Angel Pérez angelmpa
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| # El mejor día de la semana para vender | |
| # Definir una función para calcular los rendimientos semanales | |
| def weekly_returns(df): | |
| # Ordenar por fecha ascendente | |
| df = df.sort_values('date') | |
| # Calcular el rendimiento entre cada día de la semana y el último día de la semana | |
| weekly_returns = df.groupby(df['date'].dt.weekday)['price'].apply(lambda x: x.iloc[-1]/x.iloc[0]-1) | |
| return weekly_returns | |
| # Calcular los rendimientos semanales |
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| # El mejor día de la semana para comprar | |
| # Agregar una columna al DataFrame que contenga el nombre del día de la semana correspondiente a cada fecha | |
| bitcoin['day'] = bitcoin['date'].dt.day_name() | |
| ethereum['day'] = ethereum['date'].dt.day_name() | |
| binance['day'] = binance['date'].dt.day_name() | |
| tether['day'] = tether['date'].dt.day_name() | |
| usd['day'] = usd['date'].dt.day_name() | |
| # Calcular la media de la columna "price" para cada día de la semana | |
| btc_grouped = bitcoin.groupby('day')['price'].mean() |
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| # Convertir las fechas a formato de fechas de Pandas | |
| bitcoin['date'] = pd.to_datetime(bitcoin['date']) | |
| ethereum['date'] = pd.to_datetime(ethereum['date']) | |
| tether['date'] = pd.to_datetime(tether['date']) | |
| binance['date'] = pd.to_datetime(binance['date']) | |
| usd['date'] = pd.to_datetime(usd['date']) |
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| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import numpy as np | |
| import calendar | |
| # Cargar los datos desde el archivo csv | |
| bitcoin = pd.read_csv('./../data/bitcoin.csv') | |
| ethereum = pd.read_csv('./../data/ethereum.csv') | |
| tether = pd.read_csv('./../data/tether.csv') |
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| # El mejor día de la semana para vender | |
| # Definir una función para calcular los rendimientos semanales | |
| def weekly_returns(df): | |
| # Ordenar por fecha ascendente | |
| df = df.sort_values('date') | |
| # Calcular el rendimiento entre cada día de la semana y el último día de la semana | |
| weekly_returns = df.groupby(df['date'].dt.weekday)['price'].apply(lambda x: x.iloc[-1]/x.iloc[0]-1) | |
| return weekly_returns |
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| # El mejor día de la semana para comprar | |
| # Agregar una columna al DataFrame que contenga el nombre del día de la semana correspondiente a cada fecha | |
| bitcoin['day'] = bitcoin['date'].dt.day_name() | |
| ethereum['day'] = ethereum['date'].dt.day_name() | |
| binance['day'] = binance['date'].dt.day_name() | |
| # Calcular la media de la columna "Close" para cada día de la semana | |
| btc_grouped = bitcoin.groupby('day')['price'].mean() | |
| eth_grouped = ethereum.groupby('day')['price'].mean() |
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| # Convertir las fechas a formato de fechas de Pandas | |
| bitcoin['date'] = pd.to_datetime(bitcoin['date']) | |
| ethereum['date'] = pd.to_datetime(ethereum['date']) | |
| binance['date'] = pd.to_datetime(binance['date']) |
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| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import numpy as np | |
| import calendar | |
| # Cargar los datos desde el archivo csv | |
| bitcoin = pd.read_csv('./../data/bitcoin.csv') | |
| ethereum = pd.read_csv('./../data/ethereum.csv') | |
| binance = pd.read_csv('./../data/binancecoin.csv') |
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| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import seaborn as sns | |
| import numpy as np | |
| import calendar |