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pickstock.py File Reference
Go to the source code of this file.
Variables | |
| GamsInteractive | pickstock.gams = GamsInteractive() |
| str | pickstock.url = "https://github.com/daveh19/pydataberlin2017/raw/master/notebooks/dowjones2016.csv" |
| pd | pickstock.price_data = pd.read_csv(url) |
| GamsInteractive | pickstock.m = gams.exchange_container |
| GamsInteractive | pickstock.date = m.addSet('date', description='trading date') |
| GamsInteractive | pickstock.symbol = m.addSet('symbol', description='stock symbol') |
| GamsInteractive | pickstock.price = m.addParameter('price', [date, symbol], domain_forwarding=True, records=price_data, description='price of stock on date') |
| GamsInteractive | pickstock.d = m.addAlias('d', date) |
| GamsInteractive | pickstock.s = m.addAlias('s', symbol) |
| GamsInteractive | pickstock.avgprice = m.addParameter('avgprice', [symbol], description='average price of stock') |
| The mean price per stock is calculated in GAMS. | |
| GamsInteractive | pickstock.weight = m.addParameter('weight', [symbol], description='weight of stock') |
| The averages can be used in order to calculate weights. | |
| GamsInteractive | pickstock.contribution = m.addParameter('contribution', [date,symbol]) |
| Compute the contributions using weight and price. | |
| GamsInteractive | pickstock.index = m.addParameter('index', [date], description='Dow Jones index') |
| Compute index values. | |
| GamsInteractive | pickstock.trainingdays = m.addParameter('trainingdays', records = 100) |
| Create a plot showing the symbol and index values over time. | |
| GamsInteractive | pickstock.maxstock = m.addParameter('maxstock', records = 3, description='maximum number of stocks to select') |
| GamsInteractive | pickstock.ds = m.addSet('ds', [date], description='selected dates') |
| GamsInteractive | pickstock.p = m.addVariable('p', 'binary', [symbol], description = 'is stock included?') |
| Declaration of the variables and equations used to formulate the optimization model. | |
| GamsInteractive | pickstock.w = m.addVariable('w', 'positive', [symbol], description = 'what part of the portfolio') |
| GamsInteractive | pickstock.slpos = m.addVariable('slpos', 'positive', [date], description = 'positive slack') |
| GamsInteractive | pickstock.slneg = m.addVariable('slneg', 'positive', [date], description = 'negative slack') |
| GamsInteractive | pickstock.obj = m.addVariable('obj', 'free', description = 'objective') |
| GamsInteractive | pickstock.fund = m.addParameter('fund', [date], description='Index fund report parameter') |
| Defining the actual model We know come to the decision problem, where we want to pick a small subset of the stocks together with some weights, such that this portfolio has a similar behavior to our overall Dow Jones index. | |
| GamsInteractive | pickstock.error = m.addParameter('error', [date], description='Absolute error') |
| pickstock.closedown | |
| Plotting of the results. | |