def get_historical_fundamentals(ticker: str, d: date,
manager: CollectionManager):
"""
Gets all of the fundamental data for a ticker before some date and after
:param ticker: stock ticker
:param d: date
:param manager: collection manager for the fundamentals database
:return: past fundamentals, announcement dates, and future (test) fundamentals
"""
current_day = dt(d.year, d.month, d.day)
allTickersFundamentals = manager.find({
'ticker': ticker,
'date': {
'$lte': current_day
}
}).sort_values('date')
test = manager.find({
'ticker': ticker,
'date': {
'$gte': current_day
}
}).sort_values('date')
return allTickersFundamentals[features], [
announce.date()
for announce in allTickersFundamentals['date'].tolist()
], test
def calculate_performance(ticker, dates1: list, dates2: list):
"""
Gets how much the stock has changed since the last
quarter reportings.
:param ticker: stock ticker
:param date1: beginining of the quarter
:param date2: end of the quarter
:return: percent change in stock price
"""
ticker = ticker.lower()
manager = CollectionManager('5Y_technicals', 'AlgoTradingDB')
prices = manager.find({'ticker': ticker})
dates = [
dt.strptime(priceDate, '%Y-%m-%d').date()
for priceDate in prices['date']
]
pricesStart = [
prices[prices['date'] == str(d1)]['vwap'].values[0]
for d1 in get_next_trading_day(dates, dates1)
]
pricesEnd = [
prices[prices['date'] == str(d2)]['vwap'].values[0]
for d2 in get_next_trading_day(dates, dates2)
]
manager.close()
performances = [((p[0] - p[1]) / p[0])
for p in zip(pricesStart, pricesEnd)]
return performances
def get_all_fundamentals(stocks: list, tradeDate: date):
"""
Gets all of the fundamentals for a list of tickers and list of quarters
:param tickers: stocks
:param quarters: list of quarters
:param final: whether this is a final prediction
:return: Xs and ys
"""
manager = CollectionManager('10y_Fundamentals', 'AlgoTradingDB')
tickers_set = set(stocks)
all_fundamental_tickers = set(manager.find({})["ticker"])
tickers = list(tickers_set.intersection(all_fundamental_tickers))
allFundamentals = pd.DataFrame()
performances = pd.DataFrame()
quarters = 17
allTest = pd.DataFrame()
testDates = []
for ticker in tickers:
data, announcementDates, test = get_historical_fundamentals(
ticker, tradeDate, manager)
nextAnnouncementDates = announcementDates[1:] + [
dt.strptime('2018-02-05', '%Y-%m-%d').date()
]
performance = calculate_performance(ticker, announcementDates,
nextAnnouncementDates)
if len(testDates) == 0:
testDates = test['date'].tolist()
if len(performance) != 17:
performance = performance[len(performance) - 17:]
performances[ticker] = performance
else:
performances[ticker] = performance
for index, funds in data.iterrows():
tempDF = pd.DataFrame()
tempDF['fundamentals'] = list(funds)[:-1]
tempDF['ticker'] = [ticker for i in range(len(funds) - 1)]
tempDF['quarter'] = [index for j in range(len(funds) - 1)]
allFundamentals = pd.concat([allFundamentals, tempDF])
for index, testFunds in test.iterrows():
temp = pd.DataFrame()
temp['fundamentals'] = list(testFunds)[:-1]
temp['ticker'] = [ticker for k in range(len(testFunds) - 1)]
temp['quarter'] = [index for l in range(len(testFunds) - 1)]
allTest = pd.concat([allTest, temp])
manager.close()
trainingData = []
for quarter in range(quarters):
q = []
for ticker in tickers:
tickerdata = allFundamentals[allFundamentals['ticker'] == ticker]
quarterdata = tickerdata[tickerdata['quarter'] ==
quarter]['fundamentals']
q.append(quarterdata.tolist())
trainingData.append(np.array(q))
trainingDataX = np.array(trainingData)
trainingDataY = find_best_stock(performances)
allTestY = []
quarterLen = len(allTest['quarter'].unique())
if quarterLen == 1:
fix = allTest.copy()
fix['quarter'] = [2 for i in range(len(fix))]
allTest = pd.concat([allTest, fix])
for testQuarter in range(2):
testQ = []
for tick in tickers:
tickData = allTest[allTest['ticker'] == tick]
testQuarterData = tickData[tickData['quarter'] ==
testQuarter]['fundamentals']
if testQuarterData.shape[0] != 15:
print('ERROR ' + tick)
testQ.append(testQuarterData.tolist()[:-4])
allTestY.append(np.array(testQ))
return trainingDataX, trainingDataY, np.array(allTestY), testDates, tickers