Building Synthetic Factor Data for Alphalens Analysis
Summary
This notebook creates a small synthetic price panel and a date-indexed factor with missing observations, then prepares them for Alphalens. It assigns assets to groups and uses a utility function to combine factor values with forward returns over selected holding periods. The resulting data feeds full and event return tear sheets, illustrating how the same factor can be examined under different portfolio assumptions.
The examples compare long-only and long-short analysis, and show runs with and without group neutralization, including group-level reporting. The plotted outputs provide demonstrations of the library’s reporting workflow, not evidence that the factor predicts real returns. The dataset is deliberately constructed and contains only a few assets over a short span, so its returns and diagnostics cannot establish strategy performance. Its main value is showing input formatting and how analysis settings change the evaluation view.
Key ideas
- Synthetic prices and factor observations can be combined into Alphalens-ready data with forward returns.
- The example includes missing factor observations and asset group labels.
- Tear sheets can summarize returns, information, turnover, and event behavior.
- Long-only, long-short, and group-neutral settings provide different evaluation perspectives.
- Results from constructed data demonstrate workflow rather than real-world predictive power.
Tags
Full text
# Synthetic data examples
# Synthetic data examples
In this Notebook we will build synthetic data suitable to Alphalens analysis. This is useful to understand how Alphalens expects the input to be formatted and also it is a good testing environment to experiment with Alphalens.
```python
%matplotlib inline
from numpy import nan
from pandas import (DataFrame, date_range)
import matplotlib.pyplot as plt
from alphalens.tears import (create_returns_tear_sheet,
create_information_tear_sheet,
create_turnover_tear_sheet,
create_summary_tear_sheet,
create_full_tear_sheet,
create_event_returns_tear_sheet,
create_event_study_tear_sheet)
from alphalens.utils import get_clean_factor_and_forward_returns
```
```python
#
# build price
#
price_index = date_range(start='2015-1-10', end='2015-2-28')
price_index.name = 'date'
tickers = ['A', 'B', 'C', 'D', 'E', 'F']
data = [[1.0025**i, 1.005**i, 1.00**i, 0.995**i, 1.005**i, 1.00**i]
for i in range(1, 51)]
prices = DataFrame(index=price_index, columns=tickers, data=data)
#
# build factor
#
factor_index = date_range(start='2015-1-15', end='2015-2-13')
factor_index.name = 'date'
factor = DataFrame(index=factor_index, columns=tickers,
data=[[3, 4, 2, 1, nan, nan], [3, nan, nan, 1, 4, 2],
[3, 4, 2, 1, nan, nan], [3, 4, 2, 1, nan, nan],
[3, 4, 2, 1, nan, nan], [3, 4, 2, 1, nan, nan],
[3, nan, nan, 1, 4, 2], [3, nan, nan, 1, 4, 2],
[3, 4, 2, 1, nan, nan], [3, 4, 2, 1, nan, nan],
[3, nan, nan, 1, 4, 2], [3, nan, nan, 1, 4, 2],
[3, nan, nan, 1, 4, 2], [3, nan, nan, 1, 4, 2],
[3, nan, nan, 1, 4, 2], [3, nan, nan, 1, 4, 2],
[3, nan, nan, 1, 4, 2], [3, nan, nan, 1, 4, 2],
[3, nan, nan, 1, 4, 2], [3, nan, nan, 1, 4, 2],
[3, 4, 2, 1, nan, nan], [3, 4, 2, 1, nan, nan],
[3, 4, 2, 1, nan, nan], [3, 4, 2, 1, nan, nan],
[3, 4, 2, 1, nan, nan], [3, 4, 2, 1, nan, nan],
[3, 4, 2, 1, nan, nan], [3, 4, 2, 1, nan, nan],
[3, nan, nan, 1, 4, 2], [3, nan, nan, 1, 4, 2]]) \
.stack()
factor_groups = {'A': 'Group1', 'B': 'Group2', 'C': 'Group1', 'D': 'Group2', 'E': 'Group1', 'F': 'Group2'}
```
```python
prices.plot()
```
```python
prices.head()
```
```python
factor.head(10)
```
```python
factor_data = get_clean_factor_and_forward_returns(
factor,
prices,
groupby=factor_groups,
quantiles=4,
periods=(1, 3),
filter_zscore=None)
```
```python
factor_data.head(10)
```
```python
create_full_tear_sheet(factor_data, long_short=False, group_neutral=False, by_group=False)
create_event_returns_tear_sheet(factor_data, prices, avgretplot=(3, 11),
long_short=False, group_neutral=False, by_group=False)
```
```python
create_full_tear_sheet(factor_data, long_short=True, group_neutral=False, by_group=True)
create_event_returns_tear_sheet(factor_data, prices, avgretplot=(3, 11),
long_short=True, group_neutral=False, by_group=True)
```
```python
create_full_tear_sheet(factor_data, long_short=True, group_neutral=True, by_group=True)
create_event_returns_tear_sheet(factor_data, prices, avgretplot=(3, 11),
long_short=True, group_neutral=True, by_group=True)
```
Shown in full with attribution under the source's licence. Licence: Apache-2.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.