This is a sample mean-reversion algorithm on Quantopian for you to test and adapt.
This example uses a dynamic stock selector, pipeline, to select stocks to trade.
It orders stocks from the top 1% of the previous day's dollar-volume (liquid
Algorithm investment thesis:
Top-performing stocks from last week will do worse this week, and vice-versa.
Every Monday, we rank high dollar-volume stocks based on their previous 5 day returns.
We long the bottom 10% of stocks with the WORST returns over the past 5 days.
We short the top 10% of stocks with the BEST returns over the past 5 days.
This type of algorithm may be used in live trading and in the Quantopian Open.
# Import the libraries we will use here.
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import AverageDollarVolume, Returns
Called once at the start of the program. Any one-time
startup logic goes here.
# Define context variables that can be accessed in other methods of
# the algorithm.
context.long_leverage = 0.5
context.short_leverage = -0.5
context.returns_lookback = 5
# Rebalance on the first trading day of each week at 11AM.
# Record tracking variables at the end of each day.
# Create and attach our pipeline (dynamic stock selector), defined below.
context.run_pipeline = True
A function to create our pipeline (dynamic stock selector). The pipeline is used
to rank stocks based on different factors, including builtin factors, or custom
factors that you can define. Documentation on pipeline can be found here:
# Create a pipeline object.
pipe = Pipeline()
# Create a dollar_volume factor using default inputs and window_length.
# This is a builtin factor.
dollar_volume = AverageDollarVolume(window_length=1)
# Define high dollar-volume filter to be the top 5% of stocks by dollar volume.
high_dollar_volume = dollar_volume.percentile_between(95, 100)
# Create a recent_returns factor with a 5-day returns lookback. This is
# a custom factor defined below (see RecentReturns class).
recent_returns = Returns(window_length=context.returns_lookback, mask=high_dollar_volume)
# Define high and low returns filters to be the bottom 10% and top 10% of
# securities in the high dollar-volume group.
low_returns = recent_returns.percentile_between(0,10)
high_returns = recent_returns.percentile_between(90,100)
# Add a filter to the pipeline such that only high-return and low-return
# securities are kept.
pipe.set_screen(low_returns | high_returns)
# Add the low_returns and high_returns filters as columns to the pipeline so
# that when we refer to securities remaining in our pipeline later, we know
# which ones belong to which category.
def before_trading_start(context, data):
if not context.run_pipeline:
Called every day before market open. This is where we get the securities
that made it through the pipeline.
# Pipeline_output returns a pandas DataFrame with the results of our factors
# and filters.
context.output = pipeline_output('mean_reversion_example')
# Sets the list of securities we want to long as the securities with a 'True'
# value in the low_returns column.
context.long_secs = context.output[context.output['low_returns']]
# Sets the list of securities we want to short as the securities with a 'True'
# value in the high_returns column.
context.short_secs = context.output[context.output['high_returns']]
# A list of the securities that we want to order today.
context.security_list = context.long_secs.index.union(context.short_secs.index).tolist()
# A set of the same securities, sets have faster lookup.
context.security_set = set(context.security_list)
context.run_pipeline = False
Compute weights to our long and short target positions.
# Set the allocations to even weights for each long position, and even weights
# for each short position.
long_weight = context.long_leverage / len(context.long_secs)
short_weight = context.short_leverage / len(context.short_secs)
return long_weight, short_weight
context.run_pipeline = True
This rebalancing function is called according to our schedule_function settings.
long_weight, short_weight = compute_weights(context)
# For each security in our universe, order long or short positions according
# to our context.long_secs and context.short_secs lists.
for stock in context.security_list:
if stock in context.long_secs.index:
elif stock in context.short_secs.index:
# Sell all previously held positions not in our new context.security_list.
for stock in context.portfolio.positions:
if stock not in context.security_set and data.can_trade(stock):
# Log the long and short orders each week.
log.info("This week's longs: "+", ".join([long_.symbol for long_ in context.long_secs.index]))
log.info("This week's shorts: " +", ".join([short_.symbol for short_ in context.short_secs.index]))
def record_vars(context, data):
This function is called at the end of each day and plots certain variables.
# Check how many long and short positions we have.
longs = shorts = 0
for position in context.portfolio.positions.itervalues():
if position.amount > 0:
longs += 1
if position.amount < 0:
shorts += 1
# Record and plot the leverage of our portfolio over time as well as the
# number of long and short positions. Even in minute mode, only the end-of-day
# leverage is plotted.
record(leverage = context.account.leverage, long_count=longs, short_count=shorts)