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 DownloadAndSaveStocksWithStats(tickerList: list):
for ticker in tickerList:
prices = PricingData(ticker)
print('Loading ' + ticker)
if prices.LoadHistory(requestedEndDate=GetTodaysDate()):
print('Calcualting stats ' + ticker)
prices.CalculateStats()
prices.SaveStatsToFile(includePredictions=False, verbose=True)
def OpportunityFinder(tickerList: list):
outputFolder = 'data/dailypicks/'
summaryFile = '_summary.txt'
overBoughtList = []
oversoldList = []
highDeviationList = []
for root, dirs, files in os.walk(outputFolder):
for f in files:
if f.endswith('.txt') or f.endswith('.png'):
os.unlink(os.path.join(root, f))
for ticker in tickerList:
prices = PricingData(ticker)
print('Checking ' + ticker)
if prices.LoadHistory(True):
prices.CalculateStats()
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(snapshotDate)
if psnap.low > psnap.channelHigh:
overBoughtList.append(ticker)
if psnap.high < psnap.channelLow:
oversoldList.append(ticker)
prices.GraphData(None, 60, ticker + ' 60d ' + titleStatistics,
False, True, '60d', outputFolder)
if psnap.fiveDayDeviation > .0275:
highDeviationList.append(ticker)
prices.GraphData(None, 60, ticker + ' 60d ' + titleStatistics,
False, True, '60d', outputFolder)
print('Over bought:')
print(overBoughtList)
print('Over sold:')
print(oversoldList)
print('High deviation:')
print(highDeviationList)
f = open(outputFolder + summaryFile, 'w')
f.write('Over bought:\n')
for t in overBoughtList:
f.write(t + '\n')
f.write('\nOver sold:\n')
for t in oversoldList:
f.write(t + '\n')
f.write('\nHigh deviation:\n')
for t in highDeviationList:
f.write(t + '\n')
f.close()
def OpportunityFinder(tickerList: list):
outputFolder = 'data/dailypicks/'
summaryFile = '_DailyPicks.csv'
candidates = pd.DataFrame(columns=list([
'Ticker', 'hp2Year', 'hp1Year', 'hp6mo', 'hp3mo', 'hp2mo', 'hp1mo',
'currentPrice', 'channelHigh', 'channelLow', 'shortEMA', 'longEMA',
'2yearPriceChange', '1yearPriceChange', '6moPriceChange',
'3moPriceChange', '2moPriceChange', '1moPriceChange', 'dailyGain',
'monthlyGain', 'monthlyLossStd', 'Comments'
]))
candidates.set_index(['Ticker'], inplace=True)
for root, dirs, files in os.walk(outputFolder):
for f in files:
if f.endswith('.png'): os.unlink(os.path.join(root, f))
for ticker in tickerList:
prices = PricingData(ticker)
currentDate = GetTodaysDate()
print('Checking ' + ticker)
if prices.LoadHistory(requestedEndDate=currentDate):
prices.CalculateStats()
psnap = prices.GetPriceSnapshot(AddDays(currentDate, -730))
hp2Year = psnap.fiveDayAverage
psnap = prices.GetPriceSnapshot(AddDays(currentDate, -365))
hp1Year = psnap.fiveDayAverage
psnap = prices.GetPriceSnapshot(AddDays(currentDate, -180))
hp6mo = psnap.fiveDayAverage
psnap = prices.GetPriceSnapshot(AddDays(currentDate, -90))
hp3mo = psnap.fiveDayAverage
psnap = prices.GetPriceSnapshot(AddDays(currentDate, -60))
hp2mo = psnap.fiveDayAverage
psnap = prices.GetPriceSnapshot(AddDays(currentDate, -30))
hp1mo = psnap.fiveDayAverage
psnap = prices.GetCurrentPriceSnapshot()
currentPrice = psnap.twoDayAverage
Comments = ''
if psnap.low > psnap.channelHigh:
Comments += 'OverBought; '
if psnap.high < psnap.channelLow:
Comments += 'OverSold; '
if psnap.fiveDayDeviation > .0275:
Comments += 'HighDeviation; '
if Comments != '':
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)
prices.GraphData(None, 60, ticker + ' 60d ' + titleStatistics,
False, True, '60d', outputFolder)
if (currentPrice > 0 and hp2Year > 0 and hp1Year > 0
and hp6mo > 0 and hp2mo > 0
and hp1mo > 0): #values were loaded
candidates.loc[ticker] = [
hp2Year, hp1Year, hp6mo, hp3mo, hp2mo, hp1mo,
currentPrice, psnap.channelHigh,
psnap.channelLow, psnap.shortEMA, psnap.longEMA,
(currentPrice / hp2Year) - 1,
(currentPrice / hp1Year) - 1,
(currentPrice / hp6mo) - 1, (currentPrice / hp3mo) - 1,
(currentPrice / hp2mo) - 1, (currentPrice / hp1mo) - 1,
psnap.dailyGain, psnap.monthlyGain,
psnap.monthlyLossStd, Comments
]
else:
print(ticker, currentPrice, hp2Year, hp1Year, hp6mo, hp2mo,
hp1mo)
print(candidates)
candidates.to_csv(outputFolder + summaryFile)
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)