def test_price_minute(self):
coin = 'BTC'
curr = 'USD'
price = cryptocompare.get_historical_price_minute(
coin, currency=curr, limit=3, exchange='CCCAGG', toTs=datetime.datetime.now())
for frame in price:
self.assertIn('time', frame)
def test_get_historical_price_minute_custom_limit(self):
res = cryptocompare.get_historical_price_minute(coins[2], limit=90)
self.assertTrue('Data' in res.keys(), "expected 'Data'")
# XXX: cryptocompare is off-by-one
self.assertEqual(
91, len(res['Data']),
"expected limit to match days returned, got {}".format(
len(res['Data'])))
def test_get_historical_price_minute_non_default_currency(self):
res = cryptocompare.get_historical_price_minute(coins[1],
currency='USD',
limit=10)
self.assertTrue('Data' in res.keys(), "expected 'Data'")
# XXX: cryptocompare is off-by-one
self.assertEqual(
11, len(res['Data']),
"expected limit to match days returned, got {}".format(
len(res['Data'])))
def get_history(ticker, port):
socks.set_default_proxy(socks.PROXY_TYPE_SOCKS5, '127.0.0.1', port)
socket.socket = socks.socksocket
day_30 = cryptocompare.get_historical_price_day(ticker,
curr='USD',
limit=30)
day_3 = cryptocompare.get_historical_price_hour(ticker,
curr='USD',
limit=3 * 24)
hour_1 = cryptocompare.get_historical_price_minute(ticker,
curr='USD',
limit=60)
for o, file_name in [(day_30, f"data/{ticker}_30.json"),
(day_3, f"data/{ticker}_3.json"),
(hour_1, f"data/{ticker}_1.json")]:
if o is not None:
with open(file_name, 'w') as f:
json.dump(o, f)
time.sleep(1.5)
def get_historical(self,
coin,
time_step,
nod,
end=datetime.now(),
columns=['close'],
currency="USD",
add_this_moment_price=False):
"""
:param: coin: It should be symbol. e.x 'BTC'
:param: time_step: It can be 'day' for price history in last days, 'hour' for ... and 'min' for ...
:param: nod: Number of dates. Actually number of rows in returned dataframe(Except when add_this_moment_price is True)
:param: end: Default is today date. But can pass a date to recive data from nod days before end to end day
:param: columns: A list of columns from ['high', 'low', 'open', 'volumefrom', 'volumeto', 'close'].
And if it is 'OHLC', columns will be consideres as ['open', 'high', 'low', 'close'].
:param: add_this_moment_price: Only works when columns=['close']. Adds this moment's price to dataframe.
:param: currency: currency unit
:returns: A dataframe with index of time laps and columns passed
"""
if columns == "OHLC": #Famous open, high, low, close dataframe for candle bars
columns = ['open', 'high', 'low', 'close']
if columns == "OHLCV":
columns = ['open', 'high', 'low', 'close', 'volumeto']
if time_step == "day":
data = cc.get_historical_price_day(coin,
currency,
limit=nod,
toTs=end)
elif time_step == "hour":
data = cc.get_historical_price_hour(coin,
currency,
limit=nod,
toTs=end)
elif time_step == "min":
data = cc.get_historical_price_minute(coin,
currency,
limit=nod,
toTs=end)
else:
raise ValueError(
"Only 'day', 'hour' and 'min' are valid as time_stamp")
df = pd.DataFrame.from_dict(
data) #converting json like recived data to a pandas df
index = [datetime.fromtimestamp(tstamp) for tstamp in df.time]
dates = [t + timedelta(seconds=510 * 60 - 21)
for t in index] # Converting timelaps to Tehran time zone
df.index = dates
if add_this_moment_price:
if columns == ['close'] and time_step == "min":
#Adding this moment's price to tail
price = cc.get_price(coin, currency)[coin][currency]
t = datetime.now()
now_df = pd.DataFrame({"close": price}, index=[t])
df = df.append(now_df)
else:
raise ValueError(
"add_this_moment_price will only work when dataframe is a price table and time_step is 'min'"
)
return df[columns]
def main():
analyzer = SentimentIntensityAnalyzer()
tweets = [] #Stores the tweets
dates = [] #Stores the dates of the tweets
arr_compound = [] # Stores the compund of the sentiment Analysis.
arr_positive = [] #Stores the positives of the sentiment Analysis.
arr_neutral = [] #Stores de neutral values of the sentiment Analysis.
arr_negative = [] #Store the negative values of the Sentiment Analysis.
#Getting the crypto information
crypto_data = cryptocompare.get_historical_price_minute('BTC',
curr='EUR',
limit=1)
date = datetime.datetime.fromtimestamp(
crypto_data[0]['time']).strftime('%d-%m-%Y %H:%M:%S')
open_price = crypto_data[0]['open']
close_price = crypto_data[0]['close']
high = crypto_data[0]['high']
low = crypto_data[0]['low']
volume_from = crypto_data[0]['volumefrom']
gain = compare(crypto_data[0]['close'], crypto_data[1]['close'])
print(
"================================================================================="
)
print('Crypto info date: ', date)
#Getting the tweets form the API.
for tweet in tweepy.Cursor(api.search,
q='Bitcoin -giving -Giveaway',
lang='en').items(10):
tweets.append(tweet.text)
dates.append(tweet.created_at)
print('Tweet date: ', tweet.created_at)
for tweet in tweets:
print(
"================================================================================="
)
vs = analyzer.polarity_scores(tweet)
print(tweet)
#Getting the values from the Sentiment Analysis.
full_result = "{:-<65} {}".format(tweet, str(vs))
result = full_result.split('{')
split_result = str(result[1]).split(':')
params = str(split_result).split(',')
full_neg = str(params[1]).split('"')
neg = full_neg[1]
full_neu = str(params[3]).split('"')
neu = full_neu[1]
full_pos = str(params[5]).split('"')
pos = full_pos[1]
full_comp = str(params[7]).split('}')
split_comp = str(full_comp[0]).split()
comp = split_comp[1]
#Storing the values of the analysis.
arr_compound.append(comp)
arr_positive.append(pos)
arr_neutral.append(neu)
arr_negative.append(neg)
print('Negative: ', neg)
print('Neutral: ', neu)
print('Positive: ', pos)
print('Compound: ', comp)
compound = process(arr_compound, 'compound')
positive = process(arr_positive, 'positive')
negative = process(arr_negative, 'negative')
neutral = process(arr_neutral, 'neutral')
#Creating the object to write in the csv file.
with open('Results.csv', mode='a') as csv_file:
fieldnames = [
'date', 'negative', 'neutral', 'positive', 'compound', 'open',
'close', 'high', 'low', 'volume_from', 'gain'
]
writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
record(writer, date, compound, negative, neutral, positive, open_price,
close_price, high, low, volume_from, gain)
def test_get_historical_price_minute_from_exchange_and_aggregation(self):
res = cryptocompare.get_historical_price_minute('ETH',
currency='BTC',
exchange='Cryptopia',
aggregate=5)
self.assertTrue('Data' in res.keys(), "expected 'Data'")
def test_get_historical_price_minute(self):
res = cryptocompare.get_historical_price_minute(coins[0])
self.assertTrue('Data' in res.keys(), "expected 'Data'")
# XXX: cryptocompare is off-by-one
self.assertEqual(31, len(res['Data']),
"response data limit defaults to 31 days")
print(cryptocompare.get_price(coins[1], curr=['EUR','USD','GBP'], full=True))
print('==================================================')
print(cryptocompare.get_price(coins))
print(cryptocompare.get_price(coins, curr='USD'))
print(cryptocompare.get_price(coins, curr=['EUR','USD','GBP']))
print('==================== HIST PRICE ==================')
print(cryptocompare.get_historical_price(coins[0]))
print(cryptocompare.get_historical_price(coins[0], curr='USD'))
print(cryptocompare.get_historical_price(coins[1], curr=['EUR','USD','GBP']))
print(cryptocompare.get_historical_price(coins[1], 'USD', datetime.datetime.now()))
print(cryptocompare.get_historical_price(coins[2], ['EUR','USD','GBP'], time.time(), exchange='Kraken'))
print('================== HIST PRICE MINUTE ===============')
print(cryptocompare.get_historical_price_minute(coins[0], curr='USD'))
print(cryptocompare.get_historical_price_minute(coins[0], curr='USD', limit=100))
print('================== HIST PRICE HOUR ===============')
print(cryptocompare.get_historical_price_hour(coins[0]))
print(cryptocompare.get_historical_price_hour(coins[0], curr='USD'))
print(cryptocompare.get_historical_price_hour(coins[1], curr=['EUR','USD','GBP']))
print('================== HIST PRICE DAY ================')
print(cryptocompare.get_historical_price_day(coins[0]))
print(cryptocompare.get_historical_price_day(coins[0], curr='USD'))
print(cryptocompare.get_historical_price_day(coins[1], curr=['EUR','USD','GBP']))
print('======================== AVG =====================')
print(cryptocompare.get_avg(coins[0], exchange='Coinbase'))
print(cryptocompare.get_avg(coins[0], curr='USD', exchange='Coinbase'))
def get_btc_prices(coin="BTC"):
raw = cryptocompare.get_historical_price_minute(coin, "USD", 60)
for row in raw["Data"]:
row["time"] = datetime.fromtimestamp(row["time"])
return raw["Data"]