Python TA-LIB error on Mac - Symbol not found: _TA_ACOS
I'm trying to use TA-lib in my Python programming.
I followed the instructions here: http://mrjbq7.github.io/ta-lib/install.html but kept running into errors. After a bit of trial and error (I am not a Mac expert having grown up on PCs!) I eventually got it installed after installing Brew, updating to Python 3.8.5 (Conda) and uninstalling the other version (3.9.2) that I had unwittingly installed outside Conda.
Now, when I call it from within my Python code using "import talib as ta", I get the following error:
(base) jasoncameron@Jasons-Mac-mini python % python app_prepdata.py
Traceback (most recent call last):
File "app_prepdata.py", line 4, in <module>
import talib as ta
File "/Users/jasoncameron/opt/anaconda3/lib/python3.8/site-packages/TA_Lib-0.4.19-py3.8-macosx-10.9-x86_64.egg/talib/__init__.py", line 52, in <module>
from ._ta_lib import (
ImportError: dlopen(/Users/jasoncameron/opt/anaconda3/lib/python3.8/site-packages/TA_Lib-0.4.19-py3.8-macosx-10.9-x86_64.egg/talib/_ta_lib.cpython-38-darwin.so, 2): Symbol not found: _TA_ACOS
Referenced from: /Users/jasoncameron/opt/anaconda3/lib/python3.8/site-packages/TA_Lib-0.4.19-py3.8-macosx-10.9-x86_64.egg/talib/_ta_lib.cpython-38-darwin.so
Expected in: flat namespace
in /Users/jasoncameron/opt/anaconda3/lib/python3.8/site-packages/TA_Lib-0.4.19-py3.8-macosx-10.9-x86_64.egg/talib/_ta_lib.cpython-38-darwin.so
There are some references to this error on other sites but no solutions and given it appears to have been installed successfully, I'm at a loss! Please let me know if you have any thoughts.
I'm running a new M1 Mac/Big Sur (not sure if that makes a difference) and have the following packages installed:
#
# Name Version Build Channel
_ipyw_jlab_nb_ext_conf 0.1.0 py38_0
alabaster 0.7.12 py_0
anaconda 2020.11 py38_0
anaconda-client 1.7.2 py38_0
anaconda-navigator 1.10.0 py38_0
anaconda-project 0.8.4 py_0
applaunchservices 0.2.1 py_0
appnope 0.1.0 py38_1001
appscript 1.1.1 py38haf1e3a3_0
argh 0.26.2 py38_0
argon2-cffi 20.1.0 py38haf1e3a3_1
asn1crypto 1.4.0 py_0
astroid 2.4.2 py38_0
astropy 4.0.2 py38haf1e3a3_0
async_generator 1.10 py_0
atomicwrites 1.4.0 py_0
attrs 20.3.0 pyhd3eb1b0_0
autopep8 1.5.4 py_0
babel 2.8.1 pyhd3eb1b0_0
backcall 0.2.0 py_0
backports 1.0 py_2
backports.functools_lru_cache 1.6.1 py_0
backports.shutil_get_terminal_size 1.0.0 py38_2
backports.tempfile 1.0 py_1
backports.weakref 1.0.post1 py_1
beautifulsoup4 4.9.3 pyhb0f4dca_0
bitarray 1.6.1 py38h9ed2024_0
bkcharts 0.2 py38_0
blas 1.0 mkl
bleach 3.2.1 py_0
blosc 1.20.1 hab81aa3_0
bokeh 2.2.3 py38_0
boto 2.49.0 py38_0
bottleneck 1.3.2 py38hf1fa96c_1
brotlipy 0.7.0 py38haf1e3a3_1000
bzip2 1.0.8 h1de35cc_0
ca-certificates 2020.10.14 0
certifi 2020.6.20 pyhd3eb1b0_3
cffi 1.14.3 py38hed5b41f_0
chardet 3.0.4 py38_1003
click 7.1.2 py_0
cloudpickle 1.6.0 py_0
clyent 1.2.2 py38_1
colorama 0.4.4 py_0
conda 4.9.2 py38hecd8cb5_0
conda-build 3.20.5 py38_1
conda-env 2.6.0 1
conda-package-handling 1.7.2 py38h22f3db7_0
conda-verify 3.4.2 py_1
contextlib2 0.6.0.post1 py_0
cryptography 3.1.1 py38hddc9c9b_0
curl 7.71.1 hb0a8c7a_1
cycler 0.10.0 py38_0
cython 0.29.21 py38hb1e8313_0
cytoolz 0.11.0 py38haf1e3a3_0
dask 2.30.0 py_0
dask-core 2.30.0 py_0
dbus 1.13.18 h18a8e69_0
decorator 4.4.2 py_0
defusedxml 0.6.0 py_0
diff-match-patch 20200713 py_0
distributed 2.30.1 py38hecd8cb5_0
docutils 0.16 py38_1
entrypoints 0.3 py38_0
et_xmlfile 1.0.1 py_1001
expat 2.2.10 hb1e8313_2
fastcache 1.1.0 py38h1de35cc_0
filelock 3.0.12 py_0
flake8 3.8.4 py_0
flask 1.1.2 py_0
freetype 2.10.4 ha233b18_0
fsspec 0.8.3 py_0
future 0.18.2 py38_1
get_terminal_size 1.0.0 h7520d66_0
gettext 0.19.8.1 hb0f4f8b_2
gevent 20.9.0 py38haf1e3a3_0
glib 2.66.1 h9bbe63b_0
glob2 0.7 py_0
gmp 6.1.2 hb37e062_1
gmpy2 2.0.8 py38h6ef4df4_3
greenlet 0.4.17 py38haf1e3a3_0
h5py 2.10.0 py38h3134771_0
hdf5 1.10.4 hfa1e0ec_0
heapdict 1.0.1 py_0
html5lib 1.1 py_0
icu 58.2 h0a44026_3
idna 2.10 py_0
imageio 2.9.0 py_0
imagesize 1.2.0 py_0
importlib-metadata 2.0.0 py_1
importlib_metadata 2.0.0 1
iniconfig 1.1.1 py_0
intel-openmp 2019.4 233
intervaltree 3.1.0 py_0
ipykernel 5.3.4 py38h5ca1d4c_0
ipython 7.19.0 py38h01d92e1_0
ipython_genutils 0.2.0 py38_0
ipywidgets 7.5.1 py_1
isort 5.6.4 py_0
itsdangerous 1.1.0 py_0
jbig 2.1 h4d881f8_0
jdcal 1.4.1 py_0
jedi 0.17.1 py38_0
jinja2 2.11.2 py_0
joblib 0.17.0 py_0
jpeg 9b he5867d9_2
json5 0.9.5 py_0
jsonschema 3.2.0 py_2
jupyter 1.0.0 py38_7
jupyter_client 6.1.7 py_0
jupyter_console 6.2.0 py_0
jupyter_core 4.6.3 py38_0
jupyterlab 2.2.6 py_0
jupyterlab_pygments 0.1.2 py_0
jupyterlab_server 1.2.0 py_0
keyring 21.4.0 py38_1
kiwisolver 1.3.0 py38h23ab428_0
krb5 1.18.2 h75d18d8_0
lazy-object-proxy 1.4.3 py38h1de35cc_0
lcms2 2.11 h92f6f08_0
libarchive 3.4.2 haa3ed63_0
libcurl 7.71.1 h8a08a2b_1
libcxx 10.0.0 1
libedit 3.1.20191231 h1de35cc_1
libffi 3.3 hb1e8313_2
libgfortran 3.0.1 h93005f0_2
libiconv 1.16 h1de35cc_0
liblief 0.10.1 h0a44026_0
libllvm10 10.0.1 h76017ad_5
libpng 1.6.37 ha441bb4_0
libsodium 1.0.18 h1de35cc_0
libspatialindex 1.9.3 h0a44026_0
libssh2 1.9.0 ha12b0ac_1
libtiff 4.1.0 hcb84e12_1
libxml2 2.9.10 h7cdb67c_3
libxslt 1.1.34 h83b36ba_0
llvm-openmp 10.0.0 h28b9765_0
llvmlite 0.34.0 py38h739e7dc_4
locket 0.2.0 py38_1
lxml 4.6.1 py38h63b7cb6_0
lz4-c 1.9.2 h79c402e_3
lzo 2.10 haf1e3a3_2
markupsafe 1.1.1 py38h1de35cc_1
matplotlib 3.3.2 0
matplotlib-base 3.3.2 py38h181983e_0
mccabe 0.6.1 py38_1
mistune 0.8.4 py38h1de35cc_1001
mkl 2019.4 233
mkl-service 2.3.0 py38hfbe908c_0
mkl_fft 1.2.0 py38hc64f4ea_0
mkl_random 1.1.1 py38h959d312_0
mock 4.0.2 py_0
more-itertools 8.6.0 pyhd3eb1b0_0
mpc 1.1.0 h6ef4df4_1
mpfr 4.0.2 h9066e36_1
mpmath 1.1.0 py38_0
msgpack-python 1.0.0 py38h04f5b5a_1
multipledispatch 0.6.0 py38_0
navigator-updater 0.2.1 py38_0
nbclient 0.5.1 py_0
nbconvert 6.0.7 py38_0
nbformat 5.0.8 py_0
ncurses 6.2 h0a44026_1
nest-asyncio 1.4.2 pyhd3eb1b0_0
networkx 2.5 py_0
nltk 3.5 py_0
nose 1.3.7 py38_1004
notebook 6.1.4 py38_0
numba 0.51.2 py38h6440ff4_1
numexpr 2.7.1 py38hce01a72_0
numpy 1.19.2 py38h456fd55_0
numpy-base 1.19.2 py38hcfb5961_0
numpydoc 1.1.0 pyhd3eb1b0_1
olefile 0.46 py_0
openpyxl 3.0.5 py_0
openssl 1.1.1h haf1e3a3_0
packaging 20.4 py_0
pandas 1.1.3 py38hb1e8313_0
pandoc 2.11 h0dc7051_0
pandocfilters 1.4.3 py38hecd8cb5_1
parso 0.7.0 py_0
partd 1.1.0 py_0
path 15.0.0 py38_0
path.py 12.5.0 0
pathlib2 2.3.5 py38_1
pathtools 0.1.2 py_1
patsy 0.5.1 py38_0
pcre 8.44 hb1e8313_0
pep8 1.7.1 py38_0
pexpect 4.8.0 py38_1
pickleshare 0.7.5 py38_1001
pillow 8.0.1 py38h5270095_0
pip 20.2.4 py38hecd8cb5_0
pkginfo 1.6.1 py38hecd8cb5_0
pluggy 0.13.1 py38_0
ply 3.11 py38_0
prometheus_client 0.8.0 py_0
prompt-toolkit 3.0.8 py_0
prompt_toolkit 3.0.8 0
psutil 5.7.2 py38haf1e3a3_0
ptyprocess 0.6.0 py38_0
py 1.9.0 py_0
py-lief 0.10.1 py38haf313ee_0
pycodestyle 2.6.0 py_0
pycosat 0.6.3 py38h1de35cc_1
pycparser 2.20 py_2
pycurl 7.43.0.6 py38hddc9c9b_0
pydocstyle 5.1.1 py_0
pyflakes 2.2.0 py_0
pygments 2.7.2 pyhd3eb1b0_0
pylint 2.6.0 py38_0
pyodbc 4.0.30 py38h0a44026_0
pyopenssl 19.1.0 py_1
pyparsing 2.4.7 py_0
pyqt 5.9.2 py38h655552a_2
pyrsistent 0.17.3 py38haf1e3a3_0
pysocks 1.7.1 py38_1
pytables 3.6.1 py38h4727e94_0
pytest 6.1.1 py38_0
python 3.8.5 h26836e1_1
python-dateutil 2.8.1 py_0
python-jsonrpc-server 0.4.0 py_0
python-language-server 0.35.1 py_0
python-libarchive-c 2.9 py_0
python.app 2 py38_10
pytz 2020.1 py_0
pywavelets 1.1.1 py38haf1e3a3_2
pyyaml 5.3.1 py38haf1e3a3_1
pyzmq 19.0.2 py38hb1e8313_1
qdarkstyle 2.8.1 py_0
qt 5.9.7 h468cd18_1
qtawesome 1.0.1 py_0
qtconsole 4.7.7 py_0
qtpy 1.9.0 py_0
readline 8.0 h1de35cc_0
regex 2020.10.15 py38haf1e3a3_0
requests 2.24.0 py_0
ripgrep 12.1.1 0
rope 0.18.0 py_0
rtree 0.9.4 py38_1
ruamel_yaml 0.15.87 py38haf1e3a3_1
scikit-image 0.17.2 py38h81aa140_0
scikit-learn 0.23.2 py38h959d312_0
scipy 1.5.2 py38h2515648_0
seaborn 0.11.0 py_0
send2trash 1.5.0 py38_0
setuptools 50.3.1 py38hecd8cb5_1
simplegeneric 0.8.1 py38_2
singledispatch 3.4.0.3 py_1001
sip 4.19.8 py38h0a44026_0
six 1.15.0 py38hecd8cb5_0
snowballstemmer 2.0.0 py_0
sortedcollections 1.2.1 py_0
sortedcontainers 2.2.2 py_0
soupsieve 2.0.1 py_0
sphinx 3.2.1 py_0
sphinxcontrib 1.0 py38_1
sphinxcontrib-applehelp 1.0.2 py_0
sphinxcontrib-devhelp 1.0.2 py_0
sphinxcontrib-htmlhelp 1.0.3 py_0
sphinxcontrib-jsmath 1.0.1 py_0
sphinxcontrib-qthelp 1.0.3 py_0
sphinxcontrib-serializinghtml 1.1.4 py_0
sphinxcontrib-websupport 1.2.4 py_0
spyder 4.1.5 py38_0
spyder-kernels 1.9.4 py38_0
sqlalchemy 1.3.20 py38h9ed2024_0
sqlite 3.33.0 hffcf06c_0
statsmodels 0.12.0 py38haf1e3a3_0
sympy 1.6.2 py38hecd8cb5_1
ta 0.7.0 pypi_0 pypi
ta-lib 0.4.19 pypi_0 pypi
tblib 1.7.0 py_0
terminado 0.9.1 py38_0
testpath 0.4.4 py_0
threadpoolctl 2.1.0 pyh5ca1d4c_0
tifffile 2020.10.1 py38h0cf3a3e_2
tk 8.6.10 hb0a8c7a_0
toml 0.10.1 py_0
toolz 0.11.1 py_0
tornado 6.0.4 py38h1de35cc_1
tqdm 4.50.2 py_0
traitlets 5.0.5 py_0
typing_extensions 3.7.4.3 py_0
ujson 4.0.1 py38hb1e8313_0
unicodecsv 0.14.1 py38_0
unixodbc 2.3.9 haf1e3a3_0
urllib3 1.25.11 py_0
watchdog 0.10.3 py38haf1e3a3_0
wcwidth 0.2.5 py_0
webencodings 0.5.1 py38_1
werkzeug 1.0.1 py_0
wheel 0.35.1 py_0
widgetsnbextension 3.5.1 py38_0
wrapt 1.11.2 py38h1de35cc_0
wurlitzer 2.0.1 py38_0
xlrd 1.2.0 py_0
xlsxwriter 1.3.7 py_0
xlwings 0.20.8 py38_0
xlwt 1.3.0 py38_0
xmltodict 0.12.0 py_0
xz 5.2.5 h1de35cc_0
yaml 0.2.5 haf1e3a3_0
yapf 0.30.0 py_0
zeromq 4.3.3 hb1e8313_3
zict 2.0.0 py_0
zipp 3.4.0 pyhd3eb1b0_0
zlib 1.2.11 h1de35cc_3
zope 1.0 py38_1
zope.event 4.5.0 py38_0
zope.interface 5.1.2 py38haf1e3a3_0
zstd 1.4.5 h41d2c2f_0
See also questions close to this topic
-
Print Variables in For Loop
from matplotlib.pyplot import figure T10,T11,T12,T13 = nx.random_tree(10),nx.random_tree(11),nx.random_tree(12),nx.random_tree(13) T = [T10,T11,T12,T13] for t in T: print("The Pruefer Code for -", pruefer_code(t)) fig = figure() axis = fig.add_subplot(111) nx.draw(t, **opts, ax = axis)Which results in:
The Pruefer Code for - [2, 8, 1, 9, 5, 5, 6, 8] The Pruefer Code for - [9, 6, 10, 7, 4, 8, 10, 4, 6] The Pruefer Code for - [4, 1, 4, 8, 11, 11, 8, 3, 4, 8] The Pruefer Code for - [8, 7, 11, 4, 2, 7, 9, 1, 5, 10, 7]Plus the graphs - how would I amend the code so it'd say:
The Pruefer Code for - T10 [2, 8, 1, 9, 5, 5, 6, 8] The Pruefer Code for - T11 [9, 6, 10, 7, 4, 8, 10, 4, 6] The Pruefer Code for - T12 [4, 1, 4, 8, 11, 11, 8, 3, 4, 8] The Pruefer Code for - T13 [8, 7, 11, 4, 2, 7, 9, 1, 5, 10, 7]Any help appreciated :)
-
Get number of packets received by ping command in python
I need a function that can return the number of packets received or loss percentage. Before I used the code below to get true/false if I receive any of the packets. This should work in Windows, but if somebody can do it suitable for Linux too, I would be thankful.
def ping_ip(ip): current_os = platform.system().lower() parameter = "-c" if current_os == "windows": parameter = "-n" command = ['ping', parameter, '4', ip] res = subprocess.call(command) return res == 0 -
Tkinter GUI unresponsive
I'm pretty new to python and tkinter, so pardon me if I'm being naive. I'm trying to make a GUI for solving an engineering design problem using a widely accepted method (implying the method is seamless). The code for this method takes 0.537909984588623 seconds when run independently (not in tkinter but normal code), and its not too complex or tangled. When I tried to modify this code to fit into a GUI using tkinter, it becomes unresponsive after I enter all the inputs and select a button, even though the program keeps running in the background. Also, when I forcefully close the GUI window, the jupyter kernel becomes dead. Heres a brief outline of my code:
-----------------------------------------------code begins------------------------------------------
from tkinter import * from scipy.optimize import fsolve import matplotlib import numpy as np import threading from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg from matplotlib.figure import Figure import matplotlib.pyplot as plt matplotlib.use('TkAgg') import math class MyWindow(): def __init__(self, win): self.lbl1=Label(win, text='Alpha') self.lbl2=Label(win, text='xd') self.lbl3=Label(win, text='xw') self.lbl4=Label(win, text='xf') self.lbl5=Label(win, text='q') self.lbl6=Label(win, text='Reflux Factor') self.lbl7=Label(win, text='Tray Efficiency') self.lbl8=Label(win, text='Total Number of Stages') self.lbl9=Label(win, text='Feed Stage') self.t1=Entry(bd=3) self.t2=Entry(bd=3) self.t3=Entry(bd=3) self.t4=Entry(bd=3) self.t5=Entry(bd=8) self.t6=Entry(bd=8) self.t7=Entry(bd=8) self.t8=Entry(bd=8) self.t9=Entry(bd=8) self.btn1=Button(win, text='Total Number of Stages ', command=self.stagesN) self.lbl1.place(x=100, y=80) self.t1.place(x=300, y=80) self.lbl2.place(x=100, y=130) self.t2.place(x=300, y=130) self.lbl3.place(x=100, y=180) self.t3.place(x=300, y=180) self.lbl4.place(x=100, y=230) self.t4.place(x=300, y=230) self.lbl5.place(x=100, y=280) self.t5.place(x=300, y=280) self.lbl6.place(x=100, y=330) self.t6.place(x=300, y=330) self.lbl7.place(x=100, y=380) self.t7.place(x=300, y=380) self.lbl8.place(x=800, y=130) self.t8.place(x=790, y=170) self.lbl9.place(x=800, y=210) self.t9.place(x=790, y=260) self.btn1.place(x= 500, y= 75) def originalEq(self,xa,relative_volatility): ya=(relative_volatility*xa)/(1+(relative_volatility-1)*xa) return ya def equilibriumReal(self,xa,relative_volatility,nm): ya=(relative_volatility*xa)/(1+(relative_volatility-1)*xa) ya=((ya-xa)*nm)+xa return ya def equilibriumReal2(self,ya,relative_volatility,nm): a=((relative_volatility*nm)-nm-relative_volatility+1) b=((ya*relative_volatility)-ya+nm-1-(relative_volatility*nm)) c=ya xa=(-b-np.sqrt((b**2)-(4*a*c)))/(2*a) return xa def stepping_ESOL(self,x1,y1,relative_volatility,R,xd,nm): x2=self.equilibriumReal2(y1,relative_volatility,nm) y2=(((R*x2)/(R+1))+(xd/(R+1))) return x1,x2,y1,y2 def stepping_SSOL(self,x1,y1,relative_volatility,\ ESOL_q_x,ESOL_q_y,xb,nm): x2=self.equilibriumReal2(y1,relative_volatility,nm) m=((xb-ESOL_q_y)/(xb-ESOL_q_x)) c=ESOL_q_y-(m*ESOL_q_x) y2=(m*x2)+c return x1,x2,y1,y2 def stagesN(self): relative_volatility=float(self.t1.get()) nm=float(self.t7.get()) xd=float(self.t2.get()) xb=float(self.t3.get()) xf=float(self.t4.get()) q=float(self.t5.get()) R_factor=float(self.t6.get()) xa=np.linspace(0,1,100) ya_og=self.originalEq(xa[:],relative_volatility) ya_eq=self.equilibriumReal(xa[:],relative_volatility,nm) x_line=xa[:] y_line=xa[:] al=relative_volatility a=((al*q)/(q-1))-al+(al*nm)-(q/(q-1))+1-nm b=(q/(q-1))-1+nm+((al*xf)/(1-q))-(xf/(1-q))-(al*nm) c=xf/(1-q) if q>1: q_eqX=(-b+np.sqrt((b**2)-(4*a*c)))/(2*a) else: q_eqX=(-b-np.sqrt((b**2)-(4*a*c)))/(2*a) q_eqy=self.equilibriumReal(q_eqX,relative_volatility,nm) theta_min=xd*(1-((xd-q_eqy)/(xd-q_eqX))) R_min=(xd/theta_min)-1 R=R_factor*R_min theta=(xd/(R+1)) ESOL_q_x=((theta-(xf/(1-q)))/((q/(q-1))-((xd-theta)/xd))) ESOL_q_y=(ESOL_q_x*((xd-theta)/xd))+theta x1,x2,y1,y2=self.stepping_ESOL(xd,xd,relative_volatility,R,xd,nm) step_count=1 while x2>ESOL_q_x: x1,x2,y1,y2=self.stepping_ESOL(x2,y2,relative_volatility,R,xd,nm) step_count+=1 feed_stage=step_count x1,x2,y1,y2=self.stepping_SSOL(x1,y1,relative_volatility\ ,ESOL_q_x,ESOL_q_y,xb,nm) step_count+=1 while x2>xb: x1,x2,y1,y2=self.stepping_SSOL(x2,y2,relative_volatility\ ,ESOL_q_x,ESOL_q_y,xb,nm) step_count+=1 xb_actual=x2 stagesN=step_count-1 self.t8.insert(END, str(stagesN)) return window=Tk() mywin=MyWindow(window) window.title('DColumn') window.geometry("1500x1500") window.mainloop()-------------------------------------------Code end--------------------------------------------
I read on other articles that using multiple threads brings down the load on mainloop and prevents freezing. But like I said, the code isnt very complex. Is it still because of everythings running on the mainloop? Or is there something more than meets the eye? Is multithreading the only way to go past this point?
Thanks in advance.
-
Anaconda navigator on windows doesn't launch even ater reinstalling and following other ways of troubleshooting
I'm having a bit of trouble having the anaconda navigator to open.
I've tried following this tutorial (https://www.datacamp.com/community/tutorials/installing-anaconda-windows) but when i do
where condaI get
INFO: Could not find files for the given pattern(s).In the command prompt.
I've also tried this
A) Use the key combination Win-key + R then type cmd and hit return to launch the terminal window and then type: cd C:\Anaconda\Scripts (or whatever directory path yours is).
B) Navigate using windows explorer to that Scripts directory then type cmd in the address bar of that window and hit return (that will launch the terminal already set to that directory).
Next type the follow commands waiting in between for each to complete:
activate root conda update -n root conda conda update --all
I've reinstalled anaconda 3 times now and I'm not sure what else to google.
-
python pulp script error when executing in console
I installed pulp via anaconda distribution on Ubuntu 20.04. For developing my python code I use the WingIDE. Everything runs fine in the IDE. However, if I execute my script directly in the console via
.../anaconda/bin/pyton myscript.pyI get the following error
File "/home/icke/anaconda3/lib/python3.8/site-packages/pulp/pulp.py", line 1899, in solve status = solver.actualSolve(self, **kwargs) AttributeError: 'NoneType' object has no attribute 'actualSolve'Why is that and how can I fix this error?
The pulp test
.../anaconda/bin/pulptestruns flawlessly. Any suggestions? -
SPRAK CONTEXT DOES NOT LOAD
Since yesterday I have been having issues initiating a spark session on my Jupyter Notebook. I like to note that I have all the packages install, and I use Spark almost every day. Here is what happen;
All the packages are imported without showing an error; when I try to initiate the spark session, it does not show an error, but it doesn't load. Literally, it can be loading the whole night without showing an error. I have tried to reinstall all spark packages, and it didn't work. I have deleted all packages installed for the past 2 years and reinstall them again one by one with no success. I have no idea what to do since it doesn't show any error... Any idea what's going on?enter image description here
-
Is there an error in Pythons talib.ATR method?
Here is the data:
high = np.array([10697.12, 10706.16, 10744.75, 10747.88, 10745.42]) low = np.array([10683.51, 10694.72, 10705.16, 10728.22, 10727.29]) close = np.array([10696.47, 10705.16, 10728.23, 10742.46, 10730.27])Method 1: Input the data into Average True Range method directly
talib.ATR(high, low, close, timeperiod=3) output: array([nan, nan, nan, 23.56333333, 21.75222222])Method 2: Calculate True Range first, and then take average
talib.TRANGE(high, low, close) output: array([nan, 11.44, 39.59, 19.66, 18.13]) taking 3 day average: (11.44+39.59+19.66)/3=23.56 (39.59+19.66+18.13)/3=25.79 So the Average True Range array should be: array([nan, nan, nan, 23.56, 25.79])The last value in the Average True Range array from the first method is different from the second method. (21.75 vs 25.79)
What is wrong here?
-
Ta-lib : Indicators work with stock but not with cryptocurrency
I have a problem when using the Ta-lib library. I'm trying to find the RSI and the Bollinger Bands of BTC-USD. The problem is that when I try to find them for any cryptocurrencies, it gives no value (NaN). But when I try to find them for any stocks it works. When I display the data (Open Close High Low Volume) it works all the time (for cryptocurrencies and stocks). It gives me correctly : picture_0
But when I calculate the RSI and the BBANDS for cryptocurrencies it doesn't work.
Here is my code :
def get_indicators(data): # Get RSI data["rsi"] = talib.RSI(data["Close"], timeperiod=7) data["upper"], data["middle"], data["lower"] = talib.BBANDS(data["Close"], timeperiod=10, nbdevup=2, nbdevdn=2) # pprint(upper) # pprint(middle) # pprint(lower) return data df2 = get_indicators(df) df2When I run that, it gives me that, when my df is the data frame of a stock: picture_1
When I run that, when my df is the data frame of cryptocurrency, it gives me : picture_2
In the beginning, I thought that the problem was with the indicator, so I tried to replace RSI with ROC to see if it changes something.
So, I replace
data["rsi"] = talib.ROC(data["Close"], timeperiod=7)withdata["roc"] = talib.ROC(data["Close"], timeperiod=7)and this time it calcultated the ROC correctly for both stocks and cryptocurrencies. It gave me:I don't understand where the problem is, it is possible to calculate RSI and Bollinger Bands for stocks but not for cryptocurrencies. Can someone please help me?
