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.'
)
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()
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()
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)
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)
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')
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)