ich ziehe mir Daten vom Broker, filter mir diese nach den Stunden 8:oo - 20:oo, berechne von jedem 13-Stunden-Block max, min, idxmax und idxmin und brauche dann für die spätere Arbeit alle dates von den rows/Stunden des kleinsten Wertes/Kurses.
Ich kann leider kein csv dranhängen, Attachments werden hier nicht unterstützt? Wenn ich nur einen kurzen Zeitraum über ein paar Tage wähle, läuft es durch; es passiert erst nach hunderten von Zeilen.
Hier kommt nach einigen Tausend rows der bekannte "IndexError: single positional indexer is out-of-bounds", aber es ist nichts out-of-bounds. Das Problem liegt in der 4rt-letzten Zeile:
Code: Alles auswählen
the_date = df_820.query('index=={}'.format(valmin))['time'].dt.date.iloc[0]
Code: Alles auswählen
NOK, low-hr < high.hr
the_date: 2023-07-24
NOK, low-hr < high.hr
Traceback (most recent call last):
File "c:/Users/moneyKI/Documents/PyAnalytics/bsc/ustec_indexoutofrange_shorter.py", line 91, in <module>
the_date = df_820.query('index=={}'.format(valmin))['time'].dt.date.iloc[0]
File "C:\ProgramData\Anaconda3\envs\Py38\lib\site-packages\pandas\core\indexing.py", line 1073, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "C:\ProgramData\Anaconda3\envs\Py38\lib\site-packages\pandas\core\indexing.py", line 1625, in _getitem_axis
self._validate_integer(key, axis)
File "C:\ProgramData\Anaconda3\envs\Py38\lib\site-packages\pandas\core\indexing.py", line 1557, in _validate_integer
raise IndexError("single positional indexer is out-of-bounds")
IndexError: single positional indexer is out-of-bounds
PS C:\Users\moneyKI\Documents\PyAnalytics> Man kann sich den dataframe ansehen und ich kann das Problem nicht nachvollziehen. Dazu, wenn ich den Zeitraum ändere, läuft es manchmal durch oder stockt an einer anderen Stelle.
Ich verstehe es halt leider nicht.
Hier der Code:
Code: Alles auswählen
import MetaTrader5 as mt5
import pandas as pd
import numpy as np
import pytz
from datetime import datetime
from datetime import timedelta
import time
pd.set_option('display.max_rows', None)
pd.options.display.width = None
# establish connection to MetaTrader 5 terminal
if not mt5.initialize():
print("initialize() failed, error code =",mt5.last_error())
quit()
account = your account
authorized = mt5.login(account, password="yourPW", server="yourServer)
if authorized:
account_info_dict = mt5.account_info()._asdict()
for prop in account_info_dict:
print(" {}={}".format(prop, account_info_dict[prop]))
else:
print("failed to connect at account #{}, error code: {}".format(account, mt5.last_error()))
# set time zone to UTC
timezone = pytz.timezone("Etc/UTC")
# select symbol
selected=mt5.symbol_select("USDJPY",True)
if not selected:
print("failed to select symbol")
mt5.shutdown()
quit()
# create 'datetime' object in UTC time zone to avoid the implementation of a local time zone offset
utc_from = datetime(2022, 1, 1, tzinfo=timezone)
utc_to = datetime(2023, 10, 17, tzinfo=timezone)
# get the bar data
rates_H1 = mt5.copy_rates_range("EURUSD", mt5.TIMEFRAME_H1, utc_from, utc_to)
# shut down connection to the MetaTrader 5 terminal
mt5.shutdown()
# put received rates into dataframe
df_H1 = pd.DataFrame(rates_H1)
# write df to csv
df_H1.to_csv('~/df_H1.csv', index=False, sep='\t', encoding='utf-8', header=True, float_format='%.3f')
# convert time in seconds into the datetime format
df_H1['time']=pd.to_datetime(df_H1['time'], unit='s')
df_H1_hl = df_H1[["time", "high", "low"]]
# 1. get 8:oo-20:oo
filter820 = df_H1.time.dt.hour.between(8, 20, 'both')
df_820 = df_H1_hl[filter820]
# 2. get max, maxindex, min and minindex of each 8-20 period
n = 13 # 8:oo-20:oo = 13hrs/rows
max820 = df_820.groupby(np.arange(len(df_820))//n)['high'].max()
list_idxmax = df_820.groupby(np.arange(len(df_820))//n)['high'].idxmax()
min820 = df_820.groupby(np.arange(len(df_820))//n)['low'].min()
list_idxmin = df_820.groupby(np.arange(len(df_820))//n)['low'].idxmin()
df_820.reset_index(inplace=True)
# making copy of df_820 in which matched rows are deleted
df_result = df_820
# Taking indizes from max-/min-values to find the time/hr of the max/min values
for i in range(len(list_idxmax)):
valmax = list_idxmax[i]
valmin = list_idxmin[i]
if valmin > valmax:
print("OK, low.hr > high.hr")
else:
print("NOK, low-hr < high.hr")
the_date = df_820.query('index=={}'.format(valmin))['time'].dt.date.iloc[0]
print("the_date it fails: ", the_date)
df_tmp = df_820[df_820.time.dt.date == the_date]
df_result.drop(df_tmp.index, axis=0, inplace=True)Der df hat ein wie üblich stabiles Format:
Code: Alles auswählen
time high low
2022-01-03 08:00:00 1.135 1.134
2022-01-03 09:00:00 1.135 1.133
2022-01-03 10:00:00 1.135 1.134
2022-01-03 11:00:00 1.136 1.134
2022-01-03 12:00:00 1.136 1.135
2022-01-03 13:00:00 1.137 1.135
2022-01-03 14:00:00 1.136 1.135
2022-01-03 15:00:00 1.136 1.135
2022-01-03 16:00:00 1.135 1.130
2022-01-03 17:00:00 1.131 1.129
2022-01-03 18:00:00 1.130 1.128
2022-01-03 19:00:00 1.129 1.128
2022-01-03 20:00:00 1.130 1.128
2022-01-04 08:00:00 1.131 1.129
2022-01-04 09:00:00 1.129 1.128
2022-01-04 10:00:00 1.130 1.129
2022-01-04 11:00:00 1.131 1.128
2022-01-04 12:00:00 1.131 1.129
2022-01-04 13:00:00 1.130 1.128
2022-01-04 14:00:00 1.128 1.127
2022-01-04 15:00:00 1.129 1.127
2022-01-04 16:00:00 1.129 1.128
2022-01-04 17:00:00 1.132 1.128
2022-01-04 18:00:00 1.132 1.129
2022-01-04 19:00:00 1.130 1.129
2022-01-04 20:00:00 1.129 1.128
2022-01-05 08:00:00 1.130 1.129
2022-01-05 09:00:00 1.130 1.129
2022-01-05 10:00:00 1.131 1.130
2022-01-05 11:00:00 1.131 1.130
2022-01-05 12:00:00 1.131 1.130
2022-01-05 13:00:00 1.132 1.131
2022-01-05 14:00:00 1.133 1.130
2022-01-05 15:00:00 1.133 1.130