Canalyst Candas Data Science Library

Name

Canalyst Candas

Description

Built by a former PM / analyst to give anyone with a little bit of Python knowledge the ability to scale their investment process. Access, manipulate, and visualize Canalyst models, without opening Excel. Work with full fundamental models, create and calculate scenarios, and visualize actionable investment ideas.

Hosted collaborative Jupyterhub server available at Candas Cloud

• Rather than simply deliver data, Candas serves the actual model in a Python class. Like a calculator, this allows for custom scenario evaluation for one or more companies at a time.
• Use Candas to search for KPIs by partial or full description, filter by “key driver” – model driver, sector, category, or query against values for a screener-like functionality. Search either our full model dataset or our guidance dataset for companies which provide guidance.
• Discover the KPIs with the greatest impact on stock price, and evaluate those KPIs based on changing P&L scenarios.
• Visualize P&L statements in node trees with common size % and values attached. Use the built-in charting tools to efficiently make comparisons.

In short, a data science library using Canalyst's API, developed for securities analysis using Python.

• Search KPI
• Company data Dataframes (one company or many)
• Charts
• Model update (scenario analysis)
• Visualize formula builds

Installation

Installation instructions can be found on our PyPI page

Usage

Search Guidance:

Candas is built to facilitate easy discovery of guidance in our Modelverse. You can search guidance for key items, either filtered by a ticker / ticker list or just across the entire Modelverse.

Guidance Example:

canalyst_search.search_guidance_time_series(ticker = "", #any ticker or list of tickers 
                sector="Consumer", #path in our nomenclature is a hierarchy of sectors
                file_name="", #file name is a proxy for company name
                time_series_name="", #our range name
                time_series_description="china", #human readable row header
                most_recent=True) #most recent item or all items 

Search KPI:

Candas is also built to facilitate easy discovery of KPI names in our Modelverse.

KPI Search Example:

canalyst_search.search_time_series(ticker = "",
                 sector="Thrifts",
                 category="",
                 unit_type="percentage",
                 mo_only=True,
                 period_duration_type='fiscal_quarter',
                 time_series_name='',
                 time_series_description='total revenue growth', #guessing on the time series name
                 query = 'value > 5')

ModelSet:

The core objects in Candas are Models. Models can be arranged in a set by instantiating a ModelFrame. Instantiate a config object to handle authentication.

model_set = cd.ModelSet(ticker_list=[ticker_list],config=config)

With modelset, the model_frame attribute returns Pandas dataframes. The parameters for model_frame():

• time_series_name: Send in a partial string as time series name, model_frame will regex search for it
• pivot: Pivot allows for excel-model style wide data (good for comp screens)
• mrq: True / False filters to ONLY the most recent quarter
• period_duration_type: is fiscal_quarter or fiscal_year or blank for both
• is_historical: True will filter to only historical, False only forecasts, or blank for both
• n_periods: defaults to 12 but most of our models go back to 2013
• mrq_notation: applies to pivot, and will filter to historical data and apply MRQ-n notation on the columns (a way to handle off fiscal reporters in comp screens)

Example:

model_set.model_frame(time_series_name="MO_RIS_REV",
                  is_driver="",
                  pivot=False,
                  mrq=False,
                  period_duration_type='fiscal_quarter', #or fiscal_year
                  is_historical="",
                  n_periods=12,
                  mrq_notation=False)
`

Charting:

Candas has a Canalyst standard charting library which allows for easy visualizations.

Chart Example:

df_plot = df[df['ticker'].isin(['AZUL US','MESA US'])][['ticker','period_name','value']].pivot_table(values="value", index=["period_name"],columns=["ticker"]).reset_index()
p = cd.Chart(df_plot['period_name'],df_plot[["AZUL US", "MESA US"]],["AZUL US", "MESA US"], [["Periods", "Actual"]], title="MO_MA_Fuel")
p.show()

Scenario Analysis:

Candas can arrange a forecast and send it to our scenario engine via the fit() function, and get changed outputs vs the default.

Example:

return_series = "MO_RIS_EPS_WAD_Adj"
list_output = []
for ts in time_series_names:
    df_params = model_set.forecast_frame(ts,
                             n_periods=-1,
                             function_name='multiply',
                             function_value=(1.1))
    dicts_output=model_set.fit(df_params,return_series)
    for key in dicts_output.keys():
        list_output.append(dicts_output[key].head(1))

ModelMap:

Candas can show a node tree at any level of the PNL

Example:

model_set.create_model_map(ticker=ticker,time_series_name="MO_RIS_REV",col_for_labels = "time_series_description").show() #launches in a separate browser window

ModelMap and Scenario Engine Together: ModelMap example: Node Chart for Fuel Margin

KPI Importance / Scenario Engine:

Use the same node tree to extract key drivers, then use our scenario engine to rank order 1% changes in KPI driver vs subsequent revenue change

Example:

#use the same node tree to extract key drivers (red nodes in the map)
df = model_set.models[ticker].key_driver_map("MO_RIS_REV")
return_series = 'MO_RIS_REV'
driver_list_df = []
for i, row in df.iterrows():

    time_series_name = row['time_series_name']
    print(f"scenario: move {time_series_name} 1% and get resultant change in {return_series}")

    #create a param dataframe for each time series name in our list
    df_1_param = model_set.forecast_frame(time_series_name,
                         n_periods=-1,
                         function_name='multiply',
                         function_value=1.01)


    d_output=model_set.fit(df_1_param,return_series) #our fit function will return a link to scenario engine JSON for audit

    df_output = model_set.filter_summary(d_output,period_type='Q')

    df_merge = pd.merge(df_output,df_1_param,how='inner',left_on=['ticker','period_name'],right_on=['ticker','period_name'])

    driver_list_df.append(df_merge) #append to a list for concatenating at the end
df = pd.concat(driver_list_df).sort_values('diff',ascending=False)[['ticker','time_series_name_y','diff']]
df = df.rename(columns={'time_series_name_y':'time_series_name'})
df['diff'] = df['diff']-1
df = df.sort_values('diff')
df.plot(x='time_series_name',y='diff',kind='barh',title=ticker+" Key Drivers Revenue Sensitivity")

Support

[email protected]

Contributing

Project is currently only open to contributors through discussion with the maintainer.

Authors and acknowledgment

[email protected]

License

APL 2.0

Project status

Ongoing