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//authenticate hf with token
import huggingface_hub
huggingface_hub.login()
//Use the model
sentiment_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
result = sentiment_pipeline("Tesla's latest earnings report exceeded expectations, leading to a surge in stock price.")
print(result)

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# load data frame in google sheet

import gspread
from google.oauth2.service_account import Credentials
from google.auth import default

# Define the scope of access you need
scope = ['https://spreadsheets.google.com/feeds',
         'https://www.googleapis.com/auth/drive']
creds, _ = default()

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import {google} from 'googleapis'
 

export async function getSheetData() {
    const auth = new google.auth.GoogleAuth({
        credentials: {
            client_email: process.env.SERVICE_EMAIL,
            private_key: process.env.SERVICE_KEY.replace(/\\n/g, "\n"),
        },
        scopes: ['https://www.googleapis.com/auth/spreadsheets.readonly']

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//target type
 export type LatestInvoice = {

  id: string;

  name: string;

  image_url: string;

  email: string;

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UPDATE TABLE_NAME SET COL1= REPLACE(COL1,'''','"')

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 static class BackgroundDataFetcher
 {
     private static int batchSize = 2;
     public static BindingList<LedgerModel> ledgers = new BindingList<LedgerModel>();
     
     public static async Task FetchAsync(int ledId = 0)
     {
         try
         {
             

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# I encounter proplem while using Historical data
# For yfinance most of the case the data is in good shape
# The first thing I am looking for index column, of numbers. if it exist, need to replace with datetime
# the last thing I want make sure that the date column is in panda datetime type otherwise have to covert it. 

df =pd.DataFrame(data['Success'])
df =df[['datetime','open','high','low','close','volume']]
df['datetime']= pd.to_datetime(df['datetime']) 
df.set_index('datetime', inplace=True)

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import pandas as pd
import numpy as np
series=pd.read_excel(r'C:/Users/hp/OneDrive/documents/mahdata.xlsx',sheet_name='EASY', usecols='B:G',header=None, 
                   index_col=False)
series= series.to_numpy() 
data = np.flip(series,axis=0)

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Managing conda environments
It is recommended to install Zipline in an isolated conda environment. Installing Zipline in conda environments will not interfere your default Python deployment or site-packages, which will prevent any possible conflict with your global libraries. For more information on conda environment, see the Conda User Guide.

Assuming conda has been set up, you can create a conda environment:

$ conda create -n env_zipline python=3.10
Now you have set up an isolated environment called env_zipline, a sandbox-like structure to install Zipline. Then you should activate the conda environment by using the command

$ conda activate env_zipline
You can install Zipline by running

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Conda Error

`conda error error: argument COMMAND: invalid choice: 'activate'`
# solution
`conda init powershell`