Basic Overview

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    = '2016-07-16'       # 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)

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

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
from pandas import read_csv

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

portfolio.displayAllRuns()

View Results

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%

Examples continued...

Visualize Model

This feature will give you an immediate sense of how predictable your data is.

backtest.visualizeModel()

User Defined Trading Logic

These functions will tell your portfolio simulation how to trade. We tried to balance simplicity and functionality to allow for intricate trading strategies.

def buyLogic(self, confidence, data, testDay): 
    quote = data["Close"][testDay]                           # 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, data, testDay):
    quote = data["Close"][testDay]                       
    
    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, nextDayPerformance, data, testDay):
    quote = data["Close"][testDay]                        
                                                          
    # Case 1: Prediction is buy, price increases
    if prediction == 1 and nextDayPerformance > 0:
        
        if nextDayPerformance >= 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 nextDayPerformance <= 0: pass 

                                                             # If I bought shares
                                                             # and price decreases
                                                             # hold position
    
    # Case 3: Prediction is sell, price decreases
    elif prediction == -1 and nextDayPerformance <= 0:
        
        if nextDayPerformance <= -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 nextDayPerformance > 0: pass
            
                                                             # If I sold shares
                                                             # and price increases
                                                             # hold position
    # Case 5: No confident prediction was made

Multivariate Functionality

Remember, 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 features

Flexible Data Handling

Download 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,SSO,SCC
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

Social Sentiment Scores

The examples shown use data derived from a project where we are data mining social media and performing stock sentiment analysis.

https://github.com/anfederico/Stocktalk