Testing an Overnight Gap Factor with Intraday Alphalens Returns
Notebook Alphalens
Summary
This notebook demonstrates an Alphalens workflow for evaluating a daily stock factor based on the gap between the prior close and current open. It defines an example universe of large-cap equities with sector labels, calculates the gap, and aligns the factor timestamp with the market open. The intended trade enters at the open and exits at the close, with no overnight position; a second return period spans the open to the following open.
The notebook emphasizes that pricing inputs must represent prices available after the factor observation and must be aligned carefully to avoid lookahead bias. It combines open and close prices, groups assets by sector, and generates return and event-return tear sheets. The evidence consists of the analysis outputs shown in the notebook, but the factor is explicitly presented as a test of whether the gap contains alpha or is noise; no numerical result or conclusion is stated in the text. The example uses a limited stock universe and a short historical sample, so it does not establish general performance.
Key ideas
- The example factor ranks stocks by the overnight return from the previous close to the current open.
- Open-to-close returns model a same-day trade that avoids overnight exposure.
- Factor timestamps and subsequent pricing data must be aligned to prevent lookahead bias.
- Sector labels can be passed to Alphalens to group stocks in the analysis.
- The notebook presents tear sheets but does not state a definitive performance conclusion.
Tags
Full text
# Alphalens: intraday factor
# Alphalens: intraday factor
In this notebook we use Alphalens to analyse the performance of an intraday factor, which is computed daily but the stocks are bought at marker open and sold at market close with no overnight positions.
```python
%pylab inline --no-import-all
import alphalens
import pandas as pd
import numpy as np
import datetime
```
```python
import warnings
warnings.filterwarnings('ignore')
```
Below is a simple mapping of tickers to sectors for a small universe of large cap stocks.
```python
sector_names = {
0 : "information_technology",
1 : "financials",
2 : "health_care",
3 : "industrials",
4 : "utilities",
5 : "real_estate",
6 : "materials",
7 : "telecommunication_services",
8 : "consumer_staples",
9 : "consumer_discretionary",
10 : "energy"
}
ticker_sector = {
"ACN" : 0, "ATVI" : 0, "ADBE" : 0, "AMD" : 0, "AKAM" : 0, "ADS" : 0, "GOOGL" : 0, "GOOG" : 0,
"APH" : 0, "ADI" : 0, "ANSS" : 0, "AAPL" : 0, "AMAT" : 0, "ADSK" : 0, "ADP" : 0, "AVGO" : 0,
"AMG" : 1, "AFL" : 1, "ALL" : 1, "AXP" : 1, "AIG" : 1, "AMP" : 1, "AON" : 1, "AJG" : 1, "AIZ" : 1, "BAC" : 1,
"BK" : 1, "BBT" : 1, "BRK.B" : 1, "BLK" : 1, "HRB" : 1, "BHF" : 1, "COF" : 1, "CBOE" : 1, "SCHW" : 1, "CB" : 1,
"ABT" : 2, "ABBV" : 2, "AET" : 2, "A" : 2, "ALXN" : 2, "ALGN" : 2, "AGN" : 2, "ABC" : 2, "AMGN" : 2, "ANTM" : 2,
"BCR" : 2, "BAX" : 2, "BDX" : 2, "BIIB" : 2, "BSX" : 2, "BMY" : 2, "CAH" : 2, "CELG" : 2, "CNC" : 2, "CERN" : 2,
"MMM" : 3, "AYI" : 3, "ALK" : 3, "ALLE" : 3, "AAL" : 3, "AME" : 3, "AOS" : 3, "ARNC" : 3, "BA" : 3, "CHRW" : 3,
"CAT" : 3, "CTAS" : 3, "CSX" : 3, "CMI" : 3, "DE" : 3, "DAL" : 3, "DOV" : 3, "ETN" : 3, "EMR" : 3, "EFX" : 3,
"AES" : 4, "LNT" : 4, "AEE" : 4, "AEP" : 4, "AWK" : 4, "CNP" : 4, "CMS" : 4, "ED" : 4, "D" : 4, "DTE" : 4,
"DUK" : 4, "EIX" : 4, "ETR" : 4, "ES" : 4, "EXC" : 4, "FE" : 4, "NEE" : 4, "NI" : 4, "NRG" : 4, "PCG" : 4,
"ARE" : 5, "AMT" : 5, "AIV" : 5, "AVB" : 5, "BXP" : 5, "CBG" : 5, "CCI" : 5, "DLR" : 5, "DRE" : 5,
"EQIX" : 5, "EQR" : 5, "ESS" : 5, "EXR" : 5, "FRT" : 5, "GGP" : 5, "HCP" : 5, "HST" : 5, "IRM" : 5, "KIM" : 5,
"APD" : 6, "ALB" : 6, "AVY" : 6, "BLL" : 6, "CF" : 6, "DWDP" : 6, "EMN" : 6, "ECL" : 6, "FMC" : 6, "FCX" : 6,
"IP" : 6, "IFF" : 6, "LYB" : 6, "MLM" : 6, "MON" : 6, "MOS" : 6, "NEM" : 6, "NUE" : 6, "PKG" : 6, "PPG" : 6,
"T" : 7, "CTL" : 7, "VZ" : 7,
"MO" : 8, "ADM" : 8, "BF.B" : 8, "CPB" : 8, "CHD" : 8, "CLX" : 8, "KO" : 8, "CL" : 8, "CAG" : 8,
"STZ" : 8, "COST" : 8, "COTY" : 8, "CVS" : 8, "DPS" : 8, "EL" : 8, "GIS" : 8, "HSY" : 8, "HRL" : 8,
"AAP" : 9, "AMZN" : 9, "APTV" : 9, "AZO" : 9, "BBY" : 9, "BWA" : 9, "KMX" : 9, "CCL" : 9,
"APC" : 10, "ANDV" : 10, "APA" : 10, "BHGE" : 10, "COG" : 10, "CHK" : 10, "CVX" : 10, "XEC" : 10, "CXO" : 10,
"COP" : 10, "DVN" : 10, "EOG" : 10, "EQT" : 10, "XOM" : 10, "HAL" : 10, "HP" : 10, "HES" : 10, "KMI" : 10
}
```
```python
import pandas_datareader.data as web
tickers = list(ticker_sector.keys())
pan = web.DataReader(tickers, "google", datetime.datetime(2017, 1, 1), datetime.datetime(2017, 6, 1))
```
Our example factor ranks the stocks based on their overnight price gap (yesterday close to today open price). We'll see if the factor has some alpha or if it is pure noise.
```python
today_open = pan['Open']
today_close = pan['Close']
yesterday_close = today_close.shift(1)
```
```python
factor = (today_open - yesterday_close) / yesterday_close
```
The pricing data passed to alphalens should contain the entry price for the assets so it must reflect the next available price after a factor value was observed at a given timestamp. Those prices must not be used in the calculation of the factor values for that time. Always double check to ensure you are not introducing lookahead bias to your study.
The pricing data must also contain the exit price for the assets, for period 1 the price at the next timestamp will be used, for period 2 the price after 2 timestamps will be used and so on.
There are no restrinctions/assumptions on the time frequencies a factor should be computed at and neither on the specific time a factor should be traded (trading at the open vs trading at the close vs intraday trading), it is only required that factor and price DataFrames are properly aligned given the rules above.
In our example, we want to buy the stocks at marker open, so the need the open price at the exact timestamps as the factor valules, and we want to sell the stocks at market close so we will add the close prices too, which will be used to compute period 1 forward returns as they appear just after the factor values timestamps. The returns computed by Alphalens will therefore be based on the difference between open to close assets prices.
If we had other prices we could compute other period returns, for example one hour after market open and 2 hours and so on. We could have added those prices right after the open prices and instruct Alphalens to compute 1, 2, 3... periods too and not only period 1 like in this example.
```python
# Fix time as Yahoo doesn't set it
today_open.index += pd.Timedelta('9h30m')
today_close.index += pd.Timedelta('16h')
# pricing will contain both open and close
pricing = pd.concat([today_open, today_close]).sort_index()
```
```python
pricing.head()
```
```python
# Align factor to open price
factor.index += pd.Timedelta('9h30m')
factor = factor.stack()
factor.index = factor.index.set_names(['date', 'asset'])
```
```python
factor.unstack().head()
```
# Run Alphalens
Period 1 will show returns from market open to market close while period 2 will show returns from today open to tomorrow open
```python
non_predictive_factor_data = alphalens.utils.get_clean_factor_and_forward_returns(factor,
pricing,
periods=(1,2),
groupby=ticker_sector,
groupby_labels=sector_names)
```
```python
alphalens.tears.create_returns_tear_sheet(non_predictive_factor_data)
```
```python
alphalens.tears.create_event_returns_tear_sheet(non_predictive_factor_data, pricing)
```
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.