diff options
Diffstat (limited to 'lstm.py')
| -rwxr-xr-x | lstm.py | 106 |
1 files changed, 85 insertions, 21 deletions
@@ -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() |