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First attempt at fundamental algo. Having some problems.

I am having problems getting the following fundamental algo to run. Does someone mind helping me?

import pandas as pd
import numpy as np

def initialize(context):
context.stocks = []

def before_trading_start(context, data):
fundamental_df = get_fundamentals(
query(
fundamentals.asset_classification.morningstar_industry_group_code,
fundamentals.company_reference.country_id,
fundamentals.income_statement.operating_revenue,
fundamentals.income_statement.other_operating_revenue,
fundamentals.income_statement.non_operating_income,
fundamentals.income_statement.general_and_administrative_expense,
fundamentals.income_statement.cost_of_revenue,
fundamentals.balance_sheet.real_estate,
fundamentals.balance_sheet.stockholders_equity,
fundamentals.balance_sheet.preferred_securities_outside_stock_equity,
fundamentals.balance_sheet.preferred_stock_equity,
fundamentals.balance_sheet.ordinary_shares_number
)

    .filter(fundamentals.asset_classification.morningstar_industry_group_code == 10428)  
    .filter(fundamentals.company_reference.country_id == "USA")  
    .filter(fundamentals.balance_sheet.ordinary_shares_number > 0)  

)  


context.stocks = []  
for stock in fundamental_df:  
    OR = fundamental_df[stock]['operating_revenue'] or 0  
    CR = fundamental_df[stock]['cost_of_revenue'] or 0  
    SE = fundamental_df[stock]['stockholders_equity'] or 0  
    PSOSE = fundamental_df[stock]['preferred_securities_outside_stock_equity'] or 0  
    PSE = fundamental_df[stock]['preferred_stock_equity'] or 0  
    OSN = fundamental_df[stock]['ordinary_shares_number'] or 0  

    cashFlow = (OR - CR)*4  
    NAV = cashFlow + SE - PSOSE - PSE  
    intrinsicValue = NAV/float(OSN)  
    last = data[stock][symbol].price  

    if last <= .8*intrinsicValue:  
        context.stock.ammend(stock)  

context.fundamental_df = fundamental_df[context.stocks]  
update_universe(context.fundamental_df.columns.values)  

def handle_data(context, data):

if len(context.stocks) == 0:  
    weight = 0  
else:  
    weight = 1.0/len(context.stocks)  


for stock in context.portfolio.positions:  
    order_target_percent(stock, 0)  


for stock in context.stock:  
    if weight != 0:  
        order_target_percent(stock, weight)  
1 response

You're on the right track! On line 44, this is the proper syntax to query for the current stock price:

last = data[stock].price  

The KeyError you're seeing in the fundamental query is because you're requesting a stock price in a bar the stock didn't trade. Hence, there's no price to return. Since every stock doesn't trade every minute, you need to add a guard:

 for stock in fundamental_df:  
        if stock in data:  
          last = data[stock].price  

I made these minor adjustments and now the code compiles.

Clone Algorithm
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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
# Backtest ID: 56844a348b1bd711c82153fc
There was a runtime error.
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