update

4 files changed, 139 insertions, 25 deletions

diff --git a/cnn.py b/cnn.py
index 7d402c3..93e3650 100755
--- a/cnn.py
+++ b/cnn.py
@@ -1,4 +1,5 @@
 #!/usr/bin/python3
+# _*_ coding=utf-8 _*_
 
 import argparse
 import code
diff --git a/lstm.py b/lstm.py
index ae04d9f..751b60d 100755
--- a/lstm.py
+++ b/lstm.py
@@ -1,4 +1,6 @@
 #!/usr/bin/python3
+# _*_ coding=utf-8 _*_
+#original source:https://github.com/dashee87/blogScripts/blob/master/Jupyter/2017-11-20-predicting-cryptocurrency-prices-with-deep-learning.ipynb
 
 import argparse
 import code
@@ -16,6 +18,9 @@ from keras.layers import Activation, Dense
 from keras.layers import LSTM
 from keras.layers import Dropout
 
+window_len = 10
+split_date = "2017-06-01"
+
 def SigHandler_SIGINT(signum, frame):
     print()
     sys.exit(0)
@@ -28,23 +33,50 @@ class Argparser(object):
         parser.add_argument("--dbg", action="store_true", help="debug", default=False)
         self.args = parser.parse_args()
 
-def getData_CMC(crypto):
-    coin_market_info = pd.read_html("https://coinmarketcap.com/currencies/"+crypto+"/historical-data/?start=20130428&end="+time.strftime("%Y%m%d"))[0]
-    coin_market_info =  coin_market_info.assign(Date=pd.to_datetime(coin_market_info['Date']))
-    #new_list = list(coin_market_info.keys())
-    #print(repr(new_list))
-    #for k,v in coin_market_info.items():
-        #print(repr(k) + " : " + repr(v))
-    if crypto == "ethereum": coin_market_info.loc[coin_market_info["Market Cap"]=="-","Market Cap"]=0
-    if crypto == "dogecoin": coin_market_info.loc[coin_market_info["Volume"]=="-","Volume"]=0
-    #coin_market_info.loc[coin_market_info['High']=="-",'High']=0
-    #coin_market_info.loc[coin_market_info['Low']=="-",'Low']=0
-    #coin_market_info.loc[coin_market_info['Open']=="-",'Open']=0
-    #coin_market_info.loc[coin_market_info['Close']=="-",'Close']=0
+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 =  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
+    market_info["Volume"] = market_info["Volume"].astype("int64")
+    market_info.columns = market_info.columns.str.replace("*", "")
+    print(type(market_info))
     print(crypto + " head: ")
-    print(coin_market_info.head())
-    #print(repr(coin_market_info))
-    return coin_market_info
+    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 = market_info[['Date']+[metric for metric in ['Close','Volume','close_off_high','volatility']]]
+    model_data = model_data.sort_values(by='Date')
+    print(model_data.head())
+    return model_data
+
+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)
+    test_set = test_set.drop('Date', 1)
+    norm_cols = [coin+metric for coin in [] for metric in ['Close', 'Volume']]
+    LSTM_training_inputs = []
+    for i in range(len(training_set) - window_len):
+        temp_set = training_set[i:(i+window_len)].copy()
+        for col in norm_cols:
+            temp_set.loc[:, col] = temp_set[col]/temp_set[col].iloc[0] -1
+        LSTM_training_inputs.append(temp_set)
+    LSTM_training_outputs = (training_set["Close"][window_len:].values/training_set["Close"][:-window_len].values) - 1
+    LSTM_test_inputs = []
+    for i in range(len(test_set)-window_len):
+        temp_set = test_set[i:(i+window_len)].copy()
+        for col in norm_cols:
+            temp_set.loc[:, col] = temp_set[col]/temp_set[col].iloc[0] - 1
+        LSTM_test_inputs.append(temp_set)
+    LSTM_test_outputs = (test_set['Close'][window_len:].values/test_set['Close'][:-window_len].values)-1
+    print(LSTM_training_inputs[0])
+    LSTM_training_inputs = [np.array(LSTM_training_input) for LSTM_training_input in LSTM_training_inputs]
+    LSTM_training_inputs = np.array(LSTM_training_inputs)
+
+    LSTM_test_inputs = [np.array(LSTM_test_inputs) for LSTM_test_inputs in LSTM_test_inputs]
+    LSTM_test_inputs = np.array(LSTM_test_inputs)
+    return LSTM_training_inputs, LSTM_test_inputs, training_set
 
 def build_model(inputs, output_size, neurons, activ_func="linear", dropout=0.25, loss="mae", optimizer="adam"):
     model = Sequential()
@@ -55,15 +87,47 @@ def build_model(inputs, output_size, neurons, activ_func="linear", dropout=0.25,
     model.compile(loss=loss, optimizer=optimizer)
     return model
 
+def lstm_type_1(crypto, crypto_short):
+    model_data = getData_CMC(crypto, crypto_short)
+    np.random.seed(202)
+    training_inputs, test_inputs, training_set = get_sets(crypto, model_data)
+    model = build_model(training_inputs, output_size=1, neurons=20)
+    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_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 = get_sets(crypto, model_data)
+    model = build_model(training_inputs, output_size=pred_range, neurons=neuron_count)
+    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):
+        training_outputs.append((training_set['Close'][i:i+pred_range].values/training_set['Close'].values[i-window_len])-1)
+    training_outputs = np.array(training_outputs)
+    history = model.fit(training_inputs[:-pred_range], training_outputs, epochs=50, batch_size=1, verbose=2, shuffle=True)
+
+def lstm_type_3(crypto, crypto_short, pred_range, neuron_count):
+    model_data = getData_CMC(crypto, crypto_short)
+    np.random.seed(202)
+    training_inputs, test_inputs, training_set = get_sets(crypto, model_data)
+    model = build_model(training_inputs, output_size=1, neurons=neuron_count)
+    training_outputs = (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1
+    training_outputs = []
+    for rand_seed in range(775, 800):
+        print(rand_seed)
+        np.random.seed(rand_seed)
+        temp_model = build_model(training_inputs, output_size=1, neurons=neuron_count)
+        temp_model.fit(training_inputs, (training_set['Close'][window_len:].values/training_set['Close'][:-window_len].values)-1, epochs=50, batch_size=1, verbose=0, shuffle=True)
+        temp_model.save(crypto + '_model_randseed_%d.h5'%rand_seed)
+
 # write code here
 def premain(argparser):
     signal.signal(signal.SIGINT, SigHandler_SIGINT)
     #here
-    #getData_CMC("bitcoin")
-    eth_data = getData_CMC("ethereum")
-    doge_data = getData_CMC("dogecoin")
-    np.random.seed(202)
-    eth_model = build_model(LSTM_training_inputs, output_size=1, neurons=20)
+    #lstm_type_1("ethereum", "ether")
+    #lstm_type_2("ethereum", "ether", 5, 20)
+    lstm_type_3("ethereum", "ether", 5, 20)
 
 def main():
     argparser = Argparser()
diff --git a/marionette.py b/marionette.py
index 2222940..baa6f04 100755
--- a/marionette.py
+++ b/marionette.py
@@ -1,4 +1,5 @@
 #!/usr/bin/python3
+# _*_ coding=utf-8 _*_
 
 import argparse
 import code
@@ -31,10 +32,7 @@ class Argparser(object):
         parser.add_argument("--dbg", action="store_true", help="debug", default=False)
         self.args = parser.parse_args()
 
-# write code here
-def premain(argparser):
-    signal.signal(signal.SIGINT, SigHandler_SIGINT)
-    #here
+def cnn_type_1():
     url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"
     names = ["sepal-length", "sepal-width", "petal-length", "petal-width", "class"]
     dataset = pandas.read_csv(url, names=names)
@@ -84,6 +82,12 @@ def premain(argparser):
     print(confusion_matrix(Y_validation, predictions))
     print(classification_report(Y_validation, predictions))
 
+# write code here
+def premain(argparser):
+    signal.signal(signal.SIGINT, SigHandler_SIGINT)
+    #here
+    cnn_type_1()
+
 def main():
     argparser = Argparser()
     if argparser.args.dbg:
diff --git a/seer.py b/seer.py
new file mode 100755
index 0000000..c63c6cf
--- /dev/null
+++ b/seer.py
@@ -0,0 +1,45 @@
+#!/usr/bin/python3
+# _*_ coding=utf-8 _*_
+
+import argparse
+import code
+import readline
+import signal
+import sys
+from ltsm import lstm_type_1, lstm_type_2, lstm_type_3
+from cnn import cnn_type_1
+
+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()
+
+# 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()