added knn material

kNN/knn_model.R

@@ -0,0 +1,59 @@
+## read dat file into R
+# use library
+library(tidyverse)
+# read binary file
+base_buy <- readBin("test_data/Buy_Position8132102.dat",what = "double",n = 60000, endian = "little") %>% 
+  # convert vector to matrix
+  matrix(ncol = 6, byrow = TRUE) %>%
+  # convert to dataframe
+  as.data.frame()
+
+nrow(base_buy)
+
+base_buy$V6 <- as.factor(base_buy$V6)
+
+## explore this data visually
+# one vector over another one
+ggplot(base_buy, aes(V1, V2, col = V6))+geom_point()
+ggplot(base_buy, aes(V1, V3, col = V6))+geom_point()
+
+# make this data as long format
+base_buy_long <- base_buy %>% gather(key = "vector", value = "base_value", -V6)
+# visualize it
+ggplot(base_buy_long, aes(x = vector, y = base_value, col = V6))+geom_jitter()
+# with facets
+ggplot(base_buy_long, aes(x = vector, y = base_value, col = V6))+geom_jitter()+facet_grid(vector ~ .)
+# probably better ones
+ggplot(base_buy_long, aes(x = vector, y = base_value, col = V6))+geom_jitter()+facet_grid(.~ vector)
+ggplot(base_buy_long, aes(x = vector, y = base_value, col = V6))+geom_jitter()+facet_grid(V6 ~ vector)
+
+
+library(caret)
+
+# Create index to split based on labels  
+index <- createDataPartition(base_buy$V6, p=0.75, list=FALSE)
+
+# Subset training set with index
+d.training <- base_buy[index,]
+
+# Subset test set with index
+d.test <- base_buy[-index,]
+
+# Train a model
+model_knn <- train(x = d.training[ , 1:5],
+                   y = d.training[ , 6],
+                   method = 'knn')
+
+# Predict the probabilities (newdata should come from new observations)
+predict_prob <- predict(object = model_knn, type = "prob", newdata = d.test[,1:5])
+
+# Predict the classes
+predict_class <- predict(object = model_knn, type = "raw", newdata = d.test[,1:5])
+
+
+# Evaluate the predictions
+table(predict_class)
+
+# Confusion matrix 
+confusionMatrix(predict_class,d.test[,6])
+

kNN/read_binary_files.R

@@ -0,0 +1,3 @@
+# read dat file
+readBin("test_data/Buy_Position8132101.dat",what = "double",n = 600, endian = "little")
+

model/LOG/2018-11-03-75-15C.csv

@@ -0,0 +1,3 @@
+ExpectedPnL,AchievedPnL,FinalOutcome,FinalQuality,new_or_old,num_bars,timeframe,model_type
+138912.2999999999,94146.69999999994,VeryGood,0.6777419993206203,NEW,75,15,C
+138912.2999999999,70123.49999999997,VeryGood,0.5048041098557838,PREV,75,15,C

model/LOG/2018-11-03-75-15R.csv

@@ -0,0 +1,3 @@
+ExpectedPnL,AchievedPnL,TotalTrades,TPSL_Level,FinalOutcome,FinalQuality,new_or_old,num_bars,timeframe,model_type
+79029,71362.09999999998,2373,10,VeryGood,0.9029862444128276,NEW,75,15,R
+83044,55962.09999999996,1487,30,VeryGood,0.6738849276601737,PREV,75,15,R

model/LOG/2018-11-03-75-1C.csv

@@ -0,0 +1,3 @@
+ExpectedPnL,AchievedPnL,FinalOutcome,FinalQuality,new_or_old,num_bars,timeframe,model_type
+50947.299999999974,17466.699999999975,VeryGood,0.34283857958549574,NEW,75,1,C
+50947.299999999974,17107.100000000013,VeryGood,0.3357803056574537,PREV,75,1,C

model/LOG/2018-11-03-75-1R.csv

@@ -0,0 +1,3 @@
+ExpectedPnL,AchievedPnL,TotalTrades,TPSL_Level,FinalOutcome,FinalQuality,new_or_old,num_bars,timeframe,model_type
+9864,4470.100000000012,529,10,VeryGood,0.45317315031252003,NEW,75,1,R
+20402,8083.899999999953,1071,10,VeryGood,0.39623075974791083,PREV,75,1,R

model/LOG/2018-11-03-75-60C.csv

@@ -0,0 +1,3 @@
+ExpectedPnL,AchievedPnL,FinalOutcome,FinalQuality,new_or_old,num_bars,timeframe,model_type
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+97012.50000000001,54150.69999999998,VeryGood,0.558182707838492,PREV,75,60,C

model/LOG/2018-11-03-75-60R.csv

@@ -0,0 +1,3 @@
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+68139,38653.10000000002,474,80,VeryGood,0.5672683770421224,PREV,75,60,R

model/LOG/2018-11-10-75-15C.csv

@@ -0,0 +1,3 @@
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+136220.69999999995,68827.09999999989,VeryGood,0.5052616815907842,PREV,75,15,C

model/LOG/2018-11-10-75-15R.csv

@@ -0,0 +1,3 @@
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model/LOG/2018-11-10-75-1C.csv

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model/LOG/2018-11-10-75-1R.csv

@@ -0,0 +1,3 @@
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model/LOG/2018-11-10-75-60C.csv

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model/LOG/2018-11-10-75-60R.csv

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model/LOG/2018-11-17-75-15C.csv

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model/LOG/2018-11-17-75-15R.csv

@@ -0,0 +1,3 @@
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model/LOG/2018-11-17-75-1C.csv

@@ -0,0 +1,3 @@
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model/LOG/2018-11-17-75-1R.csv

@@ -0,0 +1,3 @@
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model/LOG/2018-11-17-75-60C.csv

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model/LOG/2018-11-17-75-60R.csv

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model/LOG/2018-11-24-75-15C.csv

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model/LOG/2018-11-24-75-15R.csv

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model/LOG/2018-11-24-75-1C.csv

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model/LOG/2018-11-24-75-1R.csv

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model/LOG/2018-11-24-75-60C.csv

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model/LOG/2018-11-24-75-60R.csv

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model/LOG/2018-12-01-75-15C.csv

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model/LOG/2018-12-01-75-15R.csv

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model/LOG/2018-12-01-75-1C.csv

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model/LOG/2018-12-01-75-1R.csv

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model/LOG/2018-12-01-75-60C.csv

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model/LOG/2018-12-01-75-60R.csv

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model/LOG/2018-12-08-75-15C.csv

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model/LOG/2018-12-08-75-15R.csv

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model/LOG/2018-12-08-75-1C.csv

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model/LOG/2018-12-08-75-1R.csv

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model/LOG/2018-12-08-75-60C.csv

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model/LOG/2018-12-08-75-60R.csv

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model/LOG/2018-12-15-75-15C.csv

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model/LOG/2018-12-15-75-15R.csv

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model/LOG/2018-12-15-75-1C.csv

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model/LOG/2018-12-15-75-1R.csv

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model/LOG/2018-12-15-75-60C.csv

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model/LOG/2018-12-15-75-60R.csv

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model/LOG/2018-12-22-75-15C.csv

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model/LOG/2018-12-22-75-15R.csv

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model/LOG/2018-12-22-75-1C.csv

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model/LOG/2018-12-22-75-1R.csv

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model/LOG/2018-12-22-75-60C.csv

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model/LOG/2018-12-22-75-60R.csv

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+78613,43856.899999999994,651,60,VeryGood,0.5578835554451762,PREV,75,60,R

model/LOG/2018-12-29-75-15C.csv

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+143397.59999999995,70630.20000000003,VeryGood,0.49254799209153605,PREV,75,15,C

model/LOG/2018-12-29-75-15R.csv

@@ -0,0 +1,3 @@
+ExpectedPnL,AchievedPnL,TotalTrades,TPSL_Level,FinalOutcome,FinalQuality,new_or_old,num_bars,timeframe,model_type
+70755,67621.70000000006,1662,20,VeryGood,0.955716202451112,NEW,75,15,R
+80868,58409.00000000002,1478,30,VeryGood,0.7222758065955933,PREV,75,15,R

model/LOG/2018-12-29-75-1C.csv

@@ -0,0 +1,3 @@
+ExpectedPnL,AchievedPnL,FinalOutcome,FinalQuality,new_or_old,num_bars,timeframe,model_type
+52198.89999999992,19171.700000000004,VeryGood,0.3672816853089216,NEW,75,1,C
+52198.89999999992,18477.899999999958,VeryGood,0.35399021750651766,PREV,75,1,C

model/LOG/2018-12-29-75-1R.csv

@@ -0,0 +1,3 @@
+ExpectedPnL,AchievedPnL,TotalTrades,TPSL_Level,FinalOutcome,FinalQuality,new_or_old,num_bars,timeframe,model_type
+14382,4915.2999999999965,679,10,VeryGood,0.3417674847603427,NEW,75,1,R
+22506,8659.099999999988,1155,10,VeryGood,0.38474628816872647,PREV,75,1,R

model/LOG/2018-12-29-75-60C.csv

@@ -0,0 +1,3 @@
+ExpectedPnL,AchievedPnL,FinalOutcome,FinalQuality,new_or_old,num_bars,timeframe,model_type
+99324.20000000003,54975.00000000003,VeryGood,0.5534904881655323,NEW,75,60,C
+99324.20000000003,51256.59999999996,VeryGood,0.5160534889623536,PREV,75,60,C

model/LOG/2018-12-29-75-60R.csv

@@ -0,0 +1,3 @@
+ExpectedPnL,AchievedPnL,TotalTrades,TPSL_Level,FinalOutcome,FinalQuality,new_or_old,num_bars,timeframe,model_type
+77663,47852.20000000001,747,40,VeryGood,0.6161518347010136,NEW,75,60,R
+93470,50251.900000000016,972,30,VeryGood,0.5376259756738784,PREV,75,60,R

model/LOG/AI_T-60.csv

@@ -1,2 +1,2 @@
 TPSL_Level
-100
+40