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Python for Finance (O'Reilly)

Python 0.55% CSS 0.01% HTML 48.72% Jupyter Notebook 50.72%

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py4fi's Issues

Error in your formula, chapter 3, pg. 51 for vega of a call

Hello,
the formula you have for vega of a call is incorrect. Its not N(d1).

【chapter6】data['Mov_Vol'] = data['Return'].rolling(window=252).std() * math.sqrt(252)

Rolling.std(ddof=1, *args, **kwargs) calculates rolling standard deviation, normalized by N-1 by default. It means, by default, it uses the sample standard deviation function, in which the denominator is a N-1. The denominator can be set to 1 in this case, if std(ddof=251), then N-251=1. I guess the multiplying of math.sqrt(252) here may be a mistake of confusing rolling.std with numpy.std. The default ddof in numpy.std is 0, which means its denominator is a N. In that case, multiplying of math.sqrt(252) can offset the denominator of 252 exactly.

mcs_full_vector_numpy.py has a drift a S[0]

Hi @yhilpisch, the fixed for #4 by setting the first time slice (t=0) to 0 does not correctly fixed the issue. On first look, it appears that it has solved the issue. However, on closer inspection of the equation, I notice that even though setting the first time slice (t=0) to 0, a drift will still be included at time slice resulting in an over-estimation.

Example in 6.4 not working ( Netfonds's API )

The API seems not working. I wonder whether I've got it wrong?
This is URL I get from frombook, but the page is empty
http://hopey.netfonds.no/posdump.php?date=20140922&paper=AAPL.O&csv_format=csv

This is URL I get from IPython. No data, only column name
http://www.netfonds.no/quotes/posdump.php?date=20140922&paper=NKE.N&csv_format=csv

Do anyone figure out the solution ?

mcs_full_vector_numpy.py does not converge to the true price.

Hi @yhilpisch,

Thank you for the great work as an undergraduate student I have learn a lot from your book!

Example 3-4 code which estimates the theoretical value of a European call option via Monte Carlo simulation appears to yield different results from that of bsm_call_value() function in Chapter 3.

Using the analytical formula for the valuation of European call option in BSM model for S0 = 100, K = 105, T = 1.0, r = 0.05 and sigma = 0.2, we arrive at a European call price of 8.02135.

Using the code from example 3-4 which estimates the theoretical value of a European call option via Monte Carlo simulation using the same parameter should yield the same result as that of the analytical formula. However, the results yield by mcs_full_vector_numpy.py appears to be significantly different from that of bsm_call_value().

My suspect is that random.standard_normal((M + 1, I)) should be changed to random.standard_normal((M, I)) such that the calculation converge to the true price.

Conda environment creation fails

The creation of the conda enviornment as shown in the readme fails on windows with the last version of conda (conda 4.5.11).

conda env create -f py4fi_conda.yml

Solving environment: failed

ResolvePackageNotFound:
  - ncurses==6.0=hd04f020_2
  - requests==2.18.4=py36h4516966_1
  - xlsxwriter==1.0.2=py36h3736301_0
  - tornado==4.5.2=py36h468dda9_0
  - itsdangerous==0.24=py36h49fbb8d_1
  - cycler==0.10.0=py36hfc81398_0
  - jinja2==2.9.6=py36hde4beb4_1
  - gmp==6.1.2=hb37e062_1
  - tk==8.6.7=h35a86e2_3
  - lzo==2.10=hb6b8854_1
  - testpath==0.3.1=py36h625a49b_0
  - appnope==0.1.0=py36hf537a9a_0
  - setuptools==36.5.0=py36h2134326_0
  - cffi==1.10.0=py36h880867e_1
  - jupyter==1.0.0=py36h598a6cc_0
  - numba==0.35.0=np113py36_6
  - pandocfilters==1.4.2=py36h3b0b094_1
  - pyqt==5.6.0=py36he5c6137_6
  - jupyter_core==4.3.0=py36h93810fe_0
  - cryptography==2.0.3=py36h22d4226_1
  - expat==2.2.5=hb8e80ba_0
  - chardet==3.0.4=py36h96c241c_1
  - patsy==0.4.1=py36ha1b3fa5_0
  - zeromq==4.2.2=ha360ad0_2
  - jedi==0.10.2=py36h6325097_0
  - zlib==1.2.11=hf3cbc9b_2
  - sqlite==3.20.1=h7e4c145_2
  - traitlets==4.3.2=py36h65bd3ce_0
  - python==3.6.3=h5ce8c04_4
  - mpc==1.0.3=hc455b36_4
  - werkzeug==0.12.2=py36h168efa1_0
  - matplotlib==2.1.0=py36h5068139_0
  - pexpect==4.2.1=py36h3eac828_0
  - wheel==0.29.0=py36h3597b6d_1
  - html5lib==0.999999999=py36h79312fd_0
  - pysocks==1.6.7=py36hfa33cec_1
  - libedit==3.1=hb4e282d_0
  - jpeg==9b=he5867d9_2
  - mpmath==0.19=py36h9185fea_2
  - nbconvert==5.3.1=py36h810822e_0
  - xlwt==1.2.0=py36h5ad1178_0
  - pyzmq==16.0.2=py36h087ffad_2
  - fastcache==1.0.2=py36h8606a76_0
  - pytz==2017.2=py36h2e7dfbc_1
  - pyopenssl==17.2.0=py36h5d7bf08_0
  - nbformat==4.4.0=py36h827af21_0
  - pcre==8.41=hfb6ab37_1
  - gettext==0.19.8.1=h15daf44_3
  - libiconv==1.15=hdd342a3_7
  - urllib3==1.22=py36h68b9469_0
  - certifi==2017.7.27.1=py36hd973bb6_0
  - bleach==2.0.0=py36h8fcea71_0
  - ipython_genutils==0.2.0=py36h241746c_0
  - numexpr==2.6.2=py36h8fc668d_2
  - prompt_toolkit==1.0.15=py36haeda067_0
  - widgetsnbextension==3.0.2=py36h91f43ea_1
  - hdf5==1.10.1=ha036c08_1
  - sympy==1.1.1=py36h7f3cf04_0
  - qt==5.6.2=h9975529_14
  - flask==0.12.2=py36h5658096_0
  - ipykernel==4.6.1=py36h3208c25_0
  - libffi==3.2.1=h475c297_4
  - gmpy2==2.0.8=py36h7ef02cb_1
  - freetype==2.8=h12048fb_1
  - asn1crypto==0.22.0=py36hb705621_1
  - xlrd==1.1.0=py36h336f4a2_1
  - intel-openmp==2018.0.0=h68bdfb3_7
  - pymc3==3.2=py36h1e7238b_0
  - openpyxl==2.4.8=py36he899640_1
  - readline==7.0=hc1231fa_4
  - pip==9.0.1=py36h1555ced_4
  - libgfortran==3.0.1=h93005f0_2
  - pycparser==2.18=py36h724b2fc_1
  - libgpuarray==0.6.9=0
  - python-dateutil==2.6.1=py36h86d2abb_1
  - six==1.11.0=py36h0e22d5e_1
  - libsodium==1.0.13=hba5e272_2
  - numpy==1.13.3=py36h2cdce51_0
  - simplegeneric==0.8.1=py36he5b5b09_0
  - ptyprocess==0.5.2=py36he6521c3_0
  - wcwidth==0.1.7=py36h8c6ec74_0
  - ipython==6.1.0=py36hf612aae_1
  - bzip2==1.0.6=h649919c_2
  - icu==58.2=h4b95b61_1
  - qtconsole==4.3.1=py36hd96c0ff_0
  - click==6.7=py36hec950be_0
  - jupyter_client==5.1.0=py36hf6c435f_0
  - libcxx==4.0.1=h579ed51_0
  - libpng==1.6.32=h6184301_3
  - dbus==1.10.22=h50d9ad6_0
  - terminado==0.6=py36h656782e_0
  - mako==1.0.7=py36h55379d4_0
  - pyparsing==2.2.0=py36hb281f35_0
  - pytables==3.4.2=py36hfbd7ab0_2
  - et_xmlfile==1.0.1=py36h1315bdc_0
  - nose==1.3.7=py36h73fae2b_2
  - markupsafe==1.0=py36h3a1e703_1
  - tqdm==4.19.4=py36he502594_0
  - entrypoints==0.2.3=py36hd81d71f_2
  - h5py==2.7.0=py36h6400cee_1
  - pandoc==1.19.2.1=ha5e8f32_1
  - scikit-learn==0.19.1=py36hffbff8c_0
  - decorator==4.1.2=py36h69a1b52_0
  - notebook==5.2.1=py36h640abe8_0
  - mkl==2018.0.0=h5ef208c_6
  - cython==0.26.1=py36hd51f8eb_0
  - jupyter_console==5.2.0=py36hccf5b1c_1
  - pandas==0.21.0=py36hfed917e_1
  - jsonschema==2.6.0=py36hb385e00_0
  - mkl-service==1.1.2=py36h7ea6df4_4
  - jdcal==1.3=py36h1986823_0
  - ipywidgets==7.0.0=py36h24d3910_0
  - glib==2.53.6=h33f6a65_2
  - ca-certificates==2017.08.26=ha1e5d58_0
  - pygments==2.2.0=py36h240cd3f_0
  - sip==4.18.1=py36h2824476_2
  - xz==5.2.3=h0278029_2
  - mpfr==3.1.5=h7fa3772_1
  - openssl==1.0.2m=h86d3e6a_1
  - webencodings==0.5.1=py36h3b9701d_1
  - mistune==0.8.1=py36h638d0ca_0
  - pickleshare==0.7.4=py36hf512f8e_0
  - scipy==1.0.0=py36h1de22e9_0
  - libcxxabi==4.0.1=hebd6815_0
  - idna==2.6=py36h8628d0a_1

i'm pretty sure it's a version mismatch - but couldn't figure out a way to fix it

according to an issuei found (ContinuumIO/anaconda-issues#9480 (comment)) exporting via conda env export --no-builds > environment.yml should help fix the issue.

Q about bsm_vega?

If I understand it right, the Vega of an option, the derivative of the norm cdf is needed. The notation you used in your book is N'(d_1), with a prime. The calculation in bsm_vega has used:

 stats.norm.cdf( )

which doesn't appear to be correct. Shouldn't this be just the norm pdf? ie. exp(-x^2) normalized?

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yhilpisch / py4fi Goto Github PK

py4fi's People

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py4fi's Issues

Error in your formula, chapter 3, pg. 51 for vega of a call

(+_+)

tyu

【chapter6】data['Mov_Vol'] = data['Return'].rolling(window=252).std() * math.sqrt(252)

mcs_full_vector_numpy.py has a drift a S[0]

Example in 6.4 not working ( Netfonds's API )

mcs_full_vector_numpy.py does not converge to the true price.

Conda environment creation fails

Q about bsm_vega?

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent

Jobs