raposatech

# A few helper functions
def calcReturns(df):
  # Helper function to avoid repeating too much code
  df['returns'] = df['Close'] / df['Close'].shift(1)
  df['log_returns'] = np.log(df['returns'])
  df['strat_returns'] = df['position'].shift(1) * df['returns']
  df['strat_log_returns'] = df['position'].shift(1) * \
    df['log_returns']
  df['cum_returns'] = np.exp(df['log_returns'].cumsum()) - 1
  df['strat_cum_returns'] = np.exp(

eatonphil

(defsystem :cl-docker
    :depends-on (:cl-ppcre)
    :serial t
    :components ((:file "package")
                 (:file "docker")))

tartakynov

import numpy as np
import pylab as pl
from numpy import fft
    
def fourierExtrapolation(x, n_predict):
    n = x.size
    n_harm = 10                     # number of harmonics in model
    t = np.arange(0, n)
    p = np.polyfit(t, x, 1)         # find linear trend in x
    x_notrend = x - p[0] * t        # detrended x

jteneycke

In your shell

sudo apt-get install sbcl
curl -O http://beta.quicklisp.org/quicklisp.lisp
sbcl --load quicklisp.lisp

Inside the context of sbcl

tshatrov

1. Install a Common Lisp implementation, or several of them

• SBCL for Windows with threads: https://github.com/akovalenko/sbcl-win32-threads/wiki
• CLISP is slow, and singlethreaded but makes relatively small binaries
• CCL is reportedly good?

2. Install Emacs. Official GNU Emacs Windows binaries should work: http://ftp.gnu.org/gnu/emacs/windows/

3. Create a directory where you will store your Lisp projects (you could use Quicklisp's default one but screw that, "c:/lisp" is better than "c:/users/<username>/quicklisp/local-projects")