Visualization API

Price chart visualizations using Plotly.

Provides interactive price charts including: - Candlestick charts - OHLCV charts with volume - Multi-ticker comparison - Price with moving averages

quantlab.visualization.price_charts.create_candlestick_chart(df, ticker, show_volume=True, show_range_selector=True, height=600, intraday=False)[source]

Create interactive OHLC candlestick chart with optional volume.

Parameters:

df (DataFrame) – DataFrame with columns: date, open, high, low, close, volume
ticker (str) – Stock ticker symbol
show_volume (bool) – Include volume subplot
show_range_selector (bool) – Add date range selector buttons
height (int) – Chart height in pixels

Return type:

Figure

Returns:

Plotly figure object

Example

>>> import pandas as pd
>>> df = pd.DataFrame({
...     'date': pd.date_range('2024-01-01', periods=30),
...     'open': np.random.randn(30).cumsum() + 100,
...     'high': np.random.randn(30).cumsum() + 102,
...     'low': np.random.randn(30).cumsum() + 98,
...     'close': np.random.randn(30).cumsum() + 100,
...     'volume': np.random.randint(1000000, 5000000, 30)
... })
>>> fig = create_candlestick_chart(df, ticker="AAPL")
>>> fig.show()

quantlab.visualization.price_charts.create_price_line_chart(df, ticker, price_column='close', show_volume=False, moving_averages=None, height=600, intraday=False)[source]

Create line chart for price with optional moving averages.

Parameters:

df (DataFrame) – DataFrame with columns: date, price_column, (optional) volume
ticker (str) – Stock ticker symbol
price_column (str) – Column to use for price (default: ‘close’)
show_volume (bool) – Include volume subplot
moving_averages (Optional[List[int]]) – List of MA periods (e.g., [20, 50, 200])
height (int) – Chart height in pixels

Return type:

Figure

Returns:

Plotly figure object

Example

>>> fig = create_price_line_chart(
...     df,
...     ticker="AAPL",
...     moving_averages=[20, 50, 200]
... )
>>> fig.show()

quantlab.visualization.price_charts.create_multi_ticker_comparison(data_dict, price_column='close', normalize=True, height=600)[source]

Create comparison chart for multiple tickers.

Parameters:

data_dict (Dict[str, DataFrame]) – Dict mapping ticker -> DataFrame with date and price columns
price_column (str) – Column to use for price
normalize (bool) – Normalize to percentage change from first date
height (int) – Chart height in pixels

Return type:

Figure

Returns:

Plotly figure object

Example

>>> data = {
...     'AAPL': df_aapl,
...     'GOOGL': df_googl,
...     'MSFT': df_msft
... }
>>> fig = create_multi_ticker_comparison(data, normalize=True)
>>> fig.show()

Options strategy visualization charts using Plotly.

Provides interactive visualizations for options including: - Payoff diagrams - Greeks surface plots (2D and 3D) - Options chain heatmaps - Greeks timeline projections

quantlab.visualization.options_charts.create_payoff_diagram(prices, pnls, strategy_name, current_price, breakeven_points=None, max_profit=None, max_loss=None, height=600)[source]

Create interactive options strategy payoff diagram.

Parameters:

prices (ndarray) – Array of underlying prices
pnls (ndarray) – Array of corresponding P&L values
strategy_name (str) – Name of options strategy
current_price (float) – Current underlying price
breakeven_points (Optional[List[float]]) – List of breakeven prices
max_profit (Optional[float]) – Maximum profit value
max_loss (Optional[float]) – Maximum loss value
height (int) – Chart height in pixels

Return type:

Figure

Returns:

Plotly figure object

Example

>>> prices = np.linspace(90, 110, 100)
>>> pnls = np.maximum(prices - 100, 0) - 5  # Long call at 100, premium 5
>>> fig = create_payoff_diagram(
...     prices, pnls,
...     strategy_name="Long Call",
...     current_price=100,
...     breakeven_points=[105],
...     max_profit=None,  # Unlimited
...     max_loss=-500
... )
>>> fig.show()

quantlab.visualization.options_charts.create_greeks_heatmap(greeks_data, greek_name='delta', height=600)[source]

Create heatmap showing Greek values across strikes and expirations.

Parameters:

greeks_data (DataFrame) – DataFrame with columns: strike, expiration, greek_value
greek_name (str) – Name of Greek to display (delta, gamma, theta, vega, etc.)
height (int) – Chart height in pixels

Return type:

Figure

Returns:

Plotly figure object

Example

>>> df = pd.DataFrame({
...     'strike': [95, 100, 105] * 3,
...     'expiration': ['2024-01-01'] * 3 + ['2024-02-01'] * 3 + ['2024-03-01'] * 3,
...     'delta': [0.8, 0.5, 0.2, 0.7, 0.5, 0.3, 0.6, 0.5, 0.4]
... })
>>> fig = create_greeks_heatmap(df, greek_name='delta')
>>> fig.show()

quantlab.visualization.options_charts.create_greeks_timeline(timeline_data, strategy_name, greeks_to_show=None, height=700)[source]

Create timeline showing Greeks evolution over time.

Parameters:

timeline_data (DataFrame) – DataFrame with columns: days_forward, delta, gamma, theta, vega, etc.
strategy_name (str) – Name of options strategy
greeks_to_show (Optional[List[str]]) – List of Greeks to display (default: all first-order)
height (int) – Chart height in pixels

Return type:

Figure

Returns:

Plotly figure object with subplots

Example

>>> df = pd.DataFrame({
...     'days_forward': range(0, 30),
...     'delta': np.random.randn(30).cumsum() + 50,
...     'gamma': np.random.randn(30).cumsum() + 5,
...     'theta': np.random.randn(30).cumsum() - 10,
...     'vega': np.random.randn(30).cumsum() + 20
... })
>>> fig = create_greeks_timeline(df, strategy_name="Long Call")
>>> fig.show()

quantlab.visualization.options_charts.create_greeks_3d_surface(price_range, time_range, greek_values, greek_name, strategy_name, current_price, height=700)[source]

Create 3D surface plot showing Greek sensitivity to price and time.

Parameters:

price_range (ndarray) – Array of underlying prices
time_range (ndarray) – Array of days forward
greek_values (ndarray) – 2D array of Greek values (time × price)
greek_name (str) – Name of Greek (delta, gamma, theta, vega)
strategy_name (str) – Name of options strategy
current_price (float) – Current underlying price
height (int) – Chart height in pixels

Return type:

Figure

Returns:

Plotly figure object

Example

>>> prices = np.linspace(90, 110, 50)
>>> days = np.linspace(0, 30, 30)
>>> P, D = np.meshgrid(prices, days)
>>> delta_values = 0.5 * np.exp(-(P - 100)**2 / 100) * (1 - D / 30)
>>> fig = create_greeks_3d_surface(
...     prices, days, delta_values,
...     greek_name='delta',
...     strategy_name='Long Call',
...     current_price=100
... )
>>> fig.show()

quantlab.visualization.options_charts.create_strategy_comparison(comparison_data, current_price, height=600)[source]

Create comparison chart for multiple options strategies.

Parameters:

comparison_data (Dict[str, Tuple[ndarray, ndarray]]) – Dict mapping strategy_name -> (prices, pnls)
current_price (float) – Current underlying price
height (int) – Chart height in pixels

Return type:

Figure

Returns:

Plotly figure object

Example

>>> prices = np.linspace(90, 110, 100)
>>> data = {
...     'Long Call': (prices, np.maximum(prices - 100, 0) - 5),
...     'Long Put': (prices, np.maximum(100 - prices, 0) - 5),
...     'Straddle': (prices, np.abs(prices - 100) - 10)
... }
>>> fig = create_strategy_comparison(data, current_price=100)
>>> fig.show()