#Author: johannes <
[email protected]
>, 22.12.17
#License: MIT License (
http://opensource.org/licenses/MIT
)
#I currently can't get this working, can anyone point out my mistakes. I would also like to know how
#to plot it as a histogram also. I am very new python.
#File "/Users/jeosullivan/Analytics/BTC.py", line 19, in <module>
#data = json.loads(urllib.urlopen(url).read())
#AttributeError: module 'urllib' has no attribute 'urlopen'
import pandas as pd
import urllib, json
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.mlab as mlab
from scipy.stats import norm
coinAcronym = "BTC"
exchange = "Kraken"
tradeCurrency = "EUR"
timeFrame = 30
url = "https://min-api.cryptocompare.com/data/histoday?fsym=" + coinAcronym + "&tsym=" + tradeCurrency + "&limit=" + str(timeFrame) + "&e=" + exchange
data = json.loads(urllib.urlopen(url).read())
df = pd.DataFrame(data['Data'])
df.columns = [['close', 'high', 'low', 'open', 'time', 'volumefrom', 'volumeto']]
df.time = pd.to_datetime(df['time'], unit='s')
df = df.set_index(df.time)
df2 = pd.DataFrame()
df2 = df.close
returns = df2.pct_change()
volatility = returns.std()
numberSimulations = 100
predictedDays = 30
lastPrice = df.close[-1]
print(lastPrice)
results = pd.DataFrame()
sim = 0
while (sim < numberSimulations):
prices = [] #Empty list for each new simulation
days = 0 #reset days counter
prices.append(lastPrice) #Fill list with initial price
while (days < predictedDays):
prices.append(prices[days] * (1 + np.random.normal(0, volatility))) #Add random new price and add to list
days = days + 1 #increment days counter
results[str(sim)] = pd.Series(prices).values #Add column to pandas data frame
sim = sim + 1 #increment simulation counter
fig = plt.figure()
plt.plot(results)
plt.ylabel('Price in Euro')
plt.xlabel('Simulated days')
plt.show()
I am using Python 3.6 in Sypder and I am now getting an error in one of the libraries. "line 528, in f
if value.lower() in _unit_map:
AttributeError: 'tuple' object has no attribute 'lower'". It makes no sense at all. All . I want is a bootstrap template for Monte Carlo so I can link in the apis I want.
Can you share the link of the script?
The error is pointing to the variable "value", that is a tuple but the method lower() needs a string.
You have to check the content of "value".
from datetime import datetime, timedelta, time
import numpy as np
from collections import MutableMapping
from pandas._libs import tslib
from pandas._libs.tslibs.strptime import array_strptime
from pandas._libs.tslibs import parsing, conversion
from pandas._libs.tslibs.parsing import ( # noqa
parse_time_string,
DateParseError,
_format_is_iso,
_guess_datetime_format)
from pandas.core.dtypes.common import (
_ensure_object,
is_datetime64_ns_dtype,
is_datetime64_dtype,
is_datetime64tz_dtype,
is_integer_dtype,
is_integer,
is_float,
is_list_like,
is_scalar,
is_numeric_dtype)
from pandas.core.dtypes.generic import (
ABCIndexClass, ABCSeries,
ABCDataFrame)
from pandas.core.dtypes.missing import notna
from pandas.core import algorithms
def _guess_datetime_format_for_array(arr, **kwargs):
# Try to guess the format based on the first non-NaN element
non_nan_elements = notna(arr).nonzero()[0]
if len(non_nan_elements):
return _guess_datetime_format(arr[non_nan_elements[0]], **kwargs)
def _maybe_cache(arg, format, cache, tz, convert_listlike):
Create a cache of unique dates from an array of dates
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
format : string
Strftime format to parse time
cache : boolean
True attempts to create a cache of converted values
tz : string
Timezone of the dates
convert_listlike : function
Conversion function to apply on dates
Returns
-------
cache_array : Series
Cache of converted, unique dates. Can be empty
from pandas import Series
cache_array = Series()
if cache:
# Perform a quicker unique check
from pandas import Index
if not Index(arg).is_unique:
unique_dates = algorithms.unique(arg)
cache_dates = convert_listlike(unique_dates, True, format, tz=tz)
cache_array = Series(cache_dates, index=unique_dates)
return cache_array
def _convert_and_box_cache(arg, cache_array, box, errors, name=None):
Convert array of dates with a cache and box the result
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
cache_array : Series
Cache of converted, unique dates
box : boolean
True boxes result as an Index-like, False returns an ndarray
errors : string
'ignore' plus box=True will convert result to Index
name : string, default None
Name for a DatetimeIndex
Returns
-------
result : datetime of converted dates
Returns:
- Index-like if box=True
- ndarray if box=False
from pandas import Series, DatetimeIndex, Index
result = Series(arg).map(cache_array)
if box:
if errors == 'ignore':
return Index(result)
else:
return DatetimeIndex(result, name=name)
return result.values
def to_datetime(arg, errors='raise', dayfirst=False, yearfirst=False,
utc=None, box=True, format=None, exact=True,
unit=None, infer_datetime_format=False, origin='unix',
cache=False):
Convert argument to datetime.
Parameters
----------
arg : integer, float, string, datetime, list, tuple, 1-d array, Series
.. versionadded:: 0.18.1
or DataFrame/dict-like
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as NaT
- If 'ignore', then invalid parsing will return the input
dayfirst : boolean, default False
Specify a date parse order if `arg` is str or its list-likes.
If True, parses dates with the day first, eg 10/11/12 is parsed as
2012-11-10.
Warning: dayfirst=True is not strict, but will prefer to parse
with day first (this is a known bug, based on dateutil behavior).
yearfirst : boolean, default False
Specify a date parse order if `arg` is str or its list-likes.
- If True parses dates with the year first, eg 10/11/12 is parsed as
2010-11-12.
- If both dayfirst and yearfirst are True, yearfirst is preceded (same
as dateutil).
Warning: yearfirst=True is not strict, but will prefer to parse
with year first (this is a known bug, based on dateutil beahavior).
.. versionadded:: 0.16.1
utc : boolean, default None
Return UTC DatetimeIndex if True (converting any tz-aware
datetime.datetime objects as well).
box : boolean, default True
- If True returns a DatetimeIndex
- If False returns ndarray of values.
format : string, default None
strftime to parse time, eg "%d/%m/%Y", note that "%f" will parse
all the way up to nanoseconds.
exact : boolean, True by default
- If True, require an exact format match.
- If False, allow the format to match anywhere in the target string.
unit : string, default 'ns'
unit of the arg (D,s,ms,us,ns) denote the unit, which is an
integer or float number. This will be based off the origin.
Example, with unit='ms' and origin='unix' (the default), this
would calculate the number of milliseconds to the unix epoch start.
infer_datetime_format : boolean, default False
If True and no `format` is given, attempt to infer the format of the
datetime strings, and if it can be inferred, switch to a faster
method of parsing them. In some cases this can increase the parsing
speed by ~5-10x.
origin : scalar, default is 'unix'
Define the reference date. The numeric values would be parsed as number
of units (defined by `unit`) since this reference date.
- If 'unix' (or POSIX) time; origin is set to 1970-01-01.
- If 'julian', unit must be 'D', and origin is set to beginning of
Julian Calendar. Julian day number 0 is assigned to the day starting
at noon on January 1, 4713 BC.
- If Timestamp convertible, origin is set to Timestamp identified by
origin.
.. versionadded:: 0.20.0
cache : boolean, default False
If True, use a cache of unique, converted dates to apply the datetime
conversion. May produce sigificant speed-up when parsing duplicate date
strings, especially ones with timezone offsets.
.. versionadded:: 0.23.0
Returns
-------
ret : datetime if parsing succeeded.
Return type depends on input:
- list-like: DatetimeIndex
- Series: Series of datetime64 dtype
- scalar: Timestamp
In case when it is not possible to return designated types (e.g. when
any element of input is before Timestamp.min or after Timestamp.max)
return will have datetime.datetime type (or corresponding
array/Series).
Examples
--------
Assembling a datetime from multiple columns of a DataFrame. The keys can be
common abbreviations like ['year', 'month', 'day', 'minute', 'second',
'ms', 'us', 'ns']) or plurals of the same
>>> df = pd.DataFrame({'year': [2015, 2016],
'month': [2, 3],
'day': [4, 5]})
>>> pd.to_datetime(df)
0 2015-02-04
1 2016-03-05
dtype: datetime64[ns]
If a date does not meet the `timestamp limitations
<http://pandas.pydata.org/pandas-docs/stable/timeseries.html
#timeseries-timestamp-limits>`_, passing errors='ignore'
will return the original input instead of raising any exception.
Passing errors='coerce' will force an out-of-bounds date to NaT,
in addition to forcing non-dates (or non-parseable dates) to NaT.
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='ignore')
datetime.datetime(1300, 1, 1, 0, 0)
>>> pd.to_datetime('13000101', format='%Y%m%d', errors='coerce')
Passing infer_datetime_format=True can often-times speedup a parsing
if its not an ISO8601 format exactly, but in a regular format.
>>> s = pd.Series(['3/11/2000', '3/12/2000', '3/13/2000']*1000)
>>> s.head()
0 3/11/2000
1 3/12/2000
2 3/13/2000
3 3/11/2000
4 3/12/2000
dtype: object
>>> %timeit pd.to_datetime(s,infer_datetime_format=True)
100 loops, best of 3: 10.4 ms per loop
>>> %timeit pd.to_datetime(s,infer_datetime_format=False)
1 loop, best of 3: 471 ms per loop
Using a unix epoch time
>>> pd.to_datetime(1490195805, unit='s')
Timestamp('2017-03-22 15:16:45')
>>> pd.to_datetime(1490195805433502912, unit='ns')
Timestamp('2017-03-22 15:16:45.433502912')
.. warning:: For float arg, precision rounding might happen. To prevent
unexpected behavior use a fixed-width exact type.
Using a non-unix epoch origin
>>> pd.to_datetime([1, 2, 3], unit='D',
origin=pd.Timestamp('1960-01-01'))
0 1960-01-02
1 1960-01-03
2 1960-01-04
See also
--------
pandas.DataFrame.astype : Cast argument to a specified dtype.
pandas.to_timedelta : Convert argument to timedelta.
from pandas.core.indexes.datetimes import DatetimeIndex
tz = 'utc' if utc else None
def _convert_listlike(arg, box, format, name=None, tz=tz):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
# these are shortcutable
if is_datetime64tz_dtype(arg):
if not isinstance(arg, DatetimeIndex):
return DatetimeIndex(arg, tz=tz, name=name)
if utc:
arg = arg.tz_convert(None).tz_localize('UTC')
return arg
elif is_datetime64_ns_dtype(arg):
if box and not isinstance(arg, DatetimeIndex):
return DatetimeIndex(arg, tz=tz, name=name)
except ValueError:
return arg
elif unit is not None:
if format is not None:
raise ValueError("cannot specify both format and unit")
arg = getattr(arg, 'values', arg)
result = tslib.array_with_unit_to_datetime(arg, unit,
errors=errors)
if box:
if errors == 'ignore':
from pandas import Index
return Index(result)
return DatetimeIndex(result, tz=tz, name=name)
return result
elif getattr(arg, 'ndim', 1) > 1:
raise TypeError('arg must be a string, datetime, list, tuple, '
'1-d array, or Series')
arg = _ensure_object(arg)
require_iso8601 = False
if infer_datetime_format and format is None:
format = _guess_datetime_format_for_array(arg, dayfirst=dayfirst)
if format is not None:
# There is a special fast-path for iso8601 formatted
# datetime strings, so in those cases don't use the inferred
# format because this path makes process slower in this
# special case
format_is_iso8601 = _format_is_iso(format)
if format_is_iso8601:
require_iso8601 = not infer_datetime_format
format = None
result = None
if format is not None:
# shortcut formatting here
if format == '%Y%m%d':
result = _attempt_YYYYMMDD(arg, errors=errors)
except:
raise ValueError("cannot convert the input to "
"'%Y%m%d' date format")
# fallback
if result is None:
result = array_strptime(arg, format, exact=exact,
errors=errors)
except tslib.OutOfBoundsDatetime:
if errors == 'raise':
raise
result = arg
except ValueError:
# if format was inferred, try falling back
# to array_to_datetime - terminate here
# for specified formats
if not infer_datetime_format:
if errors == 'raise':
raise
result = arg
if result is None and (format is None or infer_datetime_format):
result = tslib.array_to_datetime(
errors=errors,
utc=utc,
dayfirst=dayfirst,
yearfirst=yearfirst,
require_iso8601=require_iso8601
if is_datetime64_dtype(result) and box:
result = DatetimeIndex(result, tz=tz, name=name)
return result
except ValueError as e:
values, tz = conversion.datetime_to_datetime64(arg)
return DatetimeIndex._simple_new(values, name=name, tz=tz)
except (ValueError, TypeError):
raise e
if arg is None:
return None
# handle origin
if origin == 'julian':
original = arg
j0 = tslib.Timestamp(0).to_julian_date()
if unit != 'D':
raise ValueError("unit must be 'D' for origin='julian'")
arg = arg - j0
except:
raise ValueError("incompatible 'arg' type for given "
"'origin'='julian'")
# premptively check this for a nice range
j_max = tslib.Timestamp.max.to_julian_date() - j0
j_min = tslib.Timestamp.min.to_julian_date() - j0
if np.any(arg > j_max) or np.any(arg < j_min):
raise tslib.OutOfBoundsDatetime(
"{original} is Out of Bounds for "
"origin='julian'".format(original=original))
elif origin not in ['unix', 'julian']:
# arg must be a numeric
original = arg
if not ((is_scalar(arg) and (is_integer(arg) or is_float(arg))) or
is_numeric_dtype(np.asarray(arg))):
raise ValueError(
"'{arg}' is not compatible with origin='{origin}'; "
"it must be numeric with a unit specified ".format(
arg=arg,
origin=origin))
# we are going to offset back to unix / epoch time
offset = tslib.Timestamp(origin)
except tslib.OutOfBoundsDatetime:
raise tslib.OutOfBoundsDatetime(
"origin {origin} is Out of Bounds".format(origin=origin))
except ValueError:
raise ValueError("origin {origin} cannot be converted "
"to a Timestamp".format(origin=origin))
if offset.tz is not None:
raise ValueError(
"origin offset {} must be tz-naive".format(offset))
offset -= tslib.Timestamp(0)
# convert the offset to the unit of the arg
# this should be lossless in terms of precision
offset = offset // tslib.Timedelta(1, unit=unit)
# scalars & ndarray-like can handle the addition
if is_list_like(arg) and not isinstance(
arg, (ABCSeries, ABCIndexClass, np.ndarray)):
arg = np.asarray(arg)
arg = arg + offset
if isinstance(arg, tslib.Timestamp):
result = arg
elif isinstance(arg, ABCSeries):
cache_array = _maybe_cache(arg, format, cache, tz, _convert_listlike)
if not cache_array.empty:
result = arg.map(cache_array)
else:
from pandas import Series
values = _convert_listlike(arg._values, True, format)
result = Series(values, index=arg.index, name=arg.name)
elif isinstance(arg, (ABCDataFrame, MutableMapping)):
result = _assemble_from_unit_mappings(arg, errors=errors)
elif isinstance(arg, ABCIndexClass):
cache_array = _maybe_cache(arg, format, cache, tz, _convert_listlike)
if not cache_array.empty:
result = _convert_and_box_cache(arg, cache_array, box, errors,
name=arg.name)
else:
result = _convert_listlike(arg, box, format, name=arg.name)
elif is_list_like(arg):
cache_array = _maybe_cache(arg, format, cache, tz, _convert_listlike)
if not cache_array.empty:
result = _convert_and_box_cache(arg, cache_array, box, errors)
else:
result = _convert_listlike(arg, box, format)
else:
result = _convert_listlike(np.array([arg]), box, format)[0]
return result
# mappings for assembling units
_unit_map = {'year': 'year',
'years': 'year',
'month': 'month',
'months': 'month',
'day': 'day',
'days': 'day',
'hour': 'h',
'hours': 'h',
'minute': 'm',
'minutes': 'm',
'second': 's',
'seconds': 's',
'ms': 'ms',
'millisecond': 'ms',
'milliseconds': 'ms',
'us': 'us',
'microsecond': 'us',
'microseconds': 'us',
'ns': 'ns',
'nanosecond': 'ns',
'nanoseconds': 'ns'
def _assemble_from_unit_mappings(arg, errors):
assemble the unit specified fields from the arg (DataFrame)
Return a Series for actual parsing
Parameters
----------
arg : DataFrame
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as NaT
- If 'ignore', then invalid parsing will return the input
Returns
-------
Series
from pandas import to_timedelta, to_numeric, DataFrame
arg = DataFrame(arg)
if not arg.columns.is_unique:
raise ValueError("cannot assemble with duplicate keys")
# replace passed unit with _unit_map
def f(value):
if value in _unit_map:
return _unit_map[value]
# m is case significant
if value.lower() in _unit_map:
return _unit_map[value.lower()]
return value
unit = {k: f(k) for k in arg.keys()}
unit_rev = {v: k for k, v in unit.items()}
# we require at least Ymd
required = ['year', 'month', 'day']
req = sorted(list(set(required) - set(unit_rev.keys())))
if len(req):
raise ValueError("to assemble mappings requires at least that "
"[year, month, day] be specified: [{required}] "
"is missing".format(required=','.join(req)))
# keys we don't recognize
excess = sorted(list(set(unit_rev.keys()) - set(_unit_map.values())))
if len(excess):
raise ValueError("extra keys have been passed "
"to the datetime assemblage: "
"[{excess}]".format(excess=','.join(excess)))
def coerce(values):
# we allow coercion to if errors allows
values = to_numeric(values, errors=errors)
# prevent overflow in case of int8 or int16
if is_integer_dtype(values):
values = values.astype('int64', copy=False)
return values
values = (coerce(arg[unit_rev['year']]) * 10000 +
coerce(arg[unit_rev['month']]) * 100 +
coerce(arg[unit_rev['day']]))
values = to_datetime(values, format='%Y%m%d', errors=errors)
except (TypeError, ValueError) as e:
raise ValueError("cannot assemble the "
"datetimes: {error}".format(error=e))
for u in ['h', 'm', 's', 'ms', 'us', 'ns']:
value = unit_rev.get(u)
if value is not None and value in arg:
values += to_timedelta(coerce(arg[value]),
unit=u,
errors=errors)
except (TypeError, ValueError) as e:
raise ValueError("cannot assemble the datetimes [{value}]: "
"{error}".format(value=value, error=e))
return values
def _attempt_YYYYMMDD(arg, errors):
""" try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,
arg is a passed in as an object dtype, but could really be ints/strings
with nan-like/or floats (e.g. with nan)
Parameters
----------
arg : passed value
errors : 'raise','ignore','coerce'
def calc(carg):
# calculate the actual result
carg = carg.astype(object)
parsed = parsing.try_parse_year_month_day(carg / 10000,
carg / 100 % 100,
carg % 100)
return tslib.array_to_datetime(parsed, errors=errors)
def calc_with_mask(carg, mask):
result = np.empty(carg.shape, dtype='M8[ns]')
iresult = result.view('i8')
iresult[~mask] = tslib.iNaT
result[mask] = calc(carg[mask].astype(np.float64).astype(np.int64)). \
astype('M8[ns]')
return result
# try intlike / strings that are ints
return calc(arg.astype(np.int64))
except:
# a float with actual np.nan
carg = arg.astype(np.float64)
return calc_with_mask(carg, notna(carg))
except:
# string with NaN-like
mask = ~algorithms.isin(arg, list(tslib.nat_strings))
return calc_with_mask(arg, mask)
except:
return None
# Fixed time formats for time parsing
_time_formats = ["%H:%M", "%H%M", "%I:%M%p", "%I%M%p",
"%H:%M:%S", "%H%M%S", "%I:%M:%S%p", "%I%M%S%p"]
def _guess_time_format_for_array(arr):
# Try to guess the format based on the first non-NaN element
non_nan_elements = notna(arr).nonzero()[0]
if len(non_nan_elements):
element = arr[non_nan_elements[0]]
for time_format in _time_formats:
datetime.strptime(element, time_format)
return time_format
except ValueError:
return None
def to_time(arg, format=None, infer_time_format=False, errors='raise'):
Parse time strings to time objects using fixed strptime formats ("%H:%M",
"%H%M", "%I:%M%p", "%I%M%p", "%H:%M:%S", "%H%M%S", "%I:%M:%S%p",
"%I%M%S%p")
Use infer_time_format if all the strings are in the same format to speed
up conversion.
Parameters
----------
arg : string in time format, datetime.time, list, tuple, 1-d array, Series
format : str, default None
Format used to convert arg into a time object. If None, fixed formats
are used.
infer_time_format: bool, default False
Infer the time format based on the first non-NaN element. If all
strings are in the same format, this will speed up conversion.
errors : {'ignore', 'raise', 'coerce'}, default 'raise'
- If 'raise', then invalid parsing will raise an exception
- If 'coerce', then invalid parsing will be set as None
- If 'ignore', then invalid parsing will return the input
Returns
-------
datetime.time
from pandas.core.series import Series
def _convert_listlike(arg, format):
if isinstance(arg, (list, tuple)):
arg = np.array(arg, dtype='O')
elif getattr(arg, 'ndim', 1) > 1:
raise TypeError('arg must be a string, datetime, list, tuple, '
'1-d array, or Series')
arg = _ensure_object(arg)
if infer_time_format and format is None:
format = _guess_time_format_for_array(arg)
times = []
if format is not None:
for element in arg:
times.append(datetime.strptime(element, format).time())
except (ValueError, TypeError):
if errors == 'raise':
msg = ("Cannot convert {element} to a time with given "
"format {format}").format(element=element,
format=format)
raise ValueError(msg)
elif errors == 'ignore':
return arg
else:
times.append(None)
else:
formats = _time_formats[:]
format_found = False
for element in arg:
time_object = None
for time_format in formats:
time_object = datetime.strptime(element,
time_format).time()
if not format_found:
# Put the found format in front
fmt = formats.pop(formats.index(time_format))
formats.insert(0, fmt)
format_found = True
break
except (ValueError, TypeError):
continue
if time_object is not None:
times.append(time_object)
elif errors == 'raise':
raise ValueError("Cannot convert arg {arg} to "
"a time".format(arg=arg))
elif errors == 'ignore':
return arg
else:
times.append(None)
return times
if arg is None:
return arg
elif isinstance(arg, time):
return arg
elif isinstance(arg, Series):
values = _convert_listlike(arg._values, format)
return Series(values, index=arg.index, name=arg.name)
elif isinstance(arg, ABCIndexClass):
return _convert_listlike(arg, format)
elif is_list_like(arg):
return _convert_listlike(arg, format)
return _convert_listlike(np.array([arg]), format)[0]
def format(dt):
"""Returns date in YYYYMMDD format."""
return dt.strftime('%Y%m%d')
OLE_TIME_ZERO = datetime(1899, 12, 30, 0, 0, 0)
def ole2datetime(oledt):
"""function for converting excel date to normal date format"""
val = float(oledt)
# Excel has a bug where it thinks the date 2/29/1900 exists
# we just reject any date before 3/1/1900.
if val < 61:
msg = "Value is outside of acceptable range: {value}".format(value=val)
raise ValueError(msg)
return OLE_TIME_ZERO + timedelta(days=val)
Error:
#line 528, in f
#if value.lower() in _unit_map:
#AttributeError: 'tuple' object has no attribute 'lower'
Test
everything
in a Python shell (iPython,
Azure Notebook
, etc.)
-
Someone gave you an advice you liked? Test it -
maybe the advice was actually bad
.
-
Someone gave you an advice you think is bad? Test it before arguing -
maybe it was good
.
-
You posted a claim that something you did not test works?
Be prepared to eat your hat
.
