Algorithmic Trading
With Machine & Deep Learning
Workshop by Dr. Yves J. Hilpisch | The Python Quants GmbH
London, 12. October 2017
from fastai import * | |
from fastai.text import * | |
bs=48 | |
path = untar_data(URLs.IMDB) | |
path.ls() | |
data_lm = (TextList.from_folder(path) | |
.filter_by_folder(include=['train', 'test', 'unsup']) |
❯ bind | grep forward | |
bind --preset -M insert -k right forward-char | |
bind --preset -M insert \e\[C forward-char | |
bind --preset -M insert \e\eOC nextd-or-forward-word | |
bind --preset -M insert \e\e\[C nextd-or-forward-word | |
bind --preset -M insert \eO3C nextd-or-forward-word | |
bind --preset -M insert \e\[3C nextd-or-forward-word | |
bind --preset -M insert \e\[1\;3C nextd-or-forward-word | |
bind --preset -M insert \e\[1\;9C nextd-or-forward-word | |
bind --preset -M insert \e\eOB history-token-search-forward |
import spacy | |
import nltk | |
from nltk.corpus import gutenberg | |
#nlp = spacy.load('en') | |
nlp = spacy.load('en_core_web_sm') | |
import random | |
import pandas as pd | |
import matplotlib.pyplot as plt | |
import re |
I hereby claim:
To claim this, I am signing this object:
### Keybase proof | |
I hereby claim: | |
* I am svenski on github. | |
* I am svenski (https://keybase.io/svenski) on keybase. | |
* I have a public key ASBG_J1MEir5FWPQaxh5q4xSvGmIwymFiS7Unbi_RPbAgAo | |
To claim this, I am signing this object: |
### Keybase proof | |
I hereby claim: | |
* I am svenski on github. | |
* I am svenski (https://keybase.io/svenski) on keybase. | |
* I have a public key ASBG_J1MEir5FWPQaxh5q4xSvGmIwymFiS7Unbi_RPbAgAo | |
To claim this, I am signing this object: |
import matplotlib.pyplot as plt | |
def plotTSP(paths, points, num_iters=1): | |
""" | |
path: List of lists with the different orders in which the nodes are visited | |
points: coordinates for the different nodes | |
num_iters: number of paths that are in the path list | |
""" |
Tim Group office temperature | |
============================ | |
```{r tempRead, echo = FALSE, message = FALSE, warning = FALSE} | |
library(ggplot2) | |
library(data.table) | |
library(lubridate) | |
tt <- (read.csv("temp.csv", header = FALSE) |