Use of Naive Bayes to predict price direction in R

Discussion in 'Strategy Development' started by arsene007, Aug 8, 2019.

  1. In the past posts, I have mainly been talking about automated trading strategies based on simple logic, rule-based and technical analysis driven. In this post I want to share how we can use machine learning algorithms, particularly those that are suited for classification problems to predict the next day market direction. Yes, I mean only the next day price direction, and not the next month or next 6 months. The reason why I am focusing on such a small time horizon if because it should in theory be easier to predict the short-term, rather than the medium or long term.Let’s jump into it.

    I will use light crude oil futures data. I have the data loaded from a csv file. The data contains, daily Open, high, low and close price of crude oil futures (CL on Nymex). I then format or change the dataframe into an xts (specific time series format) in order to plot it with a candlestick plotting function from the Quantmod package.

    library(tidyverse)
    library(lubridate)
    library(quantmod)
    library(ggplot2)


    price <- read.csv("wtiDaily.csv")
    setup_data <- function(pricedata) {


    #Small function to format the data into xts format
    names(pricedata) <- c("Date", "Open", "High", "Low", "Close")
    dates <- parse_date_time(x = pricedata[,1], "mdy_HM", truncated = 3)
    pricedata <- pricedata[,2:5]
    pricedata <- xts(pricedata, order.by = dates)
    }


    price <- setup_data(price)
    ema7 <- EMA(price$Close, n = 7)
    ema20 <- EMA(price$Close, n = 20)
    ema50 <- EMA(price$Close, n = 50)
    ema70 <- EMA(price$Close, n = 70)
    After having formatted the data, i use the function chartSeries to plot the data.


    chartSeries(price, TA=NULL, subset = '2017-06::')
    addEMA(n = 7,col = "orange")
    addEMA(n = 20,col = "red"

    [​IMG]

    Now that i have the graph, i need to explain how i am planning to use machine learning predict the next day price direction. Another of saying this is also predicting the next day candle type. Since a daily bull/green candle is equivalent to price going higher on that day, and a bear/red candle is equivalent to price going lower on that day; It means i will be predicting whether the next day’s candle will be a bull or a bear candle. In order to do so, looking at the chart above, my hypothesis is if a use some variables as predictor variables or features in my machine learning algorithm, i should be able to predict fairly well the next day’s candle and therefore have an increasing equity curve. For variables or features, i will use the following:

    • Close price in relation to exponential moving average of the last 7 periods
    • today’s candle type (bull or bear)
    • previous day’s candle type (bull or bear)
    • daily return
    #Defining the individual features/variables
    candle.type.current <- data.frame(ifelse( price$Close > price$Open, "bull", "bear"))
    candle.type.previous <- data.frame(lag(candle.type.current$Close, n = 1))
    candle.next.day <- data.frame(lead(candle.type.current$Close, n = 1))
    position.to.ema7 <- data.frame(ifelse(price$Close > ema7, "above", "below"))
    dailywin <- data.frame(abs(price$Close - price$Open))
    candle.nextday.win <- lead(dailywin$Close, 1)


    #Making up the dataframe with all the features as columns
    dailyprice <- data.frame(candle.type.current,candle.type.previous,
    position.to.ema7, dailywin,
    candle.nextday.win, candle.next.day)


    # naming the dataframe columns
    names(dailyprice) <- c("candle.type.current", "candle.type.previous",
    "position.to.ema7", "dailywin",
    "candle.nextday.win","candle.next.day")


    #filtering out the data with NAs
    dailyprice <- slice(dailyprice, 7:length(dailyprice$candle.type.previous))


    After defining the features and i then go ahead and divide the data into train and test set. The data contains about 500 obversations (Trading days from June 2017 to August 2018). I use 300 data points as training set and the rest as test set. I also create a “formula”, specifying what is my target variable (what i am trying to predict) and the features.

    #Splitting the data into training and testing
    testRange <- 300:500
    trainRange <- 1:300
    test <- dailyprice[testRange,]
    train <- dailyprice[trainRange,]


    #Defining the formula: Target variables and predictors
    target <- "candle.next.day"
    predictors.variable <- c("candle.type.current", "candle.type.previous",
    "position.to.ema7", "dailywin")


    predictors <- paste(predictors.variable, collapse = "+")
    formula <- as.formula(paste(target, "~", predictors, sep = ""))


    #function for processing predictions
    predictedReturn <- function(df, pred){
    df$pred <- pred
    df$prediReturn <- ifelse(df$candle.next.day != df$pred, -df$candle.nextday.win, df$candle.nextday.win)
    df$cumReturn <- cumsum(df$prediReturn)
    return(df)
    }

    Now let’s go ahead and train our machine learning model. Here i train a naive bayes algorithm (Learn more about Naive Bayes ).

    library(naivebayes)
    # Naivebayes model
    nb <- naive_bayes(formula, data = train)
    plot(nb)


    # Prediction
    nb.pred <- predict(nb, test)
    nb.test <- predictedReturn(test, nb.pred)


    #Plotting the net daily returns and cumulative returns
    plot(nb.test$prediReturn, type = "line")
    plot(nb.test$cumReturn, type = "line")
    #Confusion matrix
    confusionMatrix.nb <- table(nb.test$candle.next.day, nb.test$pred)
    print(confusionMatrix.nb)


    #Calculating accuracy
    nb.misclserror <- mean(nb.test$candle.next.day != nb.test$pred)
    print(paste("Accuracy", 1-nb.misclserror))

    So the model evaluation on the test set is not dissapointing. It returns an accuracy of 57% which although it might seem low, is not a bad result in the trading arena, for such a limited amount of data. Obvisouly, more validation tests should be performed by changing the training and test window, carry out more serious cross validation and idealy use more data. Nevertheless, the equity curve looks interesting it is going higher (moneyyyyyy). We should not understimate the magnitude of the drawdowns, but i can say it looks good.

    bear bull
    bear 24 59
    bull 28 90
    [1] "Accuracy 0.567
    [​IMG]

    I explain in more details how to add more features and also test other classification-based machine learning algorithms to predict next day’s price on Udemy. I felt i needed to create a course to share this knowledge and help others create their own trading strategies using machine learning with R.
     
    ironchef, zdreg, nooby_mcnoob and 2 others like this.
  2. RedDuke

    RedDuke

    How many years your dataset goes back???

    When analyzing daily bars you need min 25-30 years of reliable data for your backrest to have at least some assurance it is not totally curve fitted. Oil only became electronic in mid 2000s. Not sure where one can get pit traded quality data.
     
  3. 2016. Agree the more data, the better. But a strategy of 6 to 7 years of data can surprisingly deliver good results on unseen data/live trading.
     
    ironchef and tommcginnis like this.
  4. RedDuke

    RedDuke

    Any strategy on data sample 2016-Now on daily bars can easily produce anomaly that will loose equity with deployed live.

    Just to illustrate an example, I once created 1 hour strategy (commodity) and tested it on 10 years of high quality data (both long and short were balanced). It is really hard to curve fit 1 hour bars over 10 years. It managed to loose money from get go and was later redesigned.
     
  5. tommcginnis

    tommcginnis

    ((Thanks for posting a GREAT thread. I am way bummed that I can't get into it *nearly* as much as I'd like. Nice looking stuff, though. I'll certainly follow this thread with interest.))

    The "25-30+ years" thing is just nice in theory, but commonly unworkable in practice. And in fact, *much* is dependent on the area of inquiry. For example, if you're looking at time-related data, any amount of seasonality means that 25 years of data lends you only n=25 of Spring, School's Out, Harvest/tilling, Thanksgiving→Christmas→New Years, etc etc. That's a pretty thinnish pool. But if your attention is intra-day, then you have >6,000 market closes, Friday(s), Monday afternoons, lunch-hours, Hong Kong opens, etc. -- just how many of those do you need?

    But while you're trying to stretch out your data by doing *anything* you can to build "n= ", there is the *way* ugly thought that there would be ANY AMOUNT OF DATA STABILITY for more than a 5-year swath. The sample pool over which you're dipping changes in content, character, and quantities, EVERY DAY to some degree, but hugely so over 5-10 years. To imagine that TWENTY YEARS of decision-making, conflicts, data availability+completeness, connectivity amongst decision-makers, the financial markets into which this milieu is being poured :confused::confused::confused: -- to imagine that data from day 1 are comparable to data from day 6,028??? NO.

    So what do you end up with? A lie. False input comparisons leading to bad analysis leading to false output conclusions.

    For myself, I concluded in 2009 that the world had changed, and that I needed to pay *strict* heed to these dangers in any use of data preceding March, 2009. In the 10 years since, I have seen zero reason to question that caution.
     
    Last edited: Aug 8, 2019
  6. Atomated

    Atomated

    Trying to predict any type of asset price is not an ideal strategy. It is like predicting a random walk. You can clearly see that the data you are looking at is significantly long-biased. If you randomly make entry and exit trades, you can probably achieve close (if not greater) than the accuracy of 0.567 you achieved. Try to do it for a bunch of simulations. The same type of fallacy can be found when simulating random trades in the equity market since ~2008. People backtest a strategy with between 50-60% accuracy and then start a fund and trade only to realize that the market is also skewed the same way (long-biased 50-60%), erasing any profits.

    Predicting asset prices is not a great way to go about automated trading. It is much more beneficial to try to optimize functions and algorithms as opposed to directly trying to predict something that is essentially random.

    On a side note, if you want to play around with a bunch of models in R, I suggest using the caret package. It is really easy to use and has great functionality.
     
    arsene007 likes this.
  7. There are far fewer trading days in that time span than 500. From 20170601 through 20180815 inclusive, the date range in your chart, there are 305 WTI trading dates.

    Also, the word "data" is plural.
     
    Overnight likes this.
  8. Thank you for posting this explanation. It is interesting to see how you have set up your analysis.
     
  9. I guess @arsene007 means to say June 2017 to August 2019 (this year, not last year). That would make for about 500 trading days.
     
  10. IAS_LLC

    IAS_LLC

    What is this, a Medium.com dry run?
     
    #10     Aug 8, 2019