brettkelly

# Python OAuth example

import evernote.edam.userstore.constants as UserStoreConstants
import evernote.edam.type.ttypes as Types
from evernote.api.client import EvernoteClient

##
# Helper function to turn query string parameters into a 
# Python dictionary
##

jlln

def splitDataFrameList(df,target_column,separator):
    ''' df = dataframe to split,
    target_column = the column containing the values to split
    separator = the symbol used to perform the split

    returns: a dataframe with each entry for the target column separated, with each element moved into a new row. 
    The values in the other columns are duplicated across the newly divided rows.
    '''
    def splitListToRows(row,row_accumulator,target_column,separator):
        split_row = row[target_column].split(separator)

StuartGordonReid

import math
import numpy
import random
import decimal
import scipy.linalg
import numpy.random as nrand
import matplotlib.pyplot as plt

"""
Note that this Gist uses the Model Parameters class found here  - https://gist.github.com/StuartGordonReid/f01f479c783dd40cc21e

arshpreetsingh

from sklearn.qda import QDA
#from sklearn.ensemble import RandomForestRegressor
from sklearn import preprocessing
import numpy as np
import pandas as pd

def initialize(context):
    context.assets = sid(8554) # Trade SPY
    context.model = QDA()
    context.lookback = 5 # Look back 

antonio-catalano

"It's a reworked version of Problem 14 that you find here: http://interactivepython.org/runestone/static/pythonds/Recursion/pythondsProgrammingExercises.html"

dizio_1 = { (2,2): 1, (3,4): 2, (4,6): 3, (5,7): 4, (9,10): 5, (9,11): 6, (13,15): 7, (16,19): 8, \
 (25,37): 9, (35,45):10  }

def createTab ( _Dict ): #create the graphic tab
    dizio = _Dict
    print('','Item', 'Weight', 'Value','\n', sep="       " )
    for key in dizio:
        spazio = "           "

druska

/*****************************************************************************
 *                QuantCup 1:   Price-Time Matching Engine
 *
 * Submitted by: voyager
 *
 * Design Overview:
 *   In this implementation, the limit order book is represented using
 *   a flat linear array (pricePoints), indexed by the numeric price value.
 *   Each entry in this array corresponds to a specific price point and holds
 *   an instance of struct pricePoint. This data structure maintains a list

MLWhiz

from fastapi import FastAPI
from pydantic import BaseModel
import torchvision
from torchvision import transforms
import torch
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
from PIL import Image
import numpy as np
import cv2
import io, json

ih2502mk

Lecture 1: Introduction to Research — [📝Lecture Notebooks] [▶️Video]
Lecture 2: Introduction to Python — [📝Lecture Notebooks] [▶️Video]
Lecture 3: Introduction to NumPy — [📝Lecture Notebooks] [▶️Video]
Lecture 4: Introduction to pandas — [📝Lecture Notebooks] [▶️Video]
Lecture 5: Plotting Data — [📝Lecture Notebooks] [[▶️Vide

SolarisSy

{
    "workbench.colorTheme": "One Dark Pro Darker",
    "workbench.editorAssociations": {
        "*.ipynb": "jupyter-notebook",
        "*.pyc": "default"
    },
    "terminal.integrated.sendKeybindingsToShell": true,
    "notebook.cellToolbarLocation": {
        "default": "right",
        "jupyter-notebook": "left"

Kurry

Prompt for AI Bot:

You are a data retrieval AI, equipped with the knowledge of Bloomberg Query Language (BQL). Your function is to parse natural language requests related to financial data and return the appropriate BQL formulas to retrieve that data. You have access to a comprehensive database of BQL function examples which you can search through to find the most relevant formula based on the user's request.

When you receive a request, you should:

1. Identify the key elements of the request:
   • The financial instrument (e.g., "AAPL US Equity")
   • The data type needed (e.g., daily prices)

• The time frame (e.g., last year)