Classic Values classicvalues

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classicvalues / primary_accent_colors.py

Last active November 2, 2024 05:53

Color Query via Colorsys and Random on Python

	import colorsys
	import random
	import os

	# Define output directory
	output_dir = "School/Stanford_University/Programs/Leadership_Education_for_Aspiring_Physicians/Research/Color/Query"
	os.makedirs(output_dir, exist_ok=True)

	# Function to convert RGB values to hex
	def rgb_to_hex(rgb):

classicvalues / scholarly_query.py

Created November 2, 2024 02:00

Top Cited Research Fetcher by CrossRef API Query

	import requests
	import os
	import csv

	# Define input and output paths
	query_file_path = "/School/Stanford_University/Programs/Leadership_Education_for_Aspiring_Physicians/Research/Literature/Scholarly/Query/scholar_queries.csv"
	output_dir = "/School/Stanford_University/Programs/Leadership_Education_for_Aspiring_Physicians/Research/Literature/Scholarly/Results"
	os.makedirs(output_dir, exist_ok=True)

	# Fetch results using CrossRef API

classicvalues / gist:56821d6a25b74755d9f09980e5b97d53

Created November 1, 2024 19:24

Source List .d for Stanford University

	Types: deb
	URIs: https://debian.stanford.edu/ubuntu
	Suites: noble noble-proposed noble-updates noble-security noble-backports
	Components: main universe restricted multiverse
	Signed-By: /usr/share/keyrings/ubuntu-archive-keyring.gpg

classicvalues / binance_all.py

Created September 2, 2024 22:40

Binance Trading -ALL ASSETS- API Code

	from binance.client import Client
	from datetime import datetime, timedelta
	import time

	# Set up Binance API credentials
	api_key = 'YOUR_BINANCE_API_KEY'
	api_secret = 'YOUR_BINANCE_API_SECRET'

	# Login to Binance
	client = Client(api_key, api_secret)

classicvalues / td_ameritrade_all.py

Created September 2, 2024 22:38

TD Ameritrade Trade -ALL ASSETS- API Code

	from td.client import TDClient
	from datetime import datetime, timedelta
	import time

	# Set up TD Ameritrade API credentials
	consumer_key = 'YOUR_TD_AMERITRADE_CONSUMER_KEY'
	redirect_uri = 'YOUR_REDIRECT_URI'
	credentials_path = 'path_to_credentials.json' # Path where credentials will be stored

	# Login to TD Ameritrade

classicvalues / robinhood_all.py

Created September 2, 2024 22:34

Robinhood Trading -ALL ASSETS- API Code

	import robin_stocks.robinhood as r
	import time
	from datetime import datetime, timedelta

	# Set up Robinhood API credentials
	username = 'your_robinhood_username'
	password = 'your_robinhood_password'

	# Login to Robinhood
	r.login(username, password)

classicvalues / interactive_brokers_all.py

Created September 2, 2024 22:31

Interactive Brokers Trading -ALL ASSETS- API Code

	from ib_insync import *
	from datetime import datetime, timedelta
	import time

	# Set up Interactive Brokers connection
	ib = IB()
	ib.connect('127.0.0.1', 7497, clientId=1) # Modify port and clientId as necessary

	# Define the trading parameters
	symbol = 'GOOGL'

classicvalues / alpaca_all.py

Last active September 2, 2024 22:23

Alpaca Trading -ALL ASSETS- API Code

	import alpaca_trade_api as tradeapi
	import time
	from datetime import datetime, timedelta

	# Set up Alpaca API credentials
	API_KEY = 'your_api_key'
	API_SECRET = 'your_api_secret'
	BASE_URL = 'https://paper-api.alpaca.markets' # Use the paper trading API for testing

	# Initialize the Alpaca API

classicvalues / Sentament_Base_Words.py

Created August 3, 2024 00:06

Sentiment-based Word Collection Creation

	# Load model directly
	from transformers import AutoTokenizer, AutoModelForCausalLM

	tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3.1-405B")
	model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3.1-405B")

	# Define a function to generate words based on sentiments
	def generate_words(sentiment, num_words):
	# Tokenize the sentiment text
	inputs = tokenizer.encode_plus(

classicvalues / Trained_LLM_from_Literature.py

Created July 3, 2024 03:22

A Trained and Fine-Tuned LLM via Publicly Available Literature

	import fitz # PyMuPDF
	import re
	import torch
	from torch.utils.data import Dataset, DataLoader
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from transformers import AdamW

	device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

	def extract_text_from_pdf(pdf_path):