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authorbloodstalker <thabogre@gmail.com>2018-09-03 12:20:59 +0000
committerbloodstalker <thabogre@gmail.com>2018-09-03 12:20:59 +0000
commit93c602dcc2b408c1a6307d65e80f48da9115370c (patch)
tree9e208ec032ded9cfa75667d233c97889c5b0f901
parentupdate (diff)
downloadseer-93c602dcc2b408c1a6307d65e80f48da9115370c.tar.gz
seer-93c602dcc2b408c1a6307d65e80f48da9115370c.zip
update
Diffstat (limited to '')
-rwxr-xr-xcnn.py186
1 files changed, 156 insertions, 30 deletions
diff --git a/cnn.py b/cnn.py
index 93e3650..8e8eb98 100755
--- a/cnn.py
+++ b/cnn.py
@@ -1,5 +1,6 @@
#!/usr/bin/python3
# _*_ coding=utf-8 _*_
+# original source-https://medium.com/@huangkh19951228/predicting-cryptocurrency-price-with-tensorflow-and-keras-e1674b0dc58a
import argparse
import code
@@ -14,6 +15,15 @@ import urllib3
import requests
from pathlib import Path
from sklearn.preprocessing import MinMaxScaler
+import h5py
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation, Flatten, Reshape
+from keras.layers import Conv1D, MaxPooling1D, LeakyReLU, PReLU
+from keras.layers import LSTM
+from keras.utils import np_utils
+from keras.callbacks import CSVLogger, ModelCheckpoint
+import tensorflow as tf
+from keras.backend.tensorflow_backend import set_session
def SigHandler_SIGINT(signum, frame):
print()
@@ -38,7 +48,7 @@ class PastSampler(object):
if self.sliding_window:
I = np.arange(M) + np.arange(A.shape[0] - M + 1).reshape(-1,1)
else:
- if A.shapep[0]%M == 0:
+ if A.shape[0]%M == 0:
I = np.arange(M) + np.arange(0, A.shape[0], M).reshape(-1,1)
else:
I = np.arange(M) + np.arange(0, A.shape[0] - M, M).reshape(-1,1)
@@ -48,43 +58,159 @@ class PastSampler(object):
return B[:, :ci], B[:, ci:]
def getData(symbol_str):
- data_file = Path("./" + symbol_str + ".csv")
+ data_file = Path("./cnn/" + symbol_str + ".csv")
original_columns =["close", "date", "high", "low", "open"]
new_columns = ["Close", "Timestamp", "High", "Low", "Open"]
columns = ["Close"]
- if data_file.is_file():
- original_data_file = pd.read_csv(data_file).loc[:, columns]
- return pd.read_csv(data_file).loc[:, columns], original_data_file
- else:
- url = "https://poloniex.com/public?command=returnChartData&currencyPair=USDT_" + symbol_str + "&start=1356998100&end=9999999999&period=300"
- r = requests.get(url)
- d = json.loads(r.content.decode("utf-8"))
- df = pd.DataFrame(d)
-
- df = df.loc[:, original_columns]
- df.columns = new_columns
- df.to_csv(symbol_str + ".csv", index=None)
- df = pd.read_csv(data_file)
- time_stamps = df["Timestamp"]
- df = df.loc[:, columns]
- original_data_file = pd.read_csv(data_file).loc[:, columns]
- return df
+ url = "https://poloniex.com/public?command=returnChartData&currencyPair=USDT_" + symbol_str + "&start=1356998100&end=9999999999&period=300"
+ r = requests.get(url)
+ d = json.loads(r.content.decode("utf-8"))
+ df = pd.DataFrame(d)
+
+ df = df.loc[:, original_columns]
+ df.columns = new_columns
+ df.to_csv("./cnn/" + symbol_str + ".csv", index=None)
+ df = pd.read_csv(data_file)
+ time_stamps = df["Timestamp"]
+ df = df.loc[:, columns]
+ original_df = pd.read_csv(data_file).loc[:, columns]
+ return df, original_df, time_stamps
+
+def Scaler(df, original_df, time_stamps, symbol_str):
+ file_name="./cnn/" + symbol_str + "_close.h5"
+ scaler = MinMaxScaler()
+ columns= ["Close"]
+ for c in columns:
+ df[c] = scaler.fit_transform(df[c].values.reshape(-1,1))
+ A = np.array(df)[:,None,:]
+ original_A = np.array(original_df)[:,None,:]
+ time_stamps = np.array(time_stamps)[:,None,None]
+ NPS, NFS = 256, 16
+ ps = PastSampler(NPS, NFS, sliding_window=False)
+ B, Y = ps.transform(A)
+ input_times, output_times = ps.transform(time_stamps)
+ original_B, original_Y = ps.transform(original_A)
+
+ with h5py.File(file_name, "w") as f:
+ f.create_dataset("inputs", data=B)
+ f.create_dataset("outputs", data=Y)
+ f.create_dataset("input_times", data=input_times)
+ f.create_dataset("output_times", data=output_times)
+ f.create_dataset("original_datas", data=np.array(original_df))
+ f.create_dataset("original_inputs", data=original_B)
+ f.create_dataset("original_outputs", data=original_Y)
+
+def cnn_type_1(symbol_str):
+ df, original_df, time_stamps = getData(symbol_str)
+ Scaler(df, original_df, time_stamps, symbol_str)
+ # run on gpu
+ '''
+ os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
+ os.environ["CUDA_VISIBLE_DEVICES"] = "1"
+ os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"
+ '''
+
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ set_session(tf.Session(config=config))
+
+ with h5py.File("".join("./cnn/" + symbol_str + "_close.h5"), "r") as hf:
+ datas = hf["inputs"].value
+ labels = hf["outputs"].value
+
+ output_file_name = "cnn/" + symbol_str + "_CNN_2_relu"
+ step_size = datas.shape[1]
+ batch_size = 8
+ nb_features = datas.shape[2]
+
+ epochs = 100
+
+ #split training validation
+ training_size = int(0.8* datas.shape[0])
+ training_datas = datas[:training_size,:]
+ training_labels = labels[:training_size,:]
+ validation_datas = datas[training_size:,:]
+ validation_labels = labels[training_size:,:]
+
+ model = Sequential()
+
+ # 2 Layers
+ model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=3, filters=8, kernel_size=20))
+ model.add(Dropout(0.5))
+ model.add(Conv1D( strides=4, filters=nb_features, kernel_size=16))
+
+ '''
+ # 3 Layers
+ model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=3, filters=8, kernel_size=8))
+ #model.add(LeakyReLU())
+ model.add(Dropout(0.5))
+ model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=8))
+ #model.add(LeakyReLU())
+ model.add(Dropout(0.5))
+ model.add(Conv1D( strides=2, filters=nb_features, kernel_size=8))
+ # 4 layers
+ model.add(Conv1D(activation='relu', input_shape=(step_size, nb_features), strides=2, filters=8, kernel_size=2))
+ #model.add(LeakyReLU())
+ model.add(Dropout(0.5))
+ model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=2))
+ #model.add(LeakyReLU())
+ model.add(Dropout(0.5))
+ model.add(Conv1D(activation='relu', strides=2, filters=8, kernel_size=2))
+ #model.add(LeakyReLU())
+ model.add(Dropout(0.5))
+ model.add(Conv1D( strides=2, filters=nb_features, kernel_size=2))
+ '''
+
+ model.compile(loss='mse', optimizer='adam')
+ model.fit(training_datas, training_labels,verbose=1, batch_size=batch_size,validation_data=(validation_datas,validation_labels), epochs = epochs, callbacks=[CSVLogger(output_file_name+'.csv', append=True),ModelCheckpoint(output_file_name+'-{epoch:02d}-{val_loss:.5f}.hdf5', monitor='val_loss', verbose=1,mode='min')])
+
+def lstm_type_cnn_1(symbol_str):
+ df, original_df, time_stamps = getData(symbol_str)
+ Scaler(df, original_df, time_stamps, symbol_str)
+ os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
+ os.environ['CUDA_VISIBLE_DEVICES'] = '1'
+ os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
+
+ config = tf.ConfigProto()
+ config.gpu_options.allow_growth = True
+ set_session(tf.Session(config=config))
+
+ with h5py.File("".join("./cnn/" + symbol_str + "_close.h5"), "r") as hf:
+ datas = hf['inputs'].value
+ labels = hf['outputs'].value
+
+ step_size = datas.shape[1]
+ units= 50
+ second_units = 30
+ batch_size = 8
+ nb_features = datas.shape[2]
+ epochs = 100
+ output_size=16
+ output_file_name = "cnn/" + symbol_str + "_CNN_LSTM_2_relu"
+ #split training validation
+ training_size = int(0.8* datas.shape[0])
+ training_datas = datas[:training_size,:]
+ training_labels = labels[:training_size,:,0]
+ validation_datas = datas[training_size:,:]
+ validation_labels = labels[training_size:,:,0]
+
+ #build model
+ model = Sequential()
+ model.add(LSTM(units=units,activation='tanh', input_shape=(step_size,nb_features),return_sequences=False))
+ model.add(Dropout(0.8))
+ model.add(Dense(output_size))
+ model.add(LeakyReLU())
+ model.compile(loss='mse', optimizer='adam')
+ model.fit(training_datas, training_labels, batch_size=batch_size,validation_data=(validation_datas,validation_labels), epochs = epochs, callbacks=[CSVLogger(output_file_name+'.csv', append=True),ModelCheckpoint(output_file_name+'-{epoch:02d}-{val_loss:.5f}.hdf5', monitor='val_loss', verbose=1,mode='min')])
+
+
# write code here
def premain(argparser):
signal.signal(signal.SIGINT, SigHandler_SIGINT)
#here
- columns = ["Close"]
- btc_df, orig_btc = getData("BTC")
- eth_df, orig_eth = getData("ETH")
- scaler = MinMaxScaler()
- for c in columns:
- btc_df[c] = scaler.fit_transform(btc_df[c].values.reshape(-1, 1))
- eth_df[c] = scaler.fit_transform(eth_df[c].values.reshape(-1, 1))
-
- A = np.array(eth_df)[:,None,:]
- original_A = np.array(orig_eth)[:,None,:]
- time_stamps = np.array(time_stamps)[:, None, None]
+ #cnn_type_1("ETH")
+ lstm_type_cnn_1("ETH")
def main():
argparser = Argparser()