Using predictionIO to run predictions using Yahoo Finance

This tutorial assumes you have gone through the quickstart guide for PredictionIO.

Setting up the environment

Step 1: Get your Pandas

Where: PredictionIO-Python-SDK

Run: sudo pip install pandas==0.13.1

pip command not found? install python from curl -O

https://bootstrap.pypa.io/get-pip.py

python get-pip.py

and then run sudo pip install pandas

Step 2: Edit import_yahoo.py

Where: PredictionIO-Python-SDK/examples/import_yahoo.py

At the end of file, find the following:

if __name__ == '__main__':
  #import_all(app_id=?)
  import_data_with_gaps(app_id=1)
  #import_one(app_id=1)

And, uncomment the first import, replacing app_id with your own id. Next, comment the second import statement (import_data_with_gaps).

Step 3: Import Yahoo Finance data.

Where: PredictionIO-Python-SDK/examples

Run: sudo python -m examples.import_yahoo

Step 4: Now make the distribution of PredictionIO

Where: cloned PredictionIO directory (with source code, make sure code is updated, git pull)

./make-distribution.sh

Step 5: Edit scala-stock

go to examples/scala-stock/src/main/scala

Edit YahooDataSource.scala

Go to end of file to PredefinedDSP object

Edit app_id to match the one from step 2

Step 6: Run scala-stock

Go to PredictionIO/examples/scala-stock

Now type:

../../bin/pio run --asm io.prediction.examples.stock.YahooDataSourceRun -- --master <Your spark master address found at http:local8080> --driver-memory <4-12G>

Step 7: Open dashboard and view results

In PredictionIO folder

Type /bin/pio dashboard

go to url: http://localhost:9000 to view output

---

#PredictionIO Scala Stock Tutorial

##Implementing New Indicators

If you don't need to introduce any new indicators, skip this step. To introduce a new indicator, go to Indicators.scala and implement the BaseIndicator class. 'ShiftsIndicator' can serve as an example for how to do this:

abstract class BaseIndicator extends Serializable {
    def getTraining(logPrice: Series[DateTime, Double]): Series[DateTime, Double]
	def getOne(input: Series[DateTime, Double]): Double
	def minWindowSize(): Int
}

####getTraining ######Parameters: This function takes in a Series of logPrices created from the closing prices of the YahooFinanace Data imported in YahooFinance.scala. logPrices refers to the logarithm of each price in the series. ######Functionality: Performs a transformation over every value in the series to return a training series that will be used in the regression. ####getOne() ######Parameters: This function takes in a smaller window of the price series representing the closing price of a single stock over minWindowSize(). The size of this series is defined by the return value of minWindowSize() ######Functionality: This function performs the same function as getTraining() over a smaller window defined by the return value of minWindowSize(). ####minWindowSize() ######Functionality: Returns the minimum window sized required to do a single calculation of the Indicator function implemented by getTraining(). For example, an indicator that requires seeing the previous day to make a calculation would have a minWindowSize of 2 days.

##Running New Indicators To change which indicators to include in your run, open YahooDataSource.scala. This file imports the Yahoo finance data, prepares it, and runs the predictive engine.

Navigate to Workflow.run() in the YahooDataSourceRun object:

   Workflow.run(
      dataSourceClassOpt = Some(classOf[YahooDataSource]),
      dataSourceParams = dsp,
      preparatorClassOpt = Some(IdentityPreparator(classOf[DataSource])),
      algorithmClassMapOpt = Some(Map(
        //"" -> classOf[MomentumStrategy]
        "" -> classOf[RegressionStrategy]
      )),
      //algorithmParamsList = Seq(("", momentumParams)),
      algorithmParamsList = Seq(("", RegressionStrategyParams(Seq[(String, BaseIndicator)](
        ("RSI1", new RSIIndicator(period=1)), 
        ("RSI5", new RSIIndicator(period=5)), 
        ("RSI22", new RSIIndicator(period=22))), 
      200))),
      servingClassOpt = Some(FirstServing(classOf[EmptyStrategy])),
      metricsClassOpt = Some(classOf[BacktestingMetrics]),
      metricsParams = metricsParams,
      params = WorkflowParams(
        verbose = 0,
        saveModel = false,

algorithmParamsList ()

Edit these parameters to include any newly implemented indicator functions the predictive model should include. In this example you would change 'RSIIndicator' to your new indicator. If you would like to modify the strategy used, make sure to also change the constructor parameter, i.e. 'RegressionStrategyParams'.

Viewing Your Results

To view the backtesting metrics, open up localhost:9000 in your browser and click on the 'HTML' button for the run you want to see the results of. On this page, the sharpe ratio is indicative of how effective your indicator implementations are in predictions.

Tips and Tricks

To reduce run time run your code on a smaller dataset:

1. Open YahooDataSource.scala
2. Look for line val dsp = PredefinedDSP.BigSP500 and comment it out
3. Uncomment out the line: val dsp = PredefinedDSP.SmallSP500
4. In the PredefinedDSP object, switch the appId for BigSP500with the appId for SmallSP500. *Note: It should match the appID in your runnable

To view standard output:

1. Open localhost:8080 (spark)
2. Click on the most recent worker
3. Click on stdout or stderr to view the data in 100kb increments

---

OLD DOCUMENTATION

(This doc is out-of-sync with the actual implementation)

How to implement a stock prediction algorithm

Fetch data from external source

You only need to do it once. (Unless you want to update your data.)

We provide a script which extracts historical data of SP500 stocks from Yahoo and store the data in your local data storage. Make sure you have setup storage according to storage setup instructions. Feel free to substitute with your favorite stock data source.

Specify environment. (Temporary.)

$ cd $PIO_HOME/examples
$ set -a
$ source ../conf/pio-env.sh
$ set +a

where $PIO_HOME is the root directory of PredictionIO's code tree.

Run the fetch script.

$ ../sbt/sbt "runMain io.prediction.examples.stock.FetchMain"

As SP500 constituents change all the time, the hardcoded list may not reflect the current state and the script may fail to extract delisted tickers. Whilst the stock engine is designed to accomodate missing / incomplete data, you may as well update the ticker list in stock engine settings.

High Level Description

A stock prediction algorithms employs a rolling window estimation method. For a given window with testingWindowSize, the algorithm trains a model with data in the window, and then the model is evaluated with another set of data.

For example, it builds a model with data from Aug 31, 2012 to Aug 31, 2013, and evaluate the model with data from Sept 1, 2013 to Oct 1, 2013. And then, the training is rolled one month forward: model training with data from Sept 30, 2012 to Sept 30, 2013, and evaluation with data from Oct 1, 2013 to Nov 1, 2013, and so on.

Training Data	Testing Data
2012-08-31 -> 2013-08-31	2013-09-01 -> 2013-10-01
2012-09-30 -> 2013-09-30	2013-10-01 -> 2013-11-01
2012-10-31 -> 2013-10-31	2013-11-01 -> 2013-12-01
2012-11-30 -> 2013-11-30	2013-12-01 -> 2014-01-01
2012-12-31 -> 2013-12-31	2014-01-01 -> 2014-02-01
...	...

For an algorithm developer, the task is to create a model with the training data, and then make prediction based on testing data.

Key Data Structures

Training Data

Training data has 4 main fields. tickers is the list of tickers that is used for training. mktTicker is the ticker of market, for calculating beta related metrics. timeIndex is a list of dates representing the time window used for training. price is a map from ticker to array of stock price, the array is of the same length as timeIndex. Notice that only tickers that are active through out the whole time window is included, i.e. for example, if the TrainingData's time window is from 2012-01-01 to 2013-01-01, Facebook (FB) will not be included in the training data.

class TrainingData(
  val tickers: Seq[String],
  val mktTicker: String,
  val timeIndex: Array[DateTime],
  val price: Array[(String, Array[Double])])

Query

Query is the input for prediction. Most of the fields resemble TrainingData, with an additional field tomorrow indicating the date of the prediction output. Algorithm builders take this as input, together with the trained model created with TrainingData to make prediction.

class Query(
  val mktTicker: String,
  val tickerList: Seq[String],
  val timeIndex: Array[DateTime],
  val price: Array[(String, Array[Double])],
  val tomorrow: DateTime)

Target

Target is the output for prediction. It is essentially a map from ticker to predicted return. Notice that the prediction need not to match Query, if an algorithm cannot make prediction some symbols, it can just leave them out.

The Algorithm

Stock prediction algorithms should extends StockAlgorithm class.

abstract class StockAlgorithm[P <: Params : ClassTag, M : ClassTag]
  extends LAlgorithm[P, TrainingData, M, Query, Target] {
  def train(trainingData: TrainingData): M
  def predict(model: M, query: Query): Target
}

RegressionAlgorithm

RegressionAlgrotihm creates a linear model for each stock using a vector comprised of the 1-day, 1-week, and 1-month return of the stock.

RandomAlgorithm

RandomAlgorithm produces a gaussian random variable with scaling and drift.

Evaluation: Backtesting Metrics

This is the most common method in quantitative equity research. We test the stock prediction algorithm against historical data. For each day in the evaluation period, we open or close positions according to the prediction of the algorithm. This allows us to simulate the daily P/L, volatility, and drawdown of the prediction algorithm.

BacktestingMetrics takes three parameters: enterThreshold the minimum predicted return to open a new position, exitThreshold the maximum predicted return to close an existing position, and maxPositions is the maximum number of open positions. Everyday, this metrics adjusts its portfolio based on the stock algorithm's prediction Target. For a current position, if its predicted return is lower than exitThreshold, then metrics will close this positions. On the other hand, metrics will look at all stocks that have predicted return higher than enterThreshold, and will repeatedly open new ones with maximum predicted value until it reaches maxPositions.

First Evaluation: Demo1

stock.Demo1 shows a sample Runner program. To run this evaluation, you have to specify two sets of parameters:

1. DataSourceParams governs the ticker and the time window you wish to evaluate. Here is the parameter we used in stock.Demo1.

   val dataSourceParams = new DataSourceParams(
     baseDate = new DateTime(2004, 1, 1, 0, 0),
     fromIdx = 400,
     untilIdx = 1000,
     trainingWindowSize = 300,
     evaluationInterval = 20,
     marketTicker = "SPY",
     tickerList = Seq("AAPL"))

   This means we start our evaluation at 400 market days after the first day of 2004 until 1200 days. Our evalutionInterval is 20 and trainingWindowSize is 300, meaning that we use day 100 until day 400 as the first slice of training data, and evaluate with data from day 400 until 420. Then, this process repeats, day 120 until 420 for training, and evaluation with data from day 420 until 440, until it reaches untilIdx. We only specify one ticker GOOGL for now, but our stock engine actually supports multiple tickers.

2. BacktestingParams governs the backtesting evaluation.

   val backtestingParams = BacktestingParams(enterThreshold = 0.001,
                                             exitThreshold = 0.0)

   As explained above, the backtesting evaluator opens a new long position when the prediction of stock is higher than 0.001, and will exit such position when the prediction is lower than 0.0.

You can run the evaluation with the following command.

$ cd $PIO_HOME/examples
$ ../bin/pio-run io.prediction.examples.stock.Demo1

You should see that we are trading from April 2005 until Dec 2007, the NAV went from $1,000,000 to $1,433,449.24. YaY!!!

(Disclaimer: I cherrypicked this parameter to make the demo looks nice. A buy-and-hold strategy of AAPL from April 2005 until 2007 performs even better. And, you will lose money if you let it run for longer: set untilIdx = 1400.)

Second Evaluation

stock.Demo2 shows us how to run backtesting against a basket of stocks. You simply specify a list of tickers in the tickerList. DataSource wil l handle cases where the ticker was absent.

In BacktestingParams, you may allow more stocks to be held concurrently. The backtesting class essentially divides the current NAV by the maxPositions. The demo is run the same way, by specifying the running main class.

$ cd $PIO_HOME/examples
$ ../bin/pio-run io.prediction.examples.stock.Demo2

The result is not as great, of course.

Third Evaluation

Now, you may start wondering what actually contribute to the profit, and may want to dive deeper into the data. DailyMetrics is a helper metrics which helps you to understand the performance of different parameter settings. It aggregates the prediction results for different enterThreshold, and output the average / stdev return of the prediction algorithm.

All you need is to change the metrics variable to DailyMetrics. Demo3 shows the actual code. Try it out with:

$ cd $PIO_HOME/examples
$ ../bin/pio-run io.prediction.examples.stock.Demo3

Last Words

The current version is only a proof-of-concept for a stock engine using PredictionIO infrastructure. A lot of possible improvements can be done:

• Use spark broadcast variable to deliver the whole dataset, instead of repeatedly copy to each Query and TrainingData. StockEngine should wrap around the data object and provide the correct slice of data and prevent look-ahead bias.
• Better backtesting metrics report. Should also calculate various important metrics like daily / annual return and volatility, sharpe and other metrics, drawdown, etc.
• Better backtesting method. Should be able to handle cost of capital, margin, transaction costs, variable position sizes, etc.
• And a lot more.....