Time Series Visualization

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
import numpy as np

np.random.seed(42)
dates = pd.date_range('2022-01-01', '2024-12-31', freq='D')
n = len(dates)

# Simulate realistic data with trend + seasonality + noise
trend = np.linspace(50000, 85000, n)
seasonality = 15000 * np.sin(2 * np.pi * np.arange(n) / 365)
noise = np.random.normal(0, 3000, n)
revenue = trend + seasonality + noise

df = pd.DataFrame({'Date': dates, 'Revenue': revenue})
df = df.set_index('Date')

# Moving averages
df['MA_7']  = df['Revenue'].rolling(7).mean()
df['MA_30'] = df['Revenue'].rolling(30).mean()
df['MA_90'] = df['Revenue'].rolling(90).mean()

fig, ax = plt.subplots(figsize=(14, 6))
ax.plot(df.index, df['Revenue'], color='#90CAF9', linewidth=0.8, alpha=0.6, label='Daily Revenue')
ax.plot(df.index, df['MA_7'],  color='#42A5F5', linewidth=1.5, label='7-day MA')
ax.plot(df.index, df['MA_30'], color='#1565C0', linewidth=2, label='30-day MA')
ax.plot(df.index, df['MA_90'], color='#FF5722', linewidth=2.5, label='90-day MA (Trend)')

# Format x-axis for dates
ax.xaxis.set_major_locator(mdates.MonthLocator(bymonth=[1, 4, 7, 10]))
ax.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
ax.xaxis.set_minor_locator(mdates.MonthLocator())
plt.setp(ax.get_xticklabels(), rotation=30, ha='right')

ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'${x/1000:.0f}K'))
ax.set_title('Revenue Trend with Moving Averages (2022-2024)', fontsize=14, fontweight='bold')
ax.legend(loc='upper left')
ax.grid(True, alpha=0.3)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
plt.tight_layout()
plt.savefig('time_series_ma.png', dpi=150)
plt.show()

from statsmodels.tsa.seasonal import seasonal_decompose

# Monthly data for cleaner decomposition
monthly = df['Revenue'].resample('ME').mean()
result = seasonal_decompose(monthly, model='additive', period=12)

fig, axes = plt.subplots(4, 1, figsize=(13, 12), sharex=True)

monthly.plot(ax=axes[0], title='Original Series', color='#1565C0')
result.trend.plot(ax=axes[1], title='Trend Component', color='#4CAF50')
result.seasonal.plot(ax=axes[2], title='Seasonal Component', color='#FF9800')
result.resid.plot(ax=axes[3], title='Residual (Noise)', color='#E91E63', style='.', markersize=6)

for ax in axes:
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.grid(True, alpha=0.3)

plt.suptitle('Seasonal Decomposition of Revenue', fontsize=14, fontweight='bold')
plt.tight_layout()
plt.savefig('seasonal_decomp.png', dpi=150)
plt.show()

import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
import numpy as np
from matplotlib.patches import FancyArrowPatch

np.random.seed(42)
dates = pd.date_range('2024-01-01', periods=252, freq='B')  # Business days
returns = np.random.normal(0.0005, 0.02, 252)
prices = pd.Series(150 * np.cumprod(1 + returns), index=dates)

# Simulate OHLC data
df = pd.DataFrame({'Date': dates})
df['Close'] = prices.values
df['Open']  = df['Close'].shift(1).fillna(prices.values[0])
df['High']  = df[['Open', 'Close']].max(axis=1) * (1 + np.abs(np.random.normal(0, 0.005, 252)))
df['Low']   = df[['Open', 'Close']].min(axis=1) * (1 - np.abs(np.random.normal(0, 0.005, 252)))
df['Volume']= np.random.randint(1000000, 5000000, 252)

# Indicators
df['MA20'] = df['Close'].rolling(20).mean()
df['MA50'] = df['Close'].rolling(50).mean()
df['Upper_BB'] = df['MA20'] + 2 * df['Close'].rolling(20).std()
df['Lower_BB'] = df['MA20'] - 2 * df['Close'].rolling(20).std()

fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10), sharex=True, gridspec_kw={'height_ratios': [3, 1]})

# Price + indicators
ax1.plot(df['Date'], df['Close'], 'b-', linewidth=1.5, label='Close Price')
ax1.plot(df['Date'], df['MA20'], '--', color='orange', linewidth=1.5, label='MA20')
ax1.plot(df['Date'], df['MA50'], '--', color='red', linewidth=1.5, label='MA50')
ax1.fill_between(df['Date'], df['Upper_BB'], df['Lower_BB'], alpha=0.1, color='blue', label='Bollinger Bands')

# Bullish/bearish annotation
max_idx = df['Close'].idxmax()
ax1.annotate(f'Peak: ${df["Close"].max():.2f}',
              xy=(max_idx, df['Close'].max()),
              xytext=(max_idx - pd.Timedelta(days=30), df['Close'].max() + 5),
              arrowprops=dict(arrowstyle='->', color='green'), color='green', fontweight='bold')

ax1.set_title('Stock Price Analysis with Technical Indicators', fontsize=13, fontweight='bold')
ax1.set_ylabel('Price ($)')
ax1.legend(loc='upper left', fontsize=9)
ax1.grid(True, alpha=0.3)
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)

# Volume
colors_vol = ['#4CAF50' if c > o else '#F44336' for c, o in zip(df['Close'], df['Open'])]
ax2.bar(df['Date'], df['Volume'], color=colors_vol, alpha=0.8, width=1)
ax2.set_ylabel('Volume')
ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, _: f'{x/1e6:.1f}M'))
ax2.set_title('Trading Volume', fontsize=11)
ax2.xaxis.set_major_formatter(mdates.DateFormatter('%b %Y'))
ax2.grid(True, alpha=0.3)
ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_visible(False)

plt.setp(ax2.get_xticklabels(), rotation=30, ha='right')
plt.tight_layout()
plt.savefig('stock_analysis.png', dpi=150, bbox_inches='tight')
plt.show()