Results: Successfully achieved the requirements 1. Bitcoin Volume Trends: Visualized and analyzed the volume trends over the years to identify patterns and significant changes in trading activity. 2. Market Cap Analysis: Calculated and represented the market capitalization of Bitcoin, gaining insights into its growth and fluctuations. 3. Volatility Examination: Explored the volatility patterns of Bitcoin's price to understand its stability and potential risks. 4. Cumulative Return: Computed the cumulative return on investment for Bitcoin throughout the years. 5. Moving Averages: Implemented and visualized moving averages to spot trends and potential entry/exit points in the market. 6. Daily Price Change: Studied the daily percentage price change of Bitcoin to assess its price movements. 7. Relative Strength Index (RSI) of Bitcoin: Calculated and analyzed the RSI to understand the asset's overbought and oversold conditions. ➡️ Process: 1. Data Collection and Understanding: Obtained the dataset containing Bitcoin price and trading information from 2014 to 2023. 2. Data Preprocessing: Cleaned and prepared the data for analysis, handling missing values and outliers. 3. Data Modeling: Utilized various statistical and analytical techniques to derive valuable insights from the data. 4. Data Visualization: Represented the findings using Candlestick charts, Line graphs, and Bar charts for enhanced understanding and visualization.

1. Bitcoin Price Analysis (2014 - 2023)
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.graph_objects as go
data = pd.read_csv('BTC-USD.csv')
Price by Date Graph
fig, ax = plt.subplots(figsize=(20, 8))
ax.plot(data['Date'], data['Close'], color='green')
ax.xaxis.set_major_locator(plt.MaxNLocator(15))
ax.set_xlabel('Date', fontsize=14)
ax.set_ylabel('Price in USD', fontsize=14)
plt.title('Bitcoin Prices', fontsize=18)
plt.grid()
plt.show()

2. Bitcoin Volume Trend
fig, ax = plt.subplots(figsize=(20, 8))
ax.plot(data['Date'], data['Volume'])
ax.xaxis.set_major_locator(plt.MaxNLocator(15))
ax.set_xlabel('Date', fontsize=14)
ax.set_ylabel('Volumes', fontsize=14)
plt.title('Bitcoin Volume Trends', fontsize=18)
plt.grid()
plt.show()
Finding Market cap
data['Market Cap'] = data['Open'] * data['Volume']
print(data['Market Cap'])

3. Market cap by Date graph
fig, ax = plt.subplots(figsize=(20, 8))
ax.plot(data['Date'], data['Market Cap'], color='Orange')
ax.xaxis.set_major_locator(plt.MaxNLocator(15))
ax.set_xlabel('Date', fontsize=14)
ax.set_ylabel('Market Cap', fontsize=14)
plt.title('Market Cap', fontsize=18)
plt.grid()
plt.show()
Calculating Volatility
data['vol'] = (data['Close'] / data['Close'].shift(1)) - 1
print(data['vol'])
fig, ax = plt.subplots(figsize=(20, 8))
ax.plot(data['Date'], data['vol'], color='purple')
ax.xaxis.set_major_locator(plt.MaxNLocator(15))
plt.title('Volatility', fontsize=14)
plt.grid()
plt.show()

4. Cumulative Return Calculation
data['Cumulative Return'] = (1 + data['vol']).cumprod()
print(data['Cumulative Return'])
fig, ax = plt.subplots(figsize=(20, 8))
ax.bar(data['Date'], data['Cumulative Return'], color='Brown')
ax.xaxis.set_major_locator(plt.MaxNLocator(15))
ax.set_xlabel('Date', fontsize=14)
ax.set_ylabel('Cumulative Return', fontsize=14)
plt.title('Cumulative Return', fontsize=14)
plt.grid()
plt.show()

5. Calculating Moving Averages
data['MA for 10 days'] = data['Open'].rolling(10).mean()
data['MA for 20 days'] = data['Open'].rolling(20).mean()
data['MA for 50 days'] = data['Open'].rolling(50).mean()
data['MA for 100 days'] = data['Open'].rolling(100).mean()
truncated_data = data.truncate()
truncated_data[['Adj Close', 'MA for 10 days', 'MA for 20 days', 'MA
for 50 days', 'MA for 100 days']].plot(subplots=False, figsize=(12, 5))
plt.title('Bitcoin Stock: Adjusted Close Price and Moving Averages')
plt.xlabel('Date')
plt.ylabel('Price')
plt.show()

6. Daily Price Change
data = data.sort_values(by='Date')
data['Daily_Price_Change'] = data['Close'].diff()
print(data)
fig1, ax = plt.subplots(figsize=(20, 6))
ax.plot(data['Date'], data['Daily_Price_Change'], color='b',
label='Daily Price Change')
ax.xaxis.set_major_locator(plt.MaxNLocator(15))
plt.xlabel('Date')
plt.ylabel('Daily Price Change')
plt.title('Daily Price Change of Bitcoin', fontsize=14)
plt.grid()
plt.show()

7. Candlestick Chart
data['Date'] = pd.to_datetime(data['Date'])
data = data.sort_values(by='Date')
fig = go.Figure(data=[go.Candlestick(x=data['Date'],
open=data['Open'],
high=data['High'],
low=data['Low'],
close=data['Close'])])
fig.update_layout(title='Bitcoin Candlestick Chart',
xaxis_title='Date',
yaxis_title='Price')
fig.show()
Relative Strength Index of Bitcoin
data['Date'] = pd.to_datetime(data['Date'])
data = data.sort_values(by='Date')
data['Daily_Price_Change'] = data['Close'].diff()
def calculate_rsi(data, window=14):
delta = data['Daily_Price_Change']
gain = delta.where(delta > 0, 0)
loss = -delta.where(delta < 0, 0)

8. avg_gain = gain.rolling(window=window, min_periods=1).mean()
avg_loss = loss.rolling(window=window, min_periods=1).mean()
relative_strength = avg_gain / avg_loss
rsi = 100 - (100 / (1 + relative_strength))
return rsi
data['RSI'] = calculate_rsi(data)
fig, ax = plt.subplots(figsize=(20, 8))
ax.plot(data['Date'], data['RSI'], color='b', label='RSI')
ax.axhline(y=70, color='r', linestyle='--', label='Overbought (70)')
ax.axhline(y=30, color='g', linestyle='--', label='Oversold (30)')
ax.set_xlabel('Date', fontsize=14)
ax.set_ylabel('RSI', fontsize=14)
plt.title('Relative Strength Index (RSI) of Bitcoin', fontsize=18)
plt.grid()
plt.show()