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AlphaReport
AlphaReport is experimental and might change quickly.
AlphaReport creates a suite of plots to help you understand your data sets and evaluate the performance of your investment strategies.
Use AlphaReport to:
- Look for patterns in your data
- Generate and research new strategy ideas
- Understand your data's statistical properties
- Find features in your data and assess their predictive power
- Analyze a strategy's potential to deliver future returns
AlphaReport has 6 plot categories:
- Distributions: Visualize marginal and joint probability distributions of feature and target variables.
- Temporal: Find periodic or delayed signals, assess stationarity, and view time-shifted comparisons of the data.
- Metrics: Show statistical metrics about the relationships between feature and target variables over varying timescales.
- Classification: Assess the ability of feature variables to predict the sign of target variables.
- Clustering: Perform a principal component analysis of the data and find similarities between variables.
- Machine learning: Build and evaluate prediction models using logistic regression, decision trees, and neural networks.
To view a list of available plots in each category:
import sigtech.framework as sig
sig.experimental_AlphaReport.available_plots().sort_values('Category')
Assess the ability of daily Fama-French index data to predict the movement of a strategy on the day after.
- 1.Import basic Python libraries, the SigTech framework, and the AlphaReport class:
import pandas as pd
import datetime as dtm
import sigtech.framework as sig
from sigtech.framework.experimental.analytics.alpha_report.alpha_report \
import AlphaReport
sig.init()
- 2.Create example feature data from the Fama and French index history:
# Create example feature data from Fama and French
# index history, delayed by 1 day
fama_french = sig.obj.get('US 3F_DAILY FAMA-FRENCH INDEX')
start_date = dtm.date(2018, 1, 4)
end_date = dtm.date(2022, 6, 30)
features = fama_french.history_df(
fields=fama_french.history_fields
).drop(columns=['rf'])
- 3.Delay the feature data by 1 day:
features = features.shift()
- 4.Create example target data from the percentage change series of a single-stock reinvestment strategy:
strat = sig.get_single_stock_strategy('1000045.SINGLE_STOCK.TRADABLE')
targets = strat.history().pct_change().dropna()
- 5.Create an AlphaReport object and display it interactively:
ar = AlphaReport(
x_df=features.loc[start_date: end_date].to_frame('Fama-French Index'),
y_df=targets.loc[start_date: end_date].to_frame('Strategy')
)
ar.interactive_report()
Last modified 9mo ago