def main():
# Get currency pairs sorted by 24-hour volume
polo = poloniex.Poloniex(timeout=10, coach=True)
sorted_tickers = sorted(polo.returnTicker().items(),
key=lambda x: float(x[1]['baseVolume']),
reverse=True)
currency_pairs = [(k, float(v['baseVolume'])) for k, v in sorted_tickers
if 'BTC_' in k or k == 'USDT_BTC']
volume_texts = []
for currency_pair, volume_24h in currency_pairs[0:DISPLAY_NUMS]:
print("currency_pair={}".format(currency_pair))
volume_texts.append(
get_volumes(currency_pair, volume_24h, MINUTES, TICKS))
body = textwrap.dedent(u"""
Date : {date}
Ticks : {minutes} mins x {ticks}
{volume_texts}
""").format(
date=datetime.now().strftime('%Y/%m/%d %H:%M'),
minutes=MINUTES,
ticks=TICKS,
volume_texts='\n\n'.join(volume_texts),
).strip()
print('----------')
print(body)
print('----------')
email_client = EmailClient()
# Send an email to myself
email_client.send_test_email(u"Poloniex trade volumes", body)
class MyListener(tweepy.StreamListener):
def __init__(self):
super(MyListener, self).__init__()
self.email_client = EmailClient()
def on_status(self, status):
if str(status.user.id) in FOLLOW_IDS.keys():
print('------------------------------')
print('status={}'.format(status))
logger.debug('CC official tweeted. user.id={}'.format(
status.user.id))
symbol = FOLLOW_IDS[str(status.user.id)]
is_retweet = hasattr(status, 'retweeted_status')
is_reply = (status.in_reply_to_user_id is not None)
subject = u'[CC] Official Tweet Notification ({})'.format(symbol)
body = textwrap.dedent(u"""
Date : {date}
Name : {name} (@{screen_name})
Symbol : {symbol}
Followers count : {followers_count}
Tweet count : {tweet_count}
Retweet? : {is_retweet}
Reply? : {is_reply}
==========
{text}
==========
URL : https://bittrex.com/Market/Index?MarketName=BTC-{symbol}
""").format(
date=datetime.now().strftime('%Y/%m/%d %H:%M:%S'),
name=status.user.name,
screen_name=status.user.screen_name,
symbol=symbol,
followers_count='{:,}'.format(status.user.followers_count),
tweet_count='{:,}'.format(status.user.statuses_count),
is_retweet=is_retweet,
is_reply=is_reply,
text=status.text,
).strip()
self.email_client.send_test_email(subject, body)
def on_error(self, status_code):
print('Got an error with status code: {}'.format(status_code))
logger.error('Got an error with status code: {}'.format(status_code))
if status_code == 420:
return False
# See: https://stackoverflow.com/questions/14177327/tweepy-stops-after-a-few-hours
def on_disconnect(self, notice):
print('Got a disconnect signal with notice: {}'.format(notice))
logger.error('Got a disconnect signal with notice: {}'.format(notice))
return False
def main():
# Get currency pairs sorted by 24-hour volume
polo = poloniex.Poloniex(timeout=10, coach=True)
sorted_tickers = sorted(polo.returnTicker().items(),
key=lambda x: float(x[1]['baseVolume']),
reverse=True)
currency_pairs = [k for k, v in sorted_tickers if 'BTC_' in k]
chart_texts = []
for currency_pair in currency_pairs[0:DISPLAY_NUMS]:
print("currency_pair={}".format(currency_pair))
data = get_chart_data(currency_pair, MINUTES_PER_TICK, TICKS)
chart_texts.append(
textwrap.dedent(u"""
{currency_pair}
Latest price : {latest_price} ({price_rate} from highest)
Latest timestamp : {latest_timestamp}
Highest price : {highest_price}
Highest timestamp : {highest_timestamp} ({timestamp_diff} before)
Price up-downs : {price_up_downs} (past 3 ticks)
Price deviations : {deviations_by_sigma} (σ)
1 sigma price : {sigma_percent}
Latest volumes : {latest_volumes} (past 3 ticks, {latest_volumes_rate} of total)
Volume up-downs : {volume_up_downs} (past 3 ticks)
""").strip().format(**data))
body = textwrap.dedent(u"""
Date : {date}
Ticks : {minutes} mins x {ticks} ({hours} hours)
{chart_texts}
""").format(
date=datetime.now().strftime('%Y/%m/%d %H:%M:%S'),
minutes=MINUTES_PER_TICK,
ticks=TICKS,
hours=TARGET_HOURS,
chart_texts='\n\n'.join(chart_texts),
).strip()
# print('----------')
# print(body)
# print('----------')
email_client = EmailClient()
# Send an email to myself
email_client.send_test_email(u"Poloniex high break (and sigma)", body)
def main():
now = datetime.now()
# Get all markets
res = requests.get('https://api.coinmarketcap.com/v1/ticker/')
markets = res.json()
print('Number of markets={}'.format(len(markets)))
# Pop base currency and USDT (Tether)
symbols = ['{}/{}'.format(x['symbol'], BASE_CURRENCY) for x in markets if x['symbol'] not in (BASE_CURRENCY, 'USDT')]
print(symbols)
# TODO: ZRX
symbols.append('ZRX/{}'.format(BASE_CURRENCY))
symbol_texts = []
for symbol in symbols[:DISPLAY_NUMBER_OF_ALTCOINS]:
ex_count = 0
max_bid = 0
max_bid_ex_name = ''
max_bid_ex_url = ''
max_bid_volume = 0
min_ask = float('inf')
min_ask_ex_name = ''
min_ask_ex_url = ''
min_ask_volume = 0
for ex, ex_url in EXCHANGES:
try:
ex_ticker = ex.fetch_ticker(symbol)
# TODO: Also catch urllib.error.HTTPError?
except ccxt.errors.RequestTimeout:
continue
except TypeError:
# Looks not to deal in this exchange
continue
except Exception as e:
logger.warning(e)
continue
ex_count += 1
ex_ask = ex_ticker.get('ask') or 0
ex_bid = ex_ticker.get('bid') or 0
ex_volume = ex_ticker.get('quoteVolume') or 0 # Volume
# print('-' * 30)
# print('{}'.format(ex.name))
# print('Ask: {:.8f}'.format(ex_ask))
# print('Bid: {:.8f}'.format(ex_bid))
# print('Volume: {:.1f}'.format(ex_volume))
if MIN_VOLUME < ex_volume:
# Price to sell
if max_bid < ex_bid:
max_bid = ex_bid
max_bid_ex_name = ex.name
max_bid_ex_url = ex_url
max_bid_volume = ex_volume
# Price to buy
if ex_ask < min_ask:
min_ask = ex_ask
min_ask_ex_name = ex.name
min_ask_ex_url = ex_url
min_ask_volume = ex_volume
profit = max_bid - min_ask
profit_rate = (profit / min_ask) if min_ask != 0 else 0 # TODO: else float('inf') ?
if profit_rate >= PROFIT_RATE_THRESHOLD:
symbol_text = textwrap.dedent(u"""
{symbol} ({ex_count})
--------------------
Sell at : {max_bid_ex_name:10} {max_bid_ex_url}
Buy at : {min_ask_ex_name:10} {min_ask_ex_url}
Bid : {max_bid:.8f} ({max_bid_volume:.1f} {base_currency}/day)
Ask : {min_ask:.8f} ({min_ask_volume:.1f} {base_currency}/day)
Profit : {profit:.8f} ({profit_rate:+.1%})
""").format(
symbol=symbol,
base_currency=BASE_CURRENCY.lower(),
ex_count=ex_count,
max_bid_ex_name=max_bid_ex_name,
max_bid_ex_url=max_bid_ex_url.format(*symbol.split('/')),
max_bid=max_bid,
max_bid_volume=max_bid_volume,
min_ask_ex_name=min_ask_ex_name,
min_ask_ex_url=min_ask_ex_url.format(*symbol.split('/')),
min_ask=min_ask,
min_ask_volume=min_ask_volume,
profit=profit,
profit_rate=profit_rate,
).strip()
# print('=' * 40)
# print(symbol_text)
symbol_texts.append((symbol_text, profit_rate))
# Sort by profit rate
symbol_texts = [x[0] for x in sorted(symbol_texts, key=lambda x: x[1], reverse=True)]
body = textwrap.dedent(u"""
Date : {date}
Top {number_of_altcoins} Currencies from {number_of_exchanges} Exchanges
Volume Threshold : {min_volume} {base_currency}/day
{symbol_text}
""").format(
date=now.strftime('%Y/%m/%d %H:%M:%S'),
number_of_altcoins=DISPLAY_NUMBER_OF_ALTCOINS,
number_of_exchanges=len(EXCHANGES),
min_volume=MIN_VOLUME,
base_currency=BASE_CURRENCY.lower(),
symbol_text='\n\n'.join(symbol_texts),
).strip()
print(body)
email_client = EmailClient()
# Send an email to myself
email_client.send_test_email(u'[CC] Check Price Difference (by {})'.format(BASE_CURRENCY), body)
def main():
now = datetime.now()
# Get markets
exchange = ccxt.bittrex()
markets = exchange.fetch_tickers()
print('Number of markets={}'.format(len(markets)))
# Altcoins
altcoins = [
dict(v, symbol=k.split('/')[0]) for k, v in markets.items()
if '/BTC' in k
]
# Sort by baseVolume (Note: baseVolume might be None, so 'or 0')
altcoins.sort(key=lambda x: float(x['quoteVolume'] or 0), reverse=True)
print('Number of altcoins={}'.format(len(altcoins)))
print([x['symbol'] for x in altcoins])
# Get altcoin data from MongoDB
collection = MongoClient().bittrex['tickers']
chart_texts = []
for i, symbol in enumerate(
[x['symbol'] for x in altcoins[:DISPLAY_NUMBER_OF_ALTCOINS]]):
print('==========')
print(symbol)
tickers = list(
collection.find(
{
'symbol': symbol,
'timestamp': {
'$gte': now - timedelta(minutes=TARGET_MINUTES)
}
},
{
'_id': False
},
).sort('timestamp', ASCENDING))
# pprint(tickers)
chart_dict = tickers_to_chart_dict(tickers)
chart_text = textwrap.dedent(u"""
{i}. {symbol}
-----------------
Latest price : {latest_price:.8f} ({price_diff_rate:+.1%} from highest)
Highest price : {highest_price:.8f} ({highest_timestamp_diff} before)
Price up-downs : {price_up_downs}
Price deviations : {price_diff_by_sigma} (σ)
1 sigma price : {sigma_percent:.1%}
""").format(
i=i + 1,
symbol=symbol,
latest_price=chart_dict['latest_price'],
highest_price=chart_dict['highest_price'],
highest_timestamp=chart_dict['highest_timestamp'].strftime(
'%m/%d %H:%M'),
price_up_downs=', '.join(
['{:+.1%}'.format(v) for v in chart_dict['price_up_downs']]),
highest_timestamp_diff=chart_dict['highest_timestamp_diff'],
price_diff_rate=(chart_dict['latest_price'] -
chart_dict['highest_price']) /
chart_dict['highest_price'],
price_diff_by_sigma=', '.join([
'{:.2f}'.format(v) for v in chart_dict['price_diff_by_sigma']
]),
sigma_percent=chart_dict['sigma_percent'],
).strip()
print('----------')
print(chart_text)
chart_texts.append(chart_text)
body = textwrap.dedent(u"""
Date : {date}
Ticks : {minutes} mins x {ticks:,.0f} ({hours:,.0f} hours)
{chart_texts}
""").format(
date=now.strftime('%Y/%m/%d %H:%M:%S'),
minutes=MINUTES_PER_TICK,
ticks=TARGET_MINUTES / MINUTES_PER_TICK,
hours=TARGET_MINUTES / 60,
chart_texts='\n\n'.join(chart_texts),
).strip()
print('==========')
print(body)
email_client = EmailClient()
# Send an email to myself
email_client.send_test_email(u'[CC] Bittrex Tickers', body)
def main():
now = datetime.now()
# Symbols
db_symbols = MongoClient().cctweet['symbols']
symbols = list(
db_symbols.find({'time': {
'$exists': False
}}, {'_id': False},
sort=[('count', DESCENDING)]))
for symbol in symbols:
# TODO: Need fix? INTERVAL_MINUTES * 1.5
prev = db_symbols.find_one(
{
'key': symbol['key'],
'time': {
'$gte': now - timedelta(minutes=INTERVAL_MINUTES * 1.5)
}
},
sort=[('time', DESCENDING)])
if prev is not None:
symbol.update({
'diff':
float(symbol['count'] - prev['count']) / float(prev['count'])
})
else:
symbol.update({'diff': float('inf')})
# Hashtags
db_hashtags = MongoClient().cctweet['hashtags']
hashtags = list(
db_hashtags.find({'time': {
'$exists': False
}}, {'_id': False},
sort=[('count', DESCENDING)]))
for hashtag in hashtags:
# TODO: Need fix? INTERVAL_MINUTES * 1.5
prev = db_hashtags.find_one(
{
'key': hashtag['key'],
'time': {
'$gte': now - timedelta(minutes=INTERVAL_MINUTES * 1.5)
}
},
sort=[('time', DESCENDING)])
if prev is not None:
hashtag.update({
'diff':
float(hashtag['count'] - prev['count']) / float(prev['count'])
})
else:
hashtag.update({'diff': float('inf')})
# Update time
db_symbols.update({'time': {
'$exists': False
}}, {'$set': {
'time': now
}},
multi=True)
db_hashtags.update({'time': {
'$exists': False
}}, {'$set': {
'time': now
}},
multi=True)
# Cleanup
db_symbols.remove({'time': {'$lt': now - timedelta(hours=24)}})
db_hashtags.remove({'time': {'$lt': now - timedelta(hours=24)}})
body = textwrap.dedent(u"""
Date : {date}
$ Top Symbols
=============
{top_symbols}
# Top Hashtags
==============
{top_hashtags}
""").format(
date=now.strftime('%Y/%m/%d %H:%M'),
top_symbols='\n'.join([
u'{}: {} ({})'.format(d['key'], d['count'],
'{:+.0%}'.format(d['diff']))
for d in symbols[0:SYMBOL_DISPLAY_SIZE]
]),
top_hashtags='\n'.join([
u'{}: {} ({})'.format(d['key'], d['count'],
'{:+.0%}'.format(d['diff']))
for d in hashtags[0:HASHTAG_DISPLAY_SIZE]
]),
).strip()
email_client = EmailClient()
email_client.send_test_email(u'CC Tweet Summary', body)
def main():
# Get all markets
res = requests.get('https://api.coinmarketcap.com/v1/ticker/')
markets = res.json()
print('Number of markets={}'.format(len(markets)))
# Sort by 24h_volume_usd (Note: some 24h_volume_usd are None, so 'or 0')
markets.sort(key=lambda x: float(x['24h_volume_usd'] or 0), reverse=True)
# Pop BTC and USDT (Tether)
markets = [x for x in markets if x['symbol'] not in ('BTC', 'USDT')]
# pprint(markets)
# Get Bitcoin market
res = requests.get(
'https://api.coinmarketcap.com/v1/ticker/{}/'.format('bitcoin'))
bitcoin = res.json()[0]
df = pd.DataFrame(markets)
df['price_usd'] = df['price_usd'].astype(float)
df['price_btc'] = df['price_btc'].astype(float)
df['24h_volume_usd'] = df['24h_volume_usd'].astype(float)
df['market_cap_usd'] = df['market_cap_usd'].astype(float)
df['available_supply'] = df['available_supply'].astype(float)
df['total_supply'] = df['total_supply'].astype(float)
df['percent_change_1h'] = df['percent_change_1h'].astype(float)
df['percent_change_24h'] = df['percent_change_24h'].astype(float)
df['percent_change_7d'] = df['percent_change_7d'].astype(float)
df['last_updated'] = df['last_updated'].apply(
lambda x: datetime.fromtimestamp(float(x)))
print(df[0:10])
df_top10 = df[0:10]
df_top20 = df[0:20]
df_top30 = df[0:30]
df_top50 = df[0:50]
df_top51_100 = df[50:100]
df_top100 = df[0:100]
body = textwrap.dedent(u"""
Bitcoin
=======
(1h) price change : {bitcoin_1h_change:+.1f}%
(24h) price change : {bitcoin_24h_change:+.1f}%
(7d) price change : {bitcoin_7d_change:+.1f}%
Top 10 markets
==============
(1h) price up/down : {top10_1h_up} / {top10_1h_down}
(1h) price mean : {top10_1h_mean:+.1f}%
(24h) price up/down : {top10_24h_up} / {top10_24h_down}
(24h) price mean : {top10_24h_mean:+.1f}%
(7d) price up/down : {top10_7d_up} / {top10_7d_down}
(7d) price mean : {top10_7d_mean:+.1f}%
Top 20 markets
==============
(1h) price up/down : {top20_1h_up} / {top20_1h_down}
(1h) price mean : {top20_1h_mean:+.1f}%
(24h) price up/down : {top20_24h_up} / {top20_24h_down}
(24h) price mean : {top20_24h_mean:+.1f}%
(7d) price up/down : {top20_7d_up} / {top20_7d_down}
(7d) price mean : {top20_7d_mean:+.1f}%
Top 30 markets
==============
(1h) price up/down : {top30_1h_up} / {top30_1h_down}
(1h) price mean : {top30_1h_mean:+.1f}%
(24h) price up/down : {top30_24h_up} / {top30_24h_down}
(24h) price mean : {top30_24h_mean:+.1f}%
(7d) price up/down : {top30_7d_up} / {top30_7d_down}
(7d) price mean : {top30_7d_mean:+.1f}%
Top 50 markets
==============
(1h) price up/down : {top50_1h_up} / {top50_1h_down}
(1h) price mean : {top50_1h_mean:+.1f}%
(24h) price up/down : {top50_24h_up} / {top50_24h_down}
(24h) price mean : {top50_24h_mean:+.1f}%
(7d) price up/down : {top50_7d_up} / {top50_7d_down}
(7d) price mean : {top50_7d_mean:+.1f}%
Top 51-100 markets
==============
(1h) price up/down : {top51_100_1h_up} / {top51_100_1h_down}
(1h) price mean : {top51_100_1h_mean:+.1f}%
(24h) price up/down : {top51_100_24h_up} / {top51_100_24h_down}
(24h) price mean : {top51_100_24h_mean:+.1f}%
(7d) price up/down : {top51_100_7d_up} / {top51_100_7d_down}
(7d) price mean : {top51_100_7d_mean:+.1f}%
Top 100 markets
===============
(1h) price up/down : {top100_1h_up} / {top100_1h_down}
(1h) price mean : {top100_1h_mean:+.1f}%
(24h) price up/down : {top100_24h_up} / {top100_24h_down}
(24h) price mean : {top100_24h_mean:+.1f}%
(7d) price up/down : {top100_7d_up} / {top100_7d_down}
(7d) price mean : {top100_7d_mean:+.1f}%
""").format(
bitcoin_1h_change=float(bitcoin['percent_change_1h']),
bitcoin_24h_change=float(bitcoin['percent_change_24h']),
bitcoin_7d_change=float(bitcoin['percent_change_7d']),
top10_1h_up=len(df_top10[df_top10['percent_change_1h'] > 0]),
top10_1h_down=len(df_top10[df_top10['percent_change_1h'] <= 0]),
top10_1h_mean=df_top10.mean()['percent_change_1h'],
top10_24h_up=len(df_top10[df_top10['percent_change_24h'] > 0]),
top10_24h_down=len(df_top10[df_top10['percent_change_24h'] <= 0]),
top10_24h_mean=df_top10.mean()['percent_change_24h'],
top10_7d_up=len(df_top10[df_top10['percent_change_7d'] > 0]),
top10_7d_down=len(df_top10[df_top10['percent_change_7d'] <= 0]),
top10_7d_mean=df_top10.mean()['percent_change_7d'],
top20_1h_up=len(df_top20[df_top20['percent_change_1h'] > 0]),
top20_1h_down=len(df_top20[df_top20['percent_change_1h'] <= 0]),
top20_1h_mean=df_top20.mean()['percent_change_1h'],
top20_24h_up=len(df_top20[df_top20['percent_change_24h'] > 0]),
top20_24h_down=len(df_top20[df_top20['percent_change_24h'] <= 0]),
top20_24h_mean=df_top20.mean()['percent_change_24h'],
top20_7d_up=len(df_top20[df_top20['percent_change_7d'] > 0]),
top20_7d_down=len(df_top20[df_top20['percent_change_7d'] <= 0]),
top20_7d_mean=df_top20.mean()['percent_change_7d'],
top30_1h_up=len(df_top30[df_top30['percent_change_1h'] > 0]),
top30_1h_down=len(df_top30[df_top30['percent_change_1h'] <= 0]),
top30_1h_mean=df_top30.mean()['percent_change_1h'],
top30_24h_up=len(df_top30[df_top30['percent_change_24h'] > 0]),
top30_24h_down=len(df_top30[df_top30['percent_change_24h'] <= 0]),
top30_24h_mean=df_top30.mean()['percent_change_24h'],
top30_7d_up=len(df_top30[df_top30['percent_change_7d'] > 0]),
top30_7d_down=len(df_top30[df_top30['percent_change_7d'] <= 0]),
top30_7d_mean=df_top30.mean()['percent_change_7d'],
top50_1h_up=len(df_top50[df_top50['percent_change_1h'] > 0]),
top50_1h_down=len(df_top50[df_top50['percent_change_1h'] <= 0]),
top50_1h_mean=df_top50.mean()['percent_change_1h'],
top50_24h_up=len(df_top50[df_top50['percent_change_24h'] > 0]),
top50_24h_down=len(df_top50[df_top50['percent_change_24h'] <= 0]),
top50_24h_mean=df_top50.mean()['percent_change_24h'],
top50_7d_up=len(df_top50[df_top50['percent_change_7d'] > 0]),
top50_7d_down=len(df_top50[df_top50['percent_change_7d'] <= 0]),
top50_7d_mean=df_top50.mean()['percent_change_7d'],
top51_100_1h_up=len(
df_top51_100[df_top51_100['percent_change_1h'] > 0]),
top51_100_1h_down=len(
df_top51_100[df_top51_100['percent_change_1h'] <= 0]),
top51_100_1h_mean=df_top51_100.mean()['percent_change_1h'],
top51_100_24h_up=len(
df_top51_100[df_top51_100['percent_change_24h'] > 0]),
top51_100_24h_down=len(
df_top51_100[df_top51_100['percent_change_24h'] <= 0]),
top51_100_24h_mean=df_top51_100.mean()['percent_change_24h'],
top51_100_7d_up=len(
df_top51_100[df_top51_100['percent_change_7d'] > 0]),
top51_100_7d_down=len(
df_top51_100[df_top51_100['percent_change_7d'] <= 0]),
top51_100_7d_mean=df_top51_100.mean()['percent_change_7d'],
top100_1h_up=len(df_top100[df_top100['percent_change_1h'] > 0]),
top100_1h_down=len(df_top100[df_top100['percent_change_1h'] <= 0]),
top100_1h_mean=df_top100.mean()['percent_change_1h'],
top100_24h_up=len(df_top100[df_top100['percent_change_24h'] > 0]),
top100_24h_down=len(df_top100[df_top100['percent_change_24h'] <= 0]),
top100_24h_mean=df_top100.mean()['percent_change_24h'],
top100_7d_up=len(df_top100[df_top100['percent_change_7d'] > 0]),
top100_7d_down=len(df_top100[df_top100['percent_change_7d'] <= 0]),
top100_7d_mean=df_top100.mean()['percent_change_7d'],
).strip()
email_client = EmailClient()
# Send an email to myself
email_client.send_test_email(u'[CC] Entire Market Mood', body)