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May 9, 2018 03:17
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| #IF YOU FOUND THIS USEFUL, Please Donate some Bitcoin .... 1FWt366i5PdrxCC6ydyhD8iywUHQ2C7BWC | |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| import numpy as np | |
| import pandas as pd | |
| from subprocess import check_output | |
| from keras.layers.core import Dense, Activation, Dropout | |
| from keras.layers.recurrent import LSTM | |
| from keras.layers.advanced_activations import LeakyReLU, PReLU | |
| from keras.models import Sequential | |
| from sklearn.cross_validation import train_test_split | |
| import math | |
| from sklearn.metrics import mean_squared_error | |
| from sklearn.preprocessing import MinMaxScaler | |
| import matplotlib.pyplot as plt | |
| from numpy import newaxis | |
| from keras import backend as K | |
| import math | |
| import os | |
| import requests | |
| import json | |
| import time | |
| import random | |
| import uuid | |
| #ALL EPICS | |
| epic_ids = ["CS.D.AUDUSD.TODAY.IP", "CS.D.EURCHF.TODAY.IP", "CS.D.EURGBP.TODAY.IP", "CS.D.EURJPY.TODAY.IP", "CS.D.EURUSD.TODAY.IP", "CS.D.GBPEUR.TODAY.IP", "CS.D.GBPUSD.TODAY.IP", "CS.D.USDCAD.TODAY.IP", "CS.D.USDCHF.TODAY.IP", "CS.D.USDJPY.TODAY.IP", "CS.D.CADCHF.TODAY.IP", "CS.D.CADJPY.TODAY.IP", "CS.D.CHFJPY.TODAY.IP", "CS.D.EURCAD.TODAY.IP", "CS.D.EURSGD.TODAY.IP", "CS.D.EURZAR.TODAY.IP", "CS.D.GBPCAD.TODAY.IP", "CS.D.GBPCHF.TODAY.IP", "CS.D.GBPJPY.TODAY.IP", "CS.D.GBPSGD.TODAY.IP", "CS.D.GBPZAR.TODAY.IP", "CS.D.MXNJPY.TODAY.IP", "CS.D.NOKJPY.TODAY.IP", "CS.D.PLNJPY.TODAY.IP", "CS.D.SEKJPY.TODAY.IP", "CS.D.SGDJPY.TODAY.IP", "CS.D.USDSGD.TODAY.IP", "CS.D.USDZAR.TODAY.IP", "CS.D.AUDCAD.TODAY.IP", "CS.D.AUDCHF.TODAY.IP", "CS.D.AUDEUR.TODAY.IP", "CS.D.AUDGBP.TODAY.IP", "CS.D.AUDJPY.TODAY.IP", "CS.D.AUDNZD.TODAY.IP", "CS.D.AUDSGD.TODAY.IP", "CS.D.EURAUD.TODAY.IP", "CS.D.EURNZD.TODAY.IP", "CS.D.GBPAUD.TODAY.IP", "CS.D.GBPNZD.TODAY.IP", "CS.D.NZDAUD.TODAY.IP", "CS.D.NZDCAD.TODAY.IP", "CS.D.NZDCHF.TODAY.IP", "CS.D.NZDEUR.TODAY.IP", "CS.D.NZDGBP.TODAY.IP", "CS.D.NZDJPY.TODAY.IP", "CS.D.NZDUSD.TODAY.IP", "CS.D.CHFHUF.TODAY.IP", "CS.D.EURCZK.TODAY.IP", "CS.D.EURHUF.TODAY.IP", "CS.D.EURILS.TODAY.IP", "CS.D.EURMXN.TODAY.IP", "CS.D.EURPLN.TODAY.IP", "CS.D.EURTRY.TODAY.IP", "CS.D.GBPCZK.TODAY.IP", "CS.D.GBPHUF.TODAY.IP", "CS.D.GBPILS.TODAY.IP", "CS.D.GBPMXN.TODAY.IP", "CS.D.GBPPLN.TODAY.IP", "CS.D.GBPTRY.TODAY.IP", "CS.D.TRYJPY.TODAY.IP", "CS.D.USDCZK.TODAY.IP", "CS.D.USDHUF.TODAY.IP", "CS.D.USDILS.TODAY.IP", "CS.D.USDMXN.TODAY.IP", "CS.D.USDPLN.TODAY.IP", "CS.D.USDTRY.TODAY.IP", "CS.D.CADNOK.TODAY.IP", "CS.D.CHFNOK.TODAY.IP", "CS.D.EURDKK.TODAY.IP", "CS.D.EURNOK.TODAY.IP", "CS.D.EURSEK.TODAY.IP", "CS.D.GBPDKK.TODAY.IP", "CS.D.GBPNOK.TODAY.IP", "CS.D.GBPSEK.TODAY.IP", "CS.D.NOKSEK.TODAY.IP", "CS.D.USDDKK.TODAY.IP", "CS.D.USDNOK.TODAY.IP", "CS.D.USDSEK.TODAY.IP", "CS.D.AUDCNH.TODAY.IP", "CS.D.CADCNH.TODAY.IP", "CS.D.CNHJPY.TODAY.IP", "CS.D.BRLJPY.TODAY.IP", "CS.D.GBPINR.TODAY.IP", "CS.D.USDBRL.TODAY.IP", "CS.D.USDIDR.TODAY.IP", "CS.D.USDINR.TODAY.IP", "CS.D.USDKRW.TODAY.IP", "CS.D.USDMYR.TODAY.IP", "CS.D.USDPHP.TODAY.IP", "CS.D.USDTWD.TODAY.IP", "CS.D.EURCNH.TODAY.IP", "CS.D.sp_EURRUB.TODAY.IP", "CS.D.GBPCNH.TODAY.IP", "CS.D.NZDCNH.TODAY.IP", "CS.D.USDCNH.TODAY.IP", "CS.D.sp_USDRUB.TODAY.IP"] | |
| #ALL EPICS | |
| #TESTING | |
| decent_epics = ['CS.D.AUDUSD.TODAY.IP', 'CS.D.EURGBP.TODAY.IP', 'CS.D.EURJPY.TODAY.IP', 'CS.D.EURUSD.TODAY.IP', 'CS.D.GBPUSD.TODAY.IP', 'CS.D.USDCAD.TODAY.IP', 'CS.D.USDCHF.TODAY.IP', 'CS.D.USDJPY.TODAY.IP', 'CS.D.AUDEUR.TODAY.IP', 'CS.D.AUDGBP.TODAY.IP', 'CS.D.AUDJPY.TODAY.IP', 'CS.D.EURAUD.TODAY.IP', 'CS.D.GBPAUD.TODAY.IP'] | |
| #TESTING | |
| # TIME_FRAMES = ["MINUTE/1", "MINUTE_2/1", "MINUTE_3/1", "MINUTE_5/1", "MINUTE_10/1", | |
| # "MINUTE_15/1", "MINUTE_30/1", "HOUR/1", "HOUR_2/1", "HOUR_3/1", "HOUR_4/1", | |
| # "DAY/1", "WEEK/1", "MONTH/1"] | |
| TIME_FRAMES = ["MINUTE_15/16"] | |
| ####################################################################### | |
| # REAL_OR_NO_REAL = 'https://demo-api.ig.com/gateway/deal' | |
| # API_ENDPOINT = "https://demo-api.ig.com/gateway/deal/session" | |
| # API_KEY = '****' | |
| # API_KEY = '****' | |
| # ############################################################# | |
| # #API_KEY = '****' #<- DO NOT USE!! | |
| # data = {"identifier":"****","password": "****"} | |
| ####################################################################### | |
| # FOR REAL.... | |
| ####################################################################### | |
| ####################################################################### | |
| ####################################################################### | |
| REAL_OR_NO_REAL = 'https://api.ig.com/gateway/deal' | |
| API_ENDPOINT = "https://api.ig.com/gateway/deal/session" | |
| API_KEY = '*****' | |
| #################################################### | |
| #API_KEY = '****' #<- DO NOT USE | |
| data = {"identifier":"****","password": "****"} | |
| headers = {'Content-Type':'application/json; charset=utf-8', | |
| 'Accept':'application/json; charset=utf-8', | |
| 'X-IG-API-KEY':API_KEY, | |
| 'Version':'2' | |
| } | |
| r = requests.post(API_ENDPOINT, data=json.dumps(data), headers=headers) | |
| headers_json = dict(r.headers) | |
| CST_token = headers_json["CST"] | |
| print (R"CST : " + CST_token) | |
| x_sec_token = headers_json["X-SECURITY-TOKEN"] | |
| print (R"X-SECURITY-TOKEN : " + x_sec_token) | |
| #GET ACCOUNTS | |
| base_url = REAL_OR_NO_REAL + '/accounts' | |
| authenticated_headers = {'Content-Type':'application/json; charset=utf-8', | |
| 'Accept':'application/json; charset=utf-8', | |
| 'X-IG-API-KEY':API_KEY, | |
| 'CST':CST_token, | |
| 'X-SECURITY-TOKEN':x_sec_token} | |
| auth_r = requests.get(base_url, headers=authenticated_headers) | |
| d = json.loads(auth_r.text) | |
| # print ("-----------------DEBUG-----------------") | |
| # print ("#################DEBUG#################") | |
| # print(auth_r.status_code) | |
| # print(auth_r.reason) | |
| # print (auth_r.text) | |
| # print ("-----------------DEBUG-----------------") | |
| # print ("#################DEBUG#################") | |
| for i in d['accounts']: | |
| if str(i['accountType']) == "SPREADBET": | |
| print ("Spreadbet Account ID is : " + str(i['accountId'])) | |
| spreadbet_acc_id = str(i['accountId']) | |
| #SET SPREAD BET ACCOUNT AS DEFAULT | |
| base_url = REAL_OR_NO_REAL + '/session' | |
| data = {"accountId":spreadbet_acc_id,"defaultAccount": "True"} | |
| auth_r = requests.put(base_url, data=json.dumps(data), headers=authenticated_headers) | |
| # print ("-----------------DEBUG-----------------") | |
| # print ("#################DEBUG#################") | |
| # print(auth_r.status_code) | |
| # print(auth_r.reason) | |
| # print (auth_r.text) | |
| # print ("-----------------DEBUG-----------------") | |
| # print ("#################DEBUG#################") | |
| #ERROR about account ID been the same, Ignore! | |
| ################################################################################### | |
| ##########################END OF LOGIN CODE######################################## | |
| ##########################END OF LOGIN CODE######################################## | |
| ##########################END OF LOGIN CODE######################################## | |
| ##########################END OF LOGIN CODE######################################## | |
| ################################################################################### | |
| #PROGRAMMABLE VALUES | |
| #SET INITIAL VARIABLES, Hacky for now! | |
| orderType_value = "MARKET" | |
| size_value = "1" | |
| expiry_value = "DFB" | |
| guaranteedStop_value = True | |
| currencyCode_value = "GBP" | |
| forceOpen_value = True | |
| stopDistance_value = "0" #Make this a global variable for ease! | |
| real_trader_multiplier = 0.6 | |
| #fix random seed for reproducibility | |
| np.random.seed(7) | |
| #prices to use | |
| close = [] | |
| def humanize_time(secs): | |
| mins, secs = divmod(secs, 60) | |
| hours, mins = divmod(mins, 60) | |
| return '%02d:%02d:%02d' % (hours, mins, secs) | |
| def lowest_spread_epic(): | |
| spreads_and_epics = [] | |
| i_count = 0 | |
| pick_from_epics = [] | |
| for epic_id in epic_ids: | |
| tmp_lst = [] | |
| base_url = REAL_OR_NO_REAL + '/markets/'+ epic_id | |
| auth_r = requests.get(base_url, headers=authenticated_headers) | |
| d = json.loads(auth_r.text) | |
| # print(auth_r.status_code) | |
| # print(auth_r.reason) | |
| # print (auth_r.text) | |
| # print (epic_id) | |
| try: | |
| i_count = i_count + 1 | |
| if epic_id.find('MXN') != -1: | |
| print ("!!DEBUG!!...skipping, FOUND MXN in..." + str(epic_id)) | |
| elif epic_id.find('SEK') != -1: | |
| print ("!!DEBUG!!...skipping, FOUND SEK in..." + str(epic_id)) | |
| elif epic_id.find('NOK') != -1: | |
| print ("!!DEBUG!!...skipping, FOUND NOK in..." + str(epic_id)) | |
| elif epic_id.find('CNH') != -1: | |
| print ("!!DEBUG!!...skipping, FOUND CNH in..." + str(epic_id)) | |
| else: | |
| bid_price = d['snapshot']['bid'] | |
| ask_price = d['snapshot']['offer'] | |
| spread = float(bid_price) - float(ask_price) | |
| if float(spread) > -2: | |
| tmp_lst.append(epic_id) | |
| pick_from_epics.append(epic_id) | |
| # print ("bid : " + str(bid_price)) | |
| # print ("ask : " + str(ask_price)) | |
| # print ("-------------------------") | |
| # print ("spread : " + str(spread)) | |
| # print ("-------------------------") | |
| tmp_lst.append(spread) | |
| spreads_and_epics.append(tmp_lst) | |
| print ("!!DEBUG!!...FOUND GOOD EPIC..." + str(i_count) + "/" + str(len(epic_ids))) | |
| time.sleep(1) | |
| else: | |
| print ("!!DEBUG!!...skipping, NO GOOD EPIC....Checking next epic spreads..." + str(i_count) + "/" + str(len(epic_ids))) | |
| time.sleep(1) | |
| except Exception: | |
| pass | |
| #sorted_list = sorted(spreads_and_epics, key=operator.itemgetter(1)) | |
| # for i in range(len(sorted_list)): | |
| # print(sorted_list[i]) | |
| #print (max(sorted_list, key=lambda x: x[1])) | |
| #lowest_epic = max(sorted_list, key=lambda x: x[1]) | |
| #return (str(lowest_epic[0])) | |
| #random.shuffle(pick_from_epics) | |
| #return(str(random.choice(pick_from_epics))) | |
| return pick_from_epics #Return a list of epics with decent spread, Then choose from later. | |
| def calculate_stop_loss(d): | |
| price_ranges = [] | |
| closing_prices = [] | |
| first_time_round_loop = True | |
| TR_prices = [] | |
| price_compare = "bid" | |
| for i in d['prices']: | |
| if first_time_round_loop == True: | |
| #First time round loop cannot get previous | |
| closePrice = i['closePrice'][price_compare] | |
| closing_prices.append(closePrice) | |
| high_price = i['highPrice'][price_compare] | |
| low_price = i['lowPrice'][price_compare] | |
| price_range = float(high_price - closePrice) | |
| price_ranges.append(price_range) | |
| first_time_round_loop = False | |
| else: | |
| prev_close = closing_prices[-1] | |
| ############################### | |
| closePrice = i['closePrice'][price_compare] | |
| closing_prices.append(closePrice) | |
| high_price = i['highPrice'][price_compare] | |
| low_price = i['lowPrice'][price_compare] | |
| price_range = float(high_price - closePrice) | |
| price_ranges.append(price_range) | |
| TR = max(high_price-low_price, abs(high_price-prev_close), abs(low_price-prev_close)) | |
| #print (TR) | |
| TR_prices.append(TR) | |
| return str(int(float(max(TR_prices)))) | |
| def are_we_going_to_trade(pred_ict_price, epic_id): | |
| base_url = REAL_OR_NO_REAL + '/markets/'+ epic_id | |
| auth_r = requests.get(base_url, headers=authenticated_headers) | |
| d = json.loads(auth_r.text) | |
| MARKET_ID = d['instrument']['marketId'] | |
| CURRENT_PRICE_bid = float(d['snapshot']['bid']) | |
| high_DAY = float(d['snapshot']['high']) | |
| low_DAY = float(d['snapshot']['low']) | |
| netChange = float(d['snapshot']['netChange']) | |
| day_RANGE = float(high_DAY) - float(low_DAY) | |
| if day_RANGE < 0: | |
| day_RANGE = day_RANGE * -1 #Make positive | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| print ("!!DEBUG!!...day_RANGE is ..." + str(day_RANGE)) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| # stopDistance_value = str(int(netChange)) | |
| # stopDistance_value = str(int(day_RANGE)) | |
| #stopDistance_value = str(int(netChange) + int(day_RANGE)) | |
| price_diff = float(CURRENT_PRICE_bid) - float(pred_ict_price) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| print ("!!DEBUG!!...price difference is ..." + str(price_diff)) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| if float(price_diff) > 0: | |
| print ("!!DEBUG!!...SELL") | |
| DIRECTION_TO_TRADE = "SELL" | |
| limitDistance_value = str(int(float(price_diff * real_trader_multiplier))) | |
| else: | |
| print ("!!DEBUG!!...BUY") | |
| DIRECTION_TO_TRADE = "BUY" | |
| limitDistance_value = str(int(float(price_diff * real_trader_multiplier * -1))) | |
| if int(limitDistance_value) < 1 or int(stopDistance_value) < 1: | |
| print ("!!DEBUG!!...Bailing Ooooot, Limit Distance/Stop Loss Below 1, Risk/Reward Not worth a trade") | |
| return None #Really don't bother! | |
| ############################################################################################################### | |
| print ("Order will be a " + str(DIRECTION_TO_TRADE) + " Order, With a limit of: " + str(limitDistance_value)) | |
| print ("stopDistance_value for " + str(epic_id) + " will bet set at " + str(stopDistance_value)) | |
| ############################################################################################################### | |
| #MAKE AN ORDER | |
| base_url = REAL_OR_NO_REAL + '/positions/otc' | |
| data = {"direction":DIRECTION_TO_TRADE,"epic": epic_id, "limitDistance":limitDistance_value, "orderType":orderType_value, "size":size_value,"expiry":expiry_value,"guaranteedStop":guaranteedStop_value,"currencyCode":currencyCode_value,"forceOpen":forceOpen_value,"stopDistance":stopDistance_value} | |
| r = requests.post(base_url, data=json.dumps(data), headers=authenticated_headers) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| print (r.status_code) | |
| print (r.reason) | |
| print (r.text) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| d = json.loads(r.text) | |
| deal_ref = d['dealReference'] | |
| time.sleep(1) | |
| #CONFIRM MARKET ORDER | |
| base_url = REAL_OR_NO_REAL + '/confirms/'+ deal_ref | |
| auth_r = requests.get(base_url, headers=authenticated_headers) | |
| d = json.loads(auth_r.text) | |
| DEAL_ID = d['dealId'] | |
| print("DEAL ID : " + str(d['dealId'])) | |
| print(d['dealStatus']) | |
| print(d['reason']) | |
| if str(d['reason']) != "SUCCESS": | |
| print ("!!DEBUG!!...!!some thing occurred ERROR!!") | |
| time.sleep(360) #TO DO :- Remove this and treat like a normal error...Eventually! But for now acts as very basic rate limiting | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| print ("!!DEBUG!! Resuming...") | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| else: | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| print ("!!DEBUG!!...Yay, ORDER OPEN") | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| def create_dataset(dataset, look_back=1): | |
| dataX, dataY = [], [] | |
| for i in range(len(dataset)-look_back-1): | |
| dataX.append(dataset[i:(i+look_back), 0]) | |
| dataY.append(dataset[i + look_back, 0]) | |
| return np.array(dataX), np.array(dataY) | |
| def plot_results_multiple(predicted_data, true_data,length): | |
| print ("!!DEBUG!!...graphing disabled in Linux") | |
| #plt.plot(scaler.inverse_transform(true_data.reshape(-1, 1))[length:],'k') | |
| #plt.plot(scaler.inverse_transform(np.array(predicted_data).reshape(-1, 1))[length:], 'k') | |
| #plt.show() | |
| def predict_sequences_multiple(model, firstValue,length): | |
| prediction_seqs = [] | |
| curr_frame = firstValue | |
| for i in range(length): | |
| predicted = [] | |
| #print(model.predict(curr_frame[newaxis,:,:])) | |
| predicted.append(model.predict(curr_frame[newaxis,:,:])[0,0]) | |
| curr_frame = curr_frame[0:] | |
| curr_frame = np.insert(curr_frame[0:], i+1, predicted[-1], axis=0) | |
| prediction_seqs.append(predicted[-1]) | |
| return prediction_seqs | |
| if __name__ == '__main__': | |
| rudimentary_loop_counter = 0 | |
| while True: | |
| K.clear_session() #Hopefully, Speed things up later! | |
| ####################Refresh epics###################### | |
| ####################Refresh epics###################### | |
| ####################Refresh epics###################### | |
| rudimentary_loop_counter = rudimentary_loop_counter + 1 | |
| if rudimentary_loop_counter > 30: | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| print ("!!DEBUG!!...Refreshing epic list...") | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| rudimentary_loop_counter = 0 | |
| decent_epics = lowest_spread_epic() | |
| print (decent_epics) | |
| ####################Refresh epics###################### | |
| ####################Refresh epics###################### | |
| ####################Refresh epics###################### | |
| random.shuffle(decent_epics) | |
| epic_id = random.choice(decent_epics) | |
| print ("!!DEBUG!!...chosen epic is ..." + str(epic_id)) | |
| time.sleep(3) #Generic Rate Limit for IG Index API.... | |
| close = [] #MUST MUST CLEAR OUT THIS EACH TIME ROUND LOOP!!! | |
| for item in TIME_FRAMES: | |
| base_url = REAL_OR_NO_REAL + "/prices/" + epic_id + "/" + item | |
| # Price resolution (MINUTE, MINUTE_2, MINUTE_3, MINUTE_5, MINUTE_10, MINUTE_15, MINUTE_30, HOUR, HOUR_2, HOUR_3, HOUR_4, DAY, WEEK, MONTH) | |
| auth_r = requests.get(base_url, headers=authenticated_headers) | |
| d = json.loads(auth_r.text) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| print(auth_r.status_code) | |
| print(auth_r.reason) | |
| # print (auth_r.text) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| for i in d['prices']: | |
| #snapshotTime = i['snapshotTime'] #NOT IN USE CURRENTLY | |
| if i['closePrice']['bid'] is not None: | |
| closePrice = i['closePrice']['bid'] | |
| close.append(closePrice) | |
| # if i['highPrice']['bid'] is not None: | |
| # highPrice = i['highPrice']['bid'] | |
| # close.append(highPrice) | |
| # if i['lowPrice']['bid'] is not None: | |
| # lowPrice = i['lowPrice']['bid'] | |
| # close.append(lowPrice) | |
| base_url = REAL_OR_NO_REAL + '/prices/'+ epic_id + '/HOUR_4/4' | |
| # Price resolution (MINUTE, MINUTE_2, MINUTE_3, MINUTE_5, MINUTE_10, MINUTE_15, MINUTE_30, HOUR, HOUR_2, HOUR_3, HOUR_4, DAY, WEEK, MONTH) | |
| auth_r = requests.get(base_url, headers=authenticated_headers) | |
| d = json.loads(auth_r.text) | |
| stopDistance_value = calculate_stop_loss(d) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| print ("!!DEBUG ... " + str(stopDistance_value)) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| #load the dataset | |
| prices_dataset = pd.DataFrame(close) | |
| prices_dataset = prices_dataset.fillna(method='pad') | |
| prices_dataset = prices_dataset.values | |
| prices_dataset = prices_dataset.astype('float64') | |
| ################################################################################### | |
| ##########################END OF DATA CODE######################################### | |
| ##########################END OF DATA CODE######################################### | |
| ##########################END OF DATA CODE######################################### | |
| ##########################END OF DATA CODE######################################### | |
| ################################################################################### | |
| scaler = MinMaxScaler(feature_range=(0, 1)) | |
| prices_dataset = scaler.fit_transform(prices_dataset) | |
| train_size = int(len(prices_dataset) * 0.75) | |
| test_size = len(prices_dataset) - train_size | |
| train = prices_dataset[0:train_size,:] | |
| test = prices_dataset[train_size:len(prices_dataset),:] | |
| # reshape into X=t and Y=t+1 | |
| #safer to manually code it than use train_split library | |
| look_back = 1 | |
| trainX, trainY = create_dataset(train, look_back) | |
| testX, testY = create_dataset(test, look_back) | |
| trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) | |
| testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) | |
| #Now lets contruct LSTM model, which works better than RNN in this case | |
| model = Sequential() | |
| model.add(LSTM(return_sequences=True, input_shape=(None, 1), units=50, use_bias = False)) | |
| model.add(Dropout(0.2)) | |
| model.add(LSTM(100,return_sequences=False)) | |
| model.add(Dense(1, activation = None, use_bias = False)) | |
| model.add(LeakyReLU(alpha=0.1)) | |
| start = time.time() | |
| model.compile(loss = 'mse', optimizer = 'adam', metrics = ['accuracy']) | |
| print ('compilation time : ', time.time() - start) | |
| model.fit(trainX, trainY, batch_size=1, epochs=400, validation_split=0.05) | |
| predict_length=1 | |
| predictions = predict_sequences_multiple(model, testX[-1], predict_length) | |
| print("#######################PREDICTIONS############################") | |
| print("#######################PREDICTIONS############################") | |
| print("#######################PREDICTIONS############################") | |
| print("#######################PREDICTIONS############################") | |
| print(scaler.inverse_transform(np.array(predictions).reshape(-1, 1))) | |
| plot_results_multiple(predictions, testY, predict_length) | |
| print("#######################PREDICTIONS############################") | |
| print("#######################PREDICTIONS############################") | |
| print("#######################PREDICTIONS############################") | |
| print("#######################PREDICTIONS############################") | |
| # create and fit the LSTM network | |
| model = Sequential() | |
| model.add(LSTM(4, input_shape=(look_back, 1),use_bias = False)) | |
| model.add(Dense(1, activation = None, use_bias = False)) | |
| model.add(Dropout(0.2)) | |
| model.add(LeakyReLU(alpha=0.1)) | |
| start = time.time() | |
| model.compile(loss = 'mse', optimizer = 'adam', metrics = ['accuracy']) | |
| print ('compilation time : ', time.time() - start) | |
| model.fit(trainX, trainY, epochs=400, batch_size=1, validation_split=0.05) | |
| # make predictions | |
| trainPredict = model.predict(trainX) | |
| testPredict = model.predict(testX) | |
| # invert predictions and targets to unscaled | |
| trainPredict = scaler.inverse_transform(trainPredict) | |
| trainY = scaler.inverse_transform([trainY]) | |
| testPredict = scaler.inverse_transform(testPredict) | |
| testY = scaler.inverse_transform([testY]) | |
| # calculate root mean squared error | |
| trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0])) | |
| print('Train Score: %.2f RMSE' % (trainScore)) | |
| testScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0])) | |
| print('Test Score: %.2f RMSE' % (testScore)) | |
| # shift predictions of training data for plotting | |
| trainPredictPlot = np.empty_like(prices_dataset) | |
| trainPredictPlot[:, :] = np.nan | |
| trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict | |
| # shift predictions of test data for plotting | |
| testPredictPlot = np.empty_like(prices_dataset) | |
| testPredictPlot[:, :] = np.nan | |
| testPredictPlot[len(trainPredict)+(look_back*2)+1:len(prices_dataset)-1, :] = testPredict | |
| futurePredict = scaler.inverse_transform(np.array(predictions).reshape(-1, 1)) | |
| # plot baseline and predictions | |
| # plt.plot(scaler.inverse_transform(prices_dataset), 'b--') | |
| # plt.plot([None for _ in range(prices_dataset.shape[0])] + [x for x in futurePredict], color='red', linestyle='--', linewidth=2, marker='s') | |
| # plt.plot(trainPredictPlot, 'g') | |
| # plt.plot(testPredictPlot, 'r') | |
| # plt.grid(True) | |
| # graph_file_name = str(uuid.uuid4()) + ".png" | |
| # plt.title(str(graph_file_name) + " " + "(" + str(epic_id) + ")") | |
| # plt.savefig(str(graph_file_name)) | |
| # #plt.show() | |
| # plt.clf() | |
| pred_ict_price = np.amax(futurePredict) | |
| are_we_going_to_trade(pred_ict_price, epic_id) | |
| ############################################## | |
| ############################################## | |
| ############################################## | |
| remaining_allowance = d['allowance']['remainingAllowance'] | |
| reset_time = humanize_time(int(d['allowance']['allowanceExpiry'])) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
| print ("Remaining API Calls left: " + str(remaining_allowance)) | |
| print ("Time to API Key reset: " + str(reset_time)) | |
| print ("-----------------DEBUG-----------------") | |
| print ("#################DEBUG#################") | |
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