Financial Charts¶

Create professional financial visualizations with Matplotlib.

Mental Model

Financial charts combine multiple plot types on shared time axes: line plots for prices, bar charts for volume, fill_between for ranges, and twin axes for overlaying different scales. The key technique is using twinx() to pair price and volume on the same figure without distorting either scale.

Visualization vs Analysis

This page demonstrates plotting techniques, not trading strategies. Moving average crossovers, bullish/bearish shading, and volume overlays are shown as Matplotlib patterns — they are not validated signals. In real finance:

Moving averages are lagging indicators — they react to past data, not future prices
Crossover "signals" produce many false positives in sideways markets
twinx() can visually mislead by implying correlation between differently scaled axes

Always apply statistical rigor before interpreting any chart pattern as meaningful.

Stock prices often resemble random walks (see Brownian Motion). This means visual patterns like crossovers can appear meaningful even when they are mostly noise — a moving average crossover on a random walk will produce "signals" at exactly the same rate as on real data.

Offline Usage

Examples use yfinance for real data, but all techniques work equally well with synthetic data (np.cumsum(np.random.randn(250))) for offline development and testing.

Stock Price Chart¶

Basic price chart with volume:

```python import matplotlib.pyplot as plt import yfinance as yf

def download(ticker, start=None, end=None): if start is None: return yf.Ticker(ticker).history(period="max") else: return yf.Ticker(ticker).history(start=start, end=end)

def main(): df = download('AAPL', start='2020-07-01', end='2020-12-31')

DATE = df.index[50:]
y_price = df['Close'].loc[DATE]
y_volume = df['Volume'].loc[DATE]

fig, ax = plt.subplots(figsize=(15, 4))

ax.plot(DATE, y_price, label='AAPL', color='b')
ax.set_xlabel('DATE')
ax.set_ylabel('STOCK PRICE')
ax.set_title('AAPL')

ax2 = ax.twinx()
ax2.fill_between(DATE, y_volume, color='gray', alpha=0.3)
ax2.set_ylim([0.0, 1e9])
ax2.set_ylabel('VOLUME')

ax.legend()
plt.show()

if name == "main": main() ```

Moving Average Crossover¶

```python import matplotlib.pyplot as plt import yfinance as yf

def download(ticker, start=None, end=None): if start is None: return yf.Ticker(ticker).history(period="max") else: return yf.Ticker(ticker).history(start=start, end=end)

def main(): df = download('AAPL', start='2020-07-01', end='2020-12-31') df['M15'] = df['Close'].rolling(15).mean() df['M50'] = df['Close'].rolling(50).mean()

DATE = df.index[50:]
y = df['Close'].loc[DATE]
y_15 = df['M15'].loc[DATE]
y_50 = df['M50'].loc[DATE]

fig, ax = plt.subplots(figsize=(15, 4))

ax.plot(DATE, y, label='AAPL', color='b')
ax.plot(DATE, y_15, label='MA15', color='g')
ax.plot(DATE, y_50, label='MA50', color='r')

ax.fill_between(DATE, y_15, y_50,
                where=(y_15 > y_50), interpolate=True,
                color='green', alpha=0.3, label='Bullish')
ax.fill_between(DATE, y_15, y_50,
                where=(y_15 <= y_50), interpolate=True,
                color='red', alpha=0.3, label='Bearish')

ax.set_xlabel('DATE')
ax.set_ylabel('STOCK PRICE')
ax.set_title('AAPL - Moving Average Crossover')
ax.legend()

plt.show()

if name == "main": main() ```

Marking Events¶

Highlight specific dates on a chart:

```python import matplotlib.pyplot as plt import pandas as pd import yfinance as yf

def download_stock_prices(ticker, start, end): return yf.download(ticker, start=start, end=end)

def display_stock_prices(data, ticker, event_date=None): fig, ax = plt.subplots(figsize=(12, 3)) ax.plot(data['Close'], label=ticker)

if event_date:
    date = pd.to_datetime(event_date)
    ax.plot(date, data.loc[date, 'Close'], 'ro', ms=10)
    ax.annotate(f'{event_date}',
                xy=(date, data.loc[date, 'Close']),
                xytext=(date, data.loc[date, 'Close'] + 5),
                fontsize=10, ha='center',
                arrowprops=dict(arrowstyle='->', color='red'))

ax.set_xlabel('Date')
ax.set_ylabel('Price (USD)')
ax.legend()
plt.show()

def main(): ticker = 'AAPL' data = download_stock_prices(ticker, '2023-01-01', '2024-12-31') display_stock_prices(data, ticker, event_date='2023-12-20')

if name == "main": main() ```

Multi-Asset Comparison¶

```python import matplotlib.pyplot as plt import yfinance as yf import pandas as pd

def main(): tickers = ['AAPL', 'GOOGL', 'MSFT'] start = '2023-01-01' end = '2024-01-01'

fig, ax = plt.subplots(figsize=(12, 4))

for ticker in tickers:
    data = yf.download(ticker, start=start, end=end)
    # Normalize to starting price
    normalized = data['Close'] / data['Close'].iloc[0] * 100
    ax.plot(normalized, label=ticker)

ax.set_xlabel('Date')
ax.set_ylabel('Normalized Price (Base=100)')
ax.set_title('Tech Stock Comparison')
ax.legend()
ax.grid(True, alpha=0.3)

plt.show()

if name == "main": main() ```

Price with Date Formatting¶

```python import matplotlib.pyplot as plt import matplotlib.dates as mpl_dates import yfinance as yf

def main(): df = yf.Ticker('AAPL').history(start='2020-07-01', end='2020-12-31')

fig, ax = plt.subplots(figsize=(15, 4))
ax.plot(df.index, df['Close'])

ax.set_xlabel('Date')
ax.set_ylabel('Close Price')
ax.set_title('AAPL Stock Price')

# Format dates
date_format = mpl_dates.DateFormatter('%b %d, %Y')
ax.xaxis.set_major_formatter(date_format)
ax.xaxis.set_major_locator(mpl_dates.MonthLocator())

fig.autofmt_xdate()
plt.show()

if name == "main": main() ```

Key Takeaways¶

Use twinx() for price and volume on same chart
fill_between() visualizes moving average crossovers
Mark events with annotate()
Normalize prices for multi-asset comparison
Use DateFormatter for proper date display
autofmt_xdate() prevents label overlap

Exercises¶

Exercise 1. Write code that creates a stock price chart with a secondary y-axis for volume using ax.twinx(). Use synthetic data: generate 100 dates, a random-walk price series starting at 100, and random volume data.

Solution to Exercise 1

```python import matplotlib.pyplot as plt import numpy as np

np.random.seed(42) n = 100 dates = np.arange(n) price = 100 + np.cumsum(np.random.randn(n) * 0.5) volume = np.random.randint(1000, 5000, n)

fig, ax1 = plt.subplots(figsize=(12, 5)) ax1.plot(dates, price, 'b-', label='Price') ax1.set_xlabel('Day') ax1.set_ylabel('Price', color='blue')

ax2 = ax1.twinx() ax2.fill_between(dates, volume, color='gray', alpha=0.3, label='Volume') ax2.set_ylabel('Volume', color='gray')

ax1.set_title('Price and Volume Chart') ax1.legend(loc='upper left') plt.tight_layout() plt.show() ```

Exercise 2. Explain the purpose of fill_between() in a moving average crossover chart. When is the filled region green versus red?

Solution to Exercise 2

fill_between() fills the area between two moving average lines (e.g., a short-term MA and a long-term MA). The filled region is green when the short-term MA is above the long-term MA, signaling a bullish (upward) trend. The filled region is red when the short-term MA is below the long-term MA, signaling a bearish (downward) trend. The where parameter controls which condition determines the fill, and interpolate=True ensures smooth transitions at crossover points.

Exercise 3. Create a figure that plots three synthetic "stock" series (random walks), normalizes each to start at 100, and plots them on the same axes with a legend. Add appropriate labels and title.

Solution to Exercise 3

```python import matplotlib.pyplot as plt import numpy as np

np.random.seed(42) n = 200 days = np.arange(n)

stocks = {} for name in ['Stock A', 'Stock B', 'Stock C']: raw = 100 + np.cumsum(np.random.randn(n) * 1.5) normalized = raw / raw[0] * 100 stocks[name] = normalized

fig, ax = plt.subplots(figsize=(12, 5)) for name, values in stocks.items(): ax.plot(days, values, label=name)

ax.set_xlabel('Day') ax.set_ylabel('Normalized Price (Base=100)') ax.set_title('Multi-Asset Comparison') ax.legend() ax.grid(True, alpha=0.3) plt.tight_layout() plt.show() ```

Exercise 4. Write code that plots a synthetic price series and marks a specific point on the chart using ax.annotate() with an arrow. Add a title and axis labels.

Solution to Exercise 4

```python import matplotlib.pyplot as plt import numpy as np

np.random.seed(42) n = 100 days = np.arange(n) price = 100 + np.cumsum(np.random.randn(n) * 0.8)

event_day = 60 event_price = price[event_day]

fig, ax = plt.subplots(figsize=(12, 5)) ax.plot(days, price, 'b-')

ax.plot(event_day, event_price, 'ro', markersize=10) ax.annotate(f'Event at day {event_day}', xy=(event_day, event_price), xytext=(event_day + 10, event_price + 3), fontsize=10, ha='left', arrowprops=dict(arrowstyle='->', color='red'))

ax.set_xlabel('Day') ax.set_ylabel('Price') ax.set_title('Price Chart with Annotated Event') plt.tight_layout() plt.show() ```