Ejemplo n.º 1
0
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 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 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 CalculatePriceCorrelation(tickerList: list):
    datafileName = 'data/_priceCorrelations.csv'
    summaryfileName = 'data/_priceCorrelationTop10.txt'
    result = pandas.DataFrame()
    startDate = str(datetime.datetime.now().date() +
                    datetime.timedelta(days=-365))
    endDate = str(datetime.datetime.now().date())
    for ticker in tickerList:
        prices = PricingData(ticker)
        print('Loading ' + ticker)
        if prices.LoadHistory(True):
            prices.TrimToDateRange(startDate, endDate)
            prices.NormalizePrices()
            result[ticker] = prices.GetPriceHistory(['Average'])
    result = result.corr()
    result.to_csv(datafileName)

    f = open(summaryfileName, 'w')
    for ticker in tickerList:
        topTen = result.nsmallest(10, ticker)
        print(topTen[ticker])
        f.write(ticker + '\n')
        f.write(topTen[ticker].to_string(header=True, index=True) + '\n')
        f.write('\n')
    f.close()
Ejemplo n.º 5
0
def SampleCNN(ticker: str):
    #Print sample CNN graphs of ticker, CNN will treat price data as picture and anticipate the next picture
    plot = PlotHelper()
    prices = PricingData(ticker)
    print('Loading ' + ticker)
    if prices.LoadHistory():
        prices.NormalizePrices()
        window_size = 80
        target_size = 10
        daysInTraining = 800
        sampleData = prices.GetPriceHistory()
        endDate = sampleData.index.max()
        cuttoffDate = endDate - BDay(window_size)
        startDate = cuttoffDate - BDay(daysInTraining)
        print(dataFolder + 'samples\CNNsampleLearning', startDate, cuttoffDate,
              endDate)
        for i in range(0, 10):
            ii = i * window_size
            d1 = startDate + BDay(ii)
            d2 = d1 + BDay(target_size)
            print(d1, d2, window_size, target_size)
            plot.PlotDataFrameDateRange(
                sampleData[['Average']], d1, window_size,
                'Sample image ' + str(i), 'Date', 'Price',
                dataFolder + 'samples/CNN' + str(i) + 'Sample')
            plot.PlotDataFrameDateRange(
                sampleData[['Average']], d2, target_size,
                'Target image ' + str(i), 'Date', 'Price',
                dataFolder + 'samples/CNN' + str(i) + 'Target')
Ejemplo n.º 6
0
def CalculatePriceCorrelation(tickerList: list):
    datafileName = 'data/_priceCorrelations.csv'
    summaryfileName = 'data/_priceCorrelationTop10.txt'
    result = None
    startDate = str(AddDays(GetTodaysDate(), -365))
    endDate = str(GetTodaysDate())
    for ticker in tickerList:
        prices = PricingData(ticker)
        print('Loading ' + ticker)
        if prices.LoadHistory(requestedEndDate=GetTodaysDate()):
            prices.TrimToDateRange(startDate, endDate)
            prices.NormalizePrices()
            x = prices.GetPriceHistory(['Average'])
            x.rename(index=str, columns={"Average": ticker}, inplace=True)
            if result is None:
                result = x
            else:
                result = result.join(x, how='outer')
    result = result.corr()
    result.to_csv(datafileName)

    f = open(summaryfileName, 'w')
    for ticker in tickerList:
        topTen = result.nsmallest(10, ticker)
        print(topTen[ticker])
        f.write(ticker + '\n')
        f.write(topTen[ticker].to_string(header=True, index=True) + '\n')
        f.write('\n')
    f.close()
    print(
        'Intended to create stability, in practice, this is a great way to pair well performing stocks with poor performing or volatile stocks.'
    )
Ejemplo n.º 7
0
def CalculatePriceCorrelation(tickerList: list):
    datafileName = 'data/_priceCorrelations.csv'
    summaryfileName = 'data/_priceCorrelationTop10.txt'
    result = None
    startDate = str(datetime.datetime.now().date() +
                    datetime.timedelta(days=-365))
    endDate = str(datetime.datetime.now().date())
    for ticker in tickerList:
        prices = PricingData(ticker)
        print('Loading ' + ticker)
        if prices.LoadHistory():
            prices.TrimToDateRange(startDate, endDate)
            prices.NormalizePrices()
            x = prices.GetPriceHistory(['Average'])
            x.rename(index=str, columns={"Average": ticker}, inplace=True)
            if result is None:
                result = x
            else:
                result = result.join(x, how='outer')
    result = result.corr()
    result.to_csv(datafileName)

    f = open(summaryfileName, 'w')
    for ticker in tickerList:
        topTen = result.nsmallest(10, ticker)
        print(topTen[ticker])
        f.write(ticker + '\n')
        f.write(topTen[ticker].to_string(header=True, index=True) + '\n')
        f.write('\n')
    f.close()
Ejemplo n.º 8
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def RunPredictions(ticker: str = '^SPX', numberOfLearningPasses: int = 750):
    #Runs three prediction models (Linear, LSTM, CCN) predicting a target price 4, 20, and 60 days in the future.
    prices = PricingData(ticker)
    print('Loading ' + ticker)
    if prices.LoadHistory():
        prices.TrimToDateRange('1/1/1950', '3/1/2018')
        prices.NormalizePrices()
        for ii in [4, 20, 60]:
            for i in range(0, 3):
                PredictPrices(prices, i, ii, numberOfLearningPasses)
Ejemplo n.º 9
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def RunPredictions(ticker: str = '^SPX', numberOfLearningPasses: int = 750):
    prices = PricingData(ticker)
    CreateFolder(dataFolder)
    print('Loading ' + ticker)
    if prices.LoadHistory():
        prices.TrimToDateRange('1/1/1950', '3/1/2018')
        prices.NormalizePrices()
        for ii in [4, 20, 60]:
            for i in range(0, 3):
                PredictPrices(prices, i, ii, numberOfLearningPasses)
Ejemplo n.º 10
0
def SampleGraphs(ticker:str, daysInGraph:int):
	plot = PlotHelper()
	prices = PricingData(ticker)
	print('Loading ' + ticker)
	if prices.LoadHistory(True):
		prices.NormalizePrices()
		sampleData = prices.GetPriceHistory()
		d = sampleData.index[-1]  
		for i in range(0,200, 10): 	 #Add new days to the end for crystal ball predictions
			sampleDate = d - BDay(i) #pick business day to plot
			plot.PlotDataFrameDateRange(sampleData[['Open','High', 'Low','Close']], sampleDate, daysInGraph, 'Sample window ' + str(daysInGraph), 'Date', 'Price', dataFolder + 'samples/sample' + str(i) + '_' + str(daysInGraph)) 
Ejemplo n.º 11
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def SampleLSTM(ticker:str):
	plot = PlotHelper()
	prices = PricingData(ticker)
	print('Loading ' + ticker)
	CreateFolder(dataFolder + 'samples')
	if prices.LoadHistory(True):
		prices.NormalizePrices()
		daysInTarget = 15
		daysInTraining = 200
		sampleData = prices.GetPriceHistory()
		endDate  = sampleData.index.max()
		cuttoffDate = endDate - BDay(daysInTarget)
		startDate = cuttoffDate - BDay(daysInTraining)
		print(dataFolder + 'samples/LSTMsampleLearning', startDate, cuttoffDate, endDate)
		plot.PlotDataFrameDateRange(sampleData[['Average']], cuttoffDate, daysInTraining, 'Learn from this series of days', 'Date', 'Price', dataFolder + 'samples/LSTMLearning') 
		plot.PlotDataFrameDateRange(sampleData[['Average']], endDate, daysInTarget, 'Predict what happens after this series of days', 'Date', 'Price', dataFolder + 'samples/LSTMTarget') 
Ejemplo n.º 12
0
def TrainTickerRaw(ticker: str = '^SPX',
                   UseLSTM: bool = True,
                   prediction_target_days: int = 5,
                   epochs: int = 500,
                   usePercentages: bool = False,
                   hidden_layer_size: int = 512,
                   dropout: bool = True,
                   dropout_rate: float = 0.01,
                   learning_rate: float = 2e-5):
    plot = PlotHelper()
    prices = PricingData(ticker)
    print('Loading ' + ticker)
    if prices.LoadHistory(True):
        prices.TrimToDateRange('1/1/2000', '3/1/2018')
        if usePercentages:
            prices.ConvertToPercentages(
            )  #Percentages don't work well I suspect because small errors have a huge impact when you revert back to the original prices and they roll forward
        else:
            prices.NormalizePrices()
        prices.CalculateStats()
        model = StockPredictionNN(baseModelName=ticker, UseLSTM=UseLSTM)
        if UseLSTM:
            window_size = 1
            modelDescription = ticker + '_LSTM'
            modelDescription += '_epochs' + str(epochs) + '_histwin' + str(
                window_size) + '_daysforward' + str(prediction_target_days)
            if usePercentages: modelDescription += '_percentages'
            FieldList = ['Average']
            model.LoadSource(sourceDF=prices.GetPriceHistory(),
                             FieldList=FieldList,
                             window_size=window_size)
            model.LoadTarget(targetDF=None,
                             prediction_target_days=prediction_target_days)
            model.MakeBatches(batch_size=128, train_test_split=.93)
            model.BuildModel(layer_count=1,
                             hidden_layer_size=hidden_layer_size,
                             dropout=dropout,
                             dropout_rate=dropout_rate,
                             learning_rate=learning_rate)
            model.DisplayModel()
            model.Train(epochs=epochs)
            model.Predict(True)
            model.Save()
            #model.DisplayDataSample()
        else:  #CNN
            window_size = 16 * prediction_target_days
            modelDescription = ticker + '_CNN'
            modelDescription += '_epochs' + str(epochs) + '_histwin' + str(
                window_size) + '_daysforward' + str(prediction_target_days)
            if usePercentages: modelDescription += '_percentages'
            #FieldList = None
            FieldList = ['High', 'Low', 'Open', 'Close']
            model.LoadSource(sourceDF=prices.GetPriceHistory(),
                             FieldList=FieldList,
                             window_size=window_size)
            model.LoadTarget(targetDF=None,
                             prediction_target_days=prediction_target_days)
            model.MakeBatches(batch_size=64, train_test_split=.93)
            model.BuildModel(layer_count=1,
                             hidden_layer_size=hidden_layer_size,
                             dropout=dropout,
                             dropout_rate=dropout_rate,
                             learning_rate=learning_rate)
            model.DisplayModel()
            model.Train(epochs=epochs)
            model.Predict(True)
            model.Save()
        if usePercentages:
            predDF = model.GetTrainingResults(True, True)
            predDF = predDF.loc[:, ['Average', 'Average_Predicted']]
            print('Unraveling percentages..')
            predDF['Average_Predicted'].fillna(0, inplace=True)
            predDF.iloc[0] = prices.CTPFactor['Average']
            for i in range(1, predDF.shape[0]):
                predDF.iloc[i] = (1 + predDF.iloc[i]) * predDF.iloc[i - 1]
            print(predDF)
            predDF['PercentageDeviation'] = abs(
                (predDF['Average'] - predDF['Average_Predicted']) /
                predDF['Average'])
            predDF.to_csv(dataFolder + modelDescription + '.csv')
            plot.PlotDataFrame(predDF[['Average', 'Average_Predicted']],
                               modelDescription, 'Date', 'Price', True,
                               dataFolder + modelDescription)
            plot.PlotDataFrameDateRange(
                predDF[['Average', 'Average_Predicted']], None, 160,
                modelDescription + '_last160ays', 'Date', 'Price',
                dataFolder + modelDescription + '_last160Days')
            plot.PlotDataFrameDateRange(
                predDF[['Average', 'Average_Predicted']], None, 500,
                modelDescription + '_last500Days', 'Date', 'Price',
                dataFolder + modelDescription + '_last500Days')
        else:
            model.PredictionResultsSave(modelDescription, True, True)
            model.PredictionResultsPlot(modelDescription, True, False)