alphazwest

import sys
import os


def print_dir_tree(dirname: str, indent: int=0):

    # Prints the indendations
    for i in range(indent):
        print("    ", end="")
        

alphazwest

import time
import requests

# use a context manager to make an HTTP request and file
with requests.get("https://www.example.com/file.txt", stream=True) as r:
    with open('download.txt', 'wb') as file:

        # Get the total size, in bytes, from the response header
        total_size = int(r.headers.get('Content-Length'))

alphazwest

import requests
with requests.get('https://www.example.com/file.txt')as r:
    with open('download.txt', 'wb')as file:
        file.write(r.raw)

alphazwest

import requests
import shutil
from tqdm.auto import tqdm
# make an HTTP request within a context manager
with requests.get("https://www.example.com/file.txt", stream=True) as r:
    
    # check header to get content length, in bytes
    total_length = int(r.headers.get("Content-Length"))
    
    # implement progress bar via tqdm

alphazwest

""" 
Sources like OpenSea will pull metadata from a contract URI
specified in a e.g. tokenURI function within a contract.
Sample Reference: https://docs.opensea.io/docs/metadata-standards
OpenSea particularly will use values stored within the "attributes"
key, found within a JSON-formatted, to display verifiably-rare
characteristics of an NFT.

Notes:
    This illustrates poor attempts at randomness

alphazwest

# Get pricing data
import yfinance as yf

# Get the previous 6 months pricing data for ETH
ETH = yf.Ticker('ETH-USD').history(period='6mo', interval="1d")[["High", "Low"]]

# Calculate Daily Range for each period and normalize as pct change
ETH['dr_pct'] = ETH.apply(lambda x: 100 * (x["High"] / x["Low"] - 1), axis=1)

# Calculate the average daily range over a 20-period interval

alphazwest

# Get High/Low for each period
daily_ranges = [d[1] / d[0] for d in data]

# Get SMA of daily Ranges
sma = mean(daily_ranges)

# Calculate
modified_adr = 100 * (sma - 1)

>>> 85.0

alphazwest

# Print rows # 18-21
print(ETH.iloc[17:21])

                   High          Low          dr         adr
Date                                                        
2021-06-04  2857.165527  2562.637451  294.528076         NaN
2021-06-05  2817.484863  2558.233643  259.251221         NaN  
2021-06-06  2743.441162  2616.162354  127.278809  435.919141
2021-06-07  2845.185059  2583.995117  261.189941  433.175586

alphazwest

# Add a simple moving average
ETH["adr"] = ETH['dr'].rolling(window=20).mean()

# View result
                   High          Low           dr         adr
Date                                                         
2021-05-18  3562.465088  3246.404053   316.061035         NaN
2021-05-19  3437.935791  1952.460205  1485.475586         NaN
2021-05-20  2993.145264  2170.229004   822.916260         NaN
2021-05-21  2938.205078  2113.347168   824.857910         NaN

alphazwest

# Calculate Daily Range for each period
ETH['dr'] = ETH.apply(lambda x: x["High"] - x["Low"], axis=1)


# Result
                   High          Low           dr
Date                                             
2021-05-18  3562.465088  3246.404053   316.061035
2021-05-19  3437.935791  1952.460205  1485.475586
2021-05-20  2993.145264  2170.229004   822.916260