update

3 files changed, 191 insertions, 0 deletions

diff --git a/cnn.py b/cnn.py
new file mode 100755
index 0000000..7d402c3
--- /dev/null
+++ b/cnn.py
@@ -0,0 +1,104 @@
+#!/usr/bin/python3
+
+import argparse
+import code
+import readline
+import signal
+import sys
+import json
+import numpy as np
+import os
+import pandas as pd
+import urllib3
+import requests
+from pathlib import Path
+from sklearn.preprocessing import MinMaxScaler
+
+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()
+
+class PastSampler(object):
+    def __init__(self, N, K, sliding_window=True):
+        self.N = N
+        self.K = K
+        self.sliding_window = sliding_window
+
+    def transform(self, A):
+        M = self.N + self.K
+        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:
+                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)
+
+        B = A[I].reshape(-1, M*A.shape[1], A.shape[2])
+        ci = self.N*A.shape[1]
+        return B[:, :ci], B[:, ci:]
+
+def getData(symbol_str):
+    data_file = Path("./" + 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
+
+# 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]
+
+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()
diff --git a/lstm.py b/lstm.py
new file mode 100755
index 0000000..ae04d9f
--- /dev/null
+++ b/lstm.py
@@ -0,0 +1,84 @@
+#!/usr/bin/python3
+
+import argparse
+import code
+import readline
+import signal
+import sys
+import pandas as pd
+import json
+import os
+import numpy as np
+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
+
+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 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
+    print(crypto + " head: ")
+    print(coin_market_info.head())
+    #print(repr(coin_market_info))
+    return coin_market_info
+
+def build_model(inputs, output_size, neurons, activ_func="linear", dropout=0.25, loss="mae", optimizer="adam"):
+    model = Sequential()
+    model.add(LSTM(neurons, input_shape=(inputs.shape[1], inputs.shape[2])))
+    model.add(Dropout(dropout))
+    model.add(Dense(units=output_size))
+    model.add(Activation(activ_func))
+    model.compile(loss=loss, optimizer=optimizer)
+    return model
+
+# 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)
+
+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()
diff --git a/run.sh b/run.sh
new file mode 100755
index 0000000..9e7cb89
--- /dev/null
+++ b/run.sh
@@ -0,0 +1,3 @@
+#!/usr/bin/bash
+
+./lstm.py