diff options
| -rwxr-xr-x | cnn.py | 104 | ||||
| -rwxr-xr-x | lstm.py | 84 | ||||
| -rwxr-xr-x | run.sh | 3 |
3 files changed, 191 insertions, 0 deletions
@@ -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¤cyPair=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() @@ -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() @@ -0,0 +1,3 @@ +#!/usr/bin/bash + +./lstm.py |