ryanrosenberg

library(tidyverse)

#### clear workspace ####
rm(list=ls())

#### read in the html file and clean up the data ####
msgs <- readLines("~/Documents/gosschat.html")
msgs <- gsub(" ", msgs, replacement="__")
msgs <- gsub("\"", msgs, replacement="##")
msgs <- gsub("\'", msgs, replacement="##")

ryanrosenberg

library(tidyverse)

month_vec <- c(rep(1, 31),
               rep(2, 28),
               rep(3, 31),
               rep(4, 30),
               rep(5, 31),
               rep(6, 30),
               rep(7, 31),
               rep(8, 31),

ryanrosenberg

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()

animesh-agarwal

# 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)

animesh-agarwal

# imports
import numpy as np


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

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

fnneves

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))

fnneves

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

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

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'])

emmagrimaldi

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)