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Noob new help with normalization signals

I cannot get this code to work and I do not understand why. I converted the price to normalization and I was thinking that I would be able to place orders off of it. I keep getting errors on everything that I try to do. Any help is greatly appreciated.

def initialize(context):
context.spy = sid(8554)
schedule_function(rebalance, date_rule=date_rules.every_day())

def rebalance(context,data):
Range = data.history(context.spy,fields='price',
bar_count=200,
frequency='1m')

# normalized data on the es to build signal
normalized = (Range-Range.mean())/Range.std()

if normalized > 1:
order(context.spy,100)

3 responses

The error you are probably seeing is this

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

That is generated on the line

if normalized > 1:

The issue is that 'normalized' is a pandas series and not a scaler value. One can't generally check if a series is greater than a value. That's what the error is trying to convey.

The series 'normalized' contains 200 values. One for each of the 200 days of prices in 'Range'. Each value is normalized per the calculation. Probably what you are most concerned with is the last normalized value. Something like this may be what you want.

if normalized[-1] > 1:

That compares the most current normalized value to 1.

It's very helpful to attach a backtest of ones algo for others to debug. Click the 'Attach' box in the upper right corner of the edit box when writing a post.

Good luck!

Clone Algorithm
3
Loading...
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.
"""
import quantopian.algorithm as algo
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.filters import QTradableStocksUS

def initialize(context):
    context.spy = sid(8554)
    schedule_function(rebalance, date_rule=date_rules.every_day())

def rebalance(context,data):
    Range = data.history(context.spy,fields='price',
    bar_count=200,
    frequency='1m')

    # normalized data on the es to build signal
    normalized = (Range-Range.mean())/Range.std()
    if normalized[-1] > 1:
        order(context.spy,100)
    
There was a runtime error.
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Thank you so much for taking the time to reply!!! I didn't know how to attach a backtest, I will learn that today. And you hit the nail on the head. I am a self-teaching myself to code, so if you have any useful hints that randomly pop in your head, please shoot me a message! It will not fall on deaf ears, I can assure you.

@Josh

Here are a couple of links you may want to check out (thanks to a long time community member Blue Seahawk). Might have ideas for you. Always feel free to post here or send a support request via the 'contact support' link at the bottom right of every page.

https://www.quantopian.com/posts/index-of-tools-blue-seahawk
https://www.quantopian.com/posts/long-and-short-values-counts-and-sids-lists-plus-other-useful-items

Good luck.