diff options
Diffstat (limited to 'lstm.py')
| -rwxr-xr-x | lstm.py | 41 |
1 files changed, 30 insertions, 11 deletions
@@ -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(): |