Using stock historical data, train a supervised learning algorithm with any combination of financial indicators. Rapidly backtest your model for accuracy and simulate investment portfolio performance.
pip3 install clairvoyantDuring the testing period, the model signals to buy or sell based on its prediction for price movement the following day. By putting your trading algorithm aside and testing for signal accuracy alone, you can rapidly build and test more reliable models.
from clairvoyant import Backtest, History
# Testing performance on a single stock
variables = ["SSO", "SSC"] # Financial indicators of choice
trainStart = '2013-03-01' # Start of training period
trainEnd = '2015-07-15' # End of training period
testStart = '2015-07-16' # Start of testing period
testEnd = '2016-07-16' # End of testing period
buyThreshold = 0.65 # Confidence threshold for predicting buy (default = 0.65)
sellThreshold = 0.65 # Confidence threshold for predicting sell (default = 0.65)
C = 1 # Penalty parameter (default = 1)
gamma = 10 # Kernel coefficient (default = 10)
continuedTraining = False # Continue training during testing period? (default = false)
backtest = Backtest(variables, trainStart, trainEnd, testStart, testEnd)
cols = {'Date': 'date', 'Open': 'open', 'Close': 'close',
'SSO': 'sentiment', 'SSC': 'influence'} # Define a column map
data = History("Stocks/SBUX.csv", col_map=cols) # Read in data
backtest.stocks.append("SBUX") # Inform the model which stock is being tested
for i in range(0,10): # Run the model 10-15 times
backtest.runModel(data)
# Testing performance across multiple stocks
stocks = ["AAPL", "ADBE", "AMGN", "AMZN",
"BIIB", "EBAY", "GILD", "GRPN",
"INTC", "JBLU", "MSFT", "NFLX",
"SBUX", "TSLA", "VRTX", "YHOO"]
for stock in stocks:
data = History(f'Stocks/{stock}.csv', col_map=cols)
backtest.stocks.append(stock)
for i in range(0,10):
backtest.runModel(data)
backtest.displayConditions()
backtest.displayStats() Conditions X1: SSO X2: SSC Buy Threshold: 65.0% Sell Threshold: 65.0% C: 1 gamma: 10 Continued Training: False
Stats Stock(s): AAPL | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 ADBE | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 AMGN | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 AMZN | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 BIIB | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 EBAY | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 GILD | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 GRPN | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 INTC | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 JBLU | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 MSFT | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 NFLX | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 SBUX | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 TSLA | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 VRTX | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016 YHOO | Training: 03/01/2013-07/15/2015 Testing: 07/16/2015-07/15/2016
Total Buys: 39 Buy Accuracy: 62.86% Total Sells: 20 Sell Accuracy: 70.41%
Once you've established your model can accurately predict price movement a day in advance, simulate a portfolio and test your performance with a particular stock. User defined trading logic lets you control the flow of your capital based on the model's confidence in its prediction and the following next day outcome.
from clairvoyant import Portfolio, History
variables = ["SSO", "SSC", "SSL"] # Financial indicators of choice
trainStart = '2013-03-01' # Start of training period
trainEnd = '2015-07-15' # End of training period
testStart = '2015-07-16' # Start of testing period
testEnd = '2016-07-16' # End of testing period
buyThreshold = 0.65 # Confidence threshold for predicting buy (default = 0.65)
sellThreshold = 0.65 # Confidence threshold for predicting sell (default = 0.65)
C = 1 # Penalty parameter (default = 1)
gamma = 10 # Kernel coefficient (default = 10)
continuedTraining = False # Continue training during testing period? (default = false)
startingBalance = 1000000 # Starting balance of portfolio
# User defined trading logic (see below)
class MyStrategy(Portfolio):
def buyLogic(self, confidence, row, colmap):
...
def sellLogic(self, confidence, row, colmap):
...
def nextPeriodLogic(self, prediction, nextDayPerformance, row, colmap):
...
portfolio = MyStrategy(variables, trainStart, trainEnd, testStart, testEnd)
data = History("Stocks/YHOO.csv", col_map=cols)
for i in range(0,5):
portfolio.runModel(data, startingBalance)
portfolio.displayLastRun()
portfolio.displayAllRuns()Run #1 Buying Power: $664488.82 Shares: 10682 Total Value: $1130971.76 Run #2 Buying Power: $588062.6 Shares: 10654 Total Value: $1053322.78 Run #3 Buying Power: $787542.42 Shares: 7735 Total Value: $1125329.87 Run #4 Buying Power: $783145.32 Shares: 7692 Total Value: $1119054.96 Run #5 Buying Power: $648025.83 Shares: 10418 Total Value: $1102979.9
Performance across all runs Runs: 5 Average Performance: 10.63%
This feature will give you an immediate sense of how predictable your data is.
backtest.visualizeModel()
These functions will tell your portfolio simulation how to trade. We tried to balance simplicity and functionality to allow for intricate trading strategies.
class MyStrategy(Portfolio):
def buyLogic(self, confidence, row, colmap):
quote = getattr(row, colmap['Close']) # Leave as is
if confidence >= 0.75: # If model signals buy
shareOrder = int((self.buyingPower*0.3)/quote) # and is 75-100% confident
self.buyShares(shareOrder, quote) # invest 30% of buying power
elif confidence >= 0.70: # If model is 70-75% confident
shareOrder = int((self.buyingPower*0.2)/quote) # invest 20% of buying power
self.buyShares(shareOrder, quote)
elif confidence >= 0.65: # If model is 65-70% confident
shareOrder = int((self.buyingPower*0.1)/quote) # invest 10% of buying power
self.buyShares(shareOrder, quote)
def sellLogic(self, confidence, row, colmap):
quote = getattr(row, colmap['Close'])
if confidence >= 0.65: # If model signals sell
self.sellShares(self.shares, quote) # and is 65-100% confident
# sell all shares
def nextDayLogic(self, prediction, nextPeriodPerformance, row, colmap):
quote = getattr(row, colmap['Close'])
# Case 1: Prediction is buy, price increases
if prediction == 1 and nextPeriodPerformance > 0:
if nextPeriodPerformance >= 0.025: # If I bought shares
self.sellShares(self.shares, quote) # and price increases >= 2.5%
# sell all shares
# Case 2: Prediction is buy, price decreases
elif prediction == 1 and nextPeriodPerformance <= 0: pass
# If I bought shares
# and price decreases
# hold position
# Case 3: Prediction is sell, price decreases
elif prediction == -1 and nextPeriodPerformance <= 0:
if nextPeriodPerformance <= -0.025: # If I sold shares
shareOrder = int((self.buyingPower*0.2)/quote) # and price decreases >= 2.5%
self.buyShares(shareOrder, quote) # reinvest 20% of buying power
# Case 4: Prediction is sell, price increases
elif prediction == -1 and nextPeriodPerformance > 0: pass
# If I sold shares
# and price increases
# hold position
# Case 5: No confident prediction was madeRemember, more is not always better!
variables = ["SSO"] # 1 feature
variables = ["SSO", "SSC"] # 2 features
variables = ["SSO", "SSC", "RSI"] # 3 features
variables = ["SSO", "SSC", "RSI", ... , Xn] # n featuresDownload historical data directly from popular distribution sources. Clairvoyant is flexible with most date formats and will ignore unused columns in the dataset. If it can't find the date specified, it will choose a suitable alternative.
Date,Open,High,Low,Close,Volume,influence,sentiment
03/01/2013,27.72,27.98,27.52,27.95,34851872,65.7894736842,-0.121
03/04/2013,27.85,28.15,27.7,28.15,38167504,75.9450171821,0.832
03/05/2013,28.29,28.54,28.16,28.35,41437136,84.9230769231,0.151
03/06/2013,28.21,28.23,27.78,28.09,51448912,80.7799442897,-0.689
03/07/2013,28.11,28.28,28.005,28.14,29197632,73.5368956743,-0.821
The examples shown use data derived from a project where we are data mining social media and performing stock sentiment analysis.
https://github.com/uclatommy/TweetFeels