Help needed with momentum rotation strategy

I'm just learning python and cannot get this code to run properly.
Can anyone identify what I am doing incorrectly in the code

Heres what I am trying to do

1. take the daily log returns of XLF and XLU
2. subtract XLU daily log return from XLF log return. (how much more XLF has returned each day)
3. take the cumulative sum
4. get the 200 day moving average of the cumulative sum
5. if the cumulative sum is greater than its 200 day moving average then buy XLF otherwise buy XLU

edit: fyi, the problem in the attached algorithm is that it doesn't rotate between ETFs. I think something is wrong with the IF statement, I cant figure out what I need to do to fix the code though

5
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
 Returns 1 Month 3 Month 6 Month 12 Month
 Alpha 1 Month 3 Month 6 Month 12 Month
 Beta 1 Month 3 Month 6 Month 12 Month
 Sharpe 1 Month 3 Month 6 Month 12 Month
 Sortino 1 Month 3 Month 6 Month 12 Month
 Volatility 1 Month 3 Month 6 Month 12 Month
 Max Drawdown 1 Month 3 Month 6 Month 12 Month
"""
This is a template algorithm on Quantopian for you to adapt and fill in.
"""
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
import quantopian.pipeline.filters as Filters
from datetime import datetime, timedelta
import numpy as np
import pandas as pd

def initialize(context):

#context.spy = sid(8554)
#context.shy = sid(23921)
context.xlf = [sid(19656)]
context.xlu = [sid(19660)]

"""
Called once at the start of the algorithm.
"""
# Rebalance every day, 1 hour after market open.
schedule_function(
rebalance,
date_rules.every_day(),
time_rules.market_open(hours=1),
)

# Record tracking variables at the end of each day.
schedule_function(
record_vars,
date_rules.every_day(),
time_rules.market_close(),
)

# Create our dynamic stock selector.
attach_pipeline(make_pipeline(context), 'pipeline')

def make_pipeline(context):
"""
A function to create our dynamic stock selector (pipeline). Documentation
on pipeline can be found here:
https://www.quantopian.com/help#pipeline-title
"""

# Base universe set to the QTradableStocksUS
universe = Filters.StaticAssets(context.xlf)

# Factor of yesterday's close price.
yesterday_close = USEquityPricing.close.latest

pipe = Pipeline(
columns={
'close': yesterday_close,
},
screen=universe
)
return pipe

"""
Called every day before market open.
"""
context.output = pipeline_output('pipeline')

# These are the securities that we are interested in trading each day.
context.security_list = context.output.index.tolist()

def rebalance(context, data):

XLF = data.history(context.xlf, "price", 600, "1d")
XLU = data.history(context.xlu, "price", 600, "1d")

XLF_pct_change = XLF.iloc[[0, -1]].pct_change() + 1
XLU_pct_change = XLU.iloc[[0, -1]].pct_change() + 1

XLF_log_change = np.log(XLF_pct_change)
XLU_log_change = np.log(XLU_pct_change)

XLF_XLU = XLF_log_change.ix[-1] - XLU_log_change.ix[-1]

price = XLF_XLU.cumsum(axis = 0)
ma_price = price.rolling(5).mean()
above_ma = np.where(price>=ma_price, True, False)

if above_ma[-1]:
order_target_percent(sid(19656), 1.0)
else:
order_target_percent(sid(19660), 1.0)
return

"""
Execute orders according to our schedule_function() timing.
"""
pass

def record_vars(context, data):
"""
Plot variables at the end of each day.
"""
pass

def handle_data(context, data):
"""
Called every minute.
"""
pass
There was a runtime error.
3 responses

this notebook shows what I am trying to do.

1
Notebook previews are currently unavailable.

Hi Patrick,

It looks like the issue occurs when you subtract XLU_log_change from XLF_log_change. In the way it's currently implemented, it will return NaNs for every day -- you can verify this by setting a breakpoint after line 87 and checking XLF_XLU.

I've attached a modified version of your algorithm that fixes this problem. (You'll probably still need to tweak it a bit to get exactly what you want, though.)

12
Backtest from to with initial capital
Total Returns
--
Alpha
--
Beta
--
Sharpe
--
Sortino
--
Max Drawdown
--
Benchmark Returns
--
Volatility
--
 Returns 1 Month 3 Month 6 Month 12 Month
 Alpha 1 Month 3 Month 6 Month 12 Month
 Beta 1 Month 3 Month 6 Month 12 Month
 Sharpe 1 Month 3 Month 6 Month 12 Month
 Sortino 1 Month 3 Month 6 Month 12 Month
 Volatility 1 Month 3 Month 6 Month 12 Month
 Max Drawdown 1 Month 3 Month 6 Month 12 Month
"""
This is a template algorithm on Quantopian for you to adapt and fill in.
"""
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
import quantopian.pipeline.filters as Filters
from datetime import datetime, timedelta
import numpy as np
import pandas as pd

def initialize(context):

#context.spy = sid(8554)
#context.shy = sid(23921)
context.xlf = [sid(19656)]
context.xlu = [sid(19660)]

"""
Called once at the start of the algorithm.
"""
# Rebalance every day, 1 hour after market open.
schedule_function(
rebalance,
date_rules.every_day(),
time_rules.market_open(hours=1),
)

# Record tracking variables at the end of each day.
schedule_function(
record_vars,
date_rules.every_day(),
time_rules.market_close(),
)

# Create our dynamic stock selector.
attach_pipeline(make_pipeline(context), 'pipeline')

def make_pipeline(context):
"""
A function to create our dynamic stock selector (pipeline). Documentation
on pipeline can be found here:
https://www.quantopian.com/help#pipeline-title
"""

# Base universe set to the QTradableStocksUS
universe = Filters.StaticAssets(context.xlf)

# Factor of yesterday's close price.
yesterday_close = USEquityPricing.close.latest

pipe = Pipeline(
columns={
'close': yesterday_close,
},
screen=universe
)
return pipe

"""
Called every day before market open.
"""
context.output = pipeline_output('pipeline')

# These are the securities that we are interested in trading each day.
context.security_list = context.output.index.tolist()

def rebalance(context, data):

# Get series of XLF/XLU prices
XLF = data.history(context.xlf, "price", 600, "1d")
XLU = data.history(context.xlu, "price", 600, "1d")

# Calculate percent change (and add 100%)
XLF_pct_change = XLF.pct_change()
XLF_pct_change += 1
XLU_pct_change = XLU.pct_change()
XLU_pct_change += 1

# Calculate log returns
XLF_log_change = np.log(XLF_pct_change)
XLU_log_change = np.log(XLU_pct_change)

# Rename columns so we can subtract properly
XLF_log_change.columns = ['log_change']
XLU_log_change.columns = ['log_change']

# Calculate difference in log returns
XLF_XLU = XLF_log_change.subtract(XLU_log_change)

# Calculate cumulative sum
price = XLF_XLU.cumsum(axis = 0)
ma_price = price.rolling(5).mean()
above_ma = np.where(price>=ma_price, True, False)

if above_ma[-1]:
order_target_percent(sid(19656), 1.0)
else:
order_target_percent(sid(19660), 1.0)
return

def record_vars(context, data):
"""
Plot variables at the end of each day.
"""
pass

def handle_data(context, data):
"""
Called every minute.
"""
pass
There was a runtime error.
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Lucy, thank you for taking the time to help. This is the syntax I was looking for