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CloudQuant Charting

CloudQuant Charting is a Python library for creating branded financial visualizations using the CloudQuant Data Liberator API. Import statement: 
from liberator import charting

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Chart Types

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Histogram

Generates distribution/histogram charts with CloudQuant branding. Returns: plotly.graph_objs._figure.Figure Required arguments:
  • df - pandas DataFrame containing data
  • col - string name of column to visualize
Optional arguments:
  • title, xlabel, ylabel - support HTML tags like <b>, <i>, <br>
  • histnorm - normalization type: 'percent', 'probability', 'density', or 'probability density'
  • width, height - dimensions in pixels (min 400px)
  • overlay_df, overlay_col - add comparison series
Example: 
import liberator
from liberator import charting

res = liberator.query(name="daily_bars")
df_bars = liberator.get_dataframe(res)
df_bars = df_bars.sort_values(by=['volume'], ascending=[False])
change = df_bars.close - df_bars.open
df_bars["change"] = change

fig = charting.Histogram(
    df=df_bars[0:100],
    col="change",
    title="<b>Change Px Distribution</b> <br>of last trading day's top 100 High Volume Stocks",
    width=800,
    height=400,
    histnorm="probability"
)
fig.show()

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Candlestick

Generates OHLC candlestick charts with optional technical studies and overlays. Returns: Single figure or array of figures (depending on studies) Required arguments:
  • df - DataFrame with OHLC data (minimum 20 bars)
Optional arguments:
  • title, xlabel, ylabel - chart labels with HTML support
  • width, height - dimensions (min 700px width, 400px height)
  • open, high, low, close, timestamp - column names
  • entry_px, entry_time, close_px, close_time, entry_side - trade annotations
  • overlay_df, overlay_col, overlay_col2 - secondary axis data
Supported Studies (Single Figure): BBANDS, DEMA, EMA, HT_TRENDLINE, KAMA, MA, MAMA, MIDPOINT, MIDPRICE, SAR, SAREXT, SMA, T3, TEMA, TRIMA, WMA Supported Studies (Array of Figures): ADX, ADXR, APO, AROON, AROONOSC, BOP, CCI, CMO, DX, MACD, MACDEXT, MFI, MINUS_DI, MINUS_DM, MOM, PLUS_DI, PLUS_DM, PPO, ROC, ROCP, ROCR, ROCR100, RSI, STOCH, STOCHF, STOCHRSI, TRIX, ULTOSC, WILLR, ATR, MACDFIX, NATR, TRANGE Example: 
import liberator
import pandas as pd
from liberator import charting
from datetime import datetime, timedelta

back_to = (datetime.now() + timedelta(days=-360)).strftime('%Y-%m-%d 23:59:59')
symbol = "SPY"

res = liberator.query(name="daily_bars", symbols=symbol, back_to=back_to)
df_bars = liberator.get_dataframe(res)

res = liberator.query(name="eia_gas_prices", back_to=back_to)
gaspx = liberator.get_dataframe(res)

fig = charting.Candlestick(
    df=df_bars,
    overlay_df=gaspx,
    overlay_col="retail_gas_price",
    height=700,
    width=800,
    xlabel="Date",
    title="SPY vs Retail Gas Prices"
)
fig.show()

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LineChart

Creates line charts with support for studies and secondary Y-axis. Returns: plotly.graph_objs._figure.Figure Required arguments:
  • df - pandas DataFrame
  • cols - list of column names to plot
  • x_column - column name for X-axis (defaults to index)
Optional arguments:
  • title, xlabel, ylabel - chart labels with HTML support
  • width, height - dimensions
  • y2axis_name - activates secondary Y-axis
  • overlay_df, overlay_cols - secondary axis data
  • study - technical study name
  • study_columns - columns for study calculation
  • timeperiod - study periods
Supported Studies: BBANDS, MAMA, DEMA, EMA, HT_TRENDLINE, KAMA, MA, MIDPOINT, MIDPRICE, SAR, SAREXT, SMA, T3, TEMA, TRIMA, WMA Example: 
import cloudquantcharting
import liberator

res = liberator.query(
    symbols='AMZN',
    name='Twitter',
    back_to='12/22/2020 08:00:00',
    as_of='12/22/2020 16:00:00'
)
twitter_df = liberator.get_dataframe(res)

fig = cloudquantcharting.LineChart(
    df=twitter_df,
    cols=['s-score', 's-buzz', 's', 's-mean', 'sv-score', 's-dispersion', 's-buzz', 's-delta'],
    x_column="timestamp"
)
fig.show()

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BarChart

Generates bar charts, including stacked bars for repeated X values. Returns: plotly.graph_objs._figure.Figure Required arguments:
  • df - pandas DataFrame
  • xcol - column name for X-axis
  • ycol - numeric column for Y-axis
Optional arguments:
  • title, xlabel, ylabel - labels with HTML support
  • width, height - dimensions
  • singlecolor - boolean for uniform color (defaults True)
  • orientation - 'v' (vertical) or 'h' (horizontal)
Example: 
import liberator
import pandas as pd
from liberator import charting

res = liberator.query(name="daily_bars")
df_bars = liberator.get_dataframe(res)
df_bars = df_bars.sort_values(by=['volume'], ascending=[False])

fig = charting.BarChart(
    df=df_bars[0:20],
    xcol="symbol",
    ycol="spread",
    title="Spread Price of High Volume Stocks",
    width=800,
    height=400
)
fig.show()

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PieChart

Generates pie/donut charts with customizable hole size. Returns: plotly.graph_objs._figure.Figure Required arguments:
  • df - pandas DataFrame
  • labelcol - column name for labels
  • valuecol - numeric column for slice size
Optional arguments:
  • title - chart title with HTML support
  • width, height - dimensions
  • hole - donut hole size (0-1, where 0 = no hole)
  • colors - color sequence: default CQ palette, or 'reds', 'greens', 'blues'
Example: 
import liberator
import pandas as pd
from liberator import charting

values = [
    ['Jan', '150000', 'USD'],
    ['Feb', '200000', 'USD'],
    ['Mar', '330000', 'USD'],
    ['Apr', '440000', 'USD'],
    ['May', '555000', 'USD'],
    ['June', '355000', 'USD'],
    ['July', '150000', 'USD'],
    ['Aug', '130000', 'USD'],
    ['Sept', '330000', 'USD'],
    ['Oct', '440000', 'USD'],
    ['Nov', '130000', 'USD'],
    ['Dec', '330000', 'USD']
]
df = pd.DataFrame(values, columns=["month", "sales", "currency"])
title = "<B>Sales</b><br>by month"

fig = charting.PieChart(
    df=df,
    labelcol='month',
    valuecol="sales",
    title=title,
    height=700
)
fig.show()

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GroupedBarChart

Generates grouped bar charts comparing multiple numeric columns. Returns: plotly.graph_objs._figure.Figure Required arguments:
  • df - pandas DataFrame
  • groups - column name for grouping
  • values - list of numeric column names
Optional arguments:
  • title, group_label, value_label - labels with HTML support
  • width, height - dimensions
  • colors - color list (cycles through bars)
  • orientation - 'v' or 'h'
Example: 
import liberator
import pandas as pd
from liberator import charting

dataset_name = 'reddit_wallstreetbets_comments'
df = liberator.get_dataframe(liberator.query(name=dataset_name))
df = df.loc[df['vader_body_sentiment_compound'] > 0]
df = df.sort_values(by=['vader_body_sentiment_compound'], ascending=[False])

charting.GroupedBarChart(
    df=df[0:5],
    groups="symbol",
    values=["vader_body_sentiment_pos", "vader_body_sentiment_compound", "textblob_body_sentiment_subjectivity"],
    title="WS Bets Sentiment",
    group_label="Symbol",
    value_label="sentiment score"
)

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ScatterPlot

Generates scatter plots with optional bubble sizing and color coding. Returns: plotly.graph_objs._figure.Figure Required arguments:
  • df - pandas DataFrame
  • x_column - numeric column for horizontal axis
  • y_column - numeric column(s) for vertical axis
Optional arguments:
  • title, xlabel, ylabel - labels with HTML support
  • width, height - dimensions
  • size_column - numeric column for bubble size (negative values show red/green)
  • size_multiplier - multiplier to adjust bubble sizes
Example: 
import liberator
import pandas as pd
from liberator import charting

dataset_name = 'reddit_wallstreetbets_comments'
df = liberator.get_dataframe(liberator.query(name=dataset_name))
df = df.loc[df['vader_body_sentiment_compound'] > 0]
df = df.sort_values(by=['vader_body_sentiment_compound'], ascending=[False])

fig = charting.ScatterPlot(
    df=df[0:200],
    x_column="textblob_body_sentiment_subjectivity",
    y_column=['vader_body_sentiment_neu'],
    title="WS Bets Sentiment",
    size_multiplier=.15,
    width=800,
    height=800
)
fig.show()

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addNotes

Adds annotations/callouts to existing charts for highlighting specific data points. Returns: plotly.graph_objs._figure.Figure Arguments:
  • thefig - figure from a CloudQuant charting function
  • notes - list of dictionaries with x, y, and note keys
Example: 
import liberator
import pandas as pd
from liberator import charting
from datetime import datetime, timedelta

back_to = (datetime.now() + timedelta(days=-2)).strftime('%Y-%m-%d 23:59:59')
symbol = "AAPL"

res = liberator.query(name="minute_bars", symbols=symbol, back_to=back_to)
df_bars = liberator.get_dataframe(res)

fig = charting.Candlestick(df=df_bars, title=symbol)

mydata = []
data = {'x': df_bars.timestamp[7], 'y': df_bars.open[7], 'note': "T1"}
mydata.append(data)

data = {'x': df_bars.timestamp[155], 'y': df_bars.open[155], 'note': "T2"}
mydata.append(data)

data = {'x': df_bars.timestamp[1000], 'y': df_bars.open[1000], 'note': "T3"}
mydata.append(data)

fig = charting.addNotes(fig, mydata)
fig.show()