Basic Overview

Schematic

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.

Visualize the Learning Process

Install

pip install clairvoyant

Code Examples

Backtesting Signal Accuracy

During 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
from pandas import read_csv

# 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    = '2016-09-17'       # End of training 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)

data = read_csv("Stocks/SBUX.csv")      # Read in data
data = data.round(3)                    # Round all values                  
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 = read_csv('Stocks/%s.csv' % stock)
    data = data.round(3)
    backtest.stocks.append(stock)
    for i in range(0,10):
        backtest.runModel(data)

backtest.displayConditions()
backtest.displayStats()        

View Results

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%

Portfolio Simulation

from clairvoyant import Portfolio
from pandas import read_csv

variables  = ["SSO", "SC"]      # 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    = '2016-07-17'       # End of training 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)
def buyLogic(self, confidence, data, testDay)
def sellLogic(self, confidence, data, testDay)
def nextDayLogic(self, prediction, nextDayPerformance, data, testDay)

portfolio = Portfolio(variables, trainStart, trainEnd, testStart, testEnd)

data = read_csv("Stocks/SBUX.csv")
data = data.round(3)
for i in range(0,5):
    portfolio.runModel(data, startingBalance, buyLogic, sellLogic, nextDayLogic)
    portfolio.displayLastRun()

portfolio.displayAllRuns()

View Results

Other Features

Multivariate Functionality

variables = ["SSO"]                            # 1 feature
variables = ["SSO", "SSC"]                     # 2 features
variables = ["SSO", "SSC", "RSI"]              # 3 features
variables = ["SSO", "SSC", "RSI", ... , Xn]    # n features

Visualize Model

backtest.visualizeModel()