Slides of my speech at the "Practical application of Machine Learning / Deep Learning in NLP" elective.

1. Exploring ML methods to increase the profitability of the trading strategy
Always in touch with the future!

2. About
• Author:
Zakharov Denis Arturovich,
almost graduate student from SPbSUT,
Security Engineer
Google IT Support Professional Certificate
• Research advisor:
Kulikov Evgeny
Master Of ML.
@MisterZurg
2
About speaker

3. 3
Used libraries

4. Before applying any statistical model on a time series, we want to ensure it’s stationary.
• Mean of the series should not be a function of time;
4
Preparing data

5. Before applying any statistical model on a time series, we want to ensure it’s stationary.
• Mean of the series should not be a function of time;
• Variance of the series should not be a function of time;
5
Preparing data

6. Before applying any statistical model on a time series, we want to ensure it’s stationary.
• Mean of the series should not be a function of time;
• Variance of the series should not be a function of time;
• Covariance of the i-th term and the (i + m)-th term series should not be a function of time.
6
Preparing data

7. 7
Damn…

8. # MyBoxCocks использует преобразование Бокса-
Кокса для перевода ряда к стационарному виду
def MyBoxCocks(data: pd.Series):
bc, _ = boxcox(data)
# Отрисовка графика :/
pd.Series(bc).dropna().plot(figsize=(20,8))
return data
8
Box-Cox transformation

9. 9
Box-Cox transformation

10. # MyDifferentiation преобразует ВР к стационарному виду
с через вычисление разницы следующего члена временного
ряда от предыдущего y(t+1) = y(t) + delta
def MyDifferentiation(data: pd.Series):
data = np.array(data)
data = pd.Series(data).iloc[1:] - pd.Series(data).shift(1).dropna()
data.dropna().plot(figsize=(20,8))
return data
10
Differentiation

11. 11
Differentiation

12. # MyFractionDifferentiation преобразует ВР к стационарно
му виду с через y(t+1) = y(t)* delta
def MyFractionDifferentiation(data: pd.Series, delta):
data = np.array(data)
data = pd.Series(data) / pd.Series(data).shift(delta) - 1
data.dropna().plot(figsize=(20,8))
return data
12
Fractional-Differentiation

13. 13
Fractional-Differentiation

14. def MyRoolingMean(data: pd.Series):
# rolling_mean = data.rolling
data = np.array(data)
data = pd.Series(data).rolling(window=1).mean()
data.plot(figsize=(20,8))
return data
14
Rolling Mean

15. 15
Rolling Mean

16. Golang: panic: runtime error:
invalid memory address or nil pointer dereference
𝑑𝑁
𝑑𝑡
=-λ𝑁
16
Exponential decay

17. 17
Linear Regression

18. 18
Linear Regression

19. 19
L1 - Regularization
L1 protects against unnecessary signs

20. 20
L2 - Regularization
L2 protects against unnecessary emissions

21. Principle profit success_deals failed_deals MSE MAE MAPE
LinearRegression
()
1.4335475632639079 337 220 0.006 0.060 0.0006
MLPRegressor() 2.0903450560914356 358 447 0.00524 0.0535 0.0005
DecisionTreeRegr
essor()
1.5805405381988527 238 446 0.00606 0.0602 0.0006
RandomForrest() 1.5678023683607978 243 433 0.009 0.0832 0.0008
21
Regressors

22. Principle profit success_deals failed_deals MSE MAE MAPE
LGBMRegressor() -31.96544957 195 1226 454.05 7.54936 0.19667
XGBRegressor() -7.43470097 264 1211 462.805 7.62564 0.20109
CatBoostRegressor()
-48.52380958
190 1174 441.43 7.42007 0.1923
22
Regressors

23. 23
JPX Tokyo Stock Exchange Prediction

24. 24
Plans…

25. Q
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26. Елистратова Женя, Иванов Григорий, Федотов Станислав, Павловская Анастасия,
Артемьев Михаил, Ушаков Роман, Губко Павел, Лунёв Кирилл, Буркина Мария,
Учаева Евгения, Василевский Борис, Иванов Сергей, Синицин Филипп, Шаграев
Алексей, Кантор Виктор, Голиков Евгений, Ашуха Арсений, Янина Анастасия,
Чирикова Анастасия, Тяпкин Даниил, Нейчев Радослав, Норкин Дмитрий, Симаков
Миша “Учебник по машинному обучению” [Electronic resource] / ml-handbook /
Access mode : https://ml-
handbook.ru/?utm_source=yandex&utm_medium=institutions&utm_campaign=shad.
Cory Maklin “ARIMA Model Python Example — Time Series Forecasting” / [Electronic
resource] / towardsdatascience / Access mode :
https://towardsdatascience.com/machine-learning-part-19-time-series-and-
autoregressive-integrated-moving-average-model-arima-c1005347b0d7
Used materials:

27. Denis Zakharov
2022
Thank yoU!