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# CS-677: Predicting Daily Trading Labels Assignment 2 solution

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In many data science applications, you want to identify patterns, labels or classes based on available data. In this assignment we will focus on discovering patterns in your past stock
behavior.
To each trading day i you will assign a ”trading” label ” + ” or
” − ”. depending whether the corresponding daily return for
that day ri ≥ 0 or ri < 0. We will call these ”true” labels and
we compute these for all days in all 5 years.
We will use years 1,2 ans 3 as training years and we will use
years 4 and 5 as testing years. For each day in years 4 and 5 we
will predict a label based on some patterns that we observe in
training years. We will call these ”predicted” labels. We know
the ”true” labels for years 4 and 5 and we compute ”predicted”
labels for years 4 and 5. Therefore, we can analyze how good
are our predictions for all labels, ”+” labels only and ”-” labels
only in years 4 and 5.
Question 1: You have a csv table of daily returns for your
stosk and for S&P-500 (”spy” ticker).
1. For each file, read them into a pandas frame and add a
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BU MET CS-677: Data Science With Python, v.2.0 CS-677: Predicting Daily Trading Labels
column ”True Label”. In that column, for each day (row)
i with daily return ri ≥ 0 you assign a ” + ” label (”up
day”). For each day i with daily return ri < 0 you assign
” − ” (”down days”). You do this for every day for all 5
years both both tickers.
For example, if your initial dataframe were
Date · · · Return
1/2/2015 · · · 0.015
1/3/2015 · · · -0.01
1/6/2015 · · · 0.02
· · · · · · · · ·
· · · · · · · · ·
12/30/2019 · · · 0
12/31/2019 · · · -0.03
Table 1: Initial data
data as shown in Table 2.
Your daily ”true labels” sequence is +, −, +, · · · +, −.
2. take years 1,2 and 3. Let L be the number of trading days.
750 days. Let L
− be all trading days with − labels and
let L
+ be all trading days with + labels. Assuming that
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BU MET CS-677: Data Science With Python, v.2.0 CS-677: Predicting Daily Trading Labels
Date · · · Return True Label
1/2/2015 · · · 0.015 +
1/3/2015 · · · -0.01 −
1/6/2015 · · · 0.02 +
· · · · · · · · · · · ·
· · · · · · · · · · · ·
12/30/2019 · · · 0 +
12/31/2019 · · · -0.03 −
all days are independent of each other and that the ratio
of ”up” and ”down” days remains the same in the future,
compute the default probability p

that the next day is a
”up” day.
3. take years 1, 2 and 3 What is the probability that after
seeing k consecutive ”down days”, the next day is an ”up
day”? For example, if k = 3, what is the probability of seeing ”−, −, −, +” as opposed to seeing ”−, −, −, −”. Compute this for k = 1, 2, 3.
4. take years 1, 2 and 3. What is the probability that after
seeing k consecutive ”up days”, the next day is still an
”up day”? For example, if k = 3, what is the probability
of seeing ”+, +, +, +” as opposed to seeing ”+, +, +, −”?
Compute this for k = 1, 2, 3.
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BU MET CS-677: Data Science With Python, v.2.0 CS-677: Predicting Daily Trading Labels
Predicting labels: We will now describe a procedure to
predict labels for each day in years 4 and 5 from ”true” labels
in training years 1,2 and 3.
For each day d in year 4 and 5, we look at the pattern of
last W true labels (including this day d). By looking at the
frequency of this pattern and true label for the next day in the
training set, we will predict label for day d + 1. Here W is the
hyperparameter that we will choose based on our prediction
accuracy.
Suppose W = 3. You look at a partuclar day d and suppose
that the sequence of last W labels is s = ”−, +, −”. We want
to predict the label for next day d + 1. To do this, we count
the number of sequences of length W + 1 in the training set
where the first W labels coincide with s. In other words, we
count the number N−(s) of sequences ”s, −” and the number
of sequences N+(s) of sequences ”s, +”. If N+(s) ≥ N−(s)
then the next day is assigned ”+”. If N+(s) < N−(s) then the
next day is assigned ”−”. In the unlikely event that N+(s) =
N−(s) = 0 we will assign a label based on default probability
p

that we computed in the previous question.
Question 2:
1. for W = 2, 3, 4, compute predicted labels for each day in
year 4 and 5 based on true labels in years 1,2 and 3 only.
Perform this for your ticker and for ”spy”.
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BU MET CS-677: Data Science With Python, v.2.0 CS-677: Predicting Daily Trading Labels
2. for each W = 2, 3, 4, compute the accuracy – what percentage of true labels (both positive and negative) have you
predicted correctly for the last two years.
3. which W∗ value gave you the highest accuracy for your
stock and and which W∗ valuegave you the highest accuracy
for S&P-500?
Question 3. One of the most powerful methods to (potentially) improve predictions is to combine predictions by some
”averaging”. This is called ensemble learning. Let us consider
the following procedure: for every day d, you have 3 predicted
labels: for W = 2, W = 3 and W = 4. Let us compute an
”ensemble” label for day d by taking the majority of your labels for that day. For example, if your predicted labels were
”−”,”−” and ”+”, then we would take ”−” as ensemble label
for day d (the majority of three labels is ”−”). If, on the other
hand, your predicted labels were ”−”, ”+” and ”+” then we
would take ”+” as ensemble label for day d (the majority of
predicted labels is ”+”). Compute such ensemble labels and
1. compute ensemble labels for year 4 and 5 for both your
stock and S&P-500.
2. for both S&P-500 and your ticker, what percentage of labels
in year 4 and 5 do you compute correctly by using ensemble?
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BU MET CS-677: Data Science With Python, v.2.0 CS-677: Predicting Daily Trading Labels
3. did you improve your accuracy on predicting ”−” labels by
using ensemble compared to W = 2, 3, 4?
4. did you improve your accuracy on predicting ”+” labels by
using ensemble compared to W = 2, 3, 4?
Question 4: For W = 2, 3, 4 and ensemble, compute the
following (both for your ticker and ”spy”) statistics based on
years 4 and 5:
1. TP – true positives (your predicted label is + and true label
is +
2. FP – false positives (your predicted label is + but true label
is −
3. TN – true negativess (your predicted label is − and true
label is −
4. FN – false negatives (your predicted label is − but true label
is +
5. TPR = TP/(TP + FN) – true positive rate. This is the fraction of positive labels that your predicted correctly. This is
also called sensitivity, recall or hit rate.
6. TNR = TN/(TN + FP) – true negative rate. This is the
fraction of negative labels that your predicted correctly.
This is also called specificity or selectivity.
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BU MET CS-677: Data Science With Python, v.2.0 CS-677: Predicting Daily Trading Labels
7. summarize your findings in the table as shown below:
W ticker TP FP TN FN accuracy TPR TNR
2 S&P-500
3 S&P-500
4 S&P-500
ensemble S&P-500