def generate_coins(specific_hour):
coins = []
#OMG
coin = Coin()
coin.name = 'omg'
coin.treshold = 0.65
one_day_before = specific_hour + timedelta(days=-1)
coin.loadtime = one_day_before.strftime("%Y-%m-%d")
coin.hashtags = ['omg', 'omisego']
coins.append(coin)
#NEO
coin = Coin()
coin.name = 'neo'
coin.treshold = 0.51
one_day_before = specific_hour + timedelta(days=-1)
coin.loadtime = one_day_before.strftime("%Y-%m-%d")
coin.hashtags = ['neo']
#coins.append(coin)
#XVG
coin = Coin()
coin.name = 'xvg'
coin.treshold = 0.55
one_day_before = specific_hour + timedelta(days=-1)
coin.loadtime = one_day_before.strftime("%Y-%m-%d")
coin.hashtags = ['xvg', 'verge']
#coins.append(coin)
return coins
def test_date_time_binance3(self):
bapi = BinanceApi()
coin = Coin()
coin.name = 'OMG'
one_day_before = datetime.now() + timedelta(days=-1)
coin.loadtime = one_day_before.strftime("%Y-%m-%d")
spech = datetime.now() + timedelta(hours=-1)
print("spec_closed_hour: ")
pricehourly = bapi.collect_coindata(coin, spech)
print(pricehourly)
def test_tweetio_read_db2(self):
coin = Coin()
coin.name = 'omg'
yesterday = date.today() + timedelta(days=-1)
coin.loadtime = yesterday.strftime("%Y-%m-%d")
coin.hashtags = ['omg', 'omisego']
tio = TweetIO()
df = tio.read_db_tweet_last_n_hour_by_coin(coin)
print(df)
def test_tweetio_read_train(self):
pd.set_option("display.max_rows",100)
pd.set_option("display.max_columns",100)
coin = Coin()
coin.name = 'neo'
coin.read_from_storeage('train')
print(coin.data_to_predict)
print("test case finishes.")
def test_date_time_binance2(self):
bapi = BinanceApi()
coin = Coin()
coin.name = 'OMG'
spech = datetime.now() + timedelta(hours=-1)
print("spec_closed_hour: ")
json_spec_hour = bapi.get_last_n_hour_by_specific_hour_by_coin(
coin, spech, 1)
self.print_unix_as_date(json_spec_hour[0][0])
self.print_unix_as_date(json_spec_hour[0][6])
print(json_spec_hour)
def test_tweetio_read_db3(self):
tio = TweetIO()
coin = Coin()
coin.name = 'neo'
nowdt = datetime.now()
dfnow1 = tio.read_db_tweet_last_n_hour_by_specific_hour_by_coin(
coin, nowdt + timedelta(hours=-1))
print("dfnow1:")
print(dfnow1)
print("test case finishes.")
def list_coins(self, dir):
ret = list()
for dirname, dirnames, filenames in os.walk(dir):
filenames.sort()
# print path to all subdirectories first.
for filename in filenames:
if (filename.endswith('.csv')):
path = os.path.join(dirname, filename)
name = filename[:-4]
c = Coin()
c.path = path
c.name = name
ret.append(c)
return ret
def test_tweetio_read_predict(self):
pd.set_option("display.max_rows",100)
pd.set_option("display.max_columns",100)
tio = TweetIO()
coin = Coin()
coin.name = 'omg'
nowdt = datetime.now()
argtime='2018-04-14_16-00-00'
coin.read_from_storeage('rt','runtime/'+argtime+'/')
print(coin.data_to_predict)
print("test case finishes.")
def test_increase_one_hour(self):
cointrain = CoinTrain()
#df=pd.DataFrame(index=['year', 'month','day','hour'],columns=[i],data=)
coin = Coin()
coin.path = "./data/altcoin-1hour/neo.csv"
coin.name = "neo"
coin.ico = "2017-12-31"
coin.read_from_storeage("prepare2")
gtdf = coin.gtdf
idx = gtdf.index
gtdf.index = idx.set_names(['year', 'month', 'day', 'hour'])
print(gtdf['sum_neumulfollower'].tail(27))
gtdf = cointrain.increase_by_one_hour(gtdf)
print(gtdf['sum_neumulfollower'].tail(27))
def test_tweetio_read_db1(self):
tio = TweetIO()
coin = Coin()
coin.name = 'neo'
nowdt = datetime.now()
dfnow1 = tio.read_db_retweet_last_n_hour_by_specific_hour_by_coin(
coin, nowdt)
dfnow2 = tio.read_db_retweet_last_n_hour_by_coin(coin)
print("dfnow1:")
print(dfnow1)
print("dfnow2:")
print(dfnow2)
print("equality check: ")
self.assertTrue(dfnow1.equals(dfnow2))
print("test case finishes.")
from coins.coinprice import CoinPrice
from twitter.statistics import Statistics
from twitter.tweetio import TweetIO
from twitter.sentiment import SentimentAnalyzer
from coins.coin import Coin
import pandas as pd
print('Main starts')
#cinfo=CoinInfo()
#coinlist=cinfo.list_coins('./data/altcoin-1hour')
## choosing first one: neo
#coin=coinlist[19]
coin = Coin()
coin.path = "./data/altcoin-1hour/ada.csv"
coin.name = "ada"
coin.ico = "2017-10-01"
#coin.ico="2016-02-17"
tweetio = TweetIO()
coin.read_from_storeage("prepare1")
print(coin.tweets.columns)
print(coin.retweets.columns)
df = tweetio.sort_and_clip(coin.tweets, coin.ico)
coin.tweets = df
## MULTIPLYING RETWEETS FOLLOWERS
print("multiplying nr. of retweet followers by sentiments.")
sentanalyzer = SentimentAnalyzer()
sentanalyzer.merge_tweets_with_retweets(coin)
print('Main starts searching for best model')
pd.set_option("display.max_rows", 100)
pd.set_option("display.max_columns", 100)
cinfo = CoinInfo()
#coinlist=cinfo.list_coins('./data/altcoin-1hour')
## choosing coin
coin = Coin()
#coin.path="./data/altcoin-1hour/neo.csv"
#coin.name="neo"
#coin.ico="2017-05-01"
coin.path = "./data/altcoin-1hour/omg.csv"
coin.name = "omg"
coin.ico = "2017-09-01"
starttime = datetime.now()
print(str(starttime))
coin.read_from_storeage("prepare2")
print(coin.pricehourly.head())
possibleraise = [1.5, 1.1]
possibledeclineratio = [-0.5, -0.667]
possibleoffset = [3, 4]
countphase = 0
for declineratio in possibledeclineratio:
for raisei in possibleraise:
from keras.layers import Embedding
from sklearn.model_selection import train_test_split
import keras.backend as K
print('Main starts plotting')
pd.set_option("display.max_rows", 100)
pd.set_option("display.max_columns", 100)
cinfo = CoinInfo()
#coinlist=cinfo.list_coins('./data/altcoin-1hour')
## choosing coin
coin = Coin()
coin.path = "./data/altcoin-1hour/neo.csv"
coin.name = "neo"
coin.ico = "2017-05-01"
tweetio = TweetIO()
coin.read_from_storeage("prepare2")
#print(coin.pricehourly.head())
cointrain = CoinTrain()
X = cointrain.create_buy_sig(coin, aimraise=10, declinelimit=-5, offset=12)
X_gtdf = cointrain.increase_by_one_hour(coin.gtdf)
X_grtdf = cointrain.increase_by_one_hour(coin.grtdf)
#Converting X to the same multi index type
times = pd.DatetimeIndex(X.index)
def generate_coins(specific_hour):
coins = []
#OMG
coin = Coin()
coin.name = 'omg'
coin.treshold = 0.81
one_day_before = specific_hour + timedelta(days=-1)
coin.loadtime = one_day_before.strftime("%Y-%m-%d")
coin.hashtags = ['omg', 'omisego']
coin.target = 1.7
coin.stoploss = coin.target / (-0.5)
coin.timelimit = 3
coin.modelfile='omg/found_omg_bsig34.0_prec0.7083333333333334_tre64_aimr1.7_decr-0' \
'.5_offs3_actmet_tanh_keras_model.h5'
coin.scalerfile = 'omg/found_omg_bsig34.0_prec0.7083333333333334_tre64_aimr1.7_decr-0.5_offs3_actmet_tanh_scaler.pkl'
coins.append(coin)
#NEO
coin = Coin()
coin.name = 'neo'
coin.treshold = 0.72
one_day_before = specific_hour + timedelta(days=-1)
coin.loadtime = one_day_before.strftime("%Y-%m-%d")
coin.hashtags = ['neo']
coin.target = 1.7
coin.stoploss = coin.target / (-0.667)
coin.timelimit = 3
coin.modelfile = 'neo/found_neo_bsig18.0_prec0.782608695652174_tre72_aimr1.7_decr-0.667_offs4_actmet_relu_keras_model.h5'
coin.scalerfile = 'neo/found_neo_bsig18.0_prec0.782608695652174_tre72_aimr1.7_decr-0.667_offs4_actmet_relu_scaler.pkl'
coins.append(coin)
#XVG
coin = Coin()
coin.name = 'xvg'
coin.treshold = 0.99
one_day_before = specific_hour + timedelta(days=-1)
coin.loadtime = one_day_before.strftime("%Y-%m-%d")
coin.hashtags = ['xvg', 'verge']
#coins.append(coin)
#LSK
coin = Coin()
coin.name = 'lsk'
coin.treshold = 0.61
one_day_before = specific_hour + timedelta(days=-1)
coin.loadtime = one_day_before.strftime("%Y-%m-%d")
coin.hashtags = ['lsk', 'lisk']
#coins.append(coin)
#ADA
coin = Coin()
coin.name = 'ada'
coin.treshold = 0.56
one_day_before = specific_hour + timedelta(days=-1)
coin.loadtime = one_day_before.strftime("%Y-%m-%d")
coin.hashtags = ['ada', 'cardano']
#coins.append(coin)
#GNT
coin = Coin()
coin.name = 'gnt'
coin.treshold = 0.55 #buysig 3, at prec 1.0!
one_day_before = specific_hour + timedelta(days=-1)
coin.loadtime = one_day_before.strftime("%Y-%m-%d")
coin.hashtags = ['gnt', 'golem']
#coins.append(coin)
return coins
