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Using Java & Genetic Algorithms to Beat the Market Matthew Ring JavaOne 2011 BOF Session 22382 [20111007 15:11CDT – MRing – added results pages]

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401K Problem?

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I can't speak for you, but...

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I know, I'll write software to time the market!

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Give it the 'ole college try, son!

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Shoestring Toolkit <ul><li>K.I.S.S. – Let's trade stocks.

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Cheap or free market data. Especially daily OHLCV data.

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Generally Liquid.

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Low transaction fees.

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Many brokers offer equity trading APIs. </li></ul><ul><li>Java – My primary language.

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Free.

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Huge number and variety of open source libraries. </li></ul>

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Some Styles of Trading <ul><li>HFT </li></ul>Market-making or scalping (opinions vary) in sub-second windows. <ul><li>Day Trading </li></ul>Buying & selling a stock in the same trading day, attempting to profit on same-day price moves. <ul><li>Position Trading </li></ul>Attempting to profit on trends over a window of a few days to a few years <ul><li>Swing Trading </li></ul>Attempting to profit on volatility over a window of a few days to a few weeks

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How do humans get an edge or time the market? <ul><li>Dumb luck.

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Inside information.

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Gut instinct.

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Find repeating patterns in the market.

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Manipulate other participants.

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Better or faster information.

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Faster execution. </li></ul>

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So, for which edge should *I* try to write software? <ul><li>I like the sound of “Find repeating patterns in the market”

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Patterns? … Signals?

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Technical Analysis!

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TA-Lib : Technical Analysis Library – Open-source API for C/C++, Java , Perl, Python and 100% Managed .NET </li></ul>

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What is Technical Analysis? <ul><li>Bands

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Oscillators

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Moving averages

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Candlesticks

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Trading decisions as reactions to these signals

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Voodoo? </li></ul>

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OK, how can I automate this? <ul><li>Convert market data to signals.

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The software interprets the signals & decides how to trade. </li><ul><li>Refinement towards implementation – Map a discrete set of signal responses to a discrete set of trading decisions. </li></ul><li>Simulate trades based on these decisions.

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Goal is optimize for profitable trades, of course. </li></ul>

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Leads us to: Optimization Algos and/or Machine Learning! <ul><li>Artificial Neural Networks

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Simulated Annealing

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Emergent Behavior (swarm, cellular automata)

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Genetic Programming

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Genetic Algorithms

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Support Vector Machines </li></ul>

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Genetic Algorithms Sources: http://www.slideshare.net/kancho/genetic-algorithm-by-example (Nobal Niraula)

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Genetic Algorithms Sources: http://www.slideshare.net/Mathijsje/genetic-algorithms-7276974 (Mathijs van Meerkerk)

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Some possibly familiar examples of GA...

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<ul><li>Open Source, Java, created by Dan Dyer

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Provides easy-to-follow examples & interface-based API. </li></ul>

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Our Machine Market Data Operational Settings Trading Decisions ? Backtesting Feedback GA

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Piecing our Machine Together <ul><li>Get the OHLCV market data as CSV files, load into DB. (boring, will not be shown here)

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Generate signals from market data with TA-LIB.

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Generate choices for trading decisions.

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React to the signals

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Map reactions to choices

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Test the choices made. </li></ul>

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Generate Signals

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Generate Choices for Trading Decisions What Decisions? How about... <ul><li>When to buy?

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How much to buy?

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How long to hold?

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Predicted low price (for buying)?

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Predicted high price (for selling)?

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Price at which I should sell prior to end of holding period (opportunistic sell)? </li></ul>

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Generate Choices for Trading Decisions

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Generate Choices for Trading Decisions

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Generate Choices for Trading Decisions <ul><li>Recall that number of choices for each decision is purposely limited.

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Choice values are generated from 'random' doubles, scaled to meet their various requirements. </li></ul>

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React to the signals <ul><li>Each signal feeds, one day at a time, into a device I named Thresholds.

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Each Thresholds instance is initialized w/ a 'random', sorted array of doubles, scaled to span the range of the signal it is to consume.

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This initialization step establishes both the number of bins and the bin boundaries. </li></ul>public int findBin(double testVal)

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React to the signals <ul><li>A Thresholds selects a bin based on the signal level via a simple sigVal < binBoundary check.

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The bin index is the reaction.

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Sort of like an A/D converter, if you pretend that the signal is analog, sampled daily.

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A subclass provides a memory of its previous reaction, so it reacts to both the current signal level and the change from its previous reaction. </li></ul>

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Summary, so far... <ul><li>Daily stock OHLCV data become Signals.

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I have chosen 24 various TA signals.

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I have defined 6 trading-related decisions: Buy?, How much?, Holding period? Low price guess? High price guess? Opportunistic sell price?

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For each decision, for each signal, a Thresholds instance will react. </li></ul>

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Converting reactions to decisions <ul><li>Each threshold bin in each decision layer is mapped to one of the decision choices.

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The mappings are established 'randomly'

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The choice that gets the most bin reactions is selected.

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Winner take-all voting.

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Note that some mappings will effectively abstain from voting

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Ties are broken based on iteration order. </li></ul>

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Converting reactions to decisions <ul><li>Now to get a little fancier...

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Added combinational voting to each layer.

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Some 'randomly' selected bins are ANDed with others, then 'randomly' mapped to choices for the given decision layer.

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More opinions in the mix. </li></ul>

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Backtesting the decisions <ul><li>Rules, laws & things to keep in mind </li><ul><li>Cash account trades settle in T+3 days.

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Avoid free-riding: </li><ul><li>Buy X at T

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Sell X at T', where T' < T+3

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Buy Y on T' with proceeds from sale of X

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Cannot sell Y before T'+3

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If you do, the Federal Reserve Board requires that your broker freeze your account for 90 days </li></ul></ul></ul>

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Backtesting the decisions <ul><li>More rules, laws & things to keep in mind </li><ul><li>Avoid day trading: </li><ul><li>Buy X on T

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Sell X later on T

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If you do this more than 3x in a 5-day period, you will be classified as a Pattern Day Trader

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FINRA rules require that Pattern Day Traders use margin accounts with a Min. balance of $25K </li></ul><li>When backtesting: </li><ul><li>Allow for slippage (be pessimistic in fill price)

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Market will eat up high bids & low asks. </li></ul></ul></ul>

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Backtesting the decisions <ul><li>Based on the last slides, I usually set minHoldDays=3

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Iterate signal data while trying to create trades based on machine advice & market actuals.

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Remember to skip first max 'lookback' number of days, as many signals involve moving averages.

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Strongly bias 'buy' fills toward the high, 'sell' fills toward the low. </li></ul>

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Backtesting the decisions <ul><li>Check proposed buy & sell prices against the current day open as a sanity check.

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Sell is greedy – will try to sell at Max(predicted high price, chosen opportunistic sell price)

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Any position where holdDays >= maxHoldDays is dumped at the end of the day at a price biased toward the low and the close. </li></ul>

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Evolving the Candidates <ul><li>Notice that the previous slides have mentioned 'random' & 'randomly' several times.

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This is where the GA comes into play.

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The genome, a double[6850] serves as the settings, the 'randomness', for each candidate. </li></ul>

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Evolving the Candidates <ul><li>The trades generated during backtesting will be used to score the individual candidate.

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The GA will select the fittest candidates for mating, based on their scores.

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The GA will recombine (crossover) and mutate (spontaneously change values) mated genomes. </li></ul>

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More Details <ul><li>I have simplified some things for this presentation.

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I pre-screen the stocks used, for swing trading appropriateness. I don't want to wait while it runs through the whole Nasdaq.

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I backtest & evolve on a subset of available data.

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Then I try (backtest w/o evolution) the top evolved candidates against recent out-of-sample data. </li></ul>

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Other Comments <ul><li>The GA is the easy part.

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Designing the machine to work with it is a bit of an art.

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Reduce the solution space by limiting choices. </li><ul><li>Ex: Pre-screen stocks, favor discrete choices </li></ul><li>Allow the GA plenty of wiggle room. </li><ul><li>Ex: Large genome, multiple voting schemes </li></ul><li>How meta to get? I hard-coded some values that could have been part of the genome. </li></ul>

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Last Minute Notes <ul><li>Termination Condition: Num of stagnant generations

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Scoring – I'm using the ending account balance (cash + approx val of open positions) as the score.

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The chosen scoring formula has a critical impact on the out come. It provides the 'motivation' for the GA to 'improve'.

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What happens when you try a score like numGoodTrades / numBadTrades?

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Watchmaker only supports positive double score values.

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The Trade PnL calculation includes transaction costs (my target broker is $2.50 a trade) </li></ul>

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Relevant Links <ul><li>http://watchmaker.uncommons.org/

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http://www.eoddata.com/

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http://ta-lib.org/

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http://www.slideshare.net/kancho/genetic-algorithm-by-example

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http://www.slideshare.net/Mathijsje/genetic-algorithms-7276974 </li></ul>

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Results – About the demo you didn't get to see (sorry). <ul><li>Pick 4 random stocks from the list of pre-screened Nasdaq stocks.

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Each model starts w/ $2000.

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Evolve & test models on 20080101 thru (Today - 45 trading days). This is the in-sample data.

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Stagnation Condition: quit after 7 generations w/ no improvement.

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Keep the best 2 evolved individuals for each stock, for a total of 8 reserved candidates.

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Test them on out-of sample data of (Today - 44 trading days) thru Today.

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Print out the trading results of the top 3 out-of-sample tests. </li></ul>

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Results – Talkin' 'bout an evolution... Run 1 Run 2 Best candidate score Vs. the number of generations Yes, the score is in $, but since this is in-sample data, please take it with a grain of salt.

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Results – Out-of-sample trades Run 1 Run 2

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Results – Discussion of <ul><li>These 2 demo runs took about 5 mins each. I cut corners with some of my settings (number of stocks examined; stagnation), in order to keep the run time short.

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The graphs show that evolution is a choppy process, due in part to my use of aggressive crossover & mutation strategies as well my choice not to enable elitism (best members transcend their own generation). </li></ul>

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Results – Conclusions <ul><li>On average, I am making about 3% per simulated trade. The max gain was about 10%, the max loss was about 7%. The standard deviation was about 4.5%.

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So, the generated models appear to have predictive power.

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The validity of these results depends on my belief that my backtesting routine is both reasonable & bug-free.

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No guarantee that the evolved models will continue to work when facing new, out-of-sample data. I'm still just betting. But, maybe, with an edge. </li></ul>