MetaTrader 5 和 R 进行算法交易新手指南·进阶篇

class="macro">#Import the MT5R Library
library(mt5R)
class="macro">#Import xts to help us plot
library(xts)
class="macro">#Import quantmod to help us plot
library(quantmod)
class="macro">#Check if our connection is established
MT5.Ping()
class="macro">#Global variables
MARKET_SYMBOL <- "Volatility class="num">75 Index"
class="macro">#Request historical price data
price_data <- MT5.GetSymbol(MARKET_SYMBOL, iTF = class="num">1, iRows=class="num">5000,xts = TRUE)
class="macro">#Plotting a line chart
lineChart(price_data, name = MARKET_SYMBOL)
class="macro">#We can also add technical indicators to the plot
addRSI(n=class="num">60)

class="macro">#Add moving average
price_data$SMA_20 <- SMA(price_data$Close,n = class="num">20)
class="macro">#Add RSI to the dataframe
price_data$RSI_20 <- RSI(price_data$Close,n=class="num">20)
class="macro">#Add ATR to the dataframe
price_data$ATR_20 <- ATR(price_data[,c("High","Low","Close")], n=class="num">20)
class="macro">#Drop missing values
price_data <- na.omit(price_data)
class="macro">#Next close price
price_data$Next_Close <- lag(price_data$Close,-class="num">1)
class="macro">#Setting up our target
price_data$Target <- ifelse( ( (price_data$Next_Close) > price_data$Close) , class="num">1 , class="num">0)
class="macro">#Train test split
train_set <- price_data[class="num">1:class="num">4000,]
train_y <- price_data[class="num">1:class="num">4000,c("Target")]
test_set <- price_data[class="num">4001:class="num">4980,]
test_y <- price_data[class="num">4001:class="num">4980,c("Target")]
class="macro">#Fitting models
library(MASS)
class="macro">#Quadratic Discriminant Analysis
class="macro">#Using OHLC Data
qda  <- qda(Target ~ Open+High+Low+Close,data = train_set)
qda

class="macro">#Evaluating model performance
class="macro">#Custom Quadratic Discriminant Analysis
qda_predictionts <- predict(qda,test_set)
qda_predictionts <- qda_predictionts$class
class="macro">#Confusion matrix
table(qda_predictionts,test_y)

class="macro">#Keeping track of the last trade
last_trade <- -class="num">1
while(TRUE){
  class="macro">#Fetching current market data
  print("Fetching market information")
  data <- MT5.GetSymbol(MARKET_SYMBOL,iTF=class="num">1, iRows = class="num">2)
  data <- data[class="num">1,]
  
  class="macro">#Forecasting market move
  qda_forecast <- predict(qda,data)
  qda_forecast <- qda_forecast$class
  print("Model forecast: ")
  print(qda_forecast)
  class="macro">#Checking if we have no open positions
  current_positions <- MT5$positions_total()
  
  class="macro">#If we have no open positions, open a position following the model forecast
  if(current_positions == class="num">0){
    print("We have no open positions. Opening a new position")
    
    class="macro">#A Forecast of class="num">1 means buy
    if(qda_forecast == class="num">1){
      print("Opening Buy Position")
      MT5$Buy(MARKET_SYMBOL,symbol_info$volume_min)
      last_trade <- class="num">1
    }
    class="macro">#A Forecast of class="num">0 means sell
    if (qda_forecast == class="num">0){
      print("Opening Sell Position")
      MT5$Sell(MARKET_SYMBOL,symbol_info$volume_min)
      last_trade <- class="num">0
    }
  }
  
  else{
      class="macro">#Are we anticipating a move against our open position?
      if(last_trade != qda_forecast){
        class="macro">#If we are, let&class="macro">#x27;s close our position
        print("Closing open position")
        MT5$Close(MARKET_SYMBOL)
        class="macro">#Reset last trade
        last_trade <- -class="num">1
      }
      
      else{
        class="macro">#Otherwise everything is expected to be fine
        print("The current position is aligned with the market")
        info <- MT5$account_info()
        print("Current Profit")
        print(info$profit)
      }
      
  }
  class="macro">#Wait for a new candle to form 
  Sys.sleep(class="num">60)
}