def DownloadAndGraphStocks(tickerList: list):
for ticker in tickerList:
prices = PricingData(ticker)
print('Loading ' + ticker)
if prices.LoadHistory(True):
print('Calcualting stats ' + ticker)
prices.NormalizePrices()
prices.CalculateStats()
prices.PredictPrices(2, 15)
prices.NormalizePrices()
prices.SaveStatsToFile(True)
psnap = prices.GetCurrentPriceSnapshot()
titleStatistics = ' 5/15 dev: ' + str(
round(psnap.fiveDayDeviation * 100, 2)) + '/' + str(
round(psnap.fifteenDayDeviation * 100, 2)) + '% ' + str(
psnap.low) + '/' + str(
psnap.nextDayTarget) + '/' + str(
psnap.high) + ' ' + str(
psnap.snapshotDate)[:10]
print('Graphing ' + ticker + ' ' + str(psnap.snapshotDate)[:10])
for days in [90, 180, 365, 2190, 4380]:
prices.GraphData(None,
days,
ticker + '_days' + str(days) + ' ' +
titleStatistics, (days < 1000),
True,
str(days).rjust(4, '0') + 'd',
trimHistoricalPredictions=False)
def DownloadAndGraphStocks(tickerList: list):
for ticker in tickerList:
prices = PricingData(ticker)
print('Loading ' + ticker)
if prices.LoadHistory(requestedEndDate=GetTodaysDate()):
print('Calcualting stats ' + ticker)
prices.NormalizePrices()
prices.CalculateStats()
prices.PredictPrices(2, 15)
prices.NormalizePrices()
#prices.SaveStatsToFile(includePredictions=True, verbose=True)
psnap = prices.GetCurrentPriceSnapshot()
titleStatistics = ' 5/15 dev: ' + str(
round(psnap.fiveDayDeviation * 100, 2)) + '/' + str(
round(psnap.fifteenDayDeviation * 100, 2)) + '% ' + str(
psnap.low) + '/' + str(
psnap.nextDayTarget) + '/' + str(
psnap.high) + ' ' + str(
psnap.snapShotDate)[:10]
print('Graphing ' + ticker + ' ' + str(psnap.snapShotDate)[:10])
for days in [90, 180, 365, 2190, 4380]:
prices.GraphData(endDate=None,
daysToGraph=days,
graphTitle=ticker + '_days' + str(days) +
' ' + titleStatistics,
includePredictions=(days < 1000),
saveToFile=True,
fileNameSuffix=str(days).rjust(4, '0') + 'd',
trimHistoricalPredictions=False)
def PlotPrediction(ticker: str = '^SPX',
predictionMethod: int = 0,
daysToGraph: int = 60,
daysForward: int = 5,
learnhingEpochs: int = 500):
print('Plotting predictions for ' + ticker)
prices = PricingData(ticker)
if prices.LoadHistory(True):
prices.NormalizePrices()
prices.PredictPrices(predictionMethod, daysForward, learnhingEpochs)
prices.NormalizePrices()
prices.GraphData(None, daysToGraph,
ticker + ' ' + str(daysToGraph) + 'days', True, True,
str(daysToGraph) + 'days')
prices.SaveStatsToFile(True)
def TestPredictionModels(ticker: str = '^SPX',
numberOfLearningPasses: int = 300):
#Simple procedure to test different prediction methods 4,20,60 days in the future
plot = PlotHelper()
prices = PricingData(ticker)
if prices.LoadHistory():
prices.TrimToDateRange('1/1/2000', '3/1/2018')
print('Loading ' + ticker)
for daysForward in [4, 20, 60]:
for predictionMethod in range(0, 5):
modelDescription = ticker + '_method' + str(
predictionMethod) + '_epochs' + str(
numberOfLearningPasses) + '_daysforward' + str(
daysForward)
print('Predicting ' + str(daysForward) +
' days using method ' + modelDescription)
prices.PredictPrices(predictionMethod, daysForward,
numberOfLearningPasses)
predDF = prices.pricePredictions.copy()
predDF = predDF.join(prices.GetPriceHistory())
predDF['PercentageDeviation'] = abs(
(predDF['Average'] - predDF['estAverage']) /
predDF['Average'])
averageDeviation = predDF['PercentageDeviation'].tail(
round(predDF.shape[0] / 4)).mean(
) #Average of the last 25% to account for training.
print('Average deviation: ', averageDeviation * 100, '%')
predDF.to_csv(dataFolder + modelDescription + '.csv')
plot.PlotDataFrame(predDF[['estAverage', 'Average']],
modelDescription, 'Date', 'Price', True,
dataFolder + modelDescription)
plot.PlotDataFrameDateRange(
predDF[['Average', 'estAverage']], None, 160,
modelDescription + '_last160ays', 'Date', 'Price',
dataFolder + modelDescription + '_last160Days')
plot.PlotDataFrameDateRange(
predDF[['Average', 'estAverage']], None, 500,
modelDescription + '_last500Days', 'Date', 'Price',
dataFolder + modelDescription + '_last500Days')