulation = function(S0, T, n, m, term)
     forward = vcat(term[1], forward_term(term))
 
     # And an S0 to kick things off.
     final_motion = ones(m) * S0
     for i=1:T
-        μ = (forward[i] - 1 - q)
-        year_motion = simulate_gbm(final_motion[:,end], μ, σ, 1, n)
+        μ = (forward[i] - 1 - q)
+        year_motion = simulate_gbm(final_motion[:,end], μ, σ, 1, n)
         final_motion = hcat(final_motion, year_motion)
     end
     return final_motion
@@ -3517,7 +3517,7 @@ fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-20")
 tic()
 full_simulation(S0, T, n, m, term)
 time = toq()
-@printf("Time to run simulation: %.2fs", time)
+@printf("Time to run simulation: %.2fs", time)
 

 
 

@@ -3562,7 +3562,7 @@ fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-20")
 
In [9]:

 

     

-
call_barrier = S0
+
call_barrier = S0
 strike = S0
 protection_barrier = S0 * .6
 coupon = nominal * .07
@@ -3576,7 +3576,7 @@ fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-20")
 
     for i=1:t
         price = year_prices[i]
-        if price ≥ call_barrier
+        if price ≥ call_barrier
             return (nominal + coupon*i) * exp((prod(forward_structure[i:end])-1)*(t-i))
         end
     end
@@ -3606,13 +3606,13 @@ fig.select("#fig-0378e04b897742b597befd2e8e1c169e-element-20")
     tic()
     mean_payoffs[i] = athena()
     time = toq()
-    @printf("Mean of simulation %i: \$%.4f; Simulation time: %.2fs\n", i, mean_payoffs[i], time)
+    @printf("Mean of simulation %i: \$%.4f; Simulation time: %.2fs\n", i, mean_payoffs[i], time)
 end
 
 final_mean = mean(mean_payoffs)
-println("Mean over $num_simulations simulations: $(mean(mean_payoffs))")
+println("Mean over $num_simulations simulations: $(mean(mean_payoffs))")
 pv = final_mean * (exp(-(prod(forward_structure)-1)*T))
-@printf("Present value of Athena note: \$%.2f, notional: \$%.2f", pv, nominal)
+@printf("Present value of Athena note: \$%.2f, notional: \$%.2f", pv, nominal)
 

 
 

@@ -3673,7 +3673,7 @@ Present value of Athena note: $95.00, notional: $100.00

 
In [10]:

 

     

-
call_barrier = S0
+
call_barrier = S0
 coupon_barrier = S0 * .8
 protection_barrier = S0 * .6
 coupon = nominal * .06
@@ -3686,9 +3686,9 @@ Present value of Athena note: $95.00, notional: $100.00

 
     for i=1:t
         price = year_prices[i]
-        if price ≥ call_barrier
+        if price ≥ call_barrier
             return (nominal + coupon + total_coupons)*exp((prod(forward_structure[i:end])-1)*(t-i))
-        elseif price ≥ coupon_barrier
+        elseif price ≥ coupon_barrier
             total_coupons = total_coupons * exp(forward_structure[i]-1) + coupon
         else
             total_coupons *= exp(forward_structure[i]-1)
@@ -3720,13 +3720,13 @@ Present value of Athena note: $95.00, notional: $100.00

     tic()
     mean_payoffs[i] = phoenix_no_memory()
     time = toq()
-    @printf("Mean of simulation %i: \$%.4f; Simulation time: %.2fs\n", i, mean_payoffs[i], time)
+    @printf("Mean of simulation %i: \$%.4f; Simulation time: %.2fs\n", i, mean_payoffs[i], time)
 end
 
 final_mean = mean(mean_payoffs)
-println("Mean over $num_simulations simulations: $(mean(mean_payoffs))")
+println("Mean over $num_simulations simulations: $(mean(mean_payoffs))")
 pv = final_mean * exp(-(prod(forward_structure)-1)*(T))
-@printf("Present value of Phoenix without memory note: \$%.2f", pv)
+@printf("Present value of Phoenix without memory note: \$%.2f", pv)
 

 
 

@@ -3773,7 +3773,7 @@ Present value of Phoenix without memory note: $97.44

 
In [11]:

 

     

-
call_barrier = S0
+
call_barrier = S0
 coupon_barrier = S0 * .8
 protection_barrier = S0 * .6
 coupon = nominal * .07
@@ -3827,14 +3827,14 @@ Present value of Phoenix without memory note: $97.44

     tic()
     mean_payoffs[i] = phoenix_with_memory()
     time = toq()
-    @printf("Mean of simulation %i: \$%.4f; Simulation time: %.2fs\n",
+    @printf("Mean of simulation %i: \$%.4f; Simulation time: %.2fs\n",
         i, mean_payoffs[i], time)
 end
 
 final_mean = mean(mean_payoffs)
-println("Mean over $num_simulations simulations: $(mean(mean_payoffs))")
+println("Mean over $num_simulations simulations: $(mean(mean_payoffs))")
 pv = final_mean * exp(-(prod(forward_structure)-1)*(T))
-@printf("Present value of Phoenix with memory note: \$%.2f", pv)
+@printf("Present value of Phoenix with memory note: \$%.2f", pv)
 

 
 

diff --git a/categories.html b/categories.html
index 2e0e432..77aebb2 100644
--- a/categories.html
+++ b/categories.html
@@ -82,6 +82,8 @@
         

             
Blog

             

+            	
Wed 08 June 2016

+            	
Event Studies and Earnings Releases

             	
Sun 15 May 2016

             	
The Unfair Casino

             	
Wed 06 April 2016

diff --git a/category/blog.html b/category/blog.html
index 0365414..782a8a4 100644
--- a/category/blog.html
+++ b/category/blog.html
@@ -83,6 +83,8 @@
         

             
Blog

             

+            	
Wed 08 June 2016

+            	
Event Studies and Earnings Releases

             	
Sun 15 May 2016

             	
The Unfair Casino

             	
Wed 06 April 2016

diff --git a/category/blog2.html b/category/blog2.html
index 0365414..782a8a4 100644
--- a/category/blog2.html
+++ b/category/blog2.html
@@ -83,6 +83,8 @@
         

             
Blog

             

+            	
Wed 08 June 2016

+            	
Event Studies and Earnings Releases

             	
Sun 15 May 2016

             	
The Unfair Casino

             	
Wed 06 April 2016

diff --git a/cloudy-in-seattle.html b/cloudy-in-seattle.html
index 7bd28c3..a9ae5c5 100644
--- a/cloudy-in-seattle.html
+++ b/cloudy-in-seattle.html
@@ -4,7 +4,7 @@
         
         
         
-        
+        
         
         
 
@@ -92,7 +92,7 @@
 
In [1]:

 

     

-
import pickle
+
import pickle
 import pandas as pd
 import numpy as np
 from bokeh.plotting import output_notebook, figure, show
@@ -359,7 +359,7 @@
 
In [2]:

 

     

-
city_forecasts = pickle.load(open('city_forecasts.p', 'rb'))
+
city_forecasts = pickle.load(open('city_forecasts.p', 'rb'))
 forecasts_df = pd.DataFrame.from_dict(city_forecasts)
 

 
@@ -373,24 +373,24 @@
 
In [3]:

 

     

-
cities = ['binghamton', 'cary', 'nyc', 'seattle']
+
cities = ['binghamton', 'cary', 'nyc', 'seattle']
 city_colors = {cities[i]: Palette[i] for i in range(0, 4)}
 
 def safe_cover(frame):
-    if frame and 'cloudCover' in frame:
-        return frame['cloudCover']
+    if frame and 'cloudCover' in frame:
+        return frame['cloudCover']
     else:
         return np.NaN
 
 def monthly_avg_cloudcover(city, year, month):
     dates = pd.DatetimeIndex(start=datetime(year, month, 1, 12),
                              end=datetime(year, month + 1, 1, 12),
-                             freq='D', closed='left')
-    cloud_cover_vals = list(map(lambda x: safe_cover(forecasts_df[city][x]['currently']), dates))
+                             freq='D', closed='left')
+    cloud_cover_vals = list(map(lambda x: safe_cover(forecasts_df[city][x]['currently']), dates))
     cloud_cover_samples = len(list(filter(lambda x: x is not np.NaN, cloud_cover_vals)))
-    # Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.
+    # Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.
     with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
+        warnings.simplefilter('ignore')
         return np.nanmean(cloud_cover_vals), cloud_cover_samples
 

 
@@ -404,23 +404,23 @@
 
In [4]:

 

     

-
years = range(1990, 2016)
+
years = range(1990, 2016)
 def city_avg_cc(city, month):
     return [monthly_avg_cloudcover(city, y, month) for y in years]
 
 months = [
-    ('July', 7),
-    ('August', 8),
-    ('September', 9),
-    ('October', 10),
-    ('November', 11)
+    ('July', 7),
+    ('August', 8),
+    ('September', 9),
+    ('October', 10),
+    ('November', 11)
 ]
 
 for month, month_id in months:
     month_averages = {city: city_avg_cc(city, month_id) for city in cities}
-    f = figure(title="{} Average Cloud Cover".format(month),
-               x_axis_label='Year',
-               y_axis_label='Cloud Cover Percentage')
+    f = figure(title="{} Average Cloud Cover".format(month),
+               x_axis_label='Year',
+               y_axis_label='Cloud Cover Percentage')
     for city in cities:
         f.line(years, [x[0] for x in month_averages[city]],
                legend=city, color=city_colors[city])
@@ -609,21 +609,21 @@
 
In [5]:

 

     

-
def safe_precip(frame):
-    if frame and 'precipProbability' in frame:
-        return frame['precipProbability']
+
def safe_precip(frame):
+    if frame and 'precipProbability' in frame:
+        return frame['precipProbability']
     else:
         return np.NaN
 
 def monthly_avg_precip(city, year, month):
     dates = pd.DatetimeIndex(start=datetime(year, month, 1, 12),
                              end=datetime(year, month + 1, 1, 12),
-                             freq='D', closed='left')
-    precip_vals = list(map(lambda x: safe_precip(forecasts_df[city][x]['currently']), dates))
+                             freq='D', closed='left')
+    precip_vals = list(map(lambda x: safe_precip(forecasts_df[city][x]['currently']), dates))
     precip_samples = len(list(filter(lambda x: x is not np.NaN, precip_vals)))
-    # Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.
+    # Ignore an issue with nanmean having all NaN values. We'll discuss the data issues below.
     with warnings.catch_warnings():
-        warnings.simplefilter('ignore')
+        warnings.simplefilter('ignore')
         return np.nanmean(precip_vals), precip_samples
 
 def city_avg_precip(city, month):
@@ -631,9 +631,9 @@
 
 for month, month_id in months:
     month_averages = {city: city_avg_cc(city, month_id) for city in cities}
-    f = figure(title="{} Average Precipitation Chance".format(month),
-               x_axis_label='Year',
-               y_axis_label='Precipitation Chance Percentage')
+    f = figure(title="{} Average Precipitation Chance".format(month),
+               x_axis_label='Year',
+               y_axis_label='Precipitation Chance Percentage')
     for city in cities:
         f.line(years, [x[0] for x in month_averages[city]],
                legend=city, color=city_colors[city])
diff --git a/complaining-about-the-weather.html b/complaining-about-the-weather.html
index 3dce470..08d7adc 100644
--- a/complaining-about-the-weather.html
+++ b/complaining-about-the-weather.html
@@ -92,7 +92,7 @@
 
In [1]:

 

     

-
from bokeh.plotting import figure, output_notebook, show
+
from bokeh.plotting import figure, output_notebook, show
 from bokeh.palettes import PuBuGn9 as Palette
 import pandas as pd
 import numpy as np
@@ -352,7 +352,7 @@
 
In [2]:

 

     

-
city_forecasts = pickle.load(open('city_forecasts.p', 'rb'))
+
city_forecasts = pickle.load(open('city_forecasts.p', 'rb'))
 forecast_df = pd.DataFrame.from_dict(city_forecasts)
 

 
@@ -366,31 +366,31 @@
 
In [3]:

 

     

-
cary_forecast = forecast_df['cary']
+
cary_forecast = forecast_df['cary']
 years = range(1990, 2016)
 months = range(7, 12)
-months_str = ['July', 'August', 'September', 'October', 'November']
+months_str = ['July', 'August', 'September', 'October', 'November']
 
 def safe_cover(frame):
-    if frame and 'cloudCover' in frame:
-        return frame['cloudCover']
+    if frame and 'cloudCover' in frame:
+        return frame['cloudCover']
     else:
         return np.NaN
 
 def monthly_avg_cloudcover(year, month):
     dates = pd.DatetimeIndex(start=datetime(year, month, 1, 12),
                              end=datetime(year, month + 1, 1, 12),
-                             freq='D', closed='left')
-    cloud_cover_vals = list(map(lambda x: safe_cover(cary_forecast[x]['currently']), dates))
+                             freq='D', closed='left')
+    cloud_cover_vals = list(map(lambda x: safe_cover(cary_forecast[x]['currently']), dates))
     cloud_cover_samples = len(list(filter(lambda x: x is not np.NaN, cloud_cover_vals)))
     return np.nanmean(cloud_cover_vals), cloud_cover_samples
 
 
 monthly_cover_vals = [[monthly_avg_cloudcover(y, m)[0] for y in years] for m in months]
 
-f = figure(title='Monthly Average Cloud Cover',
+f = figure(title='Monthly Average Cloud Cover',
            x_range=(1990, 2015),
-          x_axis_label='Year')
+          x_axis_label='Year')
 for x in range(0, len(months)):
     f.line(years, monthly_cover_vals[x], legend=months_str[x], color=Palette[x])
 show(f)
@@ -454,11 +454,11 @@
 
In [4]:

 

     

-
def monthly_cloudy_days(year, month):
+
def monthly_cloudy_days(year, month):
     dates = pd.DatetimeIndex(start=datetime(year, month, 1, 12),
                              end=datetime(year, month + 1, 1, 12),
-                             freq='D', closed='left')
-    cloud_cover_vals = list(map(lambda x: safe_cover(cary_forecast[x]['currently']), dates))
+                             freq='D', closed='left')
+    cloud_cover_vals = list(map(lambda x: safe_cover(cary_forecast[x]['currently']), dates))
     cloud_cover_samples = len(list(filter(lambda x: x is not np.NaN, cloud_cover_vals)))
     cloudy_days = [cover > .5 for cover in cloud_cover_vals]
     return np.count_nonzero(cloudy_days), cloud_cover_samples
@@ -466,16 +466,16 @@
 monthly_days_vals = [[monthly_cloudy_days(y, m)[0] for y in years] for m in months]
 monthly_cover_samples = [[monthly_cloudy_days(y, m)[1] for y in years] for m in months]
 
-f = figure(title='Monthly Cloudy Days',
+f = figure(title='Monthly Cloudy Days',
            x_range=(1990, 2015),
-          x_axis_label='Year')
+          x_axis_label='Year')
 for x in range(0, len(months)):
     f.line(years, monthly_days_vals[x], legend=months_str[x], color=Palette[x])
 show(f)
 
-f = figure(title='Monthly Cloud Cover Samples',
+f = figure(title='Monthly Cloud Cover Samples',
           x_range=(1990, 2015),
-          x_axis_label='Year',
+          x_axis_label='Year',
           height=300)
 for x in range(0, len(months)):
     f.line(years, monthly_cover_samples[x], legend=months_str[x], color=Palette[x])
@@ -570,25 +570,25 @@
 
In [5]:

 

     

-
def safe_precip(frame):
-    if frame and 'precipProbability' in frame:
-        return frame['precipProbability']
+
def safe_precip(frame):
+    if frame and 'precipProbability' in frame:
+        return frame['precipProbability']
     else:
         return np.NaN
 
 def monthly_avg_precip(year, month):
     dates = pd.DatetimeIndex(start=datetime(year, month, 1, 12),
                              end=datetime(year, month + 1, 1, 12),
-                             freq='D', closed='left')
-    precip_vals = list(map(lambda x: safe_precip(cary_forecast[x]['currently']), dates))
+                             freq='D', closed='left')
+    precip_vals = list(map(lambda x: safe_precip(cary_forecast[x]['currently']), dates))
     precip_samples = len(list(filter(lambda x: x is not np.NaN, precip_vals)))
     return np.nanmean(precip_vals), precip_samples
 
 monthly_avg_precip_vals = [[monthly_avg_precip(y, m)[0] for y in years] for m in months]
 
-f = figure(title='Monthly Average Precipitation Chance',
+f = figure(title='Monthly Average Precipitation Chance',
            x_range=(1990, 2015),
-          x_axis_label='Year')
+          x_axis_label='Year')
 for x in range(0, len(months)):
     f.line(years, monthly_avg_precip_vals[x], legend=months_str[x], color=Palette[x])
 show(f)
@@ -651,11 +651,11 @@
 
In [6]:

 

     

-
def monthly_rainy_days(year, month):
+
def monthly_rainy_days(year, month):
     dates = pd.DatetimeIndex(start=datetime(year, month, 1, 12),
                              end=datetime(year, month + 1, 1, 12),
-                             freq='D', closed='left')
-    precip_prob_vals = list(map(lambda x: safe_precip(cary_forecast[x]['currently']), dates))
+                             freq='D', closed='left')
+    precip_prob_vals = list(map(lambda x: safe_precip(cary_forecast[x]['currently']), dates))
     precip_prob_samples = len(list(filter(lambda x: x is not np.NaN, precip_prob_vals)))
     precip_days = [prob > .25 for prob in precip_prob_vals]
     return np.count_nonzero(precip_days), precip_prob_samples
@@ -663,16 +663,16 @@
 monthly_precip_days_vals = [[monthly_rainy_days(y, m)[0] for y in years] for m in months]
 monthly_precip_samples = [[monthly_rainy_days(y, m)[1] for y in years] for m in months]
 
-f = figure(title='Monthly Rainy Days',
+f = figure(title='Monthly Rainy Days',
            x_range=(1990, 2015),
-          x_axis_label='Year')
+          x_axis_label='Year')
 for x in range(0, len(months)):
     f.line(years, monthly_precip_days_vals[x], legend=months_str[x], color=Palette[x])
 show(f)
 
-f = figure(title='Monthly Rainy Days Samples',
+f = figure(title='Monthly Rainy Days Samples',
           x_range=(1990, 2015),
-          x_axis_label='Year',
+          x_axis_label='Year',
           height=300)
 for x in range(0, len(months)):
     f.line(years, monthly_precip_samples[x], legend=months_str[x], color=Palette[x])
@@ -770,69 +770,69 @@
 

 

 
Generating the Forecast file
The following code was generates the file that was used throughout the blog post. Please note that I'm retrieving data for other cities to use in a future blog post, only Cary data was used for this post.

-
import pandas as pd
+
import pandas as pd
 from functools import reduce
 import requests
 from datetime import datetime
 
-# Coordinate data from http://itouchmap.com/latlong.html
+# Coordinate data from http://itouchmap.com/latlong.html
 cary_loc = (35.79154,-78.781117)
 nyc_loc = (40.78306,-73.971249)
 seattle_loc = (47.60621,-122.332071)
 binghamton_loc = (42.098687,-75.917974)
 cities = {
-    'cary': cary_loc,
-    'nyc': nyc_loc,
-    'seattle': seattle_loc,
-    'binghamton': binghamton_loc
+    'cary': cary_loc,
+    'nyc': nyc_loc,
+    'seattle': seattle_loc,
+    'binghamton': binghamton_loc
 }
 
-apikey = '' # My super-secret API Key
+apikey = '' # My super-secret API Key
 
 def get_forecast(lat, long, date=None):
-    forecast_base = "https://api.forecast.io/forecast/"
+    forecast_base = "https://api.forecast.io/forecast/"
     if date is None:
-        url = forecast_base + apikey + '/{},{}'.format(lat, long)
+        url = forecast_base + apikey + '/{},{}'.format(lat, long)
     else:
         epoch = int(date.timestamp())
-        url = forecast_base + apikey + '/{},{},{}'.format(lat, long, epoch)
+        url = forecast_base + apikey + '/{},{},{}'.format(lat, long, epoch)
 
     return requests.get(url).json()
 
 years = range(1990,2016)
-# For datetimes, the 12 is for getting the weather at noon.
-# We're doing this over midnight because we're more concerned
-# with what people see, and people don't typically see the weather
-# at midnight.
+# For datetimes, the 12 is for getting the weather at noon.
+# We're doing this over midnight because we're more concerned
+# with what people see, and people don't typically see the weather
+# at midnight.
 dt_indices = [pd.date_range(start=datetime(year, 7, 1, 12),
                           end=datetime(year, 11, 30, 12))
               for year in years]
 dt_merge = reduce(lambda x, y: x.union(y), dt_indices)
 
-# Because we have to pay a little bit to use the API, we use for loops here
-# instead of a comprehension - if something breaks, we want to preserve the
-# data already retrieved
+# Because we have to pay a little bit to use the API, we use for loops here
+# instead of a comprehension - if something breaks, we want to preserve the
+# data already retrieved
 city_forecasts = {}
 for city, loc in cities.items():
-    print("Retrieving data for {} starting at {}".format(city,
-                                                         datetime.now().strftime("%I:%M:%S %p")))
+    print("Retrieving data for {} starting at {}".format(city,
+                                                         datetime.now().strftime("%I:%M:%S %p")))
     for dt in dt_merge:
         try:
             city_forecasts[(city, dt)] = get_forecast(*loc, dt)
         except Exception as e:
             print(e)
             city_forecasts[(city, dt)] = None
-print("End forecast retrieval: {}".format(datetime.now().strftime("%I:%M:%S %p")))
+print("End forecast retrieval: {}".format(datetime.now().strftime("%I:%M:%S %p")))
 
 import pickle
-pickle.dump(city_forecasts, open('city_forecasts.p', 'wb'))
+pickle.dump(city_forecasts, open('city_forecasts.p', 'wb'))
 
-### Output:
-# Retrieving data for binghamton starting at 05:13:42 PM
-# Retrieving data for seattle starting at 05:30:51 PM
-# Retrieving data for nyc starting at 05:48:30 PM
-# Retrieving data for cary starting at 06:08:32 PM
-# End forecast retrieval: 06:25:21 PM
+### Output:
+# Retrieving data for binghamton starting at 05:13:42 PM
+# Retrieving data for seattle starting at 05:30:51 PM
+# Retrieving data for nyc starting at 05:48:30 PM
+# Retrieving data for cary starting at 06:08:32 PM
+# End forecast retrieval: 06:25:21 PM
 

 
 

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+        Event Studies and Earnings Releases - Bradlee Speice
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Event Studies and Earnings Releases

+                      
 Bradlee Speice, Wed 08 June 2016,  Blog

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+earnings, event study                      

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Or, being suspicious of market insiders.

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Use the button below to show the code I've used to generate this article. Because there is a significant amount more code involved than most other posts I've written, it's hidden by default to allow people to concentrate on the important bits.

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In [1]:

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from IPython.display import HTML
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+HTML('''<script>
+code_show=true; 
+function code_toggle() {
+ if (code_show){
+ $('div.input').hide();
+ } else {
+ $('div.input').show();
+ }
+ code_show = !code_show
+} 
+$( document ).ready(code_toggle);
+</script>
+<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>''')
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The Market Just Knew
I recently saw two examples of stock charts that have kept me thinking for a while. And now that the semester is complete, I finally have enough time to really look at them and give them the treatment they deserve. The first is good old Apple:

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In [2]:

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from secrets import QUANDL_KEY
+import matplotlib.pyplot as plt
+from matplotlib.dates import date2num
+from matplotlib.finance import candlestick_ohlc
+from matplotlib.dates import DateFormatter, WeekdayLocator,\
+    DayLocator, MONDAY
+import quandl
+from datetime import datetime
+import pandas as pd
+%matplotlib inline
+
+def fetch_ticker(ticker, start, end):
+    # Quandl is currently giving me issues with returning
+    # the entire dataset and not slicing server-side.
+    # So instead, we'll do it client-side!
+    q_format = '%Y-%m-%d'
+    ticker_data = quandl.get('YAHOO/' + ticker,
+                             start_date=start.strftime(q_format),
+                             end_date=end.strftime(q_format),
+                             authtoken=QUANDL_KEY)
+    return ticker_data
+
+def ohlc_dataframe(data, ax=None):
+    # Much of this code re-used from:
+    # http://matplotlib.org/examples/pylab_examples/finance_demo.html
+    if ax is None:
+        f, ax = plt.subplots()
+    
+    vals = [(date2num(date), *(data.loc[date]))
+            for date in data.index]
+    candlestick_ohlc(ax, vals)
+    
+    mondays = WeekdayLocator(MONDAY)
+    alldays = DayLocator()
+    weekFormatter = DateFormatter('%b %d')
+    ax.xaxis.set_major_locator(mondays)
+    ax.xaxis.set_minor_locator(alldays)
+    ax.xaxis.set_major_formatter(weekFormatter)
+    return ax
+
+AAPL = fetch_ticker('AAPL', datetime(2016, 3, 1), datetime(2016, 5, 1))
+ax = ohlc_dataframe(AAPL)
+plt.vlines(date2num(datetime(2016, 4, 26, 12)),
+           ax.get_ylim()[0], ax.get_ylim()[1],
+           color='b',
+          label='Earnings Release')
+plt.legend(loc=3)
+plt.title("Apple Price 3/1/2016 - 5/1/2016");
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The second chart is from Facebook:

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In [3]:

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FB = fetch_ticker('FB', datetime(2016, 3, 1), datetime(2016, 5, 5))
+ax = ohlc_dataframe(FB)
+plt.vlines(date2num(datetime(2016, 4, 27, 12)),
+           ax.get_ylim()[0], ax.get_ylim()[1],
+           color='b', label='Earnings Release')
+plt.title('Facebook Price 3/5/2016 - 5/5/2016')
+plt.legend(loc=2);
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These two charts demonstrate two very specific phonomena: how the market prepares for earnings releases. Let's look at those charts again, but with some extra information. As we're about the see, the market "knew" in advance that Apple was going to perform poorly. The market expected that Facebook was going to perform poorly, and instead shot the lights out. Let's see that trend in action:

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In [4]:

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def plot_hilo(ax, start, end, data):
+    ax.plot([date2num(start), date2num(end)],
+            [data.loc[start]['High'], data.loc[end]['High']],
+            color='b')
+    ax.plot([date2num(start), date2num(end)],
+            [data.loc[start]['Low'], data.loc[end]['Low']],
+            color='b')
+
+f, axarr = plt.subplots(1, 2)
+
+ax_aapl = axarr[0]
+ax_fb = axarr[1]
+
+# Plot the AAPL trend up and down
+ohlc_dataframe(AAPL, ax=ax_aapl)
+plot_hilo(ax_aapl, datetime(2016, 3, 1), datetime(2016, 4, 15), AAPL)
+plot_hilo(ax_aapl, datetime(2016, 4, 18), datetime(2016, 4, 26), AAPL)
+ax_aapl.vlines(date2num(datetime(2016, 4, 26, 12)),
+               ax_aapl.get_ylim()[0], ax_aapl.get_ylim()[1],
+               color='g', label='Earnings Release')
+ax_aapl.legend(loc=2)
+ax_aapl.set_title('AAPL Price History')
+
+# Plot the FB trend down and up
+ohlc_dataframe(FB, ax=ax_fb)
+plot_hilo(ax_fb, datetime(2016, 3, 30), datetime(2016, 4, 27), FB)
+plot_hilo(ax_fb, datetime(2016, 4, 28), datetime(2016, 5, 5), FB)
+ax_fb.vlines(date2num(datetime(2016, 4, 27, 12)),
+             ax_fb.get_ylim()[0], ax_fb.get_ylim()[1],
+             color='g', label='Earnings Release')
+ax_fb.legend(loc=2)
+ax_fb.set_title('FB Price History')
+
+f.set_size_inches(18, 6)
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As we can see above, the market broke a prevailing trend on Apple in order to go down, and ultimately predict the earnings release. For Facebook, the opposite happened. While the trend was down, the earnings were fantastic and the market corrected itself much higher.

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Formulating the Question
While these are two specific examples, there are plenty of other examples you could cite one way or another. Even if the preponderance of evidence shows that the market correctly predicts earnings releases, we need not accuse people of collusion; for a company like Apple with many suppliers we can generally forecast how Apple has done based on those same suppliers.

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The question then, is this: how well does the market predict the earnings releases? It's an incredibly broad question that I want to disect in a couple of different ways:

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  1. Given a stock that has been trending down over the past N days before an earnings release, how likely does it continue downward after the release?
    2. 
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  2. Given a stock trending up, how likely does it continue up?
    3. 
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  3. Is there a difference in accuracy between large- and small-cap stocks?
    4. 
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  4. How often, and for how long, do markets trend before an earnings release?
    5. 
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+
I want to especially thank Alejandro Saltiel for helping me retrieve the data. He's great. And now for all of the interesting bits.

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Event Studies
Before we go too much further, I want to introduce the actual event study. Each chart intends to capture a lot of information and present an easy-to-understand pattern:

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In [5]:

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import numpy as np
+import pandas as pd
+from pandas.tseries.holiday import USFederalHolidayCalendar
+from pandas.tseries.offsets import CustomBusinessDay
+from datetime import datetime, timedelta
+
+# If you remove rules, it removes them from *all* calendars
+# To ensure we don't pop rules we don't want to, first make
+# sure to fully copy the object
+trade_calendar = USFederalHolidayCalendar()
+trade_calendar.rules.pop(6) # Remove Columbus day
+trade_calendar.rules.pop(7) # Remove Veteran's day
+TradeDay = lambda days: CustomBusinessDay(days, calendar=trade_calendar)
+
+def plot_study(array):
+    # Given a 2-d array, we assume the event happens at index `lookback`,
+    # and create all of our summary statistics from there.
+    lookback = int((array.shape[1] - 1) / 2)
+    norm_factor = np.repeat(array[:,lookback].reshape(-1, 1), array.shape[1], axis=1)
+    centered_data = array / norm_factor - 1
+    lookforward = centered_data.shape[1] - lookback
+    means = centered_data.mean(axis=0)
+    lookforward_data = centered_data[:,lookforward:]
+    std_dev = np.hstack([0, lookforward_data.std(axis=0)])
+    maxes = lookforward_data.max(axis=0)
+    mins = lookforward_data.min(axis=0)
+    
+    f, axarr = plt.subplots(1, 2)
+    range_begin = -lookback
+    range_end = lookforward
+    axarr[0].plot(range(range_begin, range_end), means)
+    axarr[1].plot(range(range_begin, range_end), means)
+    axarr[0].fill_between(range(0, range_end),
+                     means[-lookforward:] + std_dev,
+                     means[-lookforward:] - std_dev,
+                    alpha=.5, label="$\pm$ 1 s.d.")
+    axarr[1].fill_between(range(0, range_end),
+                     means[-lookforward:] + std_dev,
+                     means[-lookforward:] - std_dev,
+                    alpha=.5, label="$\pm$ 1 s.d.")
+    
+    max_err = maxes - means[-lookforward+1:]
+    min_err = means[-lookforward+1:] - mins
+    axarr[0].errorbar(range(1, range_end),
+                  means[-lookforward+1:],
+                  yerr=[min_err, max_err], label='Max & Min')
+    axarr[0].legend(loc=2)
+    axarr[1].legend(loc=2)
+    
+    axarr[0].set_xlim((-lookback-1, lookback+1))
+    axarr[1].set_xlim((-lookback-1, lookback+1))
+    
+def plot_study_small(array):
+    # Given a 2-d array, we assume the event happens at index `lookback`,
+    # and create all of our summary statistics from there.
+    lookback = int((array.shape[1] - 1) / 2)
+    norm_factor = np.repeat(array[:,lookback].reshape(-1, 1), array.shape[1], axis=1)
+    centered_data = array / norm_factor - 1
+    lookforward = centered_data.shape[1] - lookback
+    means = centered_data.mean(axis=0)
+    lookforward_data = centered_data[:,lookforward:]
+    std_dev = np.hstack([0, lookforward_data.std(axis=0)])
+    maxes = lookforward_data.max(axis=0)
+    mins = lookforward_data.min(axis=0)
+    
+    range_begin = -lookback
+    range_end = lookforward
+    plt.plot(range(range_begin, range_end), means)
+    plt.fill_between(range(0, range_end),
+                     means[-lookforward:] + std_dev,
+                     means[-lookforward:] - std_dev,
+                    alpha=.5, label="$\pm$ 1 s.d.")
+    
+    max_err = maxes - means[-lookforward+1:]
+    min_err = means[-lookforward+1:] - mins
+    plt.errorbar(range(1, range_end),
+                  means[-lookforward+1:],
+                  yerr=[min_err, max_err], label='Max & Min')
+    plt.legend(loc=2)
+    plt.xlim((-lookback-1, lookback+1))
+    
+def fetch_event_data(ticker, events, horizon=5):
+    # Use horizon+1 to account for including the day of the event,
+    # and half-open interval - that is, for a horizon of 5,
+    # we should be including 11 events. Additionally, using the
+    # CustomBusinessDay means we automatically handle issues if
+    # for example a company reports Friday afternoon - the date
+    # calculator will turn this into a "Saturday" release, but
+    # we effectively shift that to Monday with the logic below.
+    td_back = TradeDay(horizon+1)
+    td_forward = TradeDay(horizon+1)
+    
+    start_date = min(events) - td_back
+    end_date = max(events) + td_forward
+    total_data = fetch_ticker(ticker, start_date, end_date)
+    event_data = [total_data.ix[event-td_back:event+td_forward]\
+                      [0:horizon*2+1]\
+                      ['Adjusted Close']
+                  for event in events]
+    return np.array(event_data)
+
+# Generate a couple of random events
+
+event_dates = [datetime(2016, 5, 27) - timedelta(days=1) - TradeDay(x*20) for x in range(1, 40)]
+data = fetch_event_data('CELG', event_dates)
+plot_study_small(data)
+plt.legend(loc=3)
+plt.gcf().set_size_inches(12, 6);
+
+
+plt.annotate('Mean price for days leading up to each event',
+             (-5, -.01), (-4.5, .025),
+             arrowprops=dict(facecolor='black', shrink=0.05))
+plt.annotate('', (-.1, .005), (-.5, .02),
+             arrowprops={'facecolor': 'black', 'shrink': .05})
+plt.annotate('$\pm$ 1 std. dev. each day', (5, .055), (2.5, .085),
+            arrowprops={'facecolor': 'black', 'shrink': .05})
+plt.annotate('Min/Max each day', (.9, -.07), (-1, -.1),
+            arrowprops={'facecolor': 'black', 'shrink': .05});
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And as a quick textual explanation as well:

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  • The blue line represents the mean price for each day, represented as a percentage of the price on the '0-day'. For example, if we defined an 'event' as whenever the stock price dropped for three days, we would see a decreasing blue line to the left of the 0-day.
    
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  • The blue shaded area represents one standard deviation above and below the mean price for each day following an event. This is intended to give us an idea of what the stock price does in general following an event.
    
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  • The green bars are the minimum and maximum price for each day following an event. This instructs us as to how much it's possible for the stock to move.
    
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Event Type 1: Trending down over the past N days
The first type of event I want to study is how stocks perform when they've been trending down over the past couple of days prior to a release. However, we need to clarify what exactly is meant by "trending down." To do so, we'll use the following metric: the midpoint between each day's opening and closing price goes down over a period of N days.

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It's probably helpful to have an example:

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+
In [6]:

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+    

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f, axarr = plt.subplots(1, 2)
+f.set_size_inches(18, 6)
+
+FB_plot = axarr[0]
+ohlc_dataframe(FB[datetime(2016, 4, 18):], FB_plot)
+
+FB_truncated = FB[datetime(2016, 4, 18):datetime(2016, 4, 27)]
+midpoint = FB_truncated['Open']/2 + FB_truncated['Close']/2
+FB_plot.plot(FB_truncated.index, midpoint, label='Midpoint')
+FB_plot.vlines(date2num(datetime(2016, 4, 27, 12)),
+               ax_fb.get_ylim()[0], ax_fb.get_ylim()[1],
+               color='g', label='Earnings Release')
+FB_plot.legend(loc=2)
+FB_plot.set_title('FB Midpoint Plot')
+
+AAPL_plot = axarr[1]
+ohlc_dataframe(AAPL[datetime(2016, 4, 10):], AAPL_plot)
+AAPL_truncated = AAPL[datetime(2016, 4, 10):datetime(2016, 4, 26)]
+midpoint = AAPL_truncated['Open']/2 + AAPL_truncated['Close']/2
+AAPL_plot.plot(AAPL_truncated.index, midpoint, label='Midpoint')
+AAPL_plot.vlines(date2num(datetime(2016, 4, 26, 12)),
+                 ax_aapl.get_ylim()[0], ax_aapl.get_ylim()[1],
+                 color='g', label='Earnings Release')
+AAPL_plot.legend(loc=3)
+AAPL_plot.set_title('AAPL Midpoint Plot');
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Given these charts, we can see that FB was trending down for the four days preceding the earnings release, and AAPL was trending down for a whopping 8 days (we don't count the peak day). This will define the methodology that we will use for the study.

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So what are the results? For a given horizon, how well does the market actually perform?

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In [7]:

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# Read in the events for each stock;
+# The file was created using the first code block in the Appendix
+import yaml
+from dateutil.parser import parse
+from progressbar import ProgressBar
+
+data_str = open('earnings_dates.yaml', 'r').read()
+# Need to remove invalid lines
+filtered = filter(lambda x: '{' not in x, data_str.split('\n'))
+earnings_data = yaml.load('\n'.join(filtered))
+
+# Convert our earnings data into a list of (ticker, date) pairs
+# to make it easy to work with.
+# This is horribly inefficient, but should get us what we need
+ticker_dates = []
+for ticker, date_list in earnings_data.items():
+    for iso_str in date_list:
+        ticker_dates.append((ticker, parse(iso_str)))
+
+def does_trend_down(ticker, event, horizon):
+    # Figure out if the `event` has a downtrend for
+    # the `horizon` days preceding it
+    # As an interpretation note: it is assumed that
+    # the closing price of day `event` is the reference
+    # point, and we want `horizon` days before that.
+    # The price_data.hdf was created in the second appendix code block
+    try:
+        ticker_data = pd.read_hdf('price_data.hdf', ticker)
+        data = ticker_data[event-TradeDay(horizon):event]
+        midpoints = data['Open']/2 + data['Close']/2
+
+        # Shift dates one forward into the future and subtract
+        # Effectively: do we trend down over all days?
+        elems = midpoints - midpoints.shift(1)
+        return len(elems)-1 == len(elems.dropna()[elems <= 0])
+    except KeyError:
+        # If the stock doesn't exist, it doesn't qualify as trending down
+        # Mostly this is here to make sure the entire analysis doesn't 
+        # blow up if there were issues in data retrieval
+        return False
+
+def study_trend(horizon, trend_function):
+    five_day_events = np.zeros((1, horizon*2 + 1))
+    invalid_events = []
+    for ticker, event in ProgressBar()(ticker_dates):
+        if trend_function(ticker, event, horizon):
+            ticker_data = pd.read_hdf('price_data.hdf', ticker)
+            event_data = ticker_data[event-TradeDay(horizon):event+TradeDay(horizon)]['Close']
+
+            try:
+                five_day_events = np.vstack([five_day_events, event_data])
+            except ValueError:
+                # Sometimes we don't get exactly the right number of values due to calendar
+                # issues. I've fixed most everything I can, and the few issues that are left
+                # I assume don't systemically bias the results (i.e. data could be missing
+                # because it doesn't exist, etc.). After running through, ~1% of events get
+                # discarded this way
+                invalid_events.append((ticker, event))
+            
+
+    # Remove our initial zero row
+    five_day_events = five_day_events[1:,:]
+    plot_study(five_day_events)
+    plt.gcf().suptitle('Action over {} days: {} events'
+                       .format(horizon,five_day_events.shape[0]))
+    plt.gcf().set_size_inches(18, 6)
+    
+# Start with a 5 day study
+study_trend(5, does_trend_down)
+

+
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When a stock has been trending down for 5 days, once the earnings are announced it really doesn't move on average. However, the variability is incredible. This implies two important things:

+
    
+
  1. The market is just as often wrong about an earnings announcement before it happens as it is correct
    2. 
+
  2. The incredible width of the min/max bars and standard deviation area tell us that the market reacts violently after the earnings are released.
    3. 
+

+
Let's repeat the same study, but over a time horizon of 8 days and 3 days. Presumably if a stock has been going down for 8 days at a time before the earnings, the market should be more accurate.

+
+

+

+

+

+

+
In [8]:

+

+    

+
# 8 day study next
+study_trend(8, does_trend_down)
+

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However, looking only at stocks that trended down for 8 days prior to a release, the same pattern emerges: on average, the stock doesn't move, but the market reaction is often incredibly violent.

+
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+

+

+

+
In [9]:

+

+    

+
# 3 day study after that
+study_trend(3, does_trend_down)
+

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Finally, when we look at a 3-day horizon, we start getting some incredible outliers. Stocks have a potential to move over ~300% up, and the standard deviation width is again, incredible. The results for a 3-day horizon follow the same pattern we've seen in the 5- and 8-day horizons.

+
+

+

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+

+
Event Type 2: Trending up for N days
We're now going to repeat the analysis, but do it for uptrends instead. That is, instead of looking at stocks that have been trending down over the past number of days, we focus only on stocks that have been trending up.

+
+

+

+

+

+

+
In [10]:

+

+    

+
def does_trend_up(ticker, event, horizon):
+    # Figure out if the `event` has an uptrend for
+    # the `horizon` days preceding it
+    # As an interpretation note: it is assumed that
+    # the closing price of day `event` is the reference
+    # point, and we want `horizon` days before that.
+    # The price_data.hdf was created in the second appendix code block
+    try:
+        ticker_data = pd.read_hdf('price_data.hdf', ticker)
+        data = ticker_data[event-TradeDay(horizon):event]
+        midpoints = data['Open']/2 + data['Close']/2
+
+        # Shift dates one forward into the future and subtract
+        # Effectively: do we trend down over all days?
+        elems = midpoints - midpoints.shift(1)
+        return len(elems)-1 == len(elems.dropna()[elems >= 0])
+    except KeyError:
+        # If the stock doesn't exist, it doesn't qualify as trending down
+        # Mostly this is here to make sure the entire analysis doesn't 
+        # blow up if there were issues in data retrieval
+        return False
+
+study_trend(5, does_trend_up)
+

+
+

+

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+
The patterns here are very similar. With the exception of noting that stocks can go to nearly 400% after an earnings announcement (most likely this included a takeover announcement, etc.), we still see large min/max bars and wide standard deviation of returns.

+
We'll repeat the pattern for stocks going up for both 8 and 3 days straight, but at this point, the results should be very predictable:

+
+

+

+

+

+

+
In [11]:

+

+    

+
study_trend(8, does_trend_up)
+

+
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+

+

+
In [12]:

+

+    

+
study_trend(3, does_trend_up)
+

+
+

+

+

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100% (47578 of 47578) |###########################################################| Elapsed Time: 0:26:56 Time: 0:26:56
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+
Conclusion and Summary
I guess the most important thing to summarize with is this: looking at the entire market, stock performance prior to an earnings release has no bearing on the stock's performance. Honestly: given the huge variability of returns after an earnings release, even when the stock has been trending for a long time, you're best off divesting before an earnings release and letting the market sort itself out.

+
However, there is a big caveat. These results are taken when we look at the entire market. So while we can say that the market as a whole knows nothing and just reacts violently, I want to take a closer look into this data. Does the market typically perform poorly on large-cap/high liquidity stocks? Do smaller companies have investors that know them better and can thus predict performance better? Are specific market sectors better at prediction? Presumably technology stocks are more volatile than the industrials.

+
So there are some more interesting questions I still want to ask with this data. Knowing that the hard work of data processing is largely already done, it should be fairly simple to continue this analysis and get much more refined with it. Until next time.

+
+

+

+

+

+

+

+

+

+
Appendix
Export event data for Russell 3000 companies:

+
import pandas as pd
+from html.parser import HTMLParser
+from datetime import datetime, timedelta
+import requests
+import re
+from dateutil import parser
+import progressbar
+from concurrent import futures
+import yaml
+
+class EarningsParser(HTMLParser):
+    store_dates = False
+    earnings_offset = None
+    dates = []
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.dates = []
+
+    def handle_starttag(self, tag, attrs):
+        if tag == 'table':
+            self.store_dates = True
+
+    def handle_data(self, data):
+        if self.store_dates:
+            match = re.match(r'\d+/\d+/\d+', data)
+            if match:
+                self.dates.append(match.group(0))
+
+        # If a company reports before the bell, record the earnings date
+        # being at midnight the day before. Ex: WMT reports 5/19/2016,
+        # but we want the reference point to be the closing price on 5/18/2016
+        if 'After Close' in data:
+            self.earnings_offset = timedelta(days=0)
+        elif 'Before Open' in data:
+            self.earnings_offset = timedelta(days=-1)
+
+    def handle_endtag(self, tag):
+        if tag == 'table':
+            self.store_dates = False
+
+def earnings_releases(ticker):
+    #print("Looking up ticker {}".format(ticker))
+    user_agent = 'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:46.0) '\
+        'Gecko/20100101 Firefox/46.0'
+    headers = {'user-agent': user_agent}
+    base_url = 'http://www.streetinsider.com/ec_earnings.php?q={}'\
+        .format(ticker)
+    e = EarningsParser()
+    s = requests.Session()
+    a = requests.adapters.HTTPAdapter(max_retries=0)
+    s.mount('http://', a)
+    e.feed(str(s.get(base_url, headers=headers).content))
+
+    if e.earnings_offset is not None:
+        dates = map(lambda x: parser.parse(x) + e.earnings_offset, e.dates)
+        past = filter(lambda x: x < datetime.now(), dates)
+        return list(map(lambda d: d.isoformat(), past))
+
+# Use a Russell-3000 ETF tracker (ticker IWV) to get a list of holdings
+r3000 = pd.read_csv('https://www.ishares.com/us/products/239714/'
+                    'ishares-russell-3000-etf/1449138789749.ajax?'
+                    'fileType=csv&fileName=IWV_holdings&dataType=fund',
+                    header=10)
+r3000_equities = r3000[(r3000['Exchange'] == 'NASDAQ') |
+                       (r3000['Exchange'] == 'New York Stock Exchange Inc.')]
+
+dates_file = open('earnings_dates.yaml', 'w')
+
+with futures.ThreadPoolExecutor(max_workers=8) as pool:
+    fs = {pool.submit(earnings_releases, r3000_equities.ix[t]['Ticker']): t
+          for t in r3000_equities.index}
+    pbar = progressbar.ProgressBar(term_width=80,
+                                   max_value=r3000_equities.index.max())
+
+    for future in futures.as_completed(fs):
+        i = fs[future]
+        pbar.update(i)
+        dates_file.write(yaml.dump({r3000_equities.ix[i]['Ticker']:
+            future.result()}))
+

+
Downloading stock price data needed for the event studies:

+
from secrets import QUANDL_KEY
+import pandas as pd
+import yaml
+from dateutil.parser import parse
+from datetime import timedelta
+import quandl
+from progressbar import ProgressBar
+
+def fetch_ticker(ticker, start, end):
+    # Quandl is currently giving me issues with returning
+    # the entire dataset and not slicing server-side.
+    # So instead, we'll do it client-side!
+    q_format = '%Y-%m-%d'
+    ticker_data = quandl.get('YAHOO/' + ticker,
+                             start_date=start.strftime(q_format),
+                             end_date=end.strftime(q_format),
+                             authtoken=QUANDL_KEY)
+    return ticker_data
+
+data_str = open('earnings_dates.yaml', 'r').read()
+# Need to remove invalid lines
+filtered = filter(lambda x: '{' not in x, data_str.split('\n'))
+earnings_data = yaml.load('\n'.join(filtered))
+
+# Get the first 1500 keys - split up into two statements
+# because of Quandl rate limits
+tickers = list(earnings_data.keys())
+
+price_dict = {}
+invalid_tickers = []
+for ticker in ProgressBar()(tickers[0:1500]):
+    try:
+        # Replace '.' with '-' in name for some tickers
+        fixed = ticker.replace('.', '-')
+        event_strs = earnings_data[ticker]
+        events = [parse(event) for event in event_strs]
+        td = timedelta(days=20)
+        price_dict[ticker] = fetch_ticker(fixed,
+            min(events)-td, max(events)+td)
+    except quandl.NotFoundError:
+        invalid_tickers.append(ticker)
+
+# Execute this after 10 minutes have passed
+for ticker in ProgressBar()(tickers[1500:]):
+    try:
+        # Replace '.' with '-' in name for some tickers
+        fixed = ticker.replace('.', '-')
+        event_strs = earnings_data[ticker]
+        events = [parse(event) for event in event_strs]
+        td = timedelta(days=20)
+        price_dict[ticker] = fetch_ticker(fixed,
+            min(events)-td, max(events)+td)
+    except quandl.NotFoundError:
+        invalid_tickers.append(ticker)
+
+prices_store = pd.HDFStore('price_data.hdf')
+for ticker, prices in price_dict.items():
+    prices_store[ticker] = prices
+

+
+

+

+

+
+
+
+
+
+    

+        

+            
+        
+    

+        
+    

+         
+
+        
+        

+            

+                

+                    

+                        

+                        
    
+                            
    • 
+                        

+                    

+                    

+                        

+                        
+                    

+                    

+                    
 
+                    

+                        

+                            Proudly powered by pelican

+                            Theme and code by molivier

+                            
+                        

+                    

+                

+            

+        

+        
+    
+
\ No newline at end of file
diff --git a/feeds/all.atom.xml b/feeds/all.atom.xml
index f436db0..90913f7 100644
--- a/feeds/all.atom.xml
+++ b/feeds/all.atom.xml
@@ -1,5 +1,5241 @@
 
-Bradlee Speicehttps://bspeice.github.io/2016-05-15T00:00:00-04:00The Unfair Casino2016-05-15T00:00:00-04:00Bradlee Speicetag:bspeice.github.io,2016-05-15:the-unfair-casino.html<p>
+Bradlee Speicehttps://bspeice.github.io/2016-06-08T00:00:00-04:00Event Studies and Earnings Releases2016-06-08T00:00:00-04:00Bradlee Speicetag:bspeice.github.io,2016-06-08:event-studies-and-earnings-releases.html<script type="text/javascript" src="https://cdn.jsdelivr.net/jquery/3.0.0/jquery.min.js"></script>
+
+<p>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Or, being suspicious of market insiders.</p>
+<hr>
+<p>Use the button below to show the code I've used to generate this article. Because there is a significant amount more code involved than most other posts I've written, it's hidden by default to allow people to concentrate on the important bits.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[1]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">HTML</span>
+
+<span class="n">HTML</span><span class="p">(</span><span class="s">&#39;&#39;&#39;&lt;script&gt;</span>
+<span class="s">code_show=true; </span>
+<span class="s">function code_toggle() {</span>
+<span class="s"> if (code_show){</span>
+<span class="s"> $(&#39;div.input&#39;).hide();</span>
+<span class="s"> } else {</span>
+<span class="s"> $(&#39;div.input&#39;).show();</span>
+<span class="s"> }</span>
+<span class="s"> code_show = !code_show</span>
+<span class="s">} </span>
+<span class="s">$( document ).ready(code_toggle);</span>
+<span class="s">&lt;/script&gt;</span>
+<span class="s">&lt;form action=&quot;javascript:code_toggle()&quot;&gt;&lt;input type=&quot;submit&quot; value=&quot;Click here to toggle on/off the raw code.&quot;&gt;&lt;/form&gt;&#39;&#39;&#39;</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt output_prompt">Out[1]:</div>
+
+<div class="output_html rendered_html output_subarea output_execute_result">
+<script>
+code_show=true; 
+function code_toggle() {
+ if (code_show){
+ $('div.input').hide();
+ } else {
+ $('div.input').show();
+ }
+ code_show = !code_show
+} 
+$( document ).ready(code_toggle);
+</script>
+<form action="javascript:code_toggle()"><input type="submit" value="Click here to toggle on/off the raw code."></form>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="The-Market-Just-Knew">The Market Just Knew<a class="anchor-link" href="#The-Market-Just-Knew">&#182;</a></h1><p>I recently saw two examples of stock charts that have kept me thinking for a while. And now that the semester is complete, I finally have enough time to really look at them and give them the treatment they deserve. The first is good old Apple:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[2]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="kn">from</span> <span class="nn">secrets</span> <span class="k">import</span> <span class="n">QUANDL_KEY</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib.dates</span> <span class="k">import</span> <span class="n">date2num</span>
+<span class="kn">from</span> <span class="nn">matplotlib.finance</span> <span class="k">import</span> <span class="n">candlestick_ohlc</span>
+<span class="kn">from</span> <span class="nn">matplotlib.dates</span> <span class="k">import</span> <span class="n">DateFormatter</span><span class="p">,</span> <span class="n">WeekdayLocator</span><span class="p">,</span>\
+    <span class="n">DayLocator</span><span class="p">,</span> <span class="n">MONDAY</span>
+<span class="kn">import</span> <span class="nn">quandl</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">datetime</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="o">%</span><span class="k">matplotlib</span> inline
+
+<span class="k">def</span> <span class="nf">fetch_ticker</span><span class="p">(</span><span class="n">ticker</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">):</span>
+    <span class="c"># Quandl is currently giving me issues with returning</span>
+    <span class="c"># the entire dataset and not slicing server-side.</span>
+    <span class="c"># So instead, we&#39;ll do it client-side!</span>
+    <span class="n">q_format</span> <span class="o">=</span> <span class="s">&#39;%Y-%m-%d&#39;</span>
+    <span class="n">ticker_data</span> <span class="o">=</span> <span class="n">quandl</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">&#39;YAHOO/&#39;</span> <span class="o">+</span> <span class="n">ticker</span><span class="p">,</span>
+                             <span class="n">start_date</span><span class="o">=</span><span class="n">start</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="n">q_format</span><span class="p">),</span>
+                             <span class="n">end_date</span><span class="o">=</span><span class="n">end</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="n">q_format</span><span class="p">),</span>
+                             <span class="n">authtoken</span><span class="o">=</span><span class="n">QUANDL_KEY</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ticker_data</span>
+
+<span class="k">def</span> <span class="nf">ohlc_dataframe</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="k">None</span><span class="p">):</span>
+    <span class="c"># Much of this code re-used from:</span>
+    <span class="c"># http://matplotlib.org/examples/pylab_examples/finance_demo.html</span>
+    <span class="k">if</span> <span class="n">ax</span> <span class="ow">is</span> <span class="k">None</span><span class="p">:</span>
+        <span class="n">f</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
+    
+    <span class="n">vals</span> <span class="o">=</span> <span class="p">[(</span><span class="n">date2num</span><span class="p">(</span><span class="n">date</span><span class="p">),</span> <span class="o">*</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">date</span><span class="p">]))</span>
+            <span class="k">for</span> <span class="n">date</span> <span class="ow">in</span> <span class="n">data</span><span class="o">.</span><span class="n">index</span><span class="p">]</span>
+    <span class="n">candlestick_ohlc</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">vals</span><span class="p">)</span>
+    
+    <span class="n">mondays</span> <span class="o">=</span> <span class="n">WeekdayLocator</span><span class="p">(</span><span class="n">MONDAY</span><span class="p">)</span>
+    <span class="n">alldays</span> <span class="o">=</span> <span class="n">DayLocator</span><span class="p">()</span>
+    <span class="n">weekFormatter</span> <span class="o">=</span> <span class="n">DateFormatter</span><span class="p">(</span><span class="s">&#39;%b %d&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_locator</span><span class="p">(</span><span class="n">mondays</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_minor_locator</span><span class="p">(</span><span class="n">alldays</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_major_formatter</span><span class="p">(</span><span class="n">weekFormatter</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ax</span>
+
+<span class="n">AAPL</span> <span class="o">=</span> <span class="n">fetch_ticker</span><span class="p">(</span><span class="s">&#39;AAPL&#39;</span><span class="p">,</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">1</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">ohlc_dataframe</span><span class="p">(</span><span class="n">AAPL</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">date2num</span><span class="p">(</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">26</span><span class="p">,</span> <span class="mi">12</span><span class="p">)),</span>
+           <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span>
+           <span class="n">color</span><span class="o">=</span><span class="s">&#39;b&#39;</span><span class="p">,</span>
+          <span class="n">label</span><span class="o">=</span><span class="s">&#39;Earnings Release&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&quot;Apple Price 3/1/2016 - 5/1/2016&quot;</span><span class="p">);</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The second chart is from Facebook:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[3]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">FB</span> <span class="o">=</span> <span class="n">fetch_ticker</span><span class="p">(</span><span class="s">&#39;FB&#39;</span><span class="p">,</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">))</span>
+<span class="n">ax</span> <span class="o">=</span> <span class="n">ohlc_dataframe</span><span class="p">(</span><span class="n">FB</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">date2num</span><span class="p">(</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">12</span><span class="p">)),</span>
+           <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ax</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span>
+           <span class="n">color</span><span class="o">=</span><span class="s">&#39;b&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;Earnings Release&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Facebook Price 3/5/2016 - 5/5/2016&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">);</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>These two charts demonstrate two very specific phonomena: how the market prepares for earnings releases. Let's look at those charts again, but with some extra information. As we're about the see, the market "knew" in advance that Apple was going to perform poorly. The market expected that Facebook was going to perform poorly, and instead shot the lights out. Let's see that trend in action:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[4]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">plot_hilo</span><span class="p">(</span><span class="n">ax</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">date2num</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="n">date2num</span><span class="p">(</span><span class="n">end</span><span class="p">)],</span>
+            <span class="p">[</span><span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">start</span><span class="p">][</span><span class="s">&#39;High&#39;</span><span class="p">],</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">end</span><span class="p">][</span><span class="s">&#39;High&#39;</span><span class="p">]],</span>
+            <span class="n">color</span><span class="o">=</span><span class="s">&#39;b&#39;</span><span class="p">)</span>
+    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">date2num</span><span class="p">(</span><span class="n">start</span><span class="p">),</span> <span class="n">date2num</span><span class="p">(</span><span class="n">end</span><span class="p">)],</span>
+            <span class="p">[</span><span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">start</span><span class="p">][</span><span class="s">&#39;Low&#39;</span><span class="p">],</span> <span class="n">data</span><span class="o">.</span><span class="n">loc</span><span class="p">[</span><span class="n">end</span><span class="p">][</span><span class="s">&#39;Low&#39;</span><span class="p">]],</span>
+            <span class="n">color</span><span class="o">=</span><span class="s">&#39;b&#39;</span><span class="p">)</span>
+
+<span class="n">f</span><span class="p">,</span> <span class="n">axarr</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+
+<span class="n">ax_aapl</span> <span class="o">=</span> <span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">ax_fb</span> <span class="o">=</span> <span class="n">axarr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+
+<span class="c"># Plot the AAPL trend up and down</span>
+<span class="n">ohlc_dataframe</span><span class="p">(</span><span class="n">AAPL</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax_aapl</span><span class="p">)</span>
+<span class="n">plot_hilo</span><span class="p">(</span><span class="n">ax_aapl</span><span class="p">,</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">15</span><span class="p">),</span> <span class="n">AAPL</span><span class="p">)</span>
+<span class="n">plot_hilo</span><span class="p">(</span><span class="n">ax_aapl</span><span class="p">,</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">18</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">26</span><span class="p">),</span> <span class="n">AAPL</span><span class="p">)</span>
+<span class="n">ax_aapl</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">date2num</span><span class="p">(</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">26</span><span class="p">,</span> <span class="mi">12</span><span class="p">)),</span>
+               <span class="n">ax_aapl</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ax_aapl</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span>
+               <span class="n">color</span><span class="o">=</span><span class="s">&#39;g&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;Earnings Release&#39;</span><span class="p">)</span>
+<span class="n">ax_aapl</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax_aapl</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s">&#39;AAPL Price History&#39;</span><span class="p">)</span>
+
+<span class="c"># Plot the FB trend down and up</span>
+<span class="n">ohlc_dataframe</span><span class="p">(</span><span class="n">FB</span><span class="p">,</span> <span class="n">ax</span><span class="o">=</span><span class="n">ax_fb</span><span class="p">)</span>
+<span class="n">plot_hilo</span><span class="p">(</span><span class="n">ax_fb</span><span class="p">,</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">30</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">27</span><span class="p">),</span> <span class="n">FB</span><span class="p">)</span>
+<span class="n">plot_hilo</span><span class="p">(</span><span class="n">ax_fb</span><span class="p">,</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">28</span><span class="p">),</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">FB</span><span class="p">)</span>
+<span class="n">ax_fb</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">date2num</span><span class="p">(</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">12</span><span class="p">)),</span>
+             <span class="n">ax_fb</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ax_fb</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span>
+             <span class="n">color</span><span class="o">=</span><span class="s">&#39;g&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;Earnings Release&#39;</span><span class="p">)</span>
+<span class="n">ax_fb</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">ax_fb</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s">&#39;FB Price History&#39;</span><span class="p">)</span>
+
+<span class="n">f</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>As we can see above, the market broke a prevailing trend on Apple in order to go down, and ultimately predict the earnings release. For Facebook, the opposite happened. While the trend was down, the earnings were fantastic and the market corrected itself much higher.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Formulating-the-Question">Formulating the Question<a class="anchor-link" href="#Formulating-the-Question">&#182;</a></h1><p>While these are two specific examples, there are plenty of other examples you could cite one way or another. Even if the preponderance of evidence shows that the market correctly predicts earnings releases, we need not accuse people of collusion; for a company like Apple with many suppliers we can generally forecast how Apple has done based on those same suppliers.</p>
+<p>The question then, is this: <strong>how well does the market predict the earnings releases?</strong> It's an incredibly broad question that I want to disect in a couple of different ways:</p>
+<ol>
+<li>Given a stock that has been trending down over the past N days before an earnings release, how likely does it continue downward after the release?</li>
+<li>Given a stock trending up, how likely does it continue up?</li>
+<li>Is there a difference in accuracy between large- and small-cap stocks?</li>
+<li>How often, and for how long, do markets trend before an earnings release?</li>
+</ol>
+<p><strong>I want to especially thank Alejandro Saltiel for helping me retrieve the data.</strong> He's great. And now for all of the interesting bits.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Event-Studies">Event Studies<a class="anchor-link" href="#Event-Studies">&#182;</a></h1><p>Before we go too much further, I want to introduce the actual event study. Each chart intends to capture a lot of information and present an easy-to-understand pattern:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[5]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">pandas.tseries.holiday</span> <span class="k">import</span> <span class="n">USFederalHolidayCalendar</span>
+<span class="kn">from</span> <span class="nn">pandas.tseries.offsets</span> <span class="k">import</span> <span class="n">CustomBusinessDay</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="k">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
+
+<span class="c"># If you remove rules, it removes them from *all* calendars</span>
+<span class="c"># To ensure we don&#39;t pop rules we don&#39;t want to, first make</span>
+<span class="c"># sure to fully copy the object</span>
+<span class="n">trade_calendar</span> <span class="o">=</span> <span class="n">USFederalHolidayCalendar</span><span class="p">()</span>
+<span class="n">trade_calendar</span><span class="o">.</span><span class="n">rules</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">6</span><span class="p">)</span> <span class="c"># Remove Columbus day</span>
+<span class="n">trade_calendar</span><span class="o">.</span><span class="n">rules</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">7</span><span class="p">)</span> <span class="c"># Remove Veteran&#39;s day</span>
+<span class="n">TradeDay</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">days</span><span class="p">:</span> <span class="n">CustomBusinessDay</span><span class="p">(</span><span class="n">days</span><span class="p">,</span> <span class="n">calendar</span><span class="o">=</span><span class="n">trade_calendar</span><span class="p">)</span>
+
+<span class="k">def</span> <span class="nf">plot_study</span><span class="p">(</span><span class="n">array</span><span class="p">):</span>
+    <span class="c"># Given a 2-d array, we assume the event happens at index `lookback`,</span>
+    <span class="c"># and create all of our summary statistics from there.</span>
+    <span class="n">lookback</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">norm_factor</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span><span class="n">lookback</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">centered_data</span> <span class="o">=</span> <span class="n">array</span> <span class="o">/</span> <span class="n">norm_factor</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">lookforward</span> <span class="o">=</span> <span class="n">centered_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">lookback</span>
+    <span class="n">means</span> <span class="o">=</span> <span class="n">centered_data</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">lookforward_data</span> <span class="o">=</span> <span class="n">centered_data</span><span class="p">[:,</span><span class="n">lookforward</span><span class="p">:]</span>
+    <span class="n">std_dev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="n">lookforward_data</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)])</span>
+    <span class="n">maxes</span> <span class="o">=</span> <span class="n">lookforward_data</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">mins</span> <span class="o">=</span> <span class="n">lookforward_data</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    
+    <span class="n">f</span><span class="p">,</span> <span class="n">axarr</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">range_begin</span> <span class="o">=</span> <span class="o">-</span><span class="n">lookback</span>
+    <span class="n">range_end</span> <span class="o">=</span> <span class="n">lookforward</span>
+    <span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">range_begin</span><span class="p">,</span> <span class="n">range_end</span><span class="p">),</span> <span class="n">means</span><span class="p">)</span>
+    <span class="n">axarr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">range_begin</span><span class="p">,</span> <span class="n">range_end</span><span class="p">),</span> <span class="n">means</span><span class="p">)</span>
+    <span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">range_end</span><span class="p">),</span>
+                     <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="p">:]</span> <span class="o">+</span> <span class="n">std_dev</span><span class="p">,</span>
+                     <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="p">:]</span> <span class="o">-</span> <span class="n">std_dev</span><span class="p">,</span>
+                    <span class="n">alpha</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&quot;$\pm$ 1 s.d.&quot;</span><span class="p">)</span>
+    <span class="n">axarr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">range_end</span><span class="p">),</span>
+                     <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="p">:]</span> <span class="o">+</span> <span class="n">std_dev</span><span class="p">,</span>
+                     <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="p">:]</span> <span class="o">-</span> <span class="n">std_dev</span><span class="p">,</span>
+                    <span class="n">alpha</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&quot;$\pm$ 1 s.d.&quot;</span><span class="p">)</span>
+    
+    <span class="n">max_err</span> <span class="o">=</span> <span class="n">maxes</span> <span class="o">-</span> <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="o">+</span><span class="mi">1</span><span class="p">:]</span>
+    <span class="n">min_err</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="o">+</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="n">mins</span>
+    <span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">range_end</span><span class="p">),</span>
+                  <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="o">+</span><span class="mi">1</span><span class="p">:],</span>
+                  <span class="n">yerr</span><span class="o">=</span><span class="p">[</span><span class="n">min_err</span><span class="p">,</span> <span class="n">max_err</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;Max &amp; Min&#39;</span><span class="p">)</span>
+    <span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">axarr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    
+    <span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">((</span><span class="o">-</span><span class="n">lookback</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">lookback</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
+    <span class="n">axarr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">((</span><span class="o">-</span><span class="n">lookback</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">lookback</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
+    
+<span class="k">def</span> <span class="nf">plot_study_small</span><span class="p">(</span><span class="n">array</span><span class="p">):</span>
+    <span class="c"># Given a 2-d array, we assume the event happens at index `lookback`,</span>
+    <span class="c"># and create all of our summary statistics from there.</span>
+    <span class="n">lookback</span> <span class="o">=</span> <span class="nb">int</span><span class="p">((</span><span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">norm_factor</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">repeat</span><span class="p">(</span><span class="n">array</span><span class="p">[:,</span><span class="n">lookback</span><span class="p">]</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">array</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">centered_data</span> <span class="o">=</span> <span class="n">array</span> <span class="o">/</span> <span class="n">norm_factor</span> <span class="o">-</span> <span class="mi">1</span>
+    <span class="n">lookforward</span> <span class="o">=</span> <span class="n">centered_data</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">lookback</span>
+    <span class="n">means</span> <span class="o">=</span> <span class="n">centered_data</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">lookforward_data</span> <span class="o">=</span> <span class="n">centered_data</span><span class="p">[:,</span><span class="n">lookforward</span><span class="p">:]</span>
+    <span class="n">std_dev</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="n">lookforward_data</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)])</span>
+    <span class="n">maxes</span> <span class="o">=</span> <span class="n">lookforward_data</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">mins</span> <span class="o">=</span> <span class="n">lookforward_data</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    
+    <span class="n">range_begin</span> <span class="o">=</span> <span class="o">-</span><span class="n">lookback</span>
+    <span class="n">range_end</span> <span class="o">=</span> <span class="n">lookforward</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="n">range_begin</span><span class="p">,</span> <span class="n">range_end</span><span class="p">),</span> <span class="n">means</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">range_end</span><span class="p">),</span>
+                     <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="p">:]</span> <span class="o">+</span> <span class="n">std_dev</span><span class="p">,</span>
+                     <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="p">:]</span> <span class="o">-</span> <span class="n">std_dev</span><span class="p">,</span>
+                    <span class="n">alpha</span><span class="o">=.</span><span class="mi">5</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&quot;$\pm$ 1 s.d.&quot;</span><span class="p">)</span>
+    
+    <span class="n">max_err</span> <span class="o">=</span> <span class="n">maxes</span> <span class="o">-</span> <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="o">+</span><span class="mi">1</span><span class="p">:]</span>
+    <span class="n">min_err</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="o">+</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="n">mins</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">range_end</span><span class="p">),</span>
+                  <span class="n">means</span><span class="p">[</span><span class="o">-</span><span class="n">lookforward</span><span class="o">+</span><span class="mi">1</span><span class="p">:],</span>
+                  <span class="n">yerr</span><span class="o">=</span><span class="p">[</span><span class="n">min_err</span><span class="p">,</span> <span class="n">max_err</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;Max &amp; Min&#39;</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">((</span><span class="o">-</span><span class="n">lookback</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">lookback</span><span class="o">+</span><span class="mi">1</span><span class="p">))</span>
+    
+<span class="k">def</span> <span class="nf">fetch_event_data</span><span class="p">(</span><span class="n">ticker</span><span class="p">,</span> <span class="n">events</span><span class="p">,</span> <span class="n">horizon</span><span class="o">=</span><span class="mi">5</span><span class="p">):</span>
+    <span class="c"># Use horizon+1 to account for including the day of the event,</span>
+    <span class="c"># and half-open interval - that is, for a horizon of 5,</span>
+    <span class="c"># we should be including 11 events. Additionally, using the</span>
+    <span class="c"># CustomBusinessDay means we automatically handle issues if</span>
+    <span class="c"># for example a company reports Friday afternoon - the date</span>
+    <span class="c"># calculator will turn this into a &quot;Saturday&quot; release, but</span>
+    <span class="c"># we effectively shift that to Monday with the logic below.</span>
+    <span class="n">td_back</span> <span class="o">=</span> <span class="n">TradeDay</span><span class="p">(</span><span class="n">horizon</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    <span class="n">td_forward</span> <span class="o">=</span> <span class="n">TradeDay</span><span class="p">(</span><span class="n">horizon</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
+    
+    <span class="n">start_date</span> <span class="o">=</span> <span class="nb">min</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">-</span> <span class="n">td_back</span>
+    <span class="n">end_date</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">events</span><span class="p">)</span> <span class="o">+</span> <span class="n">td_forward</span>
+    <span class="n">total_data</span> <span class="o">=</span> <span class="n">fetch_ticker</span><span class="p">(</span><span class="n">ticker</span><span class="p">,</span> <span class="n">start_date</span><span class="p">,</span> <span class="n">end_date</span><span class="p">)</span>
+    <span class="n">event_data</span> <span class="o">=</span> <span class="p">[</span><span class="n">total_data</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">event</span><span class="o">-</span><span class="n">td_back</span><span class="p">:</span><span class="n">event</span><span class="o">+</span><span class="n">td_forward</span><span class="p">]</span>\
+                      <span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">horizon</span><span class="o">*</span><span class="mi">2</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>\
+                      <span class="p">[</span><span class="s">&#39;Adjusted Close&#39;</span><span class="p">]</span>
+                  <span class="k">for</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">events</span><span class="p">]</span>
+    <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">event_data</span><span class="p">)</span>
+
+<span class="c"># Generate a couple of random events</span>
+
+<span class="n">event_dates</span> <span class="o">=</span> <span class="p">[</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">27</span><span class="p">)</span> <span class="o">-</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">-</span> <span class="n">TradeDay</span><span class="p">(</span><span class="n">x</span><span class="o">*</span><span class="mi">20</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">40</span><span class="p">)]</span>
+<span class="n">data</span> <span class="o">=</span> <span class="n">fetch_event_data</span><span class="p">(</span><span class="s">&#39;CELG&#39;</span><span class="p">,</span> <span class="n">event_dates</span><span class="p">)</span>
+<span class="n">plot_study_small</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">6</span><span class="p">);</span>
+
+
+<span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s">&#39;Mean price for days leading up to each event&#39;</span><span class="p">,</span>
+             <span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span> <span class="o">-.</span><span class="mi">01</span><span class="p">),</span> <span class="p">(</span><span class="o">-</span><span class="mf">4.5</span><span class="p">,</span> <span class="o">.</span><span class="mi">025</span><span class="p">),</span>
+             <span class="n">arrowprops</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">facecolor</span><span class="o">=</span><span class="s">&#39;black&#39;</span><span class="p">,</span> <span class="n">shrink</span><span class="o">=</span><span class="mf">0.05</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s">&#39;&#39;</span><span class="p">,</span> <span class="p">(</span><span class="o">-.</span><span class="mi">1</span><span class="p">,</span> <span class="o">.</span><span class="mi">005</span><span class="p">),</span> <span class="p">(</span><span class="o">-.</span><span class="mi">5</span><span class="p">,</span> <span class="o">.</span><span class="mi">02</span><span class="p">),</span>
+             <span class="n">arrowprops</span><span class="o">=</span><span class="p">{</span><span class="s">&#39;facecolor&#39;</span><span class="p">:</span> <span class="s">&#39;black&#39;</span><span class="p">,</span> <span class="s">&#39;shrink&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">05</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s">&#39;$\pm$ 1 std. dev. each day&#39;</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="o">.</span><span class="mi">055</span><span class="p">),</span> <span class="p">(</span><span class="mf">2.5</span><span class="p">,</span> <span class="o">.</span><span class="mi">085</span><span class="p">),</span>
+            <span class="n">arrowprops</span><span class="o">=</span><span class="p">{</span><span class="s">&#39;facecolor&#39;</span><span class="p">:</span> <span class="s">&#39;black&#39;</span><span class="p">,</span> <span class="s">&#39;shrink&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">05</span><span class="p">})</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">annotate</span><span class="p">(</span><span class="s">&#39;Min/Max each day&#39;</span><span class="p">,</span> <span class="p">(</span><span class="o">.</span><span class="mi">9</span><span class="p">,</span> <span class="o">-.</span><span class="mi">07</span><span class="p">),</span> <span class="p">(</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-.</span><span class="mi">1</span><span class="p">),</span>
+            <span class="n">arrowprops</span><span class="o">=</span><span class="p">{</span><span class="s">&#39;facecolor&#39;</span><span class="p">:</span> <span class="s">&#39;black&#39;</span><span class="p">,</span> <span class="s">&#39;shrink&#39;</span><span class="p">:</span> <span class="o">.</span><span class="mi">05</span><span class="p">});</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>And as a quick textual explanation as well:</p>
+<ul>
+<li><p>The blue line represents the mean price for each day, represented as a percentage of the price on the '0-day'. For example, if we defined an 'event' as whenever the stock price dropped for three days, we would see a decreasing blue line to the left of the 0-day.</p>
+</li>
+<li><p>The blue shaded area represents one standard deviation above and below the mean price for each day following an event. This is intended to give us an idea of what the stock price does in general following an event.</p>
+</li>
+<li><p>The green bars are the minimum and maximum price for each day following an event. This instructs us as to how much it's possible for the stock to move.</p>
+</li>
+</ul>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Event-Type-1:-Trending-down-over-the-past-N-days">Event Type 1: Trending down over the past N days<a class="anchor-link" href="#Event-Type-1:-Trending-down-over-the-past-N-days">&#182;</a></h1><p>The first type of event I want to study is how stocks perform when they've been trending down over the past couple of days prior to a release. However, we need to clarify what exactly is meant by "trending down." To do so, we'll use the following metric: <strong>the midpoint between each day's opening and closing price goes down over a period of N days</strong>.</p>
+<p>It's probably helpful to have an example:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[6]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">f</span><span class="p">,</span> <span class="n">axarr</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="n">f</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
+
+<span class="n">FB_plot</span> <span class="o">=</span> <span class="n">axarr</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+<span class="n">ohlc_dataframe</span><span class="p">(</span><span class="n">FB</span><span class="p">[</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">18</span><span class="p">):],</span> <span class="n">FB_plot</span><span class="p">)</span>
+
+<span class="n">FB_truncated</span> <span class="o">=</span> <span class="n">FB</span><span class="p">[</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">18</span><span class="p">):</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">27</span><span class="p">)]</span>
+<span class="n">midpoint</span> <span class="o">=</span> <span class="n">FB_truncated</span><span class="p">[</span><span class="s">&#39;Open&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span> <span class="o">+</span> <span class="n">FB_truncated</span><span class="p">[</span><span class="s">&#39;Close&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span>
+<span class="n">FB_plot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">FB_truncated</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">midpoint</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;Midpoint&#39;</span><span class="p">)</span>
+<span class="n">FB_plot</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">date2num</span><span class="p">(</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">27</span><span class="p">,</span> <span class="mi">12</span><span class="p">)),</span>
+               <span class="n">ax_fb</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ax_fb</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span>
+               <span class="n">color</span><span class="o">=</span><span class="s">&#39;g&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;Earnings Release&#39;</span><span class="p">)</span>
+<span class="n">FB_plot</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+<span class="n">FB_plot</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s">&#39;FB Midpoint Plot&#39;</span><span class="p">)</span>
+
+<span class="n">AAPL_plot</span> <span class="o">=</span> <span class="n">axarr</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+<span class="n">ohlc_dataframe</span><span class="p">(</span><span class="n">AAPL</span><span class="p">[</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">10</span><span class="p">):],</span> <span class="n">AAPL_plot</span><span class="p">)</span>
+<span class="n">AAPL_truncated</span> <span class="o">=</span> <span class="n">AAPL</span><span class="p">[</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">10</span><span class="p">):</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">26</span><span class="p">)]</span>
+<span class="n">midpoint</span> <span class="o">=</span> <span class="n">AAPL_truncated</span><span class="p">[</span><span class="s">&#39;Open&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span> <span class="o">+</span> <span class="n">AAPL_truncated</span><span class="p">[</span><span class="s">&#39;Close&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span>
+<span class="n">AAPL_plot</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">AAPL_truncated</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">midpoint</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;Midpoint&#39;</span><span class="p">)</span>
+<span class="n">AAPL_plot</span><span class="o">.</span><span class="n">vlines</span><span class="p">(</span><span class="n">date2num</span><span class="p">(</span><span class="n">datetime</span><span class="p">(</span><span class="mi">2016</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">26</span><span class="p">,</span> <span class="mi">12</span><span class="p">)),</span>
+                 <span class="n">ax_aapl</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">0</span><span class="p">],</span> <span class="n">ax_aapl</span><span class="o">.</span><span class="n">get_ylim</span><span class="p">()[</span><span class="mi">1</span><span class="p">],</span>
+                 <span class="n">color</span><span class="o">=</span><span class="s">&#39;g&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s">&#39;Earnings Release&#39;</span><span class="p">)</span>
+<span class="n">AAPL_plot</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
+<span class="n">AAPL_plot</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s">&#39;AAPL Midpoint Plot&#39;</span><span class="p">);</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Given these charts, we can see that FB was trending down for the four days preceding the earnings release, and AAPL was trending down for a whopping 8 days (we don't count the peak day). This will define the methodology that we will use for the study.</p>
+<p>So what are the results? For a given horizon, how well does the market actually perform?</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[7]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="c"># Read in the events for each stock;</span>
+<span class="c"># The file was created using the first code block in the Appendix</span>
+<span class="kn">import</span> <span class="nn">yaml</span>
+<span class="kn">from</span> <span class="nn">dateutil.parser</span> <span class="k">import</span> <span class="n">parse</span>
+<span class="kn">from</span> <span class="nn">progressbar</span> <span class="k">import</span> <span class="n">ProgressBar</span>
+
+<span class="n">data_str</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s">&#39;earnings_dates.yaml&#39;</span><span class="p">,</span> <span class="s">&#39;r&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+<span class="c"># Need to remove invalid lines</span>
+<span class="n">filtered</span> <span class="o">=</span> <span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s">&#39;{&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">x</span><span class="p">,</span> <span class="n">data_str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span><span class="p">))</span>
+<span class="n">earnings_data</span> <span class="o">=</span> <span class="n">yaml</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">filtered</span><span class="p">))</span>
+
+<span class="c"># Convert our earnings data into a list of (ticker, date) pairs</span>
+<span class="c"># to make it easy to work with.</span>
+<span class="c"># This is horribly inefficient, but should get us what we need</span>
+<span class="n">ticker_dates</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">ticker</span><span class="p">,</span> <span class="n">date_list</span> <span class="ow">in</span> <span class="n">earnings_data</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="k">for</span> <span class="n">iso_str</span> <span class="ow">in</span> <span class="n">date_list</span><span class="p">:</span>
+        <span class="n">ticker_dates</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">ticker</span><span class="p">,</span> <span class="n">parse</span><span class="p">(</span><span class="n">iso_str</span><span class="p">)))</span>
+
+<span class="k">def</span> <span class="nf">does_trend_down</span><span class="p">(</span><span class="n">ticker</span><span class="p">,</span> <span class="n">event</span><span class="p">,</span> <span class="n">horizon</span><span class="p">):</span>
+    <span class="c"># Figure out if the `event` has a downtrend for</span>
+    <span class="c"># the `horizon` days preceding it</span>
+    <span class="c"># As an interpretation note: it is assumed that</span>
+    <span class="c"># the closing price of day `event` is the reference</span>
+    <span class="c"># point, and we want `horizon` days before that.</span>
+    <span class="c"># The price_data.hdf was created in the second appendix code block</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">ticker_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="s">&#39;price_data.hdf&#39;</span><span class="p">,</span> <span class="n">ticker</span><span class="p">)</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">ticker_data</span><span class="p">[</span><span class="n">event</span><span class="o">-</span><span class="n">TradeDay</span><span class="p">(</span><span class="n">horizon</span><span class="p">):</span><span class="n">event</span><span class="p">]</span>
+        <span class="n">midpoints</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s">&#39;Open&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span> <span class="o">+</span> <span class="n">data</span><span class="p">[</span><span class="s">&#39;Close&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span>
+
+        <span class="c"># Shift dates one forward into the future and subtract</span>
+        <span class="c"># Effectively: do we trend down over all days?</span>
+        <span class="n">elems</span> <span class="o">=</span> <span class="n">midpoints</span> <span class="o">-</span> <span class="n">midpoints</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">elems</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">elems</span><span class="o">.</span><span class="n">dropna</span><span class="p">()[</span><span class="n">elems</span> <span class="o">&lt;=</span> <span class="mi">0</span><span class="p">])</span>
+    <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
+        <span class="c"># If the stock doesn&#39;t exist, it doesn&#39;t qualify as trending down</span>
+        <span class="c"># Mostly this is here to make sure the entire analysis doesn&#39;t </span>
+        <span class="c"># blow up if there were issues in data retrieval</span>
+        <span class="k">return</span> <span class="k">False</span>
+
+<span class="k">def</span> <span class="nf">study_trend</span><span class="p">(</span><span class="n">horizon</span><span class="p">,</span> <span class="n">trend_function</span><span class="p">):</span>
+    <span class="n">five_day_events</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="n">horizon</span><span class="o">*</span><span class="mi">2</span> <span class="o">+</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="n">invalid_events</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">for</span> <span class="n">ticker</span><span class="p">,</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">ProgressBar</span><span class="p">()(</span><span class="n">ticker_dates</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">trend_function</span><span class="p">(</span><span class="n">ticker</span><span class="p">,</span> <span class="n">event</span><span class="p">,</span> <span class="n">horizon</span><span class="p">):</span>
+            <span class="n">ticker_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="s">&#39;price_data.hdf&#39;</span><span class="p">,</span> <span class="n">ticker</span><span class="p">)</span>
+            <span class="n">event_data</span> <span class="o">=</span> <span class="n">ticker_data</span><span class="p">[</span><span class="n">event</span><span class="o">-</span><span class="n">TradeDay</span><span class="p">(</span><span class="n">horizon</span><span class="p">):</span><span class="n">event</span><span class="o">+</span><span class="n">TradeDay</span><span class="p">(</span><span class="n">horizon</span><span class="p">)][</span><span class="s">&#39;Close&#39;</span><span class="p">]</span>
+
+            <span class="k">try</span><span class="p">:</span>
+                <span class="n">five_day_events</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">([</span><span class="n">five_day_events</span><span class="p">,</span> <span class="n">event_data</span><span class="p">])</span>
+            <span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
+                <span class="c"># Sometimes we don&#39;t get exactly the right number of values due to calendar</span>
+                <span class="c"># issues. I&#39;ve fixed most everything I can, and the few issues that are left</span>
+                <span class="c"># I assume don&#39;t systemically bias the results (i.e. data could be missing</span>
+                <span class="c"># because it doesn&#39;t exist, etc.). After running through, ~1% of events get</span>
+                <span class="c"># discarded this way</span>
+                <span class="n">invalid_events</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">ticker</span><span class="p">,</span> <span class="n">event</span><span class="p">))</span>
+            
+
+    <span class="c"># Remove our initial zero row</span>
+    <span class="n">five_day_events</span> <span class="o">=</span> <span class="n">five_day_events</span><span class="p">[</span><span class="mi">1</span><span class="p">:,:]</span>
+    <span class="n">plot_study</span><span class="p">(</span><span class="n">five_day_events</span><span class="p">)</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">suptitle</span><span class="p">(</span><span class="s">&#39;Action over {} days: {} events&#39;</span>
+                       <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">horizon</span><span class="p">,</span><span class="n">five_day_events</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+    <span class="n">plt</span><span class="o">.</span><span class="n">gcf</span><span class="p">()</span><span class="o">.</span><span class="n">set_size_inches</span><span class="p">(</span><span class="mi">18</span><span class="p">,</span> <span class="mi">6</span><span class="p">)</span>
+    
+<span class="c"># Start with a 5 day study</span>
+<span class="n">study_trend</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">does_trend_down</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>100% (47578 of 47578) |###########################################################| Elapsed Time: 0:21:38 Time: 0:21:38
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>When a stock has been trending down for 5 days, once the earnings are announced it really doesn't move on average. However, the variability is <em>incredible</em>. This implies two important things:</p>
+<ol>
+<li>The market is just as often wrong about an earnings announcement before it happens as it is correct</li>
+<li>The incredible width of the min/max bars and standard deviation area tell us that the market reacts <em>violently</em> after the earnings are released.</li>
+</ol>
+<p>Let's repeat the same study, but over a time horizon of 8 days and 3 days. Presumably if a stock has been going down for 8 days at a time before the earnings, the market should be more accurate.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[8]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="c"># 8 day study next</span>
+<span class="n">study_trend</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="n">does_trend_down</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>100% (47578 of 47578) |###########################################################| Elapsed Time: 0:20:29 Time: 0:20:29
+</pre>
+</div>
+</div>
+
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+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>However, looking only at stocks that trended down for 8 days prior to a release, the same pattern emerges: on average, the stock doesn't move, but the market reaction is often incredibly violent.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[9]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="c"># 3 day study after that</span>
+<span class="n">study_trend</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">does_trend_down</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>100% (47578 of 47578) |###########################################################| Elapsed Time: 0:26:26 Time: 0:26:26
+</pre>
+</div>
+</div>
+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>Finally, when we look at a 3-day horizon, we start getting some incredible outliers. Stocks have a potential to move over ~300% up, and the standard deviation width is again, incredible. The results for a 3-day horizon follow the same pattern we've seen in the 5- and 8-day horizons.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Event-Type-2:-Trending-up-for-N-days">Event Type 2: Trending up for N days<a class="anchor-link" href="#Event-Type-2:-Trending-up-for-N-days">&#182;</a></h1><p>We're now going to repeat the analysis, but do it for uptrends instead. That is, instead of looking at stocks that have been trending down over the past number of days, we focus only on stocks that have been trending up.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[10]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">does_trend_up</span><span class="p">(</span><span class="n">ticker</span><span class="p">,</span> <span class="n">event</span><span class="p">,</span> <span class="n">horizon</span><span class="p">):</span>
+    <span class="c"># Figure out if the `event` has an uptrend for</span>
+    <span class="c"># the `horizon` days preceding it</span>
+    <span class="c"># As an interpretation note: it is assumed that</span>
+    <span class="c"># the closing price of day `event` is the reference</span>
+    <span class="c"># point, and we want `horizon` days before that.</span>
+    <span class="c"># The price_data.hdf was created in the second appendix code block</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="n">ticker_data</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_hdf</span><span class="p">(</span><span class="s">&#39;price_data.hdf&#39;</span><span class="p">,</span> <span class="n">ticker</span><span class="p">)</span>
+        <span class="n">data</span> <span class="o">=</span> <span class="n">ticker_data</span><span class="p">[</span><span class="n">event</span><span class="o">-</span><span class="n">TradeDay</span><span class="p">(</span><span class="n">horizon</span><span class="p">):</span><span class="n">event</span><span class="p">]</span>
+        <span class="n">midpoints</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="s">&#39;Open&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span> <span class="o">+</span> <span class="n">data</span><span class="p">[</span><span class="s">&#39;Close&#39;</span><span class="p">]</span><span class="o">/</span><span class="mi">2</span>
+
+        <span class="c"># Shift dates one forward into the future and subtract</span>
+        <span class="c"># Effectively: do we trend down over all days?</span>
+        <span class="n">elems</span> <span class="o">=</span> <span class="n">midpoints</span> <span class="o">-</span> <span class="n">midpoints</span><span class="o">.</span><span class="n">shift</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">elems</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">elems</span><span class="o">.</span><span class="n">dropna</span><span class="p">()[</span><span class="n">elems</span> <span class="o">&gt;=</span> <span class="mi">0</span><span class="p">])</span>
+    <span class="k">except</span> <span class="ne">KeyError</span><span class="p">:</span>
+        <span class="c"># If the stock doesn&#39;t exist, it doesn&#39;t qualify as trending down</span>
+        <span class="c"># Mostly this is here to make sure the entire analysis doesn&#39;t </span>
+        <span class="c"># blow up if there were issues in data retrieval</span>
+        <span class="k">return</span> <span class="k">False</span>
+
+<span class="n">study_trend</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="n">does_trend_up</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>100% (47578 of 47578) |###########################################################| Elapsed Time: 0:22:51 Time: 0:22:51
+</pre>
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<p>The patterns here are very similar. With the exception of noting that stocks can go to nearly 400% after an earnings announcement (most likely this included a takeover announcement, etc.), we still see large min/max bars and wide standard deviation of returns.</p>
+<p>We'll repeat the pattern for stocks going up for both 8 and 3 days straight, but at this point, the results should be very predictable:</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing code_cell rendered">
+<div class="input">
+<div class="prompt input_prompt">In&nbsp;[11]:</div>
+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">study_trend</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span> <span class="n">does_trend_up</span><span class="p">)</span>
+</pre></div>
+
+</div>
+</div>
+</div>
+
+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>100% (47578 of 47578) |###########################################################| Elapsed Time: 0:20:51 Time: 0:20:51
+</pre>
+</div>
+</div>
+
+<div class="output_area"><div class="prompt"></div>
+
+
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+<div class="input">
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+<div class="inner_cell">
+    <div class="input_area">
+<div class=" highlight hl-ipython3"><pre><span class="n">study_trend</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">does_trend_up</span><span class="p">)</span>
+</pre></div>
+
+</div>
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+<div class="output_wrapper">
+<div class="output">
+
+
+<div class="output_area"><div class="prompt"></div>
+<div class="output_subarea output_stream output_stderr output_text">
+<pre>100% (47578 of 47578) |###########################################################| Elapsed Time: 0:26:56 Time: 0:26:56
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+
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+"
+>
+</div>
+
+</div>
+
+</div>
+</div>
+
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Conclusion-and-Summary">Conclusion and Summary<a class="anchor-link" href="#Conclusion-and-Summary">&#182;</a></h1><p>I guess the most important thing to summarize with is this: <strong>looking at the entire market, stock performance prior to an earnings release has no bearing on the stock's performance.</strong> Honestly: given the huge variability of returns after an earnings release, even when the stock has been trending for a long time, you're best off divesting before an earnings release and letting the market sort itself out.</p>
+<p><em>However</em>, there is a big caveat. These results are taken when we look at the entire market. So while we can say that the market as a whole knows nothing and just reacts violently, I want to take a closer look into this data. Does the market typically perform poorly on large-cap/high liquidity stocks? Do smaller companies have investors that know them better and can thus predict performance better? Are specific market sectors better at prediction? Presumably technology stocks are more volatile than the industrials.</p>
+<p>So there are some more interesting questions I still want to ask with this data. Knowing that the hard work of data processing is largely already done, it should be fairly simple to continue this analysis and get much more refined with it. Until next time.</p>
+
+</div>
+</div>
+</div>
+<div class="cell border-box-sizing text_cell rendered">
+<div class="prompt input_prompt">
+</div>
+<div class="inner_cell">
+<div class="text_cell_render border-box-sizing rendered_html">
+<h1 id="Appendix">Appendix<a class="anchor-link" href="#Appendix">&#182;</a></h1><p>Export event data for Russell 3000 companies:</p>
+<div class="highlight"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
+<span class="kn">from</span> <span class="nn">html.parser</span> <span class="kn">import</span> <span class="n">HTMLParser</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span><span class="p">,</span> <span class="n">timedelta</span>
+<span class="kn">import</span> <span class="nn">requests</span>
+<span class="kn">import</span> <span class="nn">re</span>
+<span class="kn">from</span> <span class="nn">dateutil</span> <span class="kn">import</span> <span class="n">parser</span>
+<span class="kn">import</span> <span class="nn">progressbar</span>
+<span class="kn">from</span> <span class="nn">concurrent</span> <span class="kn">import</span> <span class="n">futures</span>
+<span class="kn">import</span> <span class="nn">yaml</span>
+
+<span class="k">class</span> <span class="nc">EarningsParser</span><span class="p">(</span><span class="n">HTMLParser</span><span class="p">):</span>
+    <span class="n">store_dates</span> <span class="o">=</span> <span class="bp">False</span>
+    <span class="n">earnings_offset</span> <span class="o">=</span> <span class="bp">None</span>
+    <span class="n">dates</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">def</span> <span class="nf">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
+        <span class="nb">super</span><span class="p">()</span><span class="o">.</span><span class="n">__init__</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">dates</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">def</span> <span class="nf">handle_starttag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">,</span> <span class="n">attrs</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">tag</span> <span class="o">==</span> <span class="s">&#39;table&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">store_dates</span> <span class="o">=</span> <span class="bp">True</span>
+
+    <span class="k">def</span> <span class="nf">handle_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">store_dates</span><span class="p">:</span>
+            <span class="n">match</span> <span class="o">=</span> <span class="n">re</span><span class="o">.</span><span class="n">match</span><span class="p">(</span><span class="s">r&#39;\d+/\d+/\d+&#39;</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">match</span><span class="p">:</span>
+                <span class="bp">self</span><span class="o">.</span><span class="n">dates</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">match</span><span class="o">.</span><span class="n">group</span><span class="p">(</span><span class="mi">0</span><span class="p">))</span>
+
+        <span class="c"># If a company reports before the bell, record the earnings date</span>
+        <span class="c"># being at midnight the day before. Ex: WMT reports 5/19/2016,</span>
+        <span class="c"># but we want the reference point to be the closing price on 5/18/2016</span>
+        <span class="k">if</span> <span class="s">&#39;After Close&#39;</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">earnings_offset</span> <span class="o">=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="s">&#39;Before Open&#39;</span> <span class="ow">in</span> <span class="n">data</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">earnings_offset</span> <span class="o">=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
+
+    <span class="k">def</span> <span class="nf">handle_endtag</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">tag</span><span class="p">):</span>
+        <span class="k">if</span> <span class="n">tag</span> <span class="o">==</span> <span class="s">&#39;table&#39;</span><span class="p">:</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">store_dates</span> <span class="o">=</span> <span class="bp">False</span>
+
+<span class="k">def</span> <span class="nf">earnings_releases</span><span class="p">(</span><span class="n">ticker</span><span class="p">):</span>
+    <span class="c">#print(&quot;Looking up ticker {}&quot;.format(ticker))</span>
+    <span class="n">user_agent</span> <span class="o">=</span> <span class="s">&#39;Mozilla/5.0 (Windows NT 10.0; WOW64; rv:46.0) &#39;</span>\
+        <span class="s">&#39;Gecko/20100101 Firefox/46.0&#39;</span>
+    <span class="n">headers</span> <span class="o">=</span> <span class="p">{</span><span class="s">&#39;user-agent&#39;</span><span class="p">:</span> <span class="n">user_agent</span><span class="p">}</span>
+    <span class="n">base_url</span> <span class="o">=</span> <span class="s">&#39;http://www.streetinsider.com/ec_earnings.php?q={}&#39;</span>\
+        <span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">ticker</span><span class="p">)</span>
+    <span class="n">e</span> <span class="o">=</span> <span class="n">EarningsParser</span><span class="p">()</span>
+    <span class="n">s</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">Session</span><span class="p">()</span>
+    <span class="n">a</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">adapters</span><span class="o">.</span><span class="n">HTTPAdapter</span><span class="p">(</span><span class="n">max_retries</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">s</span><span class="o">.</span><span class="n">mount</span><span class="p">(</span><span class="s">&#39;http://&#39;</span><span class="p">,</span> <span class="n">a</span><span class="p">)</span>
+    <span class="n">e</span><span class="o">.</span><span class="n">feed</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">s</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">base_url</span><span class="p">,</span> <span class="n">headers</span><span class="o">=</span><span class="n">headers</span><span class="p">)</span><span class="o">.</span><span class="n">content</span><span class="p">))</span>
+
+    <span class="k">if</span> <span class="n">e</span><span class="o">.</span><span class="n">earnings_offset</span> <span class="ow">is</span> <span class="ow">not</span> <span class="bp">None</span><span class="p">:</span>
+        <span class="n">dates</span> <span class="o">=</span> <span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">parser</span><span class="o">.</span><span class="n">parse</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="o">+</span> <span class="n">e</span><span class="o">.</span><span class="n">earnings_offset</span><span class="p">,</span> <span class="n">e</span><span class="o">.</span><span class="n">dates</span><span class="p">)</span>
+        <span class="n">past</span> <span class="o">=</span> <span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="n">x</span> <span class="o">&lt;</span> <span class="n">datetime</span><span class="o">.</span><span class="n">now</span><span class="p">(),</span> <span class="n">dates</span><span class="p">)</span>
+        <span class="k">return</span> <span class="nb">list</span><span class="p">(</span><span class="nb">map</span><span class="p">(</span><span class="k">lambda</span> <span class="n">d</span><span class="p">:</span> <span class="n">d</span><span class="o">.</span><span class="n">isoformat</span><span class="p">(),</span> <span class="n">past</span><span class="p">))</span>
+
+<span class="c"># Use a Russell-3000 ETF tracker (ticker IWV) to get a list of holdings</span>
+<span class="n">r3000</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s">&#39;https://www.ishares.com/us/products/239714/&#39;</span>
+                    <span class="s">&#39;ishares-russell-3000-etf/1449138789749.ajax?&#39;</span>
+                    <span class="s">&#39;fileType=csv&amp;fileName=IWV_holdings&amp;dataType=fund&#39;</span><span class="p">,</span>
+                    <span class="n">header</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+<span class="n">r3000_equities</span> <span class="o">=</span> <span class="n">r3000</span><span class="p">[(</span><span class="n">r3000</span><span class="p">[</span><span class="s">&#39;Exchange&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s">&#39;NASDAQ&#39;</span><span class="p">)</span> <span class="o">|</span>
+                       <span class="p">(</span><span class="n">r3000</span><span class="p">[</span><span class="s">&#39;Exchange&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="s">&#39;New York Stock Exchange Inc.&#39;</span><span class="p">)]</span>
+
+<span class="n">dates_file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s">&#39;earnings_dates.yaml&#39;</span><span class="p">,</span> <span class="s">&#39;w&#39;</span><span class="p">)</span>
+
+<span class="k">with</span> <span class="n">futures</span><span class="o">.</span><span class="n">ThreadPoolExecutor</span><span class="p">(</span><span class="n">max_workers</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span> <span class="k">as</span> <span class="n">pool</span><span class="p">:</span>
+    <span class="n">fs</span> <span class="o">=</span> <span class="p">{</span><span class="n">pool</span><span class="o">.</span><span class="n">submit</span><span class="p">(</span><span class="n">earnings_releases</span><span class="p">,</span> <span class="n">r3000_equities</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">t</span><span class="p">][</span><span class="s">&#39;Ticker&#39;</span><span class="p">]):</span> <span class="n">t</span>
+          <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">r3000_equities</span><span class="o">.</span><span class="n">index</span><span class="p">}</span>
+    <span class="n">pbar</span> <span class="o">=</span> <span class="n">progressbar</span><span class="o">.</span><span class="n">ProgressBar</span><span class="p">(</span><span class="n">term_width</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span>
+                                   <span class="n">max_value</span><span class="o">=</span><span class="n">r3000_equities</span><span class="o">.</span><span class="n">index</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
+
+    <span class="k">for</span> <span class="n">future</span> <span class="ow">in</span> <span class="n">futures</span><span class="o">.</span><span class="n">as_completed</span><span class="p">(</span><span class="n">fs</span><span class="p">):</span>
+        <span class="n">i</span> <span class="o">=</span> <span class="n">fs</span><span class="p">[</span><span class="n">future</span><span class="p">]</span>
+        <span class="n">pbar</span><span class="o">.</span><span class="n">update</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+        <span class="n">dates_file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">yaml</span><span class="o">.</span><span class="n">dump</span><span class="p">({</span><span class="n">r3000_equities</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="s">&#39;Ticker&#39;</span><span class="p">]:</span>
+            <span class="n">future</span><span class="o">.</span><span class="n">result</span><span class="p">()}))</span>
+</pre></div>
+<p>Downloading stock price data needed for the event studies:</p>
+<div class="highlight"><pre><span class="kn">from</span> <span class="nn">secrets</span> <span class="kn">import</span> <span class="n">QUANDL_KEY</span>
+<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
+<span class="kn">import</span> <span class="nn">yaml</span>
+<span class="kn">from</span> <span class="nn">dateutil.parser</span> <span class="kn">import</span> <span class="n">parse</span>
+<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">timedelta</span>
+<span class="kn">import</span> <span class="nn">quandl</span>
+<span class="kn">from</span> <span class="nn">progressbar</span> <span class="kn">import</span> <span class="n">ProgressBar</span>
+
+<span class="k">def</span> <span class="nf">fetch_ticker</span><span class="p">(</span><span class="n">ticker</span><span class="p">,</span> <span class="n">start</span><span class="p">,</span> <span class="n">end</span><span class="p">):</span>
+    <span class="c"># Quandl is currently giving me issues with returning</span>
+    <span class="c"># the entire dataset and not slicing server-side.</span>
+    <span class="c"># So instead, we&#39;ll do it client-side!</span>
+    <span class="n">q_format</span> <span class="o">=</span> <span class="s">&#39;%Y-%m-</span><span class="si">%d</span><span class="s">&#39;</span>
+    <span class="n">ticker_data</span> <span class="o">=</span> <span class="n">quandl</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s">&#39;YAHOO/&#39;</span> <span class="o">+</span> <span class="n">ticker</span><span class="p">,</span>
+                             <span class="n">start_date</span><span class="o">=</span><span class="n">start</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="n">q_format</span><span class="p">),</span>
+                             <span class="n">end_date</span><span class="o">=</span><span class="n">end</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="n">q_format</span><span class="p">),</span>
+                             <span class="n">authtoken</span><span class="o">=</span><span class="n">QUANDL_KEY</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">ticker_data</span>
+
+<span class="n">data_str</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s">&#39;earnings_dates.yaml&#39;</span><span class="p">,</span> <span class="s">&#39;r&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
+<span class="c"># Need to remove invalid lines</span>
+<span class="n">filtered</span> <span class="o">=</span> <span class="nb">filter</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span> <span class="s">&#39;{&#39;</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">x</span><span class="p">,</span> <span class="n">data_str</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span><span class="p">))</span>
+<span class="n">earnings_data</span> <span class="o">=</span> <span class="n">yaml</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s">&#39;</span><span class="se">\n</span><span class="s">&#39;</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">filtered</span><span class="p">))</span>
+
+<span class="c"># Get the first 1500 keys - split up into two statements</span>
+<span class="c"># because of Quandl rate limits</span>
+<span class="n">tickers</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">earnings_data</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
+
+<span class="n">price_dict</span> <span class="o">=</span> <span class="p">{}</span>
+<span class="n">invalid_tickers</span> <span class="o">=</span> <span class="p">[]</span>
+<span class="k">for</span> <span class="n">ticker</span> <span class="ow">in</span> <span class="n">ProgressBar</span><span class="p">()(</span><span class="n">tickers</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1500</span><span class="p">]):</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="c"># Replace &#39;.&#39; with &#39;-&#39; in name for some tickers</span>
+        <span class="n">fixed</span> <span class="o">=</span> <span class="n">ticker</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">&#39;.&#39;</span><span class="p">,</span> <span class="s">&#39;-&#39;</span><span class="p">)</span>
+        <span class="n">event_strs</span> <span class="o">=</span> <span class="n">earnings_data</span><span class="p">[</span><span class="n">ticker</span><span class="p">]</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="p">[</span><span class="n">parse</span><span class="p">(</span><span class="n">event</span><span class="p">)</span> <span class="k">for</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">event_strs</span><span class="p">]</span>
+        <span class="n">td</span> <span class="o">=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+        <span class="n">price_dict</span><span class="p">[</span><span class="n">ticker</span><span class="p">]</span> <span class="o">=</span> <span class="n">fetch_ticker</span><span class="p">(</span><span class="n">fixed</span><span class="p">,</span>
+            <span class="nb">min</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="o">-</span><span class="n">td</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="o">+</span><span class="n">td</span><span class="p">)</span>
+    <span class="k">except</span> <span class="n">quandl</span><span class="o">.</span><span class="n">NotFoundError</span><span class="p">:</span>
+        <span class="n">invalid_tickers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ticker</span><span class="p">)</span>
+
+<span class="c"># Execute this after 10 minutes have passed</span>
+<span class="k">for</span> <span class="n">ticker</span> <span class="ow">in</span> <span class="n">ProgressBar</span><span class="p">()(</span><span class="n">tickers</span><span class="p">[</span><span class="mi">1500</span><span class="p">:]):</span>
+    <span class="k">try</span><span class="p">:</span>
+        <span class="c"># Replace &#39;.&#39; with &#39;-&#39; in name for some tickers</span>
+        <span class="n">fixed</span> <span class="o">=</span> <span class="n">ticker</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span><span class="s">&#39;.&#39;</span><span class="p">,</span> <span class="s">&#39;-&#39;</span><span class="p">)</span>
+        <span class="n">event_strs</span> <span class="o">=</span> <span class="n">earnings_data</span><span class="p">[</span><span class="n">ticker</span><span class="p">]</span>
+        <span class="n">events</span> <span class="o">=</span> <span class="p">[</span><span class="n">parse</span><span class="p">(</span><span class="n">event</span><span class="p">)</span> <span class="k">for</span> <span class="n">event</span> <span class="ow">in</span> <span class="n">event_strs</span><span class="p">]</span>
+        <span class="n">td</span> <span class="o">=</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
+        <span class="n">price_dict</span><span class="p">[</span><span class="n">ticker</span><span class="p">]</span> <span class="o">=</span> <span class="n">fetch_ticker</span><span class="p">(</span><span class="n">fixed</span><span class="p">,</span>
+            <span class="nb">min</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="o">-</span><span class="n">td</span><span class="p">,</span> <span class="nb">max</span><span class="p">(</span><span class="n">events</span><span class="p">)</span><span class="o">+</span><span class="n">td</span><span class="p">)</span>
+    <span class="k">except</span> <span class="n">quandl</span><span class="o">.</span><span class="n">NotFoundError</span><span class="p">:</span>
+        <span class="n">invalid_tickers</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ticker</span><span class="p">)</span>
+
+<span class="n">prices_store</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">HDFStore</span><span class="p">(</span><span class="s">&#39;price_data.hdf&#39;</span><span class="p">)</span>
+<span class="k">for</span> <span class="n">ticker</span><span class="p">,</span> <span class="n">prices</span> <span class="ow">in</span> <span class="n">price_dict</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+    <span class="n">prices_store</span><span class="p">[</span><span class="n">ticker</span><span class="p">]</span> <span class="o">=</span> <span class="n">prices</span>
+</pre></div>
+
+</div>
+</div>
+</div></p>
+<script type="text/x-mathjax-config">
+//MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\(','\)']]}});
+MathJax.Hub.Config({tex2jax: {inlineMath: [['\$','\$']]}});
+</script>
+
+<script async src='https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS_CHTML'></script>The Unfair Casino2016-05-15T00:00:00-04:00Bradlee Speicetag:bspeice.github.io,2016-05-15:the-unfair-casino.html<p>
 <div class="cell border-box-sizing text_cell rendered">
 <div class="prompt input_prompt">
 </div>
@@ -19,7 +5255,7 @@
 <div class="prompt input_prompt">In&nbsp;[1]:</div>
 <div class="inner_cell">
     <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<div class=" highlight hl-ipython3"><pre><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
 <span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
 <span class="o">%</span><span class="k">matplotlib</span> inline
@@ -27,8 +5263,8 @@
 <span class="n">fair_1</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span>
 <span class="n">fair_2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">7</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span>
 
-<span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">fair_1</span> <span class="o">+</span> <span class="n">fair_2</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;hist&#39;</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">11</span><span class="p">);</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Fair Distribution&#39;</span><span class="p">);</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">fair_1</span> <span class="o">+</span> <span class="n">fair_2</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;hist&#39;</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">11</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;Fair Distribution&#39;</span><span class="p">);</span>
 </pre></div>
 
 </div>
@@ -229,7 +5465,7 @@ D_2 = \left\{
 <div class="prompt input_prompt">In&nbsp;[2]:</div>
 <div class="inner_cell">
     <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">unfair_die</span><span class="p">(</span><span class="n">p_vals</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
+<div class=" highlight hl-ipython3"><pre><span class="k">def</span> <span class="nf">unfair_die</span><span class="p">(</span><span class="n">p_vals</span><span class="p">,</span> <span class="n">n</span><span class="p">):</span>
     <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">multinomial</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">p_vals</span><span class="p">,</span> <span class="n">n</span><span class="p">)</span>
     <span class="k">return</span> <span class="n">x</span><span class="o">.</span><span class="n">nonzero</span><span class="p">()[</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="mi">1</span>
 
@@ -239,8 +5475,8 @@ D_2 = \left\{
 <span class="n">x1</span> <span class="o">=</span> <span class="n">unfair_die</span><span class="p">(</span><span class="n">d1</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span>
 <span class="n">x2</span> <span class="o">=</span> <span class="n">unfair_die</span><span class="p">(</span><span class="n">d2</span><span class="p">,</span> <span class="mi">10000</span><span class="p">)</span>
 
-<span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">x1</span> <span class="o">+</span> <span class="n">x2</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s1">&#39;hist&#39;</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">11</span><span class="p">);</span>
-<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;$D_1$ biased low, $D_2$ biased high&#39;</span><span class="p">);</span>
+<span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">x1</span> <span class="o">+</span> <span class="n">x2</span><span class="p">)</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">kind</span><span class="o">=</span><span class="s">&#39;hist&#39;</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">11</span><span class="p">);</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s">&#39;$D_1$ biased low, $D_2$ biased high&#39;</span><span class="p">);</span>
 </pre></div>
 
 </div>
@@ -432,14 +5668,14 @@ D_2 = \left\{
 <div class="prompt input_prompt">In&nbsp;[3]:</div>
 <div class="inner_cell">
     <div class="input_area">
-<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="o">/</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">12</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">12</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">12</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">12</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">3</span><span class="p">]</span>
+<div class=" highlight hl-ipython3"><pre><span class="n">d1</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1</span><span class="o">/</span><span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="mi">12<