manuelinfosec

def median(data: list, days: int = 14) -> float:
    data = sorted(data[-days:])
    length = len(data)
    half = int(length / 2)
    
    if length % 2 == 0:
        new = data[half-1:half+1]
        return round(sum(new) / len(new),2)
    else:
        return data[half]

manuelinfosec

def mean(data: list, days: int = 14) -> float:
    return round(sum(data[-days:]) / len(data[-days:]),2)

series = df['Close'].to_list()
mean_price = mean(series)
print(f"${mean_price}")

# Outputs: $8851.92

manuelinfosec

def mean(series: pd.Series, days: int = 14) -> float:
    return round(series.iloc[-days:].mean(),2)

series = df['Close']
mean_price = mean(series)
print(f"${mean_price}")

# Outputs: $8851.92

manuelinfosec

def mean(series: pd.Series, days: int = 14) -> float:
    return round(series.iloc[-days:].mean(),2)

manuelinfosec

def mean(data: list, days: int = 14) -> float:
    return round(sum(data[-days:]) / len(data[-days:]),2)

manuelinfosec

df = pd.read_csv("btcusdt_jan_2020.csv")
df
# Outputs: 
#  <DataFrame with 31 OHLC prices from 1st of January, 2020>

manuelinfosec

import numpy as np
import pandas as pd
from prettytable import PrettyTable

manuelinfosec

import math
import numpy as np 

from matplotlib import pyplot as plt

t = np.linspace(0,39 * math.pi/2, 1000)
x = t * np.cos(t) ** 3
y = 9 * t * np.sqrt(abs(np.cos(t))) + t * np.sin(0.2 * t) * np.cos(4 * t)

plt.plot(x, y, c = "green")

manuelinfosec

data_df.iloc[:, 1:5] = data_df.iloc[:, 1:5].astype(float).round(decimals=2)

manuelinfosec

data_df['OpenTime'] = open_times
data_df['CloseTime'] = close_times