update

20 files changed, 94 insertions, 14 deletions

diff --git a/dlstuff/four.py b/dlstuff/four.py
index 983386a..90f58b9 100755
--- a/dlstuff/four.py
+++ b/dlstuff/four.py
@@ -1,4 +1,4 @@
-#!/usr/bin/python3
+#!python
 # _*_ coding=utf-8 _*_
 
 import argparse
@@ -48,6 +48,7 @@ def premain(argparser):
     signal.signal(signal.SIGINT, SigHandler_SIGINT)
     #here
     (train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()
+    print(type(train_data))
     mean = train_data.mean(axis=0)
     train_data -= mean
     std = train_data.std(axis=0)
diff --git a/dlstuff/two.py b/dlstuff/two.py
index 8287708..1d4a614 100755
--- a/dlstuff/two.py
+++ b/dlstuff/two.py
@@ -66,14 +66,12 @@ def premain(argparser):
     x_test = vectorize_sequences(test_data)
     y_train = np.asarray(train_labels).astype("float32")
     y_test = np.asarray(test_labels).astype("float32")
-
     model = models.Sequential()
     model.add(layers.Dense(16, kernel_regularizer=regularizers.l2(0.001), activation="relu", input_shape=(10000,)))
     model.add(layers.Dropout(0.5))
     model.add(layers.Dense(16, kernel_regularizer=regularizers.l2(0.001), activation="relu"))
     model.add(layers.Dropout(0.5))
     model.add(layers.Dense(1, activation="sigmoid"))
-
     x_val = x_train[:10000]
     partial_x_train = x_train[10000:]
     y_val = y_train[:10000]
diff --git a/lstm-models/ethereum_model_randseed_775.h5 b/lstm-models/ethereum_model_randseed_775.h5
index 27fc878..e58c5d7 100644
--- a/lstm-models/ethereum_model_randseed_775.h5
+++ b/lstm-models/ethereum_model_randseed_775.h5
diff --git a/lstm-models/ethereum_model_randseed_776.h5 b/lstm-models/ethereum_model_randseed_776.h5
index e45229a..ab642c0 100644
--- a/lstm-models/ethereum_model_randseed_776.h5
+++ b/lstm-models/ethereum_model_randseed_776.h5
diff --git a/lstm-models/ethereum_model_randseed_777.h5 b/lstm-models/ethereum_model_randseed_777.h5
index e341f72..d7c5276 100644
--- a/lstm-models/ethereum_model_randseed_777.h5
+++ b/lstm-models/ethereum_model_randseed_777.h5
diff --git a/lstm-models/ethereum_model_randseed_778.h5 b/lstm-models/ethereum_model_randseed_778.h5
index a53d9bf..1d15190 100644
--- a/lstm-models/ethereum_model_randseed_778.h5
+++ b/lstm-models/ethereum_model_randseed_778.h5
diff --git a/lstm-models/ethereum_model_randseed_779.h5 b/lstm-models/ethereum_model_randseed_779.h5
index 01a57f4..30ea4b4 100644
--- a/lstm-models/ethereum_model_randseed_779.h5
+++ b/lstm-models/ethereum_model_randseed_779.h5
diff --git a/lstm-models/ethereum_model_randseed_780.h5 b/lstm-models/ethereum_model_randseed_780.h5
index 5882283..9973650 100644
--- a/lstm-models/ethereum_model_randseed_780.h5
+++ b/lstm-models/ethereum_model_randseed_780.h5
diff --git a/lstm-models/ethereum_model_randseed_781.h5 b/lstm-models/ethereum_model_randseed_781.h5
index 4bda2c7..7ea593c 100644
--- a/lstm-models/ethereum_model_randseed_781.h5
+++ b/lstm-models/ethereum_model_randseed_781.h5
diff --git a/lstm-models/ethereum_model_randseed_782.h5 b/lstm-models/ethereum_model_randseed_782.h5
index d6d2177..32b08ba 100644
--- a/lstm-models/ethereum_model_randseed_782.h5
+++ b/lstm-models/ethereum_model_randseed_782.h5
diff --git a/lstm-models/ethereum_model_randseed_783.h5 b/lstm-models/ethereum_model_randseed_783.h5
index f52f6f9..13f3112 100644
--- a/lstm-models/ethereum_model_randseed_783.h5
+++ b/lstm-models/ethereum_model_randseed_783.h5
diff --git a/lstm-models/ethereum_model_randseed_784.h5 b/lstm-models/ethereum_model_randseed_784.h5
index 6d9b7a5..024c70f 100644
--- a/lstm-models/ethereum_model_randseed_784.h5
+++ b/lstm-models/ethereum_model_randseed_784.h5
diff --git a/lstm-models/ethereum_model_randseed_785.h5 b/lstm-models/ethereum_model_randseed_785.h5
index 370cb02..bd1ac60 100644
--- a/lstm-models/ethereum_model_randseed_785.h5
+++ b/lstm-models/ethereum_model_randseed_785.h5
diff --git a/lstm-models/ethereum_model_randseed_786.h5 b/lstm-models/ethereum_model_randseed_786.h5
index 43153a1..748f4cf 100644
--- a/lstm-models/ethereum_model_randseed_786.h5
+++ b/lstm-models/ethereum_model_randseed_786.h5
diff --git a/lstm-models/ethereum_model_randseed_787.h5 b/lstm-models/ethereum_model_randseed_787.h5
index 2bb8ddc..ad30e52 100644
--- a/lstm-models/ethereum_model_randseed_787.h5
+++ b/lstm-models/ethereum_model_randseed_787.h5
diff --git a/lstm-models/ethereum_model_randseed_788.h5 b/lstm-models/ethereum_model_randseed_788.h5
index 74a5a8c..15eac0b 100644
--- a/lstm-models/ethereum_model_randseed_788.h5
+++ b/lstm-models/ethereum_model_randseed_788.h5
diff --git a/lstm-models/ethereum_model_randseed_789.h5 b/lstm-models/ethereum_model_randseed_789.h5
index 4af9d00..14862c9 100644
--- a/lstm-models/ethereum_model_randseed_789.h5
+++ b/lstm-models/ethereum_model_randseed_789.h5
diff --git a/lstm-models/ethereum_model_randseed_790.h5 b/lstm-models/ethereum_model_randseed_790.h5
index 45ba97b..cf56e88 100644
--- a/lstm-models/ethereum_model_randseed_790.h5
+++ b/lstm-models/ethereum_model_randseed_790.h5
diff --git a/lstm.py b/lstm.py
index 765b0e1..e221e35 100755
--- a/lstm.py
+++ b/lstm.py
@@ -1,4 +1,4 @@
-#!/usr/bin/python3
+#!python
 # _*_ coding=utf-8 _*_
 #original source:https://github.com/dashee87/blogScripts/blob/master/Jupyter/2017-11-20-predicting-cryptocurrency-prices-with-deep-learning.ipynb
 
@@ -22,7 +22,7 @@ from keras.layers import Dropout
 from keras.models import load_model
 
 window_len = 10
-split_date = "2017-06-01"
+split_date = "2018-03-01"
 
 def SigHandler_SIGINT(signum, frame):
     print()
@@ -37,22 +37,29 @@ class Argparser(object):
         self.args = parser.parse_args()
 
 def getData_CMC(crypto, crypto_short):
-    market_info = pd.read_html("https://coinmarketcap.com/currencies/"+crypto+"/historical-data/?start=20130428&end="+time.strftime("%Y%m%d"))[0]
+    market_info = pd.read_html("https://coinmarketcap.com/currencies/"+crypto+"/historical-data/?start=20160428&end="+time.strftime("%Y%m%d"))[0]
+    print(type(market_info))
     market_info =  market_info.assign(Date=pd.to_datetime(market_info['Date']))
-    if crypto == "ethereum": market_info.loc[market_info["Market Cap"]=="-","Market Cap"]=0
-    if crypto == "dogecoin": market_info.loc[market_info["Volume"]=="-","Volume"]=0
+    #print(market_info)
+    #if crypto == "ethereum": market_info.loc[market_info["Market Cap"]=="-","Market Cap"]=0
+    #if crypto == "dogecoin": market_info.loc[market_info["Volume"]=="-","Volume"]=0
     market_info["Volume"] = market_info["Volume"].astype("int64")
     market_info.columns = market_info.columns.str.replace("*", "")
-    print(type(market_info))
-    print(crypto + " head: ")
-    print(market_info.head())
+    #print(type(market_info))
+    #print(crypto + " head: ")
+    #print(market_info.head())
     kwargs = {'close_off_high': lambda x: 2*(x['High']- x['Close'])/(x['High']-x['Low'])-1, 'volatility': lambda x: (x['High']- x['Low'])/(x['Open'])}
     market_info = market_info.assign(**kwargs)
     model_data = market_info[['Date']+[coin+metric for coin in [""] for metric in ['Close','Volume','close_off_high','volatility']]]
     model_data = model_data.sort_values(by='Date')
-    print(model_data.head())
+    #print(model_data.head())
+    print(type(model_data))
     return model_data
 
+def getData_Stock(name, period):
+    info = pd.from_csv(path="./data/"+name+"/"+period+".csv")
+    return info
+
 def get_sets(crypto, model_data):
     training_set, test_set = model_data[model_data['Date']<split_date], model_data[model_data['Date']>=split_date]
     training_set = training_set.drop('Date', 1)
@@ -93,15 +100,26 @@ def lstm_type_1(crypto, crypto_short):
     model_data = getData_CMC(crypto, crypto_short)
     np.random.seed(202)
     training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data)
-    model = build_model(training_inputs, output_size=1, neurons=20)
+    model = build_model(training_inputs, output_size=1, neurons=20, loss="mse")
     training_outputs = (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1
     history = model.fit(training_inputs, training_outputs, epochs=50, batch_size=1, verbose=2, shuffle=True)
 
+def lstm_type_4(crypto, crypto_short, crypto2, crypto_short2):
+    model_data = getData_CMC(crypto, crypto_short)
+    model_data2 = getData_CMC(crypto2, crypto_short2)
+    np.random.seed(202)
+    training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data)
+    training_inputs2, test_inputs2, training_set2, test_set2 = get_sets(crypto2, model_data2)
+    return
+    model = build_model(training_inputs/training_inputs2, output_size=1, neurons=20, loss="mse")
+    training_outputs = ((training_set['Close'][window_len:].values)/(training_set['Close'][:-window_len].values))-1
+    history = model.fit(training_inputs/training_inputs2, training_outputs, epochs=10, batch_size=1, verbose=2, shuffle=True)
+
 def lstm_type_2(crypto, crypto_short, pred_range, neuron_count):
     model_data = getData_CMC(crypto, crypto_short)
     np.random.seed(202)
     training_inputs, test_inputs, training_set, test_set = get_sets(crypto, model_data)
-    model = build_model(training_inputs, output_size=pred_range, neurons=neuron_count)
+    model = build_model(training_inputs, output_size=pred_range, neurons=neuron_count, loss="mse")
     training_outputs = (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1
     training_outputs = []
     for i in range(window_len, len(training_set['Close'])-pred_range):
@@ -139,6 +157,7 @@ def premain(argparser):
     lstm_type_1("ethereum", "ether")
     #lstm_type_2("ethereum", "ether", 5, 20)
     #lstm_type_3("ethereum", "ether", 5, 20)
+    #lstm_type_4("ethereum", "ether", "dogecoin", "doge")
     #load_models("ethereum", "eth")
 
 def main():
diff --git a/stock.py b/stock.py
new file mode 100755
index 0000000..6ad60ed
--- /dev/null
+++ b/stock.py
@@ -0,0 +1,62 @@
+#!python
+# _*_ coding=utf-8 _*_
+
+import argparse
+import code
+import readline
+import signal
+import sys
+import pandas as pd
+import numpy as np
+import json
+import os
+import urllib3
+import time
+from keras.models import Sequential
+from keras.layers import Activation, Dense
+from keras.layers import LSTM
+from keras.layers import Dropout
+from keras.models import load_model
+
+def SigHandler_SIGINT(signum, frame):
+    print()
+    sys.exit(0)
+
+class Argparser(object):
+    def __init__(self):
+        parser = argparse.ArgumentParser()
+        parser.add_argument("--string", type=str, help="string")
+        parser.add_argument("--bool", action="store_true", help="bool", default=False)
+        parser.add_argument("--dbg", action="store_true", help="debug", default=False)
+        self.args = parser.parse_args()
+
+def build_model(train_data):
+    model = models.Sequential()
+    model.add(layers.Dense(64, activation="relu", input_shape=(train_data.shape[1],)))
+    model.add(layers.Dense(64, activation="relu"))
+    model.add(layers.Dense(1))
+    model.compile(optimizer="rmsprop", loss="mse", metrics=["acc"])
+    return model
+
+# write code here
+def premain(argparser):
+    signal.signal(signal.SIGINT, SigHandler_SIGINT)
+    #here
+
+def main():
+    argparser = Argparser()
+    if argparser.args.dbg:
+        try:
+            premain(argparser)
+        except Exception as e:
+            print(e.__doc__)
+            if e.message: print(e.message)
+            variables = globals().copy()
+            variables.update(locals())
+            shell = code.InteractiveConsole(variables)
+            shell.interact(banner="DEBUG REPL")
+    else:
+        premain(argparser)
+
+if __name__ == "__main__":
+    main()