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Spread.for.ASP.NET.v4.0.3525.for.DotNET.Framework.v3.5.Incl.Keygen-BEAN.exe. 5399552. FarPoint.Spread.for.Windows.Forms.v4.0.2022.for.DotNET.Framework.v2.0. FarPoint Spread For Windows Forms V4.0.2022 For DotNET Framework V2.0-BEAN, Unrated. FarPoint Spread For Windows Forms V4.0.3501 For DotNET Framework. Out[5]: KMeans(n_clusters=4, random_state=0). In [6]: y_ = model.predict(x). In [7]: y_. Out[7]: array([3, 3, 1, 2, 1, 1, 3, 2, 1, 2, 2, 3, 2, 0, 0, 3, 2.

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MSE={MSE:.5f}')
plt.plot(x, np.polyval(reg, x), label=f'deg={deg}')
plt.legend();
deg=1 13
Scikit-learn
The following Python code uses the MLPRegressor class of scikit-learn, which
implements a DNN for estimation. DNNs are sometimes also called multi-layer per‐
ceptron (MLP).3 The results are not perfect, as Figure 1-5 and the MSE illustrate.
However, they are quite good already for the simple configuration used:
In [29]: from sklearn.neural_network import MLPRegressor

In [30]: model = MLPRegressor(hidden_layer_sizes=3 * [256],


learning_rate_init=0.03,
max_iter=5000)

In [31]: model.fit(x.reshape(-1, 1), y)


Out[31]: MLPRegressor(hidden_layer_sizes=[256, 256, 256], learning_rate_init=0.03,
max_iter=5000)

In [32]: y_ = model.predict(x.reshape(-1, 1))

In [33]: MSE = ((y - y_) ** 2).mean()


MSE
Out[33]: 0.021662355744355866

In [34]: plt.figure(figsize=(10, 6))


plt.plot(x, y, 'ro', label='sample data')
plt.plot(x, y_, lw=3.0, label='dnn estimation')
plt.legend();

Instantiates the MLPRegressor object

Implements the fitting or learning step

Implements the prediction step


Just having a look at the results in Figure 1-4 and Figure 1-5, one might assume that
the methods and approaches are not too dissimilar after all. However, there is a fun‐
damental difference worth highlighting. Although the OLS regression approach, as
shown explicitly for the simple linear regression, is based on the calculation of certain
well-specified quantities and parameters, the neural network approach relies on incre‐
mental learning. This in turn means that a set of parameters, the weights within the
neural network, are first initialized randomly and then adjusted gradually given the
differences between the neural network output and the sample output values. This
approach lets you retrain (update) a neural network incrementally.

3 For details, see sklearn.neural_network.MLPRegressor. For more background, see Goodfellow et al. (2016,
ch. 6).

14 Chapter 1: Artificial Intelligence


network increases by more than 10 percentage points, to a level of about 50%, which
is to be expected given the nature of the labels data. It is now in line with an unin‐
formed baseline algorithm:
In [77]: factor = 50

In [78]: big = pd.DataFrame(np.random.randint(0, 2, (factor * n, f)),


columns=fcols)

In [79]: big['l'] = np.random.randint(0, 2, factor * n)

In [80]: train = big[:split]


test = big[split:]

In [81]: model.fit(train[fcols], train['l'])


Out[81]: MLPClassifier(hidden_layer_sizes=[128, 128, 128], max_iter=1000,
random_state=100)

In [82]: accuracy_score(train['l'], model.predict(train[fcols]))


Out[82]: 0.9657142857142857

In [83]: accuracy_score(test['l'], model.predict(test[fcols]))


Out[83]: 0.5043407707910751

Prediction accuracy in-sample (training data set)

Prediction accuracy out-of-sample (test data set)


A quick analysis of the available data, as shown next, explains the increase in the pre‐
diction accuracy. First, all possible patterns are now represented in the data set. Sec‐
ond, all patterns have an average frequency of above 10 in the data set. In other
words, the neural network sees basically all the patterns multiple times. This allows the
neural network to “learn” that both labels 0 and 1 are equally likely for all possible
patterns. Of course, it is a rather involved way of learning this, but it is a good illus‐
tration of the fact that a relatively small data set might often be too small in the context
of neural networks:
In [84]: grouped = big.groupby(list(data.columns))

In [85]: freq = grouped['l'].size().unstack(fill_value=0)

In [86]: freq['sum'] = freq[0] + freq[1]

In [87]: freq.head(6)
Out[87]: l 0 1 sum
f0 f1 f2 f3 f4 f5 f6 f7 f8 f9
0 0 0 0 0 0 0 0 0 0 10 9 19
1 5 4 9
1 0 2 5 7
1 6 6 12

Importance of Data 5
The predictions are generated given the fitted model.

The predictions are numbers from 0 to 3, each representing one cluster.

Figure 1-1. Unsupervised learning of clusters

Once an algorithm such as KMeans is trained, it can, for instance, predict the cluster
for a new (not yet seen) combination of features values. Assume that such an algo‐
rithm is trained on features data that describes potential and real debtors of a bank. It
might learn about the creditworthiness of potential debtors by generating two clus‐
ters. New potential debtors can then be sorted into a certain cluster: “creditworthy”
versus “not creditworthy.”

Reinforcement learning
The following example is based on a coin tossing game that is played with a coin that
lands 80% of the time on heads and 20% of the time on tails. The coin tossing game is
heavily biased to emphasize the benefits of learning as compared to an uninformed
baseline algorithm. The baseline algorithm, which bets randomly and equally distrib‐
utes on heads and tails, achieves a total reward of around 50, on average, per epoch of
100 bets played:
In [9]: ssp = [1, 1, 1, 1, 0]

In [10]: asp = [1, 0]

In [11]: def epoch():


tr = 0
for _ in range(100):
a = np.random.choice(asp)
s = np.random.choice(ssp)

6 39
The DeepMind team did not stop there. AlphaZero was intended to be a general
game-playing AI agent that was supposed to be able to learn different complex board
games, such as Go, chess, and shogi. With regard to AlphaZero, the team summarizes
in Silver (2017a):
In this paper, we generalise this approach into a single AlphaZero algorithm that can
achieve, tabula rasa, superhuman performance in many challenging domains. Starting
from random play, and given no domain knowledge except the game rules, AlphaZero
achieved within 24 hours a superhuman level of play in the games of chess and shogi
(Japanese chess) as well as Go, and convincingly defeated a world-champion program
in each case.
Again, a remarkable milestone was reached by DeepMind in 2017: a game-playing AI
agent that, after less than 24 hours of self-playing and training, achieved above-
human-expert levels in three intensely studied board games with centuries-long
histories in each case.

Chess
Chess is, of course, one of the most popular board games in the world. Chess-playing
computer programs have been around since the very early days of computing, and in
particular, home computing. For example, an almost complete chess engine called ZX
Chess, which only consisted of about 672 bytes of machine code, was introduced in
1983 for the ZX-81 Spectrum home computer.6 Although an incomplete implementa‐
tion that lacked certain rules like castling, it was a great achievement at the time and
is still fascinating for computer chess fans today. The record of ZX Chess as the small‐
est chess program stood for 32 years and was broken only by BootChess in 2015, at
487 bytes.7
It can almost be considered software engineering genius to write a computer program
with such a small code base that can play a board game that has more possible per‐
mutations of a game than the universe has atoms. While not being as complex with
regard to the pure numbers as Go, chess can be considered one of the most challeng‐
ing board games, as players take decades to reach grandmaster level.
In the mid-1980s, expert-level computer chess programs were still far away, even on
better hardware with many fewer constraints than the basic home computer ZX-81
Spectrum. No wonder then that leading chess players at that time felt confident when
playing against computers. For example, Garry Kasparov (2017) recalls an event in
1985 during which he played 32 simultaneous games as follows:

6 See http://bit.ly/aiif_1k_chess for an electronic reprint of the original article published in the February 1983
issue of Your Computer and scans of the original code.
7 See http://bit.ly/aiif_bootchess for more background.

40 27
1 0 0 9 8 17
1 7 4 11

In [88]: freq['sum'].describe().astype(int)
Out[88]: count 1024
mean 12
std 3
min 2
25% 10
50% 12
75% 15
max 26
Name: sum, dtype: int64

Adds the frequency for the 0 and 1 values

Shows summary statistics for the sum values

Volume and Variety


In the context of neural networks that perform prediction tasks, the
volume and variety of the available data used to train the neural
network are decisive for its prediction performance. The numeri‐
cal, hypothetical examples in this section show that the same neural
network trained on a relatively small and not-as-varied data set
underperforms its counterpart trained on a relatively large and var‐
ied data set by more than 10 percentage points. This difference can
be considered huge given that AI practitioners and companies
often fight for improvements as small as a tenth of a percentage
point.

Big Data
What is the difference between a larger data set and a big data set? The term big data
has been used for more than a decade now to mean a number of things. For the pur‐
poses of this book, one might say that a big data set is large enough—in terms of vol‐
ume, variety, and also maybe velocity—for an AI algorithm to be trained properly
such that the algorithm performs better at a prediction task as compared to a baseline
algorithm.
The larger data set used before is still small in practical terms. However, it is large
enough to accomplish the specified goal. The required volume and variety of the data
set are mainly driven by the structure and characteristics of the features and labels
data.
In this context, assume that a retail bank implements a neural network–based classifi‐
cation approach for credit scoring. Given in-house data, the responsible data scientist

28

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Preface
Using Code Examples
You can access and execute the code that accompanies the book on the Quant Plat‐
form at https://aiif.pqp.io, for which only a free registration is required.
If you have a technical question or a problem using the code examples, please send an
email to bookquestions@oreilly.com.
This book is here to help you get your job done. In general, if example code is offered
with this book, you may use it in your programs and documentation. You do not
need to contact us for permission unless you’re reproducing a significant portion of
the code. For example, writing a program that uses several chunks of code from this
book does not require permission. Selling or distributing examples from O’Reilly
books does require permission. Answering a question by citing this book and quoting
example code does not require permission. Incorporating a significant amount of
example code from this book into your product’s documentation does require per‐
mission.
We appreciate, but generally do not require, attribution. An attribution usually
includes the title, author, publisher, and ISBN. For example, this book may be attrib‐
uted as: “Artificial Intelligence in Finance by Yves Hilpisch (O’Reilly). Copyright 2021
Yves Hilpisch, 978-1-492-05543-3.”
If you feel your use of code examples falls outside fair use or the permission given
above, feel free to contact us at permissions@oreilly.com.

O’Reilly Online Learning


For more than 40 years, O’Reilly Media has provided technol‐
ogy and business training, knowledge, and insight to help
companies succeed.

Our unique network of experts and innovators share their knowledge and expertise
through books, articles, and our online learning platform. O’Reilly’s online learning
platform gives you on-demand access to live training courses, in-depth learning
paths, interactive coding environments, and a vast collection of text and video from
O’Reilly and 200+ other publishers. For more information, visit http://oreilly.com.

Preface MSE=0.27331
deg= 9 15
model.fit(x, y, epochs=100, verbose=False)
y_ = model.predict(x)
MSE = ((y - y_.flatten()) ** 2).mean()
print(f'round={_} Table of Contents
Structured Historical Data 105
Structured Streaming Data 108
Unstructured Historical Data 110
Unstructured Streaming Data 112
Alternative Data 113
Normative Theories Revisited 117
Expected Utility and Reality 118
Mean-Variance Portfolio Theory 123
Capital Asset Pricing Model 130
Arbitrage Pricing Theory 134
Debunking Central Assumptions 143
Normally Distributed Returns 143
Linear Relationships 153
Conclusions 155
References 156
Python Code 156

5. Machine Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161


Learning 162
Data 162
Success 165
Capacity 169
Evaluation 172
Bias and Variance 178
Cross-Validation 180
Conclusions 183
References 183

6. AI-First Finance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185


Efficient Markets 186
Market Prediction Based on Returns Data 192
Market Prediction with More Features 199
Market Prediction Intraday 204
Conclusions 205
References 207

Part III. Statistical Inefficiencies


7. Dense Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
The Data 212
Baseline Prediction 214

Table of Contents Table of Contents
11. Risk Management. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 303
Trading Bot 304
Vectorized Backtesting 308
Event-Based Backtesting 311
Assessing Risk 318
Backtesting Risk Measures 322
Stop Loss 324
Trailing Stop Loss 326
Take Profit 328
Conclusions 332
References 332
Python Code 333
Finance Environment 333
Trading Bot 335
Backtesting Base Class 339
Backtesting Class 342

12. Execution and Deployment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345


Oanda Account 346
Data Retrieval 347
Order Execution 351
Trading Bot 357
Deployment 364
Conclusions 368
References 369
Python Code 369
Oanda Environment 369
Vectorized Backtesting 372
Oanda Trading Bot 373

Part V. Outlook
13. AI-Based Competition. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379
AI and Finance 380
Lack of Standardization 382
Education and Training 383
Fight for Resources 385
Market Impact 386
Competitive Scenarios 387
Risks, Regulation, and Oversight 388

Table of Contents MSE=0.22814
round=4 FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0 Chapter 1: Artificial Intelligence


CHAPTER 2
Superintelligence

The fact that there are many paths that lead to superintelligence should increase our
confidence that we will eventually get there. If one path turns out to be blocked, we can
still progress.
—Nick Bostrom (2014)

There are multiple definitions for the term technological singularity. Its use dates back
at least to the article by Vinge (1993), which the author provocatively begins like this:
Within thirty years, we will have the technological means to create superhuman intelli‐
gence. Shortly after, the human era will be ended.
For the purposes of this chapter and book, technological singularity refers to a point in
time at which certain machines achieve superhuman intelligence, or superintelligence
—this is mostly in line with the original idea of Vinge (1993). The idea and concept
was further popularized by the widely read and cited book by Kurzweil (2005). Barrat
(2013) has a wealth of historical and anecdotal information around the topic. Shana‐
han (2015) provides an informal introduction and overview of its central aspects. The
expression technological singularity itself has its origin in the concept of a singularity
in physics. It refers to the center of a black hole, where mass is highly concentrated,
gravitation becomes infinite, and traditional laws of physics break FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0. The begin‐
ning of the universe, the so-called Big Bang, FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0, is also referred to as a singularity.
Although the general ideas and concepts of the technological singularity and of
superintelligence might not have an obvious and direct relationship to AI applied to
finance, a better understanding of their background, related problems, and potential
consequences is beneficial, FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0. The insights gained in the general framework are impor‐
tant in a narrower context as well, such as for AI in finance. Those insights also help
guide the discussion about how AI might reshape the financial industry in the near
and long term.

31
“Success Stories” on page 32 takes a look at a selection of recent success stories in the
field of AI. Among others, it covers how Avast Driver Updater 2.5.6 work kay crack serial keygen company DeepMind solved the problem
of playing Atari 2600 games with neural networks. It also tells the story of how the
same company solved the problem of playing the game of Go at above-human-expert
level. The story of chess and computer programs is also recounted in that section.
“Importance of Hardware” on page 42 discusses the importance of hardware in the
context of these recent success stories. “Forms of Intelligence” on page 44 introduces
different forms of intelligence, such as artificial narrow intelligence (ANI), artificial
general intelligence (AGI), and superintelligence (SI). “Paths to Superintelligence” on
page 45 is about potential paths to superintelligence, such as whole brain emulation
(WBE), while “Intelligence Explosion” on page 50 is about what researchers call intel‐
ligence explosion. “Goals and Control” on page 50 provides a discussion of aspects
related to the so-called control problem in the context of superintelligence. Finally,
“Potential Outcomes” on page 54 briefly looks at potential future outcomes and
scenarios once superintelligence has been achieved.

Success Stories
Many ideas and algorithms in AI date back a few decades already. Over these decades
there have been longer periods of hope on the one hand and despair on the other
hand. Bostrom (2014, ch. 1) provides a review of these periods.
In 2020, one can say for sure that AI is in the middle of a period of hope, if not excite‐
ment. One reason for this is recent successes in applying AI to domains and problems
that even a few years ago seemed immune to AI dominance for decades to come. The
list of such success stories is long and growing rapidly. Therefore, this section focuses
on three such stories only. Gerrish (2018) provides a broader selection and more
detailed accounts of the single cases.

Atari
This sub-section first tells the success story of how DeepMind mastered playing Atari
2600 games with reinforcement learning and neural networks, and then illustrates the
basic approach that led to its success based on a concrete code example.

The story
The first success story is about playing Atari 2600 games on a superhuman level.1 The
Atari 2600 Video Computer System (VCS) was released in 1977 and was one of the
first widespread game-playing consoles in the 1980s. Selected popular games from

1 For background and historical information, see http://bit.ly/aiif_atari.

32 xi
Part VI
The Appendix contains Python code for interactive neural network training (see
Appendix A), classes for simple and shallow neural networks that are imple‐
mented from scratch based on plain Python code (see Appendix B), and an
example of how to use convolutional neural networks (CNNs) for financial time
series prediction (see Appendix C).

Author’s Note
The application of AI to financial trading is still a nascent field, although at the time
of writing there are a number of other books available that cover this topic to some
extent. Many of these publications, however, fail to show what it means to economi‐
cally exploit statistical inefficiencies.
Some hedge funds already claim to exclusively rely on machine learning to manage
their investors’ capital. A prominent example is The Voleon Group, a hedge fund that
reported more than $6 billion in assets under management at the end of 2019 (see Lee
and Karsh 2020). The difficulty of relying on machine learning to outsmart the finan‐
cial markets is reflected in the fund’s performance of 7% for 2019, a year during
which the S&P 500 stock index rose by almost 30%.
This book is based on years of practical experience in developing, backtesting, and
deploying AI-powered algorithmic trading strategies. The approaches and examples
presented are mostly based on my own research since the field is, by nature, FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0, not only
nascent, but also rather secretive. The exposition and the style throughout this book
are relentlessly practical, and in many instances the concrete examples are lacking
proper theoretical support and/or comprehensive empirical evidence. This book even
presents some applications and examples that might be vehemently criticized by
experts in finance and/or machine learning.
For example, some experts in machine and deep learning, such as François Chollet
(2017), outright doubt that prediction in financial markets is possible. Certain experts
in finance, such as Robert FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0 (2015), doubt that there will ever be something like
a financial singularity. Others active at the intersection of the two domains, such as
Marcos López de Prado (2018), argue that the use of machine learning for financial
trading and investing requires an industrial-scale effort with large teams and huge
budgets.
This book does not try to provide a balanced view of or a comprehensive set of refer‐
ences for all the topics covered. The presentation is driven by the personal opinions
and experiences of the author, FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0, as well as by practical considerations when providing
concrete examples and Python code. Many of the examples are also chosen and
tweaked to drive home certain points or to show encouraging results. Therefore, it
can certainly be argued that results from many examples presented in the book suffer
from data snooping and overfitting (for a discussion of these topics, see Hilpisch
2020, FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0, ch. 4).

xii Table of Contents
Structured Historical Data 105
Structured Streaming Data 108
Unstructured Historical Data 110
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Alternative Data 113
Normative Theories Revisited 117
Expected Utility and Reality Imagenomic Portraiture 3.5.5 With Crack [Latest 2022] 118
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Debunking Central Assumptions 143
Normally Distributed Returns 143
Linear Relationships 153
Conclusions FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0 155
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Success 165
Capacity 169
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Conclusions 183
References 183

6. AI-First Finance. . .FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0. 185


Efficient Markets 186
Market Prediction Based on Returns Data 192
Market Prediction with More Features 199
Market Prediction Intraday Line 6 Helix Crack Archives 204
Conclusions 205
References 207

Part III. Statistical Inefficiencies


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Kaspersky Internet Security Crack Archives Table of Contents Chapter 1: Artificial Intelligence
Assume simple OLS linear regression. In this case, the functional relationship
between the input and output values is assumed to be linear, and the problem is to
find optimal parameters α and β for the following linear equation:

f :ℝ ℝ, y FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0 α + βx

For given input values x1, x2. ., xN and output values y1, y2, FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0. ., yNoptimal in this case
means that they minimize the mean squared error (MSE) between the real output
values and the approximated output values:

1N 2
N∑
min yn − f xn
α, β n

For the case of simple linear regression, the solution α*, β* is known in closed form,
as shown in the following equation. Bars on the variables indicate sample mean val‐
ues:

Cov x, y
β* =
Var x
α* = y − βx

The following Python code calculates the optimal parameter values, linearly estimates
(approximates) the output values, and plots the linear regression line alongside the
sample data (see Figure 1-3), FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0. The linear regression FarPoint Spread for Windows Forms v4.0.2022.for DotNET Framework v2.0 does not work too well
here in approximating the functional relationship. This is confirmed by the relatively
high MSE value:
In [22]: beta = np.cov(x, y, ddof=0)[0, 1] / np.var(x)
beta
Out[22]: 1.0541666666666667

In [23]: alpha = y.mean() - beta * x.mean()


alpha
Out[23]: 3.8625000000000003

In [24]: y_ = alpha + beta * x

In [25]: MSE = ((y - y_) ** 2).mean()


MSE
Out[25]: 10.721953125

In [26]: plt.figure(figsize=(10, 6))


plt.plot(x, y, 'ro', label='sample data')
plt.plot(x, y_, lw=3.0, label='linear regression')
plt.legend();

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