mathiasfls’s gists

emmagrimaldi / annotation.py

Last active March 30, 2019 12:12

	annotations = [dict(

	# text I want to display. I used <br> to break it into two lines
	text = 'All US storm events that caused more than $50k of economic damage,<br> from 2000 until today',

	# font and border characteristics
	font = dict(color = '#FFFFFF', size = 14), borderpad = 10,

	# positional arguments
	x = 0.05, y = 0.05, xref = 'paper', yref = 'paper', align = 'left',

emmagrimaldi / plotly_data.py

Last active March 30, 2019 12:12

	data = []
	for event in event_types:
	event_data = dict(
	lat = df.loc[df['EVENT_TYPE'] == event,'BEGIN_LAT'],
	lon = df.loc[df['EVENT_TYPE'] == event,'BEGIN_LON'],
	name = event,
	marker = dict(size = 8, opacity = 0.5),
	type = 'scattermapbox'
	)
	data.append(event_data)

fnneves / 4.py

Created October 11, 2018 00:04

	frontier_x = []

	for possible_return in frontier_y:
	cons = ({'type':'eq', 'fun':check_sum},
	{'type':'eq', 'fun': lambda w: get_ret_vol_sr(w)[0] - possible_return})

	result = minimize(minimize_volatility,init_guess,method='SLSQP', bounds=bounds, constraints=cons)
	frontier_x.append(result['fun'])

fnneves / 3.py

Created October 11, 2018 00:02

Markowitz

	def get_ret_vol_sr(weights):
	weights = np.array(weights)
	ret = np.sum(log_ret.mean() * weights) * 252
	vol = np.sqrt(np.dot(weights.T, np.dot(log_ret.cov()*252, weights)))
	sr = ret/vol
	return np.array([ret, vol, sr])

	def neg_sharpe(weights):
	# the number 2 is the sharpe ratio index from the get_ret_vol_sr
	return get_ret_vol_sr(weights)[2] * -1

fnneves / 2.py

Created October 11, 2018 00:01

markowitz

	plt.figure(figsize=(12,8))
	plt.scatter(vol_arr, ret_arr, c=sharpe_arr, cmap='viridis')
	plt.colorbar(label='Sharpe Ratio')
	plt.xlabel('Volatility')
	plt.ylabel('Return')
	plt.scatter(max_sr_vol, max_sr_ret,c='red', s=50) # red dot
	plt.show()

fnneves / 1.py

Last active July 30, 2021 06:54

medium

	np.random.seed(42)
	num_ports = 6000
	all_weights = np.zeros((num_ports, len(stocks.columns)))
	ret_arr = np.zeros(num_ports)
	vol_arr = np.zeros(num_ports)
	sharpe_arr = np.zeros(num_ports)

	for x in range(num_ports):
	# Weights
	weights = np.array(np.random.random(4))

animesh-agarwal / Linear Regression using gradient descent.py

Last active June 21, 2021 19:07

	# imports
	import numpy as np


	class LinearRegressionUsingGD:
	"""Linear Regression Using Gradient Descent.

	Parameters
	----------
	eta : float

animesh-agarwal / Linear Regression using sckit-learn.py

Created September 23, 2018 09:12

	# imports
	import numpy as np
	import matplotlib.pyplot as plt
	from sklearn.linear_model import LinearRegression
	from sklearn.metrics import mean_squared_error, r2_score

	# generate random data-set
	np.random.seed(0)
	x = np.random.rand(100, 1)
	y = 2 + 3 * x + np.random.rand(100, 1)

ryanrosenberg / tribune_trivia_map.R

Created August 22, 2018 03:10

code for making a map from the Tribune's trivia guide

	library(tidyverse)
	library(rvest)
	library(leaflet)
	library(mapsapi)

	trivia <- "http://www.chicagotribune.com/redeye/culture/ct-redeye-do-trivia-chicago-bars-20180320-story.html"

	bars <- read_html(trivia) %>%
	html_nodes("a strong") %>%
	html_text()

ryanrosenberg / riddler_express_date_vandals.R

Created April 8, 2018 22:10

Solution for Riddler Express 4/8/18

Mathias Felipe mathiasfls