Machine learning in trading: theory, models, practice and algo-trading - page 2098

[Rorschach]

Maxim Dmitrievsky:
Yeah, funny, I'll take note

Some GPT-3 can also put a cotier in it.

[Maxim Dmitrievsky]

Rorschach:

Some kind of GPT-3 can also have a cotier in it.

It's expensive to convert cotier into pictures, that's why I'm in favor of one-dimensional convolutions.

[Vladimir Karputov]

How do I download the *.ipynb file from github to jupyter notebook?

add: The question is removed - something clicked and a download button appeared:

[Maxim Dmitrievsky]

Vladimir Karputov:

How do I download the *.ipynb file from github to jupyter notebook?

add: The question is removed - something clicked and a download button appeared:

Vladimir, are you with us now, on the dark side?

[Aleksey Vyazmikin]

They published my article. I invite you to read it and critique it :)

[Vladimir Karputov]

Maxim Dmitrievsky:

Vladimir, are you with us now, on the dark side?

I am just learning...

[Mihail Marchukajtes]

Maxim Dmitrievsky:

Vladimir, are you with us now, on the dark side?

Wow, the sides are already giving out! Dibs on water!!!!! :-)))))

[Vladimir Karputov]

I can't print the first five lines of the DataFrame object.

I take the script from the 'data folder'\Scripts\Python\copy_rates_from.py' package and add the lines:

from datetime import datetime
import MetaTrader5 as mt5
# выведем данные о пакете MetaTrader5
print("MetaTrader5 package author: ",mt5.__author__)
print("MetaTrader5 package version: ",mt5.__version__)

# импортируем модуль pandas для вывода полученных данных в табличной форме
import pandas as pd
pd.set_option('display.max_columns', 500) # сколько столбцов показываем
pd.set_option('display.width', 1500)      # макс. ширина таблицы для показа
# импортируем модуль pytz для работы с таймзоной
import pytz

# установим подключение к терминалу MetaTrader 5
if not mt5.initialize():
    print("initialize() failed")
    mt5.shutdown()

# установим таймзону в UTC
timezone = pytz.timezone("Etc/UTC")
# создадим объект datetime в таймзоне UTC, чтобы не применялось смещение локальной таймзоны
utc_from = datetime(2020, 1, 10, tzinfo=timezone)
# получим 10 баров с EURUSD H4 начиная с 01.10.2020 в таймзоне UTC
rates = mt5.copy_rates_from("EURUSD", mt5.TIMEFRAME_H4, utc_from, 10)

# завершим подключение к терминалу MetaTrader 5
mt5.shutdown()
# выведем каждый элемент полученных данных на новой строке
print("Выведем полученные данные как есть")
for rate in rates:
    print(rate)

# создадим из полученных данных DataFrame
rates_frame = pd.DataFrame(rates)

from datetime import datetime
import MetaTrader5 as mt5
# выведем данные о пакете MetaTrader5
print("MetaTrader5 package author: ",mt5.__author__)
print("MetaTrader5 package version: ",mt5.__version__)

# импортируем модуль pandas для вывода полученных данных в табличной форме
import pandas as pd
pd.set_option('display.max_columns', 500) # сколько столбцов показываем
pd.set_option('display.width', 1500)      # макс. ширина таблицы для показа
# импортируем модуль pytz для работы с таймзоной
import pytz

# установим подключение к терминалу MetaTrader 5
if not mt5.initialize():
    print("initialize() failed")
    mt5.shutdown()

# установим таймзону в UTC
timezone = pytz.timezone("Etc/UTC")
# создадим объект datetime в таймзоне UTC, чтобы не применялось смещение локальной таймзоны
utc_from = datetime(2020, 1, 10, tzinfo=timezone)
# получим 10 баров с EURUSD H4 начиная с 01.10.2020 в таймзоне UTC
rates = mt5.copy_rates_from("EURUSD", mt5.TIMEFRAME_H4, utc_from, 10)

# завершим подключение к терминалу MetaTrader 5
mt5.shutdown()
# выведем каждый элемент полученных данных на новой строке
print("Выведем полученные данные как есть")
for rate in rates:
    print(rate)

# создадим из полученных данных DataFrame
rates_frame = pd.DataFrame(rates)

# выведем пять первых строк (метод 'head' pandas)
print("\nВыведем пять первых строк")
rates_frame.head()

rates_frame['time']=pd.to_datetime(rates_frame['time'], unit='s')

# выведем данные
print("\nВыведем датафрейм с данными")
print(rates_frame)

rates_frame['time']=pd.to_datetime(rates_frame['time'], unit='s')

# выведем данные
print("\nВыведем датафрейм с данными")
print(rates_frame)

and the method doesn't output anything:

(1578614400, 1.11051, 1.11093, 1.11017, 1.11041, 2448, 1, 0)

Выведем пять первых строк

Выведем датафрейм с данными

[Mikhail Mishanin]

Aleksey Vyazmikin:
They published my article. I invite you to read it and critique it :)

Alexey, my question to you and to all: Why? Let's take as a target the signal of moving average crossing and not touching it on the next bar...".

You can teach the "ideal" signal. Take a ZZ (several ZZ) and cycle from the present to the past in each bar, which will go exactly up/down, for so many that, so many bars.

When I was a Neuroshell Day Trader Professional, I learned such a signal and got the first sane result.

[Aleksey Vyazmikin]

dr.mr.mom:

Alexei, a question to you and everyone else: why? Let's take as a target a signal that crosses a moving average and does not touch it on the next bar..."

You can teach an "ideal" signal. Take a ZZ (several ZZ) and cycle from the present to the past in each bar, which will go exactly up/down, for so many that, so many bars.

When I was a Neuroshell Day Trader Professional, I learned this signal and obtained the first reasonable result.

That's why at that time we didn't know that reversal points are poorly predicted by this method, and training is mostly by trend....

It is quite reasonable to use different strategies for diversification, and MO helps to improve basic strategies, which is what I suggested to use in the article.