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Conversion of Quantopian to Quantconnect

Hello everyone, 

Im very new to coding. I have been trying to backtest the following code on Quantconnect and move it from Quantopian to Quantconnect. I have been having trouble finding resources for the datasets quantopian uses in quantconnect. Feel free to run backtests on this and if anyone can help me it would be greatly appreciated! 

 

from quantopian.pipeline import Pipeline
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.data import morningstar
from quantopian.pipeline.factors import SimpleMovingAverage, AverageDollarVolume
from quantopian.pipeline.filters.morningstar import IsPrimaryShare

import numpy as np #needed for NaN handling
import math #ceil and floor are useful for rounding

from itertools import cycle

def initialize(context):
    #set_commission(commission.PerShare(cost=0.01, min_trade_cost=1.50))
    set_slippage(slippage.VolumeShareSlippage(volume_limit=.20, price_impact=0.0))
    #set_slippage(slippage.FixedSlippage(spread=0.00))
    set_commission(commission.PerTrade(cost=0.00))
    #set_slippage(slippage.FixedSlippage(spread=0.00))
    set_long_only()

    context.MaxCandidates=100
    context.MaxBuyOrdersAtOnce=30
    context.MyLeastPrice=3.00
    context.MyMostPrice=20.00
    context.MyFireSalePrice=context.MyLeastPrice
    context.MyFireSaleAge=6

    # over simplistic tracking of position age
    context.age={}
    print len(context.portfolio.positions)

    # Rebalance
    EveryThisManyMinutes=10
    TradingDayHours=6.5
    TradingDayMinutes=int(TradingDayHours*60)
    for minutez in xrange(
        1, 
        TradingDayMinutes, 
        EveryThisManyMinutes
    ):
        schedule_function(my_rebalance, date_rules.every_day(), time_rules.market_open(minutes=minutez))

    # Prevent excessive logging of canceled orders at market close.
    schedule_function(cancel_open_orders, date_rules.every_day(), time_rules.market_close(hours=0, minutes=1))

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

    # Create our pipeline and attach it to our algorithm.
    my_pipe = make_pipeline(context)
    attach_pipeline(my_pipe, 'my_pipeline')

def make_pipeline(context):
    """
    Create our pipeline.
    """

    # Filter for primary share equities. IsPrimaryShare is a built-in filter.
    primary_share = IsPrimaryShare()

    # Equities listed as common stock (as opposed to, say, preferred stock).
    # 'ST00000001' indicates common stock.
    common_stock = morningstar.share_class_reference.security_type.latest.eq('ST00000001')

    # Non-depositary receipts. Recall that the ~ operator inverts filters,
    # turning Trues into Falses and vice versa
    not_depositary = ~morningstar.share_class_reference.is_depositary_receipt.latest

    # Equities not trading over-the-counter.
    not_otc = ~morningstar.share_class_reference.exchange_id.latest.startswith('OTC')

    # Not when-issued equities.
    not_wi = ~morningstar.share_class_reference.symbol.latest.endswith('.WI')

    # Equities without LP in their name, .matches does a match using a regular
    # expression
    not_lp_name = ~morningstar.company_reference.standard_name.latest.matches('.* L[. ]?P.?$')

    # Equities with a null value in the limited_partnership Morningstar
    # fundamental field.
    not_lp_balance_sheet = morningstar.balance_sheet.limited_partnership.latest.isnull()

    # Equities whose most recent Morningstar market cap is not null have
    # fundamental data and therefore are not ETFs.
    have_market_cap = morningstar.valuation.market_cap.latest.notnull()

    # At least a certain price
    price = USEquityPricing.close.latest
    AtLeastPrice   = (price >= context.MyLeastPrice)
    AtMostPrice    = (price <= context.MyMostPrice)

    # Filter for stocks that pass all of our previous filters.
    tradeable_stocks = (
        primary_share
        & common_stock
        & not_depositary
        & not_otc
        & not_wi
        & not_lp_name
        & not_lp_balance_sheet
        & have_market_cap
        & AtLeastPrice
        & AtMostPrice
    )

    LowVar=6
    HighVar=40

    log.info('\nAlgorithm initialized variables:\n context.MaxCandidates %s \n LowVar %s \n HighVar %s'
        % (context.MaxCandidates, LowVar, HighVar)
    )

    # High dollar volume filter.
    base_universe = AverageDollarVolume(
        window_length=20,
        mask=tradeable_stocks
    ).percentile_between(LowVar, HighVar)

    # Short close price average.
    ShortAvg = SimpleMovingAverage(
        inputs=[USEquityPricing.close],
        window_length=3,
        mask=base_universe
    )

    # Long close price average.
    LongAvg = SimpleMovingAverage(
        inputs=[USEquityPricing.close],
        window_length=45,
        mask=base_universe
    )

    percent_difference = (ShortAvg - LongAvg) / LongAvg

    # Filter to select securities to long.
    stocks_worst = percent_difference.bottom(context.MaxCandidates)
    securities_to_trade = (stocks_worst)

    return Pipeline(
        columns={
            'stocks_worst': stocks_worst
        },
        screen=(securities_to_trade),
    )

def my_compute_weights(context):
    """
    Compute ordering weights.
    """
    # Compute even target weights for our long positions and short positions.
    stocks_worst_weight = 1.00/len(context.stocks_worst)

    return stocks_worst_weight

def before_trading_start(context, data):
    # Gets our pipeline output every day.
    context.output = pipeline_output('my_pipeline')

    context.stocks_worst = context.output[context.output['stocks_worst']].index.tolist()

    context.stocks_worst_weight = my_compute_weights(context)

    context.MyCandidate = cycle(context.stocks_worst)
    
    context.LowestPrice=context.MyLeastPrice #reset beginning of day
    print len(context.portfolio.positions)
    for stock in context.portfolio.positions:
        CurrPrice = float(data.current([stock], 'price'))
        if CurrPrice<context.LowestPrice:
            context.LowestPrice = CurrPrice
        if stock in context.age:
            context.age[stock] += 1
        else:
            context.age[stock] = 1
    for stock in context.age:
        if stock not in context.portfolio.positions:
            context.age[stock] = 0
        message = 'stock.symbol: {symbol}  :  age: {age}'
        log.info(message.format(symbol=stock.symbol, age=context.age[stock]))

    pass

def my_rebalance(context, data):
    BuyFactor=.99
    SellFactor=1.01
    cash=context.portfolio.cash

    cancel_open_buy_orders(context, data)

    # Order sell at profit target in hope that somebody actually buys it
    for stock in context.portfolio.positions:
        if not get_open_orders(stock):
            StockShares = context.portfolio.positions[stock].amount
            CurrPrice = float(data.current([stock], 'price'))
            CostBasis = float(context.portfolio.positions[stock].cost_basis)
            SellPrice = float(make_div_by_05(CostBasis*SellFactor, buy=False))
            
            
            if np.isnan(SellPrice) :
                pass # probably best to wait until nan goes away
            elif (stock in context.age and context.age[stock] == 1) :
                pass
            elif (
                stock in context.age 
                and context.MyFireSaleAge<=context.age[stock] 
                and (
                    context.MyFireSalePrice>CurrPrice
                    or CostBasis>CurrPrice
                )
            ):
                if (stock in context.age and context.age[stock] < 2) :
                    pass
                elif stock not in context.age:
                    context.age[stock] = 1
                else:
                    SellPrice = float(make_div_by_05(.95*CurrPrice, buy=False))
                    order(stock, -StockShares,
                        style=LimitOrder(SellPrice)
                    )
            else:
                if (stock in context.age and context.age[stock] < 2) :
                    pass
                elif stock not in context.age:
                    context.age[stock] = 1
                else:
                
                    order(stock, -StockShares,
                        style=LimitOrder(SellPrice)
                    )

    WeightThisBuyOrder=float(1.00/context.MaxBuyOrdersAtOnce)
    for ThisBuyOrder in range(context.MaxBuyOrdersAtOnce):
        stock = context.MyCandidate.next()
        PH = data.history([stock], 'price', 20, '1d')
        PH_Avg = float(PH.mean())
        CurrPrice = float(data.current([stock], 'price'))
        if np.isnan(CurrPrice):
            pass # probably best to wait until nan goes away
        else:
            if CurrPrice > float(1.25*PH_Avg):
                BuyPrice=float(CurrPrice)
            else:
                BuyPrice=float(CurrPrice*BuyFactor)
            BuyPrice=float(make_div_by_05(BuyPrice, buy=True))
            StockShares = int(WeightThisBuyOrder*cash/BuyPrice)
            order(stock, StockShares,
                style=LimitOrder(BuyPrice)
            )

#if cents not divisible by .05, round down if buy, round up if sell
def make_div_by_05(s, buy=False):
    s *= 20.00
    s =  math.floor(s) if buy else math.ceil(s)
    s /= 20.00
    return s

def my_record_vars(context, data):
    """
    Record variables at the end of each day.
    """

    # Record our variables.
    record(leverage=context.account.leverage)
    record(positions=len(context.portfolio.positions))
    if 0<len(context.age):
        MaxAge=context.age[max(context.age.keys(), key=(lambda k: context.age[k]))]
        print MaxAge
        record(MaxAge=MaxAge)
    record(LowestPrice=context.LowestPrice)

def log_open_order(StockToLog):
    oo = get_open_orders()
    if len(oo) == 0:
        return
    for stock, orders in oo.iteritems():
        if stock == StockToLog:
            for order in orders:
                message = 'Found open order for {amount} shares in {stock}'
                log.info(message.format(amount=order.amount, stock=stock))

def log_open_orders():
    oo = get_open_orders()
    if len(oo) == 0:
        return
    for stock, orders in oo.iteritems():
        for order in orders:
            message = 'Found open order for {amount} shares in {stock}'
            log.info(message.format(amount=order.amount, stock=stock))

def cancel_open_buy_orders(context, data):
    oo = get_open_orders()
    if len(oo) == 0:
        return
    for stock, orders in oo.iteritems():
        for order in orders:
            #message = 'Canceling order of {amount} shares in {stock}'
            #log.info(message.format(amount=order.amount, stock=stock))
            if 0<order.amount: #it is a buy order
                cancel_order(order)

def cancel_open_orders(context, data):
    oo = get_open_orders()
    if len(oo) == 0:
        return
    for stock, orders in oo.iteritems():
        for order in orders:
            #message = 'Canceling order of {amount} shares in {stock}'
            #log.info(message.format(amount=order.amount, stock=stock))
            cancel_order(order)

# This is the every minute stuff
def handle_data(context, data):
    pass

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The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


Update Backtest





0

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by QuantConnect. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. QuantConnect makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances. All investments involve risk, including loss of principal. You should consult with an investment professional before making any investment decisions.


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