Question

1) Create a portfolio class which stores stocks in doubly-linked list.
2) Portfolio class has load and store functions to keep all its stocks on files.
3) How do you prove that the portfolio is indeed linked correctly in both directions? (Want one answer? highlight next line)
how about print and reverse print?
4) Create your own test data such that you can demonstrate stock insertion and deletion at the beginning / middle / end of the portfolio.

Answer 1

Question: 1Create a portfolio class which stores stocks in doubly-linked list Answer def initialize(context): context stocksmbols(CERN, 'DLTR) defhandle data(context, data)://portfolio class in linked list # This will order as many shares as needed to # achieve the desired portfolio allocation. # In our case, we end up with 20% allocation for # one stock and 80% allocation for the other stock.//stocks stores the class details order target percent contextstocks[0],.2) order target percent contextstocks 1],.8) # Plot portfolio allocations pv float(context.portfolio.portfoliovalue)//stock allocation portfolioallocations [1 for stock in context stocks pos context.portfolio.positionslstock] portfolioallocations.append pos.last spos.amount/pv100 record(perc_stock_0-portfolio allocations o, perc_stock_1-portfolio allotons11)

2. Portfolio class has load and store functions to keep all its stocks on files Answer Portfolio.prototype. saveItemsfunction F ( localStorage != null) { // build array with items var items = []; for (var み= 0; < this .items(). Îength;h++) { var item this.items ( ) [İ] ; vax newItem - l : item chart : Item. chart ( ) , shares: item.shares) 1 enm 03 push (newTtem)i load and store functions in stock loca Storagel" items " ] = JSON.stringify (Items); //load store functions in local storage

rval = minmax(data) if rval is None: returo maximums. minimums rval for stock in context.stocks cu..max = maximumslstock] urprice- datalstock].price cur..position contextporttolia.positions[stock] order directioncalculate..direction stock, curmin curmax, curprice, curposition) order.amount calculate.ordeLamounticontext, stock, orderedirection. cu.pnce) # Optional: uncomment the log line below if you're looking for t goingn It will log all the information that is a 'moving part of this # algorithm. Note: if you're doing a full backtest it's a lot of log lines! logmsg. "nfs]: max (m min price p position amountnorderingn shares' log.infollogmsg.format( etail about what's s-stock, n=order, amount order(stock, order amount)

# Here we do our test to see if we should buy or sell or do nothing. This is # the main part of the algorithm. Once we establish a position (long or short) # we use the variable to remember which we took calculate. direction stock, cur min, cur.max, cur price, cur position cur ma ur ma return rections elif cur min is no cur.price cu return 0 # This method is purely for order management. It calculates and returns an order amount to place binary bets. # If signal val is -1, get to a short position f -1 * ontext.bet siz # Jf signal.val is 1, get to a long position of context.bet size def calculate order amount (context, stock, signal val, cur price)

current amountcontext portfolio eositions stock].amount abs der amount= int (context.bet amount / ce if signal val1 return 1absoder amount) - current amount return abs order amount - current amount return 0 # This is our batch transform decorator/declaration. We set the # refresh period and length of the window. In this case, once per day we re Loading # the last 10 trading days and evaluating them. @batch transform (refresh period-1, window length 10) def minmax (datapanel # We are looking for the min and the max price to return. Just # we also are logging the current price. min priceprices_dfminl because it's interesting ces df= datapaneï[ 'price'] max.Price prices df.ma x( if min price is not None andmax pice is not None return (max price, min price) else: retura None

4 Create your own test data such that you can demonstrate stock insertion and deletion at the beginning/middle/end of the portfolio Answer import talib import numpy as np import pandas as pd # Setup our variables def initialize (context): context stockssymbolSPY', 'AAPL, 'GLD', 'AMZN")//insertion # set the percent f the account to be invested per stock ontext ct per stock1. 0 / en (context.stocks) # Create a variable to track the date change context date None todays dateget datetime ).date () # Do nothing unless the date has changed def handle data (context, data) ://deletion if todays date context.date: return # set the new date

context.date todavs date # Load historical data for the stocks high - history (30, '1d', 'high') low-history ( 30, 'id', 'low') close-history ( 30, 'id', 'close price') # Iterate ver ur list of st cks for stock in context stocks: current positioncontextoportfolio positions stock] .amount slowk, slod taliboSTOCH (high[stock], lowIstock], closeLstock], fastk period 5. slow matype-O, lowd period 3 owd ma # get the most recent value slowk slowk[-1] slowd - slewdl-1] //middle insertion # If either the slowk or slowd are less than 10, the stock is # "oversold, ' a long position is pe ned If there are n shares # in the portfolio. slowk < 10 or slowd < 10 and current position <= 0: rdet target percent (stock, context long. pst. pe stock) //end of portfolio in own data # If either the slowk or slowd are larger than 90. the stock 13 # "overbought' and the position is closed. elif slewk > 90 or alwd > 90 and current position 0: order target (stock, 0)