btc_weekday_movements_analysis.py

import csv
from datetime import datetime
from collections import defaultdict

# CSV with format date,start_price,daily_high,daily_low
# Can be downloaded here: https://finance.yahoo.com/quote/BTC-USD/history/
filename = 'BTC-USD.csv'

def get_weekday(date_str):
    date_obj = datetime.strptime(date_str, '%Y-%m-%d')
    return date_obj.strftime('%A')

# Initialize dictionaries to store counts and cumulative losses
analysis_count = defaultdict(int)
analysis_loss = defaultdict(float)  # Use float for loss values
analysis_gain = defaultdict(float)

with open(filename, 'r', newline='') as csvfile:
    reader = csv.reader(csvfile)
    next(reader)  # Skip the header
    for row in reader:
        date_weekday = get_weekday(row[0])
        start_price = float(row[1])
        day_high = float(row[2])
        day_low = float(row[3])
        difference_low = (1 - (day_low / start_price)) * 100 # Calculate the loss
        difference_high = (day_high / start_price - 1) * 100

        # Increment the count sum of losses for the weekday
        analysis_count[date_weekday] += 1
        analysis_loss[date_weekday] += difference_low
        analysis_gain[date_weekday] += difference_high

# Calculate average for each weekday.
average_loss = {weekday: analysis_loss[weekday] / analysis_count[weekday] for weekday in analysis_loss}
average_gain = {weekday: analysis_gain[weekday] / analysis_count[weekday] for weekday in analysis_gain}

print('Average loss:')
for weekday, avg_loss in average_loss.items():
    print(f'{weekday}: {avg_loss:.3f}')  # Formats the average loss to two decimal places

print('Average gain:')
for weekday, avg_gain in average_gain.items():
    print(f'{weekday}: {avg_gain:.3f}')