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Hey all,

I'm new to quantoian and writing Python. I have written in Guass and MatLab, but for some reason, I'm having a little hard time here. The ideology behind my code is to check premarket data, run some fundamentals p/e and current assets>current liabilities. Then update my universe and input some geometric brownian motion to account for volatility. After which the algo is supposed to execute long only as a percentage no more than 20%. Please help as of now I'm crashing line 40. Thanks in advance.

import pandas as pd  
import numpy as np  
import urllib2  # works fine with Python 2.7.9 (not 3.4.+)  
import json  
import time  
#Function Below takes in Premarket data from Google Finance and Needs to be modified to stop running after 9:30am EST

def fetchPreMarket(symbol, exchange):  
    link = "http://finance.google.com/finance/info?client=ig&q="  
    url = link+"%s:%s" % (exchange, symbol)  
    u = urllib2.urlopen(url)  
    content = u.read()  
    data = json.loads(content[3:])  
    info = data[0]  
    t = str(info["elt"])    # time stamp  
    l = float(info["l"])    # close price (previous trading day)  
    p = float(info["el"])   # stock price in pre-market (after-hours)  
    return (t,l,p)  

p0 = 0  
while True:  
    t, l, p = fetchPreMarket("CHK","MU", "RDS.A", "LXRX", "NASDAQ")  
    if(p!=p0):  
        p0 = p  
        print("%s\t%.2f\t%.2f\t%+.2f\t%+.2f%%" % (t, l, p, p-l,  
                                                 (p/l-1)*100.))  
    time.sleep(60)

def before_trading_start(context):  
    # Query for securities based on PE ratio and their economic sector  
    fundamental_df = get_fundamentals(  
        # Retrieve data based on PE ratio and economic sector  
        query(fundamentals.valuation_ratios.pe_ratio,fundamentals.balance_sheet.current_assets, fundamentals.balance_sheet.current_liabilites)  
        )

        # Filter where the Sector code matches our technology sector code  
        # Filter where PE ratio is less than 15  
.filter(fundamentals.valuation_ratios.pe_ratio < 15)
.filter(balance_sheet.current_assests>balance_sheet.current_liablities)


        # Order by low PE ratio and limit to 20 results  
        .order_by(fundamentals.valuation_ratios.pe_ratio.desc() and fundamentals.balance_sheet.current_assests>balance_sheet.current_liablities).limit(20)  
    )  
    update_universe(context.fundamental_df.columns.values)  
 def initialize(context):  
for i in range(total_minutes):  
    # Every 45 minutes run schedule  
    if i % 45 == 0:  
      # This will start at 9:31AM and will run every 30 minutes  
      schedule_function(  
      myfunc,  
        date_rules.every_day(),  
        time_rules.market_open(minutes=i),  
        True  
      )  
       set_universe(universe.DollarVolumeUniverse(floor_percentile=1.0, ceiling_percentile=6.0))  
    context.other_stocks = symbols('MU', 'RDS.A', 'LXRX' , 'CHK')  # add some specific securities  
    #adding in Geometirc Brownian Motion to account for Volutity  
#geometric brownian motion with drift

T = 2  
mu = 0.1  
sigma = 0.01  
S0 = 20  
dt = 0.01  
N = round(T/dt)  
t = np.linspace(0, T, N)  
W = np.random.standard_normal(size = N)  
W = np.cumsum(W)*np.sqrt(dt) ### standard brownian motion ###  
X = (mu-0.5*sigma**2)*t + sigma*W  
S = S0*np.exp(X)  
# the other methods in your algorithm.


  # Algorithm will raise an exception if it attempts to place an  
  # order which would cause us to hold negative shares of any security.  
  set_long_only()

def handle_data(context, data):  
    for stock in data:  
        log.info(str(stock))



# Will be called on every trade event for the securities you specify.  
if stock pass .filter order_percent((sid), .2)  
elif order_percent((sid),.0)


        log.error  
        log.pnl  
        log.cash

5 responses

Gus Gordon

Hi Daniel,
Welcome to Quantopian!

The problem was that your filters were in the wrong spot. They should be within your get_fundamentals function. Also, some of your indentation was off, so I fixed that. For example, you were calling update_universe multiple times, when it only needs to be called once in initialize. I commented out some code at the end too where I wasn't sure what you were trying to do.

Another thing is that your fetchPreMarket function cannot use urllib2. See the modules we allow, and other help information, here. You can import external data with fetcher.

We don't currently support pre-market data, but you could try importing it via fetcher. Another option is to use data from the first bar of the day, or the last bar of the previous day. See the history function to do that.

Let me know if you need help with anything else!

Gus

import pandas as pd  
import numpy as np  
##import urllib2  # works fine with Python 2.7.9 (not 3.4.+)  
import json  
import time

#Function Below takes in Premarket data from Google Finance and Needs to be modified to stop running after 9:30am EST

def fetchPreMarket(symbol, exchange):  
    link = "http://finance.google.com/finance/info?client=ig&q="  
    url = link+"%s:%s" % (exchange, symbol)  
    u = urllib2.urlopen(url)  
    content = u.read()  
    data = json.loads(content[3:])  
    info = data[0]  
    t = str(info["elt"])    # time stamp  
    l = float(info["l"])    # close price (previous trading day)  
    p = float(info["el"])   # stock price in pre-market (after-hours)  
    return (t,l,p)  

p0 = 0  
while True:  
    t, l, p = fetchPreMarket("CHK","MU", "RDS_A", "LXRX", "NASDAQ")  
    if(p!=p0):  
        p0 = p  
        print("%s\t%.2f\t%.2f\t%+.2f\t%+.2f%%" % (t, l, p, p-l,  
                                                 (p/l-1)*100.))  
    time.sleep(60)

def before_trading_start(context):  
    # Query for securities based on PE ratio and their economic sector  
    fundamental_df = get_fundamentals(  
        # Retrieve data based on PE ratio and economic sector  
        query(  
            fundamentals.valuation_ratios.pe_ratio,  
            fundamentals.balance_sheet.current_assets,  
            fundamentals.balance_sheet.current_liabilites  
            )  
        # Filter where the Sector code matches our technology sector code  
        # Filter where PE ratio is less than 15  
        .filter(fundamentals.valuation_ratios.pe_ratio < 15)  
        .filter(balance_sheet.current_assests>balance_sheet.current_liablities)

        # Order by low PE ratio and limit to 20 results  
        .order_by(fundamentals.valuation_ratios.pe_ratio.desc() and fundamentals.balance_sheet.current_assests>balance_sheet.current_liablities).limit(20)  
    )  
    update_universe(context.fundamental_df.columns.values)  
def initialize(context):  
    set_universe(universe.DollarVolumeUniverse(floor_percentile=1.0, ceiling_percentile=6.0))  
    for i in range(total_minutes):  
        # Every 45 minutes run schedule  
        if i % 45 == 0:  
          # This will start at 9:31AM and will run every 30 minutes  
          schedule_function(myfunc, date_rules.every_day(), time_rules.market_open(minutes=i), True)  
        context.other_stocks = symbols('MU', 'RDS.A', 'LXRX' , 'CHK')  # add some specific securities  
        #adding in Geometirc Brownian Motion to account for Volutity  
    #geometric brownian motion with drift

    T = 2  
    mu = 0.1  
    sigma = 0.01  
    S0 = 20  
    dt = 0.01  
    N = round(T/dt)  
    t = np.linspace(0, T, N)  
    W = np.random.standard_normal(size = N)  
    W = np.cumsum(W)*np.sqrt(dt) ### standard brownian motion ###  
    X = (mu-0.5*sigma**2)*t + sigma*W  
    S = S0*np.exp(X)  
    # the other methods in your algorithm.


    # Algorithm will raise an exception if it attempts to place an  
    # order which would cause us to hold negative shares of any security.  
    set_long_only()

def handle_data(context, data):  
    for stock in data:  
        log.info(str(stock))


# Will be called on every trade event for the securities you specify.  
"""if stock pass .filter order_percent((sid), .2)  
elif order_percent((sid),.0)


        log.error  
        log.pnl  
        log.cash"""

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Daniel Landsman

Thanks gus. Super helpful. I really appreciate it.

Daniel Landsman

@gus, I got it sorted to line 22 and it keeps telling me there is a syntax error. I have looked at the API documentation, and I don't understand why it keeps crashing.

import pandas as pd  
import numpy as np  

def before_trading_start(context):  
    # Query for securities based on PE ratio and their economic sector  
    fundamental_df = get_fundamentals(  
        # Retrieve data based on PE ratio and economic sector  
        query(  
            fundamentals.valuation_ratios.pe_ratio,  
            fundamentals.balance_sheet.current_assets,  
            fundamentals.balance_sheet.current_liabilites  
            )  
        # Filter where the Sector code matches our technology sector code  
        # Filter where PE ratio is less than 15  
        .filter(fundamentals.valuation_ratios.pe_ratio < 15)  
        .filter(fundamentals.balance_sheet.current_assests > fundamentals.balance_sheet.current_liablities)

        # Order by low PE ratio and limit to 20 results  
  .order_by(fundamentals.valuation_ratios.pe_ratio.desc()).limit(20) and order_by(fundamentals.balance_sheet.current_assests > fundamentals.balance_sheet.current_liablities.desc()).limit(20)  
    update_universe(context.fundamental_df.columns.values)  


def initialize(context):  
    #only go long securities  
        set_long_only()


def handle_data(context, data):  
    set_universe(context.fundamental_df = fundamental_df)  
        log.info(str(stock))

    for info in range(390):  
        # Every 45 minutes run schedule  
        if i % 45 == 0:  
          # This will start at 9:31AM and will run every 45 minutes  
          schedule_function(myfunc, date_rules.every_day(), time_rules.market_open(minutes=i), True)  

        #adding in Geometirc Brownian Motion to account for Volutity  
    #geometric brownian motion with drift

    T = 2  
    mu = 0.1  
    sigma = 0.01  
    S0 = 20  
    dt = 0.01  
    N = round(T/dt)  
    t = np.linspace(0, T, N)  
    W = np.random.standard_normal(size = N)  
    W = np.cumsum(W)*np.sqrt(dt) ### standard brownian motion ###  
    X = (mu-0.5*sigma**2)*t + sigma*W  
    S = S0*np.exp(X)  
    # the other methods in your algorithm.



# Will be called on every trade event for the securities you specify.  
order_percent((sid),.1)



log.error  
log.pnl  
log.cash

Gus Gordon

Looks like you were just missing a close parentheses. I fixed a couple other things too, but it looks like you're still adjusting your main functions.

import pandas as pd  
import numpy as np  

def before_trading_start(context):  
    # Query for securities based on PE ratio and their economic sector  
    fundamental_df = get_fundamentals(  
        # Retrieve data based on PE ratio and economic sector  
        query(  
            fundamentals.valuation_ratios.pe_ratio,  
            fundamentals.balance_sheet.current_assets,  
            fundamentals.balance_sheet.current_liabilites  
            )  
        # Filter where the Sector code matches our technology sector code  
        # Filter where PE ratio is less than 15  
        .filter(fundamentals.valuation_ratios.pe_ratio < 15)  
        .filter(fundamentals.balance_sheet.current_assests > fundamentals.balance_sheet.current_liablities)

        # Order by low PE ratio and limit to 20 results  
        .order_by(fundamentals.valuation_ratios.pe_ratio.desc()).limit(20) and order_by(fundamentals.balance_sheet.current_assests > fundamentals.balance_sheet.current_liablities.desc()).limit(20))  
    update_universe(context.fundamental_df.columns.values)  


def initialize(context):  
    #only go long securities  
        set_long_only()


def handle_data(context, data):  
    #set_universe(context.fundamental_df = fundamental_df)  
    log.info(str(stock))

    for info in range(390):  
        # Every 45 minutes run schedule  
        if i % 45 == 0:  
          # This will start at 9:31AM and will run every 45 minutes  
          schedule_function(myfunc, date_rules.every_day(), time_rules.market_open(minutes=i), True)  

        #adding in Geometirc Brownian Motion to account for Volutity  
    #geometric brownian motion with drift

    T = 2  
    mu = 0.1  
    sigma = 0.01  
    S0 = 20  
    dt = 0.01  
    N = round(T/dt)  
    t = np.linspace(0, T, N)  
    W = np.random.standard_normal(size = N)  
    W = np.cumsum(W)*np.sqrt(dt) ### standard brownian motion ###  
    X = (mu-0.5*sigma**2)*t + sigma*W  
    S = S0*np.exp(X)  
    # the other methods in your algorithm.



# Will be called on every trade event for the securities you specify.  
order_percent((sid),.1)



log.error  
log.pnl  
log.cash

Disclaimer

Daniel Landsman

Thanks. And yes, still working through the main function.

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